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| python_code
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| triton_code
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GC
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/2g/c2gyn6tk4uytpd2lwz6iu2w4kczch5vnf2vcnzgbjzd34sgfx6g3.py
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv2d => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [3, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 256
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/sm/csmstye2elxev2ml7wofncqnuqbhxdgp2o3apkwco7cd56owgdf4.py
# Topologically Sorted Source Nodes: [x_l, x_r, x], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
# x => add
# x_l => convolution_1
# x_r => convolution_3
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1, 1], [0, 3], [1, 1], False, [0, 0], 1), kwargs = {})
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_2, %primals_8, %primals_9, [1, 1], [3, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %convolution_3), kwargs = {})
triton_poi_fused_add_convolution_1 = async_compile.triton('triton_poi_fused_add_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x3), xmask)
tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
args.clear()
assert_size_stride(primals_1, (256, 4, 7, 1), (28, 7, 1, 1))
assert_size_stride(primals_2, (256, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 256, 1, 7), (1792, 7, 7, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (256, 4, 1, 7), (28, 7, 7, 1))
assert_size_stride(primals_7, (256, ), (1, ))
assert_size_stride(primals_8, (4, 256, 7, 1), (1792, 7, 1, 1))
assert_size_stride(primals_9, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(3, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [x_l], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(primals_3, primals_6, stride=(1, 1), padding=(0, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 4, 4), (4096, 16, 4, 1))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
triton_poi_fused_convolution_0.run(buf4, primals_7, 16384, grid=grid(16384), stream=stream0)
del primals_7
# Topologically Sorted Source Nodes: [x_r], Original ATen: [aten.convolution]
buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1), padding=(3, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
buf6 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [x_l, x_r, x], Original ATen: [aten.convolution, aten.add]
triton_poi_fused_add_convolution_1.run(buf6, primals_5, buf5, primals_9, 256, grid=grid(256), stream=stream0)
del buf5
del primals_5
del primals_9
return (buf6, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((256, 4, 7, 1), (28, 7, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 256, 1, 7), (1792, 7, 7, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((256, 4, 1, 7), (28, 7, 7, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 256, 7, 1), (1792, 7, 1, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.parallel
class GC(nn.Module):
def __init__(self, inplanes, planes, kh=7, kw=7):
super(GC, self).__init__()
self.conv_l1 = nn.Conv2d(inplanes, 256, kernel_size=(kh, 1),
padding=(int(kh / 2), 0))
self.conv_l2 = nn.Conv2d(256, planes, kernel_size=(1, kw), padding=
(0, int(kw / 2)))
self.conv_r1 = nn.Conv2d(inplanes, 256, kernel_size=(1, kw),
padding=(0, int(kw / 2)))
self.conv_r2 = nn.Conv2d(256, planes, kernel_size=(kh, 1), padding=
(int(kh / 2), 0))
def forward(self, x):
x_l = self.conv_l2(self.conv_l1(x))
x_r = self.conv_r2(self.conv_r1(x))
x = x_l + x_r
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inplanes': 4, 'planes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 256
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x3, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (256, 4, 7, 1), (28, 7, 1, 1))
assert_size_stride(primals_2, (256,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 256, 1, 7), (1792, 7, 7, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (256, 4, 1, 7), (28, 7, 7, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (4, 256, 7, 1), (1792, 7, 1, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(3, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16384)](buf1, primals_2, 16384,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(0, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
buf3 = extern_kernels.convolution(primals_3, primals_6, stride=(1,
1), padding=(0, 3), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 4, 4), (4096, 16, 4, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_0[grid(16384)](buf4, primals_7, 16384,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
padding=(3, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
buf6 = buf2
del buf2
triton_poi_fused_add_convolution_1[grid(256)](buf6, primals_5, buf5,
primals_9, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf5
del primals_5
del primals_9
return (buf6, primals_1, primals_3, primals_4, primals_6, primals_8,
buf1, buf4)
class GCNew(nn.Module):
def __init__(self, inplanes, planes, kh=7, kw=7):
super(GCNew, self).__init__()
self.conv_l1 = nn.Conv2d(inplanes, 256, kernel_size=(kh, 1),
padding=(int(kh / 2), 0))
self.conv_l2 = nn.Conv2d(256, planes, kernel_size=(1, kw), padding=
(0, int(kw / 2)))
self.conv_r1 = nn.Conv2d(inplanes, 256, kernel_size=(1, kw),
padding=(0, int(kw / 2)))
self.conv_r2 = nn.Conv2d(256, planes, kernel_size=(kh, 1), padding=
(int(kh / 2), 0))
def forward(self, input_0):
primals_1 = self.conv_l1.weight
primals_2 = self.conv_l1.bias
primals_4 = self.conv_l2.weight
primals_5 = self.conv_l2.bias
primals_6 = self.conv_r1.weight
primals_7 = self.conv_r1.bias
primals_8 = self.conv_r2.weight
primals_9 = self.conv_r2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
HuaijiaLin/AGSS-VOS
|
GC
| false | 8,250 |
[
"MIT"
] | 11 |
e9272365aa45bf098316d7111238fe0ab8df8a17
|
https://github.com/HuaijiaLin/AGSS-VOS/tree/e9272365aa45bf098316d7111238fe0ab8df8a17
|
ChannelPool
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/7r/c7r2mp7slj6ma5k33x7azsnkn4fzyuvu5pejkuomkbcqmlt3dtua.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16) % 2
x0 = xindex % 16
x2 = (xindex // 32)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp9 = triton_helpers.maximum(tmp7, tmp8)
tmp10 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = triton_helpers.maximum(tmp9, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tmp15 = tl.full([1], 2, tl.int64)
tmp16 = tmp0 < tmp15
tmp17 = tl.load(in_ptr0 + (x0 + (64*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp18 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp19 = tmp17 + tmp18
tmp20 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp21 = tmp19 + tmp20
tmp22 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp23 = tmp21 + tmp22
tmp24 = 4.0
tmp25 = tmp23 / tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp14, tmp25, tmp26)
tmp28 = tl.where(tmp4, tmp13, tmp27)
tl.store(out_ptr0 + (x3), tmp28, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(arg0_1, buf0, 128, grid=grid(128), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.utils.model_zoo
class ChannelPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1)
.unsqueeze(1)), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 2
x0 = xindex % 16
x2 = xindex // 32
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp9 = triton_helpers.maximum(tmp7, tmp8)
tmp10 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = triton_helpers.maximum(tmp9, tmp10)
tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp13 = tl.where(tmp4, tmp11, tmp12)
tmp14 = tmp0 >= tmp3
tl.full([1], 2, tl.int64)
tmp17 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp18 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp19 = tmp17 + tmp18
tmp20 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp21 = tmp19 + tmp20
tmp22 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp23 = tmp21 + tmp22
tmp24 = 4.0
tmp25 = tmp23 / tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp14, tmp25, tmp26)
tmp28 = tl.where(tmp4, tmp13, tmp27)
tl.store(out_ptr0 + x3, tmp28, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(128)](arg0_1, buf0, 128, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
class ChannelPoolNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
HolmesShuan/AIM2020-Real-Super-Resolution
|
ChannelPool
| false | 8,251 |
[
"BSD-2-Clause"
] | 19 |
0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/tree/0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
FirstBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/qd/cqdfjibepiqhqxccoj2wu6wmu2apm7m7ts3tlk32lzzphh3lfjmx.py
# Topologically Sorted Source Nodes: [mul, leaky_relu], Original ATen: [aten.mul, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
# leaky_relu => gt, mul_1, where
# mul => mul
# Graph fragment:
# %mul : [num_users=3] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%mul, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 0.2), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_1), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tmp8 = tmp7 > tmp3
tl.store(in_out_ptr0 + (x0), tmp7, xmask)
tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0; del buf0 # reuse
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [mul, leaky_relu], Original ATen: [aten.mul, aten.leaky_relu, aten.leaky_relu_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
return (buf1, primals_1, primals_2, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels of the input tensor.
gamma: Whether the scale (weight) of the affine mapping is learnable.
beta: Whether the center (bias) of the affine mapping is learnable.
decay: Decay factor for moving average operations in this layer.
epsilon: A value added to the denominator for numerical stability.
"""
super().__init__()
self.bn = nn.BatchNorm2d(num_features=channels, affine=True,
track_running_stats=True, momentum=1 - decay, eps=epsilon)
self.bn.weight.requires_grad = gamma
self.bn.bias.requires_grad = beta
def forward(self, x):
return self.bn(x)
class FirstBlock(nn.Module):
"""Implements the first block, which is a convolutional block."""
def __init__(self, in_channels, out_channels, use_wscale=False,
wscale_gain=np.sqrt(2.0), use_bn=False, activation_type='lrelu'):
super().__init__()
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.scale = wscale_gain / np.sqrt(in_channels * 3 * 3
) if use_wscale else 1.0
self.bn = BatchNormLayer(channels=out_channels
) if use_bn else nn.Identity()
if activation_type == 'linear':
self.activate = nn.Identity()
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(
f'Not implemented activation function: {activation_type}!')
def forward(self, x):
return self.activate(self.bn(self.conv(x) * self.scale))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0(in_out_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tmp8 = tmp7 > tmp3
tl.store(in_out_ptr0 + x0, tmp7, xmask)
tl.store(out_ptr0 + x0, tmp8, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_leaky_relu_leaky_relu_backward_mul_0[grid(256)](buf1,
buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf1, primals_1, primals_2, buf2
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels of the input tensor.
gamma: Whether the scale (weight) of the affine mapping is learnable.
beta: Whether the center (bias) of the affine mapping is learnable.
decay: Decay factor for moving average operations in this layer.
epsilon: A value added to the denominator for numerical stability.
"""
super().__init__()
self.bn = nn.BatchNorm2d(num_features=channels, affine=True,
track_running_stats=True, momentum=1 - decay, eps=epsilon)
self.bn.weight.requires_grad = gamma
self.bn.bias.requires_grad = beta
def forward(self, x):
return self.bn(x)
class FirstBlockNew(nn.Module):
"""Implements the first block, which is a convolutional block."""
def __init__(self, in_channels, out_channels, use_wscale=False,
wscale_gain=np.sqrt(2.0), use_bn=False, activation_type='lrelu'):
super().__init__()
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.scale = wscale_gain / np.sqrt(in_channels * 3 * 3
) if use_wscale else 1.0
self.bn = BatchNormLayer(channels=out_channels
) if use_bn else nn.Identity()
if activation_type == 'linear':
self.activate = nn.Identity()
elif activation_type == 'lrelu':
self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(
f'Not implemented activation function: {activation_type}!')
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Hsintien-Ng/idinvert_pytorch-reproduced
|
FirstBlock
| false | 8,252 |
[
"MIT"
] | 20 |
cf3302510573138cf16202add06feae7c93624ea
|
https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea
|
InstanceNormLayer
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/au/cau3caepzzmwjjjeiplr2qx5qf4xqbtpwy7g2jzkhmbdt4dhmrhl.py
# Topologically Sorted Source Nodes: [mean, x, pow_1, mean_1, add, sqrt, x_1], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
# add => add
# mean => mean
# mean_1 => mean_1
# pow_1 => pow_1
# sqrt => sqrt
# x => sub
# x_1 => div
# Graph fragment:
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [2, 3], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [2, 3], True), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 1e-08), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
triton_per_fused_add_div_mean_pow_sqrt_sub_0 = async_compile.triton('triton_per_fused_add_div_mean_pow_sqrt_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_pow_sqrt_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mean_pow_sqrt_sub_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tmp7 = tmp0 - tmp6
tmp8 = tmp7 * tmp7
tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
tmp11 = tl.where(xmask, tmp9, 0)
tmp12 = tl.sum(tmp11, 1)[:, None]
tmp13 = tmp12 / tmp5
tmp14 = 1e-08
tmp15 = tmp13 + tmp14
tmp16 = libdevice.sqrt(tmp15)
tmp17 = tmp7 / tmp16
tl.store(out_ptr2 + (r1 + (16*x0)), tmp17, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mean, x, pow_1, mean_1, add, sqrt, x_1], Original ATen: [aten.mean, aten.sub, aten.pow, aten.add, aten.sqrt, aten.div]
stream0 = get_raw_stream(0)
triton_per_fused_add_div_mean_pow_sqrt_sub_0.run(arg0_1, buf2, 16, 16, grid=grid(16), stream=stream0)
del arg0_1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class InstanceNormLayer(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, x):
if x.ndim != 4:
raise ValueError(
f'The input tensor should be with shape [batch_size, channel, height, width], but {x.shape} is received!'
)
x = x - torch.mean(x, dim=[2, 3], keepdim=True)
x = x / torch.sqrt(torch.mean(x ** 2, dim=[2, 3], keepdim=True) +
self.eps)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mean_pow_sqrt_sub_0(in_ptr0, out_ptr2, xnumel,
rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tmp7 = tmp0 - tmp6
tmp8 = tmp7 * tmp7
tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
tmp11 = tl.where(xmask, tmp9, 0)
tmp12 = tl.sum(tmp11, 1)[:, None]
tmp13 = tmp12 / tmp5
tmp14 = 1e-08
tmp15 = tmp13 + tmp14
tmp16 = libdevice.sqrt(tmp15)
tmp17 = tmp7 / tmp16
tl.store(out_ptr2 + (r1 + 16 * x0), tmp17, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_add_div_mean_pow_sqrt_sub_0[grid(16)](arg0_1, buf2,
16, 16, XBLOCK=8, num_warps=2, num_stages=1)
del arg0_1
return buf2,
class InstanceNormLayerNew(nn.Module):
"""Implements instance normalization layer."""
def __init__(self, epsilon=1e-08):
super().__init__()
self.eps = epsilon
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Hsintien-Ng/idinvert_pytorch-reproduced
|
InstanceNormLayer
| false | 8,253 |
[
"MIT"
] | 20 |
cf3302510573138cf16202add06feae7c93624ea
|
https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea
|
ClipL1
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/2v/c2vnrsqjtx2t3nv2ncj2tm4amquh26xnsn4mjzvm6jgsoj5ihkjm.py
# Topologically Sorted Source Nodes: [sub, abs_1, clamp, loss], Original ATen: [aten.sub, aten.abs, aten.clamp, aten.mean]
# Source node to ATen node mapping:
# abs_1 => abs_1
# clamp => clamp_max, clamp_min
# loss => mean
# sub => sub
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%abs_1, 0.0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 10.0), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%clamp_max,), kwargs = {})
triton_per_fused_abs_clamp_mean_sub_0 = async_compile.triton('triton_per_fused_abs_clamp_mean_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_clamp_mean_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_clamp_mean_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = 10.0
tmp7 = triton_helpers.minimum(tmp5, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
tmp11 = 256.0
tmp12 = tmp10 / tmp11
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp12, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [sub, abs_1, clamp, loss], Original ATen: [aten.sub, aten.abs, aten.clamp, aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_abs_clamp_mean_sub_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.utils.model_zoo
class ClipL1(nn.Module):
def __init__(self, clip_min=0.0, clip_max=10.0):
super(ClipL1, self).__init__()
self.clip_max = clip_max
self.clip_min = clip_min
def forward(self, sr, hr):
loss = torch.mean(torch.clamp(torch.abs(sr - hr), self.clip_min,
self.clip_max))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_abs_clamp_mean_sub_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = 10.0
tmp7 = triton_helpers.minimum(tmp5, tmp6)
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
tmp11 = 256.0
tmp12 = tmp10 / tmp11
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp12, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_abs_clamp_mean_sub_0[grid(1)](buf1, arg0_1, arg1_1,
1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class ClipL1New(nn.Module):
def __init__(self, clip_min=0.0, clip_max=10.0):
super(ClipL1New, self).__init__()
self.clip_max = clip_max
self.clip_min = clip_min
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
HolmesShuan/AIM2020-Real-Super-Resolution
|
ClipL1
| false | 8,254 |
[
"BSD-2-Clause"
] | 19 |
0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/tree/0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
CosineSimilarity
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/kz/ckz4kdygd3gfajopqwzvtecsmzyghdhlg3mc3xzmysjall7bayjn.py
# Topologically Sorted Source Nodes: [i], Original ATen: [aten.div]
# Source node to ATen node mapping:
# i => div
# Graph fragment:
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/dw/cdwnfu4md64rrfat2jxhj4dqovlxeethbmwr4msydxra2pgc24n3.py
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
# cosine_similarity => clamp_min_2, clamp_min_3, div_2, div_3, mul, pow_5, pow_6, pow_7, pow_8, sum_3, sum_4
# Graph fragment:
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div, 2), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_5, [-1], True), kwargs = {})
# %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 0.5), kwargs = {})
# %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_6, 1e-08), kwargs = {})
# %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div, %clamp_min_2), kwargs = {})
# %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div_1, 2), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_7, [-1], True), kwargs = {})
# %pow_8 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 0.5), kwargs = {})
# %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_8, 1e-08), kwargs = {})
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div_1, %clamp_min_3), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_3, %div_2), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + (x2), xmask)
tmp17 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 * tmp30
tl.store(out_ptr0 + (x2), tmp31, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7y/c7yr3qu6y52l4ocumbg362nnuvbx6l3pybenp35zgowed55sqw6l.py
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.sum]
# Source node to ATen node mapping:
# cosine_similarity => sum_5
# Graph fragment:
# %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_poi_fused_sum_2 = async_compile.triton('triton_poi_fused_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [i], Original ATen: [aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [j], Original ATen: [aten.div]
triton_poi_fused_div_0.run(arg1_1, buf1, 256, grid=grid(256), stream=stream0)
del arg1_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1.run(buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
del buf0
del buf1
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.sum]
triton_poi_fused_sum_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
del buf2
return (buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class CosineSimilarity(nn.Module):
def __init__(self, dim=-1):
super(CosineSimilarity, self).__init__()
self.m = nn.CosineSimilarity(dim=dim)
def forward(self, i, j):
i = F.normalize(i, p=2, dim=-1)
j = F.normalize(j, p=2, dim=-1)
return self.m(i, j)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + x2, xmask)
tmp17 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-08
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 * tmp30
tl.store(out_ptr0 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused_sum_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tl.store(out_ptr0 + x0, tmp6, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_div_0[grid(256)](arg1_1, buf1, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg1_1
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_1[grid(256)](buf0
, buf1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del buf1
buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_sum_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf2
return buf3,
class CosineSimilarityNew(nn.Module):
def __init__(self, dim=-1):
super(CosineSimilarityNew, self).__init__()
self.m = nn.CosineSimilarity(dim=dim)
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IBM/aihn-ucsd
|
CosineSimilarity
| false | 8,255 |
[
"Apache-2.0"
] | 20 |
6c6a56d11c704b529a31868418e350e9760ff9d9
|
https://github.com/IBM/aihn-ucsd/tree/6c6a56d11c704b529a31868418e350e9760ff9d9
|
TemporalPooling
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ch/cchwyippaifkg3wzdlar3zblomxznwnqipberzksnr5o2fzygobs.py
# Topologically Sorted Source Nodes: [x_1, contiguous], Original ATen: [aten.avg_pool3d, aten.clone]
# Source node to ATen node mapping:
# contiguous => clone
# x_1 => avg_pool3d
# Graph fragment:
# %avg_pool3d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool3d.default](args = (%permute, [3, 1, 1], [2, 1, 1], [1, 0, 0]), kwargs = {})
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_avg_pool3d_clone_0 = async_compile.triton('triton_poi_fused_avg_pool3d_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool3d_clone_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_avg_pool3d_clone_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = (xindex // 64)
x1 = (xindex // 4) % 4
x0 = xindex % 4
x5 = xindex % 64
x4 = xindex
tmp0 = (-1) + (2*x3)
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = x1
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = x0
tmp11 = tmp10 >= tmp1
tmp12 = tmp10 < tmp3
tmp13 = tmp11 & tmp12
tmp14 = tmp5 & tmp9
tmp15 = tmp14 & tmp13
tmp16 = tl.load(in_ptr0 + ((-64) + x5 + (128*x3)), tmp15 & xmask, other=0.0)
tmp17 = 2*x3
tmp18 = tmp17 >= tmp1
tmp19 = tmp17 < tmp3
tmp20 = tmp18 & tmp19
tmp21 = tmp20 & tmp9
tmp22 = tmp21 & tmp13
tmp23 = tl.load(in_ptr0 + (x5 + (128*x3)), tmp22 & xmask, other=0.0)
tmp24 = tmp23 + tmp16
tmp25 = 1 + (2*x3)
tmp26 = tmp25 >= tmp1
tmp27 = tmp25 < tmp3
tmp28 = tmp26 & tmp27
tmp29 = tmp28 & tmp9
tmp30 = tmp29 & tmp13
tmp31 = tl.load(in_ptr0 + (64 + x5 + (128*x3)), tmp30 & xmask, other=0.0)
tmp32 = tmp31 + tmp24
tmp33 = (x0*x1) + (((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + ((-1)*x0*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + ((-1)*x1*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))) + (x0*x1*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + (((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-1)*x0*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-1)*x1*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((5) * ((5) <= (2 + (2*x3))) + (2 + (2*x3)) * ((2 + (2*x3)) < (5)))) + ((-2)*x0*x1*x3) + ((-2)*x3*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4)))*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + (2*x0*x3*((4) * ((4) <= (1 + x1)) + (1 + x1) * ((1 + x1) < (4)))) + (2*x1*x3*((4) * ((4) <= (1 + x0)) + (1 + x0) * ((1 + x0) < (4))))
tmp34 = tmp32 / tmp33
tl.store(in_out_ptr0 + (x4), tmp34, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, 4, 2, 4, 4), (128, 16, 64, 4, 1), torch.float32)
buf1 = reinterpret_tensor(buf0, (1, 2, 4, 4, 4), (128, 64, 16, 4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [x_1, contiguous], Original ATen: [aten.avg_pool3d, aten.clone]
stream0 = get_raw_stream(0)
triton_poi_fused_avg_pool3d_clone_0.run(buf1, arg0_1, 128, grid=grid(128), stream=stream0)
del arg0_1
return (reinterpret_tensor(buf1, (2, 4, 4, 4), (64, 16, 4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.distributions
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
class TemporalPooling(nn.Module):
def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
"""
Parameters
----------
frames (int): number of input frames
kernel_size
stride
mode
"""
super().__init__()
self.frames = frames
pad_size = (kernel_size - 1) // stride
if mode == 'avg':
self.pool = nn.AvgPool3d(kernel_size=(kernel_size, 1, 1),
stride=(stride, 1, 1), padding=(pad_size, 0, 0))
elif mode == 'max':
self.pool = nn.MaxPool3d(kernel_size=(kernel_size, 1, 1),
stride=(stride, 1, 1), padding=(pad_size, 0, 0))
else:
raise ValueError('only support avg or max')
def forward(self, x):
_nt, c, h, w = x.shape
x = x.view((-1, self.frames) + x.size()[1:]).transpose(1, 2)
x = self.pool(x)
x = x.transpose(1, 2).contiguous().view(-1, c, h, w)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'frames': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.distributions
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_avg_pool3d_clone_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 64
x1 = xindex // 4 % 4
x0 = xindex % 4
x5 = xindex % 64
x4 = xindex
tmp0 = -1 + 2 * x3
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = x1
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = x0
tmp11 = tmp10 >= tmp1
tmp12 = tmp10 < tmp3
tmp13 = tmp11 & tmp12
tmp14 = tmp5 & tmp9
tmp15 = tmp14 & tmp13
tmp16 = tl.load(in_ptr0 + (-64 + x5 + 128 * x3), tmp15 & xmask, other=0.0)
tmp17 = 2 * x3
tmp18 = tmp17 >= tmp1
tmp19 = tmp17 < tmp3
tmp20 = tmp18 & tmp19
tmp21 = tmp20 & tmp9
tmp22 = tmp21 & tmp13
tmp23 = tl.load(in_ptr0 + (x5 + 128 * x3), tmp22 & xmask, other=0.0)
tmp24 = tmp23 + tmp16
tmp25 = 1 + 2 * x3
tmp26 = tmp25 >= tmp1
tmp27 = tmp25 < tmp3
tmp28 = tmp26 & tmp27
tmp29 = tmp28 & tmp9
tmp30 = tmp29 & tmp13
tmp31 = tl.load(in_ptr0 + (64 + x5 + 128 * x3), tmp30 & xmask, other=0.0)
tmp32 = tmp31 + tmp24
tmp33 = x0 * x1 + (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4)) * (4 *
(4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)) + -1 * x0 * (4 * (4 <= 1 +
x1) + (1 + x1) * (1 + x1 < 4)) + -1 * x1 * (4 * (4 <= 1 + x0) + (1 +
x0) * (1 + x0 < 4)) + x0 * x1 * (5 * (5 <= 2 + 2 * x3) + (2 + 2 *
x3) * (2 + 2 * x3 < 5)) + (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4)
) * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)) * (5 * (5 <= 2 +
2 * x3) + (2 + 2 * x3) * (2 + 2 * x3 < 5)) + -1 * x0 * (4 * (4 <= 1 +
x1) + (1 + x1) * (1 + x1 < 4)) * (5 * (5 <= 2 + 2 * x3) + (2 + 2 *
x3) * (2 + 2 * x3 < 5)) + -1 * x1 * (4 * (4 <= 1 + x0) + (1 + x0) *
(1 + x0 < 4)) * (5 * (5 <= 2 + 2 * x3) + (2 + 2 * x3) * (2 + 2 * x3 <
5)) + -2 * x0 * x1 * x3 + -2 * x3 * (4 * (4 <= 1 + x0) + (1 + x0) *
(1 + x0 < 4)) * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)
) + 2 * x0 * x3 * (4 * (4 <= 1 + x1) + (1 + x1) * (1 + x1 < 4)
) + 2 * x1 * x3 * (4 * (4 <= 1 + x0) + (1 + x0) * (1 + x0 < 4))
tmp34 = tmp32 / tmp33
tl.store(in_out_ptr0 + x4, tmp34, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((1, 4, 2, 4, 4), (128, 16, 64, 4, 1),
torch.float32)
buf1 = reinterpret_tensor(buf0, (1, 2, 4, 4, 4), (128, 64, 16, 4, 1), 0
)
del buf0
get_raw_stream(0)
triton_poi_fused_avg_pool3d_clone_0[grid(128)](buf1, arg0_1, 128,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return reinterpret_tensor(buf1, (2, 4, 4, 4), (64, 16, 4, 1), 0),
class TemporalPoolingNew(nn.Module):
def __init__(self, frames, kernel_size=3, stride=2, mode='avg'):
"""
Parameters
----------
frames (int): number of input frames
kernel_size
stride
mode
"""
super().__init__()
self.frames = frames
pad_size = (kernel_size - 1) // stride
if mode == 'avg':
self.pool = nn.AvgPool3d(kernel_size=(kernel_size, 1, 1),
stride=(stride, 1, 1), padding=(pad_size, 0, 0))
elif mode == 'max':
self.pool = nn.MaxPool3d(kernel_size=(kernel_size, 1, 1),
stride=(stride, 1, 1), padding=(pad_size, 0, 0))
else:
raise ValueError('only support avg or max')
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
IBM/AdaMML
|
TemporalPooling
| false | 8,256 |
[
"Apache-2.0"
] | 32 |
be50c02188e6b31ca3a25f285b1b538c137d3d5c
|
https://github.com/IBM/AdaMML/tree/be50c02188e6b31ca3a25f285b1b538c137d3d5c
|
LastBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/y3/cy33bzqb265sy5robwugjqsh6stzmhaar3hwjz5adagksi7feinb.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mul]
# Source node to ATen node mapping:
# x_1 => mul
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 1.0), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mul]
stream0 = get_raw_stream(0)
triton_poi_fused_mul_0.run(buf1, 16, grid=grid(16), stream=stream0)
return (buf1, primals_1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.nn as nn
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels of the input tensor.
gamma: Whether the scale (weight) of the affine mapping is learnable.
beta: Whether the center (bias) of the affine mapping is learnable.
decay: Decay factor for moving average operations in this layer.
epsilon: A value added to the denominator for numerical stability.
"""
super().__init__()
self.bn = nn.BatchNorm2d(num_features=channels, affine=True,
track_running_stats=True, momentum=1 - decay, eps=epsilon)
self.bn.weight.requires_grad = gamma
self.bn.bias.requires_grad = beta
def forward(self, x):
return self.bn(x)
class LastBlock(nn.Module):
"""Implements the last block, which is a dense block."""
def __init__(self, in_channels, out_channels, use_wscale=False,
wscale_gain=1.0, use_bn=False):
super().__init__()
self.fc = nn.Linear(in_features=in_channels, out_features=
out_channels, bias=False)
self.scale = wscale_gain / np.sqrt(in_channels) if use_wscale else 1.0
self.bn = BatchNormLayer(channels=out_channels
) if use_bn else nn.Identity()
def forward(self, x):
x = x.view(x.shape[0], -1)
x = self.fc(x) * self.scale
x = x.view(x.shape[0], x.shape[1], 1, 1)
return self.bn(x).view(x.shape[0], x.shape[1])
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 * tmp1
tl.store(in_out_ptr0 + x0, tmp2, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
(1, 4), 0), out=buf0)
del primals_2
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_mul_0[grid(16)](buf1, 16, XBLOCK=16, num_warps=1,
num_stages=1)
return buf1, primals_1
class BatchNormLayer(nn.Module):
"""Implements batch normalization layer."""
def __init__(self, channels, gamma=False, beta=True, decay=0.9, epsilon
=1e-05):
"""Initializes with basic settings.
Args:
channels: Number of channels of the input tensor.
gamma: Whether the scale (weight) of the affine mapping is learnable.
beta: Whether the center (bias) of the affine mapping is learnable.
decay: Decay factor for moving average operations in this layer.
epsilon: A value added to the denominator for numerical stability.
"""
super().__init__()
self.bn = nn.BatchNorm2d(num_features=channels, affine=True,
track_running_stats=True, momentum=1 - decay, eps=epsilon)
self.bn.weight.requires_grad = gamma
self.bn.bias.requires_grad = beta
def forward(self, x):
return self.bn(x)
class LastBlockNew(nn.Module):
"""Implements the last block, which is a dense block."""
def __init__(self, in_channels, out_channels, use_wscale=False,
wscale_gain=1.0, use_bn=False):
super().__init__()
self.fc = nn.Linear(in_features=in_channels, out_features=
out_channels, bias=False)
self.scale = wscale_gain / np.sqrt(in_channels) if use_wscale else 1.0
self.bn = BatchNormLayer(channels=out_channels
) if use_bn else nn.Identity()
def forward(self, input_0):
primals_1 = self.fc.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Hsintien-Ng/idinvert_pytorch-reproduced
|
LastBlock
| false | 8,257 |
[
"MIT"
] | 20 |
cf3302510573138cf16202add06feae7c93624ea
|
https://github.com/Hsintien-Ng/idinvert_pytorch-reproduced/tree/cf3302510573138cf16202add06feae7c93624ea
|
Refine
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zs/czsabetrpdcp3wnie3fwsde6ccvxchpdbdv6h5pmwlm5ddmf4are.py
# Topologically Sorted Source Nodes: [s, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# relu => relu
# s => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bh/cbh6z6q7j2ev7jry3vfxhnujl7ykjacaqrhgcxpv3sun2pkhqeew.py
# Topologically Sorted Source Nodes: [sr, relu_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# relu_1 => relu_1
# sr => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/rh/crh2j6qyqmcfdh2dna6qyu5234c4lrboolwt7mllz44pnwhazhv7.py
# Topologically Sorted Source Nodes: [s, sr_1, s_1, m, relu_2], Original ATen: [aten.convolution, aten.add, aten.relu]
# Source node to ATen node mapping:
# m => add_1
# relu_2 => relu_2
# s => convolution
# s_1 => add
# sr_1 => convolution_2
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %convolution_2), kwargs = {})
# %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %primals_8), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_convolution_relu_2 = async_compile.triton('triton_poi_fused_add_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x3), xmask)
tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(in_out_ptr0 + (x3), tmp8, xmask)
tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/m5/cm5ddt53p46z2z7rn67i3v6kdacdect2g7434gjeybybnd4nlzzf.py
# Topologically Sorted Source Nodes: [mr_1, m_1], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
# m_1 => add_2
# mr_1 => convolution_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_11, %primals_12, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %convolution_4), kwargs = {})
triton_poi_fused_add_convolution_3 = async_compile.triton('triton_poi_fused_add_convolution_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x3), xmask)
tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_9, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_10, (4, ), (1, ))
assert_size_stride(primals_11, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_12, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [s], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [s, relu], Original ATen: [aten.convolution, aten.relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_relu_0.run(buf0, primals_2, buf1, 256, grid=grid(256), stream=stream0)
# Topologically Sorted Source Nodes: [sr], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [sr, relu_1], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 256, grid=grid(256), stream=stream0)
del primals_5
# Topologically Sorted Source Nodes: [sr_1], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = buf0; del buf0 # reuse
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [s, sr_1, s_1, m, relu_2], Original ATen: [aten.convolution, aten.add, aten.relu]
triton_poi_fused_add_convolution_relu_2.run(buf5, primals_2, buf4, primals_7, primals_8, buf6, 256, grid=grid(256), stream=stream0)
del buf4
del primals_2
del primals_7
del primals_8
# Topologically Sorted Source Nodes: [mr], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(buf6, primals_9, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
buf8 = buf7; del buf7 # reuse
# Topologically Sorted Source Nodes: [mr, relu_3], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf8, primals_10, 256, grid=grid(256), stream=stream0)
del primals_10
# Topologically Sorted Source Nodes: [mr_1], Original ATen: [aten.convolution]
buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
buf10 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [mr_1, m_1], Original ATen: [aten.convolution, aten.add]
triton_poi_fused_add_convolution_3.run(buf10, buf9, primals_12, 256, grid=grid(256), stream=stream0)
del buf9
del primals_12
return (buf10, primals_1, primals_3, primals_4, primals_6, primals_9, primals_11, buf1, buf3, buf6, buf8, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
class Refine(nn.Module):
def __init__(self, inplanes, planes, scale_factor=2):
super(Refine, self).__init__()
self.convFS1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1)
self.convFS2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convFS3 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convMM1 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convMM2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.scale_factor = scale_factor
def forward(self, f, pm):
s = self.convFS1(f)
sr = self.convFS2(F.relu(s))
sr = self.convFS3(F.relu(sr))
s = s + sr
if s.shape[-1] == pm.shape[-1]:
m = s + pm
else:
m = s + F.upsample(pm, scale_factor=self.scale_factor, mode=
'bilinear')
mr = self.convMM1(F.relu(m))
mr = self.convMM2(F.relu(mr))
m = m + mr
return m
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inplanes': 4, 'planes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_relu_2(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr3 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(in_out_ptr0 + x3, tmp8, xmask)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_convolution_3(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x3, xmask)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = tmp0 + tmp3
tl.store(in_out_ptr0 + x3, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_9, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_12, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(256)](buf0, primals_2,
buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(256)](buf3, primals_5, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = buf0
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_convolution_relu_2[grid(256)](buf5, primals_2,
buf4, primals_7, primals_8, buf6, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf4
del primals_2
del primals_7
del primals_8
buf7 = extern_kernels.convolution(buf6, primals_9, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
buf8 = buf7
del buf7
triton_poi_fused_convolution_relu_1[grid(256)](buf8, primals_10,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_10
buf9 = extern_kernels.convolution(buf8, primals_11, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 4, 4, 4), (64, 16, 4, 1))
buf10 = buf5
del buf5
triton_poi_fused_add_convolution_3[grid(256)](buf10, buf9,
primals_12, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf9
del primals_12
return (buf10, primals_1, primals_3, primals_4, primals_6, primals_9,
primals_11, buf1, buf3, buf6, buf8)
class RefineNew(nn.Module):
def __init__(self, inplanes, planes, scale_factor=2):
super(RefineNew, self).__init__()
self.convFS1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1)
self.convFS2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convFS3 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convMM1 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.convMM2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
self.scale_factor = scale_factor
def forward(self, input_0, input_1):
primals_1 = self.convFS1.weight
primals_2 = self.convFS1.bias
primals_4 = self.convFS2.weight
primals_5 = self.convFS2.bias
primals_6 = self.convFS3.weight
primals_7 = self.convFS3.bias
primals_9 = self.convMM1.weight
primals_10 = self.convMM1.bias
primals_11 = self.convMM2.weight
primals_12 = self.convMM2.bias
primals_3 = input_0
primals_8 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12])
return output[0]
|
HuaijiaLin/AGSS-VOS
|
Refine
| false | 8,258 |
[
"MIT"
] | 11 |
e9272365aa45bf098316d7111238fe0ab8df8a17
|
https://github.com/HuaijiaLin/AGSS-VOS/tree/e9272365aa45bf098316d7111238fe0ab8df8a17
|
Critic
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zm/czm6acrrgjryz6xi3wza7npycjuiqsdsygpfdo3lbzaquecrmeuj.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kv/ckvxxvyawuvakf6bxnxc5vw6k2rqjxz7ltuwj3e63t2ggkp4g736.py
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# q => relu
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 400
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/x5/cx5cvl2grwodycfylkfiinp5pp4ovaki5aibnpbdiitalm3aiire.py
# Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# q_1 => relu_1
# Graph fragment:
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_6), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2048],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 300
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (400, 8), (8, 1))
assert_size_stride(primals_4, (400, ), (1, ))
assert_size_stride(primals_5, (300, 400), (400, 1))
assert_size_stride(primals_6, (300, ), (1, ))
assert_size_stride(primals_7, (1, 300), (300, 1))
assert_size_stride(primals_8, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 400), (1, 8), 0), out=buf1)
del primals_3
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [q], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf2, primals_4, 1600, grid=grid(1600), stream=stream0)
del primals_4
buf3 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (400, 300), (1, 400), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [q_1], Original ATen: [aten.relu]
triton_poi_fused_relu_2.run(buf4, primals_6, 1200, grid=grid(1200), stream=stream0)
del primals_6
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7, (300, 1), (1, 300), 0), alpha=1, beta=1, out=buf6)
del primals_8
return (buf6, buf0, buf2, buf4, primals_7, primals_5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((400, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((300, 400), (400, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((300, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((1, 300), (300, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, state, action):
q = F.relu(self.l1(torch.cat([state, action], 1)))
q = F.relu(self.l2(q))
return self.l3(q)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 400
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 1200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 300
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (400, 8), (8, 1))
assert_size_stride(primals_4, (400,), (1,))
assert_size_stride(primals_5, (300, 400), (400, 1))
assert_size_stride(primals_6, (300,), (1,))
assert_size_stride(primals_7, (1, 300), (300, 1))
assert_size_stride(primals_8, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 400), (1,
8), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(1600)](buf2, primals_4, 1600, XBLOCK=
256, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 300), (300, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (400, 300), (
1, 400), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_2[grid(1200)](buf4, primals_6, 1200, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_6
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
(300, 1), (1, 300), 0), alpha=1, beta=1, out=buf6)
del primals_8
return buf6, buf0, buf2, buf4, primals_7, primals_5
class CriticNew(nn.Module):
def __init__(self, state_dim, action_dim):
super(CriticNew, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, input_0, input_1):
primals_3 = self.l1.weight
primals_4 = self.l1.bias
primals_5 = self.l2.weight
primals_6 = self.l2.bias
primals_7 = self.l3.weight
primals_8 = self.l3.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
HzcIrving/DLRL_PlayGround
|
Critic
| false | 8,259 |
[
"MIT"
] | 27 |
0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
https://github.com/HzcIrving/DLRL_PlayGround/tree/0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
partCE
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/u2/cu2beycg2t2ghizs6f4qom7bxbxmajhdaakuyq6y2korxywhp6ba.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# loss => amax, sub_2
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
# %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pu/cpuu4gyufyn4v3ctvhylyxrxvcaasf5acoqjpa3cheocstvrth2p.py
# Topologically Sorted Source Nodes: [sub, mul, par_scores, max_1, loss], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.max, aten.nll_loss2d_forward]
# Source node to ATen node mapping:
# loss => full_default_1, ne_1, neg, sum_3, where_1
# max_1 => max_1
# mul => mul
# par_scores => sub_1
# sub => sub
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 100000), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %mul), kwargs = {})
# %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%sub_1, 1), kwargs = {})
# %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%getitem_1, -100), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze,), kwargs = {})
# %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_1, %neg, %full_default_1), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_1,), kwargs = {})
triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1 = async_compile.triton('triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex % 16
r1 = (rindex // 16)
r2 = rindex
tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
tmp7 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
tmp8 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
tmp27 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
tmp28 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
tmp46 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
tmp47 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
tmp74 = tl.load(in_ptr2 + (r0 + (64*r1)), None)
tmp76 = tl.load(in_ptr2 + (16 + r0 + (64*r1)), None)
tmp79 = tl.load(in_ptr2 + (32 + r0 + (64*r1)), None)
tmp82 = tl.load(in_ptr2 + (48 + r0 + (64*r1)), None)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp4 = 100000.0
tmp5 = tmp3 * tmp4
tmp6 = tmp0 - tmp5
tmp9 = tmp2 - tmp8
tmp10 = tmp9 * tmp4
tmp11 = tmp7 - tmp10
tmp12 = tmp6 > tmp11
tmp13 = tmp6 == tmp11
tmp14 = tmp6 != tmp6
tmp15 = tmp11 != tmp11
tmp16 = tmp14 > tmp15
tmp17 = tmp12 | tmp16
tmp18 = tmp14 & tmp15
tmp19 = tmp13 | tmp18
tmp20 = tl.full([1, 1], 0, tl.int64)
tmp21 = tl.full([1, 1], 1, tl.int64)
tmp22 = tmp20 < tmp21
tmp23 = tmp19 & tmp22
tmp24 = tmp17 | tmp23
tmp25 = tl.where(tmp24, tmp6, tmp11)
tmp26 = tl.where(tmp24, tmp20, tmp21)
tmp29 = tmp2 - tmp28
tmp30 = tmp29 * tmp4
tmp31 = tmp27 - tmp30
tmp32 = tmp25 > tmp31
tmp33 = tmp25 == tmp31
tmp34 = tmp25 != tmp25
tmp35 = tmp31 != tmp31
tmp36 = tmp34 > tmp35
tmp37 = tmp32 | tmp36
tmp38 = tmp34 & tmp35
tmp39 = tmp33 | tmp38
tmp40 = tl.full([1, 1], 2, tl.int64)
tmp41 = tmp26 < tmp40
tmp42 = tmp39 & tmp41
tmp43 = tmp37 | tmp42
tmp44 = tl.where(tmp43, tmp25, tmp31)
tmp45 = tl.where(tmp43, tmp26, tmp40)
tmp48 = tmp2 - tmp47
tmp49 = tmp48 * tmp4
tmp50 = tmp46 - tmp49
tmp51 = tmp44 > tmp50
tmp52 = tmp44 == tmp50
tmp53 = tmp44 != tmp44
tmp54 = tmp50 != tmp50
tmp55 = tmp53 > tmp54
tmp56 = tmp51 | tmp55
tmp57 = tmp53 & tmp54
tmp58 = tmp52 | tmp57
tmp59 = tl.full([1, 1], 3, tl.int64)
tmp60 = tmp45 < tmp59
tmp61 = tmp58 & tmp60
tmp62 = tmp56 | tmp61
tmp63 = tl.where(tmp62, tmp44, tmp50)
tmp64 = tl.where(tmp62, tmp45, tmp59)
tmp65 = tl.full([1, 1], -100, tl.int64)
tmp66 = tmp64 != tmp65
tmp67 = tl.where(tmp66, tmp64, tmp20)
tmp68 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp69 = tmp67 + tmp68
tmp70 = tmp67 < 0
tmp71 = tl.where(tmp70, tmp69, tmp67)
tl.device_assert((0 <= tmp71) & (tmp71 < 4), "index out of bounds: 0 <= tmp71 < 4")
tmp73 = tl.load(in_ptr2 + (r0 + (16*tmp71) + (64*r1)), None)
tmp75 = tl_math.exp(tmp74)
tmp77 = tl_math.exp(tmp76)
tmp78 = tmp75 + tmp77
tmp80 = tl_math.exp(tmp79)
tmp81 = tmp78 + tmp80
tmp83 = tl_math.exp(tmp82)
tmp84 = tmp81 + tmp83
tmp85 = tl_math.log(tmp84)
tmp86 = tmp73 - tmp85
tmp87 = -tmp86
tmp88 = 0.0
tmp89 = tl.where(tmp66, tmp87, tmp88)
tmp90 = tl.broadcast_to(tmp89, [XBLOCK, RBLOCK])
tmp92 = tl.sum(tmp90, 1)[:, None]
tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp92, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten._log_softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__log_softmax_0.run(arg1_1, buf1, 256, grid=grid(256), stream=stream0)
buf2 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [sub, mul, par_scores, max_1, loss], Original ATen: [aten.rsub, aten.mul, aten.sub, aten.max, aten.nll_loss2d_forward]
triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1.run(arg1_1, arg0_1, buf1, buf2, 1, 64, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
del buf1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.utils.data
class partCE(nn.Module):
def __init__(self, if_average=False):
super(partCE, self).__init__()
self.crit = nn.CrossEntropyLoss(size_average=if_average)
self.maximum_score = 100000
def forward(self, scores, target):
par_scores = scores - (1 - target) * self.maximum_score
_, max_ind = torch.max(par_scores, 1)
max_ind = max_ind.detach()
loss = self.crit(scores, max_ind)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1(in_ptr0, in_ptr1,
in_ptr2, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp7 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
tmp8 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp27 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
tmp28 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp46 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
tmp47 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp74 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
tmp76 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
tmp79 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
tmp82 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp4 = 100000.0
tmp5 = tmp3 * tmp4
tmp6 = tmp0 - tmp5
tmp9 = tmp2 - tmp8
tmp10 = tmp9 * tmp4
tmp11 = tmp7 - tmp10
tmp12 = tmp6 > tmp11
tmp13 = tmp6 == tmp11
tmp14 = tmp6 != tmp6
tmp15 = tmp11 != tmp11
tmp16 = tmp14 > tmp15
tmp17 = tmp12 | tmp16
tmp18 = tmp14 & tmp15
tmp19 = tmp13 | tmp18
tmp20 = tl.full([1, 1], 0, tl.int64)
tmp21 = tl.full([1, 1], 1, tl.int64)
tmp22 = tmp20 < tmp21
tmp23 = tmp19 & tmp22
tmp24 = tmp17 | tmp23
tmp25 = tl.where(tmp24, tmp6, tmp11)
tmp26 = tl.where(tmp24, tmp20, tmp21)
tmp29 = tmp2 - tmp28
tmp30 = tmp29 * tmp4
tmp31 = tmp27 - tmp30
tmp32 = tmp25 > tmp31
tmp33 = tmp25 == tmp31
tmp34 = tmp25 != tmp25
tmp35 = tmp31 != tmp31
tmp36 = tmp34 > tmp35
tmp37 = tmp32 | tmp36
tmp38 = tmp34 & tmp35
tmp39 = tmp33 | tmp38
tmp40 = tl.full([1, 1], 2, tl.int64)
tmp41 = tmp26 < tmp40
tmp42 = tmp39 & tmp41
tmp43 = tmp37 | tmp42
tmp44 = tl.where(tmp43, tmp25, tmp31)
tmp45 = tl.where(tmp43, tmp26, tmp40)
tmp48 = tmp2 - tmp47
tmp49 = tmp48 * tmp4
tmp50 = tmp46 - tmp49
tmp51 = tmp44 > tmp50
tmp52 = tmp44 == tmp50
tmp53 = tmp44 != tmp44
tmp54 = tmp50 != tmp50
tmp55 = tmp53 > tmp54
tmp56 = tmp51 | tmp55
tmp57 = tmp53 & tmp54
tmp58 = tmp52 | tmp57
tmp59 = tl.full([1, 1], 3, tl.int64)
tmp60 = tmp45 < tmp59
tmp61 = tmp58 & tmp60
tmp62 = tmp56 | tmp61
tl.where(tmp62, tmp44, tmp50)
tmp64 = tl.where(tmp62, tmp45, tmp59)
tmp65 = tl.full([1, 1], -100, tl.int64)
tmp66 = tmp64 != tmp65
tmp67 = tl.where(tmp66, tmp64, tmp20)
tmp68 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp69 = tmp67 + tmp68
tmp70 = tmp67 < 0
tmp71 = tl.where(tmp70, tmp69, tmp67)
tl.device_assert((0 <= tmp71) & (tmp71 < 4),
'index out of bounds: 0 <= tmp71 < 4')
tmp73 = tl.load(in_ptr2 + (r0 + 16 * tmp71 + 64 * r1), None)
tmp75 = tl_math.exp(tmp74)
tmp77 = tl_math.exp(tmp76)
tmp78 = tmp75 + tmp77
tmp80 = tl_math.exp(tmp79)
tmp81 = tmp78 + tmp80
tmp83 = tl_math.exp(tmp82)
tmp84 = tmp81 + tmp83
tmp85 = tl_math.log(tmp84)
tmp86 = tmp73 - tmp85
tmp87 = -tmp86
tmp88 = 0.0
tmp89 = tl.where(tmp66, tmp87, tmp88)
tmp90 = tl.broadcast_to(tmp89, [XBLOCK, RBLOCK])
tmp92 = tl.sum(tmp90, 1)[:, None]
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp92, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((), (), torch.float32)
triton_per_fused_max_mul_nll_loss2d_forward_rsub_sub_1[grid(1)](arg1_1,
arg0_1, buf1, buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del buf1
return buf2,
class partCENew(nn.Module):
def __init__(self, if_average=False):
super(partCENew, self).__init__()
self.crit = nn.CrossEntropyLoss(size_average=if_average)
self.maximum_score = 100000
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
INK-USC/shifted-label-distribution
|
partCE
| false | 8,260 |
[
"Apache-2.0"
] | 37 |
3cf2b7ced3b2e18234db405f6014f049c4830d71
|
https://github.com/INK-USC/shifted-label-distribution/tree/3cf2b7ced3b2e18234db405f6014f049c4830d71
|
one_conv
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/so/csoofz34fiuxwjvdpudit7atl35pibxa6s4kp6wq5gg7nlgjdmxl.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %where], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16) % 8
x0 = xindex % 16
x2 = (xindex // 128)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (16*x1) + (64*x2)), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp6 & xmask, other=0.0)
tmp10 = tl.load(in_ptr2 + ((-4) + x1), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = 0.0
tmp13 = tmp11 > tmp12
tmp14 = 0.1
tmp15 = tmp11 * tmp14
tmp16 = tl.where(tmp13, tmp11, tmp15)
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp6, tmp16, tmp17)
tmp19 = tl.where(tmp4, tmp5, tmp18)
tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qg/cqghp3in456skrsqczxk64fxgnoasx3wqnhejcldnxf4o44vtala.py
# Topologically Sorted Source Nodes: [conv2d, output], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
# conv2d => convolution
# output => gt, mul, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.1), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tmp8 = tmp7 > tmp3
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_3, buf0, primals_2, buf1, 512, grid=grid(512), stream=stream0)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [conv2d, output], Original ATen: [aten.convolution, aten.leaky_relu, aten.leaky_relu_backward]
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1.run(buf0, primals_2, buf2, 256, grid=grid(256), stream=stream0)
del buf0
del primals_2
return (buf1, primals_1, primals_3, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class one_conv(nn.Module):
def __init__(self, G0, G):
super(one_conv, self).__init__()
self.conv = nn.Conv2d(G0, G, kernel_size=3, stride=1, padding=1,
bias=True)
self.relu = nn.LeakyReLU(0.1, inplace=True)
def forward(self, x):
output = self.relu(self.conv(x))
return torch.cat((x, output), dim=1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'G0': 4, 'G': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 8
x0 = xindex % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1 + 64 * x2), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp6 & xmask,
other=0.0)
tmp10 = tl.load(in_ptr2 + (-4 + x1), tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp11 = tmp9 + tmp10
tmp12 = 0.0
tmp13 = tmp11 > tmp12
tmp14 = 0.1
tmp15 = tmp11 * tmp14
tmp16 = tl.where(tmp13, tmp11, tmp15)
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp6, tmp16, tmp17)
tmp19 = tl.where(tmp4, tmp5, tmp18)
tl.store(out_ptr0 + x3, tmp19, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.1
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tmp8 = tmp7 > tmp3
tl.store(out_ptr0 + x3, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](primals_3, buf0, primals_2, buf1,
512, XBLOCK=128, num_warps=4, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_leaky_relu_leaky_relu_backward_1[grid(256)
](buf0, primals_2, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1
)
del buf0
del primals_2
return buf1, primals_1, primals_3, buf2
class one_convNew(nn.Module):
def __init__(self, G0, G):
super(one_convNew, self).__init__()
self.conv = nn.Conv2d(G0, G, kernel_size=3, stride=1, padding=1,
bias=True)
self.relu = nn.LeakyReLU(0.1, inplace=True)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Holmes-Alan/RefVAE
|
one_conv
| false | 8,261 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
ConvBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zs/czs4ezdi2shilx2y4a6pxnbbemyjgfacstkzskfo74loizhu3ggf.py
# Topologically Sorted Source Nodes: [out, out_1, prelu], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten._prelu_kernel]
# Source node to ATen node mapping:
# out => convolution
# out_1 => add, rsqrt, var_mean
# prelu => gt, mul_1, where
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [4, 4], [1, 1], False, [0, 0], 1), kwargs = {})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 128],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + (81*x3)), rmask & xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + (0))
tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(rmask & xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(rmask & xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(rmask & xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 81.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tmp24 = tmp2 - tmp12
tmp25 = tmp24 * tmp23
tmp26 = 0.0
tmp27 = tmp25 > tmp26
tmp30 = tmp29 * tmp25
tmp31 = tl.where(tmp27, tmp25, tmp30)
tl.store(in_out_ptr0 + (r2 + (81*x3)), tmp2, rmask & xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + (x3), tmp23, xmask)
tl.store(out_ptr1 + (r2 + (81*x3)), tmp31, rmask & xmask)
tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
buf1 = buf0; del buf0 # reuse
buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf5 = reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf3 # reuse
buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
# Topologically Sorted Source Nodes: [out, out_1, prelu], Original ATen: [aten.convolution, aten._native_batch_norm_legit, aten._prelu_kernel]
stream0 = get_raw_stream(0)
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0.run(buf1, buf5, primals_2, primals_4, buf2, buf6, 16, 81, grid=grid(16), stream=stream0)
del primals_2
return (buf6, primals_1, primals_3, primals_4, buf1, buf2, buf5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act = torch.nn.PReLU()
self.bn = nn.InstanceNorm2d(output_size)
def forward(self, x):
out = self.conv(x)
out = self.bn(out)
return self.act(out)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'output_size': 4, 'kernel_size': 4,
'stride': 1, 'padding': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0(
in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel,
rnumel, XBLOCK: tl.constexpr):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + 81 * x3), rmask & xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr1 + 0)
tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tl.where(rmask & xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(rmask & xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(rmask & xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 81.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tmp24 = tmp2 - tmp12
tmp25 = tmp24 * tmp23
tmp26 = 0.0
tmp27 = tmp25 > tmp26
tmp30 = tmp29 * tmp25
tmp31 = tl.where(tmp27, tmp25, tmp30)
tl.store(in_out_ptr0 + (r2 + 81 * x3), tmp2, rmask & xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr1 + (r2 + 81 * x3), tmp31, rmask & xmask)
tl.store(out_ptr0 + x3, tmp12, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(4, 4), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf5 = reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf3
buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
get_raw_stream(0)
triton_per_fused__native_batch_norm_legit__prelu_kernel_convolution_0[
grid(16)](buf1, buf5, primals_2, primals_4, buf2, buf6, 16, 81,
XBLOCK=8, num_warps=8, num_stages=1)
del primals_2
return buf6, primals_1, primals_3, primals_4, buf1, buf2, buf5
class ConvBlockNew(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlockNew, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act = torch.nn.PReLU()
self.bn = nn.InstanceNorm2d(output_size)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = self.conv.bias
primals_4 = self.act.weight
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Holmes-Alan/RefVAE
|
ConvBlock
| false | 8,262 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
BilinearMap
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/df/cdfygfm4xnb6wzvm4palmj5hunxrfqmwzgj6vkntuepuhhwswurm.py
# Topologically Sorted Source Nodes: [mul, sum_1], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
# mul => mul
# sum_1 => sum_1
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %primals_2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_poi_fused_mul_sum_0 = async_compile.triton('triton_poi_fused_mul_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 * tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 * tmp12
tmp14 = tmp10 + tmp13
tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), primals_3, out=buf0)
del primals_3
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, sum_1], Original ATen: [aten.mul, aten.sum]
stream0 = get_raw_stream(0)
triton_poi_fused_mul_sum_0.run(buf0, primals_2, buf1, 256, grid=grid(256), stream=stream0)
del buf0
return (buf1, primals_2, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.nn as nn
from torch.nn.parameter import Parameter
class BilinearMap(nn.Module):
def __init__(self, nunits):
super(BilinearMap, self).__init__()
self.map = Parameter(th.Tensor(nunits, nunits))
self.nunits = nunits
self.reset_parameters()
def reset_parameters(self):
nn.init.eye_(self.map)
def forward(self, l, r):
ncon = l.shape[1]
r.shape[2]
nunits = l.shape[3]
first = th.mm(l.view(-1, nunits), self.map).view(-1, ncon, 1, nunits)
return th.sum(first * r, dim=-1)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([16, 4, 4, 4])]
def get_init_inputs():
return [[], {'nunits': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.nn as nn
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 * tmp1
tmp5 = tmp3 * tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 * tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 * tmp12
tmp14 = tmp10 + tmp13
tl.store(out_ptr0 + x2, tmp14, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
primals_3, out=buf0)
del primals_3
buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_mul_sum_0[grid(256)](buf0, primals_2, buf1, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del buf0
return buf1, primals_2, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0)
class BilinearMapNew(nn.Module):
def __init__(self, nunits):
super(BilinearMapNew, self).__init__()
self.map = Parameter(th.Tensor(nunits, nunits))
self.nunits = nunits
self.reset_parameters()
def reset_parameters(self):
nn.init.eye_(self.map)
def forward(self, input_0, input_1):
primals_3 = self.map
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
IBM/aihn-ucsd
|
BilinearMap
| false | 8,263 |
[
"Apache-2.0"
] | 20 |
6c6a56d11c704b529a31868418e350e9760ff9d9
|
https://github.com/IBM/aihn-ucsd/tree/6c6a56d11c704b529a31868418e350e9760ff9d9
|
ResnetBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/f2/cf2npekvrfp4jsjrel6za5ovwvgxs4a2lwakd7jnsn4vg4gxjudu.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# out => convolution
# out_1 => gt, mul, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + (x3), tmp4, xmask)
tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kn/cknmhtb32jkoub44jo3jvancxhuld5lc3b6ov2b7mif7mktovzkw.py
# Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
# Source node to ATen node mapping:
# out_2 => convolution_1
# out_3 => add
# out_4 => gt_1, mul_1, where_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %primals_3), kwargs = {})
# %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.01), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %add, %mul_1), kwargs = {})
triton_poi_fused_add_convolution_leaky_relu_1 = async_compile.triton('triton_poi_fused_add_convolution_leaky_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp7 = 0.01
tmp8 = tmp4 * tmp7
tmp9 = tl.where(tmp6, tmp4, tmp8)
tl.store(out_ptr0 + (x3), tmp6, xmask)
tl.store(out_ptr1 + (x3), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.leaky_relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 256, grid=grid(256), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf5 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [out_2, out_3, out_4], Original ATen: [aten.convolution, aten.add, aten.leaky_relu]
triton_poi_fused_add_convolution_leaky_relu_1.run(buf3, primals_5, primals_3, buf4, buf5, 256, grid=grid(256), stream=stream0)
del buf3
del primals_5
return (buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
class ResnetBlock(torch.nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias
=True):
super(ResnetBlock, self).__init__()
self.conv1 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,
stride, padding, bias=bias)
self.conv2 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,
stride, padding, bias=bias)
self.act1 = torch.nn.LeakyReLU()
self.act2 = torch.nn.LeakyReLU()
def forward(self, x):
out = self.conv1(x)
out = self.act1(out)
out = self.conv2(out)
out = out + x
out = self.act2(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_filter': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_add_convolution_leaky_relu_1(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp7 = 0.01
tmp8 = tmp4 * tmp7
tmp9 = tl.where(tmp6, tmp4, tmp8)
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp9, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf0,
primals_2, buf1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_add_convolution_leaky_relu_1[grid(256)](buf3,
primals_5, primals_3, buf4, buf5, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del buf3
del primals_5
return buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4
class ResnetBlockNew(torch.nn.Module):
def __init__(self, num_filter, kernel_size=3, stride=1, padding=1, bias
=True):
super(ResnetBlockNew, self).__init__()
self.conv1 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,
stride, padding, bias=bias)
self.conv2 = torch.nn.Conv2d(num_filter, num_filter, kernel_size,
stride, padding, bias=bias)
self.act1 = torch.nn.LeakyReLU()
self.act2 = torch.nn.LeakyReLU()
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
Holmes-Alan/RefVAE
|
ResnetBlock
| false | 8,264 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
dnn
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/4g/c4guhk7x6skkidedvs2gxz2kcu6gb76l3ig5crjjvjtzvnjlhlte.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# x => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ta/ctarkhd7gqmxsmyghlzwykngqas6dfclf7bk5ijucwm3q5stwfhi.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
# x_3 => sigmoid
# Graph fragment:
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_7,), kwargs = {})
triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_sigmoid_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf10, 256, grid=grid(256), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf9, 256, grid=grid(256), stream=stream0)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf5, primals_7, buf8, 256, grid=grid(256), stream=stream0)
del primals_7
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6 # reuse
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.sigmoid]
triton_poi_fused_sigmoid_1.run(buf7, primals_9, 256, grid=grid(256), stream=stream0)
del primals_9
return (buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), buf7, primals_8, buf8, primals_6, buf9, primals_4, buf10, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class dnn(nn.Module):
def weight_init(self):
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.xavier_uniform_(self.fc2.weight)
nn.init.xavier_uniform_(self.fc3.weight)
nn.init.xavier_uniform_(self.out.weight)
def __init__(self, G_in, G_out, w1, w2, w3):
super(dnn, self).__init__()
self.fc1 = nn.Linear(G_in, w1)
self.fc2 = nn.Linear(w1, w2)
self.fc3 = nn.Linear(w2, w3)
self.out = nn.Linear(w3, G_out)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.sigmoid(self.out(x))
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'G_in': 4, 'G_out': 4, 'w1': 4, 'w2': 4, 'w3': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.sigmoid(tmp2)
tl.store(in_out_ptr0 + x2, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2)
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3,
primals_5, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf4
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf5,
primals_7, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf6
triton_poi_fused_sigmoid_1[grid(256)](buf7, primals_9, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_9
return buf7, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(
buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0
), buf7, primals_8, buf8, primals_6, buf9, primals_4, buf10
class dnnNew(nn.Module):
def weight_init(self):
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.xavier_uniform_(self.fc2.weight)
nn.init.xavier_uniform_(self.fc3.weight)
nn.init.xavier_uniform_(self.out.weight)
def __init__(self, G_in, G_out, w1, w2, w3):
super(dnnNew, self).__init__()
self.fc1 = nn.Linear(G_in, w1)
self.fc2 = nn.Linear(w1, w2)
self.fc3 = nn.Linear(w2, w3)
self.out = nn.Linear(w3, G_out)
def forward(self, input_0):
primals_1 = self.fc1.weight
primals_2 = self.fc1.bias
primals_4 = self.fc2.weight
primals_5 = self.fc2.bias
primals_6 = self.fc3.weight
primals_7 = self.fc3.bias
primals_8 = self.out.weight
primals_9 = self.out.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
Harshitmalaviya/whisper-to-normal-speech-conversion
|
dnn
| false | 8,265 |
[
"MIT"
] | 23 |
a6d411b27a3c5cc4ad12e3968350b22d88b9b4d9
|
https://github.com/Harshitmalaviya/whisper-to-normal-speech-conversion/tree/a6d411b27a3c5cc4ad12e3968350b22d88b9b4d9
|
TVLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/xy/cxyvhysnbfnp3hjbti5qrkwpcuvte3fburcim44pxxqaqbobxi7z.py
# Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add, mul], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add, aten.mul]
# Source node to ATen node mapping:
# add => add
# h_tv => sum_1
# mul => mul
# pow_1 => pow_1
# pow_2 => pow_2
# sub => sub
# sub_1 => sub_1
# w_tv => sum_2
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_3, %slice_7), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_12, %slice_16), kwargs = {})
# %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 2), kwargs = {})
triton_per_fused_add_mul_pow_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_pow_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r0 = rindex % 12
r1 = (rindex // 12)
r2 = rindex % 3
r3 = (rindex // 3)
tmp0 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (r0 + (16*r1)), rmask, other=0.0)
tmp8 = tl.load(in_ptr0 + (1 + r2 + (4*r3)), rmask, other=0.0)
tmp9 = tl.load(in_ptr0 + (r2 + (4*r3)), rmask, other=0.0)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp10 = tmp8 - tmp9
tmp11 = tmp10 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(rmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp16 = tmp7 + tmp15
tmp17 = 2.0
tmp18 = tmp16 * tmp17
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp18, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf2 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [sub, pow_1, h_tv, sub_1, pow_2, w_tv, add, mul], Original ATen: [aten.sub, aten.pow, aten.sum, aten.add, aten.mul]
stream0 = get_raw_stream(0)
triton_per_fused_add_mul_pow_sub_sum_0.run(buf2, arg0_1, 1, 192, grid=grid(1), stream=stream0)
del arg0_1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
class TVLoss(torch.nn.Module):
def __init__(self):
super(TVLoss, self).__init__()
def forward(self, x):
x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
self._tensor_size(x[:, :, 1:, :])
self._tensor_size(x[:, :, :, 1:])
h_tv = torch.pow(x[:, :, 1:, :] - x[:, :, :h_x - 1, :], 2).sum()
w_tv = torch.pow(x[:, :, :, 1:] - x[:, :, :, :w_x - 1], 2).sum()
return 2 * (h_tv + w_tv)
def _tensor_size(self, t):
return t.size()[1] * t.size()[2] * t.size()[3]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0, xnumel,
rnumel, XBLOCK: tl.constexpr):
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r0 = rindex % 12
r1 = rindex // 12
r2 = rindex % 3
r3 = rindex // 3
tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
tmp8 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0)
tmp9 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp10 = tmp8 - tmp9
tmp11 = tmp10 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(rmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp16 = tmp7 + tmp15
tmp17 = 2.0
tmp18 = tmp16 * tmp17
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp18, None)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf2 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_mul_pow_sub_sum_0[grid(1)](buf2, arg0_1, 1,
192, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
return buf2,
class TVLossNew(torch.nn.Module):
def __init__(self):
super(TVLossNew, self).__init__()
def _tensor_size(self, t):
return t.size()[1] * t.size()[2] * t.size()[3]
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Holmes-Alan/RefVAE
|
TVLoss
| false | 8,266 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
EncoderLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/3y/c3yjsyte6ypj6tbfplegsg4gibdswmi6tpejs4pygaxwycmgxs5h.py
# Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div]
# Source node to ATen node mapping:
# add => add
# mean => mean
# mul => mul
# norm => add_1
# std => sqrt, var
# sub => sub
# truediv => div
# Graph fragment:
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_2, [-1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %mean), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %sub), kwargs = {})
# %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_2, [-1]), kwargs = {correction: 1.0, keepdim: True})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_3), kwargs = {})
triton_poi_fused_add_div_mean_mul_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp2 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = 4.0
tmp10 = tmp8 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp0 * tmp11
tmp13 = tmp2 - tmp10
tmp14 = tmp13 * tmp13
tmp15 = tmp3 - tmp10
tmp16 = tmp15 * tmp15
tmp17 = tmp14 + tmp16
tmp18 = tmp5 - tmp10
tmp19 = tmp18 * tmp18
tmp20 = tmp17 + tmp19
tmp21 = tmp7 - tmp10
tmp22 = tmp21 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = 3.0
tmp25 = tmp23 / tmp24
tmp26 = libdevice.sqrt(tmp25)
tmp27 = 1e-06
tmp28 = tmp26 + tmp27
tmp29 = tmp12 / tmp28
tmp31 = tmp29 + tmp30
tl.store(out_ptr0 + (x2), tmp31, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/eg/ceg7kyuq33wggndf6pr3tkpmtjy4bsacoa2z3paagswr3wsvq4lr.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# matmul => clone
# Graph fragment:
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/k6/ck6eny5d3x47lqot42mqpldvzo6j7jifs5vaem3ya4ax2tqx4ryp.py
# Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
# Source node to ATen node mapping:
# eq => eq
# Graph fragment:
# %eq : [num_users=2] = call_function[target=torch.ops.aten.eq.Scalar](args = (%unsqueeze, 0), kwargs = {})
triton_poi_fused_eq_2 = async_compile.triton('triton_poi_fused_eq_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_eq_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = tmp0 == tmp1
tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/yw/cywbspf42lkpoxzzmwjztxpmiirzzhrlkwroigzwipg6mbbqm6sg.py
# Topologically Sorted Source Nodes: [scores, scores_1, scores_2], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
# scores => div_1
# scores_1 => full_default, where
# scores_2 => amax, exp, sub_1, sum_1
# Graph fragment:
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, 1.0), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %div_1), kwargs = {})
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [-1], True), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_div_masked_fill_3 = async_compile.triton('triton_poi_fused__softmax_div_masked_fill_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*i1', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_div_masked_fill_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = (xindex // 16)
x3 = xindex
tmp0 = tl.load(in_ptr0 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp1 = tl.load(in_ptr1 + (4*x3), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp7 = tl.load(in_ptr1 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp12 = tl.load(in_ptr1 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr0 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp17 = tl.load(in_ptr1 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp8 = tmp7 * tmp2
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = triton_helpers.maximum(tmp5, tmp9)
tmp13 = tmp12 * tmp2
tmp14 = tl.where(tmp11, tmp4, tmp13)
tmp15 = triton_helpers.maximum(tmp10, tmp14)
tmp18 = tmp17 * tmp2
tmp19 = tl.where(tmp16, tmp4, tmp18)
tmp20 = triton_helpers.maximum(tmp15, tmp19)
tmp21 = tmp5 - tmp20
tmp22 = tl_math.exp(tmp21)
tmp23 = tmp9 - tmp20
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = tmp14 - tmp20
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tmp19 - tmp20
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp28 + tmp30
tl.store(out_ptr0 + (x3), tmp20, xmask)
tl.store(out_ptr1 + (x3), tmp31, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/w3/cw3ftfqsr263uqn3jo2vzjs72ur46m2qfeii754yplfdw7xy2oht.py
# Topologically Sorted Source Nodes: [scores, scores_1, scores_2], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
# scores => div_1
# scores_1 => full_default, where
# scores_2 => amax, div_2, exp, sub_1
# Graph fragment:
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_11, 1.0), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1000000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %div_1), kwargs = {})
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [-1], True), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
# %div_2 : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_div_masked_fill_4 = async_compile.triton('triton_poi_fused__softmax_div_masked_fill_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_div_masked_fill_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = (xindex // 64)
x4 = xindex % 16
x5 = xindex
x6 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x4 + (16*x3)), xmask, eviction_policy='evict_last').to(tl.int1)
tmp1 = tl.load(in_out_ptr0 + (x5), xmask)
tmp6 = tl.load(in_ptr1 + (x6), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + (x6), xmask, eviction_policy='evict_last')
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp7 = tmp5 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp10 = tmp8 / tmp9
tl.store(in_out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pj/cpjvgoae5xnissf7uywxqqiozzft7p7zewnr46ifmiqhi6refazr.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# contiguous => clone_4
# Graph fragment:
# %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/o5/co5rm3rkza5x3azfhhhfrk5uykugfrlcma5drkjgu6vedwge3lqh.py
# Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# contiguous_1 => clone_5
# Graph fragment:
# %clone_5 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_9,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = (xindex // 4) % 4
x2 = (xindex // 16) % 4
x3 = (xindex // 64)
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/lu/clupfp5v6vnhz3xgqwadrpkejwwrfku4vc64chliaozah3tzblug.py
# Topologically Sorted Source Nodes: [x, mean_1, std_1], Original ATen: [aten.add, aten.mean, aten.std]
# Source node to ATen node mapping:
# mean_1 => mean_1
# std_1 => var_1
# x => add_2
# Graph fragment:
# %add_2 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %view_17), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [-1], True), kwargs = {})
# %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%add_2, [-1]), kwargs = {correction: 1.0, keepdim: True})
triton_poi_fused_add_mean_std_7 = async_compile.triton('triton_poi_fused_add_mean_std_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_std_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mean_std_7(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = 3.0
tmp29 = tmp27 / tmp28
tl.store(out_ptr0 + (x0), tmp16, xmask)
tl.store(in_out_ptr0 + (x0), tmp29, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/2k/c2kpyzb35xzjeem5y7lficga7cmuulafpqdcmd7wf5zp6gtbjmfh.py
# Topologically Sorted Source Nodes: [x, mean_1, sub_1, mul_1, std_1, add_3, truediv_2, norm_1], Original ATen: [aten.add, aten.mean, aten.sub, aten.mul, aten.std, aten.div]
# Source node to ATen node mapping:
# add_3 => add_3
# mean_1 => mean_1
# mul_1 => mul_1
# norm_1 => add_4
# std_1 => sqrt_1
# sub_1 => sub_2
# truediv_2 => div_3
# x => add_2
# Graph fragment:
# %add_2 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %view_17), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_2, [-1], True), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %mean_1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_13, %sub_2), kwargs = {})
# %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var_1,), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt_1, 1e-06), kwargs = {})
# %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_3), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_3, %primals_14), kwargs = {})
triton_poi_fused_add_div_mean_mul_std_sub_8 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp2 = tl.load(in_ptr2 + (x2), xmask)
tmp4 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 - tmp4
tmp6 = tmp0 * tmp5
tmp8 = libdevice.sqrt(tmp7)
tmp9 = 1e-06
tmp10 = tmp8 + tmp9
tmp11 = tmp6 / tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kp/ckp4wu25oznpu5hpwyxmm53fn2rpbixw2gq57akracpnchz7qcmt.py
# Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
# gelu => add_5, erf, mul_2, mul_3, mul_4
# Graph fragment:
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_20, 0.5), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_20, 0.7071067811865476), kwargs = {})
# %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_3,), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %add_5), kwargs = {})
triton_poi_fused_gelu_9 = async_compile.triton('triton_poi_fused_gelu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_gelu_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), None)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + (x0), tmp8, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/mv/cmvuu3ad7v7wn3urjnbejo3bxrb3eze5hd7acifzdizjsplqfdit.py
# Topologically Sorted Source Nodes: [x, x_3], Original ATen: [aten.add]
# Source node to ATen node mapping:
# x => add_2
# x_3 => add_6
# Graph fragment:
# %add_2 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %view_17), kwargs = {})
# %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_22), kwargs = {})
triton_poi_fused_add_10 = async_compile.triton('triton_poi_fused_add_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18 = args
args.clear()
assert_size_stride(primals_1, (4, ), (1, ))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4, ), (1, ))
assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_11, (4, 4), (4, 1))
assert_size_stride(primals_12, (4, ), (1, ))
assert_size_stride(primals_13, (4, ), (1, ))
assert_size_stride(primals_14, (4, ), (1, ))
assert_size_stride(primals_15, (2048, 4), (4, 1))
assert_size_stride(primals_16, (2048, ), (1, ))
assert_size_stride(primals_17, (4, 2048), (2048, 1))
assert_size_stride(primals_18, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mean, sub, mul, std, add, truediv, norm], Original ATen: [aten.mean, aten.sub, aten.mul, aten.std, aten.add, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_add_div_mean_mul_std_sub_0.run(primals_1, primals_2, primals_3, buf0, 64, grid=grid(64), stream=stream0)
del primals_1
del primals_3
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
triton_poi_fused_clone_1.run(buf2, primals_7, buf4, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_7
buf5 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0); del buf2 # reuse
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
triton_poi_fused_clone_1.run(buf1, primals_5, buf5, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_5
buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
buf7 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [eq], Original ATen: [aten.eq]
triton_poi_fused_eq_2.run(primals_10, buf7, 64, grid=grid(64), stream=stream0)
del primals_10
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0); del buf1 # reuse
buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
# Topologically Sorted Source Nodes: [scores, scores_1, scores_2], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
triton_poi_fused__softmax_div_masked_fill_3.run(buf7, buf6, buf8, buf9, 64, grid=grid(64), stream=stream0)
buf10 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6 # reuse
# Topologically Sorted Source Nodes: [scores, scores_1, scores_2], Original ATen: [aten.div, aten.masked_fill, aten._softmax]
triton_poi_fused__softmax_div_masked_fill_4.run(buf10, buf7, buf8, buf9, 256, grid=grid(256), stream=stream0)
buf11 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf9 # reuse
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.clone]
triton_poi_fused_clone_1.run(buf3, primals_9, buf11, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_9
buf12 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0); del buf3 # reuse
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
buf13 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf8 # reuse
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
triton_poi_fused_clone_5.run(buf12, buf13, 16, 4, grid=grid(16, 4), stream=stream0)
buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0); del buf12 # reuse
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_12, reinterpret_tensor(buf13, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf14)
del primals_12
buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [contiguous_1], Original ATen: [aten.clone]
triton_poi_fused_clone_6.run(buf10, buf15, 256, grid=grid(256), stream=stream0)
buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf18 = buf17; del buf17 # reuse
# Topologically Sorted Source Nodes: [x, mean_1, std_1], Original ATen: [aten.add, aten.mean, aten.std]
triton_poi_fused_add_mean_std_7.run(buf18, primals_2, buf14, buf16, 16, grid=grid(16), stream=stream0)
buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x, mean_1, sub_1, mul_1, std_1, add_3, truediv_2, norm_1], Original ATen: [aten.add, aten.mean, aten.sub, aten.mul, aten.std, aten.div]
triton_poi_fused_add_div_mean_mul_std_sub_8.run(primals_13, primals_2, buf14, buf16, buf18, primals_14, buf19, 64, grid=grid(64), stream=stream0)
del buf16
del buf18
del primals_14
buf20 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_16, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), reinterpret_tensor(primals_15, (4, 2048), (1, 4), 0), alpha=1, beta=1, out=buf20)
del primals_16
buf21 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.float32)
# Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu]
triton_poi_fused_gelu_9.run(buf20, buf21, 32768, grid=grid(32768), stream=stream0)
buf22 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(buf21, (16, 2048), (2048, 1), 0), reinterpret_tensor(primals_17, (2048, 4), (1, 2048), 0), out=buf22)
buf23 = reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0); del buf22 # reuse
# Topologically Sorted Source Nodes: [x, x_3], Original ATen: [aten.add]
triton_poi_fused_add_10.run(buf23, primals_2, buf14, primals_18, 64, grid=grid(64), stream=stream0)
del primals_18
return (buf23, reinterpret_tensor(buf15, (4, 4, 16), (64, 16, 1), 0), primals_2, primals_13, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf7, buf10, reinterpret_tensor(buf13, (16, 4), (4, 1), 0), buf14, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), buf20, reinterpret_tensor(buf21, (16, 2048), (2048, 1), 0), primals_17, primals_15, primals_11, reinterpret_tensor(buf11, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 4), 0), primals_8, primals_6, primals_4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((2048, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((4, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0)
scores = F.softmax(scores, dim=-1)
scores_attn = scores
if dropout is not None:
scores = dropout(scores)
output = torch.matmul(scores, v)
return output, scores_attn
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = self.dropout(F.gelu(self.linear_1(x)))
x = self.linear_2(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
self.out = nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
k = k.transpose(1, 2)
q = q.transpose(1, 2)
v = v.transpose(1, 2)
scores, scores_attn = attention(q, k, v, self.d_k, mask, self.dropout)
concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
output = self.out(concat)
concat_attn = scores_attn.transpose(1, 2).contiguous().view(bs, -1,
scores_attn.size(-1) * self.h)
return output, concat_attn
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
def forward(self, x):
norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
=-1, keepdim=True) + self.eps) + self.bias
return norm
class EncoderLayer(nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.attn = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, x, mask):
x2 = self.norm_1(x)
attn, concat_attn = self.attn(x2, x2, x2, mask)
x = x + self.dropout_1(attn)
x2 = self.norm_2(x)
x = x + self.dropout_2(self.ff(x2))
return x, concat_attn
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'heads': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_0(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = 4.0
tmp10 = tmp8 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp0 * tmp11
tmp13 = tmp2 - tmp10
tmp14 = tmp13 * tmp13
tmp15 = tmp3 - tmp10
tmp16 = tmp15 * tmp15
tmp17 = tmp14 + tmp16
tmp18 = tmp5 - tmp10
tmp19 = tmp18 * tmp18
tmp20 = tmp17 + tmp19
tmp21 = tmp7 - tmp10
tmp22 = tmp21 * tmp21
tmp23 = tmp20 + tmp22
tmp24 = 3.0
tmp25 = tmp23 / tmp24
tmp26 = libdevice.sqrt(tmp25)
tmp27 = 1e-06
tmp28 = tmp26 + tmp27
tmp29 = tmp12 / tmp28
tmp31 = tmp29 + tmp30
tl.store(out_ptr0 + x2, tmp31, xmask)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_eq_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 == tmp1
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_3(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * x0 + 16 * x2), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp1 = tl.load(in_ptr1 + 4 * x3, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
='evict_last').to(tl.int1)
tmp7 = tl.load(in_ptr1 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * x2), xmask,
eviction_policy='evict_last').to(tl.int1)
tmp12 = tl.load(in_ptr1 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
)
tmp16 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x2), xmask,
eviction_policy='evict_last').to(tl.int1)
tmp17 = tl.load(in_ptr1 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp8 = tmp7 * tmp2
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = triton_helpers.maximum(tmp5, tmp9)
tmp13 = tmp12 * tmp2
tmp14 = tl.where(tmp11, tmp4, tmp13)
tmp15 = triton_helpers.maximum(tmp10, tmp14)
tmp18 = tmp17 * tmp2
tmp19 = tl.where(tmp16, tmp4, tmp18)
tmp20 = triton_helpers.maximum(tmp15, tmp19)
tmp21 = tmp5 - tmp20
tmp22 = tl_math.exp(tmp21)
tmp23 = tmp9 - tmp20
tmp24 = tl_math.exp(tmp23)
tmp25 = tmp22 + tmp24
tmp26 = tmp14 - tmp20
tmp27 = tl_math.exp(tmp26)
tmp28 = tmp25 + tmp27
tmp29 = tmp19 - tmp20
tmp30 = tl_math.exp(tmp29)
tmp31 = tmp28 + tmp30
tl.store(out_ptr0 + x3, tmp20, xmask)
tl.store(out_ptr1 + x3, tmp31, xmask)
@triton.jit
def triton_poi_fused__softmax_div_masked_fill_4(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex // 64
x4 = xindex % 16
x5 = xindex
x6 = xindex // 4
tmp0 = tl.load(in_ptr0 + (x4 + 16 * x3), xmask, eviction_policy=
'evict_last').to(tl.int1)
tmp1 = tl.load(in_out_ptr0 + x5, xmask)
tmp6 = tl.load(in_ptr1 + x6, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = -1000000000.0
tmp5 = tl.where(tmp0, tmp4, tmp3)
tmp7 = tmp5 - tmp6
tmp8 = tl_math.exp(tmp7)
tmp10 = tmp8 / tmp9
tl.store(in_out_ptr0 + x5, tmp10, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
tl.store(out_ptr0 + x4, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_mean_std_7(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = 3.0
tmp29 = tmp27 / tmp28
tl.store(out_ptr0 + x0, tmp16, xmask)
tl.store(in_out_ptr0 + x0, tmp29, xmask)
@triton.jit
def triton_poi_fused_add_div_mean_mul_std_sub_8(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tl.load(in_ptr2 + x2, xmask)
tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 - tmp4
tmp6 = tmp0 * tmp5
tmp8 = libdevice.sqrt(tmp7)
tmp9 = 1e-06
tmp10 = tmp8 + tmp9
tmp11 = tmp6 / tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_gelu_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 0.7071067811865476
tmp4 = tmp0 * tmp3
tmp5 = libdevice.erf(tmp4)
tmp6 = 1.0
tmp7 = tmp5 + tmp6
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, None)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_out_ptr0 + x2, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17, primals_18
) = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4), (4, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_11, (4, 4), (4, 1))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4,), (1,))
assert_size_stride(primals_14, (4,), (1,))
assert_size_stride(primals_15, (2048, 4), (4, 1))
assert_size_stride(primals_16, (2048,), (1,))
assert_size_stride(primals_17, (4, 2048), (2048, 1))
assert_size_stride(primals_18, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_mean_mul_std_sub_0[grid(64)](primals_1,
primals_2, primals_3, buf0, 64, XBLOCK=64, num_warps=1,
num_stages=1)
del primals_1
del primals_3
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_1[grid(16, 4)](buf2, primals_7, buf4, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del primals_7
buf5 = reinterpret_tensor(buf2, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf2
triton_poi_fused_clone_1[grid(16, 4)](buf1, primals_5, buf5, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del primals_5
buf6 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf5, (16, 1, 4), (4, 0, 1), 0), out=buf6)
buf7 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
triton_poi_fused_eq_2[grid(64)](primals_10, buf7, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_10
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0)
del buf1
buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused__softmax_div_masked_fill_3[grid(64)](buf7, buf6,
buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf10 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf6
triton_poi_fused__softmax_div_masked_fill_4[grid(256)](buf10, buf7,
buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
buf11 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf9
triton_poi_fused_clone_1[grid(16, 4)](buf3, primals_9, buf11, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del primals_9
buf12 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
del buf3
extern_kernels.bmm(reinterpret_tensor(buf10, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf11, (16, 4, 1), (4, 1, 0), 0), out=buf12)
buf13 = reinterpret_tensor(buf8, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf8
triton_poi_fused_clone_5[grid(16, 4)](buf12, buf13, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
del buf12
extern_kernels.addmm(primals_12, reinterpret_tensor(buf13, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf14)
del primals_12
buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_clone_6[grid(256)](buf10, buf15, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf16 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf17 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
buf18 = buf17
del buf17
triton_poi_fused_add_mean_std_7[grid(16)](buf18, primals_2, buf14,
buf16, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf19 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_add_div_mean_mul_std_sub_8[grid(64)](primals_13,
primals_2, buf14, buf16, buf18, primals_14, buf19, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del buf16
del buf18
del primals_14
buf20 = empty_strided_cuda((16, 2048), (2048, 1), torch.float32)
extern_kernels.addmm(primals_16, reinterpret_tensor(buf19, (16, 4),
(4, 1), 0), reinterpret_tensor(primals_15, (4, 2048), (1, 4), 0
), alpha=1, beta=1, out=buf20)
del primals_16
buf21 = empty_strided_cuda((4, 4, 2048), (8192, 2048, 1), torch.float32
)
triton_poi_fused_gelu_9[grid(32768)](buf20, buf21, 32768, XBLOCK=
128, num_warps=4, num_stages=1)
buf22 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf21, (16, 2048), (2048, 1),
0), reinterpret_tensor(primals_17, (2048, 4), (1, 2048), 0),
out=buf22)
buf23 = reinterpret_tensor(buf22, (4, 4, 4), (16, 4, 1), 0)
del buf22
triton_poi_fused_add_10[grid(64)](buf23, primals_2, buf14,
primals_18, 64, XBLOCK=64, num_warps=1, num_stages=1)
del primals_18
return buf23, reinterpret_tensor(buf15, (4, 4, 16), (64, 16, 1), 0
), primals_2, primals_13, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
), buf7, buf10, reinterpret_tensor(buf13, (16, 4), (4, 1), 0
), buf14, reinterpret_tensor(buf19, (16, 4), (4, 1), 0
), buf20, reinterpret_tensor(buf21, (16, 2048), (2048, 1), 0
), primals_17, primals_15, primals_11, reinterpret_tensor(buf11, (
16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 1, 4), (4,
1, 1), 0), reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 4), 0
), primals_8, primals_6, primals_4
def attention(q, k, v, d_k, mask=None, dropout=None):
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1000000000.0)
scores = F.softmax(scores, dim=-1)
scores_attn = scores
if dropout is not None:
scores = dropout(scores)
output = torch.matmul(scores, v)
return output, scores_attn
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super().__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = self.dropout(F.gelu(self.linear_1(x)))
x = self.linear_2(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.d_k = d_model // heads
self.h = heads
self.q_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
self.out = nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
k = k.transpose(1, 2)
q = q.transpose(1, 2)
v = v.transpose(1, 2)
scores, scores_attn = attention(q, k, v, self.d_k, mask, self.dropout)
concat = scores.transpose(1, 2).contiguous().view(bs, -1, self.d_model)
output = self.out(concat)
concat_attn = scores_attn.transpose(1, 2).contiguous().view(bs, -1,
scores_attn.size(-1) * self.h)
return output, concat_attn
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
def forward(self, x):
norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim
=-1, keepdim=True) + self.eps) + self.bias
return norm
class EncoderLayerNew(nn.Module):
def __init__(self, d_model, heads, dropout=0.1):
super().__init__()
self.norm_1 = Norm(d_model)
self.norm_2 = Norm(d_model)
self.attn = MultiHeadAttention(heads, d_model, dropout=dropout)
self.ff = FeedForward(d_model, dropout=dropout)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, input_0, input_1):
primals_1 = self.norm_1.alpha
primals_3 = self.norm_1.bias
primals_5 = self.norm_2.alpha
primals_7 = self.norm_2.bias
primals_4 = self.attn.q_linear.weight
primals_9 = self.attn.q_linear.bias
primals_6 = self.attn.v_linear.weight
primals_12 = self.attn.v_linear.bias
primals_8 = self.attn.k_linear.weight
primals_13 = self.attn.k_linear.bias
primals_11 = self.attn.out.weight
primals_14 = self.attn.out.bias
primals_15 = self.ff.linear_1.weight
primals_16 = self.ff.linear_1.bias
primals_17 = self.ff.linear_2.weight
primals_18 = self.ff.linear_2.bias
primals_2 = input_0
primals_10 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18])
return output[0], output[1]
|
Hyunseung-Kim/molGCT
|
EncoderLayer
| false | 8,267 |
[
"Apache-2.0"
] | 10 |
5a2604337cf0a9d3c725295ccb7c8ea4b0144636
|
https://github.com/Hyunseung-Kim/molGCT/tree/5a2604337cf0a9d3c725295ccb7c8ea4b0144636
|
ConvRelu
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ra/cra6jsfyhayzuojfdc2sk2dfy65xbnuhresodb6dnlgcqca222ma.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# relu => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0; del buf0 # reuse
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
return (buf1, primals_1, primals_2, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block.
"""
def __init__(self, num_in, num_out):
"""Creates a `ConvReLU` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, x):
"""The networks forward pass for which autograd synthesizes the backwards pass.
Args:
x: the input tensor
Returns:
The networks output tensor.
"""
return nn.functional.relu(self.block(x), inplace=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_in': 4, 'num_out': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, buf2,
256, XBLOCK=128, num_warps=4, num_stages=1)
return buf1, primals_1, primals_2, buf2
class ConvReluNew(nn.Module):
"""3x3 convolution followed by ReLU activation building block.
"""
def __init__(self, num_in, num_out):
"""Creates a `ConvReLU` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, input_0):
primals_1 = self.block.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Iceofsky/Roofpedia
|
ConvRelu
| false | 8,268 |
[
"MIT"
] | 16 |
933dd3ff6e77ace78be6d2a23ac6692281475073
|
https://github.com/Iceofsky/Roofpedia/tree/933dd3ff6e77ace78be6d2a23ac6692281475073
|
OutPutBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/yo/cyoslma2hokg7m5472do7ezglgj4v6w2kreyk2xlbumz6sx2npub.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# x_1 => convolution
# x_2 => gt, mul, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 2
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + (x3), tmp4, xmask)
tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7r/c7r2u57hr54idc3of6lw2ouxuoyy44tzonl7cy4k7awnnjece2kt.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# x_4 => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%where, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2, ), (1, ))
assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
buf1 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.leaky_relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_3, buf1, buf2, 128, grid=grid(128), stream=stream0)
del buf0
del primals_3
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf4, primals_5, 256, grid=grid(256), stream=stream0)
del primals_5
return (buf4, primals_1, primals_2, primals_4, buf1, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((2, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 2, 1, 1), (2, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class OutPutBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_size
=1, padding=0)
self.conv2 = nn.Conv2d(self.in_chns // 2, self.out_chns,
kernel_size=1, padding=0)
self.drop1 = nn.Dropout2d(0.3)
self.drop2 = nn.Dropout2d(0.3)
self.ac1 = nn.LeakyReLU()
def forward(self, x):
x = self.drop1(x)
x = self.conv1(x)
x = self.ac1(x)
x = self.drop2(x)
x = self.conv2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 2
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (2, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (2,), (1,))
assert_size_stride(primals_4, (4, 2, 1, 1), (2, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 2, 4, 4), (32, 16, 4, 1))
buf1 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(128)](buf0,
primals_3, buf1, buf2, 128, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_1[grid(256)](buf4, primals_5, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
return buf4, primals_1, primals_2, primals_4, buf1, buf2
class OutPutBlockNew(nn.Module):
def __init__(self, in_channels, out_channels):
super(OutPutBlockNew, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns // 2, kernel_size
=1, padding=0)
self.conv2 = nn.Conv2d(self.in_chns // 2, self.out_chns,
kernel_size=1, padding=0)
self.drop1 = nn.Dropout2d(0.3)
self.drop2 = nn.Dropout2d(0.3)
self.ac1 = nn.LeakyReLU()
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
HiLab-git/WSL4MIS
|
OutPutBlock
| false | 8,269 |
[
"MIT"
] | 29 |
9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
VAE
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zm/czm6acrrgjryz6xi3wza7npycjuiqsdsygpfdo3lbzaquecrmeuj.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/y2/cy2vtwelwsa5chivt5hur5623yyfsop2gmvgbhffcqy7a4djnosn.py
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# z => relu
# Graph fragment:
# %add_tensor_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_4, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_4,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 3000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 750
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zc/czckgrlamlexpfcw75z5bczbagqb4zjdi43eltbjcilb3letpux4.py
# Topologically Sorted Source Nodes: [log_std, std], Original ATen: [aten.clamp, aten.exp, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
# log_std => clamp_max, clamp_min
# std => exp
# Graph fragment:
# %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_10), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add_tensor_2, -4), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 15), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%clamp_max,), kwargs = {})
# %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add_tensor_2, -4), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add_tensor_2, 15), kwargs = {})
# %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le_2), kwargs = {})
triton_poi_fused_clamp_exp_ge_le_logical_and_2 = async_compile.triton('triton_poi_fused_clamp_exp_ge_le_logical_and_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_exp_ge_le_logical_and_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_exp_ge_le_logical_and_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -4.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 15.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp2 >= tmp3
tmp9 = tmp2 <= tmp5
tmp10 = tmp8 & tmp9
tl.store(out_ptr0 + (x2), tmp7, xmask)
tl.store(out_ptr1 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/k3/ck3ytvs37dz3ukphv3dedkr2zmrb7zfanvmbd4o2uvqy6mw4pp4j.py
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_1 => cat_1
# Graph fragment:
# %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %add], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.load(in_ptr2 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = tl.load(in_ptr3 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp6, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kf/ckfapnrlola5dmmfr42gfkp3drujg6bdif4llskw2nmmv3updkls.py
# Topologically Sorted Source Nodes: [tanh, u], Original ATen: [aten.tanh, aten.mul]
# Source node to ATen node mapping:
# tanh => tanh
# u => mul_1
# Graph fragment:
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%addmm_6,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, 4), kwargs = {})
triton_poi_fused_mul_tanh_4 = async_compile.triton('triton_poi_fused_mul_tanh_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_tanh_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_tanh_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = libdevice.tanh(tmp0)
tmp2 = 4.0
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (750, 8), (8, 1))
assert_size_stride(primals_4, (750, ), (1, ))
assert_size_stride(primals_5, (750, 750), (750, 1))
assert_size_stride(primals_6, (750, ), (1, ))
assert_size_stride(primals_7, (4, 750), (750, 1))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, 750), (750, 1))
assert_size_stride(primals_10, (4, ), (1, ))
assert_size_stride(primals_11, (750, 8), (8, 1))
assert_size_stride(primals_12, (750, ), (1, ))
assert_size_stride(primals_13, (750, 750), (750, 1))
assert_size_stride(primals_14, (750, ), (1, ))
assert_size_stride(primals_15, (4, 750), (750, 1))
assert_size_stride(primals_16, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
del primals_2
buf1 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 750), (1, 8), 0), out=buf1)
del primals_3
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [z], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf2, primals_4, 3000, grid=grid(3000), stream=stream0)
del primals_4
buf3 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (750, 750), (1, 750), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [z_1], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf4, primals_6, 3000, grid=grid(3000), stream=stream0)
del primals_6
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7, (750, 4), (1, 750), 0), alpha=1, beta=1, out=buf5)
del primals_8
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf4, reinterpret_tensor(primals_9, (750, 4), (1, 750), 0), out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf17 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
# Topologically Sorted Source Nodes: [log_std, std], Original ATen: [aten.clamp, aten.exp, aten.ge, aten.le, aten.logical_and]
triton_poi_fused_clamp_exp_ge_le_logical_and_2.run(buf6, primals_10, buf7, buf17, 16, grid=grid(16), stream=stream0)
del primals_10
# Topologically Sorted Source Nodes: [randn_like], Original ATen: [aten.randn_like]
buf8 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
buf9 = buf8
del buf8
buf10 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat_1], Original ATen: [aten.cat]
triton_poi_fused_cat_3.run(primals_1, buf5, buf7, buf9, buf10, 32, grid=grid(32), stream=stream0)
del primals_1
buf11 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf10, reinterpret_tensor(primals_11, (8, 750), (1, 8), 0), out=buf11)
buf12 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [a], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf12, primals_12, 3000, grid=grid(3000), stream=stream0)
del primals_12
buf13 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf12, reinterpret_tensor(primals_13, (750, 750), (1, 750), 0), out=buf13)
buf14 = buf13; del buf13 # reuse
# Topologically Sorted Source Nodes: [a_1], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf14, primals_14, 3000, grid=grid(3000), stream=stream0)
del primals_14
buf15 = buf6; del buf6 # reuse
# Topologically Sorted Source Nodes: [linear_6], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_16, buf14, reinterpret_tensor(primals_15, (750, 4), (1, 750), 0), alpha=1, beta=1, out=buf15)
del primals_16
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [tanh, u], Original ATen: [aten.tanh, aten.mul]
triton_poi_fused_mul_tanh_4.run(buf15, buf16, 16, grid=grid(16), stream=stream0)
return (buf16, buf5, buf7, buf0, buf2, buf4, buf7, buf9, buf10, buf12, buf14, buf15, primals_15, primals_13, primals_11, buf17, primals_9, primals_7, primals_5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((750, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((750, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((750, 750), (750, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((750, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 750), (750, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, 750), (750, 1), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((750, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((750, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((750, 750), (750, 1), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((750, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((4, 750), (750, 1), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class VAE(nn.Module):
def __init__(self, state_dim, action_dim, latent_dim, max_action, device):
super(VAE, self).__init__()
self.e1 = nn.Linear(state_dim + action_dim, 750)
self.e2 = nn.Linear(750, 750)
self.mean = nn.Linear(750, latent_dim)
self.log_std = nn.Linear(750, latent_dim)
self.d1 = nn.Linear(state_dim + latent_dim, 750)
self.d2 = nn.Linear(750, 750)
self.d3 = nn.Linear(750, action_dim)
self.max_action = max_action
self.latent_dim = latent_dim
self.device = device
def forward(self, state, action):
z = F.relu(self.e1(torch.cat([state, action], 1)))
z = F.relu(self.e2(z))
mean = self.mean(z)
log_std = self.log_std(z).clamp(-4, 15)
std = torch.exp(log_std)
z = mean + std * torch.randn_like(std)
u = self.decode(state, z)
return u, mean, std
def decode(self, state, z=None):
if z is None:
z = torch.randn((state.shape[0], self.latent_dim)).clamp(-0.5, 0.5)
a = F.relu(self.d1(torch.cat([state, z], 1)))
a = F.relu(self.d2(a))
return self.max_action * torch.tanh(self.d3(a))
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'state_dim': 4, 'action_dim': 4, 'latent_dim': 4,
'max_action': 4, 'device': 0}]
|
import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 3000
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 750
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_clamp_exp_ge_le_logical_and_2(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = -4.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 15.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp2 >= tmp3
tmp9 = tmp2 <= tmp5
tmp10 = tmp8 & tmp9
tl.store(out_ptr0 + x2, tmp7, xmask)
tl.store(out_ptr1 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tl.load(in_ptr3 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp12 = tmp10 * tmp11
tmp13 = tmp9 + tmp12
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp6, tmp13, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_mul_tanh_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = libdevice.tanh(tmp0)
tmp2 = 4.0
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + x0, tmp3, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (750, 8), (8, 1))
assert_size_stride(primals_4, (750,), (1,))
assert_size_stride(primals_5, (750, 750), (750, 1))
assert_size_stride(primals_6, (750,), (1,))
assert_size_stride(primals_7, (4, 750), (750, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4, 750), (750, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (750, 8), (8, 1))
assert_size_stride(primals_12, (750,), (1,))
assert_size_stride(primals_13, (750, 750), (750, 1))
assert_size_stride(primals_14, (750,), (1,))
assert_size_stride(primals_15, (4, 750), (750, 1))
assert_size_stride(primals_16, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 750), (1,
8), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(3000)](buf2, primals_4, 3000, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (750, 750), (
1, 750), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_1[grid(3000)](buf4, primals_6, 3000, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_6
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
(750, 4), (1, 750), 0), alpha=1, beta=1, out=buf5)
del primals_8
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_9, (750, 4), (1,
750), 0), out=buf6)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf17 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
triton_poi_fused_clamp_exp_ge_le_logical_and_2[grid(16)](buf6,
primals_10, buf7, buf17, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_10
buf8 = torch.ops.aten.randn.default([4, 4], dtype=torch.float32,
device=device(type='cuda', index=0), pin_memory=False)
buf9 = buf8
del buf8
buf10 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
triton_poi_fused_cat_3[grid(32)](primals_1, buf5, buf7, buf9, buf10,
32, XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
buf11 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
extern_kernels.mm(buf10, reinterpret_tensor(primals_11, (8, 750), (
1, 8), 0), out=buf11)
buf12 = buf11
del buf11
triton_poi_fused_relu_1[grid(3000)](buf12, primals_12, 3000, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_12
buf13 = empty_strided_cuda((4, 750), (750, 1), torch.float32)
extern_kernels.mm(buf12, reinterpret_tensor(primals_13, (750, 750),
(1, 750), 0), out=buf13)
buf14 = buf13
del buf13
triton_poi_fused_relu_1[grid(3000)](buf14, primals_14, 3000, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_14
buf15 = buf6
del buf6
extern_kernels.addmm(primals_16, buf14, reinterpret_tensor(
primals_15, (750, 4), (1, 750), 0), alpha=1, beta=1, out=buf15)
del primals_16
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_mul_tanh_4[grid(16)](buf15, buf16, 16, XBLOCK=16,
num_warps=1, num_stages=1)
return (buf16, buf5, buf7, buf0, buf2, buf4, buf7, buf9, buf10, buf12,
buf14, buf15, primals_15, primals_13, primals_11, buf17, primals_9,
primals_7, primals_5)
class VAENew(nn.Module):
def __init__(self, state_dim, action_dim, latent_dim, max_action, device):
super(VAENew, self).__init__()
self.e1 = nn.Linear(state_dim + action_dim, 750)
self.e2 = nn.Linear(750, 750)
self.mean = nn.Linear(750, latent_dim)
self.log_std = nn.Linear(750, latent_dim)
self.d1 = nn.Linear(state_dim + latent_dim, 750)
self.d2 = nn.Linear(750, 750)
self.d3 = nn.Linear(750, action_dim)
self.max_action = max_action
self.latent_dim = latent_dim
self.device = device
def decode(self, state, z=None):
if z is None:
z = torch.randn((state.shape[0], self.latent_dim)).clamp(-0.5, 0.5)
a = F.relu(self.d1(torch.cat([state, z], 1)))
a = F.relu(self.d2(a))
return self.max_action * torch.tanh(self.d3(a))
def forward(self, input_0, input_1):
primals_3 = self.e1.weight
primals_4 = self.e1.bias
primals_5 = self.e2.weight
primals_6 = self.e2.bias
primals_7 = self.mean.weight
primals_8 = self.mean.bias
primals_9 = self.log_std.weight
primals_10 = self.log_std.bias
primals_11 = self.d1.weight
primals_12 = self.d1.bias
primals_13 = self.d2.weight
primals_14 = self.d2.bias
primals_15 = self.d3.weight
primals_16 = self.d3.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16])
return output[0], output[1], output[2]
|
HzcIrving/DLRL_PlayGround
|
VAE
| false | 8,270 |
[
"MIT"
] | 27 |
0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
https://github.com/HzcIrving/DLRL_PlayGround/tree/0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
CRF
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/sb/csbujq4zm64oktpi7gz7gmu6ncokchjdmaiollfhsvidqxvzf3z5.py
# Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add]
# Source node to ATen node mapping:
# crf_scores => add
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand_2, %expand_1), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = (xindex // 16) % 16
x5 = xindex % 16
x6 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + (x6), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_0.run(buf0, primals_3, primals_4, buf1, 1024, grid=grid(1024), stream=stream0)
del buf0
del primals_3
del primals_4
return (buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.init
import torch.nn.functional as F
class CRF(nn.Module):
"""
Conditional Random Field Module
Parameters
----------
hidden_dim : ``int``, required.
the dimension of the input features.
tagset_size : ``int``, required.
the size of the target labels.
a_num : ``int``, required.
the number of annotators.
task : ``str``, required.
the model task
if_bias: ``bool``, optional, (default=True).
whether the linear transformation has the bias term.
"""
def __init__(self, hidden_dim: 'int', tagset_size: 'int', a_num: 'int',
task: 'str', if_bias: 'bool'=True):
super(CRF, self).__init__()
self.tagset_size = tagset_size
self.a_num = a_num
self.task = task
if 'maMulVecCrowd' in self.task:
self.maMulVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
if 'maAddVecCrowd' in self.task:
self.maAddVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
if 'maCatVecCrowd' in self.task:
self.maCatVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
self.maCatVecCrowd_latent = nn.Parameter(torch.Tensor(self.
tagset_size))
if 'maMulMatCrowd' in self.task:
self.maMulMatCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size, self.tagset_size))
if 'maMulCRFCrowd' in self.task:
self.maMulCRFCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size, self.tagset_size))
if 'maMulScoreCrowd' in self.task:
self.maMulScoreCrowd = nn.Parameter(torch.Tensor(self.a_num,
self.tagset_size, self.tagset_size))
if 'maCatVecCrowd' in self.task:
self.hidden2tag = nn.Linear(hidden_dim + self.tagset_size, self
.tagset_size, bias=if_bias)
else:
self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size, bias=
if_bias)
if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
self.transitions = nn.Parameter(torch.Tensor(self.tagset_size,
self.tagset_size))
def rand_init(self):
"""
random initialization
"""
if 'maMulVecCrowd' in self.task:
self.maMulVecCrowd.data.fill_(1)
if 'maAddVecCrowd' in self.task:
self.maAddVecCrowd.data.fill_(0)
if 'maCatVecCrowd' in self.task:
self.maCatVecCrowd.data.fill_(0)
self.maCatVecCrowd_latent.data.fill_(0)
if 'maMulMatCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulMatCrowd[i])
if 'maMulCRFCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulCRFCrowd[i])
if 'maMulScoreCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulScoreCrowd[i])
utils.init_linear(self.hidden2tag)
if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
self.transitions.data.zero_()
def forward(self, feats):
"""
calculate the potential score for the conditional random field.
Parameters
----------
feats: ``torch.FloatTensor``, required.
the input features for the conditional random field, of shape (*, hidden_dim).
Returns
-------
output: ``torch.FloatTensor``.
A float tensor of shape (ins_num, from_tag_size, to_tag_size)
"""
seq_len, batch_size, hid_dim = feats.shape
ins_num = seq_len * batch_size
self.a_num * ins_num
if 'maCatVecCrowd' in self.task:
feats_expand = feats.expand(self.a_num, seq_len, batch_size,
hid_dim)
crowd_expand = self.maCatVecCrowd.unsqueeze(1).unsqueeze(2).expand(
self.a_num, seq_len, batch_size, self.tagset_size)
feats_cat = torch.cat([feats_expand, crowd_expand], 3)
scores = self.hidden2tag(feats_cat).view(self.a_num, ins_num, 1,
self.tagset_size)
else:
scores = self.hidden2tag(feats).view(1, ins_num, 1, self.
tagset_size).expand(self.a_num, ins_num, 1, self.tagset_size)
if 'maMulVecCrowd' in self.task:
crowd_expand = self.maMulVecCrowd.unsqueeze(1).unsqueeze(2).expand(
self.a_num, ins_num, 1, self.tagset_size)
scores = torch.mul(scores, crowd_expand)
if 'maAddVecCrowd' in self.task:
crowd_expand = self.maAddVecCrowd.unsqueeze(1).unsqueeze(2).expand(
self.a_num, ins_num, 1, self.tagset_size)
scores = scores + crowd_expand
if 'maMulMatCrowd' in self.task:
crowd = F.log_softmax(self.maMulMatCrowd, dim=2)
crowd = crowd.view(self.a_num, 1, self.tagset_size, self.
tagset_size).expand(self.a_num, ins_num, self.tagset_size,
self.tagset_size)
scores = torch.matmul(scores, crowd)
if 'maMulCRFCrowd' in self.task and 'latent' in self.task:
transitions = self.transitions.view(1, 1, self.tagset_size,
self.tagset_size)
transitions = torch.matmul(transitions, self.maMulCRFCrowd
).transpose(0, 1).contiguous()
elif 'maMulCRFCrowd' in self.task:
transitions = self.maMulCRFCrowd.view(self.a_num, 1, self.
tagset_size, self.tagset_size).expand(self.a_num, ins_num,
self.tagset_size, self.tagset_size)
else:
transitions = self.transitions.view(1, 1, self.tagset_size,
self.tagset_size).expand(self.a_num, ins_num, self.
tagset_size, self.tagset_size)
scores = scores.expand(self.a_num, ins_num, self.tagset_size, self.
tagset_size)
crf_scores = scores + transitions
if 'maMulScoreCrowd' in self.task:
crowd = self.maMulScoreCrowd.view(self.a_num, 1, self.
tagset_size, self.tagset_size).expand(self.a_num, ins_num,
self.tagset_size, self.tagset_size)
crf_scores = torch.matmul(crf_scores, crowd).view(self.a_num,
ins_num, self.tagset_size, self.tagset_size)
return crf_scores
def latent_forward(self, feats):
"""
ignoring crowd components
"""
seq_len, batch_size, _hid_dim = feats.shape
if 'maCatVecCrowd' in self.task:
crowd_zero = self.maCatVecCrowd_latent.view(1, 1, self.tagset_size
).expand(seq_len, batch_size, self.tagset_size)
feats = torch.cat([feats, crowd_zero], 2)
scores = self.hidden2tag(feats).view(-1, 1, self.tagset_size)
ins_num = scores.size(0)
crf_scores = scores.expand(ins_num, self.tagset_size, self.tagset_size
) + self.transitions.view(1, self.tagset_size, self.tagset_size
).expand(ins_num, self.tagset_size, self.tagset_size)
return crf_scores
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_dim': 4, 'tagset_size': 4, 'a_num': 4, 'task': [4, 4]}
]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.init
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex // 16 % 16
x5 = xindex % 16
x6 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + x6, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32
)
get_raw_stream(0)
triton_poi_fused_add_0[grid(1024)](buf0, primals_3, primals_4, buf1,
1024, XBLOCK=128, num_warps=4, num_stages=1)
del buf0
del primals_3
del primals_4
return buf1, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0)
class CRFNew(nn.Module):
"""
Conditional Random Field Module
Parameters
----------
hidden_dim : ``int``, required.
the dimension of the input features.
tagset_size : ``int``, required.
the size of the target labels.
a_num : ``int``, required.
the number of annotators.
task : ``str``, required.
the model task
if_bias: ``bool``, optional, (default=True).
whether the linear transformation has the bias term.
"""
def __init__(self, hidden_dim: 'int', tagset_size: 'int', a_num: 'int',
task: 'str', if_bias: 'bool'=True):
super(CRFNew, self).__init__()
self.tagset_size = tagset_size
self.a_num = a_num
self.task = task
if 'maMulVecCrowd' in self.task:
self.maMulVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
if 'maAddVecCrowd' in self.task:
self.maAddVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
if 'maCatVecCrowd' in self.task:
self.maCatVecCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size))
self.maCatVecCrowd_latent = nn.Parameter(torch.Tensor(self.
tagset_size))
if 'maMulMatCrowd' in self.task:
self.maMulMatCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size, self.tagset_size))
if 'maMulCRFCrowd' in self.task:
self.maMulCRFCrowd = nn.Parameter(torch.Tensor(self.a_num, self
.tagset_size, self.tagset_size))
if 'maMulScoreCrowd' in self.task:
self.maMulScoreCrowd = nn.Parameter(torch.Tensor(self.a_num,
self.tagset_size, self.tagset_size))
if 'maCatVecCrowd' in self.task:
self.hidden2tag = nn.Linear(hidden_dim + self.tagset_size, self
.tagset_size, bias=if_bias)
else:
self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size, bias=
if_bias)
if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
self.transitions = nn.Parameter(torch.Tensor(self.tagset_size,
self.tagset_size))
def rand_init(self):
"""
random initialization
"""
if 'maMulVecCrowd' in self.task:
self.maMulVecCrowd.data.fill_(1)
if 'maAddVecCrowd' in self.task:
self.maAddVecCrowd.data.fill_(0)
if 'maCatVecCrowd' in self.task:
self.maCatVecCrowd.data.fill_(0)
self.maCatVecCrowd_latent.data.fill_(0)
if 'maMulMatCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulMatCrowd[i])
if 'maMulCRFCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulCRFCrowd[i])
if 'maMulScoreCrowd' in self.task:
for i in range(self.a_num):
nn.init.eye_(self.maMulScoreCrowd[i])
utils.init_linear(self.hidden2tag)
if not ('maMulCRFCrowd' in self.task and 'latent' not in self.task):
self.transitions.data.zero_()
def latent_forward(self, feats):
"""
ignoring crowd components
"""
seq_len, batch_size, _hid_dim = feats.shape
if 'maCatVecCrowd' in self.task:
crowd_zero = self.maCatVecCrowd_latent.view(1, 1, self.tagset_size
).expand(seq_len, batch_size, self.tagset_size)
feats = torch.cat([feats, crowd_zero], 2)
scores = self.hidden2tag(feats).view(-1, 1, self.tagset_size)
ins_num = scores.size(0)
crf_scores = scores.expand(ins_num, self.tagset_size, self.tagset_size
) + self.transitions.view(1, self.tagset_size, self.tagset_size
).expand(ins_num, self.tagset_size, self.tagset_size)
return crf_scores
def forward(self, input_0):
primals_2 = self.transitions
primals_4 = self.hidden2tag.weight
primals_3 = self.hidden2tag.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
INK-USC/ConNet
|
CRF
| false | 8,271 |
[
"MIT"
] | 11 |
adb299f160556004561df302c19578200bd3835b
|
https://github.com/INK-USC/ConNet/tree/adb299f160556004561df302c19578200bd3835b
|
CGD
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/yg/cygooswl5gkxugqq2ejgag2vtcqhtumn2j3notsgzty3xoxbrq4v.py
# Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
# Source node to ATen node mapping:
# adaptive_avg_pool2d => mean
# Graph fragment:
# %mean : [num_users=4] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/wp/cwpgni2uqhs6rcp5zpreg2yqforqyf2ps3peum3jvr23zklxctyv.py
# Topologically Sorted Source Nodes: [adaptive_max_pool2d], Original ATen: [aten.adaptive_max_pool2d]
# Source node to ATen node mapping:
# adaptive_max_pool2d => adaptive_max_pool2d
# Graph fragment:
# %adaptive_max_pool2d : [num_users=1] = call_function[target=torch.ops.aten.adaptive_max_pool2d.default](args = (%primals_1, [1, 1]), kwargs = {})
triton_poi_fused_adaptive_max_pool2d_1 = async_compile.triton('triton_poi_fused_adaptive_max_pool2d_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_adaptive_max_pool2d_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_adaptive_max_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (16*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
tmp17 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
tmp29 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp14 = triton_helpers.maximum(tmp13, tmp12)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp20 = triton_helpers.maximum(tmp19, tmp18)
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tmp30 = triton_helpers.maximum(tmp29, tmp28)
tl.store(out_ptr0 + (x0), tmp30, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/fp/cfp6aijow2g5is2smjrqj7kca6itit4xrxs2tseqvxiqv3qu5hs6.py
# Topologically Sorted Source Nodes: [x0_s], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# x0_s => amax, exp, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mean, [1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bx/cbx4apfrr3crlh45z5s5g364pozucicvtftrh7pt56jdgf4a2jrr.py
# Topologically Sorted Source Nodes: [x0_s, mul_1, add_1, y1_s], Original ATen: [aten._softmax, aten.mul, aten.add, aten.tanh]
# Source node to ATen node mapping:
# add_1 => add_1
# mul_1 => mul_1
# x0_s => div, sum_1
# y1_s => tanh_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %div), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {})
# %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused__softmax_add_mul_tanh_3 = async_compile.triton('triton_poi_fused__softmax_add_mul_tanh_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_mul_tanh_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_add_mul_tanh_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp10 = tmp9 * tmp8
tmp12 = tmp10 + tmp11
tmp13 = libdevice.tanh(tmp12)
tl.store(out_ptr0 + (x2), tmp8, xmask)
tl.store(out_ptr1 + (x2), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/o2/co2tjmjjhudfqapno27v76r6iolzxn6tj4sody5vbj6edbgvy47m.py
# Topologically Sorted Source Nodes: [mul, add, y0_s, mul_2, add_2, tanh_2, add_3], Original ATen: [aten.mul, aten.add, aten.tanh]
# Source node to ATen node mapping:
# add => add
# add_2 => add_2
# add_3 => add_3
# mul => mul
# mul_2 => mul_2
# tanh_2 => tanh_2
# y0_s => tanh
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %div), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_4), kwargs = {})
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, %tanh), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_7), kwargs = {})
# %tanh_2 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh_2, 1), kwargs = {})
triton_poi_fused_add_mul_tanh_4 = async_compile.triton('triton_poi_fused_add_mul_tanh_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_tanh_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_tanh_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4)
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_out_ptr0 + (x2), xmask)
tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tmp1 * tmp2
tmp5 = tmp3 + tmp4
tmp6 = libdevice.tanh(tmp5)
tmp7 = tmp0 * tmp6
tmp9 = tmp7 + tmp8
tmp10 = libdevice.tanh(tmp9)
tmp11 = 1.0
tmp12 = tmp10 + tmp11
tl.store(in_out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/vq/cvq4aucehdpedbeboog7jds4ydv4di36f7aonm2ih2ner2milxbb.py
# Topologically Sorted Source Nodes: [mul, add, y0_s, mul_2, add_2, tanh_2, add_3, z], Original ATen: [aten.mul, aten.add, aten.tanh]
# Source node to ATen node mapping:
# add => add
# add_2 => add_2
# add_3 => add_3
# mul => mul
# mul_2 => mul_2
# tanh_2 => tanh_2
# y0_s => tanh
# z => mul_3
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, %div), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_4), kwargs = {})
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, %tanh), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_7), kwargs = {})
# %tanh_2 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh_2, 1), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %add_3), kwargs = {})
triton_poi_fused_add_mul_tanh_5 = async_compile.triton('triton_poi_fused_add_mul_tanh_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_tanh_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_tanh_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 16)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 1), (1, 1))
assert_size_stride(primals_3, (4, 1), (1, 1))
assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4, 1), (1, 1))
assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [adaptive_avg_pool2d], Original ATen: [aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_mean_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [adaptive_max_pool2d], Original ATen: [aten.adaptive_max_pool2d]
triton_poi_fused_adaptive_max_pool2d_1.run(primals_1, buf2, 16, grid=grid(16), stream=stream0)
buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
# Topologically Sorted Source Nodes: [x0_s], Original ATen: [aten._softmax]
triton_poi_fused__softmax_2.run(buf1, buf3, 16, grid=grid(16), stream=stream0)
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
# Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf2, (4, 4), (4, 1), 0), primals_3, out=buf6)
del primals_3
buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [x0_s, mul_1, add_1, y1_s], Original ATen: [aten._softmax, aten.mul, aten.add, aten.tanh]
triton_poi_fused__softmax_add_mul_tanh_3.run(buf3, buf6, primals_5, buf4, buf7, 16, grid=grid(16), stream=stream0)
del buf3
del primals_5
buf5 = buf6; del buf6 # reuse
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0), primals_2, out=buf5)
del primals_2
buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
# Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf7, (4, 4), (4, 1), 0), primals_6, out=buf8)
buf9 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [mul, add, y0_s, mul_2, add_2, tanh_2, add_3], Original ATen: [aten.mul, aten.add, aten.tanh]
triton_poi_fused_add_mul_tanh_4.run(buf9, buf8, buf5, primals_4, primals_7, 16, grid=grid(16), stream=stream0)
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, add, y0_s, mul_2, add_2, tanh_2, add_3, z], Original ATen: [aten.mul, aten.add, aten.tanh]
triton_poi_fused_add_mul_tanh_5.run(primals_1, buf9, buf10, 256, grid=grid(256), stream=stream0)
del buf9
return (buf10, primals_1, primals_4, primals_7, buf1, buf5, buf7, buf8, reinterpret_tensor(primals_6, (1, 4), (1, 1), 0), reinterpret_tensor(buf2, (4, 4), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.utils.model_zoo
class CGD(nn.Module):
def __init__(self, in_channels, bias=True, nonlinear=True):
super(CGD, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.softmax = nn.Softmax(dim=1)
self.w0 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.w1 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.w2 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.bias0 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
self.bias1 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
self.bias2 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
nn.init.xavier_uniform_(self.w0)
nn.init.xavier_uniform_(self.w1)
nn.init.xavier_uniform_(self.w2)
def forward(self, x):
b, c, _, _ = x.size()
x0 = self.avg_pool(x).view(b, c, 1, 1)
x1 = self.max_pool(x).view(b, c, 1, 1)
x0_s = self.softmax(x0)
y0 = torch.matmul(x0.view(b, c), self.w0).view(b, 1, 1, 1)
y1 = torch.matmul(x1.view(b, c), self.w1).view(b, 1, 1, 1)
y0_s = torch.tanh(y0 * x0_s + self.bias0)
y1_s = torch.tanh(y1 * x0_s + self.bias1)
y2 = torch.matmul(y1_s.view(b, c), self.w2).view(b, 1, 1, 1)
y2_s = torch.tanh(y2 * y0_s + self.bias2).view(b, c, 1, 1)
z = x * (y2_s + 1)
return z
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.model_zoo
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_adaptive_max_pool2d_1(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp7 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp9 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
)
tmp11 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp15 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp21 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp23 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp25 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp27 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp29 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
'evict_last')
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp8 = triton_helpers.maximum(tmp7, tmp6)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp14 = triton_helpers.maximum(tmp13, tmp12)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = triton_helpers.maximum(tmp17, tmp16)
tmp20 = triton_helpers.maximum(tmp19, tmp18)
tmp22 = triton_helpers.maximum(tmp21, tmp20)
tmp24 = triton_helpers.maximum(tmp23, tmp22)
tmp26 = triton_helpers.maximum(tmp25, tmp24)
tmp28 = triton_helpers.maximum(tmp27, tmp26)
tmp30 = triton_helpers.maximum(tmp29, tmp28)
tl.store(out_ptr0 + x0, tmp30, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_add_mul_tanh_3(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tmp10 = tmp9 * tmp8
tmp12 = tmp10 + tmp11
tmp13 = libdevice.tanh(tmp12)
tl.store(out_ptr0 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_add_mul_tanh_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
in_ptr3, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_out_ptr0 + x2, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp3 = tmp1 * tmp2
tmp5 = tmp3 + tmp4
tmp6 = libdevice.tanh(tmp5)
tmp7 = tmp0 * tmp6
tmp9 = tmp7 + tmp8
tmp10 = libdevice.tanh(tmp9)
tmp11 = 1.0
tmp12 = tmp10 + tmp11
tl.store(in_out_ptr0 + x2, tmp12, xmask)
@triton.jit
def triton_poi_fused_add_mul_tanh_5(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 16
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tl.store(out_ptr0 + x2, tmp2, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 1), (1, 1))
assert_size_stride(primals_3, (4, 1), (1, 1))
assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (1, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4, 1), (1, 1))
assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
triton_poi_fused_adaptive_max_pool2d_1[grid(16)](primals_1, buf2,
16, XBLOCK=16, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
triton_poi_fused__softmax_2[grid(16)](buf1, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf2, (4, 4), (4, 1), 0),
primals_3, out=buf6)
del primals_3
buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
triton_poi_fused__softmax_add_mul_tanh_3[grid(16)](buf3, buf6,
primals_5, buf4, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf3
del primals_5
buf5 = buf6
del buf6
extern_kernels.mm(reinterpret_tensor(buf1, (4, 4), (4, 1), 0),
primals_2, out=buf5)
del primals_2
buf8 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf7, (4, 4), (4, 1), 0),
primals_6, out=buf8)
buf9 = buf4
del buf4
triton_poi_fused_add_mul_tanh_4[grid(16)](buf9, buf8, buf5,
primals_4, primals_7, 16, XBLOCK=16, num_warps=1, num_stages=1)
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_tanh_5[grid(256)](primals_1, buf9, buf10,
256, XBLOCK=256, num_warps=4, num_stages=1)
del buf9
return (buf10, primals_1, primals_4, primals_7, buf1, buf5, buf7, buf8,
reinterpret_tensor(primals_6, (1, 4), (1, 1), 0),
reinterpret_tensor(buf2, (4, 4), (1, 4), 0))
class CGDNew(nn.Module):
def __init__(self, in_channels, bias=True, nonlinear=True):
super(CGDNew, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.softmax = nn.Softmax(dim=1)
self.w0 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.w1 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.w2 = nn.Parameter(torch.ones(in_channels, 1), requires_grad=True)
self.bias0 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
self.bias1 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
self.bias2 = nn.Parameter(torch.zeros(1, in_channels, 1, 1),
requires_grad=True)
nn.init.xavier_uniform_(self.w0)
nn.init.xavier_uniform_(self.w1)
nn.init.xavier_uniform_(self.w2)
def forward(self, input_0):
primals_2 = self.w0
primals_3 = self.w1
primals_6 = self.w2
primals_4 = self.bias0
primals_5 = self.bias1
primals_7 = self.bias2
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
HolmesShuan/AIM2020-Real-Super-Resolution
|
CGD
| false | 8,272 |
[
"BSD-2-Clause"
] | 19 |
0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
https://github.com/HolmesShuan/AIM2020-Real-Super-Resolution/tree/0ea4d7db0f4f7ed488cc162b90bb08fc02082106
|
_ImpalaResBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/2r/c2rkj22ztom44dtfrpj2uok5zc76tpegorlicksd2vm2decu7zzh.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%primals_1,), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bh/cbh6z6q7j2ev7jry3vfxhnujl7ykjacaqrhgcxpv3sun2pkhqeew.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# x_1 => convolution
# x_2 => relu_1
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/wu/cwu4aunzr3ywj24slclusvueucyumpdk3xwdnttwgdrxicosuhz6.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
# x_3 => convolution_1
# x_4 => add
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %primals_1), kwargs = {})
triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_convolution_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.add]
triton_poi_fused_add_convolution_2.run(buf4, primals_5, primals_1, 256, grid=grid(256), stream=stream0)
del primals_1
del primals_5
return (buf4, primals_2, primals_4, buf0, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
self.conv1 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
self.conv2 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
def forward(self, inputs):
x = self.relu(inputs)
x = self.conv1(x)
x = self.relu_inplace(x)
x = self.conv2(x)
x += inputs
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x3, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_relu_1[grid(256)](buf2, primals_3, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3
del buf3
triton_poi_fused_add_convolution_2[grid(256)](buf4, primals_5,
primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
del primals_5
return buf4, primals_2, primals_4, buf0, buf2
class _ImpalaResBlockNew(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
self.conv1 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
self.conv2 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
def forward(self, input_0):
primals_2 = self.conv1.weight
primals_3 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
IBM/vsrl-framework
|
_ImpalaResBlock
| false | 8,273 |
[
"MIT"
] | 44 |
42e0853bffb5efbb66cd97178aff9e10ad18c5a9
|
https://github.com/IBM/vsrl-framework/tree/42e0853bffb5efbb66cd97178aff9e10ad18c5a9
|
MlpNetM
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/o5/co5kwura3yutawdpcms7qvlsfuevg46xktkqymuyhafip3vp4zwg.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x => cat_addmm
# Graph fragment:
# %cat_addmm : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.cat_addmm](args = (((%primals_3, %select, %permute), (%primals_3, %select_1, %permute), (%primals_3, %select_2, %permute)), 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/tv/ctv6ewe7phoithbs7aahrs44dajxgr4b3itmcgygn4hkhjj3jcoh.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x => cat_addmm
# Graph fragment:
# %cat_addmm : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.cat_addmm](args = (((%primals_3, %select, %permute), (%primals_3, %select_1, %permute), (%primals_3, %select_2, %permute)), 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/al/calzxj7g6f6ick7gmmikjtozxgir37dxkj5zxiq6svbcnci4m2un.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x => cat_addmm
# Graph fragment:
# %cat_addmm : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.cat_addmm](args = (((%primals_3, %select, %permute), (%primals_3, %select_1, %permute), (%primals_3, %select_2, %permute)), 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/tp/ctpn2abxaozwcoujfxp5ip76tusw6bgyd3pqeehtmn7y46i3b7kk.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x_1 => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%cat_addmm,), kwargs = {})
triton_poi_fused_relu_3 = async_compile.triton('triton_poi_fused_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nx/cnxcqhxmrdktahl3p44mrkmi2vfcvoqcxwjq5yyntpao5ydw2roz.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x_3 => relu_1
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_5), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_4 = async_compile.triton('triton_poi_fused_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 12), (12, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (1, 4), (4, 1))
assert_size_stride(primals_9, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
buf6 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
buf1 = reinterpret_tensor(buf6, (4, 4), (12, 1), 0) # alias
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
extern_kernels.addmm(primals_3, buf0, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
buf2 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(primals_1, buf2, 16, grid=grid(16), stream=stream0)
buf3 = reinterpret_tensor(buf6, (4, 4), (12, 1), 4) # alias
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
extern_kernels.addmm(primals_3, buf2, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
buf4 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
triton_poi_fused_cat_2.run(primals_1, buf4, 16, grid=grid(16), stream=stream0)
buf5 = reinterpret_tensor(buf6, (4, 4), (12, 1), 8) # alias
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
extern_kernels.addmm(primals_3, buf4, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
del primals_2
del primals_3
buf7 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
triton_poi_fused_relu_3.run(buf6, buf7, 48, grid=grid(48), stream=stream0)
del buf1
del buf3
del buf5
del buf6
buf8 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf7, reinterpret_tensor(primals_4, (12, 4), (1, 12), 0), out=buf8)
buf9 = buf8; del buf8 # reuse
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu]
triton_poi_fused_relu_4.run(buf9, primals_5, 16, grid=grid(16), stream=stream0)
del primals_5
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf10)
buf11 = buf10; del buf10 # reuse
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu]
triton_poi_fused_relu_4.run(buf11, primals_7, 16, grid=grid(16), stream=stream0)
del primals_7
buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
# Topologically Sorted Source Nodes: [V], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_9, buf11, reinterpret_tensor(primals_8, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf13)
del primals_9
return (buf13, reinterpret_tensor(primals_1, (4, 4), (16, 4), 0), reinterpret_tensor(primals_1, (4, 4), (16, 4), 1), reinterpret_tensor(primals_1, (4, 4), (16, 4), 2), buf7, buf9, buf11, primals_8, primals_6, primals_4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 12), (12, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class MlpNetM(nn.Module):
"""Implements a simple fully connected mlp network."""
def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0,
agent_shuffle='none'):
super(MlpNetM, self).__init__()
self.linear1 = nn.Linear(sa_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size * 3, hidden_size)
self.linear3 = nn.Linear(hidden_size, hidden_size)
self.V = nn.Linear(hidden_size, 1)
self.V.weight.data.mul_(0.1)
self.V.bias.data.mul_(0.1)
self.n_agents = n_agents
self.agent_id = agent_id
self.agent_shuffle = agent_shuffle
def forward(self, x):
bz = x.shape[0]
if self.agent_shuffle == 'all':
x_out = []
for k in range(bz):
rand_idx = np.random.permutation(self.n_agents)
x_out.append(x[k, :, rand_idx].unsqueeze(0))
x = torch.cat(x_out, 0)
elif self.agent_shuffle == 'others':
x_out = []
for k in range(bz):
rand_idx = np.random.permutation(self.n_agents - 1)
index_except = np.concatenate([np.arange(0, self.agent_id),
np.arange(self.agent_id + 1, self.n_agents)])
except_shuffle = index_except[rand_idx]
x_tmp = x[k, :, :]
x_tmp[:, index_except] = x_tmp[:, except_shuffle]
x_out.append(x_tmp.unsqueeze(0))
x = torch.cat(x_out, 0)
elif self.agent_shuffle == 'none':
pass
else:
raise NotImplementedError('Unsupported agent_shuffle opt: %s' %
self.agent_shuffle)
x1, x2, x3 = self.linear1(x[:, :, 0]), self.linear1(x[:, :, 1]
), self.linear1(x[:, :, 2])
x = torch.cat((x1, x2, x3), 1)
x = F.relu(x)
x = self.linear2(x)
x = F.relu(x)
x = self.linear3(x)
x = F.relu(x)
V = self.V(x)
return V
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'sa_dim': 4, 'n_agents': 4, 'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_relu_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tl.store(out_ptr0 + x0, tmp2, xmask)
@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 12), (12, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (1, 4), (4, 1))
assert_size_stride(primals_9, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf6 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
buf1 = reinterpret_tensor(buf6, (4, 4), (12, 1), 0)
extern_kernels.addmm(primals_3, buf0, reinterpret_tensor(primals_2,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
buf2 = buf0
del buf0
triton_poi_fused_cat_1[grid(16)](primals_1, buf2, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf3 = reinterpret_tensor(buf6, (4, 4), (12, 1), 4)
extern_kernels.addmm(primals_3, buf2, reinterpret_tensor(primals_2,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
buf4 = buf2
del buf2
triton_poi_fused_cat_2[grid(16)](primals_1, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf5 = reinterpret_tensor(buf6, (4, 4), (12, 1), 8)
extern_kernels.addmm(primals_3, buf4, reinterpret_tensor(primals_2,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
del primals_2
del primals_3
buf7 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
triton_poi_fused_relu_3[grid(48)](buf6, buf7, 48, XBLOCK=64,
num_warps=1, num_stages=1)
del buf1
del buf3
del buf5
del buf6
buf8 = buf4
del buf4
extern_kernels.mm(buf7, reinterpret_tensor(primals_4, (12, 4), (1,
12), 0), out=buf8)
buf9 = buf8
del buf8
triton_poi_fused_relu_4[grid(16)](buf9, primals_5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf9, reinterpret_tensor(primals_6, (4, 4), (1, 4
), 0), out=buf10)
buf11 = buf10
del buf10
triton_poi_fused_relu_4[grid(16)](buf11, primals_7, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_7
buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
extern_kernels.addmm(primals_9, buf11, reinterpret_tensor(primals_8,
(4, 1), (1, 4), 0), alpha=1, beta=1, out=buf13)
del primals_9
return buf13, reinterpret_tensor(primals_1, (4, 4), (16, 4), 0
), reinterpret_tensor(primals_1, (4, 4), (16, 4), 1
), reinterpret_tensor(primals_1, (4, 4), (16, 4), 2
), buf7, buf9, buf11, primals_8, primals_6, primals_4
class MlpNetMNew(nn.Module):
"""Implements a simple fully connected mlp network."""
def __init__(self, sa_dim, n_agents, hidden_size, agent_id=0,
agent_shuffle='none'):
super(MlpNetMNew, self).__init__()
self.linear1 = nn.Linear(sa_dim, hidden_size)
self.linear2 = nn.Linear(hidden_size * 3, hidden_size)
self.linear3 = nn.Linear(hidden_size, hidden_size)
self.V = nn.Linear(hidden_size, 1)
self.V.weight.data.mul_(0.1)
self.V.bias.data.mul_(0.1)
self.n_agents = n_agents
self.agent_id = agent_id
self.agent_shuffle = agent_shuffle
def forward(self, input_0):
primals_2 = self.linear1.weight
primals_3 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_6 = self.linear3.weight
primals_7 = self.linear3.bias
primals_8 = self.V.weight
primals_9 = self.V.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
HAXRD/PIC
|
MlpNetM
| false | 8,274 |
[
"MIT"
] | 28 |
658b4dd6b01e64413d5f8f0107d9167f1bd78546
|
https://github.com/HAXRD/PIC/tree/658b4dd6b01e64413d5f8f0107d9167f1bd78546
|
predicates
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/cv/ccv6wuuk2f3t36jk6z6siizklj3kweutyi3yvvr2vh7sazv7nrd4.py
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# ret => amax, exp, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%primals_1, [1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/yt/cytqs2smvhzqhhhv5nhgfsoz7g7pop2pi3eoeztc4dtuktnwv56m.py
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# ret => div, sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten._softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__softmax_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten._softmax]
triton_poi_fused__softmax_1.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
del buf0
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [ret_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
del buf1
return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as func
class predicates(nn.Module):
def __init__(self, num_predicates, body_len):
"""
Use these to express a choice amongst predicates. For use when
learning rules.
Parameters:
----------
num_predicates: The domain size of predicates
body_len: The number of predicates to choose
"""
super().__init__()
self.log_weights = nn.Parameter(torch.zeros(body_len,
num_predicates).uniform_(-0.1, 0.1))
def forward(self, x):
"""
Forward function computes the attention weights and returns the result of mixing predicates.
Parameters:
----------
x: a 2D tensor whose number of columns should equal self.num_predicates
Returns: A 2D tensor with 1 column
-------
"""
weights = self.get_params()
ret = func.linear(x, weights)
return ret
def get_params(self):
ret = func.softmax(self.log_weights, dim=1)
return ret
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_predicates': 4, 'body_len': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as func
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__softmax_0[grid(16)](primals_1, buf0, 16, XBLOCK=
16, num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(16)](buf0, buf1, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf0
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
del buf1
return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
), primals_1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)
class predicatesNew(nn.Module):
def __init__(self, num_predicates, body_len):
"""
Use these to express a choice amongst predicates. For use when
learning rules.
Parameters:
----------
num_predicates: The domain size of predicates
body_len: The number of predicates to choose
"""
super().__init__()
self.log_weights = nn.Parameter(torch.zeros(body_len,
num_predicates).uniform_(-0.1, 0.1))
def get_params(self):
ret = func.softmax(self.log_weights, dim=1)
return ret
def forward(self, input_0):
primals_1 = self.log_weights
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
IBM/LOA
|
predicates
| false | 8,275 |
[
"MIT"
] | 12 |
9cd402c814f1d9c8b4de52ee18a3cb7ec2c6d07a
|
https://github.com/IBM/LOA/tree/9cd402c814f1d9c8b4de52ee18a3cb7ec2c6d07a
|
ConcatBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/f2/cf2npekvrfp4jsjrel6za5ovwvgxs4a2lwakd7jnsn4vg4gxjudu.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# x => convolution
# x_1 => gt, mul, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
triton_poi_fused_convolution_leaky_relu_0 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + (x3), tmp4, xmask)
tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.leaky_relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 256, grid=grid(256), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf5 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.leaky_relu]
triton_poi_fused_convolution_leaky_relu_0.run(buf3, primals_5, buf4, buf5, 256, grid=grid(256), stream=stream0)
del buf3
del primals_5
return (buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class ConcatBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlock, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
padding=0)
self.conv2 = nn.Conv2d(self.in_chns, self.out_chns, kernel_size=1,
padding=0)
self.ac1 = nn.LeakyReLU()
self.ac2 = nn.LeakyReLU()
def forward(self, x):
x = self.conv1(x)
x = self.ac1(x)
x = self.conv2(x)
x = self.ac2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf0,
primals_2, buf1, buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf5 = buf0
del buf0
triton_poi_fused_convolution_leaky_relu_0[grid(256)](buf3,
primals_5, buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf3
del primals_5
return buf5, primals_1, primals_3, primals_4, buf1, buf2, buf4
class ConcatBlockNew(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConcatBlockNew, self).__init__()
self.in_chns = in_channels
self.out_chns = out_channels
self.conv1 = nn.Conv2d(self.in_chns, self.in_chns, kernel_size=1,
padding=0)
self.conv2 = nn.Conv2d(self.in_chns, self.out_chns, kernel_size=1,
padding=0)
self.ac1 = nn.LeakyReLU()
self.ac2 = nn.LeakyReLU()
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.conv2.weight
primals_5 = self.conv2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
HiLab-git/WSL4MIS
|
ConcatBlock
| false | 8,276 |
[
"MIT"
] | 29 |
9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
predicates1
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/e2/ce2jt6xlphadls2ya3qtkdqfji5jgeyiafb6s3scf5xfh7br3ztq.py
# Topologically Sorted Source Nodes: [weights], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# weights => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%primals_1,), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(out_ptr0 + (x0), tmp2, xmask)
tl.store(out_ptr1 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ex/cexvcpsgpt2brj2udgh3bwl5roqdm6v5odbvohbmymraunikyygc.py
# Topologically Sorted Source Nodes: [beta, neg, add, clamp, ret], Original ATen: [aten.relu, aten.neg, aten.add, aten.clamp, aten.rsub, aten.ge, aten.le, aten.logical_and]
# Source node to ATen node mapping:
# add => add
# beta => relu_1
# clamp => clamp_max, clamp_min
# neg => neg
# ret => sub
# Graph fragment:
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%primals_2,), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%view_1,), kwargs = {})
# %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %relu_1), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {})
# %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add, 0), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add, 1), kwargs = {})
# %logical_and : [num_users=1] = call_function[target=torch.ops.aten.logical_and.default](args = (%ge, %le), kwargs = {})
triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1 = async_compile.triton('triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp1 = -tmp0
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = tmp1 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 1.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tmp10 = tmp8 - tmp9
tmp11 = tmp5 >= tmp6
tmp12 = tmp5 <= tmp8
tmp13 = tmp11 & tmp12
tl.store(out_ptr0 + (x2), tmp10, xmask)
tl.store(out_ptr1 + (x2), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nr/cnrqxdscuhkyzye5abpqafi3la7rmonsohhk4iip42ukkttjkdun.py
# Topologically Sorted Source Nodes: [beta], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# beta => relu_1
# Graph fragment:
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%primals_2,), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
# Topologically Sorted Source Nodes: [weights], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(primals_1, buf0, buf5, 16, grid=grid(16), stream=stream0)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1)
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [beta, neg, add, clamp, ret], Original ATen: [aten.relu, aten.neg, aten.add, aten.clamp, aten.rsub, aten.ge, aten.le, aten.logical_and]
triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1.run(buf1, primals_2, buf2, buf3, 256, grid=grid(256), stream=stream0)
del buf1
buf4 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
# Topologically Sorted Source Nodes: [beta], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_2.run(primals_2, buf4, 4, grid=grid(4), stream=stream0)
del primals_2
return (buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf3, buf4, buf5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as func
class predicates1(nn.Module):
def __init__(self, num_predicates, body_len):
super().__init__()
self.weights = nn.Parameter(torch.zeros(body_len, num_predicates).
uniform_(0.0, 0.1))
self.beta = nn.Parameter(torch.ones(1, body_len))
def forward(self, x):
beta, weights = self.get_params()
ret = 1 - torch.clamp(-func.linear(x, weights) + beta, 0, 1)
return ret
def get_params(self):
weights = func.relu(self.weights)
beta = func.relu(self.beta)
return beta, weights
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_predicates': 4, 'body_len': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.nn.functional as func
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr1 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1(in_ptr0,
in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp1 = -tmp0
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = tmp1 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 1.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tmp10 = tmp8 - tmp9
tmp11 = tmp5 >= tmp6
tmp12 = tmp5 <= tmp8
tmp13 = tmp11 & tmp12
tl.store(out_ptr0 + x2, tmp10, xmask)
tl.store(out_ptr1 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(16)](primals_1,
buf0, buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(buf0, (4, 4), (1, 4), 0), out=buf1)
del buf0
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_clamp_ge_le_logical_and_neg_relu_rsub_1[grid(256)
](buf1, primals_2, buf2, buf3, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf1
buf4 = empty_strided_cuda((1, 4), (4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_2[grid(4)](primals_2, buf4,
4, XBLOCK=4, num_warps=1, num_stages=1)
del primals_2
return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf3, buf4, buf5
class predicates1New(nn.Module):
def __init__(self, num_predicates, body_len):
super().__init__()
self.weights = nn.Parameter(torch.zeros(body_len, num_predicates).
uniform_(0.0, 0.1))
self.beta = nn.Parameter(torch.ones(1, body_len))
def get_params(self):
weights = func.relu(self.weights)
beta = func.relu(self.beta)
return beta, weights
def forward(self, input_0):
primals_1 = self.weights
primals_2 = self.beta
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
IBM/LOA
|
predicates1
| false | 8,277 |
[
"MIT"
] | 12 |
9cd402c814f1d9c8b4de52ee18a3cb7ec2c6d07a
|
https://github.com/IBM/LOA/tree/9cd402c814f1d9c8b4de52ee18a3cb7ec2c6d07a
|
qd
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/cs/ccs3vspn4kf2436g66jacyvgagqjvcahjgkcskfz7arffovv5grr.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
# h => gt, mul, where
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_1, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %primals_1, %mul), kwargs = {})
# %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %where), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr0 + (x0), tmp5, xmask)
tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
stream0 = get_raw_stream(0)
triton_poi_fused_leaky_relu_0.run(primals_1, buf0, primals_1, 256, grid=grid(256), stream=stream0)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [loc_d], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
return (reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (64, 4), (4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class qd(nn.Module):
def __init__(self, d_dim, zd_dim):
super(qd, self).__init__()
self.fc1 = nn.Linear(zd_dim, d_dim)
self.activation = nn.LeakyReLU(inplace=True)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
def forward(self, zd):
h = self.activation(zd)
loc_d = self.fc1(h)
return loc_d
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_dim': 4, 'zd_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr0 + x0, tmp5, xmask)
tl.store(out_ptr1 + x0, tmp5, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_leaky_relu_0[grid(256)](primals_1, buf0, primals_1,
256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf1)
del primals_2
del primals_3
return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
class qdNew(nn.Module):
def __init__(self, d_dim, zd_dim):
super(qdNew, self).__init__()
self.fc1 = nn.Linear(zd_dim, d_dim)
self.activation = nn.LeakyReLU(inplace=True)
torch.nn.init.xavier_uniform_(self.fc1.weight)
self.fc1.bias.data.zero_()
def forward(self, input_0):
primals_2 = self.fc1.weight
primals_3 = self.fc1.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
IamWangYunKai/DG-TrajGen
|
qd
| false | 8,278 |
[
"MIT"
] | 31 |
0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
GramMatrix
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/77/c77wsocbisn3cwqx2js5sdtxisnofx5qumhyepmyyitsesonq4um.py
# Topologically Sorted Source Nodes: [div_], Original ATen: [aten.div]
# Source node to ATen node mapping:
# div_ => div
# Graph fragment:
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 16), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = 0.0625
tmp2 = tmp0 * tmp1
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [G], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(arg0_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0), out=buf0)
del arg0_1
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [div_], Original ATen: [aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_0.run(buf1, 64, grid=grid(64), stream=stream0)
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
class GramMatrix(nn.Module):
def forward(self, input):
b, c, h, w = input.size()
F = input.view(b, c, h * w)
G = torch.bmm(F, F.transpose(1, 2))
G.div_(h * w)
return G
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_div_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = 0.0625
tmp2 = tmp0 * tmp1
tl.store(in_out_ptr0 + x0, tmp2, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(arg0_1, (4, 4, 16), (64, 16,
1), 0), reinterpret_tensor(arg0_1, (4, 16, 4), (64, 1, 16), 0),
out=buf0)
del arg0_1
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_div_0[grid(64)](buf1, 64, XBLOCK=64, num_warps=1,
num_stages=1)
return buf1,
class GramMatrixNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
IceClear/MW-GAN
|
GramMatrix
| false | 8,279 |
[
"MIT"
] | 36 |
acb962468c984681c4a21f7b5c14588ca8f58c00
|
https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00
|
MLSTM_cell
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zm/czm6acrrgjryz6xi3wza7npycjuiqsdsygpfdo3lbzaquecrmeuj.py
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# combined => cat
# Graph fragment:
# %cat : [num_users=4] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/sj/csj453dnemeic5vb74vrtqvlmefw2piyslrjpsp6rey74tg64s6n.py
# Topologically Sorted Source Nodes: [combined_d], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# combined_d => cat_1
# Graph fragment:
# %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %primals_4], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x1 = (xindex // 12)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tl.load(in_ptr2 + ((4*x1) + ((-8) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
tmp15 = tl.where(tmp9, tmp10, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pl/cplxcy4v3r3yzudhscspkmuhekmbu2kwkkzsygtsspg7gczcply3.py
# Topologically Sorted Source Nodes: [sigmoid, d_1], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
# d_1 => mul
# sigmoid => sigmoid
# Graph fragment:
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm,), kwargs = {})
# %mul : [num_users=17] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, 0.5), kwargs = {})
triton_poi_fused_mul_sigmoid_2 = async_compile.triton('triton_poi_fused_mul_sigmoid_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/x3/cx3ff36v47jn6xvrvhx7eiqdhzooeqmu2h3ri2fie6qealrxvc7b.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_2 => cat_2
# Graph fragment:
# %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_3, %div_2, %div_1, %div],), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (0))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zw/czwjo4n3regclck5sw2ycaeewamejyah5ku762ydt7yzjzqlwter.py
# Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_3 => cat_3
# Graph fragment:
# %cat_3 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_7, %div_6, %div_5, %div_4],), kwargs = {})
triton_poi_fused_cat_4 = async_compile.triton('triton_poi_fused_cat_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (1))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gc/cgcyhpnu5i5iygf4oii3mhbcxbndqtsqekrh363bhkyzj55gac7d.py
# Topologically Sorted Source Nodes: [cat_4], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_4 => cat_4
# Graph fragment:
# %cat_4 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_11, %div_10, %div_9, %div_8],), kwargs = {})
triton_poi_fused_cat_5 = async_compile.triton('triton_poi_fused_cat_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (2))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/da/cdapqxu2aqfresxzqcyu66loof63ywm273aq4yvqggwoblyle4iu.py
# Topologically Sorted Source Nodes: [cat_5], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_5 => cat_5
# Graph fragment:
# %cat_5 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_15, %div_14, %div_13, %div_12],), kwargs = {})
triton_poi_fused_cat_6 = async_compile.triton('triton_poi_fused_cat_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (3))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/je/cjefzhvmdbzaq5qkk56lvlmhm7tmym4q6qosigotepnpng6xkbsc.py
# Topologically Sorted Source Nodes: [cat_6], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_6 => cat_6
# Graph fragment:
# %cat_6 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_19, %div_18, %div_17, %div_16],), kwargs = {})
triton_poi_fused_cat_7 = async_compile.triton('triton_poi_fused_cat_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_7(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (4))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/dd/cddfcic2f2wcza72uhbohrlu2vdlnhafwjne565bmopvtg7xzprk.py
# Topologically Sorted Source Nodes: [setitem_4], Original ATen: [aten.copy]
# Source node to ATen node mapping:
# setitem_4 => copy_4
# Graph fragment:
# %copy_4 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_47, %cat_6), kwargs = {})
# %select_scatter_default_8 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_4, %copy_4, 1, 0), kwargs = {})
triton_poi_fused_copy_8 = async_compile.triton('triton_poi_fused_copy_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_copy_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = (xindex // 4)
x2 = xindex
tmp3 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tl.full([1], 1, tl.int32)
tmp5 = tmp4 == tmp1
tmp6 = tl.full([1], 3, tl.int32)
tmp7 = tmp0 == tmp6
tmp9 = tmp1 == tmp1
tmp10 = tl.full([1], 2, tl.int32)
tmp11 = tmp0 == tmp10
tmp13 = tmp0 == tmp4
tmp16 = 1.0
tmp17 = tl.where(tmp2, tmp15, tmp16)
tmp18 = tl.where(tmp9, tmp17, tmp16)
tmp19 = tl.where(tmp13, tmp14, tmp18)
tmp20 = tl.where(tmp9, tmp19, tmp18)
tmp21 = tl.where(tmp11, tmp12, tmp20)
tmp22 = tl.where(tmp9, tmp21, tmp20)
tmp23 = tl.where(tmp7, tmp8, tmp22)
tmp24 = tl.where(tmp5, tmp17, tmp16)
tmp25 = tl.where(tmp5, tmp19, tmp24)
tmp26 = tl.where(tmp5, tmp21, tmp25)
tmp27 = tl.where(tmp5, tmp23, tmp26)
tmp28 = tl.where(tmp2, tmp3, tmp27)
tl.store(out_ptr0 + (x2), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7w/c7wuknjn76zz4hh3arcrqleadlzjigfqcr2734ihvdunvc762ws6.py
# Topologically Sorted Source Nodes: [w, setitem, setitem_1, setitem_2, setitem_3, setitem_4], Original ATen: [aten.ones, aten.copy]
# Source node to ATen node mapping:
# setitem => copy
# setitem_1 => copy_1
# setitem_2 => copy_2
# setitem_3 => copy_3
# setitem_4 => copy_4
# w => full_default
# Graph fragment:
# %full_default : [num_users=3] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_3, %cat_2), kwargs = {})
# %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int, %copy, 1, 0), kwargs = {})
# %select_scatter_default_1 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%full_default, %select_scatter_default, 1, 0), kwargs = {})
# %copy_1 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_14, %cat_3), kwargs = {})
# %select_scatter_default_2 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_1, %copy_1, 1, 1), kwargs = {})
# %select_scatter_default_3 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %select_scatter_default_2, 1, 0), kwargs = {})
# %copy_2 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_25, %cat_4), kwargs = {})
# %select_scatter_default_4 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_2, %copy_2, 1, 2), kwargs = {})
# %select_scatter_default_5 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_3, %select_scatter_default_4, 1, 0), kwargs = {})
# %copy_3 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_36, %cat_5), kwargs = {})
# %select_scatter_default_6 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_3, %copy_3, 1, 3), kwargs = {})
# %select_scatter_default_7 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_5, %select_scatter_default_6, 1, 0), kwargs = {})
# %copy_4 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_47, %cat_6), kwargs = {})
# %select_scatter_default_8 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_4, %copy_4, 1, 0), kwargs = {})
# %select_scatter_default_9 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_7, %select_scatter_default_8, 1, 1), kwargs = {})
triton_poi_fused_copy_ones_9 = async_compile.triton('triton_poi_fused_copy_ones_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_ones_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_copy_ones_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x2 = (xindex // 16)
x3 = xindex
tmp3 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
tmp17 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
tmp0 = x1
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = tmp0 == tmp4
tmp6 = x0
tmp7 = tl.full([1], 3, tl.int32)
tmp8 = tmp6 == tmp7
tmp10 = tmp4 == tmp4
tmp11 = tl.full([1], 2, tl.int32)
tmp12 = tmp6 == tmp11
tmp14 = tmp6 == tmp1
tmp16 = tmp6 == tmp4
tmp18 = 1.0
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp10, tmp19, tmp18)
tmp21 = tl.where(tmp14, tmp15, tmp20)
tmp22 = tl.where(tmp10, tmp21, tmp20)
tmp23 = tl.where(tmp12, tmp13, tmp22)
tmp24 = tl.where(tmp10, tmp23, tmp22)
tmp25 = tl.where(tmp8, tmp9, tmp24)
tmp26 = tl.where(tmp5, tmp19, tmp18)
tmp27 = tl.where(tmp5, tmp21, tmp26)
tmp28 = tl.where(tmp5, tmp23, tmp27)
tmp29 = tl.where(tmp5, tmp25, tmp28)
tmp30 = tl.where(tmp2, tmp3, tmp29)
tl.store(out_ptr0 + (x3), tmp30, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jz/cjzsbymqmrdnvkb2kcsdhwhdautgqjduinigo6j2h5mvuj4a2ihw.py
# Topologically Sorted Source Nodes: [cat_7], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_7 => cat_7
# Graph fragment:
# %cat_7 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_23, %div_22, %div_21, %div_20],), kwargs = {})
triton_poi_fused_cat_10 = async_compile.triton('triton_poi_fused_cat_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_10(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (5))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/be/cbelfuukvpuvtcl4cdtgqviixzc46vgnr2fdatagts3q3izqyahd.py
# Topologically Sorted Source Nodes: [cat_8], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_8 => cat_8
# Graph fragment:
# %cat_8 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_27, %div_26, %div_25, %div_24],), kwargs = {})
triton_poi_fused_cat_11 = async_compile.triton('triton_poi_fused_cat_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_11(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (6))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pu/cpulmdggw6po2g7bdsp3rxqtenifly66efjndtbhjfq77vyi3cvp.py
# Topologically Sorted Source Nodes: [cat_9], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_9 => cat_9
# Graph fragment:
# %cat_9 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_31, %div_30, %div_29, %div_28],), kwargs = {})
triton_poi_fused_cat_12 = async_compile.triton('triton_poi_fused_cat_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_12(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (7))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/53/c53spvltloio7aajlw2hnvsy3yonl3bxdpznosmbbmkihrolpxoy.py
# Topologically Sorted Source Nodes: [setitem_5, setitem_6, setitem_7], Original ATen: [aten.copy]
# Source node to ATen node mapping:
# setitem_5 => copy_5
# setitem_6 => copy_6
# setitem_7 => copy_7
# Graph fragment:
# %copy_5 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_58, %cat_7), kwargs = {})
# %select_scatter_default_10 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_5, %copy_5, 1, 1), kwargs = {})
# %select_scatter_default_11 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_9, %select_scatter_default_10, 1, 1), kwargs = {})
# %copy_6 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_69, %cat_8), kwargs = {})
# %select_scatter_default_12 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_6, %copy_6, 1, 2), kwargs = {})
# %select_scatter_default_13 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_11, %select_scatter_default_12, 1, 1), kwargs = {})
# %copy_7 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_80, %cat_9), kwargs = {})
# %select_scatter_default_14 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_7, %copy_7, 1, 3), kwargs = {})
# %select_scatter_default_15 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_13, %select_scatter_default_14, 1, 1), kwargs = {})
triton_poi_fused_copy_13 = async_compile.triton('triton_poi_fused_copy_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_copy_13(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x2 = (xindex // 16)
x3 = xindex
tmp6 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr3 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr3 + (x3), xmask)
tmp0 = x1
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tl.full([1], 3, tl.int32)
tmp5 = tmp3 == tmp4
tmp7 = tmp1 == tmp1
tmp8 = tl.full([1], 2, tl.int32)
tmp9 = tmp3 == tmp8
tmp11 = tmp3 == tmp1
tmp14 = tl.where(tmp11, tmp12, tmp13)
tmp15 = tl.where(tmp7, tmp14, tmp13)
tmp16 = tl.where(tmp9, tmp10, tmp15)
tmp17 = tl.where(tmp7, tmp16, tmp15)
tmp18 = tl.where(tmp5, tmp6, tmp17)
tmp20 = tl.where(tmp2, tmp14, tmp19)
tmp21 = tl.where(tmp2, tmp16, tmp20)
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(out_ptr0 + (x3), tmp22, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/r4/cr4prtz5u5vsa2qt6xr26ojxyztwrwxnujvm2tgw6msgynghg5gk.py
# Topologically Sorted Source Nodes: [cat_10], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_10 => cat_10
# Graph fragment:
# %cat_10 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_35, %div_34, %div_33, %div_32],), kwargs = {})
triton_poi_fused_cat_14 = async_compile.triton('triton_poi_fused_cat_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_14(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (8))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/2o/c2oknceyhq6bpsjsj5utqfxbts5hx6wovucbg37gmxrw6qnpfaae.py
# Topologically Sorted Source Nodes: [cat_11], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_11 => cat_11
# Graph fragment:
# %cat_11 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_39, %div_38, %div_37, %div_36],), kwargs = {})
triton_poi_fused_cat_15 = async_compile.triton('triton_poi_fused_cat_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_15(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (9))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/4o/c4orv6bemdyen5o37qyqzdrr5bj57qvvltz5ifj6wz4apv47w5vc.py
# Topologically Sorted Source Nodes: [cat_12], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_12 => cat_12
# Graph fragment:
# %cat_12 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_43, %div_42, %div_41, %div_40],), kwargs = {})
triton_poi_fused_cat_16 = async_compile.triton('triton_poi_fused_cat_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_16(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (10))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/mw/cmwkoucjyz5eerldomke2z4eudzpt225yokz3kcwzb6m7swcxpmn.py
# Topologically Sorted Source Nodes: [setitem_8, setitem_9, setitem_10], Original ATen: [aten.copy]
# Source node to ATen node mapping:
# setitem_10 => copy_10
# setitem_8 => copy_8
# setitem_9 => copy_9
# Graph fragment:
# %copy_8 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_91, %cat_10), kwargs = {})
# %select_scatter_default_16 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_8, %copy_8, 1, 0), kwargs = {})
# %select_scatter_default_17 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_15, %select_scatter_default_16, 1, 2), kwargs = {})
# %copy_9 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_102, %cat_11), kwargs = {})
# %select_scatter_default_18 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_9, %copy_9, 1, 1), kwargs = {})
# %select_scatter_default_19 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_17, %select_scatter_default_18, 1, 2), kwargs = {})
# %copy_10 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_113, %cat_12), kwargs = {})
# %select_scatter_default_20 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_10, %copy_10, 1, 2), kwargs = {})
# %select_scatter_default_21 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_19, %select_scatter_default_20, 1, 2), kwargs = {})
triton_poi_fused_copy_17 = async_compile.triton('triton_poi_fused_copy_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_copy_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x2 = (xindex // 16)
x3 = xindex
tmp5 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr3 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr3 + (x3), xmask)
tmp0 = x1
tmp1 = tl.full([1], 2, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tmp3 == tmp1
tmp6 = tmp1 == tmp1
tmp7 = tl.full([1], 1, tl.int32)
tmp8 = tmp3 == tmp7
tmp10 = tl.full([1], 0, tl.int32)
tmp11 = tmp3 == tmp10
tmp14 = tl.where(tmp11, tmp12, tmp13)
tmp15 = tl.where(tmp6, tmp14, tmp13)
tmp16 = tl.where(tmp8, tmp9, tmp15)
tmp17 = tl.where(tmp6, tmp16, tmp15)
tmp18 = tl.where(tmp4, tmp5, tmp17)
tmp20 = tl.where(tmp2, tmp14, tmp19)
tmp21 = tl.where(tmp2, tmp16, tmp20)
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(out_ptr0 + (x3), tmp22, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/op/copcszro27fxcknc2lgva3vgyq46oo5p6zzwpq5ftivuo2gfbcbo.py
# Topologically Sorted Source Nodes: [cat_13], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_13 => cat_13
# Graph fragment:
# %cat_13 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_47, %div_46, %div_45, %div_44],), kwargs = {})
triton_poi_fused_cat_18 = async_compile.triton('triton_poi_fused_cat_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_18(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (11))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ph/cph2pxfbmlo2gynf4xsnkbq7gsipy6q4ydcsbowhs6guoetpcqwi.py
# Topologically Sorted Source Nodes: [cat_14], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_14 => cat_14
# Graph fragment:
# %cat_14 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_51, %div_50, %div_49, %div_48],), kwargs = {})
triton_poi_fused_cat_19 = async_compile.triton('triton_poi_fused_cat_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_19(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (12))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/iu/ciugm66dfephs7ra2b7itkiuedvvixo3fhfsmgvfjnwlaj4ezhfd.py
# Topologically Sorted Source Nodes: [setitem_11, setitem_12], Original ATen: [aten.copy]
# Source node to ATen node mapping:
# setitem_11 => copy_11
# setitem_12 => copy_12
# Graph fragment:
# %copy_11 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_124, %cat_13), kwargs = {})
# %select_scatter_default_22 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_11, %copy_11, 1, 3), kwargs = {})
# %select_scatter_default_23 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_21, %select_scatter_default_22, 1, 2), kwargs = {})
# %copy_12 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_135, %cat_14), kwargs = {})
# %select_scatter_default_24 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_12, %copy_12, 1, 0), kwargs = {})
# %select_scatter_default_25 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_23, %select_scatter_default_24, 1, 3), kwargs = {})
triton_poi_fused_copy_20 = async_compile.triton('triton_poi_fused_copy_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_copy_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_copy_20(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x2 = (xindex // 16)
x3 = xindex
tmp6 = tl.load(in_ptr0 + (x2), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr2 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr2 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
tmp17 = tl.load(in_ptr2 + (x3), xmask)
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = tmp3 == tmp4
tmp7 = tl.full([1], 2, tl.int32)
tmp8 = tmp1 == tmp7
tmp9 = tmp3 == tmp1
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp14 = tl.where(tmp8, tmp12, tmp13)
tmp15 = tl.where(tmp5, tmp6, tmp14)
tmp16 = tmp0 == tmp7
tmp18 = tl.where(tmp16, tmp12, tmp17)
tmp19 = tl.where(tmp2, tmp15, tmp18)
tl.store(out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gk/cgkfgyewxaywdupi2jym4dv562rl4w2ds6ourjtbtsrjx3xvo2cf.py
# Topologically Sorted Source Nodes: [cat_15], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_15 => cat_15
# Graph fragment:
# %cat_15 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_55, %div_54, %div_53, %div_52],), kwargs = {})
triton_poi_fused_cat_21 = async_compile.triton('triton_poi_fused_cat_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_21(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (13))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ro/crodyinmla5nmp3tisk4535242r4ffzirporcsaxd3wcvf45tw33.py
# Topologically Sorted Source Nodes: [cat_16], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_16 => cat_16
# Graph fragment:
# %cat_16 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_59, %div_58, %div_57, %div_56],), kwargs = {})
triton_poi_fused_cat_22 = async_compile.triton('triton_poi_fused_cat_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_22(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (14))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/g4/cg4xvehzfsiw7cmpfzlbdsffs7odrfkjlh3bghg6xb42c35xaplx.py
# Topologically Sorted Source Nodes: [cat_17], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_17 => cat_17
# Graph fragment:
# %cat_17 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div_63, %div_62, %div_61, %div_60],), kwargs = {})
triton_poi_fused_cat_23 = async_compile.triton('triton_poi_fused_cat_23', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_23', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + (15))
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tmp41 = tl.full([1], 4, tl.int64)
tmp42 = tmp0 < tmp41
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + (x0), tmp47, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zz/czzys25sfcimsksfj5prn5er4qkt6wdpz2gs7cygb26f5ckeikvy.py
# Topologically Sorted Source Nodes: [setitem_13, setitem_14, setitem_15, mul_65, outputs, first, i_gate_1, o_gate_1, c_tilde_1, second, cell, tanh_1, hidden], Original ATen: [aten.copy, aten.mul, aten.sum, aten.neg, aten.sigmoid, aten.tanh, aten.add]
# Source node to ATen node mapping:
# c_tilde_1 => tanh
# cell => add
# first => neg
# hidden => mul_67
# i_gate_1 => sigmoid_1
# mul_65 => mul_65
# o_gate_1 => sigmoid_2
# outputs => sum_1
# second => mul_66
# setitem_13 => copy_13
# setitem_14 => copy_14
# setitem_15 => copy_15
# tanh_1 => tanh_1
# Graph fragment:
# %copy_13 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_146, %cat_15), kwargs = {})
# %select_scatter_default_26 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_13, %copy_13, 1, 1), kwargs = {})
# %select_scatter_default_27 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_25, %select_scatter_default_26, 1, 3), kwargs = {})
# %copy_14 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_157, %cat_16), kwargs = {})
# %select_scatter_default_28 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_14, %copy_14, 1, 2), kwargs = {})
# %select_scatter_default_29 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_27, %select_scatter_default_28, 1, 3), kwargs = {})
# %copy_15 : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%select_168, %cat_17), kwargs = {})
# %select_scatter_default_30 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_int_15, %copy_15, 1, 3), kwargs = {})
# %select_scatter_default_31 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_29, %select_scatter_default_30, 1, 3), kwargs = {})
# %mul_65 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %select_scatter_default_31), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_65, [0]), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
# %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm_1,), kwargs = {})
# %sigmoid_2 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%addmm_2,), kwargs = {})
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%addmm_3,), kwargs = {})
# %mul_66 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid_1), kwargs = {})
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg, %mul_66), kwargs = {})
# %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
# %mul_67 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh_1, %sigmoid_2), kwargs = {})
triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24 = async_compile.triton('triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 27, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp6 = tl.load(in_ptr1 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp11 = tl.load(in_ptr2 + (0))
tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
tmp15 = tl.load(in_ptr3 + (0))
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp17 = tl.load(in_ptr4 + (12 + x0), xmask, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr4 + (x2), xmask)
tmp28 = tl.load(in_ptr0 + (16 + x2), xmask)
tmp29 = tl.load(in_ptr1 + (1))
tmp30 = tl.broadcast_to(tmp29, [XBLOCK])
tmp31 = tl.load(in_ptr2 + (1))
tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
tmp33 = tl.load(in_ptr3 + (1))
tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
tmp35 = tl.load(in_ptr4 + (28 + x0), xmask, eviction_policy='evict_last')
tmp41 = tl.load(in_ptr4 + (16 + x2), xmask)
tmp47 = tl.load(in_ptr0 + (32 + x2), xmask)
tmp48 = tl.load(in_ptr1 + (2))
tmp49 = tl.broadcast_to(tmp48, [XBLOCK])
tmp50 = tl.load(in_ptr2 + (2))
tmp51 = tl.broadcast_to(tmp50, [XBLOCK])
tmp52 = tl.load(in_ptr3 + (2))
tmp53 = tl.broadcast_to(tmp52, [XBLOCK])
tmp54 = tl.load(in_ptr4 + (44 + x0), xmask, eviction_policy='evict_last')
tmp60 = tl.load(in_ptr4 + (32 + x2), xmask)
tmp66 = tl.load(in_ptr0 + (48 + x2), xmask)
tmp67 = tl.load(in_ptr1 + (3))
tmp68 = tl.broadcast_to(tmp67, [XBLOCK])
tmp69 = tl.load(in_ptr2 + (3))
tmp70 = tl.broadcast_to(tmp69, [XBLOCK])
tmp71 = tl.load(in_ptr3 + (3))
tmp72 = tl.broadcast_to(tmp71, [XBLOCK])
tmp73 = tl.load(in_ptr4 + (60 + x0), xmask, eviction_policy='evict_last')
tmp79 = tl.load(in_ptr4 + (48 + x2), xmask)
tmp86 = tl.load(in_ptr5 + (x2), xmask)
tmp88 = tl.load(in_ptr6 + (x2), xmask)
tmp93 = tl.load(in_ptr7 + (x2), xmask)
tmp1 = x1
tmp2 = tl.full([1], 3, tl.int32)
tmp3 = tmp1 == tmp2
tmp4 = x0
tmp5 = tmp4 == tmp2
tmp8 = tmp2 == tmp2
tmp9 = tl.full([1], 2, tl.int32)
tmp10 = tmp4 == tmp9
tmp13 = tl.full([1], 1, tl.int32)
tmp14 = tmp4 == tmp13
tmp18 = tl.where(tmp14, tmp16, tmp17)
tmp19 = tl.where(tmp8, tmp18, tmp17)
tmp20 = tl.where(tmp10, tmp12, tmp19)
tmp21 = tl.where(tmp8, tmp20, tmp19)
tmp22 = tl.where(tmp5, tmp7, tmp21)
tmp24 = tl.where(tmp3, tmp18, tmp23)
tmp25 = tl.where(tmp3, tmp20, tmp24)
tmp26 = tl.where(tmp3, tmp22, tmp25)
tmp27 = tmp0 * tmp26
tmp36 = tl.where(tmp14, tmp34, tmp35)
tmp37 = tl.where(tmp8, tmp36, tmp35)
tmp38 = tl.where(tmp10, tmp32, tmp37)
tmp39 = tl.where(tmp8, tmp38, tmp37)
tmp40 = tl.where(tmp5, tmp30, tmp39)
tmp42 = tl.where(tmp3, tmp36, tmp41)
tmp43 = tl.where(tmp3, tmp38, tmp42)
tmp44 = tl.where(tmp3, tmp40, tmp43)
tmp45 = tmp28 * tmp44
tmp46 = tmp27 + tmp45
tmp55 = tl.where(tmp14, tmp53, tmp54)
tmp56 = tl.where(tmp8, tmp55, tmp54)
tmp57 = tl.where(tmp10, tmp51, tmp56)
tmp58 = tl.where(tmp8, tmp57, tmp56)
tmp59 = tl.where(tmp5, tmp49, tmp58)
tmp61 = tl.where(tmp3, tmp55, tmp60)
tmp62 = tl.where(tmp3, tmp57, tmp61)
tmp63 = tl.where(tmp3, tmp59, tmp62)
tmp64 = tmp47 * tmp63
tmp65 = tmp46 + tmp64
tmp74 = tl.where(tmp14, tmp72, tmp73)
tmp75 = tl.where(tmp8, tmp74, tmp73)
tmp76 = tl.where(tmp10, tmp70, tmp75)
tmp77 = tl.where(tmp8, tmp76, tmp75)
tmp78 = tl.where(tmp5, tmp68, tmp77)
tmp80 = tl.where(tmp3, tmp74, tmp79)
tmp81 = tl.where(tmp3, tmp76, tmp80)
tmp82 = tl.where(tmp3, tmp78, tmp81)
tmp83 = tmp66 * tmp82
tmp84 = tmp65 + tmp83
tmp85 = -tmp84
tmp87 = libdevice.tanh(tmp86)
tmp89 = tl.sigmoid(tmp88)
tmp90 = tmp87 * tmp89
tmp91 = tmp85 + tmp90
tmp92 = libdevice.tanh(tmp91)
tmp94 = tl.sigmoid(tmp93)
tmp95 = tmp92 * tmp94
tl.store(out_ptr0 + (x2), tmp84, xmask)
tl.store(out_ptr1 + (x2), tmp92, xmask)
tl.store(out_ptr2 + (x2), tmp95, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/v2/cv2zwj6nik7eujfd6xnetpcxijdmhugq6y7fuaiunfbp5qmkapib.py
# Topologically Sorted Source Nodes: [hc], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# hc => cat_18
# Graph fragment:
# %cat_18 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_3, %view_16],), kwargs = {})
triton_poi_fused_cat_25 = async_compile.triton('triton_poi_fused_cat_25', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_25', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_25(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16)
x0 = xindex % 16
x2 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (16*x1)), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 5, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + (x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = -tmp9
tmp11 = tl.load(in_ptr2 + (x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp12 = libdevice.tanh(tmp11)
tmp13 = tl.load(in_ptr3 + (x0), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp14 = tl.sigmoid(tmp13)
tmp15 = tmp12 * tmp14
tmp16 = tmp10 + tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp6, tmp16, tmp17)
tmp19 = tl.where(tmp4, tmp5, tmp18)
tl.store(out_ptr0 + (x2), tmp19, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, 12), (12, 1))
assert_size_stride(primals_6, (4, ), (1, ))
assert_size_stride(primals_7, (4, 8), (8, 1))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, 8), (8, 1))
assert_size_stride(primals_10, (4, ), (1, ))
assert_size_stride(primals_11, (4, 8), (8, 1))
assert_size_stride(primals_12, (4, ), (1, ))
assert_size_stride(primals_13, (4, 4), (4, 1))
assert_size_stride(primals_14, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [combined], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
buf1 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
# Topologically Sorted Source Nodes: [combined_d], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(primals_1, primals_2, primals_4, buf1, 48, grid=grid(48), stream=stream0)
del primals_1
del primals_2
del primals_4
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [d], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_6, buf1, reinterpret_tensor(primals_5, (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf2)
del primals_5
del primals_6
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [sigmoid, d_1], Original ATen: [aten.sigmoid, aten.mul]
triton_poi_fused_mul_sigmoid_2.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
triton_poi_fused_cat_3.run(buf3, buf4, 4, grid=grid(4), stream=stream0)
buf5 = empty_strided_cuda((4, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [cat_3], Original ATen: [aten.cat]
triton_poi_fused_cat_4.run(buf3, buf5, 4, grid=grid(4), stream=stream0)
buf6 = empty_strided_cuda((4, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [cat_4], Original ATen: [aten.cat]
triton_poi_fused_cat_5.run(buf3, buf6, 4, grid=grid(4), stream=stream0)
buf7 = empty_strided_cuda((4, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [cat_5], Original ATen: [aten.cat]
triton_poi_fused_cat_6.run(buf3, buf7, 4, grid=grid(4), stream=stream0)
buf8 = empty_strided_cuda((4, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [cat_6], Original ATen: [aten.cat]
triton_poi_fused_cat_7.run(buf3, buf8, 4, grid=grid(4), stream=stream0)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [setitem_4], Original ATen: [aten.copy]
triton_poi_fused_copy_8.run(buf8, buf7, buf6, buf5, buf4, buf9, 16, grid=grid(16), stream=stream0)
del buf8
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [w, setitem, setitem_1, setitem_2, setitem_3, setitem_4], Original ATen: [aten.ones, aten.copy]
triton_poi_fused_copy_ones_9.run(buf9, buf7, buf6, buf5, buf4, buf10, 64, grid=grid(64), stream=stream0)
del buf4
buf11 = buf7; del buf7 # reuse
# Topologically Sorted Source Nodes: [cat_7], Original ATen: [aten.cat]
triton_poi_fused_cat_10.run(buf3, buf11, 4, grid=grid(4), stream=stream0)
buf12 = buf6; del buf6 # reuse
# Topologically Sorted Source Nodes: [cat_8], Original ATen: [aten.cat]
triton_poi_fused_cat_11.run(buf3, buf12, 4, grid=grid(4), stream=stream0)
buf13 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [cat_9], Original ATen: [aten.cat]
triton_poi_fused_cat_12.run(buf3, buf13, 4, grid=grid(4), stream=stream0)
buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [setitem_5, setitem_6, setitem_7], Original ATen: [aten.copy]
triton_poi_fused_copy_13.run(buf13, buf12, buf11, buf10, buf14, 64, grid=grid(64), stream=stream0)
buf15 = buf13; del buf13 # reuse
# Topologically Sorted Source Nodes: [cat_10], Original ATen: [aten.cat]
triton_poi_fused_cat_14.run(buf3, buf15, 4, grid=grid(4), stream=stream0)
buf16 = buf12; del buf12 # reuse
# Topologically Sorted Source Nodes: [cat_11], Original ATen: [aten.cat]
triton_poi_fused_cat_15.run(buf3, buf16, 4, grid=grid(4), stream=stream0)
buf17 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [cat_12], Original ATen: [aten.cat]
triton_poi_fused_cat_16.run(buf3, buf17, 4, grid=grid(4), stream=stream0)
buf18 = buf10; del buf10 # reuse
# Topologically Sorted Source Nodes: [setitem_8, setitem_9, setitem_10], Original ATen: [aten.copy]
triton_poi_fused_copy_17.run(buf17, buf16, buf15, buf14, buf18, 64, grid=grid(64), stream=stream0)
buf19 = buf17; del buf17 # reuse
# Topologically Sorted Source Nodes: [cat_13], Original ATen: [aten.cat]
triton_poi_fused_cat_18.run(buf3, buf19, 4, grid=grid(4), stream=stream0)
buf20 = buf16; del buf16 # reuse
# Topologically Sorted Source Nodes: [cat_14], Original ATen: [aten.cat]
triton_poi_fused_cat_19.run(buf3, buf20, 4, grid=grid(4), stream=stream0)
buf21 = buf14; del buf14 # reuse
# Topologically Sorted Source Nodes: [setitem_11, setitem_12], Original ATen: [aten.copy]
triton_poi_fused_copy_20.run(buf20, buf19, buf18, buf21, 64, grid=grid(64), stream=stream0)
del buf18
buf22 = buf20; del buf20 # reuse
# Topologically Sorted Source Nodes: [cat_15], Original ATen: [aten.cat]
triton_poi_fused_cat_21.run(buf3, buf22, 4, grid=grid(4), stream=stream0)
buf23 = buf19; del buf19 # reuse
# Topologically Sorted Source Nodes: [cat_16], Original ATen: [aten.cat]
triton_poi_fused_cat_22.run(buf3, buf23, 4, grid=grid(4), stream=stream0)
buf24 = buf15; del buf15 # reuse
# Topologically Sorted Source Nodes: [cat_17], Original ATen: [aten.cat]
triton_poi_fused_cat_23.run(buf3, buf24, 4, grid=grid(4), stream=stream0)
buf26 = buf9; del buf9 # reuse
# Topologically Sorted Source Nodes: [i_gate], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_8, buf0, reinterpret_tensor(primals_7, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf26)
del primals_7
del primals_8
buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [o_gate], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_10, buf0, reinterpret_tensor(primals_9, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf27)
del primals_10
del primals_9
buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [c_tilde], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_12, buf0, reinterpret_tensor(primals_11, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf28)
del primals_11
del primals_12
buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf31 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [setitem_13, setitem_14, setitem_15, mul_65, outputs, first, i_gate_1, o_gate_1, c_tilde_1, second, cell, tanh_1, hidden], Original ATen: [aten.copy, aten.mul, aten.sum, aten.neg, aten.sigmoid, aten.tanh, aten.add]
triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24.run(primals_3, buf24, buf23, buf22, buf21, buf28, buf26, buf27, buf25, buf30, buf31, 16, grid=grid(16), stream=stream0)
del buf21
del buf22
del buf23
del buf24
buf29 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [hc], Original ATen: [aten.cat]
triton_poi_fused_cat_25.run(primals_3, buf25, buf28, buf26, buf29, 80, grid=grid(80), stream=stream0)
buf32 = buf25; del buf25 # reuse
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_14, buf31, reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf32)
del primals_14
return (buf32, buf31, reinterpret_tensor(buf29, (4, 4, 4), (16, 4, 1), 16), buf3, primals_3, buf0, buf1, buf2, reinterpret_tensor(buf3, (1, ), (1, ), 0), reinterpret_tensor(buf3, (1, ), (1, ), 1), reinterpret_tensor(buf3, (1, ), (1, ), 2), reinterpret_tensor(buf3, (1, ), (1, ), 3), reinterpret_tensor(buf3, (1, ), (1, ), 4), reinterpret_tensor(buf3, (1, ), (1, ), 5), reinterpret_tensor(buf3, (1, ), (1, ), 6), reinterpret_tensor(buf3, (1, ), (1, ), 7), reinterpret_tensor(buf3, (1, ), (1, ), 8), reinterpret_tensor(buf3, (1, ), (1, ), 9), reinterpret_tensor(buf3, (1, ), (1, ), 10), reinterpret_tensor(buf3, (1, ), (1, ), 11), reinterpret_tensor(buf3, (1, ), (1, ), 12), reinterpret_tensor(buf3, (1, ), (1, ), 13), reinterpret_tensor(buf3, (1, ), (1, ), 14), reinterpret_tensor(buf3, (1, ), (1, ), 15), buf26, buf27, buf28, buf30, buf31, primals_13, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 12), (12, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
from torch.autograd import Variable
class MLSTM_cell(nn.Module):
def __init__(self, input_size, hidden_size, K, output_size):
super(MLSTM_cell, self).__init__()
self.hidden_size = hidden_size
self.K = K
self.output_size = output_size
self.cgate = nn.Linear(input_size + hidden_size, hidden_size)
self.igate = nn.Linear(input_size + hidden_size, hidden_size)
self.fgate = nn.Linear(input_size + 2 * hidden_size, hidden_size)
self.ogate = nn.Linear(input_size + hidden_size, hidden_size)
self.output = nn.Linear(hidden_size, output_size)
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
def get_ws(self, d):
w = [1.0] * (self.K + 1)
for i in range(0, self.K):
w[self.K - i - 1] = w[self.K - i] * (i - d) / (i + 1)
return torch.cat(w[0:self.K])
def filter_d(self, celltensor, d):
w = torch.ones(self.K, d.size(0), d.size(1), dtype=d.dtype, device=
d.device)
hidden_size = w.shape[2]
batch_size = w.shape[1]
for batch in range(batch_size):
for hidden in range(hidden_size):
w[:, batch, hidden] = self.get_ws(d[batch, hidden].view([1]))
outputs = celltensor.mul(w).sum(dim=0)
return outputs
def forward(self, sample, hidden, celltensor, d_0):
batch_size = sample.size(0)
if hidden is None:
hidden = torch.zeros(batch_size, self.hidden_size, dtype=sample
.dtype, device=sample.device)
if celltensor is None:
celltensor = torch.zeros(self.K, batch_size, self.hidden_size,
dtype=sample.dtype, device=sample.device)
if d_0 is None:
d_0 = torch.zeros(batch_size, self.hidden_size, dtype=sample.
dtype, device=sample.device)
combined = torch.cat((sample, hidden), 1)
combined_d = torch.cat((sample, hidden, d_0), 1)
d = self.fgate(combined_d)
d = self.sigmoid(d) * 0.5
first = -self.filter_d(celltensor, d)
i_gate = self.igate(combined)
o_gate = self.ogate(combined)
i_gate = self.sigmoid(i_gate)
o_gate = self.sigmoid(o_gate)
c_tilde = self.cgate(combined)
c_tilde = self.tanh(c_tilde)
second = torch.mul(c_tilde, i_gate)
cell = torch.add(first, second)
hc = torch.cat([celltensor, cell.view([-1, cell.size(0), cell.size(
1)])], 0)
hc1 = hc[1:, :]
hidden = torch.mul(self.tanh(cell), o_gate)
output = self.output(hidden)
return output, hidden, hc1, d
def init_hidden(self):
return Variable(torch.zeros(1, self.hidden_size))
def init_cell(self):
return Variable(torch.zeros(1, self.hidden_size))
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4, 4]),
torch.rand([4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'hidden_size': 4, 'K': 4, 'output_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from torch.autograd import Variable
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 48
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 12
x1 = xindex // 12
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tl.full([1], 12, tl.int64)
tmp14 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp11 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tl.where(tmp9, tmp10, tmp14)
tmp16 = tl.where(tmp4, tmp5, tmp15)
tl.store(out_ptr0 + x2, tmp16, xmask)
@triton.jit
def triton_poi_fused_mul_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tl.store(out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_cat_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 0)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 1)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 2)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 3)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_7(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 4)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_copy_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4
x2 = xindex
tmp3 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp14 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tl.full([1], 1, tl.int32)
tmp5 = tmp4 == tmp1
tmp6 = tl.full([1], 3, tl.int32)
tmp7 = tmp0 == tmp6
tmp9 = tmp1 == tmp1
tmp10 = tl.full([1], 2, tl.int32)
tmp11 = tmp0 == tmp10
tmp13 = tmp0 == tmp4
tmp16 = 1.0
tmp17 = tl.where(tmp2, tmp15, tmp16)
tmp18 = tl.where(tmp9, tmp17, tmp16)
tmp19 = tl.where(tmp13, tmp14, tmp18)
tmp20 = tl.where(tmp9, tmp19, tmp18)
tmp21 = tl.where(tmp11, tmp12, tmp20)
tmp22 = tl.where(tmp9, tmp21, tmp20)
tmp23 = tl.where(tmp7, tmp8, tmp22)
tmp24 = tl.where(tmp5, tmp17, tmp16)
tmp25 = tl.where(tmp5, tmp19, tmp24)
tmp26 = tl.where(tmp5, tmp21, tmp25)
tmp27 = tl.where(tmp5, tmp23, tmp26)
tmp28 = tl.where(tmp2, tmp3, tmp27)
tl.store(out_ptr0 + x2, tmp28, xmask)
@triton.jit
def triton_poi_fused_copy_ones_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
tmp3 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp9 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
tmp17 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp0 = x1
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = tmp0 == tmp4
tmp6 = x0
tmp7 = tl.full([1], 3, tl.int32)
tmp8 = tmp6 == tmp7
tmp10 = tmp4 == tmp4
tmp11 = tl.full([1], 2, tl.int32)
tmp12 = tmp6 == tmp11
tmp14 = tmp6 == tmp1
tmp16 = tmp6 == tmp4
tmp18 = 1.0
tmp19 = tl.where(tmp16, tmp17, tmp18)
tmp20 = tl.where(tmp10, tmp19, tmp18)
tmp21 = tl.where(tmp14, tmp15, tmp20)
tmp22 = tl.where(tmp10, tmp21, tmp20)
tmp23 = tl.where(tmp12, tmp13, tmp22)
tmp24 = tl.where(tmp10, tmp23, tmp22)
tmp25 = tl.where(tmp8, tmp9, tmp24)
tmp26 = tl.where(tmp5, tmp19, tmp18)
tmp27 = tl.where(tmp5, tmp21, tmp26)
tmp28 = tl.where(tmp5, tmp23, tmp27)
tmp29 = tl.where(tmp5, tmp25, tmp28)
tmp30 = tl.where(tmp2, tmp3, tmp29)
tl.store(out_ptr0 + x3, tmp30, xmask)
@triton.jit
def triton_poi_fused_cat_10(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 5)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_11(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 6)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_12(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 7)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_copy_13(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
tmp6 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr3 + (4 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr3 + x3, xmask)
tmp0 = x1
tmp1 = tl.full([1], 1, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tl.full([1], 3, tl.int32)
tmp5 = tmp3 == tmp4
tmp7 = tmp1 == tmp1
tmp8 = tl.full([1], 2, tl.int32)
tmp9 = tmp3 == tmp8
tmp11 = tmp3 == tmp1
tmp14 = tl.where(tmp11, tmp12, tmp13)
tmp15 = tl.where(tmp7, tmp14, tmp13)
tmp16 = tl.where(tmp9, tmp10, tmp15)
tmp17 = tl.where(tmp7, tmp16, tmp15)
tmp18 = tl.where(tmp5, tmp6, tmp17)
tmp20 = tl.where(tmp2, tmp14, tmp19)
tmp21 = tl.where(tmp2, tmp16, tmp20)
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(out_ptr0 + x3, tmp22, xmask)
@triton.jit
def triton_poi_fused_cat_14(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 8)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_15(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 9)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_16(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 10)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_copy_17(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
tmp5 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr3 + (8 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp19 = tl.load(in_ptr3 + x3, xmask)
tmp0 = x1
tmp1 = tl.full([1], 2, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tmp3 == tmp1
tmp6 = tmp1 == tmp1
tmp7 = tl.full([1], 1, tl.int32)
tmp8 = tmp3 == tmp7
tmp10 = tl.full([1], 0, tl.int32)
tmp11 = tmp3 == tmp10
tmp14 = tl.where(tmp11, tmp12, tmp13)
tmp15 = tl.where(tmp6, tmp14, tmp13)
tmp16 = tl.where(tmp8, tmp9, tmp15)
tmp17 = tl.where(tmp6, tmp16, tmp15)
tmp18 = tl.where(tmp4, tmp5, tmp17)
tmp20 = tl.where(tmp2, tmp14, tmp19)
tmp21 = tl.where(tmp2, tmp16, tmp20)
tmp22 = tl.where(tmp2, tmp18, tmp21)
tl.store(out_ptr0 + x3, tmp22, xmask)
@triton.jit
def triton_poi_fused_cat_18(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 11)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_19(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 12)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_copy_20(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x2 = xindex // 16
x3 = xindex
tmp6 = tl.load(in_ptr0 + x2, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr2 + (8 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp13 = tl.load(in_ptr2 + (12 + x0 + 16 * x2), xmask, eviction_policy=
'evict_last')
tmp17 = tl.load(in_ptr2 + x3, xmask)
tmp0 = x1
tmp1 = tl.full([1], 3, tl.int32)
tmp2 = tmp0 == tmp1
tmp3 = x0
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = tmp3 == tmp4
tmp7 = tl.full([1], 2, tl.int32)
tmp8 = tmp1 == tmp7
tmp9 = tmp3 == tmp1
tmp12 = tl.where(tmp9, tmp10, tmp11)
tmp14 = tl.where(tmp8, tmp12, tmp13)
tmp15 = tl.where(tmp5, tmp6, tmp14)
tmp16 = tmp0 == tmp7
tmp18 = tl.where(tmp16, tmp12, tmp17)
tmp19 = tl.where(tmp2, tmp15, tmp18)
tl.store(out_ptr0 + x3, tmp19, xmask)
@triton.jit
def triton_poi_fused_cat_21(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 13)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_22(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 14)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_cat_23(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp5 = tl.load(in_ptr0 + 15)
tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp7 = 0.0
tmp8 = tmp7 - tmp6
tmp9 = 1.0
tmp10 = tmp8 * tmp9
tmp11 = tmp10 * tmp9
tmp12 = tmp9 - tmp6
tmp13 = tmp11 * tmp12
tmp14 = 0.5
tmp15 = tmp13 * tmp14
tmp16 = 2.0
tmp17 = tmp16 - tmp6
tmp18 = tmp15 * tmp17
tmp19 = 0.3333333333333333
tmp20 = tmp18 * tmp19
tmp21 = 3.0
tmp22 = tmp21 - tmp6
tmp23 = tmp20 * tmp22
tmp24 = 0.25
tmp25 = tmp23 * tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp4, tmp25, tmp26)
tmp28 = tmp0 >= tmp3
tmp29 = tl.full([1], 2, tl.int64)
tmp30 = tmp0 < tmp29
tmp31 = tmp28 & tmp30
tmp32 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
tmp33 = tl.where(tmp31, tmp20, tmp32)
tmp34 = tmp0 >= tmp29
tmp35 = tl.full([1], 3, tl.int64)
tmp36 = tmp0 < tmp35
tmp37 = tmp34 & tmp36
tmp38 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
tmp39 = tl.where(tmp37, tmp15, tmp38)
tmp40 = tmp0 >= tmp35
tl.full([1], 4, tl.int64)
tmp43 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
tmp44 = tl.where(tmp40, tmp11, tmp43)
tmp45 = tl.where(tmp37, tmp39, tmp44)
tmp46 = tl.where(tmp31, tmp33, tmp45)
tmp47 = tl.where(tmp4, tmp27, tmp46)
tl.store(out_ptr0 + x0, tmp47, xmask)
@triton.jit
def triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0,
out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp6 = tl.load(in_ptr1 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp11 = tl.load(in_ptr2 + 0)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK])
tmp15 = tl.load(in_ptr3 + 0)
tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
tmp17 = tl.load(in_ptr4 + (12 + x0), xmask, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr4 + x2, xmask)
tmp28 = tl.load(in_ptr0 + (16 + x2), xmask)
tmp29 = tl.load(in_ptr1 + 1)
tmp30 = tl.broadcast_to(tmp29, [XBLOCK])
tmp31 = tl.load(in_ptr2 + 1)
tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
tmp33 = tl.load(in_ptr3 + 1)
tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
tmp35 = tl.load(in_ptr4 + (28 + x0), xmask, eviction_policy='evict_last')
tmp41 = tl.load(in_ptr4 + (16 + x2), xmask)
tmp47 = tl.load(in_ptr0 + (32 + x2), xmask)
tmp48 = tl.load(in_ptr1 + 2)
tmp49 = tl.broadcast_to(tmp48, [XBLOCK])
tmp50 = tl.load(in_ptr2 + 2)
tmp51 = tl.broadcast_to(tmp50, [XBLOCK])
tmp52 = tl.load(in_ptr3 + 2)
tmp53 = tl.broadcast_to(tmp52, [XBLOCK])
tmp54 = tl.load(in_ptr4 + (44 + x0), xmask, eviction_policy='evict_last')
tmp60 = tl.load(in_ptr4 + (32 + x2), xmask)
tmp66 = tl.load(in_ptr0 + (48 + x2), xmask)
tmp67 = tl.load(in_ptr1 + 3)
tmp68 = tl.broadcast_to(tmp67, [XBLOCK])
tmp69 = tl.load(in_ptr2 + 3)
tmp70 = tl.broadcast_to(tmp69, [XBLOCK])
tmp71 = tl.load(in_ptr3 + 3)
tmp72 = tl.broadcast_to(tmp71, [XBLOCK])
tmp73 = tl.load(in_ptr4 + (60 + x0), xmask, eviction_policy='evict_last')
tmp79 = tl.load(in_ptr4 + (48 + x2), xmask)
tmp86 = tl.load(in_ptr5 + x2, xmask)
tmp88 = tl.load(in_ptr6 + x2, xmask)
tmp93 = tl.load(in_ptr7 + x2, xmask)
tmp1 = x1
tmp2 = tl.full([1], 3, tl.int32)
tmp3 = tmp1 == tmp2
tmp4 = x0
tmp5 = tmp4 == tmp2
tmp8 = tmp2 == tmp2
tmp9 = tl.full([1], 2, tl.int32)
tmp10 = tmp4 == tmp9
tmp13 = tl.full([1], 1, tl.int32)
tmp14 = tmp4 == tmp13
tmp18 = tl.where(tmp14, tmp16, tmp17)
tmp19 = tl.where(tmp8, tmp18, tmp17)
tmp20 = tl.where(tmp10, tmp12, tmp19)
tmp21 = tl.where(tmp8, tmp20, tmp19)
tmp22 = tl.where(tmp5, tmp7, tmp21)
tmp24 = tl.where(tmp3, tmp18, tmp23)
tmp25 = tl.where(tmp3, tmp20, tmp24)
tmp26 = tl.where(tmp3, tmp22, tmp25)
tmp27 = tmp0 * tmp26
tmp36 = tl.where(tmp14, tmp34, tmp35)
tmp37 = tl.where(tmp8, tmp36, tmp35)
tmp38 = tl.where(tmp10, tmp32, tmp37)
tmp39 = tl.where(tmp8, tmp38, tmp37)
tmp40 = tl.where(tmp5, tmp30, tmp39)
tmp42 = tl.where(tmp3, tmp36, tmp41)
tmp43 = tl.where(tmp3, tmp38, tmp42)
tmp44 = tl.where(tmp3, tmp40, tmp43)
tmp45 = tmp28 * tmp44
tmp46 = tmp27 + tmp45
tmp55 = tl.where(tmp14, tmp53, tmp54)
tmp56 = tl.where(tmp8, tmp55, tmp54)
tmp57 = tl.where(tmp10, tmp51, tmp56)
tmp58 = tl.where(tmp8, tmp57, tmp56)
tmp59 = tl.where(tmp5, tmp49, tmp58)
tmp61 = tl.where(tmp3, tmp55, tmp60)
tmp62 = tl.where(tmp3, tmp57, tmp61)
tmp63 = tl.where(tmp3, tmp59, tmp62)
tmp64 = tmp47 * tmp63
tmp65 = tmp46 + tmp64
tmp74 = tl.where(tmp14, tmp72, tmp73)
tmp75 = tl.where(tmp8, tmp74, tmp73)
tmp76 = tl.where(tmp10, tmp70, tmp75)
tmp77 = tl.where(tmp8, tmp76, tmp75)
tmp78 = tl.where(tmp5, tmp68, tmp77)
tmp80 = tl.where(tmp3, tmp74, tmp79)
tmp81 = tl.where(tmp3, tmp76, tmp80)
tmp82 = tl.where(tmp3, tmp78, tmp81)
tmp83 = tmp66 * tmp82
tmp84 = tmp65 + tmp83
tmp85 = -tmp84
tmp87 = libdevice.tanh(tmp86)
tmp89 = tl.sigmoid(tmp88)
tmp90 = tmp87 * tmp89
tmp91 = tmp85 + tmp90
tmp92 = libdevice.tanh(tmp91)
tmp94 = tl.sigmoid(tmp93)
tmp95 = tmp92 * tmp94
tl.store(out_ptr0 + x2, tmp84, xmask)
tl.store(out_ptr1 + x2, tmp92, xmask)
tl.store(out_ptr2 + x2, tmp95, xmask)
@triton.jit
def triton_poi_fused_cat_25(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16
x0 = xindex % 16
x2 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 5, tl.int64)
tmp9 = tl.load(in_ptr1 + x0, tmp6 & xmask, eviction_policy='evict_last',
other=0.0)
tmp10 = -tmp9
tmp11 = tl.load(in_ptr2 + x0, tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp12 = libdevice.tanh(tmp11)
tmp13 = tl.load(in_ptr3 + x0, tmp6 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp14 = tl.sigmoid(tmp13)
tmp15 = tmp12 * tmp14
tmp16 = tmp10 + tmp15
tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
tmp18 = tl.where(tmp6, tmp16, tmp17)
tmp19 = tl.where(tmp4, tmp5, tmp18)
tl.store(out_ptr0 + x2, tmp19, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, 12), (12, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 8), (8, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4, 8), (8, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4, 8), (8, 1))
assert_size_stride(primals_12, (4,), (1,))
assert_size_stride(primals_13, (4, 4), (4, 1))
assert_size_stride(primals_14, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf1 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
triton_poi_fused_cat_1[grid(48)](primals_1, primals_2, primals_4,
buf1, 48, XBLOCK=64, num_warps=1, num_stages=1)
del primals_1
del primals_2
del primals_4
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_6, buf1, reinterpret_tensor(primals_5,
(12, 4), (1, 12), 0), alpha=1, beta=1, out=buf2)
del primals_5
del primals_6
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_mul_sigmoid_2[grid(16)](buf2, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_cat_3[grid(4)](buf3, buf4, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf5 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_cat_4[grid(4)](buf3, buf5, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf6 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_cat_5[grid(4)](buf3, buf6, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf7 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_cat_6[grid(4)](buf3, buf7, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf8 = empty_strided_cuda((4,), (1,), torch.float32)
triton_poi_fused_cat_7[grid(4)](buf3, buf8, 4, XBLOCK=4, num_warps=
1, num_stages=1)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_copy_8[grid(16)](buf8, buf7, buf6, buf5, buf4,
buf9, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf8
buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_copy_ones_9[grid(64)](buf9, buf7, buf6, buf5, buf4,
buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf4
buf11 = buf7
del buf7
triton_poi_fused_cat_10[grid(4)](buf3, buf11, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf12 = buf6
del buf6
triton_poi_fused_cat_11[grid(4)](buf3, buf12, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf13 = buf5
del buf5
triton_poi_fused_cat_12[grid(4)](buf3, buf13, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_copy_13[grid(64)](buf13, buf12, buf11, buf10,
buf14, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf15 = buf13
del buf13
triton_poi_fused_cat_14[grid(4)](buf3, buf15, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf16 = buf12
del buf12
triton_poi_fused_cat_15[grid(4)](buf3, buf16, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf17 = buf11
del buf11
triton_poi_fused_cat_16[grid(4)](buf3, buf17, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf18 = buf10
del buf10
triton_poi_fused_copy_17[grid(64)](buf17, buf16, buf15, buf14,
buf18, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf19 = buf17
del buf17
triton_poi_fused_cat_18[grid(4)](buf3, buf19, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf20 = buf16
del buf16
triton_poi_fused_cat_19[grid(4)](buf3, buf20, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf21 = buf14
del buf14
triton_poi_fused_copy_20[grid(64)](buf20, buf19, buf18, buf21, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del buf18
buf22 = buf20
del buf20
triton_poi_fused_cat_21[grid(4)](buf3, buf22, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf23 = buf19
del buf19
triton_poi_fused_cat_22[grid(4)](buf3, buf23, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf24 = buf15
del buf15
triton_poi_fused_cat_23[grid(4)](buf3, buf24, 4, XBLOCK=4,
num_warps=1, num_stages=1)
buf26 = buf9
del buf9
extern_kernels.addmm(primals_8, buf0, reinterpret_tensor(primals_7,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf26)
del primals_7
del primals_8
buf27 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_10, buf0, reinterpret_tensor(primals_9,
(8, 4), (1, 8), 0), alpha=1, beta=1, out=buf27)
del primals_10
del primals_9
buf28 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_12, buf0, reinterpret_tensor(
primals_11, (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf28)
del primals_11
del primals_12
buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf30 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf31 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_copy_mul_neg_sigmoid_sum_tanh_24[grid(16)](
primals_3, buf24, buf23, buf22, buf21, buf28, buf26, buf27,
buf25, buf30, buf31, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf21
del buf22
del buf23
del buf24
buf29 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_cat_25[grid(80)](primals_3, buf25, buf28, buf26,
buf29, 80, XBLOCK=128, num_warps=4, num_stages=1)
buf32 = buf25
del buf25
extern_kernels.addmm(primals_14, buf31, reinterpret_tensor(
primals_13, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf32)
del primals_14
return buf32, buf31, reinterpret_tensor(buf29, (4, 4, 4), (16, 4, 1), 16
), buf3, primals_3, buf0, buf1, buf2, reinterpret_tensor(buf3, (1,),
(1,), 0), reinterpret_tensor(buf3, (1,), (1,), 1), reinterpret_tensor(
buf3, (1,), (1,), 2), reinterpret_tensor(buf3, (1,), (1,), 3
), reinterpret_tensor(buf3, (1,), (1,), 4), reinterpret_tensor(buf3,
(1,), (1,), 5), reinterpret_tensor(buf3, (1,), (1,), 6
), reinterpret_tensor(buf3, (1,), (1,), 7), reinterpret_tensor(buf3,
(1,), (1,), 8), reinterpret_tensor(buf3, (1,), (1,), 9
), reinterpret_tensor(buf3, (1,), (1,), 10), reinterpret_tensor(buf3,
(1,), (1,), 11), reinterpret_tensor(buf3, (1,), (1,), 12
), reinterpret_tensor(buf3, (1,), (1,), 13), reinterpret_tensor(buf3,
(1,), (1,), 14), reinterpret_tensor(buf3, (1,), (1,), 15
), buf26, buf27, buf28, buf30, buf31, primals_13
class MLSTM_cellNew(nn.Module):
def __init__(self, input_size, hidden_size, K, output_size):
super(MLSTM_cellNew, self).__init__()
self.hidden_size = hidden_size
self.K = K
self.output_size = output_size
self.cgate = nn.Linear(input_size + hidden_size, hidden_size)
self.igate = nn.Linear(input_size + hidden_size, hidden_size)
self.fgate = nn.Linear(input_size + 2 * hidden_size, hidden_size)
self.ogate = nn.Linear(input_size + hidden_size, hidden_size)
self.output = nn.Linear(hidden_size, output_size)
self.sigmoid = nn.Sigmoid()
self.tanh = nn.Tanh()
def get_ws(self, d):
w = [1.0] * (self.K + 1)
for i in range(0, self.K):
w[self.K - i - 1] = w[self.K - i] * (i - d) / (i + 1)
return torch.cat(w[0:self.K])
def filter_d(self, celltensor, d):
w = torch.ones(self.K, d.size(0), d.size(1), dtype=d.dtype, device=
d.device)
hidden_size = w.shape[2]
batch_size = w.shape[1]
for batch in range(batch_size):
for hidden in range(hidden_size):
w[:, batch, hidden] = self.get_ws(d[batch, hidden].view([1]))
outputs = celltensor.mul(w).sum(dim=0)
return outputs
def init_hidden(self):
return Variable(torch.zeros(1, self.hidden_size))
def init_cell(self):
return Variable(torch.zeros(1, self.hidden_size))
def forward(self, input_0, input_1, input_2, input_3):
primals_7 = self.cgate.weight
primals_6 = self.cgate.bias
primals_9 = self.igate.weight
primals_8 = self.igate.bias
primals_5 = self.fgate.weight
primals_10 = self.fgate.bias
primals_11 = self.ogate.weight
primals_12 = self.ogate.bias
primals_1 = self.output.weight
primals_14 = self.output.bias
primals_2 = input_0
primals_4 = input_1
primals_3 = input_2
primals_13 = input_3
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14])
return output[0], output[1], output[2], output[3]
|
Gladys-Zhao/mRNN-mLSTM
|
MLSTM_cell
| false | 8,280 |
[
"BSD-3-Clause"
] | 15 |
23499f237ea8b0f68c96f756fbf0f4028836e64c
|
https://github.com/Gladys-Zhao/mRNN-mLSTM/tree/23499f237ea8b0f68c96f756fbf0f4028836e64c
|
EuclideanDistance
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/q3/cq37dk4eve5l2hcpqjouja2n65o4ftoycn3s44v5zrxeph62vvip.py
# Topologically Sorted Source Nodes: [i_norm, j_norm, sub, pow_1, sum_1, sqrt], Original ATen: [aten.sigmoid, aten.sub, aten.pow, aten.sum, aten.sqrt]
# Source node to ATen node mapping:
# i_norm => sigmoid
# j_norm => sigmoid_1
# pow_1 => pow_1
# sqrt => sqrt
# sub => sub
# sum_1 => sum_1
# Graph fragment:
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
# %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg1_1,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %sigmoid_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0 = async_compile.triton('triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp20 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp1 = tl.sigmoid(tmp0)
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp1 - tmp3
tmp5 = tmp4 * tmp4
tmp7 = tl.sigmoid(tmp6)
tmp9 = tl.sigmoid(tmp8)
tmp10 = tmp7 - tmp9
tmp11 = tmp10 * tmp10
tmp12 = tmp5 + tmp11
tmp14 = tl.sigmoid(tmp13)
tmp16 = tl.sigmoid(tmp15)
tmp17 = tmp14 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp12 + tmp18
tmp21 = tl.sigmoid(tmp20)
tmp23 = tl.sigmoid(tmp22)
tmp24 = tmp21 - tmp23
tmp25 = tmp24 * tmp24
tmp26 = tmp19 + tmp25
tmp27 = libdevice.sqrt(tmp26)
tl.store(out_ptr0 + (x0), tmp27, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [i_norm, j_norm, sub, pow_1, sum_1, sqrt], Original ATen: [aten.sigmoid, aten.sub, aten.pow, aten.sum, aten.sqrt]
stream0 = get_raw_stream(0)
triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
del arg0_1
del arg1_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.nn as nn
class EuclideanDistance(nn.Module):
def __init__(self):
super(EuclideanDistance, self).__init__()
self.m = nn.Sigmoid()
def forward(self, i, j):
i_norm = self.m(i)
j_norm = self.m(j)
return th.sqrt(th.sum((i_norm - j_norm) ** 2, dim=-1))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp15 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp22 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp1 = tl.sigmoid(tmp0)
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp1 - tmp3
tmp5 = tmp4 * tmp4
tmp7 = tl.sigmoid(tmp6)
tmp9 = tl.sigmoid(tmp8)
tmp10 = tmp7 - tmp9
tmp11 = tmp10 * tmp10
tmp12 = tmp5 + tmp11
tmp14 = tl.sigmoid(tmp13)
tmp16 = tl.sigmoid(tmp15)
tmp17 = tmp14 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp12 + tmp18
tmp21 = tl.sigmoid(tmp20)
tmp23 = tl.sigmoid(tmp22)
tmp24 = tmp21 - tmp23
tmp25 = tmp24 * tmp24
tmp26 = tmp19 + tmp25
tmp27 = libdevice.sqrt(tmp26)
tl.store(out_ptr0 + x0, tmp27, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_pow_sigmoid_sqrt_sub_sum_0[grid(64)](arg0_1,
arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class EuclideanDistanceNew(nn.Module):
def __init__(self):
super(EuclideanDistanceNew, self).__init__()
self.m = nn.Sigmoid()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IBM/aihn-ucsd
|
EuclideanDistance
| false | 8,281 |
[
"Apache-2.0"
] | 20 |
6c6a56d11c704b529a31868418e350e9760ff9d9
|
https://github.com/IBM/aihn-ucsd/tree/6c6a56d11c704b529a31868418e350e9760ff9d9
|
DownBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/jg/cjgtviiizh5ssfmytvsw5ugabco6mn5iifxdeperduxtp54znwav.py
# Topologically Sorted Source Nodes: [out_1, lr], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel]
# Source node to ATen node mapping:
# lr => gt, mul_1, where
# out_1 => add, rsqrt, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
# %where : [num_users=3] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 128],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit__prelu_kernel_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (81*x0)), rmask & xmask, other=0.0)
tmp26 = tl.load(in_ptr1 + (0))
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask & xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(rmask & xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 81.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp22 = tmp0 - tmp10
tmp23 = tmp22 * tmp21
tmp24 = 0.0
tmp25 = tmp23 > tmp24
tmp28 = tmp27 * tmp23
tmp29 = tl.where(tmp25, tmp23, tmp28)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp21, xmask)
tl.store(out_ptr1 + (r1 + (81*x0)), tmp29, rmask & xmask)
tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/y7/cy7wf5syp7242qc5v6om2jztrblllvhfhxdiictfx2fe3f2mpoxl.py
# Topologically Sorted Source Nodes: [hr, add, mul, residue], Original ATen: [aten.leaky_relu, aten.add, aten.mul]
# Source node to ATen node mapping:
# add => add_1
# hr => gt_1, mul_2, where_1
# mul => mul_3
# residue => add_2
# Graph fragment:
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution_1, 0), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, 0.01), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %convolution_1, %mul_2), kwargs = {})
# %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_3, 1), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_1, %add_1), kwargs = {})
# %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, %mul_3), kwargs = {})
triton_poi_fused_add_leaky_relu_mul_1 = async_compile.triton('triton_poi_fused_add_leaky_relu_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_leaky_relu_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_leaky_relu_mul_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = (xindex // 64)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (64 + x0 + (128*x1)), xmask)
tmp3 = tl.load(in_ptr0 + (x0 + (128*x1)), xmask)
tmp4 = tl.load(in_ptr1 + (x2), xmask)
tmp1 = 1.0
tmp2 = tmp0 + tmp1
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp7 = 0.01
tmp8 = tmp4 * tmp7
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = tmp9 * tmp2
tmp11 = tmp3 + tmp10
tl.store(out_ptr0 + (x2), tmp2, xmask)
tl.store(out_ptr1 + (x2), tmp11, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/dk/cdkgpa2ze742pnjuwwkdqd7raqd65bqcsvj7miczbwuml43fuqmv.py
# Topologically Sorted Source Nodes: [out_4, l_residue, add_2, mul_1, add_3], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel, aten.add, aten.mul]
# Source node to ATen node mapping:
# add_2 => add_4
# add_3 => add_5
# l_residue => gt_2, mul_5, where_2
# mul_1 => mul_6
# out_4 => add_3, rsqrt_1, var_mean_1
# Graph fragment:
# %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_3, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, 1e-05), kwargs = {})
# %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
# %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_4, 0), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %view_4), kwargs = {})
# %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_4, %mul_5), kwargs = {})
# %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_7, 1), kwargs = {})
# %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where_2, %add_4), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_6, %mul_6), kwargs = {})
triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2 = async_compile.triton('triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 128],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
x2 = xindex % 4
x3 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (r1 + (81*x0)), rmask & xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + (324 + r1 + (81*x2) + (648*x3)), rmask & xmask, other=0.0)
tmp25 = tl.load(in_ptr1 + (r1 + (81*x2) + (648*x3)), rmask & xmask, other=0.0)
tmp30 = tl.load(in_ptr2 + (0))
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(rmask & xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask & xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(rmask & xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 81.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp23 = 1.0
tmp24 = tmp22 + tmp23
tmp26 = tmp0 - tmp10
tmp27 = tmp26 * tmp21
tmp28 = 0.0
tmp29 = tmp27 > tmp28
tmp32 = tmp31 * tmp27
tmp33 = tl.where(tmp29, tmp27, tmp32)
tmp34 = tmp33 * tmp24
tmp35 = tmp25 + tmp34
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp21, xmask)
tl.store(out_ptr1 + (r1 + (81*x0)), tmp24, rmask & xmask)
tl.store(out_ptr2 + (r1 + (81*x0)), tmp35, rmask & xmask)
tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (1, ), (1, ))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (8, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_7, (1, ), (1, ))
assert_size_stride(primals_8, (8, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf4 = reinterpret_tensor(buf2, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf2 # reuse
buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_1, lr], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel]
stream0 = get_raw_stream(0)
triton_per_fused__native_batch_norm_legit__prelu_kernel_0.run(buf4, buf0, primals_3, buf1, buf5, 16, 81, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
buf6 = extern_kernels.convolution(buf5, primals_4, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 8, 4, 4), (128, 16, 4, 1))
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [hr, add, mul, residue], Original ATen: [aten.leaky_relu, aten.add, aten.mul]
triton_poi_fused_add_leaky_relu_mul_1.run(buf7, buf6, buf8, buf9, 256, grid=grid(256), stream=stream0)
del buf7
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.convolution]
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 4, 9, 9), (324, 81, 9, 1))
# Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
buf15 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 8, 9, 9), (648, 81, 9, 1))
buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf12 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf14 = reinterpret_tensor(buf12, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf12 # reuse
buf16 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
buf17 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_4, l_residue, add_2, mul_1, add_3], Original ATen: [aten._native_batch_norm_legit, aten._prelu_kernel, aten.add, aten.mul]
triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2.run(buf14, buf10, buf15, primals_7, buf11, buf16, buf17, 16, 81, grid=grid(16), stream=stream0)
del buf15
return (buf17, primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, buf0, buf1, buf4, buf5, buf6, buf8, buf9, buf10, buf11, buf14, buf16, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act = torch.nn.PReLU()
self.bn = nn.InstanceNorm2d(output_size)
def forward(self, x):
out = self.conv(x)
out = self.bn(out)
return self.act(out)
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
kernel_size, stride, padding, bias=bias)
self.act = torch.nn.LeakyReLU()
def forward(self, x):
out = self.deconv(x)
return self.act(out)
class DownBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride, padding):
super(DownBlock, self).__init__()
self.conv1 = ConvBlock(input_size, output_size, kernel_size, stride,
padding, bias=False)
self.conv2 = DeconvBlock(output_size, output_size, kernel_size,
stride, padding, bias=False)
self.conv3 = ConvBlock(output_size, output_size, kernel_size,
stride, padding, bias=False)
self.local_weight1 = nn.Conv2d(input_size, 2 * output_size,
kernel_size=1, stride=1, padding=0, bias=False)
self.local_weight2 = nn.Conv2d(output_size, 2 * output_size,
kernel_size=1, stride=1, padding=0, bias=False)
def forward(self, x):
lr = self.conv1(x)
hr = self.conv2(lr)
mean, var = self.local_weight1(x).chunk(2, dim=1)
residue = mean + hr * (1 + var)
l_residue = self.conv3(residue)
mean, var = self.local_weight2(lr).chunk(2, dim=1)
return mean + l_residue * (1 + var)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'input_size': 4, 'output_size': 4, 'kernel_size': 4,
'stride': 1, 'padding': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_0(in_out_ptr0,
in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr
):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 81 * x0), rmask & xmask, other=0.0)
tmp26 = tl.load(in_ptr1 + 0)
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(rmask & xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask & xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(rmask & xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 81.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp22 = tmp0 - tmp10
tmp23 = tmp22 * tmp21
tmp24 = 0.0
tmp25 = tmp23 > tmp24
tmp28 = tmp27 * tmp23
tmp29 = tl.where(tmp25, tmp23, tmp28)
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr1 + (r1 + 81 * x0), tmp29, rmask & xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_leaky_relu_mul_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 64
x1 = xindex // 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask)
tmp3 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask)
tmp4 = tl.load(in_ptr1 + x2, xmask)
tmp1 = 1.0
tmp2 = tmp0 + tmp1
tmp5 = 0.0
tmp6 = tmp4 > tmp5
tmp7 = 0.01
tmp8 = tmp4 * tmp7
tmp9 = tl.where(tmp6, tmp4, tmp8)
tmp10 = tmp9 * tmp2
tmp11 = tmp3 + tmp10
tl.store(out_ptr0 + x2, tmp2, xmask)
tl.store(out_ptr1 + x2, tmp11, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2,
xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
rnumel = 81
RBLOCK: tl.constexpr = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r1 = rindex
x0 = xindex
x2 = xindex % 4
x3 = xindex // 4
tmp0 = tl.load(in_ptr0 + (r1 + 81 * x0), rmask & xmask, other=0.0)
tmp22 = tl.load(in_ptr1 + (324 + r1 + 81 * x2 + 648 * x3), rmask &
xmask, other=0.0)
tmp25 = tl.load(in_ptr1 + (r1 + 81 * x2 + 648 * x3), rmask & xmask,
other=0.0)
tmp30 = tl.load(in_ptr2 + 0)
tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(rmask & xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask & xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 81, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(rmask & xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 81.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp23 = 1.0
tmp24 = tmp22 + tmp23
tmp26 = tmp0 - tmp10
tmp27 = tmp26 * tmp21
tmp28 = 0.0
tmp29 = tmp27 > tmp28
tmp32 = tmp31 * tmp27
tmp33 = tl.where(tmp29, tmp27, tmp32)
tmp34 = tmp33 * tmp24
tmp35 = tmp25 + tmp34
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(out_ptr1 + (r1 + 81 * x0), tmp24, rmask & xmask)
tl.store(out_ptr2 + (r1 + 81 * x0), tmp35, rmask & xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (1,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (8, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_7, (1,), (1,))
assert_size_stride(primals_8, (8, 4, 1, 1), (4, 1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1,
1), padding=(4, 4), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 9, 9), (324, 81, 9, 1))
buf1 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf4 = reinterpret_tensor(buf2, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf2
buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
get_raw_stream(0)
triton_per_fused__native_batch_norm_legit__prelu_kernel_0[grid(16)](
buf4, buf0, primals_3, buf1, buf5, 16, 81, XBLOCK=1, num_warps=
2, num_stages=1)
buf6 = extern_kernels.convolution(buf5, primals_4, stride=(1, 1),
padding=(4, 4), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = extern_kernels.convolution(primals_2, primals_5, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 8, 4, 4), (128, 16, 4, 1))
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_leaky_relu_mul_1[grid(256)](buf7, buf6, buf8,
buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf7
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
padding=(4, 4), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 4, 9, 9), (324, 81, 9, 1))
buf15 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 8, 9, 9), (648, 81, 9, 1))
buf11 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf12 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
buf14 = reinterpret_tensor(buf12, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf12
buf16 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32
)
buf17 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32
)
triton_per_fused__native_batch_norm_legit__prelu_kernel_add_mul_2[grid
(16)](buf14, buf10, buf15, primals_7, buf11, buf16, buf17, 16,
81, XBLOCK=1, num_warps=2, num_stages=1)
del buf15
return (buf17, primals_1, primals_2, primals_3, primals_4, primals_5,
primals_6, primals_7, primals_8, buf0, buf1, buf4, buf5, buf6, buf8,
buf9, buf10, buf11, buf14, buf16)
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size,
stride, padding, bias=bias)
self.act = torch.nn.PReLU()
self.bn = nn.InstanceNorm2d(output_size)
def forward(self, x):
out = self.conv(x)
out = self.bn(out)
return self.act(out)
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride,
padding, bias=True):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size,
kernel_size, stride, padding, bias=bias)
self.act = torch.nn.LeakyReLU()
def forward(self, x):
out = self.deconv(x)
return self.act(out)
class DownBlockNew(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size, stride, padding):
super(DownBlockNew, self).__init__()
self.conv1 = ConvBlock(input_size, output_size, kernel_size, stride,
padding, bias=False)
self.conv2 = DeconvBlock(output_size, output_size, kernel_size,
stride, padding, bias=False)
self.conv3 = ConvBlock(output_size, output_size, kernel_size,
stride, padding, bias=False)
self.local_weight1 = nn.Conv2d(input_size, 2 * output_size,
kernel_size=1, stride=1, padding=0, bias=False)
self.local_weight2 = nn.Conv2d(output_size, 2 * output_size,
kernel_size=1, stride=1, padding=0, bias=False)
def forward(self, input_0):
primals_1 = self.conv1.conv.weight
primals_3 = self.conv1.act.weight
primals_2 = self.conv2.deconv.weight
primals_4 = self.conv3.conv.weight
primals_7 = self.conv3.act.weight
primals_5 = self.local_weight1.weight
primals_8 = self.local_weight2.weight
primals_6 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
Holmes-Alan/RefVAE
|
DownBlock
| false | 8,282 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
MumfordShah_Loss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/mv/cmvz47ch3dym7sclktj42hufe6c4fpig64zsi573kcvcgtkqiuye.py
# Topologically Sorted Source Nodes: [mul, sum_1, sum_2, mul_3, sum_4, sum_5, mul_6, sum_7, sum_8, mul_9, sum_10, sum_11], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
# mul => mul
# mul_3 => mul_3
# mul_6 => mul_6
# mul_9 => mul_9
# sum_1 => sum_1
# sum_10 => sum_10
# sum_11 => sum_11
# sum_2 => sum_2
# sum_4 => sum_4
# sum_5 => sum_5
# sum_7 => sum_7
# sum_8 => sum_8
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand, %arg0_1), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2, 3]), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [2, 3]), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_2, %arg0_1), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_3, [2, 3]), kwargs = {})
# %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [2, 3]), kwargs = {})
# %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_4, %arg0_1), kwargs = {})
# %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_6, [2, 3]), kwargs = {})
# %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [2, 3]), kwargs = {})
# %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_6, %arg0_1), kwargs = {})
# %sum_10 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_9, [2, 3]), kwargs = {})
# %sum_11 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [2, 3]), kwargs = {})
triton_per_fused_mul_sum_0 = async_compile.triton('triton_per_fused_mul_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32', 11: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 8, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x1 = (xindex // 4)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp1 = tl.load(in_ptr1 + (r2 + (16*x3)), xmask, other=0.0)
tmp11 = tl.load(in_ptr0 + (16 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp17 = tl.load(in_ptr0 + (32 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp23 = tl.load(in_ptr0 + (48 + r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.sum(tmp5, 1)[:, None]
tmp7 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp12 = tmp11 * tmp1
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp18 = tmp17 * tmp1
tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
tmp21 = tl.where(xmask, tmp19, 0)
tmp22 = tl.sum(tmp21, 1)[:, None]
tmp24 = tmp23 * tmp1
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tmp27 = tl.where(xmask, tmp25, 0)
tmp28 = tl.sum(tmp27, 1)[:, None]
tl.store(out_ptr0 + (x3), tmp6, xmask)
tl.store(out_ptr1 + (x3), tmp10, xmask)
tl.store(out_ptr2 + (x3), tmp16, xmask)
tl.store(out_ptr3 + (x3), tmp10, xmask)
tl.store(out_ptr4 + (x3), tmp22, xmask)
tl.store(out_ptr5 + (x3), tmp10, xmask)
tl.store(out_ptr6 + (x3), tmp28, xmask)
tl.store(out_ptr7 + (x3), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/h2/ch2d42btsiytszj7kjoxvre7y6lc4ymkjcoznqk2c25dn2x66ban.py
# Topologically Sorted Source Nodes: [plevel, mul_1, pLoss, sum_3, plevel_1, mul_4, pLoss_1, sum_6, plevel_2, mul_7, pLoss_2, sum_9, plevel_3, mul_10, pLoss_3, sum_12], Original ATen: [aten.sub, aten.mul, aten.sum]
# Source node to ATen node mapping:
# mul_1 => mul_1
# mul_10 => mul_10
# mul_4 => mul_4
# mul_7 => mul_7
# pLoss => mul_2
# pLoss_1 => mul_5
# pLoss_2 => mul_8
# pLoss_3 => mul_11
# plevel => sub
# plevel_1 => sub_1
# plevel_2 => sub_2
# plevel_3 => sub_3
# sum_12 => sum_12
# sum_3 => sum_3
# sum_6 => sum_6
# sum_9 => sum_9
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand, %expand_1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %arg0_1), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_2,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_2, %expand_3), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %sub_1), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %arg0_1), kwargs = {})
# %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_5,), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_4, %expand_5), kwargs = {})
# %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %sub_2), kwargs = {})
# %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_7, %arg0_1), kwargs = {})
# %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_8,), kwargs = {})
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%expand_6, %expand_7), kwargs = {})
# %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_3), kwargs = {})
# %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_10, %arg0_1), kwargs = {})
# %sum_12 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_11,), kwargs = {})
triton_per_fused_mul_sub_sum_1 = async_compile.triton('triton_per_fused_mul_sub_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: '*fp32', 14: 'i32', 15: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {14: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15), equal_to_1=(14,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 13, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mul_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex % 16
r2 = (rindex // 64)
r3 = (rindex // 16)
r4 = rindex
tmp0 = tl.load(in_ptr0 + (r0 + (64*r2)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr3 + (r4), None)
tmp11 = tl.load(in_ptr0 + (16 + r0 + (64*r2)), None, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr4 + (r3), None, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr5 + (r3), None, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (32 + r0 + (64*r2)), None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr6 + (r3), None, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr7 + (r3), None, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr0 + (48 + r0 + (64*r2)), None, eviction_policy='evict_last')
tmp32 = tl.load(in_ptr8 + (r3), None, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr9 + (r3), None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 - tmp3
tmp5 = tmp4 * tmp4
tmp7 = tmp5 * tmp6
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
tmp14 = tmp12 / tmp13
tmp15 = tmp11 - tmp14
tmp16 = tmp15 * tmp15
tmp17 = tmp16 * tmp6
tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
tmp24 = tmp22 / tmp23
tmp25 = tmp21 - tmp24
tmp26 = tmp25 * tmp25
tmp27 = tmp26 * tmp6
tmp28 = tl.broadcast_to(tmp27, [RBLOCK])
tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
tmp34 = tmp32 / tmp33
tmp35 = tmp31 - tmp34
tmp36 = tmp35 * tmp35
tmp37 = tmp36 * tmp6
tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp10, None)
tl.store(out_ptr1 + (tl.full([1], 0, tl.int32)), tmp20, None)
tl.store(out_ptr2 + (tl.full([1], 0, tl.int32)), tmp30, None)
tl.store(out_ptr3 + (tl.full([1], 0, tl.int32)), tmp40, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gp/cgpnpjcg7zpsdulkqz2it5s6dulyayunkfcfizni2bezjjfrofuy.py
# Topologically Sorted Source Nodes: [loss, loss_1, loss_2, loss_3, sub_4, dH, sum_13, sub_5, dW, sum_14, loss_4, add_2], Original ATen: [aten.add, aten.sub, aten.abs, aten.sum]
# Source node to ATen node mapping:
# add_2 => add_5
# dH => abs_1
# dW => abs_2
# loss => add
# loss_1 => add_1
# loss_2 => add_2
# loss_3 => add_3
# loss_4 => add_4
# sub_4 => sub_4
# sub_5 => sub_5
# sum_13 => sum_13
# sum_14 => sum_14
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_3, 0.0), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %sum_6), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %sum_9), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %sum_12), kwargs = {})
# %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_7, %slice_11), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_4,), kwargs = {})
# %sum_13 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%abs_1,), kwargs = {})
# %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%slice_16, %slice_20), kwargs = {})
# %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_5,), kwargs = {})
# %sum_14 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%abs_2,), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_13, %sum_14), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %add_4), kwargs = {})
triton_per_fused_abs_add_sub_sum_2 = async_compile.triton('triton_per_fused_abs_add_sub_sum_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(5,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_abs_add_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = rindex < rnumel
r0 = rindex % 12
r1 = (rindex // 12)
r2 = rindex % 3
r3 = (rindex // 3)
tmp0 = tl.load(in_ptr0 + (4 + r0 + (16*r1)), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (r0 + (16*r1)), rmask, other=0.0)
tmp8 = tl.load(in_ptr0 + (1 + r2 + (4*r3)), rmask, other=0.0)
tmp9 = tl.load(in_ptr0 + (r2 + (4*r3)), rmask, other=0.0)
tmp16 = tl.load(in_ptr1 + (0))
tmp17 = tl.broadcast_to(tmp16, [XBLOCK, 1])
tmp20 = tl.load(in_ptr2 + (0))
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, 1])
tmp23 = tl.load(in_ptr3 + (0))
tmp24 = tl.broadcast_to(tmp23, [XBLOCK, 1])
tmp26 = tl.load(in_out_ptr0 + (0))
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, 1])
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp10 = tmp8 - tmp9
tmp11 = tl_math.abs(tmp10)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(rmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp18 = 0.0
tmp19 = tmp17 + tmp18
tmp22 = tmp19 + tmp21
tmp25 = tmp22 + tmp24
tmp28 = tmp25 + tmp27
tmp29 = tmp7 + tmp15
tmp30 = tmp28 + tmp29
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp30, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, sum_1, sum_2, mul_3, sum_4, sum_5, mul_6, sum_7, sum_8, mul_9, sum_10, sum_11], Original ATen: [aten.mul, aten.sum]
stream0 = get_raw_stream(0)
triton_per_fused_mul_sum_0.run(arg1_1, arg0_1, buf0, buf1, buf3, buf4, buf6, buf7, buf9, buf10, 16, 16, grid=grid(16), stream=stream0)
buf2 = empty_strided_cuda((), (), torch.float32)
buf5 = empty_strided_cuda((), (), torch.float32)
buf8 = empty_strided_cuda((), (), torch.float32)
buf11 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [plevel, mul_1, pLoss, sum_3, plevel_1, mul_4, pLoss_1, sum_6, plevel_2, mul_7, pLoss_2, sum_9, plevel_3, mul_10, pLoss_3, sum_12], Original ATen: [aten.sub, aten.mul, aten.sum]
triton_per_fused_mul_sub_sum_1.run(arg1_1, buf0, buf1, arg0_1, buf3, buf4, buf6, buf7, buf9, buf10, buf2, buf5, buf8, buf11, 1, 256, grid=grid(1), stream=stream0)
del arg1_1
del buf0
del buf1
del buf10
del buf3
del buf4
del buf6
del buf7
del buf9
buf14 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [loss, loss_1, loss_2, loss_3, sub_4, dH, sum_13, sub_5, dW, sum_14, loss_4, add_2], Original ATen: [aten.add, aten.sub, aten.abs, aten.sum]
triton_per_fused_abs_add_sub_sum_2.run(buf14, arg0_1, buf2, buf5, buf8, 1, 192, grid=grid(1), stream=stream0)
del arg0_1
del buf2
del buf5
del buf8
return (buf14, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class MumfordShah_Loss(nn.Module):
def levelsetLoss(self, output, target, penalty='l1'):
outshape = output.shape
tarshape = target.shape
self.penalty = penalty
loss = 0.0
for ich in range(tarshape[1]):
target_ = torch.unsqueeze(target[:, ich], 1)
target_ = target_.expand(tarshape[0], outshape[1], tarshape[2],
tarshape[3])
pcentroid = torch.sum(target_ * output, (2, 3)) / torch.sum(output,
(2, 3))
pcentroid = pcentroid.view(tarshape[0], outshape[1], 1, 1)
plevel = target_ - pcentroid.expand(tarshape[0], outshape[1],
tarshape[2], tarshape[3])
pLoss = plevel * plevel * output
loss += torch.sum(pLoss)
return loss
def gradientLoss2d(self, input):
dH = torch.abs(input[:, :, 1:, :] - input[:, :, :-1, :])
dW = torch.abs(input[:, :, :, 1:] - input[:, :, :, :-1])
if self.penalty == 'l2':
dH = dH * dH
dW = dW * dW
loss = torch.sum(dH) + torch.sum(dW)
return loss
def forward(self, image, prediction):
loss_level = self.levelsetLoss(image, prediction)
loss_tv = self.gradientLoss2d(image)
return loss_level + loss_tv
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7, xnumel,
rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x1 = xindex // 4
x3 = xindex
tmp0 = tl.load(in_ptr0 + (r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp1 = tl.load(in_ptr1 + (r2 + 16 * x3), xmask, other=0.0)
tmp11 = tl.load(in_ptr0 + (16 + r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp17 = tl.load(in_ptr0 + (32 + r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp23 = tl.load(in_ptr0 + (48 + r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp2 = tmp0 * tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.sum(tmp5, 1)[:, None]
tmp7 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp12 = tmp11 * tmp1
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp18 = tmp17 * tmp1
tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
tmp21 = tl.where(xmask, tmp19, 0)
tmp22 = tl.sum(tmp21, 1)[:, None]
tmp24 = tmp23 * tmp1
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tmp27 = tl.where(xmask, tmp25, 0)
tmp28 = tl.sum(tmp27, 1)[:, None]
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp10, xmask)
tl.store(out_ptr2 + x3, tmp16, xmask)
tl.store(out_ptr3 + x3, tmp10, xmask)
tl.store(out_ptr4 + x3, tmp22, xmask)
tl.store(out_ptr5 + x3, tmp10, xmask)
tl.store(out_ptr6 + x3, tmp28, xmask)
tl.store(out_ptr7 + x3, tmp10, xmask)
@triton.jit
def triton_per_fused_mul_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, out_ptr0,
out_ptr1, out_ptr2, out_ptr3, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex % 16
r2 = rindex // 64
r3 = rindex // 16
r4 = rindex
tmp0 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
)
tmp1 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr3 + r4, None)
tmp11 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp12 = tl.load(in_ptr4 + r3, None, eviction_policy='evict_last')
tmp13 = tl.load(in_ptr5 + r3, None, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp22 = tl.load(in_ptr6 + r3, None, eviction_policy='evict_last')
tmp23 = tl.load(in_ptr7 + r3, None, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
'evict_last')
tmp32 = tl.load(in_ptr8 + r3, None, eviction_policy='evict_last')
tmp33 = tl.load(in_ptr9 + r3, None, eviction_policy='evict_last')
tmp3 = tmp1 / tmp2
tmp4 = tmp0 - tmp3
tmp5 = tmp4 * tmp4
tmp7 = tmp5 * tmp6
tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0))
tmp14 = tmp12 / tmp13
tmp15 = tmp11 - tmp14
tmp16 = tmp15 * tmp15
tmp17 = tmp16 * tmp6
tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
tmp24 = tmp22 / tmp23
tmp25 = tmp21 - tmp24
tmp26 = tmp25 * tmp25
tmp27 = tmp26 * tmp6
tmp28 = tl.broadcast_to(tmp27, [RBLOCK])
tmp30 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
tmp34 = tmp32 / tmp33
tmp35 = tmp31 - tmp34
tmp36 = tmp35 * tmp35
tmp37 = tmp36 * tmp6
tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp10, None)
tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp20, None)
tl.store(out_ptr2 + tl.full([1], 0, tl.int32), tmp30, None)
tl.store(out_ptr3 + tl.full([1], 0, tl.int32), tmp40, None)
@triton.jit
def triton_per_fused_abs_add_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, in_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr):
rnumel = 192
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
rmask = rindex < rnumel
r0 = rindex % 12
r1 = rindex // 12
r2 = rindex % 3
r3 = rindex // 3
tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
tmp8 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0)
tmp9 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0)
tmp16 = tl.load(in_ptr1 + 0)
tmp17 = tl.broadcast_to(tmp16, [XBLOCK, 1])
tmp20 = tl.load(in_ptr2 + 0)
tmp21 = tl.broadcast_to(tmp20, [XBLOCK, 1])
tmp23 = tl.load(in_ptr3 + 0)
tmp24 = tl.broadcast_to(tmp23, [XBLOCK, 1])
tmp26 = tl.load(in_out_ptr0 + 0)
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, 1])
tmp2 = tmp0 - tmp1
tmp3 = tl_math.abs(tmp2)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(rmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp10 = tmp8 - tmp9
tmp11 = tl_math.abs(tmp10)
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(rmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp18 = 0.0
tmp19 = tmp17 + tmp18
tmp22 = tmp19 + tmp21
tmp25 = tmp22 + tmp24
tmp28 = tmp25 + tmp27
tmp29 = tmp7 + tmp15
tmp30 = tmp28 + tmp29
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_mul_sum_0[grid(16)](arg1_1, arg0_1, buf0, buf1,
buf3, buf4, buf6, buf7, buf9, buf10, 16, 16, XBLOCK=1,
num_warps=2, num_stages=1)
buf2 = empty_strided_cuda((), (), torch.float32)
buf5 = empty_strided_cuda((), (), torch.float32)
buf8 = empty_strided_cuda((), (), torch.float32)
buf11 = empty_strided_cuda((), (), torch.float32)
triton_per_fused_mul_sub_sum_1[grid(1)](arg1_1, buf0, buf1, arg0_1,
buf3, buf4, buf6, buf7, buf9, buf10, buf2, buf5, buf8, buf11, 1,
256, num_warps=2, num_stages=1)
del arg1_1
del buf0
del buf1
del buf10
del buf3
del buf4
del buf6
del buf7
del buf9
buf14 = buf11
del buf11
triton_per_fused_abs_add_sub_sum_2[grid(1)](buf14, arg0_1, buf2,
buf5, buf8, 1, 192, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del buf2
del buf5
del buf8
return buf14,
class MumfordShah_LossNew(nn.Module):
def levelsetLoss(self, output, target, penalty='l1'):
outshape = output.shape
tarshape = target.shape
self.penalty = penalty
loss = 0.0
for ich in range(tarshape[1]):
target_ = torch.unsqueeze(target[:, ich], 1)
target_ = target_.expand(tarshape[0], outshape[1], tarshape[2],
tarshape[3])
pcentroid = torch.sum(target_ * output, (2, 3)) / torch.sum(output,
(2, 3))
pcentroid = pcentroid.view(tarshape[0], outshape[1], 1, 1)
plevel = target_ - pcentroid.expand(tarshape[0], outshape[1],
tarshape[2], tarshape[3])
pLoss = plevel * plevel * output
loss += torch.sum(pLoss)
return loss
def gradientLoss2d(self, input):
dH = torch.abs(input[:, :, 1:, :] - input[:, :, :-1, :])
dW = torch.abs(input[:, :, :, 1:] - input[:, :, :, :-1])
if self.penalty == 'l2':
dH = dH * dH
dW = dW * dW
loss = torch.sum(dH) + torch.sum(dW)
return loss
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
HiLab-git/WSL4MIS
|
MumfordShah_Loss
| false | 8,283 |
[
"MIT"
] | 29 |
9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
https://github.com/HiLab-git/WSL4MIS/tree/9683e7c7409b95c0ac2169fe7964f6ca04c80d9a
|
DecoderBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/6w/c6wtmdwxjo7wezkht7wgzzxxrhlxzpar6hx7dzg655ig7bbf4zmm.py
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
# interpolate => _unsafe_index
# Graph fragment:
# %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 8) % 8
x0 = xindex % 8
x2 = (xindex // 64)
x4 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = x0
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + (4*tmp4) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/sm/csmdpoup66ebhswimw5bnz5yb6gm6qorrsupwp5r2vekave5fupk.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# relu => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten._unsafe_index]
stream0 = get_raw_stream(0)
triton_poi_fused__unsafe_index_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1; del buf1 # reuse
buf3 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_1.run(buf2, buf3, 1024, grid=grid(1024), stream=stream0)
return (buf2, primals_2, buf0, buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block.
"""
def __init__(self, num_in, num_out):
"""Creates a `ConvReLU` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, x):
"""The networks forward pass for which autograd synthesizes the backwards pass.
Args:
x: the input tensor
Returns:
The networks output tensor.
"""
return nn.functional.relu(self.block(x), inplace=True)
class DecoderBlock(nn.Module):
"""Decoder building block upsampling resolution by a factor of two.
"""
def __init__(self, num_in, num_out):
"""Creates a `DecoderBlock` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = ConvRelu(num_in, num_out)
def forward(self, x):
"""The networks forward pass for which autograd synthesizes the backwards pass.
Args:
x: the input tensor
Returns:
The networks output tensor.
"""
return self.block(nn.functional.interpolate(x, scale_factor=2, mode
='nearest'))
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_in': 4, 'num_out': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.backends.cudnn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 8 % 8
x0 = xindex % 8
x2 = xindex // 64
x4 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = x0
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * tmp4 + 16 * x2), xmask,
eviction_policy='evict_last')
tl.store(out_ptr0 + x4, tmp9, xmask)
@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__unsafe_index_0[grid(1024)](primals_1, buf0, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 8, 8), (256, 64, 8, 1))
buf2 = buf1
del buf1
buf3 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_1[grid(1024)](buf2, buf3,
1024, XBLOCK=256, num_warps=4, num_stages=1)
return buf2, primals_2, buf0, buf3
class ConvRelu(nn.Module):
"""3x3 convolution followed by ReLU activation building block.
"""
def __init__(self, num_in, num_out):
"""Creates a `ConvReLU` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = nn.Conv2d(num_in, num_out, kernel_size=3, padding=1,
bias=False)
def forward(self, x):
"""The networks forward pass for which autograd synthesizes the backwards pass.
Args:
x: the input tensor
Returns:
The networks output tensor.
"""
return nn.functional.relu(self.block(x), inplace=True)
class DecoderBlockNew(nn.Module):
"""Decoder building block upsampling resolution by a factor of two.
"""
def __init__(self, num_in, num_out):
"""Creates a `DecoderBlock` building block.
Args:
num_in: number of input feature maps
num_out: number of output feature maps
"""
super().__init__()
self.block = ConvRelu(num_in, num_out)
def forward(self, input_0):
primals_2 = self.block.block.weight
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Iceofsky/Roofpedia
|
DecoderBlock
| false | 8,284 |
[
"MIT"
] | 16 |
933dd3ff6e77ace78be6d2a23ac6692281475073
|
https://github.com/Iceofsky/Roofpedia/tree/933dd3ff6e77ace78be6d2a23ac6692281475073
|
_ImpalaBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/3r/c3runacu4pkgvdlmsngxxodg4pf6xmzvzxpf7xzbkjc3ay27rdj3.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# x => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/t5/ct5oo7t4ymfc2efqfz5ay5r66lcfjis6iujhbm2x7foxj7dhrh4q.py
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
# Source node to ATen node mapping:
# x_1 => _low_memory_max_pool2d_with_offsets, getitem_1
# x_2 => relu
# Graph fragment:
# %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%convolution, [3, 3], [2, 2], [1, 1], [1, 1], False), kwargs = {})
# %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%getitem,), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_relu_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 2) % 2
x0 = xindex % 2
x4 = (xindex // 2)
x3 = xindex
tmp0 = (-1) + (2*x1)
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = (-1) + (2*x0)
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + ((-5) + (2*x0) + (8*x4)), tmp10 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp12 = 2*x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + ((-4) + (2*x0) + (8*x4)), tmp16 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + (2*x0)
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + ((-3) + (2*x0) + (8*x4)), tmp23 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 2*x1
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp29 & tmp9
tmp31 = tl.load(in_ptr0 + ((-1) + (2*x0) + (8*x4)), tmp30 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = tmp29 & tmp15
tmp34 = tl.load(in_ptr0 + ((2*x0) + (8*x4)), tmp33 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp35 = triton_helpers.maximum(tmp34, tmp32)
tmp36 = tmp29 & tmp22
tmp37 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x4)), tmp36 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp38 = triton_helpers.maximum(tmp37, tmp35)
tmp39 = 1 + (2*x1)
tmp40 = tmp39 >= tmp1
tmp41 = tmp39 < tmp3
tmp42 = tmp40 & tmp41
tmp43 = tmp42 & tmp9
tmp44 = tl.load(in_ptr0 + (3 + (2*x0) + (8*x4)), tmp43 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp45 = triton_helpers.maximum(tmp44, tmp38)
tmp46 = tmp42 & tmp15
tmp47 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x4)), tmp46 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp48 = triton_helpers.maximum(tmp47, tmp45)
tmp49 = tmp42 & tmp22
tmp50 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x4)), tmp49 & xmask, eviction_policy='evict_last', other=float("-inf"))
tmp51 = triton_helpers.maximum(tmp50, tmp48)
tmp52 = tmp17 > tmp11
tmp53 = tl.full([1], 1, tl.int8)
tmp54 = tl.full([1], 0, tl.int8)
tmp55 = tl.where(tmp52, tmp53, tmp54)
tmp56 = tmp24 > tmp18
tmp57 = tl.full([1], 2, tl.int8)
tmp58 = tl.where(tmp56, tmp57, tmp55)
tmp59 = tmp31 > tmp25
tmp60 = tl.full([1], 3, tl.int8)
tmp61 = tl.where(tmp59, tmp60, tmp58)
tmp62 = tmp34 > tmp32
tmp63 = tl.full([1], 4, tl.int8)
tmp64 = tl.where(tmp62, tmp63, tmp61)
tmp65 = tmp37 > tmp35
tmp66 = tl.full([1], 5, tl.int8)
tmp67 = tl.where(tmp65, tmp66, tmp64)
tmp68 = tmp44 > tmp38
tmp69 = tl.full([1], 6, tl.int8)
tmp70 = tl.where(tmp68, tmp69, tmp67)
tmp71 = tmp47 > tmp45
tmp72 = tl.full([1], 7, tl.int8)
tmp73 = tl.where(tmp71, tmp72, tmp70)
tmp74 = tmp50 > tmp48
tmp75 = tl.full([1], 8, tl.int8)
tmp76 = tl.where(tmp74, tmp75, tmp73)
tmp77 = tl.full([1], 0, tl.int32)
tmp78 = triton_helpers.maximum(tmp77, tmp51)
tl.store(out_ptr0 + (x3), tmp51, xmask)
tl.store(out_ptr1 + (x3), tmp76, xmask)
tl.store(out_ptr2 + (x3), tmp78, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/6r/c6r5oewse6oyupxnlvgrs4vufiqqtijrjv2rebh2orpekt3jg4iz.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# x_3 => convolution_1
# x_4 => relu_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 4) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ct/cctoo55xu463f6cs7mvvibhaxbs7jtssjplld5iuuuxcrtntoaie.py
# Topologically Sorted Source Nodes: [x_5, x_6, x_7], Original ATen: [aten.convolution, aten.add, aten.relu]
# Source node to ATen node mapping:
# x_5 => convolution_2
# x_6 => add
# x_7 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %getitem), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
triton_poi_fused_add_convolution_relu_3 = async_compile.triton('triton_poi_fused_add_convolution_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 4) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = tl.full([1], 0, tl.int32)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ov/covl7ec5j6xbzpyhkqnfikcqpn4u6eya77a7gl3ep77ix374lir2.py
# Topologically Sorted Source Nodes: [x_5, x_6, x_10, x_11], Original ATen: [aten.convolution, aten.add]
# Source node to ATen node mapping:
# x_10 => convolution_4
# x_11 => add_1
# x_5 => convolution_2
# x_6 => add
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %getitem), kwargs = {})
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_4, %add), kwargs = {})
triton_poi_fused_add_convolution_4 = async_compile.triton('triton_poi_fused_add_convolution_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 4) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x3), xmask)
tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr3 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp2 + tmp7
tl.store(in_out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_9, (4, ), (1, ))
assert_size_stride(primals_10, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_11, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8)
buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.max_pool2d_with_indices, aten.relu]
triton_poi_fused_max_pool2d_with_indices_relu_1.run(buf1, buf2, buf3, buf4, 64, grid=grid(64), stream=stream0)
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 2, 2), (16, 4, 2, 1))
buf6 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_2.run(buf6, primals_5, 64, grid=grid(64), stream=stream0)
del primals_5
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 2, 2), (16, 4, 2, 1))
buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_5, x_6, x_7], Original ATen: [aten.convolution, aten.add, aten.relu]
triton_poi_fused_add_convolution_relu_3.run(buf7, primals_7, buf2, buf8, 64, grid=grid(64), stream=stream0)
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 4, 2, 2), (16, 4, 2, 1))
buf10 = buf9; del buf9 # reuse
# Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_2.run(buf10, primals_9, 64, grid=grid(64), stream=stream0)
del primals_9
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 4, 2, 2), (16, 4, 2, 1))
buf12 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [x_5, x_6, x_10, x_11], Original ATen: [aten.convolution, aten.add]
triton_poi_fused_add_convolution_4.run(buf12, primals_11, buf7, primals_7, buf2, 64, grid=grid(64), stream=stream0)
del buf2
del buf7
del primals_11
del primals_7
return (buf12, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf4, buf6, buf8, buf10, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
self.conv1 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
self.conv2 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
def forward(self, inputs):
x = self.relu(inputs)
x = self.conv1(x)
x = self.relu_inplace(x)
x = self.conv2(x)
x += inputs
return x
class _ImpalaBlock(nn.Module):
def __init__(self, n_channels_in: 'int', n_channels_out: 'int'):
super().__init__()
self.n_channels_in = n_channels_in
self.n_channels_out = n_channels_out
kernel_size = 3
padding = 1
self.conv1 = nn.Conv2d(n_channels_in, n_channels_out, kernel_size,
padding=padding)
self.pool = nn.MaxPool2d(kernel_size, stride=2, padding=padding)
self.res1 = _ImpalaResBlock(n_channels_out)
self.res2 = _ImpalaResBlock(n_channels_out)
def forward(self, x):
x = self.conv1(x)
x = self.pool(x)
x = self.res1(x)
x = self.res2(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_channels_in': 4, 'n_channels_out': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_relu_1(in_ptr0, out_ptr0,
out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 2 % 2
x0 = xindex % 2
x4 = xindex // 2
x3 = xindex
tmp0 = -1 + 2 * x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -1 + 2 * x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x4), tmp10 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp12 = 2 * x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x4), tmp16 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = 1 + 2 * x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + (-3 + 2 * x0 + 8 * x4), tmp23 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 2 * x1
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp29 & tmp9
tmp31 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x4), tmp30 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = tmp29 & tmp15
tmp34 = tl.load(in_ptr0 + (2 * x0 + 8 * x4), tmp33 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp35 = triton_helpers.maximum(tmp34, tmp32)
tmp36 = tmp29 & tmp22
tmp37 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x4), tmp36 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp38 = triton_helpers.maximum(tmp37, tmp35)
tmp39 = 1 + 2 * x1
tmp40 = tmp39 >= tmp1
tmp41 = tmp39 < tmp3
tmp42 = tmp40 & tmp41
tmp43 = tmp42 & tmp9
tmp44 = tl.load(in_ptr0 + (3 + 2 * x0 + 8 * x4), tmp43 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp45 = triton_helpers.maximum(tmp44, tmp38)
tmp46 = tmp42 & tmp15
tmp47 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x4), tmp46 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp48 = triton_helpers.maximum(tmp47, tmp45)
tmp49 = tmp42 & tmp22
tmp50 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x4), tmp49 & xmask,
eviction_policy='evict_last', other=float('-inf'))
tmp51 = triton_helpers.maximum(tmp50, tmp48)
tmp52 = tmp17 > tmp11
tmp53 = tl.full([1], 1, tl.int8)
tmp54 = tl.full([1], 0, tl.int8)
tmp55 = tl.where(tmp52, tmp53, tmp54)
tmp56 = tmp24 > tmp18
tmp57 = tl.full([1], 2, tl.int8)
tmp58 = tl.where(tmp56, tmp57, tmp55)
tmp59 = tmp31 > tmp25
tmp60 = tl.full([1], 3, tl.int8)
tmp61 = tl.where(tmp59, tmp60, tmp58)
tmp62 = tmp34 > tmp32
tmp63 = tl.full([1], 4, tl.int8)
tmp64 = tl.where(tmp62, tmp63, tmp61)
tmp65 = tmp37 > tmp35
tmp66 = tl.full([1], 5, tl.int8)
tmp67 = tl.where(tmp65, tmp66, tmp64)
tmp68 = tmp44 > tmp38
tmp69 = tl.full([1], 6, tl.int8)
tmp70 = tl.where(tmp68, tmp69, tmp67)
tmp71 = tmp47 > tmp45
tmp72 = tl.full([1], 7, tl.int8)
tmp73 = tl.where(tmp71, tmp72, tmp70)
tmp74 = tmp50 > tmp48
tmp75 = tl.full([1], 8, tl.int8)
tmp76 = tl.where(tmp74, tmp75, tmp73)
tmp77 = tl.full([1], 0, tl.int32)
tmp78 = triton_helpers.maximum(tmp77, tmp51)
tl.store(out_ptr0 + x3, tmp51, xmask)
tl.store(out_ptr1 + x3, tmp76, xmask)
tl.store(out_ptr2 + x3, tmp78, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_relu_3(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp5 = tl.full([1], 0, tl.int32)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tl.store(out_ptr0 + x3, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_convolution_4(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 4 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x3, xmask)
tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr3 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp2 + tmp7
tl.store(in_out_ptr0 + x3, tmp8, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_11, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(256)](buf1, primals_2, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8)
buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
triton_poi_fused_max_pool2d_with_indices_relu_1[grid(64)](buf1,
buf2, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 2, 2), (16, 4, 2, 1))
buf6 = buf5
del buf5
triton_poi_fused_convolution_relu_2[grid(64)](buf6, primals_5, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_5
buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 2, 2), (16, 4, 2, 1))
buf8 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
triton_poi_fused_add_convolution_relu_3[grid(64)](buf7, primals_7,
buf2, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 4, 2, 2), (16, 4, 2, 1))
buf10 = buf9
del buf9
triton_poi_fused_convolution_relu_2[grid(64)](buf10, primals_9, 64,
XBLOCK=64, num_warps=1, num_stages=1)
del primals_9
buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 4, 2, 2), (16, 4, 2, 1))
buf12 = buf11
del buf11
triton_poi_fused_add_convolution_4[grid(64)](buf12, primals_11,
buf7, primals_7, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
del buf2
del buf7
del primals_11
del primals_7
return (buf12, primals_1, primals_3, primals_4, primals_6, primals_8,
primals_10, buf1, buf3, buf4, buf6, buf8, buf10)
class _ImpalaResBlock(nn.Module):
def __init__(self, n_channels: 'int'):
super().__init__()
self.n_channels = n_channels
kernel_size = 3
padding = 1
self.relu = nn.ReLU()
self.relu_inplace = nn.ReLU()
self.conv1 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
self.conv2 = nn.Conv2d(n_channels, n_channels, kernel_size, padding
=padding)
def forward(self, inputs):
x = self.relu(inputs)
x = self.conv1(x)
x = self.relu_inplace(x)
x = self.conv2(x)
x += inputs
return x
class _ImpalaBlockNew(nn.Module):
def __init__(self, n_channels_in: 'int', n_channels_out: 'int'):
super().__init__()
self.n_channels_in = n_channels_in
self.n_channels_out = n_channels_out
kernel_size = 3
padding = 1
self.conv1 = nn.Conv2d(n_channels_in, n_channels_out, kernel_size,
padding=padding)
self.pool = nn.MaxPool2d(kernel_size, stride=2, padding=padding)
self.res1 = _ImpalaResBlock(n_channels_out)
self.res2 = _ImpalaResBlock(n_channels_out)
def forward(self, input_0):
primals_1 = self.conv1.weight
primals_2 = self.conv1.bias
primals_4 = self.res1.conv1.weight
primals_5 = self.res1.conv1.bias
primals_6 = self.res1.conv2.weight
primals_7 = self.res1.conv2.bias
primals_8 = self.res2.conv1.weight
primals_9 = self.res2.conv1.bias
primals_10 = self.res2.conv2.weight
primals_11 = self.res2.conv2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
IBM/vsrl-framework
|
_ImpalaBlock
| false | 8,285 |
[
"MIT"
] | 44 |
42e0853bffb5efbb66cd97178aff9e10ad18c5a9
|
https://github.com/IBM/vsrl-framework/tree/42e0853bffb5efbb66cd97178aff9e10ad18c5a9
|
C51ValueNetwork
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zm/czm6acrrgjryz6xi3wza7npycjuiqsdsygpfdo3lbzaquecrmeuj.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qk/cqk5cnmrffdyh6x4yseboivxypitwp75t6ywqxjlpiff6wcgokiw.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# x_1 => relu
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4, ), (1, ))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4, ), (1, ))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf2, primals_4, 16, grid=grid(16), stream=stream0)
del primals_4
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu]
triton_poi_fused_relu_1.run(buf4, primals_6, 16, grid=grid(16), stream=stream0)
del primals_6
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
del primals_8
return (buf5, buf0, buf2, buf4, primals_7, primals_5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.nn as nn
class C51ValueNetwork(nn.Module):
"""Critic - return Q value from given states and actions. """
def __init__(self, num_states, num_actions, hidden_size, v_min, v_max,
num_atoms, device='cuda'):
"""
Args:
num_states (int): state dimension
num_actions (int): action dimension
hidden_size (int): size of the hidden layers
v_min (float): minimum value for critic
v_max (float): maximum value for critic
num_atoms (int): number of atoms in distribution
init_w:
"""
super(C51ValueNetwork, self).__init__()
self.linear1 = nn.Linear(num_states + num_actions, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, num_atoms)
self.z_atoms = np.linspace(v_min, v_max, num_atoms)
self
def forward(self, state, action):
x = torch.cat([state, action], 1)
x = torch.relu(self.linear1(x))
x = torch.relu(self.linear2(x))
x = self.linear3(x)
return x
def get_probs(self, state, action):
return torch.softmax(self.forward(state, action), dim=1)
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'num_states': 4, 'num_actions': 4, 'hidden_size': 4,
'v_min': 4, 'v_max': 4, 'num_atoms': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4), (4, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
XBLOCK=32, num_warps=1, num_stages=1)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8
), 0), out=buf1)
del primals_3
buf2 = buf1
del buf1
triton_poi_fused_relu_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_4
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4
), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_relu_1[grid(16)](buf4, primals_6, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_6
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_8, buf4, reinterpret_tensor(primals_7,
(4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
del primals_8
return buf5, buf0, buf2, buf4, primals_7, primals_5
class C51ValueNetworkNew(nn.Module):
"""Critic - return Q value from given states and actions. """
def __init__(self, num_states, num_actions, hidden_size, v_min, v_max,
num_atoms, device='cuda'):
"""
Args:
num_states (int): state dimension
num_actions (int): action dimension
hidden_size (int): size of the hidden layers
v_min (float): minimum value for critic
v_max (float): maximum value for critic
num_atoms (int): number of atoms in distribution
init_w:
"""
super(C51ValueNetworkNew, self).__init__()
self.linear1 = nn.Linear(num_states + num_actions, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, num_atoms)
self.z_atoms = np.linspace(v_min, v_max, num_atoms)
self
def get_probs(self, state, action):
return torch.softmax(self.forward(state, action), dim=1)
def forward(self, input_0, input_1):
primals_3 = self.linear1.weight
primals_4 = self.linear1.bias
primals_1 = self.linear2.weight
primals_6 = self.linear2.bias
primals_2 = self.linear3.weight
primals_8 = self.linear3.bias
primals_5 = input_0
primals_7 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
HzcIrving/DLRL_PlayGround
|
C51ValueNetwork
| false | 8,286 |
[
"MIT"
] | 27 |
0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
https://github.com/HzcIrving/DLRL_PlayGround/tree/0db9a4bdb87130d1d26aea1591ef74cbe6aaa43b
|
NormalizeColorSpace
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/n2/cn2cyuftodozmxu2joymsv4yssslpmnjeonrbeayewq7pxbcxk56.py
# Topologically Sorted Source Nodes: [x, truediv], Original ATen: [aten.clamp, aten.div]
# Source node to ATen node mapping:
# truediv => div
# x => clamp_max, clamp_min
# Graph fragment:
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 0.0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 255.0), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 255.0), kwargs = {})
triton_poi_fused_clamp_div_0 = async_compile.triton('triton_poi_fused_clamp_div_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 255.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = 0.00392156862745098
tmp6 = tmp4 * tmp5
tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x, truediv], Original ATen: [aten.clamp, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_clamp_div_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
from typing import *
class NormalizeColorSpace(nn.Module):
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = x.clamp(0.0, 255.0)
return x / 255.0
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
from typing import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_clamp_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = triton_helpers.maximum(tmp0, tmp1)
tmp3 = 255.0
tmp4 = triton_helpers.minimum(tmp2, tmp3)
tmp5 = 0.00392156862745098
tmp6 = tmp4 * tmp5
tl.store(out_ptr0 + x0, tmp6, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_clamp_div_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class NormalizeColorSpaceNew(nn.Module):
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
IntelLabs/OSCAR
|
NormalizeColorSpace
| false | 8,287 |
[
"BSD-3-Clause"
] | 13 |
25d1dea35727379117e11b7238b5a0d1ed19acad
|
https://github.com/IntelLabs/OSCAR/tree/25d1dea35727379117e11b7238b5a0d1ed19acad
|
CharbonnierLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/sl/cslh7x5cazzoyhcuw5kjabl7ug63v7wdjnfcty4qpe7ykfslndal.py
# Topologically Sorted Source Nodes: [diff, mul, add, sqrt, loss], Original ATen: [aten.sub, aten.mul, aten.add, aten.sqrt, aten.sum]
# Source node to ATen node mapping:
# add => add
# diff => sub
# loss => sum_1
# mul => mul
# sqrt => sqrt
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0.001), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sqrt,), kwargs = {})
triton_per_fused_add_mul_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_mul_sqrt_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_sqrt_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_mul_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = 0.001
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp9, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [diff, mul, add, sqrt, loss], Original ATen: [aten.sub, aten.mul, aten.add, aten.sqrt, aten.sum]
stream0 = get_raw_stream(0)
triton_per_fused_add_mul_sqrt_sub_sum_0.run(arg0_1, arg1_1, buf0, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(CharbonnierLoss, self).__init__()
self.eps = eps
def forward(self, x, y):
diff = x - y
loss = torch.sum(torch.sqrt(diff * diff + self.eps))
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_mul_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = 0.001
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp9, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
get_raw_stream(0)
triton_per_fused_add_mul_sqrt_sub_sum_0[grid(1)](arg0_1, arg1_1,
buf0, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class CharbonnierLossNew(nn.Module):
"""Charbonnier Loss (L1)"""
def __init__(self, eps=0.001):
super(CharbonnierLossNew, self).__init__()
self.eps = eps
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IceClear/MW-GAN
|
CharbonnierLoss
| false | 8,288 |
[
"MIT"
] | 36 |
acb962468c984681c4a21f7b5c14588ca8f58c00
|
https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00
|
Transform
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ym/cymg6qdt5jm4qekx3x5d6hyii4szjh2aapwfe5cntjchsk67j6ee.py
# Topologically Sorted Source Nodes: [var, mean, sub, normalized_feat], Original ATen: [aten.var, aten.mean, aten.sub, aten.div]
# Source node to ATen node mapping:
# mean => mean
# normalized_feat => div
# sub => sub
# var => var
# Graph fragment:
# %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view, [2]), kwargs = {correction: 1})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view, [2]), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %expand), kwargs = {})
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %expand_1), kwargs = {})
triton_per_fused_div_mean_sub_var_0 = async_compile.triton('triton_per_fused_div_mean_sub_var_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mean_sub_var_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_mean_sub_var_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp18 = tl.sum(tmp3, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = tmp0 - tmp20
tmp22 = 15.0
tmp23 = tmp16 / tmp22
tmp24 = 1e-05
tmp25 = tmp23 + tmp24
tmp26 = libdevice.sqrt(tmp25)
tmp27 = tmp21 / tmp26
tl.store(out_ptr2 + (r1 + (16*x0)), tmp27, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7r/c7r2u57hr54idc3of6lw2ouxuoyy44tzonl7cy4k7awnnjece2kt.py
# Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# F => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%div, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7l/c7lvofwajnoxsuknxf55lbahwz4fgxvepmfscd6nrkqg6j36hgpf.py
# Topologically Sorted Source Nodes: [S_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# S_1 => amax, div_2, exp, sub_2, sum_1
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_2 = async_compile.triton('triton_per_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[64, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 64
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, float("-inf"))
tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
tmp5 = tmp0 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tmp6 / tmp10
tl.store(out_ptr2 + (r1 + (16*x0)), tmp11, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bd/cbdvv2md7wvfby3zu57fdq4vipnrreq352g2rxv2gwtxqndb2nbf.py
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange]
# Source node to ATen node mapping:
# interpolate => iota
# Graph fragment:
# %iota : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
triton_poi_fused_arange_3 = async_compile.triton('triton_poi_fused_arange_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_arange_3(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/vw/cvw5kzu7vkr3o7kascffeeebuk7q45twci6hgr7ekpzkxdp7cgnv.py
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# interpolate => add_6, add_7, convert_element_type, convert_element_type_1, mul, mul_1
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota, 1), kwargs = {})
# %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
# %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_6, torch.float32), kwargs = {})
# %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_7, 1.0), kwargs = {})
# %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/wo/cwoithqgu6etbyjigbbpcbu4ijnlxvdclyvm6ske7ntzsx4rs3bc.py
# Topologically Sorted Source Nodes: [O_2, O_3, O_6, O_7, interpolate, add_4, pad], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# O_2 => convolution_3
# O_3 => add_2
# O_6 => convolution_7
# O_7 => add_5
# add_4 => add_10
# interpolate => _unsafe_index
# pad => _unsafe_index_1, _unsafe_index_2
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_11, %primals_9, %primals_10, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_3, %primals_1), kwargs = {})
# %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_23, %primals_19, %primals_20, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_7, %primals_11), kwargs = {})
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_5, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
# %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %_unsafe_index), kwargs = {})
# %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%add_10, [None, None, %sub_7, None]), kwargs = {})
# %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_1, [None, None, None, %sub_7]), kwargs = {})
triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5 = async_compile.triton('triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i64', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x4 = (xindex // 36)
x2 = (xindex // 36) % 4
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x4)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x4)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (3 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + (3 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr5 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tl.full([XBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr4 + (tmp13 + (4*tmp9) + (16*x4)), xmask, eviction_policy='evict_last')
tmp16 = tmp14 + tmp15
tmp17 = tl.load(in_ptr6 + (tmp13 + (4*tmp9) + (16*x4)), xmask, eviction_policy='evict_last')
tmp18 = tmp16 + tmp17
tmp19 = tmp4 + tmp18
tl.store(out_ptr0 + (x7), tmp19, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4, ), (1, ))
assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_10, (4, ), (1, ))
assert_size_stride(primals_11, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_13, (4, ), (1, ))
assert_size_stride(primals_14, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_15, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_16, (4, ), (1, ))
assert_size_stride(primals_17, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_18, (4, ), (1, ))
assert_size_stride(primals_19, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_20, (4, ), (1, ))
assert_size_stride(primals_21, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_22, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [var, mean, sub, normalized_feat], Original ATen: [aten.var, aten.mean, aten.sub, aten.div]
stream0 = get_raw_stream(0)
triton_per_fused_div_mean_sub_var_0.run(primals_1, buf4, 16, 16, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution]
buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [var_1, mean_1, sub_1, normalized_feat_1], Original ATen: [aten.var, aten.mean, aten.sub, aten.div]
triton_per_fused_div_mean_sub_var_0.run(primals_4, buf10, 16, 16, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [G], Original ATen: [aten.convolution]
buf11 = extern_kernels.convolution(buf10, primals_5, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
# Topologically Sorted Source Nodes: [H], Original ATen: [aten.convolution]
buf12 = extern_kernels.convolution(primals_4, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
buf13 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [F], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf13, primals_3, 256, grid=grid(256), stream=stream0)
del primals_3
buf14 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [G], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf14, primals_6, 256, grid=grid(256), stream=stream0)
del primals_6
buf15 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [S], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf13, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf14, (4, 4, 16), (64, 16, 1), 0), out=buf15)
buf18 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [S_1], Original ATen: [aten._softmax]
triton_per_fused__softmax_2.run(buf15, buf18, 64, 16, grid=grid(64), stream=stream0)
buf19 = buf12; del buf12 # reuse
# Topologically Sorted Source Nodes: [H], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf19, primals_8, 256, grid=grid(256), stream=stream0)
del primals_8
buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [O], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf19, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf18, (4, 16, 16), (256, 1, 16), 0), out=buf20)
# Topologically Sorted Source Nodes: [O_2], Original ATen: [aten.convolution]
buf21 = extern_kernels.convolution(reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_9, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1))
buf26 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [var_2, mean_2, sub_2, normalized_feat_2], Original ATen: [aten.var, aten.mean, aten.sub, aten.div]
triton_per_fused_div_mean_sub_var_0.run(primals_11, buf26, 16, 16, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [F_2], Original ATen: [aten.convolution]
buf27 = extern_kernels.convolution(buf26, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf27, (4, 4, 4, 4), (64, 16, 4, 1))
buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [var_3, mean_3, sub_3, normalized_feat_3], Original ATen: [aten.var, aten.mean, aten.sub, aten.div]
triton_per_fused_div_mean_sub_var_0.run(primals_14, buf32, 16, 16, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [G_2], Original ATen: [aten.convolution]
buf33 = extern_kernels.convolution(buf32, primals_15, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf33, (4, 4, 4, 4), (64, 16, 4, 1))
# Topologically Sorted Source Nodes: [H_2], Original ATen: [aten.convolution]
buf34 = extern_kernels.convolution(primals_14, primals_17, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 4, 4, 4), (64, 16, 4, 1))
buf35 = buf27; del buf27 # reuse
# Topologically Sorted Source Nodes: [F_2], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf35, primals_13, 256, grid=grid(256), stream=stream0)
del primals_13
buf36 = buf33; del buf33 # reuse
# Topologically Sorted Source Nodes: [G_2], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf36, primals_16, 256, grid=grid(256), stream=stream0)
del primals_16
buf37 = buf15; del buf15 # reuse
# Topologically Sorted Source Nodes: [S_2], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf35, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf36, (4, 4, 16), (64, 16, 1), 0), out=buf37)
buf40 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [S_3], Original ATen: [aten._softmax]
triton_per_fused__softmax_2.run(buf37, buf40, 64, 16, grid=grid(64), stream=stream0)
del buf37
buf41 = buf34; del buf34 # reuse
# Topologically Sorted Source Nodes: [H_2], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf41, primals_18, 256, grid=grid(256), stream=stream0)
del primals_18
buf42 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [O_4], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf41, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf40, (4, 16, 16), (256, 1, 16), 0), out=buf42)
# Topologically Sorted Source Nodes: [O_6], Original ATen: [aten.convolution]
buf43 = extern_kernels.convolution(reinterpret_tensor(buf42, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_19, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf43, (4, 4, 4, 4), (64, 16, 4, 1))
buf44 = empty_strided_cuda((4, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange]
triton_poi_fused_arange_3.run(buf44, 4, grid=grid(4), stream=stream0)
buf45 = empty_strided_cuda((4, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [interpolate], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_4.run(buf45, 4, grid=grid(4), stream=stream0)
buf46 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [O_2, O_3, O_6, O_7, interpolate, add_4, pad], Original ATen: [aten.convolution, aten.add, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5.run(buf21, primals_10, primals_1, buf45, buf43, primals_20, primals_11, buf46, 576, grid=grid(576), stream=stream0)
del buf21
del buf43
del primals_1
del primals_10
del primals_11
del primals_20
# Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
buf47 = extern_kernels.convolution(buf46, primals_21, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf47, (4, 4, 4, 4), (64, 16, 4, 1))
buf48 = buf47; del buf47 # reuse
# Topologically Sorted Source Nodes: [conv2d_8], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf48, primals_22, 256, grid=grid(256), stream=stream0)
del primals_22
return (buf48, primals_2, primals_4, primals_5, primals_7, primals_9, primals_12, primals_14, primals_15, primals_17, primals_19, primals_21, buf4, buf10, buf18, reinterpret_tensor(buf20, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf26, buf32, buf40, reinterpret_tensor(buf42, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf44, buf45, buf46, reinterpret_tensor(buf41, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf35, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf36, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf19, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(buf13, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(buf14, (4, 16, 4), (64, 1, 16), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_20 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_21 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_22 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def mean_variance_norm(feat):
size = feat.size()
mean, std = calc_mean_std(feat)
normalized_feat = (feat - mean.expand(size)) / std.expand(size)
return normalized_feat
class SANet(nn.Module):
def __init__(self, in_planes):
super(SANet, self).__init__()
self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
self.sm = nn.Softmax(dim=-1)
self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1))
def forward(self, content, style):
F = self.f(mean_variance_norm(content))
G = self.g(mean_variance_norm(style))
H = self.h(style)
b, c, h, w = F.size()
F = F.view(b, -1, w * h).permute(0, 2, 1)
b, c, h, w = G.size()
G = G.view(b, -1, w * h)
S = torch.bmm(F, G)
S = self.sm(S)
b, c, h, w = H.size()
H = H.view(b, -1, w * h)
O = torch.bmm(H, S.permute(0, 2, 1))
b, c, h, w = content.size()
O = O.view(b, c, h, w)
O = self.out_conv(O)
O += content
return O
class Transform(nn.Module):
def __init__(self, in_planes):
super(Transform, self).__init__()
self.sanet4_1 = SANet(in_planes=in_planes)
self.sanet5_1 = SANet(in_planes=in_planes)
self.merge_conv_pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.merge_conv = nn.Conv2d(in_planes, in_planes, (3, 3))
def forward(self, content4_1, style4_1, content5_1, style5_1):
self.upsample5_1 = nn.Upsample(size=(content4_1.size()[2],
content4_1.size()[3]), mode='nearest')
return self.merge_conv(self.merge_conv_pad(self.sanet4_1(content4_1,
style4_1) + self.upsample5_1(self.sanet5_1(content5_1, style5_1))))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_planes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_div_mean_sub_var_0(in_ptr0, out_ptr2, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp18 = tl.sum(tmp3, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = tmp0 - tmp20
tmp22 = 15.0
tmp23 = tmp16 / tmp22
tmp24 = 1e-05
tmp25 = tmp23 + tmp24
tmp26 = libdevice.sqrt(tmp25)
tmp27 = tmp21 / tmp26
tl.store(out_ptr2 + (r1 + 16 * x0), tmp27, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_per_fused__softmax_2(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 64
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, float('-inf'))
tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
tmp5 = tmp0 - tmp4
tmp6 = tl_math.exp(tmp5)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tmp6 / tmp10
tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)
@triton.jit
def triton_poi_fused_arange_3(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x4 = xindex // 36
x2 = xindex // 36 % 4
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x4),
xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (3 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x1
))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr3 + (3 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0))), xmask, eviction_policy='evict_last')
tmp15 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tl.full([XBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tmp11 = tmp10 + tmp6
tmp12 = tmp10 < 0
tmp13 = tl.where(tmp12, tmp11, tmp10)
tmp14 = tl.load(in_ptr4 + (tmp13 + 4 * tmp9 + 16 * x4), xmask,
eviction_policy='evict_last')
tmp16 = tmp14 + tmp15
tmp17 = tl.load(in_ptr6 + (tmp13 + 4 * tmp9 + 16 * x4), xmask,
eviction_policy='evict_last')
tmp18 = tmp16 + tmp17
tmp19 = tmp4 + tmp18
tl.store(out_ptr0 + x7, tmp19, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_10, (4,), (1,))
assert_size_stride(primals_11, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_12, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_13, (4,), (1,))
assert_size_stride(primals_14, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_15, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_16, (4,), (1,))
assert_size_stride(primals_17, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_18, (4,), (1,))
assert_size_stride(primals_19, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_20, (4,), (1,))
assert_size_stride(primals_21, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_22, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_per_fused_div_mean_sub_var_0[grid(16)](primals_1, buf4, 16,
16, XBLOCK=8, num_warps=2, num_stages=1)
buf5 = extern_kernels.convolution(buf4, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 4, 4, 4), (64, 16, 4, 1))
buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_div_mean_sub_var_0[grid(16)](primals_4, buf10, 16,
16, XBLOCK=8, num_warps=2, num_stages=1)
buf11 = extern_kernels.convolution(buf10, primals_5, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 4, 4, 4), (64, 16, 4, 1))
buf12 = extern_kernels.convolution(primals_4, primals_7, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
buf13 = buf5
del buf5
triton_poi_fused_convolution_1[grid(256)](buf13, primals_3, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf14 = buf11
del buf11
triton_poi_fused_convolution_1[grid(256)](buf14, primals_6, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_6
buf15 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf13, (4, 16, 4), (64, 1, 16
), 0), reinterpret_tensor(buf14, (4, 4, 16), (64, 16, 1), 0),
out=buf15)
buf18 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
triton_per_fused__softmax_2[grid(64)](buf15, buf18, 64, 16, XBLOCK=
32, num_warps=4, num_stages=1)
buf19 = buf12
del buf12
triton_poi_fused_convolution_1[grid(256)](buf19, primals_8, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_8
buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf19, (4, 4, 16), (64, 16, 1
), 0), reinterpret_tensor(buf18, (4, 16, 16), (256, 1, 16), 0),
out=buf20)
buf21 = extern_kernels.convolution(reinterpret_tensor(buf20, (4, 4,
4, 4), (64, 16, 4, 1), 0), primals_9, stride=(1, 1), padding=(0,
0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
groups=1, bias=None)
assert_size_stride(buf21, (4, 4, 4, 4), (64, 16, 4, 1))
buf26 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_div_mean_sub_var_0[grid(16)](primals_11, buf26, 16,
16, XBLOCK=8, num_warps=2, num_stages=1)
buf27 = extern_kernels.convolution(buf26, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf27, (4, 4, 4, 4), (64, 16, 4, 1))
buf32 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_per_fused_div_mean_sub_var_0[grid(16)](primals_14, buf32, 16,
16, XBLOCK=8, num_warps=2, num_stages=1)
buf33 = extern_kernels.convolution(buf32, primals_15, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf33, (4, 4, 4, 4), (64, 16, 4, 1))
buf34 = extern_kernels.convolution(primals_14, primals_17, stride=(
1, 1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf34, (4, 4, 4, 4), (64, 16, 4, 1))
buf35 = buf27
del buf27
triton_poi_fused_convolution_1[grid(256)](buf35, primals_13, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_13
buf36 = buf33
del buf33
triton_poi_fused_convolution_1[grid(256)](buf36, primals_16, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_16
buf37 = buf15
del buf15
extern_kernels.bmm(reinterpret_tensor(buf35, (4, 16, 4), (64, 1, 16
), 0), reinterpret_tensor(buf36, (4, 4, 16), (64, 16, 1), 0),
out=buf37)
buf40 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
triton_per_fused__softmax_2[grid(64)](buf37, buf40, 64, 16, XBLOCK=
32, num_warps=4, num_stages=1)
del buf37
buf41 = buf34
del buf34
triton_poi_fused_convolution_1[grid(256)](buf41, primals_18, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_18
buf42 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf41, (4, 4, 16), (64, 16, 1
), 0), reinterpret_tensor(buf40, (4, 16, 16), (256, 1, 16), 0),
out=buf42)
buf43 = extern_kernels.convolution(reinterpret_tensor(buf42, (4, 4,
4, 4), (64, 16, 4, 1), 0), primals_19, stride=(1, 1), padding=(
0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0),
groups=1, bias=None)
assert_size_stride(buf43, (4, 4, 4, 4), (64, 16, 4, 1))
buf44 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused_arange_3[grid(4)](buf44, 4, XBLOCK=4, num_warps=1,
num_stages=1)
buf45 = empty_strided_cuda((4,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_4[grid(4)](buf45, 4,
XBLOCK=4, num_warps=1, num_stages=1)
buf46 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32
)
triton_poi_fused__unsafe_index_add_convolution_reflection_pad2d_5[grid
(576)](buf21, primals_10, primals_1, buf45, buf43, primals_20,
primals_11, buf46, 576, XBLOCK=256, num_warps=4, num_stages=1)
del buf21
del buf43
del primals_1
del primals_10
del primals_11
del primals_20
buf47 = extern_kernels.convolution(buf46, primals_21, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf47, (4, 4, 4, 4), (64, 16, 4, 1))
buf48 = buf47
del buf47
triton_poi_fused_convolution_1[grid(256)](buf48, primals_22, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_22
return (buf48, primals_2, primals_4, primals_5, primals_7, primals_9,
primals_12, primals_14, primals_15, primals_17, primals_19,
primals_21, buf4, buf10, buf18, reinterpret_tensor(buf20, (4, 4, 4,
4), (64, 16, 4, 1), 0), buf26, buf32, buf40, reinterpret_tensor(
buf42, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf44, buf45, buf46,
reinterpret_tensor(buf41, (4, 16, 4), (64, 1, 16), 0),
reinterpret_tensor(buf35, (4, 4, 16), (64, 16, 1), 0),
reinterpret_tensor(buf36, (4, 16, 4), (64, 1, 16), 0),
reinterpret_tensor(buf19, (4, 16, 4), (64, 1, 16), 0),
reinterpret_tensor(buf13, (4, 4, 16), (64, 16, 1), 0),
reinterpret_tensor(buf14, (4, 16, 4), (64, 1, 16), 0))
def calc_mean_std(feat, eps=1e-05):
size = feat.size()
assert len(size) == 4
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def mean_variance_norm(feat):
size = feat.size()
mean, std = calc_mean_std(feat)
normalized_feat = (feat - mean.expand(size)) / std.expand(size)
return normalized_feat
class SANet(nn.Module):
def __init__(self, in_planes):
super(SANet, self).__init__()
self.f = nn.Conv2d(in_planes, in_planes, (1, 1))
self.g = nn.Conv2d(in_planes, in_planes, (1, 1))
self.h = nn.Conv2d(in_planes, in_planes, (1, 1))
self.sm = nn.Softmax(dim=-1)
self.out_conv = nn.Conv2d(in_planes, in_planes, (1, 1))
def forward(self, content, style):
F = self.f(mean_variance_norm(content))
G = self.g(mean_variance_norm(style))
H = self.h(style)
b, c, h, w = F.size()
F = F.view(b, -1, w * h).permute(0, 2, 1)
b, c, h, w = G.size()
G = G.view(b, -1, w * h)
S = torch.bmm(F, G)
S = self.sm(S)
b, c, h, w = H.size()
H = H.view(b, -1, w * h)
O = torch.bmm(H, S.permute(0, 2, 1))
b, c, h, w = content.size()
O = O.view(b, c, h, w)
O = self.out_conv(O)
O += content
return O
class TransformNew(nn.Module):
def __init__(self, in_planes):
super(TransformNew, self).__init__()
self.sanet4_1 = SANet(in_planes=in_planes)
self.sanet5_1 = SANet(in_planes=in_planes)
self.merge_conv_pad = nn.ReflectionPad2d((1, 1, 1, 1))
self.merge_conv = nn.Conv2d(in_planes, in_planes, (3, 3))
def forward(self, input_0, input_1, input_2, input_3):
primals_2 = self.sanet4_1.f.weight
primals_3 = self.sanet4_1.f.bias
primals_5 = self.sanet4_1.g.weight
primals_6 = self.sanet4_1.g.bias
primals_7 = self.sanet4_1.h.weight
primals_8 = self.sanet4_1.h.bias
primals_9 = self.sanet4_1.out_conv.weight
primals_10 = self.sanet4_1.out_conv.bias
primals_12 = self.sanet5_1.f.weight
primals_13 = self.sanet5_1.f.bias
primals_15 = self.sanet5_1.g.weight
primals_16 = self.sanet5_1.g.bias
primals_17 = self.sanet5_1.h.weight
primals_18 = self.sanet5_1.h.bias
primals_19 = self.sanet5_1.out_conv.weight
primals_20 = self.sanet5_1.out_conv.bias
primals_21 = self.merge_conv.weight
primals_22 = self.merge_conv.bias
primals_1 = input_0
primals_4 = input_1
primals_11 = input_2
primals_14 = input_3
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22])
return output[0]
|
HalbertCH/IEContraAST
|
Transform
| false | 8,289 |
[
"MIT"
] | 39 |
50ee949f5302a7e4a3cae3226610c03462093c21
|
https://github.com/HalbertCH/IEContraAST/tree/50ee949f5302a7e4a3cae3226610c03462093c21
|
EuclideanLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ie/cie26aqrhrjj27vnihr4jur2pvlo3x4acpibdw2jj7v6zbvkxes3.py
# Topologically Sorted Source Nodes: [sub, pow_1, sum_1, diff], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div]
# Source node to ATen node mapping:
# diff => div
# pow_1 => pow_1
# sub => sub
# sum_1 => sum_1
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 8), kwargs = {})
triton_per_fused_div_pow_sub_sum_0 = async_compile.triton('triton_per_fused_div_pow_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 0.125
tmp8 = tmp6 * tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [sub, pow_1, sum_1, diff], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div]
stream0 = get_raw_stream(0)
triton_per_fused_div_pow_sub_sum_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class EuclideanLoss(nn.Module):
def __init__(self):
super(EuclideanLoss, self).__init__()
def forward(self, pre, gt):
N = pre.shape[0]
diff = torch.sum((pre - gt).pow(2)) / (N * 2)
return diff
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_div_pow_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 0.125
tmp8 = tmp6 * tmp7
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_div_pow_sub_sum_0[grid(1)](buf1, arg0_1, arg1_1, 1,
256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class EuclideanLossNew(nn.Module):
def __init__(self):
super(EuclideanLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IndigoPurple/EFENet
|
EuclideanLoss
| false | 8,290 |
[
"MIT"
] | 11 |
e88234486f19534274a0a20badc251788ac67e31
|
https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31
|
loss_Textures
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/vy/cvycdfiwg2xzdtugmmlqgcnevqpapdtxrsqgoszxptgsosb6rvuv.py
# Topologically Sorted Source Nodes: [mul_1, yi2, mul_2, mul, xi2, sub, add, out, mean], Original ATen: [aten.mul, aten.sum, aten.sub, aten.add, aten.relu, aten.mean]
# Source node to ATen node mapping:
# add => add
# mean => mean
# mul => mul
# mul_1 => mul_1
# mul_2 => mul_2
# out => relu
# sub => sub
# xi2 => sum_1
# yi2 => sum_2
# Graph fragment:
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view_1), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [2]), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 1.2), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %sum_1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0), kwargs = {})
# %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%relu,), kwargs = {})
triton_per_fused_add_mean_mul_relu_sub_sum_0 = async_compile.triton('triton_per_fused_add_mean_mul_relu_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_mul_relu_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_mean_mul_relu_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp4 = tl.load(in_ptr1 + (r0), None)
tmp1 = tmp0 * tmp0
tmp2 = 1.2
tmp3 = tmp1 * tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp3 - tmp5
tmp7 = 0.0
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
tmp14 = 256.0
tmp15 = tmp13 / tmp14
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp15, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [mul_1, yi2, mul_2, mul, xi2, sub, add, out, mean], Original ATen: [aten.mul, aten.sum, aten.sub, aten.add, aten.relu, aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_add_mean_mul_relu_sub_sum_0.run(buf1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch.nn
class loss_Textures(nn.Module):
def __init__(self, nc=1, alpha=1.2, margin=0):
super(loss_Textures, self).__init__()
self.nc = nc
self.alpha = alpha
self.margin = margin
def forward(self, x, y):
xi = x.contiguous().view(x.size(0), -1, self.nc, x.size(2), x.size(3))
yi = y.contiguous().view(y.size(0), -1, self.nc, y.size(2), y.size(3))
xi2 = torch.sum(xi * xi, dim=2)
yi2 = torch.sum(yi * yi, dim=2)
out = nn.functional.relu(yi2.mul(self.alpha) - xi2 + self.margin)
return torch.mean(out)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_mean_mul_relu_sub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp4 = tl.load(in_ptr1 + r0, None)
tmp1 = tmp0 * tmp0
tmp2 = 1.2
tmp3 = tmp1 * tmp2
tmp5 = tmp4 * tmp4
tmp6 = tmp3 - tmp5
tmp7 = 0.0
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
tmp14 = 256.0
tmp15 = tmp13 / tmp14
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp15, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_mean_mul_relu_sub_sum_0[grid(1)](buf1, arg1_1,
arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class loss_TexturesNew(nn.Module):
def __init__(self, nc=1, alpha=1.2, margin=0):
super(loss_TexturesNew, self).__init__()
self.nc = nc
self.alpha = alpha
self.margin = margin
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IceClear/MW-GAN
|
loss_Textures
| false | 8,291 |
[
"MIT"
] | 36 |
acb962468c984681c4a21f7b5c14588ca8f58c00
|
https://github.com/IceClear/MW-GAN/tree/acb962468c984681c4a21f7b5c14588ca8f58c00
|
BilinearMatrixAttention
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/p3/cp3vh5ge4dyj3r4smr5dvjovuocujri2yvhft44mt2rcrwo3tz5y.py
# Topologically Sorted Source Nodes: [final], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# final => clone
# Graph fragment:
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = (xindex // 4) % 4
x2 = (xindex // 16) % 4
x3 = (xindex // 64)
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/w2/cw2hdpxgykdf2qwrvgkllw6pflbjzobcpold6ffzzkzv4wjl3gys.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
# add => add
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%squeeze, %primals_4), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr0 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [intermediate], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (0, 4, 1), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 1, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [final], Original ATen: [aten.clone]
stream0 = get_raw_stream(0)
triton_poi_fused_clone_0.run(primals_3, buf1, 256, grid=grid(256), stream=stream0)
del primals_3
buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [final], Original ATen: [aten.bmm]
extern_kernels.bmm(buf0, reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out=buf2)
del buf0
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
triton_poi_fused_add_1.run(buf3, primals_4, 256, grid=grid(256), stream=stream0)
del primals_4
return (buf3, reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class BilinearMatrixAttention(nn.Module):
"""
Adopted from AllenNLP. For now there is no activation function
"""
def __init__(self, matrix_1_dim: 'int', matrix_2_dim: 'int',
use_input_biases: 'bool'=False, label_dim: 'int'=1) ->None:
super().__init__()
if use_input_biases:
matrix_1_dim += 1
matrix_2_dim += 1
if label_dim == 1:
self.weight_matrix = nn.Parameter(torch.Tensor(matrix_1_dim,
matrix_2_dim))
else:
self.weight_matrix = nn.Parameter(torch.Tensor(label_dim,
matrix_1_dim, matrix_2_dim))
self.bias = nn.Parameter(torch.Tensor(1))
self.use_input_biases = use_input_biases
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.weight_matrix)
self.bias.data.fill_(0)
def forward(self, matrix_1: 'torch.Tensor', matrix_2: 'torch.Tensor'
) ->torch.Tensor:
if self.use_input_biases:
bias1 = matrix_1.new_ones(matrix_1.size()[:-1] + (1,))
bias2 = matrix_2.new_ones(matrix_2.size()[:-1] + (1,))
matrix_1 = torch.cat([matrix_1, bias1], -1)
matrix_2 = torch.cat([matrix_2, bias2], -1)
weight = self.weight_matrix
if weight.dim() == 2:
weight = weight.unsqueeze(0)
intermediate = torch.matmul(matrix_1.unsqueeze(1), weight)
final = torch.matmul(intermediate, matrix_2.unsqueeze(1).transpose(
2, 3))
return final.squeeze(1) + self.bias
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'matrix_1_dim': 4, 'matrix_2_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 4
x2 = xindex // 16 % 4
x3 = xindex // 64
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
tl.store(out_ptr0 + x4, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr0 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(in_out_ptr0 + x0, tmp3, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_2, (16, 4, 4), (16, 4,
1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (0, 4, 1), 0),
out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 1, 16, 4, 1), torch
.float32)
get_raw_stream(0)
triton_poi_fused_clone_0[grid(256)](primals_3, buf1, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del primals_3
buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf0, reinterpret_tensor(buf1, (16, 4, 4), (16,
4, 1), 0), out=buf2)
del buf0
buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf2
triton_poi_fused_add_1[grid(256)](buf3, primals_4, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_4
return buf3, reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0)
class BilinearMatrixAttentionNew(nn.Module):
"""
Adopted from AllenNLP. For now there is no activation function
"""
def __init__(self, matrix_1_dim: 'int', matrix_2_dim: 'int',
use_input_biases: 'bool'=False, label_dim: 'int'=1) ->None:
super().__init__()
if use_input_biases:
matrix_1_dim += 1
matrix_2_dim += 1
if label_dim == 1:
self.weight_matrix = nn.Parameter(torch.Tensor(matrix_1_dim,
matrix_2_dim))
else:
self.weight_matrix = nn.Parameter(torch.Tensor(label_dim,
matrix_1_dim, matrix_2_dim))
self.bias = nn.Parameter(torch.Tensor(1))
self.use_input_biases = use_input_biases
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.weight_matrix)
self.bias.data.fill_(0)
def forward(self, input_0, input_1):
primals_1 = self.weight_matrix
primals_4 = self.bias
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
Impavidity/relogic
|
BilinearMatrixAttention
| false | 8,292 |
[
"MIT"
] | 24 |
f647106e143cd603b95b63e06ea530cdd516aefe
|
https://github.com/Impavidity/relogic/tree/f647106e143cd603b95b63e06ea530cdd516aefe
|
CharbonnierLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ep/cep73vddjgorwhamorfn3vcq66nxtnospefeu3y64s7xzpnmwngo.py
# Topologically Sorted Source Nodes: [sub, pow_1, add, sqrt, sum_1, diff], Original ATen: [aten.sub, aten.pow, aten.add, aten.sqrt, aten.sum, aten.div]
# Source node to ATen node mapping:
# add => add
# diff => div
# pow_1 => pow_1
# sqrt => sqrt
# sub => sub
# sum_1 => sum_1
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, 1e-06), kwargs = {})
# %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sqrt,), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 4), kwargs = {})
triton_per_fused_add_div_pow_sqrt_sub_sum_0 = async_compile.triton('triton_per_fused_add_div_pow_sqrt_sub_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = 1e-06
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 0.25
tmp11 = tmp9 * tmp10
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [sub, pow_1, add, sqrt, sum_1, diff], Original ATen: [aten.sub, aten.pow, aten.add, aten.sqrt, aten.sum, aten.div]
stream0 = get_raw_stream(0)
triton_per_fused_add_div_pow_sqrt_sub_sum_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class CharbonnierLoss(nn.Module):
def __init__(self):
super(CharbonnierLoss, self).__init__()
def forward(self, pre, gt):
N = pre.shape[0]
diff = torch.sum(torch.sqrt((pre - gt).pow(2) + 0.001 ** 2)) / N
return diff
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0,
in_ptr1, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = 1e-06
tmp5 = tmp3 + tmp4
tmp6 = libdevice.sqrt(tmp5)
tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
tmp10 = 0.25
tmp11 = tmp9 * tmp10
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_pow_sqrt_sub_sum_0[grid(1)](buf1, arg0_1,
arg1_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class CharbonnierLossNew(nn.Module):
def __init__(self):
super(CharbonnierLossNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IndigoPurple/EFENet
|
CharbonnierLoss
| false | 8,293 |
[
"MIT"
] | 11 |
e88234486f19534274a0a20badc251788ac67e31
|
https://github.com/IndigoPurple/EFENet/tree/e88234486f19534274a0a20badc251788ac67e31
|
ConvLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zo/czoefxnecwpok2nmimcnrjk6tumldvtx7xkzpmzgmnay3tkwhjwg.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# x => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8) % 8
x2 = (xindex // 64)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-2) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-2) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7x/c7xksf46hekvm7qrrnxlo34nyt2yrkxxbfiyydgamwacppezvp4w.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# x_1 => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 25) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 400, grid=grid(400), stream=stream0)
del primals_3
return (buf2, primals_2, buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size, stride=stride)
self._pad = nn.ReflectionPad2d(padding=kernel_size // 2)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = self._pad(x)
x = self._conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4,
'stride': 1}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8 % 8
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-2 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-2 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 25 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(1024)](primals_1, buf0,
1024, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5, 5), (100, 25, 5, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(400)](buf2, primals_3, 400,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return buf2, primals_2, buf0
class ConvLayerNew(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size, stride=stride)
self._pad = nn.ReflectionPad2d(padding=kernel_size // 2)
def forward(self, input_0):
primals_1 = self._conv.weight
primals_3 = self._conv.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Inkln/StyleTransferWithCatalyst
|
ConvLayer
| false | 8,294 |
[
"Apache-2.0"
] | 11 |
c3181ecdfd32160907efc2d9d917a55925c25c11
|
https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11
|
HardMish
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/wf/cwfz45lgmn4vvb2z7srvtaetpjlafulbeenz4jg3ktl6yri6sgvk.py
# Topologically Sorted Source Nodes: [mul, add, clamp, mul_1], Original ATen: [aten.mul, aten.add, aten.clamp]
# Source node to ATen node mapping:
# add => add
# clamp => clamp_max, clamp_min
# mul => mul
# mul_1 => mul_1
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 2), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 2), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %clamp_max), kwargs = {})
triton_poi_fused_add_clamp_mul_0 = async_compile.triton('triton_poi_fused_add_clamp_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 2.0
tmp4 = tmp0 + tmp3
tmp5 = 0.0
tmp6 = triton_helpers.maximum(tmp4, tmp5)
tmp7 = triton_helpers.minimum(tmp6, tmp3)
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, add, clamp, mul_1], Original ATen: [aten.mul, aten.add, aten.clamp]
stream0 = get_raw_stream(0)
triton_poi_fused_add_clamp_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn as nn
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMish(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(HardMish, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_mish(x, self.inplace)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_clamp_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.5
tmp2 = tmp0 * tmp1
tmp3 = 2.0
tmp4 = tmp0 + tmp3
tmp5 = 0.0
tmp6 = triton_helpers.maximum(tmp4, tmp5)
tmp7 = triton_helpers.minimum(tmp6, tmp3)
tmp8 = tmp2 * tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_clamp_mul_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def hard_mish(x, inplace: 'bool'=False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMishNew(nn.Module):
def __init__(self, inplace: 'bool'=False):
super(HardMishNew, self).__init__()
self.inplace = inplace
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation
|
HardMish
| false | 8,295 |
[
"Apache-2.0"
] | 34 |
2a546ef946989fc5bac8d819b3c93a9fdc83f241
|
https://github.com/JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation/tree/2a546ef946989fc5bac8d819b3c93a9fdc83f241
|
SpatialPyramidPooling
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/g6/cg6oe55l3p5bxi7j5vm2v5urvkqm27opizsjxr2rmxliltibgvru.py
# Topologically Sorted Source Nodes: [max_pool2d_2, features], Original ATen: [aten.max_pool2d_with_indices, aten.cat]
# Source node to ATen node mapping:
# features => cat
# max_pool2d_2 => _low_memory_max_pool2d_with_offsets
# Graph fragment:
# %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [5, 5], [1, 1], [2, 2], [1, 1], False), kwargs = {})
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %getitem_2, %getitem_4, %arg0_1], 1), kwargs = {})
triton_poi_fused_cat_max_pool2d_with_indices_0 = async_compile.triton('triton_poi_fused_cat_max_pool2d_with_indices_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_max_pool2d_with_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 26, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_max_pool2d_with_indices_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x7 = xindex
x3 = (xindex // 64)
x4 = xindex % 64
tmp116 = tl.load(in_ptr0 + (x7), xmask)
tmp0 = (-2) + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = (-2) + x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + ((-10) + x7), tmp10 & xmask, other=float("-inf"))
tmp12 = (-1) + x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + ((-9) + x7), tmp16 & xmask, other=float("-inf"))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + ((-8) + x7), tmp23 & xmask, other=float("-inf"))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 1 + x0
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp5 & tmp29
tmp31 = tl.load(in_ptr0 + ((-7) + x7), tmp30 & xmask, other=float("-inf"))
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = 2 + x0
tmp34 = tmp33 >= tmp1
tmp35 = tmp33 < tmp3
tmp36 = tmp34 & tmp35
tmp37 = tmp5 & tmp36
tmp38 = tl.load(in_ptr0 + ((-6) + x7), tmp37 & xmask, other=float("-inf"))
tmp39 = triton_helpers.maximum(tmp38, tmp32)
tmp40 = (-1) + x1
tmp41 = tmp40 >= tmp1
tmp42 = tmp40 < tmp3
tmp43 = tmp41 & tmp42
tmp44 = tmp43 & tmp9
tmp45 = tl.load(in_ptr0 + ((-6) + x7), tmp44 & xmask, other=float("-inf"))
tmp46 = triton_helpers.maximum(tmp45, tmp39)
tmp47 = tmp43 & tmp15
tmp48 = tl.load(in_ptr0 + ((-5) + x7), tmp47 & xmask, other=float("-inf"))
tmp49 = triton_helpers.maximum(tmp48, tmp46)
tmp50 = tmp43 & tmp22
tmp51 = tl.load(in_ptr0 + ((-4) + x7), tmp50 & xmask, other=float("-inf"))
tmp52 = triton_helpers.maximum(tmp51, tmp49)
tmp53 = tmp43 & tmp29
tmp54 = tl.load(in_ptr0 + ((-3) + x7), tmp53 & xmask, other=float("-inf"))
tmp55 = triton_helpers.maximum(tmp54, tmp52)
tmp56 = tmp43 & tmp36
tmp57 = tl.load(in_ptr0 + ((-2) + x7), tmp56 & xmask, other=float("-inf"))
tmp58 = triton_helpers.maximum(tmp57, tmp55)
tmp59 = x1
tmp60 = tmp59 >= tmp1
tmp61 = tmp59 < tmp3
tmp62 = tmp60 & tmp61
tmp63 = tmp62 & tmp9
tmp64 = tl.load(in_ptr0 + ((-2) + x7), tmp63 & xmask, other=float("-inf"))
tmp65 = triton_helpers.maximum(tmp64, tmp58)
tmp66 = tmp62 & tmp15
tmp67 = tl.load(in_ptr0 + ((-1) + x7), tmp66 & xmask, other=float("-inf"))
tmp68 = triton_helpers.maximum(tmp67, tmp65)
tmp69 = tmp62 & tmp22
tmp70 = tl.load(in_ptr0 + (x7), tmp69 & xmask, other=float("-inf"))
tmp71 = triton_helpers.maximum(tmp70, tmp68)
tmp72 = tmp62 & tmp29
tmp73 = tl.load(in_ptr0 + (1 + x7), tmp72 & xmask, other=float("-inf"))
tmp74 = triton_helpers.maximum(tmp73, tmp71)
tmp75 = tmp62 & tmp36
tmp76 = tl.load(in_ptr0 + (2 + x7), tmp75 & xmask, other=float("-inf"))
tmp77 = triton_helpers.maximum(tmp76, tmp74)
tmp78 = 1 + x1
tmp79 = tmp78 >= tmp1
tmp80 = tmp78 < tmp3
tmp81 = tmp79 & tmp80
tmp82 = tmp81 & tmp9
tmp83 = tl.load(in_ptr0 + (2 + x7), tmp82 & xmask, other=float("-inf"))
tmp84 = triton_helpers.maximum(tmp83, tmp77)
tmp85 = tmp81 & tmp15
tmp86 = tl.load(in_ptr0 + (3 + x7), tmp85 & xmask, other=float("-inf"))
tmp87 = triton_helpers.maximum(tmp86, tmp84)
tmp88 = tmp81 & tmp22
tmp89 = tl.load(in_ptr0 + (4 + x7), tmp88 & xmask, other=float("-inf"))
tmp90 = triton_helpers.maximum(tmp89, tmp87)
tmp91 = tmp81 & tmp29
tmp92 = tl.load(in_ptr0 + (5 + x7), tmp91 & xmask, other=float("-inf"))
tmp93 = triton_helpers.maximum(tmp92, tmp90)
tmp94 = tmp81 & tmp36
tmp95 = tl.load(in_ptr0 + (6 + x7), tmp94 & xmask, other=float("-inf"))
tmp96 = triton_helpers.maximum(tmp95, tmp93)
tmp97 = 2 + x1
tmp98 = tmp97 >= tmp1
tmp99 = tmp97 < tmp3
tmp100 = tmp98 & tmp99
tmp101 = tmp100 & tmp9
tmp102 = tl.load(in_ptr0 + (6 + x7), tmp101 & xmask, other=float("-inf"))
tmp103 = triton_helpers.maximum(tmp102, tmp96)
tmp104 = tmp100 & tmp15
tmp105 = tl.load(in_ptr0 + (7 + x7), tmp104 & xmask, other=float("-inf"))
tmp106 = triton_helpers.maximum(tmp105, tmp103)
tmp107 = tmp100 & tmp22
tmp108 = tl.load(in_ptr0 + (8 + x7), tmp107 & xmask, other=float("-inf"))
tmp109 = triton_helpers.maximum(tmp108, tmp106)
tmp110 = tmp100 & tmp29
tmp111 = tl.load(in_ptr0 + (9 + x7), tmp110 & xmask, other=float("-inf"))
tmp112 = triton_helpers.maximum(tmp111, tmp109)
tmp113 = tmp100 & tmp36
tmp114 = tl.load(in_ptr0 + (10 + x7), tmp113 & xmask, other=float("-inf"))
tmp115 = triton_helpers.maximum(tmp114, tmp112)
tl.store(out_ptr0 + (x4 + (256*x3)), tmp115, xmask)
tl.store(out_ptr1 + (x4 + (256*x3)), tmp116, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zh/czh2iu2vn6pigyl5h6fyfqheb4tot4inbiraysjygmf5yilwww7d.py
# Topologically Sorted Source Nodes: [features], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# features => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %getitem_2, %getitem_4, %arg0_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 64
x1 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tl.store(out_ptr0 + (x0 + (256*x1)), tmp0, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
buf0 = torch.ops.aten.max_pool2d_with_indices.default(arg0_1, [13, 13], [1, 1], [6, 6])
buf1 = buf0[0]
del buf0
# Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
buf3 = torch.ops.aten.max_pool2d_with_indices.default(arg0_1, [9, 9], [1, 1], [4, 4])
buf4 = buf3[0]
del buf3
buf10 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.float32)
buf6 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 128) # alias
buf9 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 192) # alias
# Topologically Sorted Source Nodes: [max_pool2d_2, features], Original ATen: [aten.max_pool2d_with_indices, aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_max_pool2d_with_indices_0.run(arg0_1, buf6, buf9, 256, grid=grid(256), stream=stream0)
del arg0_1
buf7 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 0) # alias
# Topologically Sorted Source Nodes: [features], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(buf1, buf7, 256, grid=grid(256), stream=stream0)
del buf1
buf8 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 64) # alias
# Topologically Sorted Source Nodes: [features], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(buf4, buf8, 256, grid=grid(256), stream=stream0)
del buf4
return (buf10, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(self, x):
features = [maxpool(x) for maxpool in self.maxpools[::-1]]
features = torch.cat(features + [x], dim=1)
return features
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_max_pool2d_with_indices_0(in_ptr0, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x7 = xindex
x3 = xindex // 64
x4 = xindex % 64
tmp116 = tl.load(in_ptr0 + x7, xmask)
tmp0 = -2 + x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tmp2 & tmp4
tmp6 = -2 + x0
tmp7 = tmp6 >= tmp1
tmp8 = tmp6 < tmp3
tmp9 = tmp7 & tmp8
tmp10 = tmp5 & tmp9
tmp11 = tl.load(in_ptr0 + (-10 + x7), tmp10 & xmask, other=float('-inf'))
tmp12 = -1 + x0
tmp13 = tmp12 >= tmp1
tmp14 = tmp12 < tmp3
tmp15 = tmp13 & tmp14
tmp16 = tmp5 & tmp15
tmp17 = tl.load(in_ptr0 + (-9 + x7), tmp16 & xmask, other=float('-inf'))
tmp18 = triton_helpers.maximum(tmp17, tmp11)
tmp19 = x0
tmp20 = tmp19 >= tmp1
tmp21 = tmp19 < tmp3
tmp22 = tmp20 & tmp21
tmp23 = tmp5 & tmp22
tmp24 = tl.load(in_ptr0 + (-8 + x7), tmp23 & xmask, other=float('-inf'))
tmp25 = triton_helpers.maximum(tmp24, tmp18)
tmp26 = 1 + x0
tmp27 = tmp26 >= tmp1
tmp28 = tmp26 < tmp3
tmp29 = tmp27 & tmp28
tmp30 = tmp5 & tmp29
tmp31 = tl.load(in_ptr0 + (-7 + x7), tmp30 & xmask, other=float('-inf'))
tmp32 = triton_helpers.maximum(tmp31, tmp25)
tmp33 = 2 + x0
tmp34 = tmp33 >= tmp1
tmp35 = tmp33 < tmp3
tmp36 = tmp34 & tmp35
tmp37 = tmp5 & tmp36
tmp38 = tl.load(in_ptr0 + (-6 + x7), tmp37 & xmask, other=float('-inf'))
tmp39 = triton_helpers.maximum(tmp38, tmp32)
tmp40 = -1 + x1
tmp41 = tmp40 >= tmp1
tmp42 = tmp40 < tmp3
tmp43 = tmp41 & tmp42
tmp44 = tmp43 & tmp9
tmp45 = tl.load(in_ptr0 + (-6 + x7), tmp44 & xmask, other=float('-inf'))
tmp46 = triton_helpers.maximum(tmp45, tmp39)
tmp47 = tmp43 & tmp15
tmp48 = tl.load(in_ptr0 + (-5 + x7), tmp47 & xmask, other=float('-inf'))
tmp49 = triton_helpers.maximum(tmp48, tmp46)
tmp50 = tmp43 & tmp22
tmp51 = tl.load(in_ptr0 + (-4 + x7), tmp50 & xmask, other=float('-inf'))
tmp52 = triton_helpers.maximum(tmp51, tmp49)
tmp53 = tmp43 & tmp29
tmp54 = tl.load(in_ptr0 + (-3 + x7), tmp53 & xmask, other=float('-inf'))
tmp55 = triton_helpers.maximum(tmp54, tmp52)
tmp56 = tmp43 & tmp36
tmp57 = tl.load(in_ptr0 + (-2 + x7), tmp56 & xmask, other=float('-inf'))
tmp58 = triton_helpers.maximum(tmp57, tmp55)
tmp59 = x1
tmp60 = tmp59 >= tmp1
tmp61 = tmp59 < tmp3
tmp62 = tmp60 & tmp61
tmp63 = tmp62 & tmp9
tmp64 = tl.load(in_ptr0 + (-2 + x7), tmp63 & xmask, other=float('-inf'))
tmp65 = triton_helpers.maximum(tmp64, tmp58)
tmp66 = tmp62 & tmp15
tmp67 = tl.load(in_ptr0 + (-1 + x7), tmp66 & xmask, other=float('-inf'))
tmp68 = triton_helpers.maximum(tmp67, tmp65)
tmp69 = tmp62 & tmp22
tmp70 = tl.load(in_ptr0 + x7, tmp69 & xmask, other=float('-inf'))
tmp71 = triton_helpers.maximum(tmp70, tmp68)
tmp72 = tmp62 & tmp29
tmp73 = tl.load(in_ptr0 + (1 + x7), tmp72 & xmask, other=float('-inf'))
tmp74 = triton_helpers.maximum(tmp73, tmp71)
tmp75 = tmp62 & tmp36
tmp76 = tl.load(in_ptr0 + (2 + x7), tmp75 & xmask, other=float('-inf'))
tmp77 = triton_helpers.maximum(tmp76, tmp74)
tmp78 = 1 + x1
tmp79 = tmp78 >= tmp1
tmp80 = tmp78 < tmp3
tmp81 = tmp79 & tmp80
tmp82 = tmp81 & tmp9
tmp83 = tl.load(in_ptr0 + (2 + x7), tmp82 & xmask, other=float('-inf'))
tmp84 = triton_helpers.maximum(tmp83, tmp77)
tmp85 = tmp81 & tmp15
tmp86 = tl.load(in_ptr0 + (3 + x7), tmp85 & xmask, other=float('-inf'))
tmp87 = triton_helpers.maximum(tmp86, tmp84)
tmp88 = tmp81 & tmp22
tmp89 = tl.load(in_ptr0 + (4 + x7), tmp88 & xmask, other=float('-inf'))
tmp90 = triton_helpers.maximum(tmp89, tmp87)
tmp91 = tmp81 & tmp29
tmp92 = tl.load(in_ptr0 + (5 + x7), tmp91 & xmask, other=float('-inf'))
tmp93 = triton_helpers.maximum(tmp92, tmp90)
tmp94 = tmp81 & tmp36
tmp95 = tl.load(in_ptr0 + (6 + x7), tmp94 & xmask, other=float('-inf'))
tmp96 = triton_helpers.maximum(tmp95, tmp93)
tmp97 = 2 + x1
tmp98 = tmp97 >= tmp1
tmp99 = tmp97 < tmp3
tmp100 = tmp98 & tmp99
tmp101 = tmp100 & tmp9
tmp102 = tl.load(in_ptr0 + (6 + x7), tmp101 & xmask, other=float('-inf'))
tmp103 = triton_helpers.maximum(tmp102, tmp96)
tmp104 = tmp100 & tmp15
tmp105 = tl.load(in_ptr0 + (7 + x7), tmp104 & xmask, other=float('-inf'))
tmp106 = triton_helpers.maximum(tmp105, tmp103)
tmp107 = tmp100 & tmp22
tmp108 = tl.load(in_ptr0 + (8 + x7), tmp107 & xmask, other=float('-inf'))
tmp109 = triton_helpers.maximum(tmp108, tmp106)
tmp110 = tmp100 & tmp29
tmp111 = tl.load(in_ptr0 + (9 + x7), tmp110 & xmask, other=float('-inf'))
tmp112 = triton_helpers.maximum(tmp111, tmp109)
tmp113 = tmp100 & tmp36
tmp114 = tl.load(in_ptr0 + (10 + x7), tmp113 & xmask, other=float('-inf'))
tmp115 = triton_helpers.maximum(tmp114, tmp112)
tl.store(out_ptr0 + (x4 + 256 * x3), tmp115, xmask)
tl.store(out_ptr1 + (x4 + 256 * x3), tmp116, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 64
x1 = xindex // 64
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 256 * x1), tmp0, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = torch.ops.aten.max_pool2d_with_indices.default(arg0_1, [13,
13], [1, 1], [6, 6])
buf1 = buf0[0]
del buf0
buf3 = torch.ops.aten.max_pool2d_with_indices.default(arg0_1, [9, 9
], [1, 1], [4, 4])
buf4 = buf3[0]
del buf3
buf10 = empty_strided_cuda((4, 16, 4, 4), (256, 16, 4, 1), torch.
float32)
buf6 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 128)
buf9 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 192)
get_raw_stream(0)
triton_poi_fused_cat_max_pool2d_with_indices_0[grid(256)](arg0_1,
buf6, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf7 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 0)
triton_poi_fused_cat_1[grid(256)](buf1, buf7, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf1
buf8 = reinterpret_tensor(buf10, (4, 4, 4, 4), (256, 16, 4, 1), 64)
triton_poi_fused_cat_1[grid(256)](buf4, buf8, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf4
return buf10,
class SpatialPyramidPoolingNew(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPoolingNew, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(pool_size, 1, pool_size //
2) for pool_size in pool_sizes])
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
IDayday/YOLOv4_CAM
|
SpatialPyramidPooling
| false | 8,296 |
[
"Apache-2.0"
] | 34 |
8df61f1c59c197126f0385c1ec1cf65a29a80cec
|
https://github.com/IDayday/YOLOv4_CAM/tree/8df61f1c59c197126f0385c1ec1cf65a29a80cec
|
decoder4
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 73728
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), None, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_3 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1
# Graph fragment:
# %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
# %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {})
# %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/iw/ciwouyfqxfi5suuzcagn3bbdbaulgyw4bpqnsdjlizmkc2oz2muq.py
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# out_3 => _unsafe_index_2
# out_4 => _unsafe_index_3, _unsafe_index_4
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
# %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 102400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 10) % 10
x0 = xindex % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 256
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (4*tmp4) + (16*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/x6/cx6kpvwny3lwf4b75yjzmpojqlofwsrfkk23uf3lterlix2wg3ah.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_5 => convolution_1
# out_6 => relu_1
# out_7 => _unsafe_index_5, _unsafe_index_6
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 102400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = (xindex // 10) % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py
# Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_16 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_12, mul_4, mul_5
# Graph fragment:
# %iota_12 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {})
# %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {})
# %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/u4/cu42vug3hzqlursadot6js3gerux56tz7joz5blmrj6a5xowpyd6.py
# Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_14 => convolution_4
# out_15 => relu_4
# out_16 => _unsafe_index_11
# out_17 => _unsafe_index_12, _unsafe_index_13
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
# %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {})
# %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {})
# %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 165888
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 18) % 18
x0 = xindex % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (8*tmp4) + (64*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/lz/clznsh6fdhksacfsunvntibvsjaoxwmok2tqs5avvjtlb7uojj5w.py
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_18 => convolution_5
# out_19 => relu_5
# out_20 => _unsafe_index_14, _unsafe_index_15
# Graph fragment:
# %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
# %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {})
# %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 165888
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = (xindex // 18) % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/6w/c6wyyvxovkkzfk6k64bfon5db5xfxiywvm7oionlfmjjhdvc6ugb.py
# Topologically Sorted Source Nodes: [out_23], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_23 => add_8, add_9, convert_element_type_8, convert_element_type_9, iota_18, mul_8, mul_9
# Graph fragment:
# %iota_18 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_18, 1), kwargs = {})
# %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {})
# %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {})
# %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {})
# %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {})
# %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_7 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/74/c74eexru54jzdd7so47hr6vfjhuchik3ect6ywzjtozle2ccoanv.py
# Topologically Sorted Source Nodes: [out_21, out_22, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_21 => convolution_6
# out_22 => relu_6
# out_23 => _unsafe_index_16
# out_24 => _unsafe_index_17, _unsafe_index_18
# Graph fragment:
# %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
# %_unsafe_index_16 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_6, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {})
# %_unsafe_index_17 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_16, [None, None, %sub_29, None]), kwargs = {})
# %_unsafe_index_18 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_17, [None, None, None, %sub_29]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 34) % 34
x0 = xindex % 34
x4 = (xindex // 1156)
x2 = (xindex // 1156) % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (16*tmp4) + (256*x4)), xmask, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/hz/chz63phj47p2gjm5xtlsmszcrnfpp4xsj7dae5glkrxk5zs7hyto.py
# Topologically Sorted Source Nodes: [out_25, out_26, out_27], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_25 => convolution_7
# out_26 => relu_7
# out_27 => _unsafe_index_19, _unsafe_index_20
# Graph fragment:
# %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
# %_unsafe_index_19 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_7, [None, None, %sub_29, None]), kwargs = {})
# %_unsafe_index_20 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_19, [None, None, None, %sub_29]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = (xindex // 34) % 34
x4 = (xindex // 1156)
x2 = (xindex // 1156) % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024*x4)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/hn/chnibi7gwebi7kg4g3szjbyckwlp74j2tqdbjdrt6nzxxpvjzwhu.py
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# out_28 => convolution_8
# Graph fragment:
# %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_20, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_10 = async_compile.triton('triton_poi_fused_convolution_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 12288
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 3
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5o/c5ojoadwtfg74b52gh7y53jwhw2ktpi5q7bj2iprlawfjiexyy3r.py
# Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_25 => convolution_7
# out_26 => relu_7
# Graph fragment:
# %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_18, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
# %le_18 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 262144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/aa/caa4f5re2hltkmtlkj3n4yzdxyuaw5kf6spnc6g2phrpe6faiy4x.py
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_21 => convolution_6
# out_22 => relu_6
# Graph fragment:
# %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
# %le_37 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_12 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jr/cjrwyczsgjgyy6qosgum4wgnyb2w3ihpqvja6fteyhda2zxoh5ot.py
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_18 => convolution_5
# out_19 => relu_5
# Graph fragment:
# %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
# %le_56 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_13 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/sz/cszsc7yevnmc6ugclb5yovyvzqbxqknvq6uzgneygczjmkvgtczc.py
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_14 => convolution_4
# out_15 => relu_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
# %le_75 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_14 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_14', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/rf/crftfxpfxphanidbjn5jncdcv65uaywlzsfsv5jzcsmdxwvjvf2c.py
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_11 => convolution_3
# out_12 => relu_3
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
# %le_94 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 256
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ia/cianovbanealq7vsk3q7ox66wiuiaoeqjzpupak5lcrtiilg2ewl.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le_151 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 256
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (256, ), (1, ))
assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_5, (256, ), (1, ))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256, ), (1, ))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256, ), (1, ))
assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (128, ), (1, ))
assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_13, (128, ), (1, ))
assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_15, (64, ), (1, ))
assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_17, (64, ), (1, ))
assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_19, (3, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1))
buf2 = empty_strided_cuda((8, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0)
buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1))
buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1))
buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1))
buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1))
buf11 = empty_strided_cuda((16, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_4.run(buf11, 16, grid=grid(16), stream=stream0)
buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf11, buf10, primals_11, buf12, 165888, grid=grid(165888), stream=stream0)
# Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.convolution]
buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf13, primals_13, buf14, 165888, grid=grid(165888), stream=stream0)
# Topologically Sorted Source Nodes: [out_21], Original ATen: [aten.convolution]
buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1))
buf16 = empty_strided_cuda((32, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_23], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_7.run(buf16, 32, grid=grid(32), stream=stream0)
buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_21, out_22, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8.run(buf16, buf15, primals_15, buf17, 295936, grid=grid(295936), stream=stream0)
# Topologically Sorted Source Nodes: [out_25], Original ATen: [aten.convolution]
buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_25, out_26, out_27], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf18, primals_17, buf19, 295936, grid=grid(295936), stream=stream0)
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1))
buf21 = buf20; del buf20 # reuse
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
triton_poi_fused_convolution_10.run(buf21, primals_19, 12288, grid=grid(12288), stream=stream0)
del primals_19
buf22 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_11.run(buf18, primals_17, buf22, 262144, grid=grid(262144), stream=stream0)
del buf18
del primals_17
buf23 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_12.run(buf15, primals_15, buf23, 65536, grid=grid(65536), stream=stream0)
del buf15
del primals_15
buf24 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_13.run(buf13, primals_13, buf24, 131072, grid=grid(131072), stream=stream0)
del buf13
del primals_13
buf25 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_14.run(buf10, primals_11, buf25, 32768, grid=grid(32768), stream=stream0)
del buf10
del primals_11
buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_15.run(buf8, primals_9, buf26, 65536, grid=grid(65536), stream=stream0)
del buf8
del primals_9
buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_15.run(buf6, primals_7, buf27, 65536, grid=grid(65536), stream=stream0)
del buf6
del primals_7
buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_15.run(buf4, primals_5, buf28, 65536, grid=grid(65536), stream=stream0)
del buf4
del primals_5
buf29 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_16.run(buf1, primals_3, buf29, 16384, grid=grid(16384), stream=stream0)
del buf1
del primals_3
return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22, buf23, buf24, buf25, buf26, buf27, buf28, buf29, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class decoder4(nn.Module):
def __init__(self):
super(decoder4, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu12 = nn.ReLU(inplace=True)
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu13 = nn.ReLU(inplace=True)
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu14 = nn.ReLU(inplace=True)
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, x):
out = self.reflecPad11(x)
out = self.conv11(out)
out = self.relu11(out)
out = self.unpool(out)
out = self.reflecPad12(out)
out = self.conv12(out)
out = self.relu12(out)
out = self.reflecPad13(out)
out = self.conv13(out)
out = self.relu13(out)
out = self.reflecPad14(out)
out = self.conv14(out)
out = self.relu14(out)
out = self.reflecPad15(out)
out = self.conv15(out)
out = self.relu15(out)
out = self.unpool2(out)
out = self.reflecPad16(out)
out = self.conv16(out)
out = self.relu16(out)
out = self.reflecPad17(out)
out = self.conv17(out)
out = self.relu17(out)
out = self.unpool3(out)
out = self.reflecPad18(out)
out = self.conv18(out)
out = self.relu18(out)
out = self.reflecPad19(out)
out = self.conv19(out)
return out
def get_inputs():
return [torch.rand([4, 512, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 10 % 10
x0 = xindex % 10
x4 = xindex // 100
x2 = xindex // 100 % 256
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1
))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0
))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = xindex // 10 % 10
x4 = xindex // 100
x2 = xindex // 100 % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 18 % 18
x0 = xindex % 18
x4 = xindex // 324
x2 = xindex // 324 % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x1))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = xindex // 18 % 18
x4 = xindex // 324
x2 = xindex // 324 % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 34 % 34
x0 = xindex % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x1))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), xmask,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, xmask)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = xindex // 34 % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 3
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_12(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_13(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_14(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19) = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (256,), (1,))
assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (128,), (1,))
assert_size_stride(primals_12, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_13, (128,), (1,))
assert_size_stride(primals_14, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_15, (64,), (1,))
assert_size_stride(primals_16, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_17, (64,), (1,))
assert_size_stride(primals_18, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_19, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0,
73728, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1))
buf2 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK
=8, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid
(102400)](buf2, buf1, primals_3, buf3, 102400, XBLOCK=512,
num_warps=8, num_stages=1)
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1))
buf5 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf4
, primals_5, buf5, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 256, 8, 8), (16384, 64, 8, 1))
buf7 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf6
, primals_7, buf7, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 256, 8, 8), (16384, 64, 8, 1))
buf9 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(102400)](buf8
, primals_9, buf9, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 128, 8, 8), (8192, 64, 8, 1))
buf11 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf11, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf12 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid
(165888)](buf11, buf10, primals_11, buf12, 165888, XBLOCK=512,
num_warps=8, num_stages=1)
buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(165888)](
buf13, primals_13, buf14, 165888, XBLOCK=1024, num_warps=4,
num_stages=1)
buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 64, 16, 16), (16384, 256, 16, 1))
buf16 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf16, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf17 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8[grid
(295936)](buf16, buf15, primals_15, buf17, 295936, XBLOCK=512,
num_warps=8, num_stages=1)
buf18 = extern_kernels.convolution(buf17, primals_16, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf18, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf19 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(295936)](
buf18, primals_17, buf19, 295936, XBLOCK=1024, num_warps=4,
num_stages=1)
buf20 = extern_kernels.convolution(buf19, primals_18, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf20, (4, 3, 32, 32), (3072, 1024, 32, 1))
buf21 = buf20
del buf20
triton_poi_fused_convolution_10[grid(12288)](buf21, primals_19,
12288, XBLOCK=128, num_warps=4, num_stages=1)
del primals_19
buf22 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_11[grid(262144)](
buf18, primals_17, buf22, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf18
del primals_17
buf23 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_12[grid(65536)](
buf15, primals_15, buf23, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf15
del primals_15
buf24 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_13[grid(131072)](
buf13, primals_13, buf24, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf13
del primals_13
buf25 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_14[grid(32768)](
buf10, primals_11, buf25, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del buf10
del primals_11
buf26 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
buf8, primals_9, buf26, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf8
del primals_9
buf27 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
buf6, primals_7, buf27, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf6
del primals_7
buf28 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_15[grid(65536)](
buf4, primals_5, buf28, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf4
del primals_5
buf29 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_16[grid(16384)](
buf1, primals_3, buf29, 16384, XBLOCK=256, num_warps=4,
num_stages=1)
del buf1
del primals_3
return (buf21, primals_2, primals_4, primals_6, primals_8, primals_10,
primals_12, primals_14, primals_16, primals_18, buf0, buf2, buf3,
buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf17, buf19, buf22,
buf23, buf24, buf25, buf26, buf27, buf28, buf29)
class decoder4New(nn.Module):
def __init__(self):
super(decoder4New, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu12 = nn.ReLU(inplace=True)
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu13 = nn.ReLU(inplace=True)
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu14 = nn.ReLU(inplace=True)
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, input_0):
primals_2 = self.conv11.weight
primals_3 = self.conv11.bias
primals_4 = self.conv12.weight
primals_5 = self.conv12.bias
primals_6 = self.conv13.weight
primals_7 = self.conv13.bias
primals_8 = self.conv14.weight
primals_9 = self.conv14.bias
primals_10 = self.conv15.weight
primals_11 = self.conv15.bias
primals_12 = self.conv16.weight
primals_13 = self.conv16.bias
primals_14 = self.conv17.weight
primals_15 = self.conv17.bias
primals_16 = self.conv18.weight
primals_17 = self.conv18.bias
primals_18 = self.conv19.weight
primals_19 = self.conv19.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19])
return output[0]
|
Holmes-Alan/RefVAE
|
decoder4
| false | 8,297 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
Normalization
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/3c/c3cacivpsbr2lx33r7cvq5ixpvvpsezpl6ffnlpsiwslr5udi262.py
# Topologically Sorted Source Nodes: [sub, truediv], Original ATen: [aten.sub, aten.div]
# Source node to ATen node mapping:
# sub => sub
# truediv => div
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %primals_1), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %primals_3), kwargs = {})
triton_poi_fused_div_sub_0 = async_compile.triton('triton_poi_fused_div_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 3
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 / tmp3
tl.store(out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (3, 1, 1), (1, 1, 1))
assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
assert_size_stride(primals_3, (3, 1, 1), (1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [sub, truediv], Original ATen: [aten.sub, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_sub_0.run(primals_2, primals_1, primals_3, buf0, 192, grid=grid(192), stream=stream0)
return (buf0, primals_1, primals_2, primals_3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((3, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((3, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class Normalization(nn.Module):
def __init__(self):
super(Normalization, self).__init__()
self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(-
1, 1, 1))
self.std = nn.Parameter(torch.tensor([0.329, 0.224, 0.225]).view(-1,
1, 1))
def forward(self, img):
return (img - self.mean) / self.std
def get_inputs():
return [torch.rand([4, 3, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 3
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 / tmp3
tl.store(out_ptr0 + x3, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (3, 1, 1), (1, 1, 1))
assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
assert_size_stride(primals_3, (3, 1, 1), (1, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_sub_0[grid(192)](primals_2, primals_1,
primals_3, buf0, 192, XBLOCK=128, num_warps=4, num_stages=1)
return buf0, primals_1, primals_2, primals_3
class NormalizationNew(nn.Module):
def __init__(self):
super(NormalizationNew, self).__init__()
self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]).view(-
1, 1, 1))
self.std = nn.Parameter(torch.tensor([0.329, 0.224, 0.225]).view(-1,
1, 1))
def forward(self, input_0):
primals_1 = self.mean
primals_3 = self.std
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Inkln/StyleTransferWithCatalyst
|
Normalization
| false | 8,298 |
[
"Apache-2.0"
] | 11 |
c3181ecdfd32160907efc2d9d917a55925c25c11
|
https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11
|
GroupNorm32
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/26/c26yotdahezpp65sumfsd7qxngqailkofzxbzr2ahmvtueumital.py
# Topologically Sorted Source Nodes: [y, mul, sigmoid, y_1], Original ATen: [aten.native_group_norm, aten.mul, aten.sigmoid]
# Source node to ATen node mapping:
# mul => mul_2
# sigmoid => sigmoid
# y => add, add_1, mul_1, rsqrt, var_mean
# y_1 => mul_3
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_5), kwargs = {})
# %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_2), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 4.0), kwargs = {})
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul_2,), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, %sigmoid), kwargs = {})
triton_per_fused_mul_native_group_norm_sigmoid_0 = async_compile.triton('triton_per_fused_mul_native_group_norm_sigmoid_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[4, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_native_group_norm_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mul_native_group_norm_sigmoid_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 4
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = (rindex // 16)
tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + (r3), None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + (r3), None, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 64.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp22 = tmp0 - tmp10
tmp23 = tmp22 * tmp21
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp28 = 4.0
tmp29 = tmp27 * tmp28
tmp30 = tl.sigmoid(tmp29)
tmp31 = tmp27 * tmp30
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp21, xmask)
tl.store(in_out_ptr1 + (r1 + (64*x0)), tmp31, xmask)
tl.store(out_ptr0 + (x0), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf3 = reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf1 # reuse
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf5 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [y, mul, sigmoid, y_1], Original ATen: [aten.native_group_norm, aten.mul, aten.sigmoid]
stream0 = get_raw_stream(0)
triton_per_fused_mul_native_group_norm_sigmoid_0.run(buf3, buf5, primals_1, primals_2, primals_3, buf0, 4, 64, grid=grid(4), stream=stream0)
return (buf5, primals_1, primals_2, primals_3, buf0, buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn.functional as F
from torch import nn
class GroupNorm32(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, x):
y = super().forward(x.float())
if self.swish == 1.0:
y = F.silu(y)
elif self.swish:
y = y * F.sigmoid(y * float(self.swish))
return y
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_groups': 1, 'num_channels': 4, 'swish': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mul_native_group_norm_sigmoid_0(in_out_ptr0,
in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
r3 = rindex // 16
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp24 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
tmp26 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tl.where(xmask, tmp1, 0)
tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.sum(tmp6, 1)[:, None]
tmp8 = tl.full([XBLOCK, 1], 64, tl.int32)
tmp9 = tmp8.to(tl.float32)
tmp10 = tmp7 / tmp9
tmp11 = tmp1 - tmp10
tmp12 = tmp11 * tmp11
tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
tmp15 = tl.where(xmask, tmp13, 0)
tmp16 = tl.sum(tmp15, 1)[:, None]
tmp17 = 64.0
tmp18 = tmp16 / tmp17
tmp19 = 1e-05
tmp20 = tmp18 + tmp19
tmp21 = libdevice.rsqrt(tmp20)
tmp22 = tmp0 - tmp10
tmp23 = tmp22 * tmp21
tmp25 = tmp23 * tmp24
tmp27 = tmp25 + tmp26
tmp28 = 4.0
tmp29 = tmp27 * tmp28
tmp30 = tl.sigmoid(tmp29)
tmp31 = tmp27 * tmp30
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp21, xmask)
tl.store(in_out_ptr1 + (r1 + 64 * x0), tmp31, xmask)
tl.store(out_ptr0 + x0, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 1, 1, 1), torch.float32)
buf1 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
buf3 = reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1, 1), 0)
del buf1
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf5 = buf4
del buf4
get_raw_stream(0)
triton_per_fused_mul_native_group_norm_sigmoid_0[grid(4)](buf3,
buf5, primals_1, primals_2, primals_3, buf0, 4, 64, XBLOCK=1,
num_warps=2, num_stages=1)
return buf5, primals_1, primals_2, primals_3, buf0, buf3
class GroupNorm32New(nn.GroupNorm):
def __init__(self, num_groups, num_channels, swish, eps=1e-05):
super().__init__(num_groups=num_groups, num_channels=num_channels,
eps=eps)
self.swish = swish
def forward(self, input_0):
primals_2 = self.weight
primals_3 = self.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Jack000/glid-3
|
GroupNorm32
| false | 8,299 |
[
"MIT"
] | 31 |
4a18efc2785339ebc743e149a7955e34fff436fb
|
https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb
|
ImageGradients
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/nd/cnd7i6ikfohhr7cv6t5hgbordc6otybc225qvzaa5aboneegp7sq.py
# Topologically Sorted Source Nodes: [conv2d, conv2d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv2d => convolution
# conv2d_1 => convolution_1
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg0_1, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 4), kwargs = {})
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%arg1_1, %arg2_1, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 4), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 16], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = (yindex // 4)
tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
tl.store(out_ptr1 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/sb/csblbppfaoexwwzbj6kzlvxsskzy4fbftk3huh4lvz7v7dj7jcoy.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16) % 8
x0 = xindex % 16
x2 = (xindex // 128)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x0) + (64*x2) + x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x0) + (64*x2) + ((-4) + x1)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 1, 3, 3), (9, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
# Topologically Sorted Source Nodes: [conv2d, conv2d_1], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(arg1_1, buf0, buf2, 16, 16, grid=grid(16, 16), stream=stream0)
del arg1_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 1, 16, 4))
del arg0_1
del buf0
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=4, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 1, 16, 4))
del arg2_1
del buf2
buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(buf1, buf3, buf4, 512, grid=grid(512), stream=stream0)
del buf1
del buf3
return (buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.utils.data
class ImageGradients(th.nn.Module):
"""
Args:
c_in(int): number of channels expected in the images.
use_sobel(bool): if True, uses a (smoother) Sobel filter instead of simple
finite differences.
"""
def __init__(self, c_in, use_sobel=True):
super(ImageGradients, self).__init__()
if use_sobel:
self.dx = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
False, groups=c_in)
self.dy = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
False, groups=c_in)
self.dx.weight.data.zero_()
self.dx.weight.data[:, :, 0, 0] = -1
self.dx.weight.data[:, :, 0, 2] = 1
self.dx.weight.data[:, :, 1, 0] = -2
self.dx.weight.data[:, :, 1, 2] = 2
self.dx.weight.data[:, :, 2, 0] = -1
self.dx.weight.data[:, :, 2, 2] = 1
self.dy.weight.data.zero_()
self.dy.weight.data[:, :, 0, 0] = -1
self.dy.weight.data[:, :, 2, 0] = 1
self.dy.weight.data[:, :, 0, 1] = -2
self.dy.weight.data[:, :, 2, 1] = 2
self.dy.weight.data[:, :, 0, 2] = -1
self.dy.weight.data[:, :, 2, 2] = 1
else:
self.dx = th.nn.Conv2d(c_in, c_in, [1, 3], padding=(0, 1), bias
=False, groups=c_in)
self.dy = th.nn.Conv2d(c_in, c_in, [3, 1], padding=(1, 0), bias
=False, groups=c_in)
self.dx.weight.data.zero_()
self.dx.weight.data[:, :, 0, 0] = -1
self.dx.weight.data[:, :, 0, 1] = 1
self.dy.weight.data.zero_()
self.dy.weight.data[:, :, 0, 0] = -1
self.dy.weight.data[:, :, 1, 0] = 1
self.dx.weight.requires_grad = False
self.dy.weight.requires_grad = False
def forward(self, im):
return th.cat([self.dx(im), self.dy(im)], 1)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'c_in': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, out_ptr1, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 16
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 8
x0 = xindex % 16
x2 = xindex // 128
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x0 + 64 * x2 + x1), tmp4 & xmask,
eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x0 + 64 * x2 + (-4 + x1)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x3, tmp10, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 3, 3), (9, 9, 3, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 1, 3, 3), (9, 9, 3, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 16)](arg1_1, buf0, buf2, 16,
16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
del arg1_1
buf1 = extern_kernels.convolution(buf0, arg0_1, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=4, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 1, 16, 4))
del arg0_1
del buf0
buf3 = extern_kernels.convolution(buf2, arg2_1, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=4, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 1, 16, 4))
del arg2_1
del buf2
buf4 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
triton_poi_fused_cat_1[grid(512)](buf1, buf3, buf4, 512, XBLOCK=128,
num_warps=4, num_stages=1)
del buf1
del buf3
return buf4,
class ImageGradientsNew(th.nn.Module):
"""
Args:
c_in(int): number of channels expected in the images.
use_sobel(bool): if True, uses a (smoother) Sobel filter instead of simple
finite differences.
"""
def __init__(self, c_in, use_sobel=True):
super(ImageGradientsNew, self).__init__()
if use_sobel:
self.dx = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
False, groups=c_in)
self.dy = th.nn.Conv2d(c_in, c_in, [3, 3], padding=1, bias=
False, groups=c_in)
self.dx.weight.data.zero_()
self.dx.weight.data[:, :, 0, 0] = -1
self.dx.weight.data[:, :, 0, 2] = 1
self.dx.weight.data[:, :, 1, 0] = -2
self.dx.weight.data[:, :, 1, 2] = 2
self.dx.weight.data[:, :, 2, 0] = -1
self.dx.weight.data[:, :, 2, 2] = 1
self.dy.weight.data.zero_()
self.dy.weight.data[:, :, 0, 0] = -1
self.dy.weight.data[:, :, 2, 0] = 1
self.dy.weight.data[:, :, 0, 1] = -2
self.dy.weight.data[:, :, 2, 1] = 2
self.dy.weight.data[:, :, 0, 2] = -1
self.dy.weight.data[:, :, 2, 2] = 1
else:
self.dx = th.nn.Conv2d(c_in, c_in, [1, 3], padding=(0, 1), bias
=False, groups=c_in)
self.dy = th.nn.Conv2d(c_in, c_in, [3, 1], padding=(1, 0), bias
=False, groups=c_in)
self.dx.weight.data.zero_()
self.dx.weight.data[:, :, 0, 0] = -1
self.dx.weight.data[:, :, 0, 1] = 1
self.dy.weight.data.zero_()
self.dy.weight.data[:, :, 0, 0] = -1
self.dy.weight.data[:, :, 1, 0] = 1
self.dx.weight.requires_grad = False
self.dy.weight.requires_grad = False
def forward(self, input_0):
arg0_1 = self.dx.weight
arg2_1 = self.dy.weight
arg1_1 = input_0
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
IlyaBizyaev/ttools
|
ImageGradients
| false | 8,300 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
decoder3
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/tg/ctga5yvj5wo2zpied3edqbxqaa6zsxsvjmztmx7kufklh74c6vkl.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 36864
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), None, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_3 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1
# Graph fragment:
# %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
# %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {})
# %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/uw/cuw2bwjzi7yls2s6pmwc256upm7wakbnbnnmg4kzboptcqkagykr.py
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# out_3 => _unsafe_index_2
# out_4 => _unsafe_index_3, _unsafe_index_4
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
# %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 51200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 10) % 10
x0 = xindex % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (4*tmp4) + (16*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/fk/cfk52xdhjt42cxoynmeuwzri2n3lcch6jizrtctuiqmsv6a2mkr3.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_5 => convolution_1
# out_6 => relu_1
# out_7 => _unsafe_index_5, _unsafe_index_6
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 51200
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = (xindex // 10) % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py
# Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_9 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_8, mul_4, mul_5
# Graph fragment:
# %iota_8 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_8, 1), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {})
# %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {})
# %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5e/c5ei33gkipof6fobyyrar2cj656rvntddykzm72jpultuhy63dhr.py
# Topologically Sorted Source Nodes: [out_8, out_relu9, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_10 => _unsafe_index_8, _unsafe_index_9
# out_8 => convolution_2
# out_9 => _unsafe_index_7
# out_relu9 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_6, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
# %_unsafe_index_7 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_2, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {})
# %_unsafe_index_8 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_7, [None, None, %sub_13, None]), kwargs = {})
# %_unsafe_index_9 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_8, [None, None, None, %sub_13]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 82944
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 18) % 18
x0 = xindex % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (8*tmp4) + (64*x4)), xmask, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/e4/ce4e2ky4yyr3wug3sp34tac3wwmg73ae4iw4t2rrdrt4b2xz6ub7.py
# Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_11 => convolution_3
# out_12 => relu_3
# out_13 => _unsafe_index_10, _unsafe_index_11
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
# %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_3, [None, None, %sub_13, None]), kwargs = {})
# %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_13]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 82944
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 18
x1 = (xindex // 18) % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x4)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nl/cnln25tqv3hxdvpkm2wirtg6quodz3iycjrmekrdjai75k7i365o.py
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# out_14 => convolution_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_11, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_7 = async_compile.triton('triton_poi_fused_convolution_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 3072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 256) % 3
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/4d/c4dp2ytqocuv466duxky6r4655dxvbtylgfdc7fmejmxdhdigbt6.py
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_11 => convolution_3
# out_12 => relu_3
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
# %le_18 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_8 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/uu/cuuo66ehlpqop6rv5jkauuvlngd34ltribbftanuodnpgbt7qamp.py
# Topologically Sorted Source Nodes: [out_8, out_relu9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_8 => convolution_2
# out_relu9 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_6, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
# %le_37 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_9 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gg/cggqdkvaubjpvgli2myetrofo7po6bgucydnzonwetaalv4i6kxe.py
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_5 => convolution_1
# out_6 => relu_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %le_56 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_10 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ok/cokdahlwoyphqouubcbdzli6rsbyw2673pgje3xakeknqlgosk5f.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le_75 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_11 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
args.clear()
assert_size_stride(primals_1, (4, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_2, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_3, (128, ), (1, ))
assert_size_stride(primals_4, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_5, (128, ), (1, ))
assert_size_stride(primals_6, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (64, ), (1, ))
assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_9, (64, ), (1, ))
assert_size_stride(primals_10, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (3, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 256, 6, 6), (9216, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 36864, grid=grid(36864), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 128, 4, 4), (2048, 16, 4, 1))
buf2 = empty_strided_cuda((8, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0)
buf3 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 51200, grid=grid(51200), stream=stream0)
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 128, 8, 8), (8192, 64, 8, 1))
buf5 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 51200, grid=grid(51200), stream=stream0)
# Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1))
buf7 = empty_strided_cuda((16, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_4.run(buf7, 16, grid=grid(16), stream=stream0)
buf8 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_8, out_relu9, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf7, buf6, primals_7, buf8, 82944, grid=grid(82944), stream=stream0)
# Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 64, 16, 16), (16384, 256, 16, 1))
buf10 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf9, primals_9, buf10, 82944, grid=grid(82944), stream=stream0)
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 3, 16, 16), (768, 256, 16, 1))
buf12 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
triton_poi_fused_convolution_7.run(buf12, primals_11, 3072, grid=grid(3072), stream=stream0)
del primals_11
buf13 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_8.run(buf9, primals_9, buf13, 65536, grid=grid(65536), stream=stream0)
del buf9
del primals_9
buf14 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_8, out_relu9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_9.run(buf6, primals_7, buf14, 16384, grid=grid(16384), stream=stream0)
del buf6
del primals_7
buf15 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_10.run(buf4, primals_5, buf15, 32768, grid=grid(32768), stream=stream0)
del buf4
del primals_5
buf16 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_11.run(buf1, primals_3, buf16, 8192, grid=grid(8192), stream=stream0)
del buf1
del primals_3
return (buf12, primals_2, primals_4, primals_6, primals_8, primals_10, buf0, buf2, buf3, buf5, buf7, buf8, buf10, buf13, buf14, buf15, buf16, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 256, 4, 4), (4096, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class decoder3(nn.Module):
def __init__(self):
super(decoder3, self).__init__()
self.reflecPad7 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv7 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu7 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad8 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv8 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu8 = nn.ReLU(inplace=True)
self.reflecPad9 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv9 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu9 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad10 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv10 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu10 = nn.ReLU(inplace=True)
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, x):
out = self.reflecPad7(x)
out = self.conv7(out)
out = self.relu7(out)
out = self.unpool(out)
out = self.reflecPad8(out)
out = self.conv8(out)
out = self.relu8(out)
out = self.reflecPad9(out)
out = self.conv9(out)
out_relu9 = self.relu9(out)
out = self.unpool2(out_relu9)
out = self.reflecPad10(out)
out = self.conv10(out)
out = self.relu10(out)
out = self.reflecPad11(out)
out = self.conv11(out)
return out
def get_inputs():
return [torch.rand([4, 256, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 10 % 10
x0 = xindex % 10
x4 = xindex // 100
x2 = xindex // 100 % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1
))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0
))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = xindex // 10 % 10
x4 = xindex // 100
x2 = xindex // 100 % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 82944
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 18 % 18
x0 = xindex % 18
x4 = xindex // 324
x2 = xindex // 324 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x1))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), xmask,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, xmask)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 82944
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 18
x1 = xindex // 18 % 18
x4 = xindex // 324
x2 = xindex // 324 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 3072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 256 % 3
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_8(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_11(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11) = args
args.clear()
assert_size_stride(primals_1, (4, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_2, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_3, (128,), (1,))
assert_size_stride(primals_4, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_7, (64,), (1,))
assert_size_stride(primals_8, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_9, (64,), (1,))
assert_size_stride(primals_10, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_11, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 256, 6, 6), (9216, 36, 6, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(36864)](primals_1, buf0,
36864, XBLOCK=256, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 128, 4, 4), (2048, 16, 4, 1))
buf2 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK
=8, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid
(51200)](buf2, buf1, primals_3, buf3, 51200, XBLOCK=256,
num_warps=4, num_stages=1)
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 128, 8, 8), (8192, 64, 8, 1))
buf5 = empty_strided_cuda((4, 128, 10, 10), (12800, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(51200)](buf4,
primals_5, buf5, 51200, XBLOCK=256, num_warps=4, num_stages=1)
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 64, 8, 8), (4096, 64, 8, 1))
buf7 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf7, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf8 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid
(82944)](buf7, buf6, primals_7, buf8, 82944, XBLOCK=512,
num_warps=8, num_stages=1)
buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 64, 16, 16), (16384, 256, 16, 1))
buf10 = empty_strided_cuda((4, 64, 18, 18), (20736, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(82944)](buf9,
primals_9, buf10, 82944, XBLOCK=512, num_warps=8, num_stages=1)
buf11 = extern_kernels.convolution(buf10, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 3, 16, 16), (768, 256, 16, 1))
buf12 = buf11
del buf11
triton_poi_fused_convolution_7[grid(3072)](buf12, primals_11, 3072,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_11
buf13 = empty_strided_cuda((4, 64, 16, 16), (16384, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_8[grid(65536)](
buf9, primals_9, buf13, 65536, XBLOCK=256, num_warps=4,
num_stages=1)
del buf9
del primals_9
buf14 = empty_strided_cuda((4, 64, 8, 8), (4096, 64, 8, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_9[grid(16384)](
buf6, primals_7, buf14, 16384, XBLOCK=128, num_warps=4,
num_stages=1)
del buf6
del primals_7
buf15 = empty_strided_cuda((4, 128, 8, 8), (8192, 64, 8, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_10[grid(32768)](
buf4, primals_5, buf15, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del buf4
del primals_5
buf16 = empty_strided_cuda((4, 128, 4, 4), (2048, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_11[grid(8192)](
buf1, primals_3, buf16, 8192, XBLOCK=128, num_warps=4, num_stages=1
)
del buf1
del primals_3
return (buf12, primals_2, primals_4, primals_6, primals_8, primals_10,
buf0, buf2, buf3, buf5, buf7, buf8, buf10, buf13, buf14, buf15, buf16)
class decoder3New(nn.Module):
def __init__(self):
super(decoder3New, self).__init__()
self.reflecPad7 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv7 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu7 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad8 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv8 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu8 = nn.ReLU(inplace=True)
self.reflecPad9 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv9 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu9 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad10 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv10 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu10 = nn.ReLU(inplace=True)
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, input_0):
primals_2 = self.conv7.weight
primals_3 = self.conv7.bias
primals_4 = self.conv8.weight
primals_5 = self.conv8.bias
primals_6 = self.conv9.weight
primals_7 = self.conv9.bias
primals_8 = self.conv10.weight
primals_9 = self.conv10.bias
primals_10 = self.conv11.weight
primals_11 = self.conv11.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11])
return output[0]
|
Holmes-Alan/RefVAE
|
decoder3
| false | 8,301 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
CMMD
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/f6/cf6zadwv7a5akbud7ttdbdc565ql6iiuysbozq7xwvkys7sxo7oz.py
# Topologically Sorted Source Nodes: [feat_v_1], Original ATen: [aten.linalg_vector_norm]
# Source node to ATen node mapping:
# feat_v_1 => pow_1, sum_1
# Graph fragment:
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2.0), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
triton_per_fused_linalg_vector_norm_0 = async_compile.triton('triton_per_fused_linalg_vector_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[4, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_linalg_vector_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 4
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(xmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/o6/co65xeotc4w3dat3ahcsyaw4klhv5mxlia7b5j6qfhr2a27ig27r.py
# Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
# mul => mul
# mul_1 => mul_1
# mul_2 => mul_2
# sum_1 => sum_3
# sum_2 => sum_4
# sum_3 => sum_5
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_2, %unsqueeze_3), kwargs = {})
# %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1]), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_4, %unsqueeze_5), kwargs = {})
# %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [-1]), kwargs = {})
triton_per_fused_mul_sum_1 = async_compile.triton('triton_per_fused_mul_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 64],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x0 = xindex % 4
x1 = (xindex // 4)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (r2 + (64*x0)), xmask, eviction_policy='evict_last', other=0.0)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp7 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr2 + (r2 + (64*x0)), xmask, eviction_policy='evict_last', other=0.0)
tmp17 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr2 + (r2 + (64*x1)), xmask, eviction_policy='evict_last', other=0.0)
tmp22 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp2 = libdevice.sqrt(tmp1)
tmp3 = 1e-12
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = tmp0 / tmp4
tmp8 = libdevice.sqrt(tmp7)
tmp9 = triton_helpers.maximum(tmp8, tmp3)
tmp10 = tmp6 / tmp9
tmp11 = tmp5 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(xmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp18 = libdevice.sqrt(tmp17)
tmp19 = triton_helpers.maximum(tmp18, tmp3)
tmp20 = tmp16 / tmp19
tmp23 = libdevice.sqrt(tmp22)
tmp24 = triton_helpers.maximum(tmp23, tmp3)
tmp25 = tmp21 / tmp24
tmp26 = tmp20 * tmp25
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
tmp29 = tl.where(xmask, tmp27, 0)
tmp30 = tl.sum(tmp29, 1)[:, None]
tmp31 = tmp5 * tmp25
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.where(xmask, tmp32, 0)
tmp35 = tl.sum(tmp34, 1)[:, None]
tl.store(out_ptr0 + (x3), tmp15, xmask)
tl.store(out_ptr1 + (x3), tmp30, xmask)
tl.store(out_ptr2 + (x3), tmp35, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ts/ctsxkk536uyp5fec3ryq3tilzzjb36yephn2kbrcplax6x4ngjxm.py
# Topologically Sorted Source Nodes: [res, mean, res_1, mean_1, add, res_2, mean_2, mul_3, sub, add_1, loss], Original ATen: [aten.pow, aten.mean, aten.add, aten.mul, aten.sub, aten.div]
# Source node to ATen node mapping:
# add => add
# add_1 => add_1
# loss => div_2
# mean => mean
# mean_1 => mean_1
# mean_2 => mean_2
# mul_3 => mul_3
# res => pow_5
# res_1 => pow_6
# res_2 => pow_7
# sub => sub
# Graph fragment:
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_3, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
# %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_4, 2), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_6,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
# %pow_7 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_5, 2), kwargs = {})
# %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_7,), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_2, 2), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mul_3), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub, 0), kwargs = {})
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 1), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_sub_2 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_sub_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_pow_sub_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mean_mul_pow_sub_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp5 = tl.load(in_ptr1 + (r0), None)
tmp10 = tl.load(in_ptr2 + (r0), None)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.sum(tmp2, 1)[:, None]
tmp6 = tmp5 * tmp5
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp11 = tmp10 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.sum(tmp12, 1)[:, None]
tmp15 = 16.0
tmp16 = tmp4 / tmp15
tmp17 = tmp9 / tmp15
tmp18 = tmp16 + tmp17
tmp19 = tmp14 / tmp15
tmp20 = 2.0
tmp21 = tmp19 * tmp20
tmp22 = tmp18 - tmp21
tmp23 = 0.0
tmp24 = tmp22 + tmp23
tmp25 = 1.0
tmp26 = tmp24 * tmp25
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp26, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
# Topologically Sorted Source Nodes: [feat_v_1], Original ATen: [aten.linalg_vector_norm]
stream0 = get_raw_stream(0)
triton_per_fused_linalg_vector_norm_0.run(arg0_1, buf0, 4, 64, grid=grid(4), stream=stream0)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
# Topologically Sorted Source Nodes: [feat_t_1], Original ATen: [aten.linalg_vector_norm]
triton_per_fused_linalg_vector_norm_0.run(arg1_1, buf1, 4, 64, grid=grid(4), stream=stream0)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, sum_1, mul_1, sum_2, mul_2, sum_3], Original ATen: [aten.mul, aten.sum]
triton_per_fused_mul_sum_1.run(arg0_1, buf0, arg1_1, buf1, buf2, buf4, buf6, 16, 64, grid=grid(16), stream=stream0)
del arg0_1
del arg1_1
del buf0
del buf1
buf3 = empty_strided_cuda((), (), torch.float32)
buf8 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [res, mean, res_1, mean_1, add, res_2, mean_2, mul_3, sub, add_1, loss], Original ATen: [aten.pow, aten.mean, aten.add, aten.mul, aten.sub, aten.div]
triton_per_fused_add_div_mean_mul_pow_sub_2.run(buf8, buf2, buf4, buf6, 1, 16, grid=grid(1), stream=stream0)
del buf2
del buf4
del buf6
return (buf8, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class CMMD(nn.Module):
def __init__(self, num_pos):
super(CMMD, self).__init__()
self.num_pos = num_pos
def forward(self, feat_v, feat_t):
feat_v = feat_v.view(feat_v.size(0), -1)
feat_v = F.normalize(feat_v, dim=-1)
feat_v_s = torch.split(feat_v, self.num_pos)
feat_t = feat_t.view(feat_t.size(0), -1)
feat_t = F.normalize(feat_t, dim=-1)
feat_t_s = torch.split(feat_t, self.num_pos)
losses = [self.mmd_loss(f_v, f_t) for f_v, f_t in zip(feat_v_s,
feat_t_s)]
loss = sum(losses) / len(losses)
return loss
def mmd_loss(self, f_v, f_t):
return self.poly_kernel(f_v, f_v).mean() + self.poly_kernel(f_t, f_t
).mean() - 2 * self.poly_kernel(f_v, f_t).mean()
def poly_kernel(self, a, b):
a = a.unsqueeze(0)
b = b.unsqueeze(1)
res = (a * b).sum(-1).pow(2)
return res
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'num_pos': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_linalg_vector_norm_0(in_ptr0, out_ptr0, xnumel, rnumel,
XBLOCK: tl.constexpr):
xnumel = 4
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.where(xmask, tmp2, 0)
tmp5 = tl.sum(tmp4, 1)[:, None]
tl.store(out_ptr0 + x0, tmp5, xmask)
@triton.jit
def triton_per_fused_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x0 = xindex % 4
x1 = xindex // 4
x3 = xindex
tmp0 = tl.load(in_ptr0 + (r2 + 64 * x0), xmask, eviction_policy=
'evict_last', other=0.0)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp7 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr2 + (r2 + 64 * x0), xmask, eviction_policy=
'evict_last', other=0.0)
tmp17 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr2 + (r2 + 64 * x1), xmask, eviction_policy=
'evict_last', other=0.0)
tmp22 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp2 = libdevice.sqrt(tmp1)
tmp3 = 1e-12
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = tmp0 / tmp4
tmp8 = libdevice.sqrt(tmp7)
tmp9 = triton_helpers.maximum(tmp8, tmp3)
tmp10 = tmp6 / tmp9
tmp11 = tmp5 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.where(xmask, tmp12, 0)
tmp15 = tl.sum(tmp14, 1)[:, None]
tmp18 = libdevice.sqrt(tmp17)
tmp19 = triton_helpers.maximum(tmp18, tmp3)
tmp20 = tmp16 / tmp19
tmp23 = libdevice.sqrt(tmp22)
tmp24 = triton_helpers.maximum(tmp23, tmp3)
tmp25 = tmp21 / tmp24
tmp26 = tmp20 * tmp25
tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
tmp29 = tl.where(xmask, tmp27, 0)
tmp30 = tl.sum(tmp29, 1)[:, None]
tmp31 = tmp5 * tmp25
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.where(xmask, tmp32, 0)
tmp35 = tl.sum(tmp34, 1)[:, None]
tl.store(out_ptr0 + x3, tmp15, xmask)
tl.store(out_ptr1 + x3, tmp30, xmask)
tl.store(out_ptr2 + x3, tmp35, xmask)
@triton.jit
def triton_per_fused_add_div_mean_mul_pow_sub_2(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp5 = tl.load(in_ptr1 + r0, None)
tmp10 = tl.load(in_ptr2 + r0, None)
tmp1 = tmp0 * tmp0
tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
tmp4 = tl.sum(tmp2, 1)[:, None]
tmp6 = tmp5 * tmp5
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp11 = tmp10 * tmp10
tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
tmp14 = tl.sum(tmp12, 1)[:, None]
tmp15 = 16.0
tmp16 = tmp4 / tmp15
tmp17 = tmp9 / tmp15
tmp18 = tmp16 + tmp17
tmp19 = tmp14 / tmp15
tmp20 = 2.0
tmp21 = tmp19 * tmp20
tmp22 = tmp18 - tmp21
tmp23 = 0.0
tmp24 = tmp22 + tmp23
tmp25 = 1.0
tmp26 = tmp24 * tmp25
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
get_raw_stream(0)
triton_per_fused_linalg_vector_norm_0[grid(4)](arg0_1, buf0, 4, 64,
XBLOCK=1, num_warps=2, num_stages=1)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
triton_per_fused_linalg_vector_norm_0[grid(4)](arg1_1, buf1, 4, 64,
XBLOCK=1, num_warps=2, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_per_fused_mul_sum_1[grid(16)](arg0_1, buf0, arg1_1, buf1,
buf2, buf4, buf6, 16, 64, XBLOCK=8, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
del buf0
del buf1
buf3 = empty_strided_cuda((), (), torch.float32)
buf8 = buf3
del buf3
triton_per_fused_add_div_mean_mul_pow_sub_2[grid(1)](buf8, buf2,
buf4, buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
del buf2
del buf4
del buf6
return buf8,
class CMMDNew(nn.Module):
def __init__(self, num_pos):
super(CMMDNew, self).__init__()
self.num_pos = num_pos
def mmd_loss(self, f_v, f_t):
return self.poly_kernel(f_v, f_v).mean() + self.poly_kernel(f_t, f_t
).mean() - 2 * self.poly_kernel(f_v, f_t).mean()
def poly_kernel(self, a, b):
a = a.unsqueeze(0)
b = b.unsqueeze(1)
res = (a * b).sum(-1).pow(2)
return res
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
JDAI-CV/CM-NAS
|
CMMD
| false | 8,302 |
[
"Apache-2.0"
] | 31 |
bbc77f427b2c8afb9f3865f5a04e86079d33dd28
|
https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28
|
qy
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/jf/cjfr4sfsrvydqmccowtorseagqzcsymbondn73vnpv23l35q756r.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x_1 => cat_1
# Graph fragment:
# %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%cat, %view_2], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 5, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1], 4, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tmp6 & tmp4
tmp8 = tl.load(in_ptr0 + ((4*(x1 // 4)) + x0), tmp7 & xmask, eviction_policy='evict_last', other=0.0)
tmp9 = 0.0
tmp10 = tmp8 > tmp9
tmp11 = 0.01
tmp12 = tmp8 * tmp11
tmp13 = tl.where(tmp10, tmp8, tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp7, tmp13, tmp14)
tmp16 = tmp0 >= tmp5
tmp17 = tmp16 & tmp4
tmp18 = tl.load(in_ptr1 + (x1), tmp17 & xmask, eviction_policy='evict_last', other=0.0)
tmp19 = tl.where(tmp6, tmp15, tmp18)
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp4, tmp19, tmp20)
tmp22 = tmp0 >= tmp3
tmp23 = tl.full([1], 6, tl.int64)
tmp24 = tmp0 < tmp23
tmp25 = tl.load(in_ptr2 + (x1), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
tmp26 = tl.where(tmp4, tmp21, tmp25)
tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bh/cbhiwirufn4xgjlassc2kuz5i566zrr37i3sefu2ywwzuzkthdsl.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
# x_3 => gt_1, mul_1, where_1
# Graph fragment:
# %add_tensor_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_5), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%add_tensor_2, 0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor_2, 0.01), kwargs = {})
# %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %add_tensor_2, %mul_1), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + (x2), tmp7, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qd/cqdja7yao6mcn2im3ot77wimzd5degbo24upnzgxqbv2njv5uffs.py
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.cos]
# Source node to ATen node mapping:
# x_9 => cos
# Graph fragment:
# %cos : [num_users=2] = call_function[target=torch.ops.aten.cos.default](args = (%addmm_3,), kwargs = {})
triton_poi_fused_cos_2 = async_compile.triton('triton_poi_fused_cos_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cos_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cos_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), None)
tmp1 = tl_math.cos(tmp0)
tl.store(out_ptr0 + (x0), tmp1, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/vw/cvwcjxztifu7pseky77dsket6nm7izi3xqkbtqtxa6b7we5vdmtx.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
# h => gt, mul, where
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_1, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %primals_1, %mul), kwargs = {})
# %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %where), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_leaky_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_3', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr1 + (x0), tmp5, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (512, 6), (6, 1))
assert_size_stride(primals_5, (512, ), (1, ))
assert_size_stride(primals_6, (512, 512), (512, 1))
assert_size_stride(primals_7, (512, ), (1, ))
assert_size_stride(primals_8, (512, 512), (512, 1))
assert_size_stride(primals_9, (512, ), (1, ))
assert_size_stride(primals_10, (256, 512), (512, 1))
assert_size_stride(primals_11, (256, ), (1, ))
assert_size_stride(primals_12, (4, 256), (256, 1))
assert_size_stride(primals_13, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6), (6, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_3, primals_2, buf0, 96, grid=grid(96), stream=stream0)
del primals_2
del primals_3
buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (6, 512), (1, 6), 0), out=buf1)
del primals_4
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf2, primals_5, 8192, grid=grid(8192), stream=stream0)
del primals_5
buf3 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_6, (512, 512), (1, 512), 0), out=buf3)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf4, primals_7, 8192, grid=grid(8192), stream=stream0)
del primals_7
buf5 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf4, reinterpret_tensor(primals_8, (512, 512), (1, 512), 0), out=buf5)
buf6 = buf5; del buf5 # reuse
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_1.run(buf6, primals_9, 8192, grid=grid(8192), stream=stream0)
del primals_9
buf7 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_11, buf6, reinterpret_tensor(primals_10, (512, 256), (1, 512), 0), alpha=1, beta=1, out=buf7)
del primals_11
buf8 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.cos]
triton_poi_fused_cos_2.run(buf7, buf8, 4096, grid=grid(4096), stream=stream0)
buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(primals_12, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf9)
del primals_13
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.leaky_relu]
triton_poi_fused_leaky_relu_3.run(primals_1, primals_1, 16, grid=grid(16), stream=stream0)
del primals_1
return (reinterpret_tensor(buf9, (16, 2), (4, 1), 0), reinterpret_tensor(buf9, (16, 2), (4, 1), 2), buf9, buf0, buf2, buf4, buf6, buf7, buf8, primals_12, primals_10, primals_8, primals_6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((512, 6), (6, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((256, 512), (512, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 256), (256, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
try:
nn.init.constant_(m.bias, 0.01)
except:
pass
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
nn.init.constant_(m.bias, 0.01)
class Generator(nn.Module):
def __init__(self, input_dim=8, output_dim=2):
super(Generator, self).__init__()
self.linear1 = nn.Linear(input_dim, 512)
self.linear2 = nn.Linear(512, 512)
self.linear3 = nn.Linear(512, 512)
self.linear4 = nn.Linear(512, 256)
self.linear5 = nn.Linear(256, output_dim)
self.apply(weights_init)
def forward(self, condition, v0, t):
x = torch.cat([condition, v0], dim=1)
x = torch.cat([x, t], dim=1)
x = self.linear1(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear2(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear3(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear4(x)
x = torch.cos(x)
x = self.linear5(x)
return x
class qy(nn.Module):
def __init__(self, zy_dim):
super(qy, self).__init__()
self.trajectory_generation = Generator(input_dim=1 + 1 + zy_dim,
output_dim=4)
def forward(self, zy, v0, t):
h = F.leaky_relu(zy, inplace=True)
condition = h.unsqueeze(1)
condition = condition.expand(h.shape[0], t.shape[-1], h.shape[-1])
condition = condition.reshape(h.shape[0] * t.shape[-1], h.shape[-1])
output = self.trajectory_generation(condition, v0.view(-1, 1), t.
view(-1, 1))
output_xy = output[:, :2]
logvar = output[:, 2:]
return output_xy, logvar
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'zy_dim': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 96
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 5, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.full([1], 4, tl.int64)
tmp6 = tmp0 < tmp5
tmp7 = tmp6 & tmp4
tmp8 = tl.load(in_ptr0 + (4 * (x1 // 4) + x0), tmp7 & xmask,
eviction_policy='evict_last', other=0.0)
tmp9 = 0.0
tmp10 = tmp8 > tmp9
tmp11 = 0.01
tmp12 = tmp8 * tmp11
tmp13 = tl.where(tmp10, tmp8, tmp12)
tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
tmp15 = tl.where(tmp7, tmp13, tmp14)
tmp16 = tmp0 >= tmp5
tmp17 = tmp16 & tmp4
tmp18 = tl.load(in_ptr1 + x1, tmp17 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp19 = tl.where(tmp6, tmp15, tmp18)
tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
tmp21 = tl.where(tmp4, tmp19, tmp20)
tmp22 = tmp0 >= tmp3
tl.full([1], 6, tl.int64)
tmp25 = tl.load(in_ptr2 + x1, tmp22 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp26 = tl.where(tmp4, tmp21, tmp25)
tl.store(out_ptr0 + x2, tmp26, xmask)
@triton.jit
def triton_poi_fused_leaky_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 512
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.01
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(in_out_ptr0 + x2, tmp7, None)
@triton.jit
def triton_poi_fused_cos_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, None)
tmp1 = tl_math.cos(tmp0)
tl.store(out_ptr0 + x0, tmp1, None)
@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, out_ptr1, xnumel, XBLOCK: tl.
constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tl.store(out_ptr1 + x0, tmp5, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (512, 6), (6, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (512, 512), (512, 1))
assert_size_stride(primals_7, (512,), (1,))
assert_size_stride(primals_8, (512, 512), (512, 1))
assert_size_stride(primals_9, (512,), (1,))
assert_size_stride(primals_10, (256, 512), (512, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (4, 256), (256, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 6), (6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(96)](primals_1, primals_3, primals_2,
buf0, 96, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
del primals_3
buf1 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf0, reinterpret_tensor(primals_4, (6, 512), (1,
6), 0), out=buf1)
del primals_4
buf2 = buf1
del buf1
triton_poi_fused_leaky_relu_1[grid(8192)](buf2, primals_5, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf3 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_6, (512, 512), (
1, 512), 0), out=buf3)
buf4 = buf3
del buf3
triton_poi_fused_leaky_relu_1[grid(8192)](buf4, primals_7, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
buf5 = empty_strided_cuda((16, 512), (512, 1), torch.float32)
extern_kernels.mm(buf4, reinterpret_tensor(primals_8, (512, 512), (
1, 512), 0), out=buf5)
buf6 = buf5
del buf5
triton_poi_fused_leaky_relu_1[grid(8192)](buf6, primals_9, 8192,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_9
buf7 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
extern_kernels.addmm(primals_11, buf6, reinterpret_tensor(
primals_10, (512, 256), (1, 512), 0), alpha=1, beta=1, out=buf7)
del primals_11
buf8 = empty_strided_cuda((16, 256), (256, 1), torch.float32)
triton_poi_fused_cos_2[grid(4096)](buf7, buf8, 4096, XBLOCK=128,
num_warps=4, num_stages=1)
buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_13, buf8, reinterpret_tensor(
primals_12, (256, 4), (1, 256), 0), alpha=1, beta=1, out=buf9)
del primals_13
triton_poi_fused_leaky_relu_3[grid(16)](primals_1, primals_1, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_1
return (reinterpret_tensor(buf9, (16, 2), (4, 1), 0),
reinterpret_tensor(buf9, (16, 2), (4, 1), 2), buf9, buf0, buf2,
buf4, buf6, buf7, buf8, primals_12, primals_10, primals_8, primals_6)
def weights_init(m):
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
try:
nn.init.constant_(m.bias, 0.01)
except:
pass
if isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight, a=0, mode='fan_in', nonlinearity
='leaky_relu')
nn.init.constant_(m.bias, 0.01)
class Generator(nn.Module):
def __init__(self, input_dim=8, output_dim=2):
super(Generator, self).__init__()
self.linear1 = nn.Linear(input_dim, 512)
self.linear2 = nn.Linear(512, 512)
self.linear3 = nn.Linear(512, 512)
self.linear4 = nn.Linear(512, 256)
self.linear5 = nn.Linear(256, output_dim)
self.apply(weights_init)
def forward(self, condition, v0, t):
x = torch.cat([condition, v0], dim=1)
x = torch.cat([x, t], dim=1)
x = self.linear1(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear2(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear3(x)
x = F.leaky_relu(x, inplace=True)
x = self.linear4(x)
x = torch.cos(x)
x = self.linear5(x)
return x
class qyNew(nn.Module):
def __init__(self, zy_dim):
super(qyNew, self).__init__()
self.trajectory_generation = Generator(input_dim=1 + 1 + zy_dim,
output_dim=4)
def forward(self, input_0, input_1, input_2):
primals_4 = self.trajectory_generation.linear1.weight
primals_5 = self.trajectory_generation.linear1.bias
primals_6 = self.trajectory_generation.linear2.weight
primals_7 = self.trajectory_generation.linear2.bias
primals_8 = self.trajectory_generation.linear3.weight
primals_9 = self.trajectory_generation.linear3.bias
primals_10 = self.trajectory_generation.linear4.weight
primals_11 = self.trajectory_generation.linear4.bias
primals_12 = self.trajectory_generation.linear5.weight
primals_13 = self.trajectory_generation.linear5.bias
primals_1 = input_0
primals_2 = input_1
primals_3 = input_2
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0], output[1]
|
IamWangYunKai/DG-TrajGen
|
qy
| false | 8,303 |
[
"MIT"
] | 31 |
0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
https://github.com/IamWangYunKai/DG-TrajGen/tree/0a8aab7e1c05111a5afe43d53801c55942e9ff56
|
decoder6
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 73728
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), None, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/wz/cwzjmrc2v3gwplve567c7534qfatyge6ptdqpizbsvkvvkqb6hmk.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16384
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 256
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/na/cna65n7bsuxz7ikiyyizb5dl3lo5l7y55o5a5potmxasqkc3gxn2.py
# Topologically Sorted Source Nodes: [conv_transpose2d, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# conv_transpose2d => convolution_1
# out_3 => relu_1
# out_4 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 102400
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = (xindex // 10) % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zc/czc6gwfowtr36eg2ru6ub5lnatitketu6t2t7b6mk3op2d7xj6ws.py
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# out_14 => convolution_5
# out_15 => relu_5
# Graph fragment:
# %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_9, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 128
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/vf/cvfoka5u5rqb3jnugnwjceihmurqgr46klczdmd2w3s3v3ovzl67.py
# Topologically Sorted Source Nodes: [conv_transpose2d_1, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# conv_transpose2d_1 => convolution_6
# out_16 => relu_6
# out_17 => _unsafe_index_10, _unsafe_index_11
# Graph fragment:
# %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_14, %primals_15, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
# %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_6,), kwargs = {})
# %_unsafe_index_10 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_6, [None, None, %sub_21, None]), kwargs = {})
# %_unsafe_index_11 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_10, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_4 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 165888
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = (xindex // 18) % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nu/cnuwumvnccleyvosfzjqcporzq6n5s7wvej7arpzw3vr2odxax2k.py
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# out_21 => convolution_8
# out_22 => relu_8
# Graph fragment:
# %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
triton_poi_fused_convolution_relu_5 = async_compile.triton('triton_poi_fused_convolution_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 64
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ef/cefecpvsbbsvl567igo6d7ekq553wgpz427ebipswmvgccpb2rem.py
# Topologically Sorted Source Nodes: [conv_transpose2d_2, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# conv_transpose2d_2 => convolution_9
# out_23 => relu_9
# out_24 => _unsafe_index_14, _unsafe_index_15
# Graph fragment:
# %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_8, %primals_20, %primals_21, [2, 2], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
# %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
# %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %sub_29, None]), kwargs = {})
# %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_29]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = (xindex // 34) % 34
x4 = (xindex // 1156)
x2 = (xindex // 1156) % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024*x4)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/a2/ca2fnx2kunjmiygzcraylypvf3srkbtj3234had4vdberjxep4cn.py
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# out_28 => convolution_11
# Graph fragment:
# %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_7 = async_compile.triton('triton_poi_fused_convolution_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16384],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 12288
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 3
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/v5/cv5tlekkioox7c4mxm6jhnv3z2cq5lr2g26dzli4ta2geffjgmtj.py
# Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_25 => convolution_10
# out_26 => relu_10
# Graph fragment:
# %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_15, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
# %le_18 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_8 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_8(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 262144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/n6/cn6viaktgazpeszmywhi45uqavrfdu6nwh6qiqlzpdv4pm2tjqph.py
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_18 => convolution_7
# out_19 => relu_7
# Graph fragment:
# %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_11, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
# %le_57 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_9 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ga/cgajxwvlesefbfuscqbx6mpkz7iwpzyxdzmeus2pslshflqvqgrj.py
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_11 => convolution_4
# out_12 => relu_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_7, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
# %le_96 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_10 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 256
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25 = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (256, ), (1, ))
assert_size_stride(primals_4, (256, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_5, (256, ), (1, ))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256, ), (1, ))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256, ), (1, ))
assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (256, ), (1, ))
assert_size_stride(primals_12, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (128, ), (1, ))
assert_size_stride(primals_14, (128, 128, 4, 4), (2048, 16, 4, 1))
assert_size_stride(primals_15, (128, ), (1, ))
assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_17, (128, ), (1, ))
assert_size_stride(primals_18, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_19, (64, ), (1, ))
assert_size_stride(primals_20, (64, 64, 4, 4), (1024, 16, 4, 1))
assert_size_stride(primals_21, (64, ), (1, ))
assert_size_stride(primals_22, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_23, (64, ), (1, ))
assert_size_stride(primals_24, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_25, (3, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf2, primals_3, 16384, grid=grid(16384), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 8, 8), (16384, 64, 8, 1))
buf4 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv_transpose2d, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_2.run(buf3, primals_5, buf4, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 256, 8, 8), (16384, 64, 8, 1))
buf6 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_2.run(buf5, primals_7, buf6, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 256, 8, 8), (16384, 64, 8, 1))
buf8 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_2.run(buf7, primals_9, buf8, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
buf9 = extern_kernels.convolution(buf8, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 256, 8, 8), (16384, 64, 8, 1))
buf10 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_2.run(buf9, primals_11, buf10, 102400, grid=grid(102400), stream=stream0)
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
buf11 = extern_kernels.convolution(buf10, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 128, 8, 8), (8192, 64, 8, 1))
buf12 = buf11; del buf11 # reuse
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_3.run(buf12, primals_13, 32768, grid=grid(32768), stream=stream0)
del primals_13
# Topologically Sorted Source Nodes: [conv_transpose2d_1], Original ATen: [aten.convolution]
buf13 = extern_kernels.convolution(buf12, primals_14, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv_transpose2d_1, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_4.run(buf13, primals_15, buf14, 165888, grid=grid(165888), stream=stream0)
# Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.convolution]
buf15 = extern_kernels.convolution(buf14, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 128, 16, 16), (32768, 256, 16, 1))
buf16 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_4.run(buf15, primals_17, buf16, 165888, grid=grid(165888), stream=stream0)
# Topologically Sorted Source Nodes: [out_21], Original ATen: [aten.convolution]
buf17 = extern_kernels.convolution(buf16, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 64, 16, 16), (16384, 256, 16, 1))
buf18 = buf17; del buf17 # reuse
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_5.run(buf18, primals_19, 65536, grid=grid(65536), stream=stream0)
del primals_19
# Topologically Sorted Source Nodes: [conv_transpose2d_2], Original ATen: [aten.convolution]
buf19 = extern_kernels.convolution(buf18, primals_20, stride=(2, 2), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf20 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv_transpose2d_2, out_23, out_24], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf19, primals_21, buf20, 295936, grid=grid(295936), stream=stream0)
# Topologically Sorted Source Nodes: [out_25], Original ATen: [aten.convolution]
buf21 = extern_kernels.convolution(buf20, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf22 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_25, out_26, out_27], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf21, primals_23, buf22, 295936, grid=grid(295936), stream=stream0)
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
buf23 = extern_kernels.convolution(buf22, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf23, (4, 3, 32, 32), (3072, 1024, 32, 1))
buf24 = buf23; del buf23 # reuse
# Topologically Sorted Source Nodes: [out_28], Original ATen: [aten.convolution]
triton_poi_fused_convolution_7.run(buf24, primals_25, 12288, grid=grid(12288), stream=stream0)
del primals_25
buf25 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_8.run(buf21, primals_23, buf25, 262144, grid=grid(262144), stream=stream0)
del buf21
del primals_23
buf26 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
# Topologically Sorted Source Nodes: [conv_transpose2d_2, out_23], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_8.run(buf19, primals_21, buf26, 262144, grid=grid(262144), stream=stream0)
del buf19
del primals_21
buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_9.run(buf15, primals_17, buf27, 131072, grid=grid(131072), stream=stream0)
del buf15
del primals_17
buf28 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [conv_transpose2d_1, out_16], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_9.run(buf13, primals_15, buf28, 131072, grid=grid(131072), stream=stream0)
del buf13
del primals_15
buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_10.run(buf9, primals_11, buf29, 65536, grid=grid(65536), stream=stream0)
del buf9
del primals_11
buf30 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_10.run(buf7, primals_9, buf30, 65536, grid=grid(65536), stream=stream0)
del buf7
del primals_9
buf31 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_10.run(buf5, primals_7, buf31, 65536, grid=grid(65536), stream=stream0)
del buf5
del primals_7
buf32 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [conv_transpose2d, out_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_10.run(buf3, primals_5, buf32, 65536, grid=grid(65536), stream=stream0)
del buf3
del primals_5
return (buf24, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, buf0, buf2, buf4, buf6, buf8, buf10, buf12, buf14, buf16, buf18, buf20, buf22, buf25, buf26, buf27, buf28, buf29, buf30, buf31, buf32, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((256, 256, 4, 4), (4096, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((128, 128, 4, 4), (2048, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_20 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_21 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_22 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_24 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_25 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class decoder6(nn.Module):
def __init__(self):
super(decoder6, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool1 = nn.ConvTranspose2d(256, 256, 4, 2, 1)
self.act1 = nn.ReLU()
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu12 = nn.ReLU(inplace=True)
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu13 = nn.ReLU(inplace=True)
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu14 = nn.ReLU(inplace=True)
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool2 = nn.ConvTranspose2d(128, 128, 4, 2, 1)
self.act2 = nn.ReLU()
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.unpool3 = nn.ConvTranspose2d(64, 64, 4, 2, 1)
self.act3 = nn.ReLU()
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, x):
out = self.reflecPad11(x)
out = self.conv11(out)
out = self.relu11(out)
out = self.act1(self.unpool1(out))
out = self.reflecPad12(out)
out = self.conv12(out)
out = self.relu12(out)
out = self.reflecPad13(out)
out = self.conv13(out)
out = self.relu13(out)
out = self.reflecPad14(out)
out = self.conv14(out)
out = self.relu14(out)
out = self.reflecPad15(out)
out = self.conv15(out)
out = self.relu15(out)
out = self.act2(self.unpool2(out))
out = self.reflecPad16(out)
out = self.conv16(out)
out = self.relu16(out)
out = self.reflecPad17(out)
out = self.conv17(out)
out = self.relu17(out)
out = self.act3(self.unpool3(out))
out = self.reflecPad18(out)
out = self.conv18(out)
out = self.relu18(out)
out = self.reflecPad19(out)
out = self.conv19(out)
return out
def get_inputs():
return [torch.rand([4, 512, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 256
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = xindex // 10 % 10
x4 = xindex // 100
x2 = xindex // 100 % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 128
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_4(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = xindex // 18 % 18
x4 = xindex // 324
x2 = xindex // 324 % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 295936
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 34
x1 = xindex // 34 % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 3
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_8(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_10(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25) = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (256,), (1,))
assert_size_stride(primals_4, (256, 256, 4, 4), (4096, 16, 4, 1))
assert_size_stride(primals_5, (256,), (1,))
assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_7, (256,), (1,))
assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_9, (256,), (1,))
assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (128,), (1,))
assert_size_stride(primals_14, (128, 128, 4, 4), (2048, 16, 4, 1))
assert_size_stride(primals_15, (128,), (1,))
assert_size_stride(primals_16, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_17, (128,), (1,))
assert_size_stride(primals_18, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_19, (64,), (1,))
assert_size_stride(primals_20, (64, 64, 4, 4), (1024, 16, 4, 1))
assert_size_stride(primals_21, (64,), (1,))
assert_size_stride(primals_22, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_23, (64,), (1,))
assert_size_stride(primals_24, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_25, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0,
73728, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 256, 4, 4), (4096, 16, 4, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_relu_1[grid(16384)](buf2, primals_3,
16384, XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 256, 8, 8), (16384, 64, 8, 1))
buf4 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_2[grid(102400)](buf3
, primals_5, buf4, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf5, (4, 256, 8, 8), (16384, 64, 8, 1))
buf6 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_2[grid(102400)](buf5
, primals_7, buf6, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf7 = extern_kernels.convolution(buf6, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 256, 8, 8), (16384, 64, 8, 1))
buf8 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_2[grid(102400)](buf7
, primals_9, buf8, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf9 = extern_kernels.convolution(buf8, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf9, (4, 256, 8, 8), (16384, 64, 8, 1))
buf10 = empty_strided_cuda((4, 256, 10, 10), (25600, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_2[grid(102400)](buf9
, primals_11, buf10, 102400, XBLOCK=512, num_warps=8, num_stages=1)
buf11 = extern_kernels.convolution(buf10, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf11, (4, 128, 8, 8), (8192, 64, 8, 1))
buf12 = buf11
del buf11
triton_poi_fused_convolution_relu_3[grid(32768)](buf12, primals_13,
32768, XBLOCK=256, num_warps=4, num_stages=1)
del primals_13
buf13 = extern_kernels.convolution(buf12, primals_14, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 128, 16, 16), (32768, 256, 16, 1))
buf14 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_4[grid(165888)](
buf13, primals_15, buf14, 165888, XBLOCK=1024, num_warps=4,
num_stages=1)
buf15 = extern_kernels.convolution(buf14, primals_16, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 128, 16, 16), (32768, 256, 16, 1))
buf16 = empty_strided_cuda((4, 128, 18, 18), (41472, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_4[grid(165888)](
buf15, primals_17, buf16, 165888, XBLOCK=1024, num_warps=4,
num_stages=1)
buf17 = extern_kernels.convolution(buf16, primals_18, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 64, 16, 16), (16384, 256, 16, 1))
buf18 = buf17
del buf17
triton_poi_fused_convolution_relu_5[grid(65536)](buf18, primals_19,
65536, XBLOCK=512, num_warps=4, num_stages=1)
del primals_19
buf19 = extern_kernels.convolution(buf18, primals_20, stride=(2, 2),
padding=(1, 1), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf20 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(295936)](
buf19, primals_21, buf20, 295936, XBLOCK=1024, num_warps=4,
num_stages=1)
buf21 = extern_kernels.convolution(buf20, primals_22, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf21, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf22 = empty_strided_cuda((4, 64, 34, 34), (73984, 1156, 34, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(295936)](
buf21, primals_23, buf22, 295936, XBLOCK=1024, num_warps=4,
num_stages=1)
buf23 = extern_kernels.convolution(buf22, primals_24, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf23, (4, 3, 32, 32), (3072, 1024, 32, 1))
buf24 = buf23
del buf23
triton_poi_fused_convolution_7[grid(12288)](buf24, primals_25,
12288, XBLOCK=256, num_warps=4, num_stages=1)
del primals_25
buf25 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_8[grid(262144)](
buf21, primals_23, buf25, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf21
del primals_23
buf26 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_8[grid(262144)](
buf19, primals_21, buf26, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf19
del primals_21
buf27 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_9[grid(131072)](
buf15, primals_17, buf27, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf15
del primals_17
buf28 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_9[grid(131072)](
buf13, primals_15, buf28, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf13
del primals_15
buf29 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_10[grid(65536)](
buf9, primals_11, buf29, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf9
del primals_11
buf30 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_10[grid(65536)](
buf7, primals_9, buf30, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf7
del primals_9
buf31 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_10[grid(65536)](
buf5, primals_7, buf31, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf5
del primals_7
buf32 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_10[grid(65536)](
buf3, primals_5, buf32, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf3
del primals_5
return (buf24, primals_2, primals_4, primals_6, primals_8, primals_10,
primals_12, primals_14, primals_16, primals_18, primals_20,
primals_22, primals_24, buf0, buf2, buf4, buf6, buf8, buf10, buf12,
buf14, buf16, buf18, buf20, buf22, buf25, buf26, buf27, buf28,
buf29, buf30, buf31, buf32)
class decoder6New(nn.Module):
def __init__(self):
super(decoder6New, self).__init__()
self.reflecPad11 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv11 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu11 = nn.ReLU(inplace=True)
self.unpool1 = nn.ConvTranspose2d(256, 256, 4, 2, 1)
self.act1 = nn.ReLU()
self.reflecPad12 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv12 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu12 = nn.ReLU(inplace=True)
self.reflecPad13 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv13 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu13 = nn.ReLU(inplace=True)
self.reflecPad14 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv14 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu14 = nn.ReLU(inplace=True)
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool2 = nn.ConvTranspose2d(128, 128, 4, 2, 1)
self.act2 = nn.ReLU()
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.unpool3 = nn.ConvTranspose2d(64, 64, 4, 2, 1)
self.act3 = nn.ReLU()
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, input_0):
primals_2 = self.conv11.weight
primals_3 = self.conv11.bias
primals_4 = self.unpool1.weight
primals_5 = self.unpool1.bias
primals_6 = self.conv12.weight
primals_7 = self.conv12.bias
primals_8 = self.conv13.weight
primals_9 = self.conv13.bias
primals_10 = self.conv14.weight
primals_11 = self.conv14.bias
primals_12 = self.conv15.weight
primals_13 = self.conv15.bias
primals_14 = self.unpool2.weight
primals_15 = self.unpool2.bias
primals_16 = self.conv16.weight
primals_17 = self.conv16.bias
primals_18 = self.conv17.weight
primals_19 = self.conv17.bias
primals_20 = self.unpool3.weight
primals_21 = self.unpool3.bias
primals_22 = self.conv18.weight
primals_23 = self.conv18.bias
primals_24 = self.conv19.weight
primals_25 = self.conv19.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25])
return output[0]
|
Holmes-Alan/RefVAE
|
decoder6
| false | 8,304 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
SP
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/xy/cxyhbkbrrxia6m4o2z5ejl3hooqt4dfspygx3pbzicxpdf4yzsef.py
# Topologically Sorted Source Nodes: [norm_G_v, norm_G_t, loss], Original ATen: [aten.div, aten.mse_loss]
# Source node to ATen node mapping:
# loss => mean, pow_5, sub
# norm_G_t => div_1
# norm_G_v => div
# Graph fragment:
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, %expand), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm_1, %expand_1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %div_1), kwargs = {})
# %pow_5 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_5,), kwargs = {})
triton_per_fused_div_mse_loss_0 = async_compile.triton('triton_per_fused_div_mse_loss_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 16],
reduction_hint=ReductionHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mse_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 10, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r1 = (rindex // 4)
tmp0 = tl.load(in_ptr0 + (r2), None)
tmp1 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + (r2), None)
tmp17 = tl.load(in_ptr1 + (4*r1), None, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (1 + (4*r1)), None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (2 + (4*r1)), None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (3 + (4*r1)), None, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 - tmp30
tmp32 = tmp31 * tmp31
tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
tmp35 = tl.sum(tmp33, 1)[:, None]
tmp36 = 16.0
tmp37 = tmp35 / tmp36
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp37, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [G_v], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 64), (64, 1), 0), reinterpret_tensor(arg0_1, (64, 4), (1, 64), 0), out=buf0)
del arg0_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [G_t], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0), reinterpret_tensor(arg1_1, (64, 4), (1, 64), 0), out=buf1)
del arg1_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [norm_G_v, norm_G_t, loss], Original ATen: [aten.div, aten.mse_loss]
stream0 = get_raw_stream(0)
triton_per_fused_div_mse_loss_0.run(buf4, buf0, buf1, 1, 16, grid=grid(1), stream=stream0)
del buf0
del buf1
return (buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class SP(nn.Module):
def __init__(self):
super(SP, self).__init__()
def forward(self, feat_v, feat_t):
feat_v = feat_v.view(feat_v.size(0), -1)
G_v = torch.mm(feat_v, feat_v.t())
norm_G_v = F.normalize(G_v, p=2, dim=1)
feat_t = feat_t.view(feat_t.size(0), -1)
G_t = torch.mm(feat_t, feat_t.t())
norm_G_t = F.normalize(G_t, p=2, dim=1)
loss = F.mse_loss(norm_G_v, norm_G_t)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_div_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r1 = rindex // 4
tmp0 = tl.load(in_ptr0 + r2, None)
tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
tmp16 = tl.load(in_ptr1 + r2, None)
tmp17 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
tmp22 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
tmp2 = tmp1 * tmp1
tmp4 = tmp3 * tmp3
tmp5 = tmp2 + tmp4
tmp7 = tmp6 * tmp6
tmp8 = tmp5 + tmp7
tmp10 = tmp9 * tmp9
tmp11 = tmp8 + tmp10
tmp12 = libdevice.sqrt(tmp11)
tmp13 = 1e-12
tmp14 = triton_helpers.maximum(tmp12, tmp13)
tmp15 = tmp0 / tmp14
tmp18 = tmp17 * tmp17
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = libdevice.sqrt(tmp27)
tmp29 = triton_helpers.maximum(tmp28, tmp13)
tmp30 = tmp16 / tmp29
tmp31 = tmp15 - tmp30
tmp32 = tmp31 * tmp31
tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
tmp35 = tl.sum(tmp33, 1)[:, None]
tmp36 = 16.0
tmp37 = tmp35 / tmp36
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp37, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 64), (64, 1), 0),
reinterpret_tensor(arg0_1, (64, 4), (1, 64), 0), out=buf0)
del arg0_1
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(arg1_1, (4, 64), (64, 1), 0),
reinterpret_tensor(arg1_1, (64, 4), (1, 64), 0), out=buf1)
del arg1_1
buf3 = empty_strided_cuda((), (), torch.float32)
buf4 = buf3
del buf3
get_raw_stream(0)
triton_per_fused_div_mse_loss_0[grid(1)](buf4, buf0, buf1, 1, 16,
XBLOCK=1, num_warps=2, num_stages=1)
del buf0
del buf1
return buf4,
class SPNew(nn.Module):
def __init__(self):
super(SPNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
JDAI-CV/CM-NAS
|
SP
| false | 8,305 |
[
"Apache-2.0"
] | 31 |
bbc77f427b2c8afb9f3865f5a04e86079d33dd28
|
https://github.com/JDAI-CV/CM-NAS/tree/bbc77f427b2c8afb9f3865f5a04e86079d33dd28
|
PSNR
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/qc/cqcc74nwqd2g2mwv6rqouvlsqfvus3f6l5sou7akewum3z4cdkxj.py
# Topologically Sorted Source Nodes: [mse, add, log10, mul], Original ATen: [aten.mse_loss, aten.add, aten.log10, aten.mul]
# Source node to ATen node mapping:
# add => add
# log10 => log10
# mse => mean, pow_1, sub
# mul => mul
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 1e-12), kwargs = {})
# %log10 : [num_users=1] = call_function[target=torch.ops.aten.log10.default](args = (%add,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%log10, -10), kwargs = {})
triton_per_fused_add_log10_mse_loss_mul_0 = async_compile.triton('triton_per_fused_add_log10_mse_loss_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_log10_mse_loss_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_log10_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.load(in_ptr1 + (r0), None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tmp9 = 1e-12
tmp10 = tmp8 + tmp9
tmp11 = libdevice.log10(tmp10)
tmp12 = -10.0
tmp13 = tmp11 * tmp12
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp13, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [mse, add, log10, mul], Original ATen: [aten.mse_loss, aten.add, aten.log10, aten.mul]
stream0 = get_raw_stream(0)
triton_per_fused_add_log10_mse_loss_mul_0.run(buf1, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.utils.data
class PSNR(th.nn.Module):
def __init__(self):
super(PSNR, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, out, ref):
mse = self.mse(out, ref)
return -10 * th.log10(mse + 1e-12)
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_log10_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.load(in_ptr1 + r0, None)
tmp2 = tmp0 - tmp1
tmp3 = tmp2 * tmp2
tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
tmp7 = 256.0
tmp8 = tmp6 / tmp7
tmp9 = 1e-12
tmp10 = tmp8 + tmp9
tmp11 = libdevice.log10(tmp10)
tmp12 = -10.0
tmp13 = tmp11 * tmp12
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, None)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf1 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_log10_mse_loss_mul_0[grid(1)](buf1, arg1_1,
arg0_1, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
return buf1,
class PSNRNew(th.nn.Module):
def __init__(self):
super(PSNRNew, self).__init__()
self.mse = th.nn.MSELoss()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IlyaBizyaev/ttools
|
PSNR
| false | 8,306 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
BicubicUpsampler
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/gh/cghnjq3bevfs6cftlpoj2dnardv2irziqemlozkyjaqcw3umhzsr.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
# x => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%arg0_1, [None, None, %clamp_max, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad2d_0 = async_compile.triton('triton_poi_fused_replication_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_replication_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8) % 8
x2 = (xindex // 64)
x3 = xindex
tmp0 = tl.load(in_ptr0 + ((4*((3) * ((3) <= (((0) * ((0) >= ((-2) + x1)) + ((-2) + x1) * (((-2) + x1) > (0))))) + (((0) * ((0) >= ((-2) + x1)) + ((-2) + x1) * (((-2) + x1) > (0)))) * ((((0) * ((0) >= ((-2) + x1)) + ((-2) + x1) * (((-2) + x1) > (0)))) < (3)))) + (16*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0))))) + (((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) * ((((0) * ((0) >= ((-2) + x0)) + ((-2) + x0) * (((-2) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(arg1_1, (1, 1, 1, 8), (8, 8, 8, 1))
assert_size_stride(arg2_1, (1, 1, 8, 1), (8, 8, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 8, 8), (64, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_replication_pad2d_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.replication_pad2d, aten.convolution]
buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2), padding=(0, 7), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 1, 8, 8), (64, 64, 8, 1))
del arg1_1
del buf0
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1), padding=(7, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
del arg2_1
del buf1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((1, 1, 1, 8), (8, 8, 8, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((1, 1, 8, 1), (8, 8, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.utils.data
class BicubicUpsampler(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BicubicUpsampler, self).__init__()
ksize = 2 * scale * 2
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
self.pad = th.nn.ReplicationPad2d((2, 2, 2, 2))
self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
stride=(1, scale), padding=(0, total_pad), groups=channels,
bias=False)
self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
stride=(scale, 1), padding=(total_pad, 0), groups=channels,
bias=False)
k_idx = th.arange(0, ksize) + 0.5
k_coord = k_idx / scale - ksize * 0.5 / scale
absx = th.abs(k_coord)
absx2 = absx.pow(2)
absx3 = absx.pow(3)
k_weight = th.zeros(ksize)
k_weight += (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2.0) * ((absx >
1.0) & (absx < 2.0))
k_weight += (1.5 * absx3 - 2.5 * absx2 + 1.0) * (absx <= 1.0)
for c in range(channels):
self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
for p in self.parameters():
p.requires_grad = False
def forward(self, x):
x = self.pad(x)
x = self.us_x(x)
x = self.us_y(x)
return x
def get_inputs():
return [torch.rand([4, 1, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8 % 8
x2 = xindex // 64
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -2 + x1) + (-2 + x1) *
(-2 + x1 > 0)) + (0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0)) *
(0 * (0 >= -2 + x1) + (-2 + x1) * (-2 + x1 > 0) < 3)) + 16 * x2 + (
3 * (3 <= 0 * (0 >= -2 + x0) + (-2 + x0) * (-2 + x0 > 0)) + (0 * (0 >=
-2 + x0) + (-2 + x0) * (-2 + x0 > 0)) * (0 * (0 >= -2 + x0) + (-2 +
x0) * (-2 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(arg1_1, (1, 1, 1, 8), (8, 8, 8, 1))
assert_size_stride(arg2_1, (1, 1, 8, 1), (8, 8, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 8, 8), (64, 64, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_replication_pad2d_0[grid(256)](arg0_1, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2),
padding=(0, 7), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 1, 8, 8), (64, 64, 8, 1))
del arg1_1
del buf0
buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1),
padding=(7, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
del arg2_1
del buf1
return buf2,
class BicubicUpsamplerNew(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BicubicUpsamplerNew, self).__init__()
ksize = 2 * scale * 2
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
self.pad = th.nn.ReplicationPad2d((2, 2, 2, 2))
self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
stride=(1, scale), padding=(0, total_pad), groups=channels,
bias=False)
self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
stride=(scale, 1), padding=(total_pad, 0), groups=channels,
bias=False)
k_idx = th.arange(0, ksize) + 0.5
k_coord = k_idx / scale - ksize * 0.5 / scale
absx = th.abs(k_coord)
absx2 = absx.pow(2)
absx3 = absx.pow(3)
k_weight = th.zeros(ksize)
k_weight += (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2.0) * ((absx >
1.0) & (absx < 2.0))
k_weight += (1.5 * absx3 - 2.5 * absx2 + 1.0) * (absx <= 1.0)
for c in range(channels):
self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
for p in self.parameters():
p.requires_grad = False
def forward(self, input_0):
arg1_1 = self.us_x.weight
arg2_1 = self.us_y.weight
arg0_1 = input_0
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
IlyaBizyaev/ttools
|
BicubicUpsampler
| false | 8,307 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
FCChain
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ee/ceelnmyyuq5vt6keqkoyx42in3lgxsjzl3w5zrsxshnjzs235atg.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# x_1 => relu
# Graph fragment:
# %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_6, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x4 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (x4), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x4), tmp4, None)
tl.store(out_ptr0 + (x4), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/2b/c2boo4jotsghgwkvi3bhh7jeeqfoifmqf3i5xpefsjudgfwsofmd.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.view]
# Source node to ATen node mapping:
# x_2 => view_7
# Graph fragment:
# %view_7 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_6, [64, 64]), kwargs = {})
triton_poi_fused_view_1 = async_compile.triton('triton_poi_fused_view_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = (xindex // 64)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (64*x1) + (256*((x1 % 4) // 4)) + (1024*(((4*((x1 // 4) % 4)) + (x1 % 4)) // 16))), None)
tl.store(out_ptr0 + (x2), tmp0, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zn/czn2rtp4skukdswua2ptjqd2a7mxp4omcm4gbcgfjj4llz6orj2f.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.view, aten.threshold_backward]
# Source node to ATen node mapping:
# x_5 => relu_2, view_17
# Graph fragment:
# %relu_2 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_15,), kwargs = {})
# %view_17 : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%view_16, [4, 4, 4, 64]), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%view_22, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_view_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_view_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_view_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_view_2(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x4 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + (x4), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x4), tmp4, None)
tl.store(out_ptr1 + (x4), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (64, 4), (4, 1))
assert_size_stride(primals_2, (64, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64, 64), (64, 1))
assert_size_stride(primals_5, (64, ), (1, ))
assert_size_stride(primals_6, (64, 64), (64, 1))
assert_size_stride(primals_7, (64, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse
buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf11, 4096, grid=grid(4096), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.view]
triton_poi_fused_view_1.run(buf1, buf2, 4096, grid=grid(4096), stream=stream0)
buf3 = reinterpret_tensor(buf1, (64, 64), (64, 1), 0); del buf1 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf3 # reuse
buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf4, primals_5, buf10, 4096, grid=grid(4096), stream=stream0)
del primals_5
buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.view]
triton_poi_fused_view_1.run(buf4, buf5, 4096, grid=grid(4096), stream=stream0)
buf6 = reinterpret_tensor(buf4, (64, 64), (64, 1), 0); del buf4 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (64, 64), (1, 64), 0), out=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf6 # reuse
buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.view, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_view_2.run(buf7, primals_7, buf8, buf9, 4096, grid=grid(4096), stream=stream0)
del buf7
del primals_7
return (buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, buf5, buf9, primals_6, buf10, primals_4, buf11, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
class FCModule(nn.Module):
"""Basic fully connected module with optional dropout.
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
activation(str): nonlinear activation function.
dropout(float): dropout ratio if defined, default to None: no dropout.
"""
def __init__(self, n_in, n_out, activation=None, dropout=None):
super(FCModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer'
self.add_module('fc', nn.Linear(n_in, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
if dropout is not None:
self.add_module('dropout', nn.Dropout(dropout, inplace=True))
_init_fc_or_conv(self.fc, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class FCChain(nn.Module):
"""Linear chain of fully connected layers.
Args:
n_in(int): number of input channels.
width(int or list of int): number of features channels in the intermediate layers.
depth(int): number of layers
activation(str): nonlinear activation function between convolutions.
dropout(float or list of float): dropout ratio if defined, default to None: no dropout.
"""
def __init__(self, n_in, width=64, depth=3, activation='relu', dropout=None
):
super(FCChain, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
assert isinstance(width, int) or isinstance(width, list
), 'Width should be a list or an int'
_in = [n_in]
if isinstance(width, int):
_in = _in + [width] * (depth - 1)
_out = [width] * depth
elif isinstance(width, list):
assert len(width
) == depth, 'Specifying width with a least: should have `depth` entries'
_in = _in + width[:-1]
_out = width
_activations = [activation] * depth
if dropout is not None:
assert isinstance(dropout, float) or isinstance(dropout, list
), 'Dropout should be a float or a list of floats'
if dropout is None or isinstance(dropout, float):
_dropout = [dropout] * depth
elif isinstance(dropout, list):
assert len(dropout
) == depth, "When specifying a list of dropout, the list should have 'depth' elements."
_dropout = dropout
for lvl in range(depth):
self.add_module('fc{}'.format(lvl), FCModule(_in[lvl], _out[lvl
], activation=_activations[lvl], dropout=_dropout[lvl]))
def forward(self, x):
for m in self.children():
x = m(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_in': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x4 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x4, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x4, tmp4, None)
tl.store(out_ptr0 + x4, tmp6, None)
@triton.jit
def triton_poi_fused_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 64
x1 = xindex // 64
x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1 + 256 * (x1 % 4 // 4) + 1024 * (
(4 * (x1 // 4 % 4) + x1 % 4) // 16)), None)
tl.store(out_ptr0 + x2, tmp0, None)
@triton.jit
def triton_poi_fused_relu_threshold_backward_view_2(in_out_ptr0, in_ptr0,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x4 = xindex
x0 = xindex % 64
tmp0 = tl.load(in_out_ptr0 + x4, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x4, tmp4, None)
tl.store(out_ptr1 + x4, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (64, 4), (4, 1))
assert_size_stride(primals_2, (64,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (64, 64), (64, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (64, 64), (64, 1))
assert_size_stride(primals_7, (64,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf0
buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf1,
primals_2, buf11, 4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
triton_poi_fused_view_1[grid(4096)](buf1, buf2, 4096, XBLOCK=256,
num_warps=4, num_stages=1)
buf3 = reinterpret_tensor(buf1, (64, 64), (64, 1), 0)
del buf1
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (64, 64), (1,
64), 0), out=buf3)
buf4 = reinterpret_tensor(buf3, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf3
buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
.bool)
triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf4,
primals_5, buf10, 4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
triton_poi_fused_view_1[grid(4096)](buf4, buf5, 4096, XBLOCK=256,
num_warps=4, num_stages=1)
buf6 = reinterpret_tensor(buf4, (64, 64), (64, 1), 0)
del buf4
extern_kernels.mm(buf5, reinterpret_tensor(primals_6, (64, 64), (1,
64), 0), out=buf6)
buf7 = reinterpret_tensor(buf6, (4, 4, 4, 64), (1024, 256, 64, 1), 0)
del buf6
buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
float32)
buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
)
triton_poi_fused_relu_threshold_backward_view_2[grid(4096)](buf7,
primals_7, buf8, buf9, 4096, XBLOCK=128, num_warps=4, num_stages=1)
del buf7
del primals_7
return buf8, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf2, buf5, buf9, primals_6, buf10, primals_4, buf11
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
class FCModule(nn.Module):
"""Basic fully connected module with optional dropout.
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
activation(str): nonlinear activation function.
dropout(float): dropout ratio if defined, default to None: no dropout.
"""
def __init__(self, n_in, n_out, activation=None, dropout=None):
super(FCModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer'
self.add_module('fc', nn.Linear(n_in, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
if dropout is not None:
self.add_module('dropout', nn.Dropout(dropout, inplace=True))
_init_fc_or_conv(self.fc, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class FCChainNew(nn.Module):
"""Linear chain of fully connected layers.
Args:
n_in(int): number of input channels.
width(int or list of int): number of features channels in the intermediate layers.
depth(int): number of layers
activation(str): nonlinear activation function between convolutions.
dropout(float or list of float): dropout ratio if defined, default to None: no dropout.
"""
def __init__(self, n_in, width=64, depth=3, activation='relu', dropout=None
):
super(FCChainNew, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
assert isinstance(width, int) or isinstance(width, list
), 'Width should be a list or an int'
_in = [n_in]
if isinstance(width, int):
_in = _in + [width] * (depth - 1)
_out = [width] * depth
elif isinstance(width, list):
assert len(width
) == depth, 'Specifying width with a least: should have `depth` entries'
_in = _in + width[:-1]
_out = width
_activations = [activation] * depth
if dropout is not None:
assert isinstance(dropout, float) or isinstance(dropout, list
), 'Dropout should be a float or a list of floats'
if dropout is None or isinstance(dropout, float):
_dropout = [dropout] * depth
elif isinstance(dropout, list):
assert len(dropout
) == depth, "When specifying a list of dropout, the list should have 'depth' elements."
_dropout = dropout
for lvl in range(depth):
self.add_module('fc{}'.format(lvl), FCModule(_in[lvl], _out[lvl
], activation=_activations[lvl], dropout=_dropout[lvl]))
def forward(self, input_0):
primals_1 = self.fc0.fc.weight
primals_2 = self.fc0.fc.bias
primals_4 = self.fc1.fc.weight
primals_5 = self.fc1.fc.bias
primals_6 = self.fc2.fc.weight
primals_7 = self.fc2.fc.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
IlyaBizyaev/ttools
|
FCChain
| false | 8,308 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
TransformerEncoderPostNormLayer
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/25/c25xezvaufcxosn2zaablzq3d4dz4ocigm5s7w52r36lqwucoptj.py
# Topologically Sorted Source Nodes: [norm_src], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# norm_src => add, rsqrt, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [1]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + (x0), tmp8, xmask)
tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/uv/cuvyzdrclvsiubqwnkqyzqogybjelcuaoywggvz52bz5abo4hmua.py
# Topologically Sorted Source Nodes: [norm_src], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# norm_src => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [1]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_1), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5o/c5oauw3z3ep2ftanta4riae32t6z5u67plt43eekrznregbmlpjr.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
# Source node to ATen node mapping:
# multi_head_attention_forward => mul_2
# Graph fragment:
# %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_3, 1.0), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kj/ckjie4jfhwucdjwrkoxyorl5ltzj7tre6m3jengzvslyxc6fnmbt.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# multi_head_attention_forward => amax, exp, sub_1
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bt/cbt34fhodpavx2oq24hpku3ag5gk3mibbr2ozkxz4z7bs2m23gfy.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# multi_head_attention_forward => div, sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/fe/cfech2rxtnpxedtqlj5kjrdsdntbttrruuwxu7hmdxbzkefn6e6q.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# multi_head_attention_forward => clone
# Graph fragment:
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 4
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x1)), xmask & ymask)
tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/4s/c4sghfflzlvhg76ckzrkj2yv7ethqif4fe437f2dxqtwuh4ahinp.py
# Topologically Sorted Source Nodes: [src, norm_src_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# norm_src_1 => var_mean_1
# src => add_2
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %squeeze), kwargs = {})
# %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [1]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_6 = async_compile.triton('triton_poi_fused_add_native_layer_norm_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + (x0), tmp16, xmask)
tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/cm/ccma2nw3h6tzmwtufo45byghevpzamho2ay363inl5jh2l67anpn.py
# Topologically Sorted Source Nodes: [src, norm_src_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# norm_src_1 => add_3, add_4, mul_3, mul_4, rsqrt_1, sub_2
# src => add_2
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %squeeze), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
# %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_9), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_1), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %primals_8), kwargs = {})
# %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_9), kwargs = {})
triton_poi_fused_add_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_native_layer_norm_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/g6/cg6zs5ingblixxdll7mhr43lbl2qywjluoo7nxxztvnae3q4rjsu.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
# Source node to ATen node mapping:
# relu => relu
# Graph fragment:
# %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_11), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_8 = async_compile.triton('triton_poi_fused_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8192],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + (x2), None)
tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/cb/ccbk5xotcsqtl2ksko2mwsbwmw3wuum5asogvfe7nzhucp3hqc2c.py
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
# src => add_2
# src_1 => add_5
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %squeeze), kwargs = {})
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_13), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %add_tensor), kwargs = {})
triton_poi_fused_add_9 = async_compile.triton('triton_poi_fused_add_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_9', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
args.clear()
assert_size_stride(primals_1, (4, ), (1, ))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, ), (1, ))
assert_size_stride(primals_10, (2048, 4), (4, 1))
assert_size_stride(primals_11, (2048, ), (1, ))
assert_size_stride(primals_12, (4, 2048), (2048, 1))
assert_size_stride(primals_13, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
# Topologically Sorted Source Nodes: [norm_src], Original ATen: [aten.native_layer_norm]
stream0 = get_raw_stream(0)
triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 4, grid=grid(4), stream=stream0)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [norm_src], Original ATen: [aten.native_layer_norm]
triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 16, grid=grid(16), stream=stream0)
del primals_1
del primals_2
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
extern_kernels.addmm(reinterpret_tensor(primals_5, (4, ), (1, ), 4), buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), alpha=1, beta=1, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
extern_kernels.addmm(reinterpret_tensor(primals_5, (4, ), (1, ), 8), buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=1, beta=1, out=buf5)
buf6 = reinterpret_tensor(buf3, (4, 4, 1), (1, 4, 16), 0); del buf3 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
triton_poi_fused_mul_2.run(buf6, primals_5, 16, grid=grid(16), stream=stream0)
del primals_5
buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
extern_kernels.bmm(buf6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1, 4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax]
triton_poi_fused__softmax_3.run(buf7, buf8, 64, grid=grid(64), stream=stream0)
buf9 = buf7; del buf7 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax]
triton_poi_fused__softmax_4.run(buf8, buf9, 64, grid=grid(64), stream=stream0)
del buf8
buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
extern_kernels.bmm(buf9, reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 1), 0), out=buf10)
buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
triton_poi_fused_clone_5.run(buf10, buf11, 4, 4, grid=grid(4, 4), stream=stream0)
buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0); del buf10 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_7, reinterpret_tensor(buf11, (4, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12)
del primals_7
buf13 = buf1; del buf1 # reuse
buf14 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [src, norm_src_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_6.run(primals_3, buf12, buf13, buf14, 4, grid=grid(4), stream=stream0)
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [src, norm_src_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_7.run(primals_3, buf12, buf13, buf14, primals_8, primals_9, buf15, 16, grid=grid(16), stream=stream0)
del buf13
del buf14
del primals_9
buf16 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf15, reinterpret_tensor(primals_10, (4, 2048), (1, 4), 0), out=buf16)
buf17 = buf16; del buf16 # reuse
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu]
triton_poi_fused_relu_8.run(buf17, primals_11, 8192, grid=grid(8192), stream=stream0)
del primals_11
buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf17, reinterpret_tensor(primals_12, (2048, 4), (1, 2048), 0), out=buf18)
buf19 = buf18; del buf18 # reuse
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add]
triton_poi_fused_add_9.run(buf19, primals_3, buf12, primals_13, 16, grid=grid(16), stream=stream0)
del primals_13
return (buf19, primals_3, primals_8, buf2, buf9, reinterpret_tensor(buf11, (4, 4), (4, 1), 0), buf12, buf15, buf17, primals_12, primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (4, 1), 32), reinterpret_tensor(primals_4, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((2048, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn.functional as F
from torch import nn
from typing import Optional
from torch.nn import LayerNorm
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
elif activation == 'gelu':
return F.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activation))
class TransformerEncoderPostNormLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.0,
activation='relu'):
super().__init__()
assert dropout == 0.0
self.self_attn = nn.MultiheadAttention(d_model, nhead)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.activation = _get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = F.relu
super().__setstate__(state)
def forward(self, src, src_mask: 'Optional[torch.Tensor]'=None,
src_key_padding_mask: 'Optional[torch.Tensor]'=None):
norm_src = self.norm1(src)
src2 = self.self_attn(norm_src, norm_src, norm_src, attn_mask=
src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + src2
norm_src = self.norm2(src)
src2 = self.linear2(self.activation(self.linear1(norm_src)))
src = src + src2
return src
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'nhead': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn.functional as F
from torch import nn
from torch.nn import LayerNorm
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 4
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_relu_8(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x2 = xindex
x0 = xindex % 2048
tmp0 = tl.load(in_out_ptr0 + x2, None)
tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, None)
@triton.jit
def triton_poi_fused_add_9(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_out_ptr0 + x2, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4,), (1,))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4), (4, 1))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (2048, 4), (4, 1))
assert_size_stride(primals_11, (2048,), (1,))
assert_size_stride(primals_12, (4, 2048), (2048, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(4)](primals_3, buf0, buf1,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(16)](primals_3, buf0,
buf1, primals_1, primals_2, buf2, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del primals_1
del primals_2
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 4),
buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), alpha=
1, beta=1, out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=
1, beta=1, out=buf5)
buf6 = reinterpret_tensor(buf3, (4, 4, 1), (1, 4, 16), 0)
del buf3
triton_poi_fused_mul_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(buf6, reinterpret_tensor(buf4, (4, 1, 4), (1, 1,
4), 0), out=buf7)
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused__softmax_3[grid(64)](buf7, buf8, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf9 = buf7
del buf7
triton_poi_fused__softmax_4[grid(64)](buf8, buf9, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del buf8
buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
extern_kernels.bmm(buf9, reinterpret_tensor(buf5, (4, 4, 1), (1, 4,
1), 0), out=buf10)
buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
triton_poi_fused_clone_5[grid(4, 4)](buf10, buf11, 4, 4, XBLOCK=4,
YBLOCK=4, num_warps=1, num_stages=1)
buf12 = reinterpret_tensor(buf10, (4, 4), (4, 1), 0)
del buf10
extern_kernels.addmm(primals_7, reinterpret_tensor(buf11, (4, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf12)
del primals_7
buf13 = buf1
del buf1
buf14 = buf0
del buf0
triton_poi_fused_add_native_layer_norm_6[grid(4)](primals_3, buf12,
buf13, buf14, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_7[grid(16)](primals_3, buf12,
buf13, buf14, primals_8, primals_9, buf15, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf13
del buf14
del primals_9
buf16 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
extern_kernels.mm(buf15, reinterpret_tensor(primals_10, (4, 2048),
(1, 4), 0), out=buf16)
buf17 = buf16
del buf16
triton_poi_fused_relu_8[grid(8192)](buf17, primals_11, 8192, XBLOCK
=256, num_warps=4, num_stages=1)
del primals_11
buf18 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf17, reinterpret_tensor(primals_12, (2048, 4),
(1, 2048), 0), out=buf18)
buf19 = buf18
del buf18
triton_poi_fused_add_9[grid(16)](buf19, primals_3, buf12,
primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_13
return (buf19, primals_3, primals_8, buf2, buf9, reinterpret_tensor(
buf11, (4, 4), (4, 1), 0), buf12, buf15, buf17, primals_12,
primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4
), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 1, 4), 0),
reinterpret_tensor(buf4, (4, 4, 1), (1, 4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 32),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 16),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 0))
def _get_activation_fn(activation):
if activation == 'relu':
return F.relu
elif activation == 'gelu':
return F.gelu
raise RuntimeError('activation should be relu/gelu, not {}'.format(
activation))
class TransformerEncoderPostNormLayerNew(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.0,
activation='relu'):
super().__init__()
assert dropout == 0.0
self.self_attn = nn.MultiheadAttention(d_model, nhead)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.activation = _get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = F.relu
super().__setstate__(state)
def forward(self, input_0):
primals_4 = self.self_attn.in_proj_weight
primals_5 = self.self_attn.in_proj_bias
primals_3 = self.self_attn.out_proj.weight
primals_1 = self.self_attn.out_proj.bias
primals_10 = self.linear1.weight
primals_11 = self.linear1.bias
primals_12 = self.linear2.weight
primals_2 = self.linear2.bias
primals_7 = self.norm1.weight
primals_8 = self.norm1.bias
primals_9 = self.norm2.weight
primals_13 = self.norm2.bias
primals_6 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
JDBumgardner/stone_ground_hearth_battles
|
TransformerEncoderPostNormLayer
| false | 8,309 |
[
"Apache-2.0"
] | 20 |
9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f
|
https://github.com/JDBumgardner/stone_ground_hearth_battles/tree/9fe095651fab60e8ddbf563f0b9b7f3e723d5f4f
|
ResidualAttentionBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/25/c25xezvaufcxosn2zaablzq3d4dz4ocigm5s7w52r36lqwucoptj.py
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# ret => add, rsqrt, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_1, [1]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + (x0), tmp8, xmask)
tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/uv/cuvyzdrclvsiubqwnkqyzqogybjelcuaoywggvz52bz5abo4hmua.py
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# ret => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_1, [1]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_2), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_3), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5o/c5oauw3z3ep2ftanta4riae32t6z5u67plt43eekrznregbmlpjr.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
# Source node to ATen node mapping:
# multi_head_attention_forward => mul_2
# Graph fragment:
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%view_9, 1.0), kwargs = {})
triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pe/cpeuwb6odun6kyfch2fujopahr225z4a5acqfktisl43nfgedsn7.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
# Source node to ATen node mapping:
# multi_head_attention_forward => mul_3
# Graph fragment:
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%permute_6, 1.0), kwargs = {})
triton_poi_fused_mul_3 = async_compile.triton('triton_poi_fused_mul_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/xj/cxj2hmquy3ddfbpokchzqnhirvhk27qcfadslgslmxc7atmgb6e6.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
# Source node to ATen node mapping:
# multi_head_attention_forward => amax, exp, sub_1
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_14, [-1], True), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_14, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__safe_softmax_4 = async_compile.triton('triton_poi_fused__safe_softmax_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__safe_softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__safe_softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bd/cbd3w5eob5otb3hdndg3dzwf6z5s6drp46xybotd3giji25blkl2.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
# Source node to ATen node mapping:
# multi_head_attention_forward => any_1, div, eq, full_default, logical_not, logical_not_1, sum_1, where
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
# %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view_14, -inf), kwargs = {})
# %logical_not : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%eq,), kwargs = {})
# %any_1 : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%logical_not, -1, True), kwargs = {})
# %logical_not_1 : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%any_1,), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%logical_not_1, %full_default, %div), kwargs = {})
triton_poi_fused__safe_softmax_5 = async_compile.triton('triton_poi_fused__safe_softmax_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__safe_softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__safe_softmax_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (x2), xmask)
tmp26 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp29 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp1 = float("-inf")
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = (tmp4 != 0)
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = (tmp9 != 0)
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = (tmp15 != 0)
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = (tmp21 != 0)
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/mo/cmom2jnedlhton5nnyviqb5zqossp7cjhsuhxlr67v53mlgbofbg.py
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# multi_head_attention_forward => clone
# Graph fragment:
# %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_6 = async_compile.triton('triton_poi_fused_clone_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 4
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x1)), xmask & ymask)
tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jk/cjk5hgimisgqfqlbn746vw3lhixtbp4vrjsjocy7rnuf2iiex4pz.py
# Topologically Sorted Source Nodes: [x, ret_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# ret_1 => var_mean_1
# x => add_2
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {})
# %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [1]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_native_layer_norm_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + (x0), tmp16, xmask)
tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/iy/ciyhj7s4akkewa7mcw5vrnzhzevc7em7zjrrh5zl54sfs3xb7lhm.py
# Topologically Sorted Source Nodes: [x, ret_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# ret_1 => add_3, add_4, mul_4, mul_5, rsqrt_1, sub_2
# x => add_2
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_8, 1e-05), kwargs = {})
# %rsqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_9), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %rsqrt_1), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %primals_8), kwargs = {})
# %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %primals_9), kwargs = {})
triton_poi_fused_add_native_layer_norm_8 = async_compile.triton('triton_poi_fused_add_native_layer_norm_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bo/cbokxp2vzoiwoa4wrpa36mavzo6xb3qpj6m5dblai76cdifhf373.py
# Topologically Sorted Source Nodes: [mul, sigmoid, input_2], Original ATen: [aten.mul, aten.sigmoid]
# Source node to ATen node mapping:
# input_2 => mul_7
# mul => mul_6
# sigmoid => sigmoid
# Graph fragment:
# %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm_4, 1.702), kwargs = {})
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul_6,), kwargs = {})
# %mul_7 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%addmm_4, %sigmoid), kwargs = {})
triton_poi_fused_mul_sigmoid_9 = async_compile.triton('triton_poi_fused_mul_sigmoid_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_sigmoid_9(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 1.702
tmp2 = tmp0 * tmp1
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/c5/cc5u3ctv3kvqkccxk6pl5ztteehlh44dlyclcjmb7nc6wusntsea.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
# x => add_2
# x_1 => add_5
# Graph fragment:
# %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %squeeze), kwargs = {})
# %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_13), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %add_tensor), kwargs = {})
triton_poi_fused_add_10 = async_compile.triton('triton_poi_fused_add_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_10', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_out_ptr0 + (x2), xmask)
tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12, ), (1, ))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, ), (1, ))
assert_size_stride(primals_10, (16, 4), (4, 1))
assert_size_stride(primals_11, (16, ), (1, ))
assert_size_stride(primals_12, (4, 16), (16, 1))
assert_size_stride(primals_13, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten.native_layer_norm]
stream0 = get_raw_stream(0)
triton_poi_fused_native_layer_norm_0.run(primals_1, buf0, buf1, 4, grid=grid(4), stream=stream0)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [ret], Original ATen: [aten.native_layer_norm]
triton_poi_fused_native_layer_norm_1.run(primals_1, buf0, buf1, primals_2, primals_3, buf2, 16, grid=grid(16), stream=stream0)
del primals_2
del primals_3
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
extern_kernels.addmm(reinterpret_tensor(primals_5, (4, ), (1, ), 8), buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=1, beta=1, out=buf5)
buf6 = reinterpret_tensor(buf3, (1, 4, 4, 1), (16, 1, 4, 16), 0); del buf3 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
triton_poi_fused_mul_2.run(buf6, primals_5, 16, grid=grid(16), stream=stream0)
buf7 = reinterpret_tensor(buf4, (1, 4, 1, 4), (16, 1, 16, 4), 0); del buf4 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul]
triton_poi_fused_mul_3.run(buf7, primals_5, 16, grid=grid(16), stream=stream0)
del primals_5
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 1), (1, 4, 0), 0), reinterpret_tensor(buf7, (4, 1, 4), (1, 0, 4), 0), out=buf8)
buf9 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
triton_poi_fused__safe_softmax_4.run(buf8, buf9, 64, grid=grid(64), stream=stream0)
buf10 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._safe_softmax]
triton_poi_fused__safe_softmax_5.run(buf8, buf9, buf10, 64, grid=grid(64), stream=stream0)
buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 0), 0), out=buf11)
buf12 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32)
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone]
triton_poi_fused_clone_6.run(buf11, buf12, 4, 4, grid=grid(4, 4), stream=stream0)
buf13 = reinterpret_tensor(buf11, (4, 4), (4, 1), 0); del buf11 # reuse
# Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_7, reinterpret_tensor(buf12, (4, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf13)
del primals_7
buf14 = buf1; del buf1 # reuse
buf15 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x, ret_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_7.run(primals_1, buf13, buf14, buf15, 4, grid=grid(4), stream=stream0)
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x, ret_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_8.run(primals_1, buf13, buf14, buf15, primals_8, primals_9, buf16, 16, grid=grid(16), stream=stream0)
del buf14
del buf15
del primals_9
buf17 = reinterpret_tensor(buf9, (4, 16), (16, 1), 0); del buf9 # reuse
# Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_11, buf16, reinterpret_tensor(primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf17)
del primals_11
buf18 = reinterpret_tensor(buf8, (4, 16), (16, 1), 0); del buf8 # reuse
# Topologically Sorted Source Nodes: [mul, sigmoid, input_2], Original ATen: [aten.mul, aten.sigmoid]
triton_poi_fused_mul_sigmoid_9.run(buf17, buf18, 64, grid=grid(64), stream=stream0)
buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(buf18, reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf19)
buf20 = buf19; del buf19 # reuse
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.add]
triton_poi_fused_add_10.run(buf20, primals_1, buf13, primals_13, 16, grid=grid(16), stream=stream0)
del primals_13
return (buf20, primals_1, primals_8, buf2, buf10, reinterpret_tensor(buf12, (4, 4), (4, 1), 0), buf13, buf16, buf17, buf18, primals_12, primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 4, 4), 0), reinterpret_tensor(buf7, (4, 4, 1), (1, 4, 16), 0), reinterpret_tensor(primals_4, (4, 4), (4, 1), 32), reinterpret_tensor(primals_4, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, 4), (4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from collections import OrderedDict
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: 'torch.Tensor'):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: 'int', n_head: 'int', attn_mask:
'torch.Tensor'=None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([('c_fc', nn.Linear(d_model,
d_model * 4)), ('gelu', QuickGELU()), ('c_proj', nn.Linear(
d_model * 4, d_model))]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: 'torch.Tensor'):
self.attn_mask = self.attn_mask if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask
)[0]
def forward(self, x: 'torch.Tensor'):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'n_head': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from collections import OrderedDict
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
@triton.jit
def triton_poi_fused_mul_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_mul_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused__safe_softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__safe_softmax_5(in_ptr0, in_ptr1, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + x2, xmask)
tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = float('-inf')
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = tmp4 != 0
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = tmp9 != 0
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = tmp15 != 0
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = tmp21 != 0
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_clone_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 4
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
@triton.jit
def triton_poi_fused_mul_sigmoid_9(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.702
tmp2 = tmp0 * tmp1
tmp3 = tl.sigmoid(tmp2)
tmp4 = tmp0 * tmp3
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_out_ptr0 + x2, xmask)
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tl.store(in_out_ptr0 + x2, tmp6, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (12, 4), (4, 1))
assert_size_stride(primals_5, (12,), (1,))
assert_size_stride(primals_6, (4, 4), (4, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
assert_size_stride(primals_10, (16, 4), (4, 1))
assert_size_stride(primals_11, (16,), (1,))
assert_size_stride(primals_12, (4, 16), (16, 1))
assert_size_stride(primals_13, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
get_raw_stream(0)
triton_poi_fused_native_layer_norm_0[grid(4)](primals_1, buf0, buf1,
4, XBLOCK=4, num_warps=1, num_stages=1)
buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(16)](primals_1, buf0,
buf1, primals_2, primals_3, buf2, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del primals_2
del primals_3
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 0), out=buf3)
buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
), 16), out=buf4)
buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=
1, beta=1, out=buf5)
buf6 = reinterpret_tensor(buf3, (1, 4, 4, 1), (16, 1, 4, 16), 0)
del buf3
triton_poi_fused_mul_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
buf7 = reinterpret_tensor(buf4, (1, 4, 1, 4), (16, 1, 16, 4), 0)
del buf4
triton_poi_fused_mul_3[grid(16)](buf7, primals_5, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 1), (1, 4, 0), 0
), reinterpret_tensor(buf7, (4, 1, 4), (1, 0, 4), 0), out=buf8)
buf9 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__safe_softmax_4[grid(64)](buf8, buf9, 64, XBLOCK=
64, num_warps=1, num_stages=1)
buf10 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__safe_softmax_5[grid(64)](buf8, buf9, buf10, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 0), 0), out=buf11)
buf12 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32)
triton_poi_fused_clone_6[grid(4, 4)](buf11, buf12, 4, 4, XBLOCK=4,
YBLOCK=4, num_warps=1, num_stages=1)
buf13 = reinterpret_tensor(buf11, (4, 4), (4, 1), 0)
del buf11
extern_kernels.addmm(primals_7, reinterpret_tensor(buf12, (4, 4), (
4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf13)
del primals_7
buf14 = buf1
del buf1
buf15 = buf0
del buf0
triton_poi_fused_add_native_layer_norm_7[grid(4)](primals_1, buf13,
buf14, buf15, 4, XBLOCK=4, num_warps=1, num_stages=1)
buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_8[grid(16)](primals_1, buf13,
buf14, buf15, primals_8, primals_9, buf16, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del buf14
del buf15
del primals_9
buf17 = reinterpret_tensor(buf9, (4, 16), (16, 1), 0)
del buf9
extern_kernels.addmm(primals_11, buf16, reinterpret_tensor(
primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf17)
del primals_11
buf18 = reinterpret_tensor(buf8, (4, 16), (16, 1), 0)
del buf8
triton_poi_fused_mul_sigmoid_9[grid(64)](buf17, buf18, 64, XBLOCK=
64, num_warps=1, num_stages=1)
buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(buf18, reinterpret_tensor(primals_12, (16, 4), (1,
16), 0), out=buf19)
buf20 = buf19
del buf19
triton_poi_fused_add_10[grid(16)](buf20, primals_1, buf13,
primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1)
del primals_13
return (buf20, primals_1, primals_8, buf2, buf10, reinterpret_tensor(
buf12, (4, 4), (4, 1), 0), buf13, buf16, buf17, buf18, primals_12,
primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4
), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 4, 4), 0),
reinterpret_tensor(buf7, (4, 4, 1), (1, 4, 16), 0),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 32),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 16),
reinterpret_tensor(primals_4, (4, 4), (4, 1), 0))
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: 'torch.Tensor'):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: 'torch.Tensor'):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlockNew(nn.Module):
def __init__(self, d_model: 'int', n_head: 'int', attn_mask:
'torch.Tensor'=None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([('c_fc', nn.Linear(d_model,
d_model * 4)), ('gelu', QuickGELU()), ('c_proj', nn.Linear(
d_model * 4, d_model))]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: 'torch.Tensor'):
self.attn_mask = self.attn_mask if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask
)[0]
def forward(self, input_0):
primals_4 = self.attn.in_proj_weight
primals_5 = self.attn.in_proj_bias
primals_1 = self.attn.out_proj.weight
primals_2 = self.attn.out_proj.bias
primals_3 = self.ln_1.weight
primals_7 = self.ln_1.bias
primals_10 = self.mlp.c_fc.weight
primals_11 = self.mlp.c_fc.bias
primals_12 = self.mlp.c_proj.weight
primals_8 = self.mlp.c_proj.bias
primals_9 = self.ln_2.weight
primals_13 = self.ln_2.bias
primals_6 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13])
return output[0]
|
Jack000/glid-3
|
ResidualAttentionBlock
| false | 8,310 |
[
"MIT"
] | 31 |
4a18efc2785339ebc743e149a7955e34fff436fb
|
https://github.com/Jack000/glid-3/tree/4a18efc2785339ebc743e149a7955e34fff436fb
|
StyleLossBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ws/cws6ubo5rtysavijeldjqsivs2p7c2rt5vegjx5u6fxmjdrzw2u3.py
# Topologically Sorted Source Nodes: [g, result], Original ATen: [aten.div, aten.mse_loss, aten.mse_loss_backward]
# Source node to ATen node mapping:
# g => div
# result => mean, pow_1, sub
# Graph fragment:
# %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 64), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, %primals_2), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %div), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 0.03125), kwargs = {})
triton_per_fused_div_mse_loss_mse_loss_backward_0 = async_compile.triton('triton_per_fused_div_mse_loss_mse_loss_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_mse_loss_mse_loss_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_div_mse_loss_mse_loss_backward_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp3 = tl.load(in_ptr1 + (r0), None)
tmp1 = 0.015625
tmp2 = tmp0 * tmp1
tmp4 = tmp2 - tmp3
tmp5 = tmp4 * tmp4
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.sum(tmp6, 1)[:, None]
tmp9 = tmp3 - tmp2
tmp10 = 0.03125
tmp11 = tmp9 * tmp10
tmp12 = 64.0
tmp13 = tmp8 / tmp12
tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp11, None)
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp13, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64, 16, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 1, 16), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf3 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [g, result], Original ATen: [aten.div, aten.mse_loss, aten.mse_loss_backward]
stream0 = get_raw_stream(0)
triton_per_fused_div_mse_loss_mse_loss_backward_0.run(buf3, buf0, primals_2, buf2, 1, 64, grid=grid(1), stream=stream0)
del buf0
del primals_2
return (buf3, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class StyleLossBlock(nn.Module):
def __init__(self, target: 'torch.Tensor'):
super().__init__()
self.stored_value = None
self._loss = F.mse_loss
self.shape = target.shape
self._target_gram_matrix = nn.Parameter(self.gram_matrix(target).data)
@staticmethod
def gram_matrix(x: 'torch.Tensor') ->torch.Tensor:
bs, ch, h, w = x.size()
f = x.view(bs, ch, w * h)
f_t = f.transpose(1, 2)
g = f.bmm(f_t) / (ch * h * w)
return g
def forward(self, input_tensor: 'torch.Tensor') ->torch.Tensor:
input_gram_matrix = self.gram_matrix(input_tensor)
result = self._loss(input_gram_matrix, self._target_gram_matrix)
return result
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'target': torch.rand([4, 4, 4, 4])}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_div_mse_loss_mse_loss_backward_0(in_out_ptr0, in_ptr0,
in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp1 = 0.015625
tmp2 = tmp0 * tmp1
tmp4 = tmp2 - tmp3
tmp5 = tmp4 * tmp4
tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
tmp8 = tl.sum(tmp6, 1)[:, None]
tmp9 = tmp3 - tmp2
tmp10 = 0.03125
tmp11 = tmp9 * tmp10
tmp12 = 64.0
tmp13 = tmp8 / tmp12
tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp11, None)
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp13, None)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 4, 16), (64,
16, 1), 0), reinterpret_tensor(primals_1, (4, 16, 4), (64, 1,
16), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
buf3 = buf1
del buf1
get_raw_stream(0)
triton_per_fused_div_mse_loss_mse_loss_backward_0[grid(1)](buf3,
buf0, primals_2, buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del buf0
del primals_2
return buf3, buf2
class StyleLossBlockNew(nn.Module):
def __init__(self, target: 'torch.Tensor'):
super().__init__()
self.stored_value = None
self._loss = F.mse_loss
self.shape = target.shape
self._target_gram_matrix = nn.Parameter(self.gram_matrix(target).data)
@staticmethod
def gram_matrix(x: 'torch.Tensor') ->torch.Tensor:
bs, ch, h, w = x.size()
f = x.view(bs, ch, w * h)
f_t = f.transpose(1, 2)
g = f.bmm(f_t) / (ch * h * w)
return g
def forward(self, input_0):
primals_2 = self._target_gram_matrix
primals_1 = input_0
output = call([primals_1, primals_2])
return output[0]
|
Inkln/StyleTransferWithCatalyst
|
StyleLossBlock
| false | 8,311 |
[
"Apache-2.0"
] | 11 |
c3181ecdfd32160907efc2d9d917a55925c25c11
|
https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11
|
BilinearUpsampler
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/qe/cqeeqbdhzjgqgez7ulct4iu2pihhnqy35eqv4vlirc2jwtjqhqtd.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
# x => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%arg0_1, [None, None, %clamp_max, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_replication_pad2d_0 = async_compile.triton('triton_poi_fused_replication_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_replication_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + ((4*((3) * ((3) <= (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0))))) + (((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) * ((((0) * ((0) >= ((-1) + x1)) + ((-1) + x1) * (((-1) + x1) > (0)))) < (3)))) + (16*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0))))) + (((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) * ((((0) * ((0) >= ((-1) + x0)) + ((-1) + x0) * (((-1) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(arg1_1, (1, 1, 1, 4), (4, 4, 4, 1))
assert_size_stride(arg2_1, (1, 1, 4, 1), (4, 4, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 6, 6), (36, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.replication_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_replication_pad2d_0.run(arg0_1, buf0, 144, grid=grid(144), stream=stream0)
del arg0_1
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.replication_pad2d, aten.convolution]
buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2), padding=(0, 3), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 1, 6, 8), (48, 48, 8, 1))
del arg1_1
del buf0
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1), padding=(3, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
del arg2_1
del buf1
return (buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((1, 1, 1, 4), (4, 4, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((1, 1, 4, 1), (4, 4, 1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
import torch.utils.data
class BilinearUpsampler(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BilinearUpsampler, self).__init__()
ksize = 2 * scale
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
self.pad = th.nn.ReplicationPad2d((1, 1, 1, 1))
self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
stride=(1, scale), padding=(0, total_pad), groups=channels,
bias=False)
self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
stride=(scale, 1), padding=(total_pad, 0), groups=channels,
bias=False)
k_idx = th.arange(0, ksize) + 0.5
k_coord = k_idx / scale - ksize * 0.5 / scale
k_weight = th.clamp(1.0 - th.abs(k_coord), min=0)
for c in range(channels):
self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
for p in self.parameters():
p.requires_grad = False
def forward(self, x):
x = self.pad(x)
x = self.us_x(x)
x = self.us_y(x)
return x
def get_inputs():
return [torch.rand([4, 1, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch as th
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -1 + x1) + (-1 + x1) *
(-1 + x1 > 0)) + (0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0)) *
(0 * (0 >= -1 + x1) + (-1 + x1) * (-1 + x1 > 0) < 3)) + 16 * x2 + (
3 * (3 <= 0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) + (0 * (0 >=
-1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -1 + x0) + (-1 +
x0) * (-1 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(arg1_1, (1, 1, 1, 4), (4, 4, 4, 1))
assert_size_stride(arg2_1, (1, 1, 4, 1), (4, 4, 1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 1, 6, 6), (36, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_replication_pad2d_0[grid(144)](arg0_1, buf0, 144,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
buf1 = extern_kernels.convolution(buf0, arg1_1, stride=(1, 2),
padding=(0, 3), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 1, 6, 8), (48, 48, 8, 1))
del arg1_1
del buf0
buf2 = extern_kernels.convolution(buf1, arg2_1, stride=(2, 1),
padding=(3, 0), dilation=(1, 1), transposed=True,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 1, 8, 8), (64, 64, 8, 1))
del arg2_1
del buf1
return buf2,
class BilinearUpsamplerNew(th.nn.Module):
def __init__(self, scale=2, channels=1):
super(BilinearUpsamplerNew, self).__init__()
ksize = 2 * scale
total_pad = ksize - scale // 2
if scale % 2 == 1:
ksize += 1
self.pad = th.nn.ReplicationPad2d((1, 1, 1, 1))
self.us_x = th.nn.ConvTranspose2d(channels, channels, (1, ksize),
stride=(1, scale), padding=(0, total_pad), groups=channels,
bias=False)
self.us_y = th.nn.ConvTranspose2d(channels, channels, (ksize, 1),
stride=(scale, 1), padding=(total_pad, 0), groups=channels,
bias=False)
k_idx = th.arange(0, ksize) + 0.5
k_coord = k_idx / scale - ksize * 0.5 / scale
k_weight = th.clamp(1.0 - th.abs(k_coord), min=0)
for c in range(channels):
self.us_x.weight.data[c, 0, 0, :].copy_(k_weight)
self.us_y.weight.data[c, 0, :, 0].copy_(k_weight)
for p in self.parameters():
p.requires_grad = False
def forward(self, input_0):
arg1_1 = self.us_x.weight
arg2_1 = self.us_y.weight
arg0_1 = input_0
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
IlyaBizyaev/ttools
|
BilinearUpsampler
| false | 8,312 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
Conv1dResBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/y7/cy75pv62flzgueapzlnlplljec2lmbrjxeuhmgi2gb5pjyl6sna4.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# y_1 => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [2], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/t6/ct6wonueojeybj6szuvdasvkbo65yaannfvrfwhvh65ngw7tp3i2.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
# Source node to ATen node mapping:
# x => add
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_2, %primals_1), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = (yindex // 4)
tmp0 = tl.load(in_ptr0 + (x2 + (5*y3)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(2,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
triton_poi_fused_add_1.run(buf1, primals_3, primals_1, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
del buf1
del primals_3
return (buf2, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
if padding == 'same':
padding = kernel_size // 2 * dilation
self.even_kernel = not bool(kernel_size % 2)
super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=
groups, bias=bias)
self.activation_fn = activation_fn(inplace=True
) if activation_fn is not None else None
self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
self.layer_norm = nn.LayerNorm(out_channels) if ln else None
def forward(self, x):
y = x.transpose(1, 2)
y = super(Conv1d, self).forward(y)
y = y.transpose(1, 2)
y = self.layer_norm(y) if self.layer_norm is not None else y
y = self.activation_fn(y) if self.activation_fn is not None else y
y = self.drop_out(y) if self.drop_out is not None else y
y = y[:, :-1, :] if self.even_kernel else y
return y
class Conv1dResBlock(Conv1d):
"""
Convolution 1d with Residual connection
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
super(Conv1dResBlock, self).__init__(in_channels, out_channels,
kernel_size, activation_fn, drop_rate, stride, padding,
dilation, groups=groups, bias=bias, ln=ln)
def forward(self, x):
residual = x
x = super(Conv1dResBlock, self).forward(x)
x = x + residual
return x
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_add_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, ynumel,
xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y3 = yindex
y0 = yindex % 4
y1 = yindex // 4
tmp0 = tl.load(in_ptr0 + (x2 + 5 * y3), xmask & ymask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(2,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
buf2 = reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0)
del buf0
triton_poi_fused_add_1[grid(16, 4)](buf1, primals_3, primals_1,
buf2, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
del buf1
del primals_3
return buf2, primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1,
4), 0)
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
if padding == 'same':
padding = kernel_size // 2 * dilation
self.even_kernel = not bool(kernel_size % 2)
super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=
groups, bias=bias)
self.activation_fn = activation_fn(inplace=True
) if activation_fn is not None else None
self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
self.layer_norm = nn.LayerNorm(out_channels) if ln else None
def forward(self, x):
y = x.transpose(1, 2)
y = super(Conv1d, self).forward(y)
y = y.transpose(1, 2)
y = self.layer_norm(y) if self.layer_norm is not None else y
y = self.activation_fn(y) if self.activation_fn is not None else y
y = self.drop_out(y) if self.drop_out is not None else y
y = y[:, :-1, :] if self.even_kernel else y
return y
class Conv1dResBlockNew(Conv1d):
"""
Convolution 1d with Residual connection
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
super(Conv1dResBlockNew, self).__init__(in_channels, out_channels,
kernel_size, activation_fn, drop_rate, stride, padding,
dilation, groups=groups, bias=bias, ln=ln)
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Jackson-Kang/VQVC-Pytorch
|
Conv1dResBlock
| false | 8,313 |
[
"MIT"
] | 13 |
d2267b5c52253b6ae11a5767963a65320ae335c2
|
https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2
|
DepthwiseSeparableConv
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/y7/cy75pv62flzgueapzlnlplljec2lmbrjxeuhmgi2gb5pjyl6sna4.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv1d => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [2], [1], False, [0], 4), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/xk/cxkffkiz6eieexc3hfbeczljvabsfcx2g3a23xartvu3zhymn52o.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv1d => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [2], [1], False, [0], 4), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 5) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/7c/c7cvvjxgsc43jjol2pfdytdwkzuhtidmyqq2chrjhigzykz7oxb2.py
# Topologically Sorted Source Nodes: [conv1d_1, out], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# conv1d_1 => convolution_1
# out => relu
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 5) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, xmask)
tl.store(out_ptr1 + (x3), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(2,), dilation=(1,), transposed=False, output_padding=(0,), groups=4, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
del buf0
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 80, grid=grid(80), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 5), (20, 5, 1))
buf4 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.bool)
buf5 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv1d_1, out], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_2.run(buf3, primals_5, buf4, buf5, 80, grid=grid(80), stream=stream0)
del buf3
del primals_5
return (reinterpret_tensor(buf5, (4, 5, 4), (20, 1, 5), 0), primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf2, buf4, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.randn(32, 300, 20)
>>> output = m(input_tensor)
"""
def __init__(self, in_ch, out_ch, k, dim=1, relu=True):
"""
:param in_ch: input hidden dimension size
:param out_ch: output hidden dimension size
:param k: kernel size
:param dim: default 1. 1D conv or 2D conv
"""
super(DepthwiseSeparableConv, self).__init__()
self.relu = relu
if dim == 1:
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
elif dim == 2:
self.depthwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
else:
raise Exception('Incorrect dimension!')
def forward(self, x):
"""
:Input: (N, L_in, D)
:Output: (N, L_out, D)
"""
x = x.transpose(1, 2)
if self.relu:
out = F.relu(self.pointwise_conv(self.depthwise_conv(x)),
inplace=True)
else:
out = self.pointwise_conv(self.depthwise_conv(x))
return out.transpose(1, 2)
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_ch': 4, 'out_ch': 4, 'k': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 5 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 5 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, xmask)
tl.store(out_ptr1 + x3, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 1, 4), (4, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(2,), dilation=(1,), transposed=False, output_padding=(
0,), groups=4, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
del buf0
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(80)](buf2, primals_3, 80,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 5), (20, 5, 1))
buf4 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.bool)
buf5 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
triton_poi_fused_convolution_relu_threshold_backward_2[grid(80)](buf3,
primals_5, buf4, buf5, 80, XBLOCK=128, num_warps=4, num_stages=1)
del buf3
del primals_5
return reinterpret_tensor(buf5, (4, 5, 4), (20, 1, 5), 0
), primals_2, primals_4, reinterpret_tensor(primals_1, (4, 4, 4), (
16, 1, 4), 0), buf2, buf4
class DepthwiseSeparableConvNew(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.randn(32, 300, 20)
>>> output = m(input_tensor)
"""
def __init__(self, in_ch, out_ch, k, dim=1, relu=True):
"""
:param in_ch: input hidden dimension size
:param out_ch: output hidden dimension size
:param k: kernel size
:param dim: default 1. 1D conv or 2D conv
"""
super(DepthwiseSeparableConvNew, self).__init__()
self.relu = relu
if dim == 1:
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
elif dim == 2:
self.depthwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
else:
raise Exception('Incorrect dimension!')
def forward(self, input_0):
primals_2 = self.depthwise_conv.weight
primals_3 = self.depthwise_conv.bias
primals_4 = self.pointwise_conv.weight
primals_5 = self.pointwise_conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
IsaacChanghau/ReLoCLNet
|
DepthwiseSeparableConv
| false | 8,314 |
[
"MIT"
] | 31 |
56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
NormalDivLoss
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/eo/ceoz2kphbqrqev46qe2nwklfye5ugifeuhwm6mzhnum2ftts7qhx.py
# Topologically Sorted Source Nodes: [diffs, truediv, abs_1, pow_1, add, distances, hist, sum_2, kl_div], Original ATen: [aten.sub, aten.div, aten.abs, aten.pow, aten.add, aten.reciprocal, aten.mul, aten.sum, aten.xlogy]
# Source node to ATen node mapping:
# abs_1 => abs_1
# add => add
# diffs => sub
# distances => mul, reciprocal
# hist => sum_1
# kl_div => div_2, eq, full_default, full_default_1, isnan, log_1, mul_1, mul_2, sub_1, sum_3, where, where_1
# pow_1 => pow_1
# sum_2 => sum_2
# truediv => div
# Graph fragment:
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%squeeze, %primals_2), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, 0.1), kwargs = {})
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%div,), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 4), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, 1), kwargs = {})
# %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1), kwargs = {})
# %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
# %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.default](args = (%sum_1,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %unsqueeze), kwargs = {})
# %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%primals_3, 0), kwargs = {})
# %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%primals_3,), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %log_1), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %mul_2), kwargs = {})
# %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%primals_3,), kwargs = {})
# %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_1, %where), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul_1), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_1,), kwargs = {})
# %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, 4), kwargs = {})
triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0 = async_compile.triton('triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.reduction(
size_hints=[1, 2048],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 7), equal_to_1=(6,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
xnumel = 1
rnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp44 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = (rindex // 400)
r4 = rindex % 400
r1 = (rindex // 4) % 100
r3 = rindex
tmp0 = tl.load(in_ptr0 + (r4 + (1600*r2)), rmask, eviction_policy='evict_first', other=0.0)
tmp1 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0)
tmp13 = tl.load(in_ptr0 + (400 + r4 + (1600*r2)), rmask, eviction_policy='evict_first', other=0.0)
tmp23 = tl.load(in_ptr0 + (800 + r4 + (1600*r2)), rmask, eviction_policy='evict_first', other=0.0)
tmp33 = tl.load(in_ptr0 + (1200 + r4 + (1600*r2)), rmask, eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 - tmp1
tmp3 = 10.0
tmp4 = tmp2 * tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = tmp5 * tmp5
tmp7 = tmp6 * tmp6
tmp8 = 1.0
tmp9 = tmp7 + tmp8
tmp10 = tl.full([1, 1], 1, tl.int32)
tmp11 = tmp10 / tmp9
tmp12 = tmp11 * tmp8
tmp14 = tmp13 - tmp1
tmp15 = tmp14 * tmp3
tmp16 = tl_math.abs(tmp15)
tmp17 = tmp16 * tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp18 + tmp8
tmp20 = tmp10 / tmp19
tmp21 = tmp20 * tmp8
tmp22 = tmp12 + tmp21
tmp24 = tmp23 - tmp1
tmp25 = tmp24 * tmp3
tmp26 = tl_math.abs(tmp25)
tmp27 = tmp26 * tmp26
tmp28 = tmp27 * tmp27
tmp29 = tmp28 + tmp8
tmp30 = tmp10 / tmp29
tmp31 = tmp30 * tmp8
tmp32 = tmp22 + tmp31
tmp34 = tmp33 - tmp1
tmp35 = tmp34 * tmp3
tmp36 = tl_math.abs(tmp35)
tmp37 = tmp36 * tmp36
tmp38 = tmp37 * tmp37
tmp39 = tmp38 + tmp8
tmp40 = tmp10 / tmp39
tmp41 = tmp40 * tmp8
tmp42 = tmp32 + tmp41
tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
tmp45 = _tmp44 + tmp43
_tmp44 = tl.where(rmask, tmp45, _tmp44)
tl.store(out_ptr0 + (tl.broadcast_to(r3, [XBLOCK, RBLOCK])), tmp42, rmask)
tmp44 = tl.sum(_tmp44, 1)[:, None]
tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp44, None)
_tmp61 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = (rindex // 4) % 100
r3 = rindex
tmp46 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0)
tmp55 = tl.load(out_ptr0 + (r3), rmask, eviction_policy='evict_first', other=0.0)
tmp47 = libdevice.isnan(tmp46).to(tl.int1)
tmp48 = 0.0
tmp49 = tmp46 == tmp48
tmp50 = tl_math.log(tmp46)
tmp51 = tmp46 * tmp50
tmp52 = tl.where(tmp49, tmp48, tmp51)
tmp53 = float("nan")
tmp54 = tl.where(tmp47, tmp53, tmp52)
tmp56 = tmp55 / tmp44
tmp57 = tl_math.log(tmp56)
tmp58 = tmp46 * tmp57
tmp59 = tmp54 - tmp58
tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
tmp62 = _tmp61 + tmp60
_tmp61 = tl.where(rmask, tmp62, _tmp61)
tmp61 = tl.sum(_tmp61, 1)[:, None]
tmp63 = 0.25
tmp64 = tmp61 * tmp63
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp64, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 100, 4), (1600, 400, 4, 1))
assert_size_stride(primals_2, (100, 1), (1, 1))
assert_size_stride(primals_3, (100, 1), (1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 100, 4), (400, 4, 1), torch.float32)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [diffs, truediv, abs_1, pow_1, add, distances, hist, sum_2, kl_div], Original ATen: [aten.sub, aten.div, aten.abs, aten.pow, aten.add, aten.reciprocal, aten.mul, aten.sum, aten.xlogy]
stream0 = get_raw_stream(0)
triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0.run(buf3, primals_1, primals_2, primals_3, buf0, buf1, 1, 1600, grid=grid(1), stream=stream0)
del buf0
return (buf3, primals_1, primals_2, primals_3, buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 100, 4), (1600, 400, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((100, 1), (1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((100, 1), (1, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
def fuzzyDist(x, a=0.1, b=2):
return 1 / (1 + (x / a).abs().pow(2 * b))
class SoftHist(nn.Module):
def __init__(self, bins, dist):
super(SoftHist, self).__init__()
bins[1] - bins[0]
self.bins = nn.Parameter(bins.unsqueeze(1))
self.dist = dist
self.pdf = lambda h: h / h.sum()
def forward(self, x):
diffs = x.squeeze() - self.bins
distances = self.dist(diffs)
hist = distances.sum(1)
hist_norm = self.pdf(hist)
return hist_norm
class NormalDivLoss(nn.Module):
def __init__(self, dist=fuzzyDist):
super(NormalDivLoss, self).__init__()
bins = torch.arange(-10, 10, 0.2)
binwidth = bins[1] - bins[0]
self.hist = SoftHist(bins, dist)
self.kl = nn.KLDivLoss(reduction='batchmean')
self.target = nn.Parameter(binwidth * torch.distributions.normal.
Normal(0, 0.3).log_prob(bins).exp().unsqueeze(1))
def forward(self, x):
hist = self.hist(x)
hist_log = torch.log(hist).unsqueeze(1)
return self.kl(hist_log, self.target)
def get_inputs():
return [torch.rand([4, 4, 100, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0(in_out_ptr0
, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
tl.constexpr, RBLOCK: tl.constexpr):
rnumel = 1600
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rbase = tl.arange(0, RBLOCK)[None, :]
_tmp44 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex // 400
r4 = rindex % 400
r1 = rindex // 4 % 100
r3 = rindex
tmp0 = tl.load(in_ptr0 + (r4 + 1600 * r2), rmask, eviction_policy=
'evict_first', other=0.0)
tmp1 = tl.load(in_ptr1 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp13 = tl.load(in_ptr0 + (400 + r4 + 1600 * r2), rmask,
eviction_policy='evict_first', other=0.0)
tmp23 = tl.load(in_ptr0 + (800 + r4 + 1600 * r2), rmask,
eviction_policy='evict_first', other=0.0)
tmp33 = tl.load(in_ptr0 + (1200 + r4 + 1600 * r2), rmask,
eviction_policy='evict_first', other=0.0)
tmp2 = tmp0 - tmp1
tmp3 = 10.0
tmp4 = tmp2 * tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = tmp5 * tmp5
tmp7 = tmp6 * tmp6
tmp8 = 1.0
tmp9 = tmp7 + tmp8
tmp10 = tl.full([1, 1], 1, tl.int32)
tmp11 = tmp10 / tmp9
tmp12 = tmp11 * tmp8
tmp14 = tmp13 - tmp1
tmp15 = tmp14 * tmp3
tmp16 = tl_math.abs(tmp15)
tmp17 = tmp16 * tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp18 + tmp8
tmp20 = tmp10 / tmp19
tmp21 = tmp20 * tmp8
tmp22 = tmp12 + tmp21
tmp24 = tmp23 - tmp1
tmp25 = tmp24 * tmp3
tmp26 = tl_math.abs(tmp25)
tmp27 = tmp26 * tmp26
tmp28 = tmp27 * tmp27
tmp29 = tmp28 + tmp8
tmp30 = tmp10 / tmp29
tmp31 = tmp30 * tmp8
tmp32 = tmp22 + tmp31
tmp34 = tmp33 - tmp1
tmp35 = tmp34 * tmp3
tmp36 = tl_math.abs(tmp35)
tmp37 = tmp36 * tmp36
tmp38 = tmp37 * tmp37
tmp39 = tmp38 + tmp8
tmp40 = tmp10 / tmp39
tmp41 = tmp40 * tmp8
tmp42 = tmp32 + tmp41
tmp43 = tl.broadcast_to(tmp42, [XBLOCK, RBLOCK])
tmp45 = _tmp44 + tmp43
_tmp44 = tl.where(rmask, tmp45, _tmp44)
tl.store(out_ptr0 + tl.broadcast_to(r3, [XBLOCK, RBLOCK]), tmp42, rmask
)
tmp44 = tl.sum(_tmp44, 1)[:, None]
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp44, None)
_tmp61 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r1 = rindex // 4 % 100
r3 = rindex
tmp46 = tl.load(in_ptr2 + r1, rmask, eviction_policy='evict_last',
other=0.0)
tmp55 = tl.load(out_ptr0 + r3, rmask, eviction_policy='evict_first',
other=0.0)
tmp47 = libdevice.isnan(tmp46).to(tl.int1)
tmp48 = 0.0
tmp49 = tmp46 == tmp48
tmp50 = tl_math.log(tmp46)
tmp51 = tmp46 * tmp50
tmp52 = tl.where(tmp49, tmp48, tmp51)
tmp53 = float('nan')
tmp54 = tl.where(tmp47, tmp53, tmp52)
tmp56 = tmp55 / tmp44
tmp57 = tl_math.log(tmp56)
tmp58 = tmp46 * tmp57
tmp59 = tmp54 - tmp58
tmp60 = tl.broadcast_to(tmp59, [XBLOCK, RBLOCK])
tmp62 = _tmp61 + tmp60
_tmp61 = tl.where(rmask, tmp62, _tmp61)
tmp61 = tl.sum(_tmp61, 1)[:, None]
tmp63 = 0.25
tmp64 = tmp61 * tmp63
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp64, None)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 100, 4), (1600, 400, 4, 1))
assert_size_stride(primals_2, (100, 1), (1, 1))
assert_size_stride(primals_3, (100, 1), (1, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 100, 4), (400, 4, 1), torch.float32)
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((), (), torch.float32)
buf3 = buf2
del buf2
get_raw_stream(0)
triton_red_fused_abs_add_div_mul_pow_reciprocal_sub_sum_xlogy_0[grid(1)
](buf3, primals_1, primals_2, primals_3, buf0, buf1, 1, 1600,
XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
del buf0
return buf3, primals_1, primals_2, primals_3, buf1
def fuzzyDist(x, a=0.1, b=2):
return 1 / (1 + (x / a).abs().pow(2 * b))
class SoftHist(nn.Module):
def __init__(self, bins, dist):
super(SoftHist, self).__init__()
bins[1] - bins[0]
self.bins = nn.Parameter(bins.unsqueeze(1))
self.dist = dist
self.pdf = lambda h: h / h.sum()
def forward(self, x):
diffs = x.squeeze() - self.bins
distances = self.dist(diffs)
hist = distances.sum(1)
hist_norm = self.pdf(hist)
return hist_norm
class NormalDivLossNew(nn.Module):
def __init__(self, dist=fuzzyDist):
super(NormalDivLossNew, self).__init__()
bins = torch.arange(-10, 10, 0.2)
binwidth = bins[1] - bins[0]
self.hist = SoftHist(bins, dist)
self.kl = nn.KLDivLoss(reduction='batchmean')
self.target = nn.Parameter(binwidth * torch.distributions.normal.
Normal(0, 0.3).log_prob(bins).exp().unsqueeze(1))
def forward(self, input_0):
primals_2 = self.target
primals_3 = self.hist.bins
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JWHan717/CS492I-Project
|
NormalDivLoss
| false | 8,315 |
[
"MIT"
] | 23 |
5da80bc41425ee90711a3de89c5501b5f7acd4b7
|
https://github.com/JWHan717/CS492I-Project/tree/5da80bc41425ee90711a3de89c5501b5f7acd4b7
|
ConvEncoder
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zw/czwngyjjwloxnp75kpzlimlcxcpvb5d44is6m4xzql4ih4sncldx.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv1d => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [3], [1], False, [0], 128), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512, 128], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 512
xnumel = 128
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 128
y1 = (yindex // 128)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (128*x2) + (16384*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (128*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ck/cckjtichnwjzj2ay2iflzrpt2ztesvrjegoe3xpqz6vrs23viw6m.py
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv1d => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [3], [1], False, [0], 128), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 128) % 128
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/b5/cb5h3hqzbyzifmvs5ny3eurysy63p6qnmo3ommm3d7wkagzrjp6u.py
# Topologically Sorted Source Nodes: [add, layer_norm], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# add => add
# layer_norm => add_1, add_2, clone_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_2, %primals_1), kwargs = {})
# %clone_1 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%add,), kwargs = {memory_format: torch.contiguous_format})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone_1, [2]), kwargs = {correction: 0, keepdim: True})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_1, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_6), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_7), kwargs = {})
triton_red_fused_add_native_layer_norm_2 = async_compile.triton('triton_red_fused_add_native_layer_norm_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.reduction(
size_hints=[512, 128],
reduction_hint=ReductionHint.OUTER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_native_layer_norm_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_red_fused_add_native_layer_norm_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
xnumel = 512
rnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x0 = xindex % 128
x1 = (xindex // 128)
x3 = xindex
tmp6_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp6_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp6_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_ptr0 + (x0 + (128*r2) + (16384*x1)), rmask & xmask, eviction_policy='evict_last', other=0.0)
tmp3 = tl.load(in_ptr1 + (r2 + (128*x3)), rmask & xmask, eviction_policy='evict_last', other=0.0)
tmp1 = tl.full([1, 1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = tmp2 + tmp3
tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
tmp6_mean_next, tmp6_m2_next, tmp6_weight_next = triton_helpers.welford_reduce(
tmp5, tmp6_mean, tmp6_m2, tmp6_weight, roffset == 0
)
tmp6_mean = tl.where(rmask & xmask, tmp6_mean_next, tmp6_mean)
tmp6_m2 = tl.where(rmask & xmask, tmp6_m2_next, tmp6_m2)
tmp6_weight = tl.where(rmask & xmask, tmp6_weight_next, tmp6_weight)
tmp6_tmp, tmp7_tmp, tmp8_tmp = triton_helpers.welford(
tmp6_mean, tmp6_m2, tmp6_weight, 1
)
tmp6 = tmp6_tmp[:, None]
tmp7 = tmp7_tmp[:, None]
tmp8 = tmp8_tmp[:, None]
tl.store(out_ptr0 + (x3), tmp6, xmask)
tmp9 = 128.0
tmp10 = tmp7 / tmp9
tmp11 = 1e-05
tmp12 = tmp10 + tmp11
tmp13 = libdevice.rsqrt(tmp12)
tl.debug_barrier()
tl.store(in_out_ptr0 + (x3), tmp13, xmask)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp14 = tl.load(in_ptr0 + (x0 + (128*r2) + (16384*x1)), rmask & xmask, eviction_policy='evict_first', other=0.0)
tmp17 = tl.load(in_ptr1 + (r2 + (128*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0)
tmp21 = tl.load(in_ptr2 + (r2), rmask, eviction_policy='evict_last', other=0.0)
tmp23 = tl.load(in_ptr3 + (r2), rmask, eviction_policy='evict_last', other=0.0)
tmp15 = tl.full([1, 1], 0, tl.int32)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = tmp16 + tmp17
tmp19 = tmp18 - tmp6
tmp20 = tmp19 * tmp13
tmp22 = tmp20 * tmp21
tmp24 = tmp22 + tmp23
tl.store(out_ptr1 + (r2 + (128*x3)), tmp24, rmask & xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (4, 128, 128), (16384, 128, 1))
assert_size_stride(primals_2, (128, 1, 7), (7, 7, 1))
assert_size_stride(primals_3, (128, ), (1, ))
assert_size_stride(primals_4, (128, 128, 1), (128, 1, 1))
assert_size_stride(primals_5, (128, ), (1, ))
assert_size_stride(primals_6, (128, ), (1, ))
assert_size_stride(primals_7, (128, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 128, 128), (16384, 128, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(primals_1, buf0, 512, 128, grid=grid(512, 128), stream=stream0)
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(3,), dilation=(1,), transposed=False, output_padding=(0,), groups=128, bias=None)
assert_size_stride(buf1, (4, 128, 128), (16384, 128, 1))
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 65536, grid=grid(65536), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf3, (4, 128, 128), (16384, 128, 1))
buf4 = buf3; del buf3 # reuse
# Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf4, primals_5, 65536, grid=grid(65536), stream=stream0)
del primals_5
buf5 = empty_strided_cuda((4, 128, 1), (128, 1, 1), torch.float32)
buf6 = empty_strided_cuda((4, 128, 1), (128, 1, 512), torch.float32)
buf8 = reinterpret_tensor(buf6, (4, 128, 1), (128, 1, 1), 0); del buf6 # reuse
buf9 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [add, layer_norm], Original ATen: [aten.add, aten.native_layer_norm]
triton_red_fused_add_native_layer_norm_2.run(buf8, buf4, primals_1, primals_6, primals_7, buf5, buf9, 512, 128, grid=grid(512), stream=stream0)
del primals_7
return (buf9, primals_1, primals_2, primals_4, primals_6, buf2, buf4, buf5, buf8, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 128, 128), (16384, 128, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((128, 1, 7), (7, 7, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((128, 128, 1), (128, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn.functional as F
import torch.nn as nn
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.randn(32, 300, 20)
>>> output = m(input_tensor)
"""
def __init__(self, in_ch, out_ch, k, dim=1, relu=True):
"""
:param in_ch: input hidden dimension size
:param out_ch: output hidden dimension size
:param k: kernel size
:param dim: default 1. 1D conv or 2D conv
"""
super(DepthwiseSeparableConv, self).__init__()
self.relu = relu
if dim == 1:
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
elif dim == 2:
self.depthwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
else:
raise Exception('Incorrect dimension!')
def forward(self, x):
"""
:Input: (N, L_in, D)
:Output: (N, L_out, D)
"""
x = x.transpose(1, 2)
if self.relu:
out = F.relu(self.pointwise_conv(self.depthwise_conv(x)),
inplace=True)
else:
out = self.pointwise_conv(self.depthwise_conv(x))
return out.transpose(1, 2)
class ConvEncoder(nn.Module):
def __init__(self, kernel_size=7, n_filters=128, dropout=0.1):
super(ConvEncoder, self).__init__()
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(n_filters)
self.conv = DepthwiseSeparableConv(in_ch=n_filters, out_ch=
n_filters, k=kernel_size, relu=True)
def forward(self, x):
"""
:param x: (N, L, D)
:return: (N, L, D)
"""
return self.layer_norm(self.dropout(self.conv(x)) + x)
def get_inputs():
return [torch.rand([4, 128, 128])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional as F
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 512
xnumel = 128
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 128
y1 = yindex // 128
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 16384 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 128 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 128 % 128
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_red_fused_add_native_layer_norm_2(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.
constexpr, RBLOCK: tl.constexpr):
xnumel = 512
rnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rbase = tl.arange(0, RBLOCK)[None, :]
x0 = xindex % 128
x1 = xindex // 128
x3 = xindex
tmp6_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp6_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
tmp6_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp0 = tl.load(in_ptr0 + (x0 + 128 * r2 + 16384 * x1), rmask &
xmask, eviction_policy='evict_last', other=0.0)
tmp3 = tl.load(in_ptr1 + (r2 + 128 * x3), rmask & xmask,
eviction_policy='evict_last', other=0.0)
tmp1 = tl.full([1, 1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp4 = tmp2 + tmp3
tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
tmp6_mean_next, tmp6_m2_next, tmp6_weight_next = (triton_helpers.
welford_reduce(tmp5, tmp6_mean, tmp6_m2, tmp6_weight, roffset == 0)
)
tmp6_mean = tl.where(rmask & xmask, tmp6_mean_next, tmp6_mean)
tmp6_m2 = tl.where(rmask & xmask, tmp6_m2_next, tmp6_m2)
tmp6_weight = tl.where(rmask & xmask, tmp6_weight_next, tmp6_weight)
tmp6_tmp, tmp7_tmp, tmp8_tmp = triton_helpers.welford(tmp6_mean,
tmp6_m2, tmp6_weight, 1)
tmp6 = tmp6_tmp[:, None]
tmp7 = tmp7_tmp[:, None]
tmp8_tmp[:, None]
tl.store(out_ptr0 + x3, tmp6, xmask)
tmp9 = 128.0
tmp10 = tmp7 / tmp9
tmp11 = 1e-05
tmp12 = tmp10 + tmp11
tmp13 = libdevice.rsqrt(tmp12)
tl.debug_barrier()
tl.store(in_out_ptr0 + x3, tmp13, xmask)
for roffset in range(0, rnumel, RBLOCK):
rindex = roffset + rbase
rmask = rindex < rnumel
r2 = rindex
tmp14 = tl.load(in_ptr0 + (x0 + 128 * r2 + 16384 * x1), rmask &
xmask, eviction_policy='evict_first', other=0.0)
tmp17 = tl.load(in_ptr1 + (r2 + 128 * x3), rmask & xmask,
eviction_policy='evict_first', other=0.0)
tmp21 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_last',
other=0.0)
tmp23 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_last',
other=0.0)
tmp15 = tl.full([1, 1], 0, tl.int32)
tmp16 = triton_helpers.maximum(tmp15, tmp14)
tmp18 = tmp16 + tmp17
tmp19 = tmp18 - tmp6
tmp20 = tmp19 * tmp13
tmp22 = tmp20 * tmp21
tmp24 = tmp22 + tmp23
tl.store(out_ptr1 + (r2 + 128 * x3), tmp24, rmask & xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 128, 128), (16384, 128, 1))
assert_size_stride(primals_2, (128, 1, 7), (7, 7, 1))
assert_size_stride(primals_3, (128,), (1,))
assert_size_stride(primals_4, (128, 128, 1), (128, 1, 1))
assert_size_stride(primals_5, (128,), (1,))
assert_size_stride(primals_6, (128,), (1,))
assert_size_stride(primals_7, (128,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 128, 128), (16384, 128, 1), torch.float32
)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(512, 128)](primals_1, buf0, 512,
128, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(3,), dilation=(1,), transposed=False, output_padding=(
0,), groups=128, bias=None)
assert_size_stride(buf1, (4, 128, 128), (16384, 128, 1))
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(65536)](buf2, primals_3, 65536,
XBLOCK=512, num_warps=4, num_stages=1)
del primals_3
buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
padding=(0,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf3, (4, 128, 128), (16384, 128, 1))
buf4 = buf3
del buf3
triton_poi_fused_convolution_1[grid(65536)](buf4, primals_5, 65536,
XBLOCK=512, num_warps=4, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((4, 128, 1), (128, 1, 1), torch.float32)
buf6 = empty_strided_cuda((4, 128, 1), (128, 1, 512), torch.float32)
buf8 = reinterpret_tensor(buf6, (4, 128, 1), (128, 1, 1), 0)
del buf6
buf9 = buf0
del buf0
triton_red_fused_add_native_layer_norm_2[grid(512)](buf8, buf4,
primals_1, primals_6, primals_7, buf5, buf9, 512, 128, XBLOCK=
64, RBLOCK=8, num_warps=4, num_stages=1)
del primals_7
return (buf9, primals_1, primals_2, primals_4, primals_6, buf2, buf4,
buf5, buf8)
class DepthwiseSeparableConv(nn.Module):
"""
Depth-wise separable convolution uses less parameters to generate output by convolution.
:Examples:
>>> m = DepthwiseSeparableConv(300, 200, 5, dim=1)
>>> input_tensor = torch.randn(32, 300, 20)
>>> output = m(input_tensor)
"""
def __init__(self, in_ch, out_ch, k, dim=1, relu=True):
"""
:param in_ch: input hidden dimension size
:param out_ch: output hidden dimension size
:param k: kernel size
:param dim: default 1. 1D conv or 2D conv
"""
super(DepthwiseSeparableConv, self).__init__()
self.relu = relu
if dim == 1:
self.depthwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv1d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
elif dim == 2:
self.depthwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=in_ch, kernel_size=k, groups=in_ch, padding=k // 2)
self.pointwise_conv = nn.Conv2d(in_channels=in_ch, out_channels
=out_ch, kernel_size=1, padding=0)
else:
raise Exception('Incorrect dimension!')
def forward(self, x):
"""
:Input: (N, L_in, D)
:Output: (N, L_out, D)
"""
x = x.transpose(1, 2)
if self.relu:
out = F.relu(self.pointwise_conv(self.depthwise_conv(x)),
inplace=True)
else:
out = self.pointwise_conv(self.depthwise_conv(x))
return out.transpose(1, 2)
class ConvEncoderNew(nn.Module):
def __init__(self, kernel_size=7, n_filters=128, dropout=0.1):
super(ConvEncoderNew, self).__init__()
self.dropout = nn.Dropout(dropout)
self.layer_norm = nn.LayerNorm(n_filters)
self.conv = DepthwiseSeparableConv(in_ch=n_filters, out_ch=
n_filters, k=kernel_size, relu=True)
def forward(self, input_0):
primals_3 = self.layer_norm.weight
primals_5 = self.layer_norm.bias
primals_2 = self.conv.depthwise_conv.weight
primals_6 = self.conv.depthwise_conv.bias
primals_4 = self.conv.pointwise_conv.weight
primals_7 = self.conv.pointwise_conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
IsaacChanghau/ReLoCLNet
|
ConvEncoder
| false | 8,316 |
[
"MIT"
] | 31 |
56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
ConvChain
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/kp/ckpxyjrryss73jxepfvclfr2v4tabryusa2wzxofuqqgnilplkae.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# x => convolution
# x_1 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 64
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/6u/c6u2biqwyvxx5yl5ps7v6bfyd6r34dhrwbiozzfxsfyp374bhaqi.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# x_4 => convolution_2
# x_5 => relu_2
# Graph fragment:
# %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[4096],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 4096
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 64
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x3), tmp4, None)
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (64, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (64, ), (1, ))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64, ), (1, ))
assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (64, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_relu_0.run(buf1, primals_3, 4096, grid=grid(4096), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 4096, grid=grid(4096), stream=stream0)
del primals_5
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
buf5 = buf4; del buf4 # reuse
buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_1.run(buf5, primals_7, buf6, 4096, grid=grid(4096), stream=stream0)
del primals_7
return (buf5, primals_1, primals_2, primals_4, primals_6, buf1, buf3, buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
def _get_norm_layer(norm_layer, channels):
valid = ['instance', 'batch']
assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
if norm_layer == 'instance':
layer = nn.InstanceNorm2d(channels, affine=True)
elif norm_layer == 'batch':
layer = nn.BatchNorm2d(channels, affine=True)
nn.init.constant_(layer.bias, 0.0)
nn.init.constant_(layer.weight, 1.0)
return layer
class ConvModule(nn.Module):
"""Basic convolution module with conv + norm(optional) + activation(optional).
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
ksize(int): size of the convolution kernel (square).
stride(int): downsampling factor
pad(bool): if True, pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
padding_mode='zero', activation=None, norm_layer=None):
super(ConvModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
n_in)
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
n_out)
assert isinstance(ksize, int
) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
ksize)
assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
padding = (ksize - 1) // 2 if pad else 0
use_bias_in_conv = norm_layer is None
conv_pad = padding
if padding_mode == 'reflection' and padding > 0:
self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
conv_pad = 0
else:
self.add_module('no-op', nn.Identity())
self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
padding=conv_pad, bias=use_bias_in_conv))
if norm_layer is not None:
self.add_module('norm', _get_norm_layer(norm_layer, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
_init_fc_or_conv(self.conv, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class ConvChain(nn.Module):
"""Linear chain of convolution layers.
Args:
n_in(int): number of input channels.
ksize(int or list of int): size of the convolution kernel (square).
width(int or list of int): number of features channels in the intermediate layers.
depth(int): number of layers
strides(list of int): stride between kernels. If None, defaults to 1 for all.
pad(bool): if True, zero pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, ksize=3, width=64, depth=3, strides=None, pad=
True, padding_mode='zero', activation='relu', norm_layer=None):
super(ConvChain, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(ksize, int) and ksize > 0 or isinstance(ksize, list
), 'Kernel size should be a positive integer or a list of integers'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
assert isinstance(width, int) or isinstance(width, list
), 'Width should be a list or an int'
_in = [n_in]
if strides is None:
_strides = [1] * depth
else:
assert isinstance(strides, list), 'strides should be a list'
assert len(strides
) == depth, 'strides should have `depth` elements'
_strides = strides
if isinstance(width, int):
_in = _in + [width] * (depth - 1)
_out = [width] * depth
elif isinstance(width, list):
assert len(width
) == depth, 'Specifying width with a list should have `depth` elements'
_in = _in + width[:-1]
_out = width
if isinstance(ksize, int):
_ksizes = [ksize] * depth
elif isinstance(ksize, list):
assert len(ksize
) == depth, "kernel size list should have 'depth' entries"
_ksizes = ksize
_activations = [activation] * depth
_norms = [norm_layer] * depth
for lvl in range(depth):
self.add_module('conv{}'.format(lvl), ConvModule(_in[lvl], _out
[lvl], _ksizes[lvl], stride=_strides[lvl], pad=pad,
activation=_activations[lvl], norm_layer=_norms[lvl]))
def forward(self, x):
for m in self.children():
x = m(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_in': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x3, tmp4, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 64
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x3, tmp4, None)
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (64, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (64,), (1,))
assert_size_stride(primals_4, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_5, (64,), (1,))
assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_7, (64,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,
1), padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 64, 4, 4), (1024, 16, 4, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_relu_0[grid(4096)](buf1, primals_3,
4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 64, 4, 4), (1024, 16, 4, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_0[grid(4096)](buf3, primals_5,
4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_5
buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1))
buf5 = buf4
del buf4
buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_1[grid(4096)](buf5
, primals_7, buf6, 4096, XBLOCK=128, num_warps=4, num_stages=1)
del primals_7
return buf5, primals_1, primals_2, primals_4, primals_6, buf1, buf3, buf6
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
def _get_norm_layer(norm_layer, channels):
valid = ['instance', 'batch']
assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
if norm_layer == 'instance':
layer = nn.InstanceNorm2d(channels, affine=True)
elif norm_layer == 'batch':
layer = nn.BatchNorm2d(channels, affine=True)
nn.init.constant_(layer.bias, 0.0)
nn.init.constant_(layer.weight, 1.0)
return layer
class ConvModule(nn.Module):
"""Basic convolution module with conv + norm(optional) + activation(optional).
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
ksize(int): size of the convolution kernel (square).
stride(int): downsampling factor
pad(bool): if True, pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
padding_mode='zero', activation=None, norm_layer=None):
super(ConvModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
n_in)
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
n_out)
assert isinstance(ksize, int
) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
ksize)
assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
padding = (ksize - 1) // 2 if pad else 0
use_bias_in_conv = norm_layer is None
conv_pad = padding
if padding_mode == 'reflection' and padding > 0:
self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
conv_pad = 0
else:
self.add_module('no-op', nn.Identity())
self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
padding=conv_pad, bias=use_bias_in_conv))
if norm_layer is not None:
self.add_module('norm', _get_norm_layer(norm_layer, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
_init_fc_or_conv(self.conv, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class ConvChainNew(nn.Module):
"""Linear chain of convolution layers.
Args:
n_in(int): number of input channels.
ksize(int or list of int): size of the convolution kernel (square).
width(int or list of int): number of features channels in the intermediate layers.
depth(int): number of layers
strides(list of int): stride between kernels. If None, defaults to 1 for all.
pad(bool): if True, zero pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, ksize=3, width=64, depth=3, strides=None, pad=
True, padding_mode='zero', activation='relu', norm_layer=None):
super(ConvChainNew, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer'
assert isinstance(ksize, int) and ksize > 0 or isinstance(ksize, list
), 'Kernel size should be a positive integer or a list of integers'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
assert isinstance(width, int) or isinstance(width, list
), 'Width should be a list or an int'
_in = [n_in]
if strides is None:
_strides = [1] * depth
else:
assert isinstance(strides, list), 'strides should be a list'
assert len(strides
) == depth, 'strides should have `depth` elements'
_strides = strides
if isinstance(width, int):
_in = _in + [width] * (depth - 1)
_out = [width] * depth
elif isinstance(width, list):
assert len(width
) == depth, 'Specifying width with a list should have `depth` elements'
_in = _in + width[:-1]
_out = width
if isinstance(ksize, int):
_ksizes = [ksize] * depth
elif isinstance(ksize, list):
assert len(ksize
) == depth, "kernel size list should have 'depth' entries"
_ksizes = ksize
_activations = [activation] * depth
_norms = [norm_layer] * depth
for lvl in range(depth):
self.add_module('conv{}'.format(lvl), ConvModule(_in[lvl], _out
[lvl], _ksizes[lvl], stride=_strides[lvl], pad=pad,
activation=_activations[lvl], norm_layer=_norms[lvl]))
def forward(self, input_0):
primals_2 = self.conv0.conv.weight
primals_3 = self.conv0.conv.bias
primals_4 = self.conv1.conv.weight
primals_5 = self.conv1.conv.bias
primals_6 = self.conv2.conv.weight
primals_7 = self.conv2.conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
IlyaBizyaev/ttools
|
ConvChain
| false | 8,317 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
Conv1d
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/y7/cy75pv62flzgueapzlnlplljec2lmbrjxeuhmgi2gb5pjyl6sna4.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# y_1 => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [2], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/xk/cxkffkiz6eieexc3hfbeczljvabsfcx2g3a23xartvu3zhymn52o.py
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# y_1 => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [2], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[128],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 5) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(primals_1, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(2,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
del buf0
buf2 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [y_1], Original ATen: [aten.convolution]
triton_poi_fused_convolution_1.run(buf2, primals_3, 80, grid=grid(80), stream=stream0)
del primals_3
return (reinterpret_tensor(buf2, (4, 4, 4), (20, 1, 5), 0), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class Conv1d(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
if padding == 'same':
padding = kernel_size // 2 * dilation
self.even_kernel = not bool(kernel_size % 2)
super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=
groups, bias=bias)
self.activation_fn = activation_fn(inplace=True
) if activation_fn is not None else None
self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
self.layer_norm = nn.LayerNorm(out_channels) if ln else None
def forward(self, x):
y = x.transpose(1, 2)
y = super(Conv1d, self).forward(y)
y = y.transpose(1, 2)
y = self.layer_norm(y) if self.layer_norm is not None else y
y = self.activation_fn(y) if self.activation_fn is not None else y
y = self.drop_out(y) if self.drop_out is not None else y
y = y[:, :-1, :] if self.even_kernel else y
return y
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 80
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 5 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_3, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
padding=(2,), dilation=(1,), transposed=False, output_padding=(
0,), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 5), (20, 5, 1))
del buf0
buf2 = buf1
del buf1
triton_poi_fused_convolution_1[grid(80)](buf2, primals_3, 80,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_3
return reinterpret_tensor(buf2, (4, 4, 4), (20, 1, 5), 0
), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0)
class Conv1dNew(nn.Conv1d):
"""
Convolution 1d
Args:
x: (N, T, C_in)
Returns:
y: (N, T, C_out)
"""
def __init__(self, in_channels, out_channels, kernel_size,
activation_fn=None, drop_rate=0.0, stride=1, padding='same',
dilation=1, groups=1, bias=True, ln=False):
if padding == 'same':
padding = kernel_size // 2 * dilation
self.even_kernel = not bool(kernel_size % 2)
super(Conv1dNew, self).__init__(in_channels, out_channels,
kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.activation_fn = activation_fn(inplace=True
) if activation_fn is not None else None
self.drop_out = nn.Dropout(drop_rate) if drop_rate > 0 else None
self.layer_norm = nn.LayerNorm(out_channels) if ln else None
def forward(self, input_0):
primals_1 = self.weight
primals_3 = self.bias
primals_2 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
Jackson-Kang/VQVC-Pytorch
|
Conv1d
| false | 8,318 |
[
"MIT"
] | 13 |
d2267b5c52253b6ae11a5767963a65320ae335c2
|
https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2
|
KLLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/3m/c3mmvssnolnbggtuenzwxjez4yavlmvt63pkzhp2niftmwmxo6gx.py
# Topologically Sorted Source Nodes: [neg, exp_min_alpha, smooth_l1, mul_1, half_alpha, add, total_kl_loss], Original ATen: [aten.neg, aten.exp, aten.smooth_l1_loss, aten.mul, aten.add, aten.sum]
# Source node to ATen node mapping:
# add => add
# exp_min_alpha => exp
# half_alpha => mul_1
# mul_1 => mul_2
# neg => neg
# smooth_l1 => abs_1, div, lt, mul, pow_1, sub, sub_1, where
# total_kl_loss => sum_1
# Graph fragment:
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg2_1,), kwargs = {})
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
# %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
# %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 1.0), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 1.0), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.5), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div, %sub_1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %where), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, 0.5), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_1), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add,), kwargs = {})
triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0 = async_compile.triton('triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 256],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel):
xnumel = 1
XBLOCK: tl.constexpr = 1
rnumel = 256
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = tl.full([1], xoffset, tl.int32)
xmask = tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
roffset = 0
rmask = tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp3 = tl.load(in_ptr1 + (r0), None)
tmp4 = tl.load(in_ptr2 + (r0), None)
tmp1 = -tmp0
tmp2 = tl_math.exp(tmp1)
tmp5 = tmp3 - tmp4
tmp6 = tl_math.abs(tmp5)
tmp7 = 1.0
tmp8 = tmp6 < tmp7
tmp9 = tmp6 * tmp6
tmp10 = 0.5
tmp11 = tmp9 * tmp10
tmp12 = tmp11 * tmp7
tmp13 = tmp6 - tmp10
tmp14 = tl.where(tmp8, tmp12, tmp13)
tmp15 = tmp2 * tmp14
tmp16 = tmp0 * tmp10
tmp17 = tmp15 + tmp16
tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp20, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
# Topologically Sorted Source Nodes: [neg, exp_min_alpha, smooth_l1, mul_1, half_alpha, add, total_kl_loss], Original ATen: [aten.neg, aten.exp, aten.smooth_l1_loss, aten.mul, aten.add, aten.sum]
stream0 = get_raw_stream(0)
triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0.run(arg2_1, arg1_1, arg0_1, buf0, 1, 256, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
del arg2_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.functional as F
from math import sqrt as sqrt
from itertools import product as product
class KLLoss(nn.Module):
"""
Kl-loss function for bounding box regression from CVPR 2019 paper:
Bounding Box Regression with Uncertainty for Accurate Object Detection
by Yihui He, Chenchen Zhu, Jianren Wang. Marios Savvides, Xiangyu Zhang
It is a replacement for the Smooth L1 loss often used in bounding box regression.
The regression loss for a coordinate depends on |xg − xe| > 1 or not:
Loss |xg − xe| ≤ 1:
Lreg1 ∝ e^{−α} * 1/2(xg − xe)^2 + 1/2α
and if |xg − xe| > 1, Loss:
Lreg2 = e^{−α} (|xg − xe| − 1/2) + 1/2α
PyTorch implementation by Jasper Bakker (JappaB @github)
"""
def __init__(self, loc_loss_weight=1.0):
super(KLLoss, self).__init__()
self.loc_loss_weight = loc_loss_weight
def forward(self, xg, xe, alpha):
"""
:param xg: The ground truth of the bounding box coordinates in x1y1x2y2 format
shape: [number_of_boxes, 4]
:param xe: The estimated bounding box coordinates in x1y1x2y2 format
shape: [number_of_boxes, 4]
:param alpha: The log(sigma^2) of the bounding box coordinates in x1y1x2y2 format
shape: [number_of_boxes, 4]
:return: total_kl_loss
"""
assert xg.shape == xe.shape and xg.shape == alpha.shape, 'The shapes of the input tensors must be the same'
smooth_l1 = F.smooth_l1_loss(xe, xg, reduction='none')
exp_min_alpha = torch.exp(-alpha)
half_alpha = 0.5 * alpha
total_kl_loss = (exp_min_alpha * smooth_l1 + half_alpha).sum()
return total_kl_loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
from math import sqrt as sqrt
from itertools import product as product
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, xnumel, rnumel):
XBLOCK: tl.constexpr = 1
RBLOCK: tl.constexpr = 256
xoffset = tl.program_id(0) * XBLOCK
tl.full([1], xoffset, tl.int32)
tl.full([RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[:]
tl.full([RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp3 = tl.load(in_ptr1 + r0, None)
tmp4 = tl.load(in_ptr2 + r0, None)
tmp1 = -tmp0
tmp2 = tl_math.exp(tmp1)
tmp5 = tmp3 - tmp4
tmp6 = tl_math.abs(tmp5)
tmp7 = 1.0
tmp8 = tmp6 < tmp7
tmp9 = tmp6 * tmp6
tmp10 = 0.5
tmp11 = tmp9 * tmp10
tmp12 = tmp11 * tmp7
tmp13 = tmp6 - tmp10
tmp14 = tl.where(tmp8, tmp12, tmp13)
tmp15 = tmp2 * tmp14
tmp16 = tmp0 * tmp10
tmp17 = tmp15 + tmp16
tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp20, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
get_raw_stream(0)
triton_per_fused_add_exp_mul_neg_smooth_l1_loss_sum_0[grid(1)](arg2_1,
arg1_1, arg0_1, buf0, 1, 256, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf0,
class KLLossNew(nn.Module):
"""
Kl-loss function for bounding box regression from CVPR 2019 paper:
Bounding Box Regression with Uncertainty for Accurate Object Detection
by Yihui He, Chenchen Zhu, Jianren Wang. Marios Savvides, Xiangyu Zhang
It is a replacement for the Smooth L1 loss often used in bounding box regression.
The regression loss for a coordinate depends on |xg − xe| > 1 or not:
Loss |xg − xe| ≤ 1:
Lreg1 ∝ e^{−α} * 1/2(xg − xe)^2 + 1/2α
and if |xg − xe| > 1, Loss:
Lreg2 = e^{−α} (|xg − xe| − 1/2) + 1/2α
PyTorch implementation by Jasper Bakker (JappaB @github)
"""
def __init__(self, loc_loss_weight=1.0):
super(KLLossNew, self).__init__()
self.loc_loss_weight = loc_loss_weight
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
JappaB/Active_Learning_Object_Detection
|
KLLoss
| false | 8,319 |
[
"MIT"
] | 21 |
3d9ad367aa872cbf3e9d71c566042c78fe2d0e76
|
https://github.com/JappaB/Active_Learning_Object_Detection/tree/3d9ad367aa872cbf3e9d71c566042c78fe2d0e76
|
ResidualBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/tt/ctthexrcgmoykvsyasq7xirwxi6m3yxgjocmuvarikaawgqvdiws.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# x => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/hc/chcdx6ttmlttffwusj5w6vjo2khj6mg5rgpxcpvcjyf4xor54xwb.py
# Topologically Sorted Source Nodes: [x_1, instance_norm], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
# instance_norm => add, rsqrt, var_mean
# x_1 => convolution
# Graph fragment:
# %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_per_fused__native_batch_norm_legit_convolution_1 = async_compile.triton('triton_per_fused__native_batch_norm_legit_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_convolution_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0, in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + (16*x3)), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tmp5 = tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(in_out_ptr0 + (r2 + (16*x3)), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + (x3), tmp23, xmask)
tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5g/c5gxks3yqvci4quqbwwhect6gkb5urcmnqe6r5cio2in2vodlfqy.py
# Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
# instance_norm => repeat
# Graph fragment:
# %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_4, [4]), kwargs = {})
triton_poi_fused_repeat_2 = async_compile.triton('triton_poi_fused_repeat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0 % 4), xmask)
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zo/czo4w4jiravqnnhuvvpxl7kim6nsxjmv6ck2k3djnldqrfvz45ss.py
# Topologically Sorted Source Nodes: [out, x_2], Original ATen: [aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => relu
# x_2 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
# %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_reflection_pad2d_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(out_ptr0 + (x3), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/le/cle3jd3j7s2us7otszcyhmv7sbn5v2qdpt3ghlkpbw7shhoxzv26.py
# Topologically Sorted Source Nodes: [x_3, out_1, out_2, out_3], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_1 => add_2, repeat_2, rsqrt_1, var_mean_1
# out_2 => add_4
# out_3 => relu_1
# x_3 => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_3, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %repeat_2 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%primals_8, [4]), kwargs = {})
# %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view_2, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
# %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_2,), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_3, %primals_1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4 = async_compile.triton('triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*i1', 9: '*fp32', 10: 'i32', 11: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 4, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr3, out_ptr4, out_ptr5, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
x0 = xindex
r3 = rindex
x1 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x0 % 4), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_out_ptr0 + (r3 + (16*x0)), xmask, other=0.0)
tmp2 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr3 + (r3 + (16*x0)), xmask, other=0.0)
tmp3 = tmp1 + tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp6 = tl.where(xmask, tmp4, 0)
tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp10 / tmp12
tmp14 = tmp4 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.where(xmask, tmp16, 0)
tmp19 = tl.sum(tmp18, 1)[:, None]
tmp20 = tmp3 - tmp13
tmp21 = 16.0
tmp22 = tmp19 / tmp21
tmp23 = 1e-05
tmp24 = tmp22 + tmp23
tmp25 = libdevice.rsqrt(tmp24)
tmp26 = tmp20 * tmp25
tmp27 = tmp26 * tmp0
tmp29 = tmp27 + tmp28
tmp31 = tmp29 + tmp30
tmp32 = tl.full([1, 1], 0, tl.int32)
tmp33 = triton_helpers.maximum(tmp32, tmp31)
tmp34 = 0.0
tmp35 = tmp33 <= tmp34
tl.store(out_ptr0 + (x0), tmp0, xmask)
tl.store(in_out_ptr0 + (r3 + (16*x0)), tmp3, xmask)
tl.store(out_ptr3 + (r3 + (16*x0)), tmp33, xmask)
tl.store(out_ptr4 + (r3 + (16*x0)), tmp35, xmask)
tl.store(out_ptr5 + (x0), tmp25, xmask)
tl.store(out_ptr1 + (x0), tmp13, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, ), (1, ))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4, ), (1, ))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1; del buf1 # reuse
buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf8 = reinterpret_tensor(buf6, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf6 # reuse
# Topologically Sorted Source Nodes: [x_1, instance_norm], Original ATen: [aten.convolution, aten._native_batch_norm_legit]
triton_per_fused__native_batch_norm_legit_convolution_1.run(buf2, buf8, primals_3, buf5, 16, 16, grid=grid(16), stream=stream0)
del primals_3
buf3 = empty_strided_cuda((16, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
triton_poi_fused_repeat_2.run(primals_4, buf3, 16, grid=grid(16), stream=stream0)
del primals_4
buf4 = empty_strided_cuda((16, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [instance_norm], Original ATen: [aten.repeat]
triton_poi_fused_repeat_2.run(primals_5, buf4, 16, grid=grid(16), stream=stream0)
del primals_5
buf9 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out, x_2], Original ATen: [aten.relu, aten.reflection_pad2d]
triton_poi_fused_reflection_pad2d_relu_3.run(buf2, buf5, buf8, buf3, buf4, buf9, 576, grid=grid(576), stream=stream0)
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
buf12 = empty_strided_cuda((16, ), (1, ), torch.float32)
buf11 = buf10; del buf10 # reuse
buf13 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
# Topologically Sorted Source Nodes: [x_3, out_1, out_2, out_3], Original ATen: [aten.convolution, aten.repeat, aten._native_batch_norm_legit, aten.add, aten.relu, aten.threshold_backward]
triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4.run(buf11, primals_8, primals_7, primals_9, primals_1, buf12, buf13, buf17, buf18, buf16, 16, 16, grid=grid(16), stream=stream0)
del primals_1
del primals_7
del primals_8
del primals_9
return (buf17, primals_2, primals_6, buf0, buf2, buf3, buf4, buf5, buf8, buf9, buf11, buf12, reinterpret_tensor(buf16, (16, ), (1, ), 0), buf18, reinterpret_tensor(buf13, (1, 16, 1, 1), (16, 1, 1, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class ConvLayer(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size, stride=stride)
self._pad = nn.ReflectionPad2d(padding=kernel_size // 2)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = self._pad(x)
x = self._conv(x)
return x
class ResidualBlock(nn.Module):
def __init__(self, channels: 'int', kernel_size: 'int'=3):
super(ResidualBlock, self).__init__()
self._conv1 = ConvLayer(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, stride=1)
self._in1 = nn.InstanceNorm2d(num_features=channels, affine=True)
self._relu = nn.ReLU()
self._conv2 = ConvLayer(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, stride=1)
self._in2 = nn.InstanceNorm2d(num_features=channels, affine=True)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
residual = x
out = self._relu(self._in1(self._conv1(x)))
out = self._in2(self._conv2(out))
out = out + residual
out = self._relu(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'channels': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_convolution_1(in_out_ptr0,
in_out_ptr1, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
x3 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (r2 + 16 * x3), xmask, other=0.0)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
tl.where(xmask, tmp3, 0)
tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tmp8 = tl.where(xmask, tmp6, 0)
tmp9 = tl.sum(tmp8, 1)[:, None]
tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp11 = tmp10.to(tl.float32)
tmp12 = tmp9 / tmp11
tmp13 = tmp3 - tmp12
tmp14 = tmp13 * tmp13
tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
tmp17 = tl.where(xmask, tmp15, 0)
tmp18 = tl.sum(tmp17, 1)[:, None]
tmp19 = 16.0
tmp20 = tmp18 / tmp19
tmp21 = 1e-05
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(in_out_ptr0 + (r2 + 16 * x3), tmp2, xmask)
tl.debug_barrier()
tl.store(in_out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr0 + x3, tmp12, xmask)
@triton.jit
def triton_poi_fused_repeat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0 % 4, xmask)
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused_reflection_pad2d_relu_3(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tl.store(out_ptr0 + x3, tmp10, xmask)
@triton.jit
def triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4(
in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1,
out_ptr3, out_ptr4, out_ptr5, xnumel, rnumel, XBLOCK: tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
x0 = xindex
r3 = rindex
x1 = xindex % 4
tmp0 = tl.load(in_ptr0 + x0 % 4, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_out_ptr0 + (r3 + 16 * x0), xmask, other=0.0)
tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp30 = tl.load(in_ptr3 + (r3 + 16 * x0), xmask, other=0.0)
tmp3 = tmp1 + tmp2
tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
tl.where(xmask, tmp4, 0)
tmp7 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
tmp9 = tl.where(xmask, tmp7, 0)
tmp10 = tl.sum(tmp9, 1)[:, None]
tmp11 = tl.full([XBLOCK, 1], 16, tl.int32)
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp10 / tmp12
tmp14 = tmp4 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.where(xmask, tmp16, 0)
tmp19 = tl.sum(tmp18, 1)[:, None]
tmp20 = tmp3 - tmp13
tmp21 = 16.0
tmp22 = tmp19 / tmp21
tmp23 = 1e-05
tmp24 = tmp22 + tmp23
tmp25 = libdevice.rsqrt(tmp24)
tmp26 = tmp20 * tmp25
tmp27 = tmp26 * tmp0
tmp29 = tmp27 + tmp28
tmp31 = tmp29 + tmp30
tmp32 = tl.full([1, 1], 0, tl.int32)
tmp33 = triton_helpers.maximum(tmp32, tmp31)
tmp34 = 0.0
tmp35 = tmp33 <= tmp34
tl.store(out_ptr0 + x0, tmp0, xmask)
tl.store(in_out_ptr0 + (r3 + 16 * x0), tmp3, xmask)
tl.store(out_ptr3 + (r3 + 16 * x0), tmp33, xmask)
tl.store(out_ptr4 + (r3 + 16 * x0), tmp35, xmask)
tl.store(out_ptr5 + x0, tmp25, xmask)
tl.store(out_ptr1 + x0, tmp13, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (4,), (1,))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576,
XBLOCK=128, num_warps=4, num_stages=1)
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = buf1
del buf1
buf5 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
buf6 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
)
buf8 = reinterpret_tensor(buf6, (1, 16, 1, 1), (16, 1, 1, 1), 0)
del buf6
triton_per_fused__native_batch_norm_legit_convolution_1[grid(16)](buf2,
buf8, primals_3, buf5, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
del primals_3
buf3 = empty_strided_cuda((16,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(16)](primals_4, buf3, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_4
buf4 = empty_strided_cuda((16,), (1,), torch.float32)
triton_poi_fused_repeat_2[grid(16)](primals_5, buf4, 16, XBLOCK=16,
num_warps=1, num_stages=1)
del primals_5
buf9 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32)
triton_poi_fused_reflection_pad2d_relu_3[grid(576)](buf2, buf5,
buf8, buf3, buf4, buf9, 576, XBLOCK=256, num_warps=4, num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1))
buf12 = empty_strided_cuda((16,), (1,), torch.float32)
buf11 = buf10
del buf10
buf13 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf16 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.
float32)
triton_per_fused__native_batch_norm_legit_add_convolution_relu_repeat_threshold_backward_4[
grid(16)](buf11, primals_8, primals_7, primals_9, primals_1,
buf12, buf13, buf17, buf18, buf16, 16, 16, XBLOCK=1, num_warps=
2, num_stages=1)
del primals_1
del primals_7
del primals_8
del primals_9
return (buf17, primals_2, primals_6, buf0, buf2, buf3, buf4, buf5, buf8,
buf9, buf11, buf12, reinterpret_tensor(buf16, (16,), (1,), 0),
buf18, reinterpret_tensor(buf13, (1, 16, 1, 1), (16, 1, 1, 1), 0))
class ConvLayer(nn.Module):
def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size:
'int', stride: 'int'):
super().__init__()
self._conv = nn.Conv2d(in_channels=in_channels, out_channels=
out_channels, kernel_size=kernel_size, stride=stride)
self._pad = nn.ReflectionPad2d(padding=kernel_size // 2)
def forward(self, x: 'torch.Tensor') ->torch.Tensor:
x = self._pad(x)
x = self._conv(x)
return x
class ResidualBlockNew(nn.Module):
def __init__(self, channels: 'int', kernel_size: 'int'=3):
super(ResidualBlockNew, self).__init__()
self._conv1 = ConvLayer(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, stride=1)
self._in1 = nn.InstanceNorm2d(num_features=channels, affine=True)
self._relu = nn.ReLU()
self._conv2 = ConvLayer(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, stride=1)
self._in2 = nn.InstanceNorm2d(num_features=channels, affine=True)
def forward(self, input_0):
primals_2 = self._conv1._conv.weight
primals_3 = self._conv1._conv.bias
primals_4 = self._in1.weight
primals_5 = self._in1.bias
primals_6 = self._conv2._conv.weight
primals_7 = self._conv2._conv.bias
primals_8 = self._in2.weight
primals_9 = self._in2.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9])
return output[0]
|
Inkln/StyleTransferWithCatalyst
|
ResidualBlock
| false | 8,320 |
[
"Apache-2.0"
] | 11 |
c3181ecdfd32160907efc2d9d917a55925c25c11
|
https://github.com/Inkln/StyleTransferWithCatalyst/tree/c3181ecdfd32160907efc2d9d917a55925c25c11
|
FixupBasicBlock
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/lw/clwmhv3ard4vibrwx3znjaja7nsm4gxiuq5s4gjf3wv47kclvepv.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
# add => add
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %primals_2), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/pq/cpqh6pncbdjs5yqop2lntujmwkodk2itembvrnwkr2h676tdleps.py
# Topologically Sorted Source Nodes: [add_1, out_1, add_2], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# add_1 => add_1
# add_2 => add_2
# out_1 => relu
# Graph fragment:
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %primals_4), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
# %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %primals_5), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp8 = tmp5 + tmp7
tmp9 = 0.0
tmp10 = tmp5 <= tmp9
tl.store(out_ptr0 + (x0), tmp8, xmask)
tl.store(out_ptr1 + (x0), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/47/c47xvberxpgbdgn3ek75i37q5oszudgkxhedwqh5uxjm5xiep43i.py
# Topologically Sorted Source Nodes: [mul, out_3, out_4, out_5], Original ATen: [aten.mul, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# mul => mul
# out_3 => add_3
# out_4 => add_4
# out_5 => relu_1
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, %primals_7), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_8), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_mul_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_add_mul_relu_threshold_backward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_relu_threshold_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + (0))
tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
tmp7 = tl.load(in_ptr3 + (x0), xmask)
tmp3 = tmp0 * tmp2
tmp6 = tmp3 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = 0.0
tmp12 = tmp10 <= tmp11
tl.store(out_ptr0 + (x0), tmp10, xmask)
tl.store(out_ptr1 + (x0), tmp12, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, ), (1, ))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (1, ), (1, ))
assert_size_stride(primals_5, (1, ), (1, ))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (1, ), (1, ))
assert_size_stride(primals_8, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [add_1, out_1, add_2], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_relu_threshold_backward_1.run(buf1, primals_4, primals_5, buf2, buf6, 256, grid=grid(256), stream=stream0)
del primals_4
del primals_5
# Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf1; del buf1 # reuse
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [mul, out_3, out_4, out_5], Original ATen: [aten.mul, aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_mul_relu_threshold_backward_2.run(buf3, primals_7, primals_8, primals_1, buf4, buf5, 256, grid=grid(256), stream=stream0)
del primals_1
del primals_8
return (buf4, primals_3, primals_6, primals_7, buf0, buf2, buf3, buf5, buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class FixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(FixupBasicBlock, self).__init__()
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = conv3x3(inplanes, planes, stride)
self.bias1b = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.bias2a = nn.Parameter(torch.zeros(1))
self.conv2 = conv3x3(planes, planes)
self.scale = nn.Parameter(torch.ones(1))
self.bias2b = nn.Parameter(torch.zeros(1))
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.relu(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = out * self.scale + self.bias2b
if self.downsample is not None:
identity = self.downsample(x + self.bias1a)
identity = torch.cat((identity, torch.zeros_like(identity)), 1)
out += identity
out = self.relu(out)
return out
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'inplanes': 4, 'planes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp6 = tl.load(in_ptr2 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp3 = tmp0 + tmp2
tmp4 = tl.full([1], 0, tl.int32)
tmp5 = triton_helpers.maximum(tmp4, tmp3)
tmp8 = tmp5 + tmp7
tmp9 = 0.0
tmp10 = tmp5 <= tmp9
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp10, xmask)
@triton.jit
def triton_poi_fused_add_mul_relu_threshold_backward_2(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp4 = tl.load(in_ptr2 + 0)
tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
tmp7 = tl.load(in_ptr3 + x0, xmask)
tmp3 = tmp0 * tmp2
tmp6 = tmp3 + tmp5
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 0, tl.int32)
tmp10 = triton_helpers.maximum(tmp9, tmp8)
tmp11 = 0.0
tmp12 = tmp10 <= tmp11
tl.store(out_ptr0 + x0, tmp10, xmask)
tl.store(out_ptr1 + x0, tmp12, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1,), (1,))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (1,), (1,))
assert_size_stride(primals_5, (1,), (1,))
assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_7, (1,), (1,))
assert_size_stride(primals_8, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](primals_1, primals_2, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_relu_threshold_backward_1[grid(256)](buf1,
primals_4, primals_5, buf2, buf6, 256, XBLOCK=128, num_warps=4,
num_stages=1)
del primals_4
del primals_5
buf3 = extern_kernels.convolution(buf2, primals_6, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf1
del buf1
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_mul_relu_threshold_backward_2[grid(256)](buf3,
primals_7, primals_8, primals_1, buf4, buf5, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del primals_1
del primals_8
return buf4, primals_3, primals_6, primals_7, buf0, buf2, buf3, buf5, buf6
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class FixupBasicBlockNew(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(FixupBasicBlockNew, self).__init__()
self.bias1a = nn.Parameter(torch.zeros(1))
self.conv1 = conv3x3(inplanes, planes, stride)
self.bias1b = nn.Parameter(torch.zeros(1))
self.relu = nn.ReLU(inplace=True)
self.bias2a = nn.Parameter(torch.zeros(1))
self.conv2 = conv3x3(planes, planes)
self.scale = nn.Parameter(torch.ones(1))
self.bias2b = nn.Parameter(torch.zeros(1))
self.downsample = downsample
def forward(self, input_0):
primals_2 = self.bias1a
primals_4 = self.bias1b
primals_5 = self.bias2a
primals_7 = self.scale
primals_8 = self.bias2b
primals_3 = self.conv1.weight
primals_6 = self.conv2.weight
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
IlanPrice/DCTpS
|
FixupBasicBlock
| false | 8,321 |
[
"MIT"
] | 12 |
e3219ac132959f484724e0d0bd48a0cb8af3d0fa
|
https://github.com/IlanPrice/DCTpS/tree/e3219ac132959f484724e0d0bd48a0cb8af3d0fa
|
TrainablePositionalEncoding
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/vi/cvihm7vuecpwbow2wnjad4or47lkrqy6id7iwi6ead23ddqqwmaj.py
# Topologically Sorted Source Nodes: [position_ids_1], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
# position_ids_1 => repeat
# Graph fragment:
# %repeat : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze, [4, 1]), kwargs = {})
triton_poi_fused_repeat_0 = async_compile.triton('triton_poi_fused_repeat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_repeat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_repeat_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
tmp0 = x0
tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/eg/ceg7bhdwcuy6ttbiuyuxwhmt264m6ng4b4og6ioyziunyqmhrcqu.py
# Topologically Sorted Source Nodes: [position_embeddings], Original ATen: [aten.embedding]
# Source node to ATen node mapping:
# position_embeddings => embedding
# Graph fragment:
# %embedding : [num_users=2] = call_function[target=torch.ops.aten.embedding.default](args = (%primals_2, %repeat), kwargs = {})
triton_poi_fused_embedding_1 = async_compile.triton('triton_poi_fused_embedding_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_embedding_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_embedding_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex % 16
x4 = xindex
tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jf/cjfvkhv36qhzyzhuqr4ydrnpvjwbxldohhidwrsa67j3s5jtjtup.py
# Topologically Sorted Source Nodes: [add, embeddings], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# add => add
# embeddings => var_mean
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %embedding), kwargs = {})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_2 = async_compile.triton('triton_poi_fused_add_native_layer_norm_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + (x2), tmp16, xmask)
tl.store(out_ptr1 + (x2), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jt/cjtcv6zwozpppau66htbz7h47ii2z42gwcw4nwxwd2m3sajfekxr.py
# Topologically Sorted Source Nodes: [add, embeddings], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# add => add
# embeddings => add_1, add_2, mul, mul_1, rsqrt, sub
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %embedding), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_3), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_4), kwargs = {})
triton_poi_fused_add_native_layer_norm_3 = async_compile.triton('triton_poi_fused_add_native_layer_norm_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex % 64
x5 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x5), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x3), tmp13, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.int64)
# Topologically Sorted Source Nodes: [position_ids_1], Original ATen: [aten.repeat]
stream0 = get_raw_stream(0)
triton_poi_fused_repeat_0.run(buf0, 16, grid=grid(16), stream=stream0)
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [position_embeddings], Original ATen: [aten.embedding]
triton_poi_fused_embedding_1.run(primals_2, buf1, 64, grid=grid(64), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
# Topologically Sorted Source Nodes: [add, embeddings], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_2.run(primals_1, buf1, buf2, buf3, 64, grid=grid(64), stream=stream0)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [add, embeddings], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_3.run(primals_1, buf1, buf2, buf3, primals_3, primals_4, buf4, 256, grid=grid(256), stream=stream0)
del buf2
del buf3
del primals_4
return (buf4, primals_1, primals_3, buf0, buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class TrainablePositionalEncoding(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
super(TrainablePositionalEncoding, self).__init__()
self.position_embeddings = nn.Embedding(max_position_embeddings,
hidden_size)
self.LayerNorm = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, input_feat):
bsz, seq_length = input_feat.shape[:2]
position_ids = torch.arange(seq_length, dtype=torch.long, device=
input_feat.device)
position_ids = position_ids.unsqueeze(0).repeat(bsz, 1)
position_embeddings = self.position_embeddings(position_ids)
embeddings = self.LayerNorm(input_feat + position_embeddings)
embeddings = self.dropout(embeddings)
return embeddings
def add_position_emb(self, input_feat):
bsz, seq_length = input_feat.shape[:2]
position_ids = torch.arange(seq_length, dtype=torch.long, device=
input_feat.device)
position_ids = position_ids.unsqueeze(0).repeat(bsz, 1)
position_embeddings = self.position_embeddings(position_ids)
return input_feat + position_embeddings
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'max_position_embeddings': 4, 'hidden_size': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_repeat_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x2 = xindex
tmp0 = x0
tl.store(out_ptr0 + x2, tmp0, xmask)
@triton.jit
def triton_poi_fused_embedding_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex % 16
x4 = xindex
tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + x4, tmp0, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 16
tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x2, tmp16, xmask)
tl.store(out_ptr1 + x2, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex % 64
x5 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x3, tmp13, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.int64)
get_raw_stream(0)
triton_poi_fused_repeat_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=
1, num_stages=1)
buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
triton_poi_fused_embedding_1[grid(64)](primals_2, buf1, 64, XBLOCK=
64, num_warps=1, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused_add_native_layer_norm_2[grid(64)](primals_1, buf1,
buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_3[grid(256)](primals_1, buf1,
buf2, buf3, primals_3, primals_4, buf4, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf2
del buf3
del primals_4
return buf4, primals_1, primals_3, buf0, buf1
class TrainablePositionalEncodingNew(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, max_position_embeddings, hidden_size, dropout=0.1):
super(TrainablePositionalEncodingNew, self).__init__()
self.position_embeddings = nn.Embedding(max_position_embeddings,
hidden_size)
self.LayerNorm = nn.LayerNorm(hidden_size)
self.dropout = nn.Dropout(dropout)
def add_position_emb(self, input_feat):
bsz, seq_length = input_feat.shape[:2]
position_ids = torch.arange(seq_length, dtype=torch.long, device=
input_feat.device)
position_ids = position_ids.unsqueeze(0).repeat(bsz, 1)
position_embeddings = self.position_embeddings(position_ids)
return input_feat + position_embeddings
def forward(self, input_0):
primals_2 = self.position_embeddings.weight
primals_3 = self.LayerNorm.weight
primals_4 = self.LayerNorm.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
IsaacChanghau/ReLoCLNet
|
TrainablePositionalEncoding
| false | 8,322 |
[
"MIT"
] | 31 |
56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
https://github.com/IsaacChanghau/ReLoCLNet/tree/56cb666ce516cce9acbcfce78fb4e95d81e11e54
|
FixupResidualChain
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/lw/clwmhv3ard4vibrwx3znjaja7nsm4gxiuq5s4gjf3wv47kclvepv.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
# Source node to ATen node mapping:
# x => add
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %primals_2), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (0))
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/fx/cfxyl7fil5puh5xllnvnypodhp2566clml7xolta3dccawk6glj2.py
# Topologically Sorted Source Nodes: [x_1, add_1, out, x_2], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# add_1 => add_1
# out => relu
# x_1 => convolution
# x_2 => add_2
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %primals_3, %primals_4, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %primals_5), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {})
# %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %primals_6), kwargs = {})
# %le_5 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp8 = tl.load(in_ptr3 + (0))
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 + tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp10 = tmp7 + tmp9
tmp11 = 0.0
tmp12 = tmp7 <= tmp11
tl.store(out_ptr0 + (x3), tmp10, xmask)
tl.store(out_ptr1 + (x3), tmp12, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/df/cdfbjg2xr76zlgllz2e47iqn5vkwjaemphyac2a7o4lzdfljxhy5.py
# Topologically Sorted Source Nodes: [x_3, mul, out_1, out_2, out_3, x_4], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu]
# Source node to ATen node mapping:
# mul => mul
# out_1 => add_3
# out_2 => add_4
# out_3 => relu_1
# x_3 => convolution_1
# x_4 => add_5
# Graph fragment:
# %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%add_2, %primals_7, %primals_8, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, %primals_9), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_10), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
# %relu_1 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
# %add_5 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_1, %primals_11), kwargs = {})
triton_poi_fused_add_convolution_mul_relu_2 = async_compile.triton('triton_poi_fused_add_convolution_mul_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + (x3), xmask)
tmp13 = tl.load(in_ptr4 + (0))
tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 0, tl.int32)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp15 = tmp12 + tmp14
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
tl.store(out_ptr0 + (x3), tmp15, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/yi/cyi2as2dup2abzoy2nrm3o4yozugua63gantx3cpc4wmp3bcewjn.py
# Topologically Sorted Source Nodes: [mul, out_1, out_2, out_3, x_7, mul_1, out_5, out_6, out_7, x_8], Original ATen: [aten.mul, aten.add, aten.relu, aten.convolution, aten.threshold_backward]
# Source node to ATen node mapping:
# mul => mul
# mul_1 => mul_1
# out_1 => add_3
# out_2 => add_4
# out_3 => relu_1
# out_5 => add_8
# out_6 => add_9
# out_7 => relu_3
# x_7 => convolution_3
# x_8 => add_10
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_1, %primals_9), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_10), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %primals_1), kwargs = {})
# %relu_1 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_4,), kwargs = {})
# %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%add_7, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, %primals_18), kwargs = {})
# %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_19), kwargs = {})
# %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %relu_1), kwargs = {})
# %relu_3 : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_9,), kwargs = {})
# %add_10 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_3, %primals_20), kwargs = {})
# %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_convolution_mul_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_add_convolution_mul_relu_threshold_backward_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*i1', 11: '*fp32', 12: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_relu_threshold_backward_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_threshold_backward_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + (x3), xmask)
tmp10 = tl.load(in_ptr4 + (0))
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp13 = tl.load(in_ptr5 + (0))
tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
tmp16 = tl.load(in_ptr6 + (x3), xmask)
tmp24 = tl.load(in_ptr7 + (0))
tmp25 = tl.broadcast_to(tmp24, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp12 = tmp9 * tmp11
tmp15 = tmp12 + tmp14
tmp17 = tmp15 + tmp16
tmp18 = tl.full([1], 0, tl.int32)
tmp19 = triton_helpers.maximum(tmp18, tmp17)
tmp20 = tmp8 + tmp19
tmp21 = triton_helpers.maximum(tmp18, tmp20)
tmp22 = 0.0
tmp23 = tmp19 <= tmp22
tmp26 = tmp21 + tmp25
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
tl.store(out_ptr0 + (x3), tmp21, xmask)
tl.store(out_ptr1 + (x3), tmp23, xmask)
tl.store(out_ptr2 + (x3), tmp26, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5q/c5qfif223ltpcxca3duvqwsq43t27metziiv43ejexavdyxtnimh.py
# Topologically Sorted Source Nodes: [x_11, mul_2, out_9, out_10, out_11], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# mul_2 => mul_2
# out_10 => add_14
# out_11 => relu_5
# out_9 => add_13
# x_11 => convolution_5
# Graph fragment:
# %convolution_5 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%add_12, %primals_25, %primals_26, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_5, %primals_27), kwargs = {})
# %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_28), kwargs = {})
# %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_13, %relu_3), kwargs = {})
# %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_14,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_5, 0), kwargs = {})
# %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_add_convolution_mul_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_add_convolution_mul_relu_threshold_backward_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*i1', 7: '*i1', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_mul_relu_threshold_backward_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_threshold_backward_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (0))
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (0))
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 0, tl.int32)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp13 = 0.0
tmp14 = tmp12 <= tmp13
tmp15 = tmp9 <= tmp13
tl.store(in_out_ptr0 + (x3), tmp2, xmask)
tl.store(out_ptr0 + (x3), tmp12, xmask)
tl.store(out_ptr1 + (x3), tmp14, xmask)
tl.store(out_ptr2 + (x3), tmp15, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1, ), (1, ))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (4, ), (1, ))
assert_size_stride(primals_5, (1, ), (1, ))
assert_size_stride(primals_6, (1, ), (1, ))
assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_8, (4, ), (1, ))
assert_size_stride(primals_9, (1, ), (1, ))
assert_size_stride(primals_10, (1, ), (1, ))
assert_size_stride(primals_11, (1, ), (1, ))
assert_size_stride(primals_12, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_13, (4, ), (1, ))
assert_size_stride(primals_14, (1, ), (1, ))
assert_size_stride(primals_15, (1, ), (1, ))
assert_size_stride(primals_16, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_17, (4, ), (1, ))
assert_size_stride(primals_18, (1, ), (1, ))
assert_size_stride(primals_19, (1, ), (1, ))
assert_size_stride(primals_20, (1, ), (1, ))
assert_size_stride(primals_21, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_22, (4, ), (1, ))
assert_size_stride(primals_23, (1, ), (1, ))
assert_size_stride(primals_24, (1, ), (1, ))
assert_size_stride(primals_25, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_26, (4, ), (1, ))
assert_size_stride(primals_27, (1, ), (1, ))
assert_size_stride(primals_28, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_1, add_1, out, x_2], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf1, primals_4, primals_5, primals_6, buf2, buf22, 256, grid=grid(256), stream=stream0)
del primals_4
del primals_5
del primals_6
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3; del buf3 # reuse
buf5 = buf1; del buf1 # reuse
# Topologically Sorted Source Nodes: [x_3, mul, out_1, out_2, out_3, x_4], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu]
triton_poi_fused_add_convolution_mul_relu_2.run(buf4, primals_8, primals_9, primals_10, primals_1, primals_11, buf5, 256, grid=grid(256), stream=stream0)
del primals_11
del primals_8
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
buf6 = extern_kernels.convolution(buf5, primals_12, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_5, add_5, out_4, x_6], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf6, primals_13, primals_14, primals_15, buf7, buf20, 256, grid=grid(256), stream=stream0)
del primals_13
del primals_14
del primals_15
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.convolution]
buf8 = extern_kernels.convolution(buf7, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
buf9 = buf8; del buf8 # reuse
buf10 = buf6; del buf6 # reuse
buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mul, out_1, out_2, out_3, x_7, mul_1, out_5, out_6, out_7, x_8], Original ATen: [aten.mul, aten.add, aten.relu, aten.convolution, aten.threshold_backward]
triton_poi_fused_add_convolution_mul_relu_threshold_backward_3.run(buf9, primals_17, primals_18, primals_19, buf4, primals_9, primals_10, primals_1, primals_20, buf10, buf21, buf11, 256, grid=grid(256), stream=stream0)
del primals_1
del primals_10
del primals_17
del primals_19
del primals_20
# Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.convolution]
buf12 = extern_kernels.convolution(buf11, primals_21, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_9, add_9, out_8, x_10], Original ATen: [aten.convolution, aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_convolution_relu_threshold_backward_1.run(buf12, primals_22, primals_23, primals_24, buf13, buf18, 256, grid=grid(256), stream=stream0)
del primals_22
del primals_23
del primals_24
# Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
buf14 = extern_kernels.convolution(buf13, primals_25, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 4, 4, 4), (64, 16, 4, 1))
buf15 = buf14; del buf14 # reuse
buf16 = buf12; del buf12 # reuse
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_11, mul_2, out_9, out_10, out_11], Original ATen: [aten.convolution, aten.mul, aten.add, aten.relu, aten.threshold_backward]
triton_poi_fused_add_convolution_mul_relu_threshold_backward_4.run(buf15, primals_26, primals_27, primals_28, buf10, buf16, buf17, buf19, 256, grid=grid(256), stream=stream0)
del buf10
del primals_26
del primals_28
return (buf16, primals_3, primals_7, primals_9, primals_12, primals_16, primals_18, primals_21, primals_25, primals_27, buf0, buf2, buf4, buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf18, buf19, buf20, buf21, buf22, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_20 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_21 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_22 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_23 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_24 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_25 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_26 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_27 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_28 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch as th
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
def _get_norm_layer(norm_layer, channels):
valid = ['instance', 'batch']
assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
if norm_layer == 'instance':
layer = nn.InstanceNorm2d(channels, affine=True)
elif norm_layer == 'batch':
layer = nn.BatchNorm2d(channels, affine=True)
nn.init.constant_(layer.bias, 0.0)
nn.init.constant_(layer.weight, 1.0)
return layer
class ConvModule(nn.Module):
"""Basic convolution module with conv + norm(optional) + activation(optional).
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
ksize(int): size of the convolution kernel (square).
stride(int): downsampling factor
pad(bool): if True, pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
padding_mode='zero', activation=None, norm_layer=None):
super(ConvModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
n_in)
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
n_out)
assert isinstance(ksize, int
) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
ksize)
assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
padding = (ksize - 1) // 2 if pad else 0
use_bias_in_conv = norm_layer is None
conv_pad = padding
if padding_mode == 'reflection' and padding > 0:
self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
conv_pad = 0
else:
self.add_module('no-op', nn.Identity())
self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
padding=conv_pad, bias=use_bias_in_conv))
if norm_layer is not None:
self.add_module('norm', _get_norm_layer(norm_layer, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
_init_fc_or_conv(self.conv, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class FixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, n_features, ksize=3, pad=True, padding_mode='zero',
activation='relu'):
super(FixupBasicBlock, self).__init__()
self.bias1a = nn.Parameter(th.zeros(1))
self.conv1 = ConvModule(n_features, n_features, ksize=ksize, stride
=1, pad=pad, activation=None, norm_layer=None, padding_mode=
padding_mode)
self.bias1b = nn.Parameter(th.zeros(1))
self.activation = _get_activation(activation)
self.bias2a = nn.Parameter(th.zeros(1))
self.conv2 = ConvModule(n_features, n_features, ksize=ksize, stride
=1, pad=pad, activation=None, norm_layer=None, padding_mode=
padding_mode)
self.scale = nn.Parameter(th.ones(1))
self.bias2b = nn.Parameter(th.zeros(1))
self.activation2 = _get_activation(activation)
self.ksize = 3
self.pad = pad
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.activation(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = out * self.scale + self.bias2b
crop = (self.ksize - 1) // 2 * 2
if crop > 0 and not self.pad:
identity = identity[:, :, crop:-crop, crop:-crop]
out += identity
out = self.activation2(out)
return out
class FixupResidualChain(nn.Module):
"""Linear chain of residual blocks.
Args:
n_features(int): number of input channels.
depth(int): number of residual blocks
ksize(int): size of the convolution kernel (square).
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
pad(bool): if True, zero pad the convs to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
"""
def __init__(self, n_features, depth=3, ksize=3, activation='relu',
norm_layer=None, pad=True, padding_mode='zero'):
super(FixupResidualChain, self).__init__()
assert isinstance(n_features, int
) and n_features > 0, 'Number of feature channels should be a positive integer'
assert isinstance(ksize, int) and ksize > 0 or isinstance(ksize, list
), 'Kernel size should be a positive integer or a list of integers'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
self.depth = depth
layers = OrderedDict()
for lvl in range(depth):
blockname = 'resblock{}'.format(lvl)
layers[blockname] = FixupBasicBlock(n_features, ksize=ksize,
activation=activation, pad=pad, padding_mode=padding_mode)
self.net = nn.Sequential(layers)
self._reset_weights()
def _reset_weights(self):
for m in self.net.modules():
if isinstance(m, FixupBasicBlock):
nn.init.normal_(m.conv1.conv.weight, mean=0, std=np.sqrt(2 /
(m.conv1.conv.weight.shape[0] * np.prod(m.conv1.conv.
weight.shape[2:]))) * self.depth ** -0.5)
nn.init.constant_(m.conv2.conv.weight, 0)
def forward(self, x):
x = self.net(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'n_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch as th
import torch.utils.data
import torch.nn as nn
from collections import OrderedDict
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + 0)
tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
tmp3 = tmp0 + tmp2
tl.store(out_ptr0 + x0, tmp3, xmask)
@triton.jit
def triton_poi_fused_add_convolution_relu_threshold_backward_1(in_ptr0,
in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp8 = tl.load(in_ptr3 + 0)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 + tmp4
tmp6 = tl.full([1], 0, tl.int32)
tmp7 = triton_helpers.maximum(tmp6, tmp5)
tmp10 = tmp7 + tmp9
tmp11 = 0.0
tmp12 = tmp7 <= tmp11
tl.store(out_ptr0 + x3, tmp10, xmask)
tl.store(out_ptr1 + x3, tmp12, xmask)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_2(in_out_ptr0, in_ptr0,
in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + x3, xmask)
tmp13 = tl.load(in_ptr4 + 0)
tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 0, tl.int32)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp15 = tmp12 + tmp14
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp15, xmask)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_threshold_backward_3(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7,
out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + x3, xmask)
tmp10 = tl.load(in_ptr4 + 0)
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp13 = tl.load(in_ptr5 + 0)
tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
tmp16 = tl.load(in_ptr6 + x3, xmask)
tmp24 = tl.load(in_ptr7 + 0)
tmp25 = tl.broadcast_to(tmp24, [XBLOCK])
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp12 = tmp9 * tmp11
tmp15 = tmp12 + tmp14
tmp17 = tmp15 + tmp16
tmp18 = tl.full([1], 0, tl.int32)
tmp19 = triton_helpers.maximum(tmp18, tmp17)
tmp20 = tmp8 + tmp19
tmp21 = triton_helpers.maximum(tmp18, tmp20)
tmp22 = 0.0
tmp23 = tmp19 <= tmp22
tmp26 = tmp21 + tmp25
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp21, xmask)
tl.store(out_ptr1 + x3, tmp23, xmask)
tl.store(out_ptr2 + x3, tmp26, xmask)
@triton.jit
def triton_poi_fused_add_convolution_mul_relu_threshold_backward_4(in_out_ptr0,
in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_out_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + 0)
tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
tmp6 = tl.load(in_ptr2 + 0)
tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
tmp9 = tl.load(in_ptr3 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp5 = tmp2 * tmp4
tmp8 = tmp5 + tmp7
tmp10 = tmp8 + tmp9
tmp11 = tl.full([1], 0, tl.int32)
tmp12 = triton_helpers.maximum(tmp11, tmp10)
tmp13 = 0.0
tmp14 = tmp12 <= tmp13
tmp15 = tmp9 <= tmp13
tl.store(in_out_ptr0 + x3, tmp2, xmask)
tl.store(out_ptr0 + x3, tmp12, xmask)
tl.store(out_ptr1 + x3, tmp14, xmask)
tl.store(out_ptr2 + x3, tmp15, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27, primals_28
) = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (1,), (1,))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (1,), (1,))
assert_size_stride(primals_6, (1,), (1,))
assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_8, (4,), (1,))
assert_size_stride(primals_9, (1,), (1,))
assert_size_stride(primals_10, (1,), (1,))
assert_size_stride(primals_11, (1,), (1,))
assert_size_stride(primals_12, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_13, (4,), (1,))
assert_size_stride(primals_14, (1,), (1,))
assert_size_stride(primals_15, (1,), (1,))
assert_size_stride(primals_16, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_17, (4,), (1,))
assert_size_stride(primals_18, (1,), (1,))
assert_size_stride(primals_19, (1,), (1,))
assert_size_stride(primals_20, (1,), (1,))
assert_size_stride(primals_21, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_22, (4,), (1,))
assert_size_stride(primals_23, (1,), (1,))
assert_size_stride(primals_24, (1,), (1,))
assert_size_stride(primals_25, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_26, (4,), (1,))
assert_size_stride(primals_27, (1,), (1,))
assert_size_stride(primals_28, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](primals_1, primals_2, buf0, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
buf1, primals_4, primals_5, primals_6, buf2, buf22, 256, XBLOCK
=128, num_warps=4, num_stages=1)
del primals_4
del primals_5
del primals_6
buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
buf4 = buf3
del buf3
buf5 = buf1
del buf1
triton_poi_fused_add_convolution_mul_relu_2[grid(256)](buf4,
primals_8, primals_9, primals_10, primals_1, primals_11, buf5,
256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_11
del primals_8
buf6 = extern_kernels.convolution(buf5, primals_12, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
buf6, primals_13, primals_14, primals_15, buf7, buf20, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_13
del primals_14
del primals_15
buf8 = extern_kernels.convolution(buf7, primals_16, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
buf9 = buf8
del buf8
buf10 = buf6
del buf6
buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_convolution_mul_relu_threshold_backward_3[grid
(256)](buf9, primals_17, primals_18, primals_19, buf4,
primals_9, primals_10, primals_1, primals_20, buf10, buf21,
buf11, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_1
del primals_10
del primals_17
del primals_19
del primals_20
buf12 = extern_kernels.convolution(buf11, primals_21, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf12, (4, 4, 4, 4), (64, 16, 4, 1))
buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf18 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_convolution_relu_threshold_backward_1[grid(256)](
buf12, primals_22, primals_23, primals_24, buf13, buf18, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_22
del primals_23
del primals_24
buf14 = extern_kernels.convolution(buf13, primals_25, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf14, (4, 4, 4, 4), (64, 16, 4, 1))
buf15 = buf14
del buf14
buf16 = buf12
del buf12
buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_add_convolution_mul_relu_threshold_backward_4[grid
(256)](buf15, primals_26, primals_27, primals_28, buf10, buf16,
buf17, buf19, 256, XBLOCK=128, num_warps=4, num_stages=1)
del buf10
del primals_26
del primals_28
return (buf16, primals_3, primals_7, primals_9, primals_12, primals_16,
primals_18, primals_21, primals_25, primals_27, buf0, buf2, buf4,
buf5, buf7, buf9, buf11, buf13, buf15, buf17, buf18, buf19, buf20,
buf21, buf22)
def _get_activation(activation):
valid = ['relu', 'leaky_relu', 'lrelu', 'tanh', 'sigmoid']
assert activation in valid, 'activation should be one of {}'.format(valid)
if activation == 'relu':
return nn.ReLU(inplace=True)
if activation == 'leaky_relu' or activation == 'lrelu':
return nn.LeakyReLU(inplace=True)
if activation == 'sigmoid':
return nn.Sigmoid()
if activation == 'tanh':
return nn.Tanh()
return None
def _init_fc_or_conv(fc_conv, activation):
gain = 1.0
if activation is not None:
gain = nn.init.calculate_gain(activation)
nn.init.xavier_uniform_(fc_conv.weight, gain)
if fc_conv.bias is not None:
nn.init.constant_(fc_conv.bias, 0.0)
def _get_norm_layer(norm_layer, channels):
valid = ['instance', 'batch']
assert norm_layer in valid, 'norm_layer should be one of {}'.format(valid)
if norm_layer == 'instance':
layer = nn.InstanceNorm2d(channels, affine=True)
elif norm_layer == 'batch':
layer = nn.BatchNorm2d(channels, affine=True)
nn.init.constant_(layer.bias, 0.0)
nn.init.constant_(layer.weight, 1.0)
return layer
class ConvModule(nn.Module):
"""Basic convolution module with conv + norm(optional) + activation(optional).
Args:
n_in(int): number of input channels.
n_out(int): number of output channels.
ksize(int): size of the convolution kernel (square).
stride(int): downsampling factor
pad(bool): if True, pad the convolutions to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
"""
def __init__(self, n_in, n_out, ksize=3, stride=1, pad=True,
padding_mode='zero', activation=None, norm_layer=None):
super(ConvModule, self).__init__()
assert isinstance(n_in, int
) and n_in > 0, 'Input channels should be a positive integer got {}'.format(
n_in)
assert isinstance(n_out, int
) and n_out > 0, 'Output channels should be a positive integer got {}'.format(
n_out)
assert isinstance(ksize, int
) and ksize > 0, 'Kernel size should be a positive integer got {}'.format(
ksize)
assert padding_mode in ['zero', 'reflection'], 'Invalid padding mode'
padding = (ksize - 1) // 2 if pad else 0
use_bias_in_conv = norm_layer is None
conv_pad = padding
if padding_mode == 'reflection' and padding > 0:
self.add_module('reflection_pad', nn.ReflectionPad2d(padding))
conv_pad = 0
else:
self.add_module('no-op', nn.Identity())
self.add_module('conv', nn.Conv2d(n_in, n_out, ksize, stride=stride,
padding=conv_pad, bias=use_bias_in_conv))
if norm_layer is not None:
self.add_module('norm', _get_norm_layer(norm_layer, n_out))
if activation is not None:
self.add_module('activation', _get_activation(activation))
_init_fc_or_conv(self.conv, activation)
def forward(self, x):
for c in self.children():
x = c(x)
return x
class FixupBasicBlock(nn.Module):
expansion = 1
def __init__(self, n_features, ksize=3, pad=True, padding_mode='zero',
activation='relu'):
super(FixupBasicBlock, self).__init__()
self.bias1a = nn.Parameter(th.zeros(1))
self.conv1 = ConvModule(n_features, n_features, ksize=ksize, stride
=1, pad=pad, activation=None, norm_layer=None, padding_mode=
padding_mode)
self.bias1b = nn.Parameter(th.zeros(1))
self.activation = _get_activation(activation)
self.bias2a = nn.Parameter(th.zeros(1))
self.conv2 = ConvModule(n_features, n_features, ksize=ksize, stride
=1, pad=pad, activation=None, norm_layer=None, padding_mode=
padding_mode)
self.scale = nn.Parameter(th.ones(1))
self.bias2b = nn.Parameter(th.zeros(1))
self.activation2 = _get_activation(activation)
self.ksize = 3
self.pad = pad
def forward(self, x):
identity = x
out = self.conv1(x + self.bias1a)
out = self.activation(out + self.bias1b)
out = self.conv2(out + self.bias2a)
out = out * self.scale + self.bias2b
crop = (self.ksize - 1) // 2 * 2
if crop > 0 and not self.pad:
identity = identity[:, :, crop:-crop, crop:-crop]
out += identity
out = self.activation2(out)
return out
class FixupResidualChainNew(nn.Module):
"""Linear chain of residual blocks.
Args:
n_features(int): number of input channels.
depth(int): number of residual blocks
ksize(int): size of the convolution kernel (square).
activation(str): nonlinear activation function between convolutions.
norm_layer(str): normalization to apply between the convolution modules.
pad(bool): if True, zero pad the convs to maintain a constant size.
padding_mode(str): 'zero' or 'reflection'.
"""
def __init__(self, n_features, depth=3, ksize=3, activation='relu',
norm_layer=None, pad=True, padding_mode='zero'):
super(FixupResidualChainNew, self).__init__()
assert isinstance(n_features, int
) and n_features > 0, 'Number of feature channels should be a positive integer'
assert isinstance(ksize, int) and ksize > 0 or isinstance(ksize, list
), 'Kernel size should be a positive integer or a list of integers'
assert isinstance(depth, int
) and depth > 0, 'Depth should be a positive integer'
self.depth = depth
layers = OrderedDict()
for lvl in range(depth):
blockname = 'resblock{}'.format(lvl)
layers[blockname] = FixupBasicBlock(n_features, ksize=ksize,
activation=activation, pad=pad, padding_mode=padding_mode)
self.net = nn.Sequential(layers)
self._reset_weights()
def _reset_weights(self):
for m in self.net.modules():
if isinstance(m, FixupBasicBlock):
nn.init.normal_(m.conv1.conv.weight, mean=0, std=np.sqrt(2 /
(m.conv1.conv.weight.shape[0] * np.prod(m.conv1.conv.
weight.shape[2:]))) * self.depth ** -0.5)
nn.init.constant_(m.conv2.conv.weight, 0)
def forward(self, input_0):
primals_2 = self.net.resblock0.bias1a
primals_5 = self.net.resblock0.bias1b
primals_6 = self.net.resblock0.bias2a
primals_9 = self.net.resblock0.scale
primals_10 = self.net.resblock0.bias2b
primals_3 = self.net.resblock0.conv1.conv.weight
primals_4 = self.net.resblock0.conv1.conv.bias
primals_7 = self.net.resblock0.conv2.conv.weight
primals_8 = self.net.resblock0.conv2.conv.bias
primals_11 = self.net.resblock1.bias1a
primals_14 = self.net.resblock1.bias1b
primals_15 = self.net.resblock1.bias2a
primals_18 = self.net.resblock1.scale
primals_19 = self.net.resblock1.bias2b
primals_12 = self.net.resblock1.conv1.conv.weight
primals_13 = self.net.resblock1.conv1.conv.bias
primals_16 = self.net.resblock1.conv2.conv.weight
primals_17 = self.net.resblock1.conv2.conv.bias
primals_20 = self.net.resblock2.bias1a
primals_23 = self.net.resblock2.bias1b
primals_24 = self.net.resblock2.bias2a
primals_27 = self.net.resblock2.scale
primals_28 = self.net.resblock2.bias2b
primals_21 = self.net.resblock2.conv1.conv.weight
primals_22 = self.net.resblock2.conv1.conv.bias
primals_25 = self.net.resblock2.conv2.conv.weight
primals_26 = self.net.resblock2.conv2.conv.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25, primals_26, primals_27, primals_28])
return output[0]
|
IlyaBizyaev/ttools
|
FixupResidualChain
| false | 8,323 |
[
"MIT"
] | 11 |
b1435b19f397ce1baff9daed3cb287e52a029fdb
|
https://github.com/IlyaBizyaev/ttools/tree/b1435b19f397ce1baff9daed3cb287e52a029fdb
|
WeightedSmoothL1Loss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/pi/cpichfxibui6mq4pmlhdz47ncspmnhv33wdmfin5ccqwxaz6gllg.py
# Topologically Sorted Source Nodes: [isnan, target, diff, n, lt, pow_1, mul, truediv, sub_1, loss], Original ATen: [aten.isnan, aten.where, aten.sub, aten.abs, aten.lt, aten.pow, aten.mul, aten.div]
# Source node to ATen node mapping:
# diff => sub
# isnan => isnan
# loss => where_1
# lt => lt
# mul => mul
# n => abs_1
# pow_1 => pow_1
# sub_1 => sub_1
# target => where
# truediv => div
# Graph fragment:
# %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%arg0_1,), kwargs = {})
# %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %arg1_1, %arg0_1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %where), kwargs = {})
# %abs_1 : [num_users=3] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
# %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%abs_1, 0.1111111111111111), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 2), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.5), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 0.1111111111111111), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%abs_1, 0.05555555555555555), kwargs = {})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %div, %sub_1), kwargs = {})
triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0 = async_compile.triton('triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask)
tmp2 = libdevice.isnan(tmp1).to(tl.int1)
tmp3 = tl.where(tmp2, tmp0, tmp1)
tmp4 = tmp0 - tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = 0.1111111111111111
tmp7 = tmp5 < tmp6
tmp8 = tmp5 * tmp5
tmp9 = 0.5
tmp10 = tmp8 * tmp9
tmp11 = 9.0
tmp12 = tmp10 * tmp11
tmp13 = 0.05555555555555555
tmp14 = tmp5 - tmp13
tmp15 = tl.where(tmp7, tmp12, tmp14)
tl.store(out_ptr0 + (x0), tmp15, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [isnan, target, diff, n, lt, pow_1, mul, truediv, sub_1, loss], Original ATen: [aten.isnan, aten.where, aten.sub, aten.abs, aten.lt, aten.pow, aten.mul, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0.run(arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
del arg1_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.nn as nn
class WeightedSmoothL1Loss(nn.Module):
"""
Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
| 0.5 * x ** 2 / beta if abs(x) < beta
smoothl1(x) = |
| abs(x) - 0.5 * beta otherwise,
where x = input - target.
"""
def __init__(self, beta: 'float'=1.0 / 9.0, code_weights: 'list'=None):
"""
Args:
beta: Scalar float.
L1 to L2 change point.
For beta values < 1e-5, L1 loss is computed.
code_weights: (#codes) float list if not None.
Code-wise weights.
"""
super(WeightedSmoothL1Loss, self).__init__()
self.beta = beta
if code_weights is not None:
self.code_weights = np.array(code_weights, dtype=np.float32)
self.code_weights = torch.from_numpy(self.code_weights)
else:
self.code_weights = None
@staticmethod
def smooth_l1_loss(diff, beta):
if beta < 1e-05:
loss = torch.abs(diff)
else:
n = torch.abs(diff)
loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
return loss
def forward(self, input: 'torch.Tensor', target: 'torch.Tensor',
weights: 'torch.Tensor'=None):
"""
Args:
input: (B, #anchors, #codes) float tensor.
Ecoded predicted locations of objects.
target: (B, #anchors, #codes) float tensor.
Regression targets.
weights: (B, #anchors) float tensor if not None.
Returns:
loss: (B, #anchors) float tensor.
Weighted smooth l1 loss without reduction.
"""
target = torch.where(torch.isnan(target), input, target)
diff = input - target
if self.code_weights is not None:
diff = diff * self.code_weights.view(1, 1, -1)
loss = self.smooth_l1_loss(diff, self.beta)
if weights is not None:
assert weights.shape[0] == loss.shape[0] and weights.shape[1
] == loss.shape[1]
loss = loss * weights.unsqueeze(-1)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask)
tmp2 = libdevice.isnan(tmp1).to(tl.int1)
tmp3 = tl.where(tmp2, tmp0, tmp1)
tmp4 = tmp0 - tmp3
tmp5 = tl_math.abs(tmp4)
tmp6 = 0.1111111111111111
tmp7 = tmp5 < tmp6
tmp8 = tmp5 * tmp5
tmp9 = 0.5
tmp10 = tmp8 * tmp9
tmp11 = 9.0
tmp12 = tmp10 * tmp11
tmp13 = 0.05555555555555555
tmp14 = tmp5 - tmp13
tmp15 = tl.where(tmp7, tmp12, tmp14)
tl.store(out_ptr0 + x0, tmp15, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_abs_div_isnan_lt_mul_pow_sub_where_0[grid(256)](arg1_1
, arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class WeightedSmoothL1LossNew(nn.Module):
"""
Code-wise Weighted Smooth L1 Loss modified based on fvcore.nn.smooth_l1_loss
https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/smooth_l1_loss.py
| 0.5 * x ** 2 / beta if abs(x) < beta
smoothl1(x) = |
| abs(x) - 0.5 * beta otherwise,
where x = input - target.
"""
def __init__(self, beta: 'float'=1.0 / 9.0, code_weights: 'list'=None):
"""
Args:
beta: Scalar float.
L1 to L2 change point.
For beta values < 1e-5, L1 loss is computed.
code_weights: (#codes) float list if not None.
Code-wise weights.
"""
super(WeightedSmoothL1LossNew, self).__init__()
self.beta = beta
if code_weights is not None:
self.code_weights = np.array(code_weights, dtype=np.float32)
self.code_weights = torch.from_numpy(self.code_weights)
else:
self.code_weights = None
@staticmethod
def smooth_l1_loss(diff, beta):
if beta < 1e-05:
loss = torch.abs(diff)
else:
n = torch.abs(diff)
loss = torch.where(n < beta, 0.5 * n ** 2 / beta, n - 0.5 * beta)
return loss
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
Jasonkks/mlcnet
|
WeightedSmoothL1Loss
| false | 8,324 |
[
"Apache-2.0"
] | 18 |
8f89c860c709733c8baa663607004fc48d76291d
|
https://github.com/Jasonkks/mlcnet/tree/8f89c860c709733c8baa663607004fc48d76291d
|
h_swish
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/lq/clqeppj4y7zg3pimgkc3jwu5feu5fyckp56vd63n4y2itru2coxs.py
# Topologically Sorted Source Nodes: [add, hardtanh, truediv, mul], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
# Source node to ATen node mapping:
# add => add
# hardtanh => clamp_max, clamp_min
# mul => mul
# truediv => div
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 3), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0.0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6.0), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 6), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %div), kwargs = {})
triton_poi_fused_add_div_hardtanh_mul_0 = async_compile.triton('triton_poi_fused_add_div_hardtanh_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_hardtanh_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 3.0
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = 0.16666666666666666
tmp8 = tmp6 * tmp7
tmp9 = tmp0 * tmp8
tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [add, hardtanh, truediv, mul], Original ATen: [aten.add, aten.hardtanh, aten.div, aten.mul]
stream0 = get_raw_stream(0)
triton_poi_fused_add_div_hardtanh_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swish(nn.Module):
def __init__(self, inplace=True):
super(h_swish, self).__init__()
self.sigmoid = h_sigmoid(inplace=inplace)
def forward(self, x):
return x * self.sigmoid(x)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_div_hardtanh_mul_0(in_ptr0, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 3.0
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = triton_helpers.maximum(tmp2, tmp3)
tmp5 = 6.0
tmp6 = triton_helpers.minimum(tmp4, tmp5)
tmp7 = 0.16666666666666666
tmp8 = tmp6 * tmp7
tmp9 = tmp0 * tmp8
tl.store(out_ptr0 + x0, tmp9, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_div_hardtanh_mul_0[grid(256)](arg0_1, buf0,
256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
class h_sigmoid(nn.Module):
def __init__(self, inplace=True):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
def forward(self, x):
return self.relu(x + 3) / 6
class h_swishNew(nn.Module):
def __init__(self, inplace=True):
super(h_swishNew, self).__init__()
self.sigmoid = h_sigmoid(inplace=inplace)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
JaminFong/dali-pytorch
|
h_swish
| false | 8,325 |
[
"Apache-2.0"
] | 41 |
7bd5d2380d210a32d24c7309da69c8d2c5db8759
|
https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759
|
injective_pad
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/h6/ch6tghwto3sunf7yucjggsdgjagmrqcpgqcdhw4lwhtwix7epmhu.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
# x_1 => constant_pad_nd
# Graph fragment:
# %constant_pad_nd : [num_users=1] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%permute, [0, 0, 0, 4], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 8
x0 = xindex % 4
x2 = (xindex // 32) % 4
x3 = (xindex // 128)
x4 = xindex
tmp0 = x1
tmp1 = tl.full([1], 4, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp2 & xmask, other=0.0)
tl.store(out_ptr0 + (x4), tmp3, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 4), (128, 32, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.constant_pad_nd]
stream0 = get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0.run(arg0_1, buf0, 512, grid=grid(512), stream=stream0)
del arg0_1
return (reinterpret_tensor(buf0, (4, 8, 4, 4), (128, 4, 32, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class injective_pad(nn.Module):
def __init__(self, pad_size):
super(injective_pad, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def forward(self, x):
x = x.permute(0, 2, 1, 3)
x = self.pad(x)
return x.permute(0, 2, 1, 3)
def inverse(self, x):
l = len(x[1])
return x[:, :l - self.pad_size, :, :]
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'pad_size': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 8
x0 = xindex % 4
x2 = xindex // 32 % 4
x3 = xindex // 128
x4 = xindex
tmp0 = x1
tmp1 = tl.full([1], 4, tl.int64)
tmp2 = tmp0 < tmp1
tmp3 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp2 &
xmask, other=0.0)
tl.store(out_ptr0 + x4, tmp3, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 4), (128, 32, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_constant_pad_nd_0[grid(512)](arg0_1, buf0, 512,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return reinterpret_tensor(buf0, (4, 8, 4, 4), (128, 4, 32, 1), 0),
class injective_padNew(nn.Module):
def __init__(self, pad_size):
super(injective_padNew, self).__init__()
self.pad_size = pad_size
self.pad = nn.ZeroPad2d((0, 0, 0, pad_size))
def inverse(self, x):
l = len(x[1])
return x[:, :l - self.pad_size, :, :]
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
JessieYuW/CrevNet-Traffic4cast
|
injective_pad
| false | 8,326 |
[
"Apache-2.0"
] | 13 |
810b2a951de1f99a07bf8cfcbd93e1fc016cce48
|
https://github.com/JessieYuW/CrevNet-Traffic4cast/tree/810b2a951de1f99a07bf8cfcbd93e1fc016cce48
|
GridMixupLoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/b5/cb5jk3crx33kn6vxoefz2ccunrf65n2pxkom4gzxn2lov6ze7r4n.py
# Topologically Sorted Source Nodes: [cross_entropy, cross_entropy_1], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# cross_entropy => amax, sub
# cross_entropy_1 => amax_1, sub_2
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax), kwargs = {})
# %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [1], True), kwargs = {})
# %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %amax_1), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
tl.store(out_ptr1 + (x3), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/k7/ck74ibzkhizd2qk4whd4yyiurqnch67rweqz3vbgxiwqp7djcpfw.py
# Topologically Sorted Source Nodes: [trues1, cross_entropy], Original ATen: [aten._to_copy, aten.nll_loss2d_forward]
# Source node to ATen node mapping:
# cross_entropy => full_default_1, ne_1, ne_2, neg, sum_2, sum_3, where_1
# trues1 => convert_element_type
# Graph fragment:
# %convert_element_type : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%select_2, torch.int64), kwargs = {})
# %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%convert_element_type, -100), kwargs = {})
# %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze,), kwargs = {})
# %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_1, %neg, %full_default_1), kwargs = {})
# %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_1,), kwargs = {})
# %ne_2 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%convert_element_type, -100), kwargs = {})
# %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_2,), kwargs = {})
triton_per_fused__to_copy_nll_loss2d_forward_1 = async_compile.triton('triton_per_fused__to_copy_nll_loss2d_forward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i64', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_nll_loss2d_forward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__to_copy_nll_loss2d_forward_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 16
r1 = (rindex // 16)
tmp0 = tl.load(in_ptr0 + (r2), None)
tmp12 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
tmp14 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
tmp17 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
tmp20 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
tmp1 = tmp0.to(tl.int64)
tmp2 = tl.full([1, 1], -100, tl.int64)
tmp3 = tmp1 != tmp2
tmp4 = tl.full([1, 1], 0, tl.int64)
tmp5 = tl.where(tmp3, tmp1, tmp4)
tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tl.device_assert((0 <= tmp9) & (tmp9 < 4), "index out of bounds: 0 <= tmp9 < 4")
tmp11 = tl.load(in_ptr1 + (r0 + (16*tmp9) + (64*r1)), None)
tmp13 = tl_math.exp(tmp12)
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tl_math.log(tmp22)
tmp24 = tmp11 - tmp23
tmp25 = -tmp24
tmp26 = 0.0
tmp27 = tl.where(tmp3, tmp25, tmp26)
tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
tmp30 = tl.sum(tmp28, 1)[:, None]
tmp31 = tmp3.to(tl.int64)
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.sum(tmp32, 1)[:, None]
tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp30, None)
tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp34, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ei/ceimgl2hwvul4obyyrtzgmervwa4zbjy7ve6mhxrqnthpt42ulbt.py
# Topologically Sorted Source Nodes: [trues2, cross_entropy_1], Original ATen: [aten._to_copy, aten.nll_loss2d_forward]
# Source node to ATen node mapping:
# cross_entropy_1 => full_default_3, ne_4, ne_5, neg_1, sum_5, sum_6, where_3
# trues2 => convert_element_type_1
# Graph fragment:
# %convert_element_type_1 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%select_3, torch.int64), kwargs = {})
# %ne_4 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%convert_element_type_1, -100), kwargs = {})
# %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze_1,), kwargs = {})
# %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_4, %neg_1, %full_default_3), kwargs = {})
# %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_3,), kwargs = {})
# %ne_5 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%convert_element_type_1, -100), kwargs = {})
# %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_5,), kwargs = {})
triton_per_fused__to_copy_nll_loss2d_forward_2 = async_compile.triton('triton_per_fused__to_copy_nll_loss2d_forward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 64],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i64', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_nll_loss2d_forward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused__to_copy_nll_loss2d_forward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 64
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 16
r1 = (rindex // 16)
tmp0 = tl.load(in_ptr0 + (64 + r2), None)
tmp12 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
tmp14 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
tmp17 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
tmp20 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
tmp1 = tmp0.to(tl.int64)
tmp2 = tl.full([1, 1], -100, tl.int64)
tmp3 = tmp1 != tmp2
tmp4 = tl.full([1, 1], 0, tl.int64)
tmp5 = tl.where(tmp3, tmp1, tmp4)
tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tl.device_assert((0 <= tmp9) & (tmp9 < 4), "index out of bounds: 0 <= tmp9 < 4")
tmp11 = tl.load(in_ptr1 + (r0 + (16*tmp9) + (64*r1)), None)
tmp13 = tl_math.exp(tmp12)
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tl_math.log(tmp22)
tmp24 = tmp11 - tmp23
tmp25 = -tmp24
tmp26 = 0.0
tmp27 = tl.where(tmp3, tmp25, tmp26)
tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
tmp30 = tl.sum(tmp28, 1)[:, None]
tmp31 = tmp3.to(tl.int64)
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.sum(tmp32, 1)[:, None]
tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp30, None)
tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp34, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zd/czdyhcm636chtdkvsfo234ieox46sxe2vfejbylo3mwgb2vsmtbq.py
# Topologically Sorted Source Nodes: [cross_entropy, mul, cross_entropy_1, sub, mul_1, loss], Original ATen: [aten.nll_loss2d_forward, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
# cross_entropy => convert_element_type_2, div
# cross_entropy_1 => convert_element_type_3, div_1
# loss => add
# mul => mul
# mul_1 => mul_1
# sub => sub_4
# Graph fragment:
# %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_2, torch.float32), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %convert_element_type_2), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %select_1), kwargs = {})
# %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_5, torch.float32), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_6, %convert_element_type_3), kwargs = {})
# %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %select_1), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %sub_4), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3 = async_compile.triton('triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*fp32', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (0))
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + (0))
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (192 + x0), xmask)
tmp8 = tl.load(in_ptr3 + (0))
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp10 = tl.load(in_ptr4 + (0))
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp4 = tmp3.to(tl.float32)
tmp5 = tmp1 / tmp4
tmp7 = tmp5 * tmp6
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp9 / tmp12
tmp14 = 1.0
tmp15 = tmp14 - tmp6
tmp16 = tmp13 * tmp15
tmp17 = tmp7 + tmp16
tl.store(out_ptr0 + (x0), tmp17, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cross_entropy, cross_entropy_1], Original ATen: [aten._log_softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__log_softmax_0.run(arg1_1, buf0, buf3, 256, grid=grid(256), stream=stream0)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((), (), torch.int64)
# Topologically Sorted Source Nodes: [trues1, cross_entropy], Original ATen: [aten._to_copy, aten.nll_loss2d_forward]
triton_per_fused__to_copy_nll_loss2d_forward_1.run(arg0_1, buf0, buf1, buf2, 1, 64, grid=grid(1), stream=stream0)
del buf0
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = empty_strided_cuda((), (), torch.int64)
# Topologically Sorted Source Nodes: [trues2, cross_entropy_1], Original ATen: [aten._to_copy, aten.nll_loss2d_forward]
triton_per_fused__to_copy_nll_loss2d_forward_2.run(arg0_1, buf3, buf4, buf5, 1, 64, grid=grid(1), stream=stream0)
del buf3
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [cross_entropy, mul, cross_entropy_1, sub, mul_1, loss], Original ATen: [aten.nll_loss2d_forward, aten.mul, aten.rsub, aten.add]
triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3.run(buf1, buf2, arg0_1, buf4, buf5, buf6, 16, grid=grid(16), stream=stream0)
del arg0_1
del buf1
del buf2
del buf4
del buf5
return (buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import math
import random
import torch
import numpy as np
import typing as t
from torch import nn
class GridMixupLoss(nn.Module):
""" Implementation of GridMixup loss
:param alpha: Percent of the first image on the crop. Can be float or Tuple[float, float]
- if float: lambda parameter gets from the beta-distribution np.random.beta(alpha, alpha)
- if Tuple[float, float]: lambda parameter gets from the uniform
distribution np.random.uniform(alpha[0], alpha[1])
:param n_holes_x: Number of holes by OX
:param hole_aspect_ratio: hole aspect ratio
:param crop_area_ratio: Define percentage of the crop area
:param crop_aspect_ratio: Define crop aspect ratio
"""
def __init__(self, alpha: 't.Union[float, t.Tuple[float, float]]'=(0.1,
0.9), n_holes_x: 't.Union[int, t.Tuple[int, int]]'=20,
hole_aspect_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0,
crop_area_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0,
crop_aspect_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0):
super().__init__()
self.alpha = alpha
self.n_holes_x = n_holes_x
self.hole_aspect_ratio = hole_aspect_ratio
self.crop_area_ratio = crop_area_ratio
self.crop_aspect_ratio = crop_aspect_ratio
if isinstance(self.n_holes_x, int):
self.n_holes_x = self.n_holes_x, self.n_holes_x
if isinstance(self.hole_aspect_ratio, float):
self.hole_aspect_ratio = (self.hole_aspect_ratio, self.
hole_aspect_ratio)
if isinstance(self.crop_area_ratio, float):
self.crop_area_ratio = self.crop_area_ratio, self.crop_area_ratio
if isinstance(self.crop_aspect_ratio, float):
self.crop_aspect_ratio = (self.crop_aspect_ratio, self.
crop_aspect_ratio)
self.loss = nn.CrossEntropyLoss()
def __str__(self):
return 'gridmixup'
@staticmethod
def _get_random_crop(height: 'int', width: 'int', crop_area_ratio:
'float', crop_aspect_ratio: 'float') ->t.Tuple:
crop_area = int(height * width * crop_area_ratio)
crop_width = int(np.sqrt(crop_area / crop_aspect_ratio))
crop_height = int(crop_width * crop_aspect_ratio)
cx = np.random.random()
cy = np.random.random()
y1 = int((height - crop_height) * cy)
y2 = y1 + crop_height
x1 = int((width - crop_width) * cx)
x2 = x1 + crop_width
return x1, y1, x2, y2
def _get_gridmask(self, image_shape: 't.Tuple[int, int]',
crop_area_ratio: 'float', crop_aspect_ratio: 'float', lam: 'float',
nx: 'int', ar: 'float') ->np.ndarray:
""" Method make grid mask
:param image_shape: Shape of the images
:param lam: Lambda parameter
:param crop_area_ratio: Ratio of the crop area
:param crop_aspect_ratio: Aspect ratio of the crop
:param nx: Amount of holes by width
:param ar: Aspect ratio of the hole
:return: Binary mask, where holes == 1, background == 0
"""
img_height, img_width = image_shape
xc1, yc1, xc2, yc2 = self._get_random_crop(height=img_height, width
=img_width, crop_area_ratio=crop_area_ratio, crop_aspect_ratio=
crop_aspect_ratio)
height = yc2 - yc1
width = xc2 - xc1
if not 1 <= nx <= width // 2:
raise ValueError(
f'The nx must be between 1 and {width // 2}.\nGive: {nx}')
patch_width = math.ceil(width / nx)
patch_height = int(patch_width * ar)
ny = math.ceil(height / patch_height)
ratio = np.sqrt(1 - lam)
hole_width = int(patch_width * ratio)
hole_height = int(patch_height * ratio)
hole_width = min(max(hole_width, 1), patch_width - 1)
hole_height = min(max(hole_height, 1), patch_height - 1)
holes = []
for i in range(nx + 1):
for j in range(ny + 1):
x1 = min(patch_width * i, width)
y1 = min(patch_height * j, height)
x2 = min(x1 + hole_width, width)
y2 = min(y1 + hole_height, height)
holes.append((x1, y1, x2, y2))
mask = np.zeros(shape=image_shape, dtype=np.uint8)
for x1, y1, x2, y2 in holes:
mask[yc1 + y1:yc1 + y2, xc1 + x1:xc1 + x2] = 1
return mask
def get_sample(self, images: 'torch.Tensor', targets: 'torch.Tensor'
) ->t.Tuple[torch.Tensor, torch.Tensor]:
""" Method returns augmented images and targets
:param images: Batch of non-augmented images
:param targets: Batch of non-augmented targets
:return: Augmented images and targets
"""
indices = torch.randperm(images.size(0))
shuffled_targets = targets[indices]
height, width = images.shape[2:]
if isinstance(self.alpha, float):
lam = np.random.beta(self.alpha, self.alpha)
else:
lam = np.random.uniform(self.alpha[0], self.alpha[1])
nx = random.randint(self.n_holes_x[0], self.n_holes_x[1])
ar = np.random.uniform(self.hole_aspect_ratio[0], self.
hole_aspect_ratio[1])
crop_area_ratio = np.random.uniform(self.crop_area_ratio[0], self.
crop_area_ratio[1])
crop_aspect_ratio = np.random.uniform(self.crop_aspect_ratio[0],
self.crop_aspect_ratio[1])
mask = self._get_gridmask(image_shape=(height, width),
crop_area_ratio=crop_area_ratio, crop_aspect_ratio=
crop_aspect_ratio, lam=lam, nx=nx, ar=ar)
lam = 1 - mask.sum() / (images.size()[-1] * images.size()[-2])
mask = torch.from_numpy(mask)
images = images * (1 - mask) + images[indices, ...] * mask
lam_list = torch.from_numpy(np.ones(shape=targets.shape) * lam)
out_targets = torch.cat([targets, shuffled_targets, lam_list], dim=1
).transpose(0, 1)
return images, out_targets
def forward(self, preds: 'torch.Tensor', trues: 'torch.Tensor'
) ->torch.Tensor:
lam = trues[-1][0].float()
trues1, trues2 = trues[0].long(), trues[1].long()
loss = self.loss(preds, trues1) * lam + self.loss(preds, trues2) * (
1 - lam)
return loss
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import math
import random
import numpy as np
import typing as t
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
tl.store(out_ptr1 + x3, tmp8, xmask)
@triton.jit
def triton_per_fused__to_copy_nll_loss2d_forward_1(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + r2, None)
tmp12 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp14 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp17 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp20 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp1 = tmp0.to(tl.int64)
tmp2 = tl.full([1, 1], -100, tl.int64)
tmp3 = tmp1 != tmp2
tmp4 = tl.full([1, 1], 0, tl.int64)
tmp5 = tl.where(tmp3, tmp1, tmp4)
tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tl.device_assert((0 <= tmp9) & (tmp9 < 4),
'index out of bounds: 0 <= tmp9 < 4')
tmp11 = tl.load(in_ptr1 + (r0 + 16 * tmp9 + 64 * r1), None)
tmp13 = tl_math.exp(tmp12)
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tl_math.log(tmp22)
tmp24 = tmp11 - tmp23
tmp25 = -tmp24
tmp26 = 0.0
tmp27 = tl.where(tmp3, tmp25, tmp26)
tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
tmp30 = tl.sum(tmp28, 1)[:, None]
tmp31 = tmp3.to(tl.int64)
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.sum(tmp32, 1)[:, None]
tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp34, None)
@triton.jit
def triton_per_fused__to_copy_nll_loss2d_forward_2(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 64
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r2 = rindex
r0 = rindex % 16
r1 = rindex // 16
tmp0 = tl.load(in_ptr0 + (64 + r2), None)
tmp12 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
tmp14 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
tmp17 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
tmp20 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
tmp1 = tmp0.to(tl.int64)
tmp2 = tl.full([1, 1], -100, tl.int64)
tmp3 = tmp1 != tmp2
tmp4 = tl.full([1, 1], 0, tl.int64)
tmp5 = tl.where(tmp3, tmp1, tmp4)
tmp6 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
tmp7 = tmp5 + tmp6
tmp8 = tmp5 < 0
tmp9 = tl.where(tmp8, tmp7, tmp5)
tl.device_assert((0 <= tmp9) & (tmp9 < 4),
'index out of bounds: 0 <= tmp9 < 4')
tmp11 = tl.load(in_ptr1 + (r0 + 16 * tmp9 + 64 * r1), None)
tmp13 = tl_math.exp(tmp12)
tmp15 = tl_math.exp(tmp14)
tmp16 = tmp13 + tmp15
tmp18 = tl_math.exp(tmp17)
tmp19 = tmp16 + tmp18
tmp21 = tl_math.exp(tmp20)
tmp22 = tmp19 + tmp21
tmp23 = tl_math.log(tmp22)
tmp24 = tmp11 - tmp23
tmp25 = -tmp24
tmp26 = 0.0
tmp27 = tl.where(tmp3, tmp25, tmp26)
tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
tmp30 = tl.sum(tmp28, 1)[:, None]
tmp31 = tmp3.to(tl.int64)
tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
tmp34 = tl.sum(tmp32, 1)[:, None]
tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, None)
tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp34, None)
@triton.jit
def triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3(in_ptr0, in_ptr1,
in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr1 + 0)
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp6 = tl.load(in_ptr2 + (192 + x0), xmask)
tmp8 = tl.load(in_ptr3 + 0)
tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
tmp10 = tl.load(in_ptr4 + 0)
tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
tmp4 = tmp3.to(tl.float32)
tmp5 = tmp1 / tmp4
tmp7 = tmp5 * tmp6
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp9 / tmp12
tmp14 = 1.0
tmp15 = tmp14 - tmp6
tmp16 = tmp13 * tmp15
tmp17 = tmp7 + tmp16
tl.store(out_ptr0 + x0, tmp17, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, buf3, 256,
XBLOCK=256, num_warps=4, num_stages=1)
del arg1_1
buf1 = empty_strided_cuda((), (), torch.float32)
buf2 = empty_strided_cuda((), (), torch.int64)
triton_per_fused__to_copy_nll_loss2d_forward_1[grid(1)](arg0_1,
buf0, buf1, buf2, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del buf0
buf4 = empty_strided_cuda((), (), torch.float32)
buf5 = empty_strided_cuda((), (), torch.int64)
triton_per_fused__to_copy_nll_loss2d_forward_2[grid(1)](arg0_1,
buf3, buf4, buf5, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
del buf3
buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
triton_poi_fused_add_mul_nll_loss2d_forward_rsub_3[grid(16)](buf1,
buf2, arg0_1, buf4, buf5, buf6, 16, XBLOCK=16, num_warps=1,
num_stages=1)
del arg0_1
del buf1
del buf2
del buf4
del buf5
return buf6,
class GridMixupLossNew(nn.Module):
""" Implementation of GridMixup loss
:param alpha: Percent of the first image on the crop. Can be float or Tuple[float, float]
- if float: lambda parameter gets from the beta-distribution np.random.beta(alpha, alpha)
- if Tuple[float, float]: lambda parameter gets from the uniform
distribution np.random.uniform(alpha[0], alpha[1])
:param n_holes_x: Number of holes by OX
:param hole_aspect_ratio: hole aspect ratio
:param crop_area_ratio: Define percentage of the crop area
:param crop_aspect_ratio: Define crop aspect ratio
"""
def __init__(self, alpha: 't.Union[float, t.Tuple[float, float]]'=(0.1,
0.9), n_holes_x: 't.Union[int, t.Tuple[int, int]]'=20,
hole_aspect_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0,
crop_area_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0,
crop_aspect_ratio: 't.Union[float, t.Tuple[float, float]]'=1.0):
super().__init__()
self.alpha = alpha
self.n_holes_x = n_holes_x
self.hole_aspect_ratio = hole_aspect_ratio
self.crop_area_ratio = crop_area_ratio
self.crop_aspect_ratio = crop_aspect_ratio
if isinstance(self.n_holes_x, int):
self.n_holes_x = self.n_holes_x, self.n_holes_x
if isinstance(self.hole_aspect_ratio, float):
self.hole_aspect_ratio = (self.hole_aspect_ratio, self.
hole_aspect_ratio)
if isinstance(self.crop_area_ratio, float):
self.crop_area_ratio = self.crop_area_ratio, self.crop_area_ratio
if isinstance(self.crop_aspect_ratio, float):
self.crop_aspect_ratio = (self.crop_aspect_ratio, self.
crop_aspect_ratio)
self.loss = nn.CrossEntropyLoss()
def __str__(self):
return 'gridmixup'
@staticmethod
def _get_random_crop(height: 'int', width: 'int', crop_area_ratio:
'float', crop_aspect_ratio: 'float') ->t.Tuple:
crop_area = int(height * width * crop_area_ratio)
crop_width = int(np.sqrt(crop_area / crop_aspect_ratio))
crop_height = int(crop_width * crop_aspect_ratio)
cx = np.random.random()
cy = np.random.random()
y1 = int((height - crop_height) * cy)
y2 = y1 + crop_height
x1 = int((width - crop_width) * cx)
x2 = x1 + crop_width
return x1, y1, x2, y2
def _get_gridmask(self, image_shape: 't.Tuple[int, int]',
crop_area_ratio: 'float', crop_aspect_ratio: 'float', lam: 'float',
nx: 'int', ar: 'float') ->np.ndarray:
""" Method make grid mask
:param image_shape: Shape of the images
:param lam: Lambda parameter
:param crop_area_ratio: Ratio of the crop area
:param crop_aspect_ratio: Aspect ratio of the crop
:param nx: Amount of holes by width
:param ar: Aspect ratio of the hole
:return: Binary mask, where holes == 1, background == 0
"""
img_height, img_width = image_shape
xc1, yc1, xc2, yc2 = self._get_random_crop(height=img_height, width
=img_width, crop_area_ratio=crop_area_ratio, crop_aspect_ratio=
crop_aspect_ratio)
height = yc2 - yc1
width = xc2 - xc1
if not 1 <= nx <= width // 2:
raise ValueError(
f'The nx must be between 1 and {width // 2}.\nGive: {nx}')
patch_width = math.ceil(width / nx)
patch_height = int(patch_width * ar)
ny = math.ceil(height / patch_height)
ratio = np.sqrt(1 - lam)
hole_width = int(patch_width * ratio)
hole_height = int(patch_height * ratio)
hole_width = min(max(hole_width, 1), patch_width - 1)
hole_height = min(max(hole_height, 1), patch_height - 1)
holes = []
for i in range(nx + 1):
for j in range(ny + 1):
x1 = min(patch_width * i, width)
y1 = min(patch_height * j, height)
x2 = min(x1 + hole_width, width)
y2 = min(y1 + hole_height, height)
holes.append((x1, y1, x2, y2))
mask = np.zeros(shape=image_shape, dtype=np.uint8)
for x1, y1, x2, y2 in holes:
mask[yc1 + y1:yc1 + y2, xc1 + x1:xc1 + x2] = 1
return mask
def get_sample(self, images: 'torch.Tensor', targets: 'torch.Tensor'
) ->t.Tuple[torch.Tensor, torch.Tensor]:
""" Method returns augmented images and targets
:param images: Batch of non-augmented images
:param targets: Batch of non-augmented targets
:return: Augmented images and targets
"""
indices = torch.randperm(images.size(0))
shuffled_targets = targets[indices]
height, width = images.shape[2:]
if isinstance(self.alpha, float):
lam = np.random.beta(self.alpha, self.alpha)
else:
lam = np.random.uniform(self.alpha[0], self.alpha[1])
nx = random.randint(self.n_holes_x[0], self.n_holes_x[1])
ar = np.random.uniform(self.hole_aspect_ratio[0], self.
hole_aspect_ratio[1])
crop_area_ratio = np.random.uniform(self.crop_area_ratio[0], self.
crop_area_ratio[1])
crop_aspect_ratio = np.random.uniform(self.crop_aspect_ratio[0],
self.crop_aspect_ratio[1])
mask = self._get_gridmask(image_shape=(height, width),
crop_area_ratio=crop_area_ratio, crop_aspect_ratio=
crop_aspect_ratio, lam=lam, nx=nx, ar=ar)
lam = 1 - mask.sum() / (images.size()[-1] * images.size()[-2])
mask = torch.from_numpy(mask)
images = images * (1 - mask) + images[indices, ...] * mask
lam_list = torch.from_numpy(np.ones(shape=targets.shape) * lam)
out_targets = torch.cat([targets, shuffled_targets, lam_list], dim=1
).transpose(0, 1)
return images, out_targets
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IlyaDobrynin/GridMixup
|
GridMixupLoss
| false | 8,327 |
[
"MIT"
] | 42 |
11b741f234832c9a15b4e650e1e4fad0e79dc63b
|
https://github.com/IlyaDobrynin/GridMixup/tree/11b741f234832c9a15b4e650e1e4fad0e79dc63b
|
DiagonalQuantizer
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/nt/cnt5xwsci4q4h3cvlzzh66flvlit77lerlwrimpneefxep4t5ytk.py
# Topologically Sorted Source Nodes: [abs_1, max_1, truediv, x, div_, round_, mul_, cos_, mul__1], Original ATen: [aten.abs, aten.max, aten.div, aten.acos, aten.round, aten.mul, aten.cos]
# Source node to ATen node mapping:
# abs_1 => abs_1
# cos_ => cos
# div_ => div_1
# max_1 => max_1
# mul_ => mul
# mul__1 => mul_1
# round_ => round_1
# truediv => div
# x => acos
# Graph fragment:
# %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
# %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%abs_1, -1, True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %getitem), kwargs = {})
# %acos : [num_users=1] = call_function[target=torch.ops.aten.acos.default](args = (%div,), kwargs = {})
# %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%acos, 0.20943951023931953), kwargs = {})
# %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%div_1,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%round_1, 0.20943951023931953), kwargs = {})
# %cos : [num_users=1] = call_function[target=torch.ops.aten.cos.default](args = (%mul,), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%cos, %getitem), kwargs = {})
triton_poi_fused_abs_acos_cos_div_max_mul_round_0 = async_compile.triton('triton_poi_fused_abs_acos_cos_div_max_mul_round_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_acos_cos_div_max_mul_round_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_abs_acos_cos_div_max_mul_round_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp2 = tl_math.abs(tmp1)
tmp4 = tl_math.abs(tmp3)
tmp5 = triton_helpers.maximum(tmp2, tmp4)
tmp7 = tl_math.abs(tmp6)
tmp8 = triton_helpers.maximum(tmp5, tmp7)
tmp10 = tl_math.abs(tmp9)
tmp11 = triton_helpers.maximum(tmp8, tmp10)
tmp12 = tmp0 / tmp11
tmp13 = libdevice.acos(tmp12)
tmp14 = 4.7746482927568605
tmp15 = tmp13 * tmp14
tmp16 = libdevice.nearbyint(tmp15)
tmp17 = 0.20943951023931953
tmp18 = tmp16 * tmp17
tmp19 = tl_math.cos(tmp18)
tmp20 = tmp19 * tmp11
tl.store(out_ptr0 + (x2), tmp20, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [abs_1, max_1, truediv, x, div_, round_, mul_, cos_, mul__1], Original ATen: [aten.abs, aten.max, aten.div, aten.acos, aten.round, aten.mul, aten.cos]
stream0 = get_raw_stream(0)
triton_poi_fused_abs_acos_cos_div_max_mul_round_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import numpy as np
import torch.cuda
import torch.fft
def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
gradient_clip=False):
class DiagonalQuantizeFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
S_scale = x.abs().max(dim=-1, keepdim=True)[0]
x = (x / S_scale).acos()
ratio = np.pi / (2 ** bit - 1)
x.div_(ratio).round_().mul_(ratio)
if phase_noise_std > 1e-05:
noise = gen_gaussian_noise(x, noise_mean=0, noise_std=
phase_noise_std, trunc_range=[-2 * phase_noise_std, 2 *
phase_noise_std], random_state=random_state)
x.add_(noise)
x.cos_().mul_(S_scale)
return x
@staticmethod
def backward(ctx, grad_output):
if gradient_clip:
grad_input = grad_output.clamp(-1, 1)
else:
grad_input = grad_output.clone()
return grad_input
return DiagonalQuantizeFunction.apply(x)
class DiagonalQuantizer(torch.nn.Module):
def __init__(self, bit, phase_noise_std=0.0, random_state=None, device=
torch.device('cuda')):
"""2021/02/18: New phase quantizer for Sigma matrix in MZI-ONN. Gaussian phase noise is supported. All singular values are normalized by a TIA gain (S_scale), the normalized singular values will be achieved by cos(phi), phi will have [0, pi] uniform quantization.
We do not consider real MZI implementation, thus voltage quantization and gamma noises are not supported.
Args:
bit (int): bitwidth for phase quantization.
phase_noise_std (float, optional): Std dev for Gaussian phase noises. Defaults to 0.
random_state (int, optional): random_state to control random noise injection. Defaults to None.
device (torch.Device, optional): torch.Device. Defaults to torch.device("cuda").
"""
super().__init__()
self.bit = bit
self.phase_noise_std = phase_noise_std
self.random_state = random_state
self.device = device
def set_phase_noise_std(self, phase_noise_std=0, random_state=None):
self.phase_noise_std = phase_noise_std
self.random_state = random_state
def forward(self, x):
x = diagonal_quantize_function(x, self.bit, self.phase_noise_std,
self.random_state, gradient_clip=True)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'bit': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.cuda
import torch.fft
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_abs_acos_cos_div_max_mul_round_0(in_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp2 = tl_math.abs(tmp1)
tmp4 = tl_math.abs(tmp3)
tmp5 = triton_helpers.maximum(tmp2, tmp4)
tmp7 = tl_math.abs(tmp6)
tmp8 = triton_helpers.maximum(tmp5, tmp7)
tmp10 = tl_math.abs(tmp9)
tmp11 = triton_helpers.maximum(tmp8, tmp10)
tmp12 = tmp0 / tmp11
tmp13 = libdevice.acos(tmp12)
tmp14 = 4.7746482927568605
tmp15 = tmp13 * tmp14
tmp16 = libdevice.nearbyint(tmp15)
tmp17 = 0.20943951023931953
tmp18 = tmp16 * tmp17
tmp19 = tl_math.cos(tmp18)
tmp20 = tmp19 * tmp11
tl.store(out_ptr0 + x2, tmp20, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_abs_acos_cos_div_max_mul_round_0[grid(256)](arg0_1,
buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return buf0,
def diagonal_quantize_function(x, bit, phase_noise_std=0, random_state=None,
gradient_clip=False):
class DiagonalQuantizeFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
S_scale = x.abs().max(dim=-1, keepdim=True)[0]
x = (x / S_scale).acos()
ratio = np.pi / (2 ** bit - 1)
x.div_(ratio).round_().mul_(ratio)
if phase_noise_std > 1e-05:
noise = gen_gaussian_noise(x, noise_mean=0, noise_std=
phase_noise_std, trunc_range=[-2 * phase_noise_std, 2 *
phase_noise_std], random_state=random_state)
x.add_(noise)
x.cos_().mul_(S_scale)
return x
@staticmethod
def backward(ctx, grad_output):
if gradient_clip:
grad_input = grad_output.clamp(-1, 1)
else:
grad_input = grad_output.clone()
return grad_input
return DiagonalQuantizeFunction.apply(x)
class DiagonalQuantizerNew(torch.nn.Module):
def __init__(self, bit, phase_noise_std=0.0, random_state=None, device=
torch.device('cuda')):
"""2021/02/18: New phase quantizer for Sigma matrix in MZI-ONN. Gaussian phase noise is supported. All singular values are normalized by a TIA gain (S_scale), the normalized singular values will be achieved by cos(phi), phi will have [0, pi] uniform quantization.
We do not consider real MZI implementation, thus voltage quantization and gamma noises are not supported.
Args:
bit (int): bitwidth for phase quantization.
phase_noise_std (float, optional): Std dev for Gaussian phase noises. Defaults to 0.
random_state (int, optional): random_state to control random noise injection. Defaults to None.
device (torch.Device, optional): torch.Device. Defaults to torch.device("cuda").
"""
super().__init__()
self.bit = bit
self.phase_noise_std = phase_noise_std
self.random_state = random_state
self.device = device
def set_phase_noise_std(self, phase_noise_std=0, random_state=None):
self.phase_noise_std = phase_noise_std
self.random_state = random_state
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
JeremieMelo/pytorch-onn
|
DiagonalQuantizer
| false | 8,328 |
[
"MIT"
] | 16 |
670996112277a6c19c7da400afbe0a4ce45ad5de
|
https://github.com/JeremieMelo/pytorch-onn/tree/670996112277a6c19c7da400afbe0a4ce45ad5de
|
AffineConstantFlow
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/gf/cgf2lqq7cbcczhsumobsfq3dn5fsi3p75lbb4mtyspfjtq5gypjr.py
# Topologically Sorted Source Nodes: [exp, mul, z], Original ATen: [aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
# exp => exp
# mul => mul
# z => add
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%primals_1,), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %exp), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_2), kwargs = {})
triton_poi_fused_add_exp_mul_0 = async_compile.triton('triton_poi_fused_add_exp_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_exp_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tl_math.exp(tmp1)
tmp3 = tmp0 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/s4/cs4g7wo7yiaa347qqj3zck6oaptt2u32r2fobo4kcyhvmxzc6uvk.py
# Topologically Sorted Source Nodes: [log_det], Original ATen: [aten.sum]
# Source node to ATen node mapping:
# log_det => sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%primals_1, [1]), kwargs = {})
triton_per_fused_sum_1 = async_compile.triton('triton_per_fused_sum_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 4],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_sum_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 4
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (r0), None)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.sum(tmp1, 1)[:, None]
tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp3, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 4), (4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [exp, mul, z], Original ATen: [aten.exp, aten.mul, aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_exp_mul_0.run(primals_3, primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0)
del primals_2
buf1 = empty_strided_cuda((1, ), (1, ), torch.float32)
# Topologically Sorted Source Nodes: [log_det], Original ATen: [aten.sum]
triton_per_fused_sum_1.run(primals_1, buf1, 1, 4, grid=grid(1), stream=stream0)
return (buf0, buf1, primals_1, primals_3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class AffineConstantFlow(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super().__init__()
self.s = nn.Parameter(torch.randn(1, dim, requires_grad=True)
) if scale else None
self.t = nn.Parameter(torch.randn(1, dim, requires_grad=True)
) if shift else None
def forward(self, x):
s = self.s if self.s is not None else x.new_zeros(x.size())
t = self.t if self.t is not None else x.new_zeros(x.size())
z = x * torch.exp(s) + t
log_det = torch.sum(s, dim=1)
return z, log_det
def backward(self, z):
s = self.s if self.s is not None else z.new_zeros(z.size())
t = self.t if self.t is not None else z.new_zeros(z.size())
x = (z - t) * torch.exp(-s)
log_det = torch.sum(-s, dim=1)
return x, log_det
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dim': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_add_exp_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
tmp2 = tl_math.exp(tmp1)
tmp3 = tmp0 * tmp2
tmp5 = tmp3 + tmp4
tl.store(out_ptr0 + x2, tmp5, xmask)
@triton.jit
def triton_per_fused_sum_1(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
constexpr):
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + r0, None)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.sum(tmp1, 1)[:, None]
tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (1, 4), (4, 1))
assert_size_stride(primals_2, (1, 4), (4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_exp_mul_0[grid(256)](primals_3, primals_1,
primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf1 = empty_strided_cuda((1,), (1,), torch.float32)
triton_per_fused_sum_1[grid(1)](primals_1, buf1, 1, 4, XBLOCK=1,
num_warps=2, num_stages=1)
return buf0, buf1, primals_1, primals_3
class AffineConstantFlowNew(nn.Module):
"""
Scales + Shifts the flow by (learned) constants per dimension.
In NICE paper there is a Scaling layer which is a special case of this where t is None
"""
def __init__(self, dim, scale=True, shift=True):
super().__init__()
self.s = nn.Parameter(torch.randn(1, dim, requires_grad=True)
) if scale else None
self.t = nn.Parameter(torch.randn(1, dim, requires_grad=True)
) if shift else None
def backward(self, z):
s = self.s if self.s is not None else z.new_zeros(z.size())
t = self.t if self.t is not None else z.new_zeros(z.size())
x = (z - t) * torch.exp(-s)
log_det = torch.sum(-s, dim=1)
return x, log_det
def forward(self, input_0):
primals_1 = self.s
primals_2 = self.t
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0], output[1]
|
JannerM/gamma-models
|
AffineConstantFlow
| false | 8,329 |
[
"MIT"
] | 32 |
4b40d828bf228385c3081d359cdc3494d70de4a1
|
https://github.com/JannerM/gamma-models/tree/4b40d828bf228385c3081d359cdc3494d70de4a1
|
SqueezeExcite
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/yg/cygooswl5gkxugqq2ejgag2vtcqhtumn2j3notsgzty3xoxbrq4v.py
# Topologically Sorted Source Nodes: [x_se], Original ATen: [aten.mean]
# Source node to ATen node mapping:
# x_se => mean
# Graph fragment:
# %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [-1, -2], True), kwargs = {})
triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[16, 16],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 16
rnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/bq/cbqs25ilz2wluqikclvslctdlvul4rcbcdk3m2pcvrxffj6hdrw5.py
# Topologically Sorted Source Nodes: [x_se_1, x_se_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
# x_se_1 => convolution
# x_se_2 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mean, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5h/c5hir5ln45yujqrysh3s4rbbaxaizyl74k7lhsfxzlfm45oosdlf.py
# Topologically Sorted Source Nodes: [x_se_3, add, relu6, truediv, x], Original ATen: [aten.convolution, aten.add, aten.hardtanh, aten.div, aten.mul]
# Source node to ATen node mapping:
# add => add
# relu6 => clamp_max, clamp_min
# truediv => div
# x => mul
# x_se_3 => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, 3.0), kwargs = {})
# %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%add, 0), kwargs = {})
# %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 6.0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %div), kwargs = {})
triton_poi_fused_add_convolution_div_hardtanh_mul_2 = async_compile.triton('triton_poi_fused_add_convolution_div_hardtanh_mul_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_div_hardtanh_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_div_hardtanh_mul_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = (xindex // 16)
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 3.0
tmp5 = tmp3 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 6.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tmp10 = 0.16666666666666666
tmp11 = tmp9 * tmp10
tmp12 = tmp0 * tmp11
tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/hz/chzn444fdowir7gm4qwrpvqa5wg45cir5iv5rjtuqqbsnatls5q5.py
# Topologically Sorted Source Nodes: [x_se_3, add], Original ATen: [aten.convolution, aten.add, aten.hardtanh_backward]
# Source node to ATen node mapping:
# add => add
# x_se_3 => convolution_1
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, 3.0), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%add, 0), kwargs = {})
# %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%add, 6), kwargs = {})
# %bitwise_or : [num_users=1] = call_function[target=torch.ops.aten.bitwise_or.Tensor](args = (%le, %ge), kwargs = {})
triton_poi_fused_add_convolution_hardtanh_backward_3 = async_compile.triton('triton_poi_fused_add_convolution_hardtanh_backward_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_hardtanh_backward_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_convolution_hardtanh_backward_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 3.0
tmp4 = tmp2 + tmp3
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tmp7 = 6.0
tmp8 = tmp4 >= tmp7
tmp9 = tmp6 | tmp8
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4, ), (1, ))
assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [x_se], Original ATen: [aten.mean]
stream0 = get_raw_stream(0)
triton_per_fused_mean_0.run(buf1, primals_1, 16, 16, grid=grid(16), stream=stream0)
# Topologically Sorted Source Nodes: [x_se_1], Original ATen: [aten.convolution]
buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
buf3 = buf2; del buf2 # reuse
# Topologically Sorted Source Nodes: [x_se_1, x_se_2], Original ATen: [aten.convolution, aten.relu]
triton_poi_fused_convolution_relu_1.run(buf3, primals_3, 16, grid=grid(16), stream=stream0)
del primals_3
# Topologically Sorted Source Nodes: [x_se_3], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_se_3, add, relu6, truediv, x], Original ATen: [aten.convolution, aten.add, aten.hardtanh, aten.div, aten.mul]
triton_poi_fused_add_convolution_div_hardtanh_mul_2.run(primals_1, buf4, primals_5, buf5, 256, grid=grid(256), stream=stream0)
buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
# Topologically Sorted Source Nodes: [x_se_3, add], Original ATen: [aten.convolution, aten.add, aten.hardtanh_backward]
triton_poi_fused_add_convolution_hardtanh_backward_3.run(buf4, primals_5, buf6, 16, grid=grid(16), stream=stream0)
del buf4
del primals_5
return (buf5, primals_1, primals_2, primals_4, buf1, buf3, buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
def hard_sigmoid(x, inplace: 'bool'=False):
if inplace:
return x.add_(3.0).clamp_(0.0, 6.0).div_(6.0)
else:
return F.relu6(x + 3.0) / 6.0
class SqueezeExcite(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
super(SqueezeExcite, self).__init__()
self.gate_fn = gate_fn
reduced_chs = _make_divisible((reduced_base_chs or in_chs) *
se_ratio, divisor)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.act1 = act_layer(inplace=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
def forward(self, x):
x_se = self.avg_pool(x)
x_se = self.conv_reduce(x_se)
x_se = self.act1(x_se)
x_se = self.conv_expand(x_se)
x = x * self.gate_fn(x_se)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_chs': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torchvision.transforms import functional as F
import torch.nn as nn
import torch.nn.functional as F
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
tl.constexpr):
xnumel = 16
RBLOCK: tl.constexpr = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r1 = rindex
x0 = xindex
tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
tmp3 = tl.where(xmask, tmp1, 0)
tmp4 = tl.sum(tmp3, 1)[:, None]
tmp5 = 16.0
tmp6 = tmp4 / tmp5
tl.debug_barrier()
tl.store(in_out_ptr0 + x0, tmp6, xmask)
@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_add_convolution_div_hardtanh_mul_2(in_ptr0, in_ptr1,
in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x4 = xindex // 16
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp4 = 3.0
tmp5 = tmp3 + tmp4
tmp6 = 0.0
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = 6.0
tmp9 = triton_helpers.minimum(tmp7, tmp8)
tmp10 = 0.16666666666666666
tmp11 = tmp9 * tmp10
tmp12 = tmp0 * tmp11
tl.store(out_ptr0 + x3, tmp12, xmask)
@triton.jit
def triton_poi_fused_add_convolution_hardtanh_backward_3(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 3.0
tmp4 = tmp2 + tmp3
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tmp7 = 6.0
tmp8 = tmp4 >= tmp7
tmp9 = tmp6 | tmp8
tl.store(out_ptr0 + x2, tmp9, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_3, (4,), (1,))
assert_size_stride(primals_4, (4, 4, 1, 1), (4, 1, 1, 1))
assert_size_stride(primals_5, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
del buf0
get_raw_stream(0)
triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
num_warps=2, num_stages=1)
buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
buf3 = buf2
del buf2
triton_poi_fused_convolution_relu_1[grid(16)](buf3, primals_3, 16,
XBLOCK=16, num_warps=1, num_stages=1)
del primals_3
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 1, 1), (4, 1, 1, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_convolution_div_hardtanh_mul_2[grid(256)](
primals_1, buf4, primals_5, buf5, 256, XBLOCK=128, num_warps=4,
num_stages=1)
buf6 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
triton_poi_fused_add_convolution_hardtanh_backward_3[grid(16)](buf4,
primals_5, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
del buf4
del primals_5
return buf5, primals_1, primals_2, primals_4, buf1, buf3, buf6
def _make_divisible(v, divisor, min_value=None):
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
:param v:
:param divisor:
:param min_value:
:return:
"""
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
if new_v < 0.9 * v:
new_v += divisor
return new_v
def hard_sigmoid(x, inplace: 'bool'=False):
if inplace:
return x.add_(3.0).clamp_(0.0, 6.0).div_(6.0)
else:
return F.relu6(x + 3.0) / 6.0
class SqueezeExciteNew(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
act_layer=nn.ReLU, gate_fn=hard_sigmoid, divisor=4, **_):
super(SqueezeExciteNew, self).__init__()
self.gate_fn = gate_fn
reduced_chs = _make_divisible((reduced_base_chs or in_chs) *
se_ratio, divisor)
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.act1 = act_layer(inplace=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
def forward(self, input_0):
primals_2 = self.conv_reduce.weight
primals_3 = self.conv_reduce.bias
primals_4 = self.conv_expand.weight
primals_5 = self.conv_expand.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
JaminFong/dali-pytorch
|
SqueezeExcite
| false | 8,330 |
[
"Apache-2.0"
] | 41 |
7bd5d2380d210a32d24c7309da69c8d2c5db8759
|
https://github.com/JaminFong/dali-pytorch/tree/7bd5d2380d210a32d24c7309da69c8d2c5db8759
|
Upsample
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ia/ciadmcoq5lravwtvr2lif5rbcgis3uetas5ofbzwogig6jezsvqp.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._unsafe_index]
# Source node to ATen node mapping:
# x_1 => _unsafe_index
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%permute, [None, None, %unsqueeze, %convert_element_type_3]), kwargs = {})
triton_poi_fused__unsafe_index_0 = async_compile.triton('triton_poi_fused__unsafe_index_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 8) % 8
x0 = xindex % 8
x2 = (xindex // 64) % 4
x3 = (xindex // 256)
x5 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = x0
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + (4*x2) + (16*tmp4) + (64*x3)), xmask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x5), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._unsafe_index]
stream0 = get_raw_stream(0)
triton_poi_fused__unsafe_index_0.run(arg0_1, buf0, 1024, grid=grid(1024), stream=stream0)
del arg0_1
return (reinterpret_tensor(buf0, (4, 8, 4, 8), (256, 8, 64, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class Upsample(nn.Upsample):
"""
Upsampling via interporlation
Args:
x: (N, T, C)
Returns:
y: (N, S * T, C)
(S: scale_factor)
"""
def __init__(self, scale_factor=2, mode='nearest'):
super(Upsample, self).__init__(scale_factor=scale_factor, mode=mode)
def forward(self, x):
x = x.transpose(1, 2)
x = super(Upsample, self).forward(x)
x = x.transpose(1, 2)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__unsafe_index_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 8 % 8
x0 = xindex % 8
x2 = xindex // 64 % 4
x3 = xindex // 256
x5 = xindex
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tmp5 = x0
tmp6 = tmp5.to(tl.float32)
tmp7 = tmp6 * tmp2
tmp8 = tmp7.to(tl.int32)
tmp9 = tl.load(in_ptr0 + (tmp8 + 4 * x2 + 16 * tmp4 + 64 * x3), xmask,
eviction_policy='evict_last')
tl.store(out_ptr0 + x5, tmp9, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__unsafe_index_0[grid(1024)](arg0_1, buf0, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
return reinterpret_tensor(buf0, (4, 8, 4, 8), (256, 8, 64, 1), 0),
class UpsampleNew(nn.Upsample):
"""
Upsampling via interporlation
Args:
x: (N, T, C)
Returns:
y: (N, S * T, C)
(S: scale_factor)
"""
def __init__(self, scale_factor=2, mode='nearest'):
super(UpsampleNew, self).__init__(scale_factor=scale_factor, mode=mode)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
Jackson-Kang/VQVC-Pytorch
|
Upsample
| false | 8,331 |
[
"MIT"
] | 13 |
d2267b5c52253b6ae11a5767963a65320ae335c2
|
https://github.com/Jackson-Kang/VQVC-Pytorch/tree/d2267b5c52253b6ae11a5767963a65320ae335c2
|
GraphConvolution
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/l7/cl7yxfbt4ykiq5jvwa5z2y7sq5svbia3hxdlhmplibtjjkzvzuil.py
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# output_1 => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mm_1,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm]
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.mm]
extern_kernels.mm(primals_3, buf0, out=buf1)
del buf0
buf2 = buf1; del buf1 # reuse
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
return (buf2, buf3, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
from torch.nn import Module
import torch
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)``
Returns
-------
output: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied.
"""
if drop_prob == 0 or drop_prob is None or not training:
return x
sz = list(x.size())
for i in shared_axes:
sz[i] = 1
mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob)
mask = mask.expand_as(x)
return x * mask
class GraphConvolution(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.0, act=F.relu):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.dropout = dropout
self.act = act
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.weight)
def forward(self, input, adj):
input = F.dropout(input, self.dropout, self.training)
support = torch.mm(input, self.weight)
output = torch.spmm(adj, support)
output = self.act(output)
return output
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'in_features': 4, 'out_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import torch.nn.functional as F
from torch.nn import Parameter
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4), (4, 1))
assert_size_stride(primals_3, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_1, primals_2, out=buf0)
del primals_2
buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(primals_3, buf0, out=buf1)
del buf0
buf2 = buf1
del buf1
buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(16)](buf2, buf3, 16,
XBLOCK=16, num_warps=1, num_stages=1)
return buf2, buf3, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)``
Returns
-------
output: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied.
"""
if drop_prob == 0 or drop_prob is None or not training:
return x
sz = list(x.size())
for i in shared_axes:
sz[i] = 1
mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob)
mask = mask.expand_as(x)
return x * mask
class GraphConvolutionNew(Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, dropout=0.0, act=F.relu):
super(GraphConvolutionNew, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.dropout = dropout
self.act = act
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self.weight)
def __repr__(self):
return self.__class__.__name__ + ' (' + str(self.in_features
) + ' -> ' + str(self.out_features) + ')'
def forward(self, input_0, input_1):
primals_1 = self.weight
primals_2 = input_0
primals_3 = input_1
output = call([primals_1, primals_2, primals_3])
return output[0]
|
IBM/graph4nlp
|
GraphConvolution
| false | 8,332 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
Context2AnswerAttention
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ra/cra6jsfyhayzuojfdc2sk2dfy65xbnuhresodb6dnlgcqca222ma.py
# Topologically Sorted Source Nodes: [context_fc], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# context_fc => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + (x0), tmp2, xmask)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/om/com5a2jdtzsd2gxozlfy64n7thpakjehaajnnx7weffss4gxj2sy.py
# Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# prob => amax, exp, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_6, [-1], True), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_6, %amax), kwargs = {})
# %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5m/c5mma4y56ura3imiphserxkqyervoqe3bptp4i4swvp3yenvzn36.py
# Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
# prob => div, sum_1
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
del primals_1
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [context_fc], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf2, buf9, 256, grid=grid(256), stream=stream0)
buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1 # reuse
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [questions_fc], Original ATen: [aten.relu, aten.threshold_backward]
triton_poi_fused_relu_threshold_backward_0.run(buf3, buf8, 256, grid=grid(256), stream=stream0)
buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
triton_poi_fused__softmax_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax]
triton_poi_fused__softmax_2.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5 # reuse
# Topologically Sorted Source Nodes: [ques_emb], Original ATen: [aten.bmm]
extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (16, 4, 4), (16, 4, 1), 0), out=buf7)
del buf6
return (reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf4, reinterpret_tensor(primals_4, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), buf8, buf9, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class Context2AnswerAttention(nn.Module):
def __init__(self, dim, hidden_size):
super(Context2AnswerAttention, self).__init__()
self.linear_sim = nn.Linear(dim, hidden_size, bias=False)
def forward(self, context, answers, out_answers, ans_mask=None):
"""
Parameters
:context, (batch_size, L, dim)
:answers, (batch_size, N, dim)
:out_answers, (batch_size, N, dim)
:ans_mask, (batch_size, N)
Returns
:ques_emb, (batch_size, L, dim)
"""
context_fc = torch.relu(self.linear_sim(context))
questions_fc = torch.relu(self.linear_sim(answers))
attention = torch.matmul(context_fc, questions_fc.transpose(-1, -2))
if ans_mask is not None:
attention = attention.masked_fill_(1 - ans_mask.byte().
unsqueeze(1), -INF)
prob = torch.softmax(attention, dim=-1)
ques_emb = torch.matmul(prob, out_answers)
return ques_emb
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
[4, 4, 4, 4])]
def get_init_inputs():
return [[], {'dim': 4, 'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.full([1], 0, tl.int32)
tmp2 = triton_helpers.maximum(tmp1, tmp0)
tmp3 = 0.0
tmp4 = tmp2 <= tmp3
tl.store(in_out_ptr0 + x0, tmp2, xmask)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = tmp1 + tmp2
tmp5 = tmp3 + tmp4
tmp7 = tmp5 + tmp6
tmp8 = tmp0 / tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
del primals_1
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf2, buf9,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf1
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3, buf8,
256, XBLOCK=128, num_warps=4, num_stages=1)
buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256,
num_warps=4, num_stages=1)
buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
del buf5
extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(primals_4, (16, 4, 4), (16, 4, 1), 0),
out=buf7)
del buf6
return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0
), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf4, reinterpret_tensor(primals_4, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0
), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), buf8, buf9
class Context2AnswerAttentionNew(nn.Module):
def __init__(self, dim, hidden_size):
super(Context2AnswerAttentionNew, self).__init__()
self.linear_sim = nn.Linear(dim, hidden_size, bias=False)
def forward(self, input_0, input_1, input_2):
primals_1 = self.linear_sim.weight
primals_2 = input_0
primals_3 = input_1
primals_4 = input_2
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
IBM/graph4nlp
|
Context2AnswerAttention
| false | 8,333 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
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decoder5
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# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/g3/cg3ccnqa6wyzxanbi6fgd6vkyhgnnoo3va4nix3ohszhmyutqgep.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
# Source node to ATen node mapping:
# out => _unsafe_index, _unsafe_index_1
# Graph fragment:
# %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {})
# %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {})
triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 73728
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = (xindex // 6) % 6
x2 = (xindex // 36)
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), None, eviction_policy='evict_last')
tl.store(out_ptr0 + (x3), tmp0, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ki/ckii6ld2pjui2kyh33ieyagpaxjso55jvdwg3kttj5omjlrhkys6.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_3 => add, add_1, convert_element_type, convert_element_type_1, iota_2, mul, mul_1
# Graph fragment:
# %iota_2 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (8,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_2, 1), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
# %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add, torch.float32), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.0), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 0.5), kwargs = {})
# %convert_element_type_1 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_1, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_1 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[8],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jb/cjbtqp44rv5g6gp6xheddrm2gqlumlvkhqfivbddyr2zt6dvb2bl.py
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# out_3 => _unsafe_index_2
# out_4 => _unsafe_index_3, _unsafe_index_4
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu, [None, None, %unsqueeze, %convert_element_type_1]), kwargs = {})
# %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_4 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_3, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 204800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 10) % 10
x0 = xindex % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 512
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (4*tmp4) + (16*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/yj/cyjjvld53umyu5izmkpdhznlumejgejh2466jzod3qgr7fvb5pdo.py
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_5 => convolution_1
# out_6 => relu_1
# out_7 => _unsafe_index_5, _unsafe_index_6
# Graph fragment:
# %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_4, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
# %_unsafe_index_5 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_1, [None, None, %sub_5, None]), kwargs = {})
# %_unsafe_index_6 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_5, [None, None, None, %sub_5]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_3 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 204800
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = (xindex // 10) % 10
x4 = (xindex // 100)
x2 = (xindex // 100) % 512
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-7) + (tl_math.abs((-1) + x0))))) + ((-8)*(tl_math.abs((-7) + (tl_math.abs((-1) + x1))))) + (64*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/od/codkvvxpur7vfcixyfgg6ayyzatao2cw5of6bxy53exotyuzofwb.py
# Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_16 => add_4, add_5, convert_element_type_4, convert_element_type_5, iota_12, mul_4, mul_5
# Graph fragment:
# %iota_12 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (16,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_12, 1), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, 0), kwargs = {})
# %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_4, torch.float32), kwargs = {})
# %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_4, 0.0), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_5, 0.5), kwargs = {})
# %convert_element_type_5 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_4 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/n4/cn4gysdmqghrvngp47oenyvvto7s3aethdrxi4sbmrcf3xjjaxe5.py
# Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_14 => convolution_4
# out_15 => relu_4
# out_16 => _unsafe_index_11
# out_17 => _unsafe_index_12, _unsafe_index_13
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
# %_unsafe_index_11 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_4, [None, None, %unsqueeze_1, %convert_element_type_5]), kwargs = {})
# %_unsafe_index_12 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_11, [None, None, %sub_21, None]), kwargs = {})
# %_unsafe_index_13 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_12, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 331776
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 18) % 18
x0 = xindex % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 256
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (8*tmp4) + (64*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qo/cqosfn35mg3vfwgwbfco5wnax7omnfdeld4mf5zgmi57rqhvu546.py
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_18 => convolution_5
# out_19 => relu_5
# out_20 => _unsafe_index_14, _unsafe_index_15
# Graph fragment:
# %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_13, %primals_12, %primals_13, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_5 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_5,), kwargs = {})
# %_unsafe_index_14 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_5, [None, None, %sub_21, None]), kwargs = {})
# %_unsafe_index_15 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_14, [None, None, None, %sub_21]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_6 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_6', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 331776
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = (xindex // 18) % 18
x4 = (xindex // 324)
x2 = (xindex // 324) % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + ((-1)*(tl_math.abs((-15) + (tl_math.abs((-1) + x0))))) + ((-16)*(tl_math.abs((-15) + (tl_math.abs((-1) + x1))))) + (256*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/6w/c6wyyvxovkkzfk6k64bfon5db5xfxiywvm7oionlfmjjhdvc6ugb.py
# Topologically Sorted Source Nodes: [out_29], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_29 => add_8, add_9, convert_element_type_8, convert_element_type_9, iota_22, mul_8, mul_9
# Graph fragment:
# %iota_22 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (32,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_22, 1), kwargs = {})
# %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, 0), kwargs = {})
# %convert_element_type_8 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_8, torch.float32), kwargs = {})
# %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_8, 0.0), kwargs = {})
# %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, 0.5), kwargs = {})
# %convert_element_type_9 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_9, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_7 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_7', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/zx/czxyrb5nedwpsce4kihomb7jh3xlalitdbzogjhvllw7tvju45wx.py
# Topologically Sorted Source Nodes: [out_27, out_28, out_29, out_30], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_27 => convolution_8
# out_28 => relu_8
# out_29 => _unsafe_index_20
# out_30 => _unsafe_index_21, _unsafe_index_22
# Graph fragment:
# %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
# %_unsafe_index_20 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_8, [None, None, %unsqueeze_2, %convert_element_type_9]), kwargs = {})
# %_unsafe_index_21 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_20, [None, None, %sub_37, None]), kwargs = {})
# %_unsafe_index_22 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_21, [None, None, None, %sub_37]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1048576],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 591872
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x1 = (xindex // 34) % 34
x0 = xindex % 34
x4 = (xindex // 1156)
x2 = (xindex // 1156) % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1)))))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (31 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0)))))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (16*tmp4) + (256*x4)), None, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/t5/ct5pmhupse6j54x6sdpzr6ounpgjr2tjepxqrep6mpshhz6d5nmj.py
# Topologically Sorted Source Nodes: [out_31, out_32, out_33], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_31 => convolution_9
# out_32 => relu_9
# out_33 => _unsafe_index_23, _unsafe_index_24
# Graph fragment:
# %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
# %_unsafe_index_23 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_9, [None, None, %sub_37, None]), kwargs = {})
# %_unsafe_index_24 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_23, [None, None, None, %sub_37]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_9 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_9', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1048576],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_9', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 591872
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 34
x1 = (xindex // 34) % 34
x4 = (xindex // 1156)
x2 = (xindex // 1156) % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + ((-1)*(tl_math.abs((-31) + (tl_math.abs((-1) + x0))))) + ((-32)*(tl_math.abs((-31) + (tl_math.abs((-1) + x1))))) + (1024*x4)), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/or/corqi4rcleb4nedtprk7hbxxrjl3jr4bncq2rzvazmq4w47jdfgd.py
# Topologically Sorted Source Nodes: [out_36], Original ATen: [aten.arange]
# Source node to ATen node mapping:
# out_36 => iota_28
# Graph fragment:
# %iota_28 : [num_users=2] = call_function[target=torch.ops.prims.iota.default](args = (64,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
triton_poi_fused_arange_10 = async_compile.triton('triton_poi_fused_arange_10', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_arange_10', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_arange_10(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/tu/ctuiw5by2zagl46pvth5lodtjm7rgoqdm2h4nc7rofycgb7l2f5p.py
# Topologically Sorted Source Nodes: [out_36], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
# Source node to ATen node mapping:
# out_36 => add_12, add_13, convert_element_type_12, convert_element_type_13, mul_12, mul_13
# Graph fragment:
# %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%iota_28, 1), kwargs = {})
# %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_12, 0), kwargs = {})
# %convert_element_type_12 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%add_12, torch.float32), kwargs = {})
# %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_12, 0.0), kwargs = {})
# %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_13, 0.5), kwargs = {})
# %convert_element_type_13 : [num_users=3] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_13, torch.int64), kwargs = {})
triton_poi_fused__to_copy_add_arange_mul_11 = async_compile.triton('triton_poi_fused__to_copy_add_arange_mul_11', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_mul_11', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_11(out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/qo/cqo35fiyesrqzo22llfixo2ceuvfeafmio72vhbbceo6rq3nomeh.py
# Topologically Sorted Source Nodes: [out_34, out_35, out_36, out_37], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_34 => convolution_10
# out_35 => relu_10
# out_36 => _unsafe_index_25
# out_37 => _unsafe_index_26, _unsafe_index_27
# Graph fragment:
# %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
# %_unsafe_index_25 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_10, [None, None, %unsqueeze_3, %convert_element_type_13]), kwargs = {})
# %_unsafe_index_26 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_25, [None, None, %sub_45, None]), kwargs = {})
# %_unsafe_index_27 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_26, [None, None, None, %sub_45]), kwargs = {})
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12 = async_compile.triton('triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2097152],
filename=__file__,
triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 66) % 66
x0 = xindex % 66
x4 = (xindex // 4356)
x2 = (xindex // 4356) % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x1)))))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (63 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x0)))))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 32, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + (32*tmp4) + (1024*x4)), xmask, eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + (x7), tmp13, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ru/cruijqz2dfjd47jaznipfhrikp7vpe4l3vowrihrs46t4ur5qh3q.py
# Topologically Sorted Source Nodes: [out_38, out_39, out_40], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
# Source node to ATen node mapping:
# out_38 => convolution_11
# out_39 => relu_11
# out_40 => _unsafe_index_28, _unsafe_index_29
# Graph fragment:
# %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
# %_unsafe_index_28 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%relu_11, [None, None, %sub_45, None]), kwargs = {})
# %_unsafe_index_29 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_28, [None, None, None, %sub_45]), kwargs = {})
triton_poi_fused_convolution_reflection_pad2d_relu_13 = async_compile.triton('triton_poi_fused_convolution_reflection_pad2d_relu_13', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[2097152],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_reflection_pad2d_relu_13', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 66
x1 = (xindex // 66) % 66
x4 = (xindex // 4356)
x2 = (xindex // 4356) % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (4095 + ((-1)*(tl_math.abs((-63) + (tl_math.abs((-1) + x0))))) + ((-64)*(tl_math.abs((-63) + (tl_math.abs((-1) + x1))))) + (4096*x4)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/34/c34mowj53rihk4ptlkhbho74czctpibkqk4yakydwhzc2nb7vgqs.py
# Topologically Sorted Source Nodes: [out_41], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# out_41 => convolution_12
# Graph fragment:
# %convolution_12 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_29, %primals_26, %primals_27, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_14 = async_compile.triton('triton_poi_fused_convolution_14', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_14', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 49152
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 4096) % 3
tmp0 = tl.load(in_out_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + (x3), tmp2, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/gw/cgww5gopybzpq23cobl4c3okhpizmjt3qwz5psrtu4u5y2aayof4.py
# Topologically Sorted Source Nodes: [out_38, out_39], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_38 => convolution_11
# out_39 => relu_11
# Graph fragment:
# %convolution_11 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_27, %primals_24, %primals_25, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_11 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_11,), kwargs = {})
# %le_18 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_11, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_15 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_15', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1048576],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_15', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1048576
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 4096) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/hu/chuaxrcezvpnqxen3zhi6dy4wtnw5xk7fgoy4m7eqxsgsej3akm7.py
# Topologically Sorted Source Nodes: [out_34, out_35], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_34 => convolution_10
# out_35 => relu_10
# Graph fragment:
# %convolution_10 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_24, %primals_22, %primals_23, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_10 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_10,), kwargs = {})
# %le_37 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_10, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_16 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_16', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_16', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 262144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 64
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/u3/cu365fyiqrrwqanu2tbumkdnzceorgvl56vwfpbp6hzh6cfd52ln.py
# Topologically Sorted Source Nodes: [out_31, out_32], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_31 => convolution_9
# out_32 => relu_9
# Graph fragment:
# %convolution_9 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_22, %primals_20, %primals_21, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_9 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_9,), kwargs = {})
# %le_56 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_9, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_17 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_17', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[524288],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_17', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 524288
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 1024) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ds/cdsftonakf5p3pywbrmqutzkxmu6uw7qadvzd2gvaemkwahcviq5.py
# Topologically Sorted Source Nodes: [out_27, out_28], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_27 => convolution_8
# out_28 => relu_8
# Graph fragment:
# %convolution_8 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_19, %primals_18, %primals_19, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_8 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_8,), kwargs = {})
# %le_75 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_8, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_18 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_18', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_18', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 128
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nc/cncr74hmgexnjng5woys5mmwhc6i42nfmpbr7ps5bjpztikrhibj.py
# Topologically Sorted Source Nodes: [out_24, out_25], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_24 => convolution_7
# out_25 => relu_7
# Graph fragment:
# %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_17, %primals_16, %primals_17, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_7 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_7,), kwargs = {})
# %le_94 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_7, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_19 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_19', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[262144],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_19', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 262144
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 256) % 256
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/k5/ck5k7e2bzuyqgp742kzidsjiq4mpdyfuxqpvwha4lv3nbv3kk6in.py
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_14 => convolution_4
# out_15 => relu_4
# Graph fragment:
# %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_10, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_4,), kwargs = {})
# %le_151 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_20 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_20', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[65536],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_20', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 65536
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 256
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/nb/cnbenfnapnsjbfdmibjqcsb2xvjvoiopdhhqmngsiygd46sc2rcg.py
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_11 => convolution_3
# out_12 => relu_3
# Graph fragment:
# %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_8, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
# %le_170 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_21 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_21', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[131072],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_21', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 131072
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 64) % 512
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ev/cevzongfbe5yon2fp6xblelcr3xptsebsdf2ugzjhpldgpbgy2ph.py
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# out_1 => convolution
# out_2 => relu
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
# %le_227 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_22 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_22', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[32768],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_22', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 32768
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = (xindex // 16) % 512
tmp0 = tl.load(in_ptr0 + (x3), None)
tmp1 = tl.load(in_ptr1 + (x1), None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + (x3), tmp6, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27 = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (512, ), (1, ))
assert_size_stride(primals_4, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_5, (512, ), (1, ))
assert_size_stride(primals_6, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_7, (512, ), (1, ))
assert_size_stride(primals_8, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_9, (512, ), (1, ))
assert_size_stride(primals_10, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_11, (256, ), (1, ))
assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (256, ), (1, ))
assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_15, (256, ), (1, ))
assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_17, (256, ), (1, ))
assert_size_stride(primals_18, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_19, (128, ), (1, ))
assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_21, (128, ), (1, ))
assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_23, (64, ), (1, ))
assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_25, (64, ), (1, ))
assert_size_stride(primals_26, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_27, (3, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.float32)
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d]
stream0 = get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 73728, grid=grid(73728), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution]
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 512, 4, 4), (8192, 16, 4, 1))
buf2 = empty_strided_cuda((8, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_1.run(buf2, 8, grid=grid(8), stream=stream0)
buf3 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_1, out_2, out_3, out_4], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2.run(buf2, buf1, primals_3, buf3, 204800, grid=grid(204800), stream=stream0)
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 512, 8, 8), (32768, 64, 8, 1))
buf5 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_5, out_6, out_7], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf4, primals_5, buf5, 204800, grid=grid(204800), stream=stream0)
# Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 512, 8, 8), (32768, 64, 8, 1))
buf7 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_8, out_9, out_10], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf6, primals_7, buf7, 204800, grid=grid(204800), stream=stream0)
# Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.convolution]
buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 512, 8, 8), (32768, 64, 8, 1))
buf9 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_11, out_12, out_13], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_3.run(buf8, primals_9, buf9, 204800, grid=grid(204800), stream=stream0)
# Topologically Sorted Source Nodes: [out_14], Original ATen: [aten.convolution]
buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 256, 8, 8), (16384, 64, 8, 1))
buf11 = empty_strided_cuda((16, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_16], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_4.run(buf11, 16, grid=grid(16), stream=stream0)
buf12 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_14, out_15, out_16, out_17], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5.run(buf11, buf10, primals_11, buf12, 331776, grid=grid(331776), stream=stream0)
# Topologically Sorted Source Nodes: [out_18], Original ATen: [aten.convolution]
buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 256, 16, 16), (65536, 256, 16, 1))
buf14 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_18, out_19, out_20], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf13, primals_13, buf14, 331776, grid=grid(331776), stream=stream0)
# Topologically Sorted Source Nodes: [out_21], Original ATen: [aten.convolution]
buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 256, 16, 16), (65536, 256, 16, 1))
buf16 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_21, out_22, out_23], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf15, primals_15, buf16, 331776, grid=grid(331776), stream=stream0)
# Topologically Sorted Source Nodes: [out_24], Original ATen: [aten.convolution]
buf17 = extern_kernels.convolution(buf16, primals_16, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 256, 16, 16), (65536, 256, 16, 1))
buf18 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_24, out_25, out_26], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_6.run(buf17, primals_17, buf18, 331776, grid=grid(331776), stream=stream0)
# Topologically Sorted Source Nodes: [out_27], Original ATen: [aten.convolution]
buf19 = extern_kernels.convolution(buf18, primals_18, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
buf20 = empty_strided_cuda((32, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_29], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_7.run(buf20, 32, grid=grid(32), stream=stream0)
buf21 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_27, out_28, out_29, out_30], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8.run(buf20, buf19, primals_19, buf21, 591872, grid=grid(591872), stream=stream0)
# Topologically Sorted Source Nodes: [out_31], Original ATen: [aten.convolution]
buf22 = extern_kernels.convolution(buf21, primals_20, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1024, 32, 1))
buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_31, out_32, out_33], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_9.run(buf22, primals_21, buf23, 591872, grid=grid(591872), stream=stream0)
# Topologically Sorted Source Nodes: [out_34], Original ATen: [aten.convolution]
buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf25 = empty_strided_cuda((64, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_36], Original ATen: [aten.arange]
triton_poi_fused_arange_10.run(buf25, 64, grid=grid(64), stream=stream0)
buf26 = empty_strided_cuda((64, ), (1, ), torch.int64)
# Topologically Sorted Source Nodes: [out_36], Original ATen: [aten.arange, aten.add, aten.mul, aten._to_copy]
triton_poi_fused__to_copy_add_arange_mul_11.run(buf26, 64, grid=grid(64), stream=stream0)
buf27 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_34, out_35, out_36, out_37], Original ATen: [aten.convolution, aten.relu, aten._unsafe_index, aten.reflection_pad2d]
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12.run(buf26, buf24, primals_23, buf27, 1115136, grid=grid(1115136), stream=stream0)
# Topologically Sorted Source Nodes: [out_38], Original ATen: [aten.convolution]
buf28 = extern_kernels.convolution(buf27, primals_24, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf29 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1), torch.float32)
# Topologically Sorted Source Nodes: [out_38, out_39, out_40], Original ATen: [aten.convolution, aten.relu, aten.reflection_pad2d]
triton_poi_fused_convolution_reflection_pad2d_relu_13.run(buf28, primals_25, buf29, 1115136, grid=grid(1115136), stream=stream0)
# Topologically Sorted Source Nodes: [out_41], Original ATen: [aten.convolution]
buf30 = extern_kernels.convolution(buf29, primals_26, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf31 = buf30; del buf30 # reuse
# Topologically Sorted Source Nodes: [out_41], Original ATen: [aten.convolution]
triton_poi_fused_convolution_14.run(buf31, primals_27, 49152, grid=grid(49152), stream=stream0)
del primals_27
buf32 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_38, out_39], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_15.run(buf28, primals_25, buf32, 1048576, grid=grid(1048576), stream=stream0)
del buf28
del primals_25
buf33 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_34, out_35], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_16.run(buf24, primals_23, buf33, 262144, grid=grid(262144), stream=stream0)
del buf24
del primals_23
buf34 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_31, out_32], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_17.run(buf22, primals_21, buf34, 524288, grid=grid(524288), stream=stream0)
del buf22
del primals_21
buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_27, out_28], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_18.run(buf19, primals_19, buf35, 131072, grid=grid(131072), stream=stream0)
del buf19
del primals_19
buf36 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_24, out_25], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_19.run(buf17, primals_17, buf36, 262144, grid=grid(262144), stream=stream0)
del buf17
del primals_17
buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_21, out_22], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_19.run(buf15, primals_15, buf37, 262144, grid=grid(262144), stream=stream0)
del buf15
del primals_15
buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_18, out_19], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_19.run(buf13, primals_13, buf38, 262144, grid=grid(262144), stream=stream0)
del buf13
del primals_13
buf39 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_14, out_15], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_20.run(buf10, primals_11, buf39, 65536, grid=grid(65536), stream=stream0)
del buf10
del primals_11
buf40 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_11, out_12], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_21.run(buf8, primals_9, buf40, 131072, grid=grid(131072), stream=stream0)
del buf8
del primals_9
buf41 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_8, out_9], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_21.run(buf6, primals_7, buf41, 131072, grid=grid(131072), stream=stream0)
del buf6
del primals_7
buf42 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_5, out_6], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_21.run(buf4, primals_5, buf42, 131072, grid=grid(131072), stream=stream0)
del buf4
del primals_5
buf43 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
triton_poi_fused_convolution_relu_threshold_backward_22.run(buf1, primals_3, buf43, 32768, grid=grid(32768), stream=stream0)
del buf1
del primals_3
return (buf31, primals_2, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, primals_20, primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7, buf9, buf11, buf12, buf14, buf16, buf18, buf20, buf21, buf23, buf25, buf26, buf27, buf29, buf32, buf33, buf34, buf35, buf36, buf37, buf38, buf39, buf40, buf41, buf42, buf43, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 512, 4, 4), (8192, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((512, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_9 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_10 = rand_strided((256, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_12 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_16 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_18 = rand_strided((128, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_19 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_20 = rand_strided((128, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_21 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_22 = rand_strided((64, 128, 3, 3), (1152, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_23 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_24 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_26 = rand_strided((3, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_27 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class decoder5(nn.Module):
def __init__(self):
super(decoder5, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu19 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad20 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv20 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu20 = nn.ReLU(inplace=True)
self.reflecPad21 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv21 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu21 = nn.ReLU(inplace=True)
self.reflecPad22 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv22 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu22 = nn.ReLU(inplace=True)
self.reflecPad23 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv23 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu23 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad24 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv24 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu24 = nn.ReLU(inplace=True)
self.reflecPad25 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv25 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu25 = nn.ReLU(inplace=True)
self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad26 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv26 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu26 = nn.ReLU(inplace=True)
self.reflecPad27 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv27 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, x):
out = self.reflecPad15(x)
out = self.conv15(out)
out = self.relu15(out)
out = self.unpool(out)
out = self.reflecPad16(out)
out = self.conv16(out)
out = self.relu16(out)
out = self.reflecPad17(out)
out = self.conv17(out)
out = self.relu17(out)
out = self.reflecPad18(out)
out = self.conv18(out)
out = self.relu18(out)
out = self.reflecPad19(out)
out = self.conv19(out)
out = self.relu19(out)
out = self.unpool2(out)
out = self.reflecPad20(out)
out = self.conv20(out)
out = self.relu20(out)
out = self.reflecPad21(out)
out = self.conv21(out)
out = self.relu21(out)
out = self.reflecPad22(out)
out = self.conv22(out)
out = self.relu22(out)
out = self.reflecPad23(out)
out = self.conv23(out)
out = self.relu23(out)
out = self.unpool3(out)
out = self.reflecPad24(out)
out = self.conv24(out)
out = self.relu24(out)
out = self.reflecPad25(out)
out = self.conv25(out)
out = self.relu25(out)
out = self.unpool4(out)
out = self.reflecPad26(out)
out = self.conv26(out)
out = self.relu26(out)
out = self.reflecPad27(out)
out = self.conv27(out)
return out
def get_inputs():
return [torch.rand([4, 512, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 6
x1 = xindex // 6 % 6
x2 = xindex // 36
x3 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 +
x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), None,
eviction_policy='evict_last')
tl.store(out_ptr0 + x3, tmp0, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_1(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 8
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 10 % 10
x0 = xindex % 10
x4 = xindex // 100
x2 = xindex // 100 % 512
x7 = xindex
tmp0 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x1
))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (7 + -1 * tl_math.abs(-7 + tl_math.abs(-1 + x0
))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 4, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 4 * tmp4 + 16 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_3(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 10
x1 = xindex // 10 % 10
x4 = xindex // 100
x2 = xindex // 100 % 512
x5 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-7 + tl_math.abs(-1 +
x0)) + -8 * tl_math.abs(-7 + tl_math.abs(-1 + x1)) + 64 * x4), None,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_4(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 18 % 18
x0 = xindex % 18
x4 = xindex // 324
x2 = xindex // 324 % 256
x7 = xindex
tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x1))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 8, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 8 * tmp4 + 64 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_6(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 18
x1 = xindex // 18 % 18
x4 = xindex // 324
x2 = xindex // 324 % 256
x5 = xindex
tmp0 = tl.load(in_ptr0 + (255 + -1 * tl_math.abs(-15 + tl_math.abs(-1 +
x0)) + -16 * tl_math.abs(-15 + tl_math.abs(-1 + x1)) + 256 * x4),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_7(out_ptr0, xnumel, XBLOCK: tl
.constexpr):
xnumel = 32
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8(in_ptr0,
in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x1 = xindex // 34 % 34
x0 = xindex % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 128
x7 = xindex
tmp0 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x1))), None, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (31 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0))), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, None, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 16, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 16 * tmp4 + 256 * x4), None,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, None)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_9(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x0 = xindex % 34
x1 = xindex // 34 % 34
x4 = xindex // 1156
x2 = xindex // 1156 % 128
x5 = xindex
tmp0 = tl.load(in_ptr0 + (1023 + -1 * tl_math.abs(-31 + tl_math.abs(-1 +
x0)) + -32 * tl_math.abs(-31 + tl_math.abs(-1 + x1)) + 1024 * x4),
None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, None)
@triton.jit
def triton_poi_fused_arange_10(out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tl.store(out_ptr0 + x0, tmp0, xmask)
@triton.jit
def triton_poi_fused__to_copy_add_arange_mul_11(out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = x0
tmp1 = tmp0.to(tl.float32)
tmp2 = 0.5
tmp3 = tmp1 * tmp2
tmp4 = tmp3.to(tl.int32)
tl.store(out_ptr0 + x0, tmp4, xmask)
@triton.jit
def triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12(in_ptr0
, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 66 % 66
x0 = xindex % 66
x4 = xindex // 4356
x2 = xindex // 4356 % 64
x7 = xindex
tmp0 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x1))), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (63 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x0))), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
tmp1 = tl.full([XBLOCK], 32, tl.int32)
tmp2 = tmp0 + tmp1
tmp3 = tmp0 < 0
tmp4 = tl.where(tmp3, tmp2, tmp0)
tmp6 = tmp5 + tmp1
tmp7 = tmp5 < 0
tmp8 = tl.where(tmp7, tmp6, tmp5)
tmp9 = tl.load(in_ptr1 + (tmp8 + 32 * tmp4 + 1024 * x4), xmask,
eviction_policy='evict_last')
tmp11 = tmp9 + tmp10
tmp12 = tl.full([1], 0, tl.int32)
tmp13 = triton_helpers.maximum(tmp12, tmp11)
tl.store(out_ptr0 + x7, tmp13, xmask)
@triton.jit
def triton_poi_fused_convolution_reflection_pad2d_relu_13(in_ptr0, in_ptr1,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 1115136
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 66
x1 = xindex // 66 % 66
x4 = xindex // 4356
x2 = xindex // 4356 % 64
x5 = xindex
tmp0 = tl.load(in_ptr0 + (4095 + -1 * tl_math.abs(-63 + tl_math.abs(-1 +
x0)) + -64 * tl_math.abs(-63 + tl_math.abs(-1 + x1)) + 4096 * x4),
xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tl.store(out_ptr0 + x5, tmp4, xmask)
@triton.jit
def triton_poi_fused_convolution_14(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 3
tmp0 = tl.load(in_out_ptr0 + x3, None)
tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(in_out_ptr0 + x3, tmp2, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_15(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 4096 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_16(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 64
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_17(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 1024 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_18(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 128
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_19(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 256 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_20(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 256
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_21(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 64 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_22(in_ptr0,
in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
x3 = xindex
x1 = xindex // 16 % 512
tmp0 = tl.load(in_ptr0 + x3, None)
tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(out_ptr0 + x3, tmp6, None)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
primals_13, primals_14, primals_15, primals_16, primals_17,
primals_18, primals_19, primals_20, primals_21, primals_22,
primals_23, primals_24, primals_25, primals_26, primals_27) = args
args.clear()
assert_size_stride(primals_1, (4, 512, 4, 4), (8192, 16, 4, 1))
assert_size_stride(primals_2, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_3, (512,), (1,))
assert_size_stride(primals_4, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_5, (512,), (1,))
assert_size_stride(primals_6, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_7, (512,), (1,))
assert_size_stride(primals_8, (512, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_9, (512,), (1,))
assert_size_stride(primals_10, (256, 512, 3, 3), (4608, 9, 3, 1))
assert_size_stride(primals_11, (256,), (1,))
assert_size_stride(primals_12, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_13, (256,), (1,))
assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_15, (256,), (1,))
assert_size_stride(primals_16, (256, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_17, (256,), (1,))
assert_size_stride(primals_18, (128, 256, 3, 3), (2304, 9, 3, 1))
assert_size_stride(primals_19, (128,), (1,))
assert_size_stride(primals_20, (128, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_21, (128,), (1,))
assert_size_stride(primals_22, (64, 128, 3, 3), (1152, 9, 3, 1))
assert_size_stride(primals_23, (64,), (1,))
assert_size_stride(primals_24, (64, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_25, (64,), (1,))
assert_size_stride(primals_26, (3, 64, 3, 3), (576, 9, 3, 1))
assert_size_stride(primals_27, (3,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 512, 6, 6), (18432, 36, 6, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_reflection_pad2d_0[grid(73728)](primals_1, buf0,
73728, XBLOCK=1024, num_warps=4, num_stages=1)
del primals_1
buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf1, (4, 512, 4, 4), (8192, 16, 4, 1))
buf2 = empty_strided_cuda((8,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_1[grid(8)](buf2, 8, XBLOCK
=8, num_warps=1, num_stages=1)
buf3 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_2[grid
(204800)](buf2, buf1, primals_3, buf3, 204800, XBLOCK=512,
num_warps=8, num_stages=1)
buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 512, 8, 8), (32768, 64, 8, 1))
buf5 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf4
, primals_5, buf5, 204800, XBLOCK=1024, num_warps=4, num_stages=1)
buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf6, (4, 512, 8, 8), (32768, 64, 8, 1))
buf7 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf6
, primals_7, buf7, 204800, XBLOCK=1024, num_warps=4, num_stages=1)
buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf8, (4, 512, 8, 8), (32768, 64, 8, 1))
buf9 = empty_strided_cuda((4, 512, 10, 10), (51200, 100, 10, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_3[grid(204800)](buf8
, primals_9, buf9, 204800, XBLOCK=1024, num_warps=4, num_stages=1)
buf10 = extern_kernels.convolution(buf9, primals_10, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf10, (4, 256, 8, 8), (16384, 64, 8, 1))
buf11 = empty_strided_cuda((16,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_4[grid(16)](buf11, 16,
XBLOCK=16, num_warps=1, num_stages=1)
buf12 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_5[grid
(331776)](buf11, buf10, primals_11, buf12, 331776, XBLOCK=512,
num_warps=8, num_stages=1)
buf13 = extern_kernels.convolution(buf12, primals_12, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf13, (4, 256, 16, 16), (65536, 256, 16, 1))
buf14 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)](
buf13, primals_13, buf14, 331776, XBLOCK=512, num_warps=8,
num_stages=1)
buf15 = extern_kernels.convolution(buf14, primals_14, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf15, (4, 256, 16, 16), (65536, 256, 16, 1))
buf16 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)](
buf15, primals_15, buf16, 331776, XBLOCK=512, num_warps=8,
num_stages=1)
buf17 = extern_kernels.convolution(buf16, primals_16, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf17, (4, 256, 16, 16), (65536, 256, 16, 1))
buf18 = empty_strided_cuda((4, 256, 18, 18), (82944, 324, 18, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_6[grid(331776)](
buf17, primals_17, buf18, 331776, XBLOCK=512, num_warps=8,
num_stages=1)
buf19 = extern_kernels.convolution(buf18, primals_18, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf19, (4, 128, 16, 16), (32768, 256, 16, 1))
buf20 = empty_strided_cuda((32,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_7[grid(32)](buf20, 32,
XBLOCK=32, num_warps=1, num_stages=1)
buf21 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_8[grid
(591872)](buf20, buf19, primals_19, buf21, 591872, XBLOCK=1024,
num_warps=4, num_stages=1)
buf22 = extern_kernels.convolution(buf21, primals_20, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf22, (4, 128, 32, 32), (131072, 1024, 32, 1))
buf23 = empty_strided_cuda((4, 128, 34, 34), (147968, 1156, 34, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_9[grid(591872)](
buf22, primals_21, buf23, 591872, XBLOCK=1024, num_warps=4,
num_stages=1)
buf24 = extern_kernels.convolution(buf23, primals_22, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf24, (4, 64, 32, 32), (65536, 1024, 32, 1))
buf25 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused_arange_10[grid(64)](buf25, 64, XBLOCK=64,
num_warps=1, num_stages=1)
buf26 = empty_strided_cuda((64,), (1,), torch.int64)
triton_poi_fused__to_copy_add_arange_mul_11[grid(64)](buf26, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf27 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
torch.float32)
triton_poi_fused__unsafe_index_convolution_reflection_pad2d_relu_12[
grid(1115136)](buf26, buf24, primals_23, buf27, 1115136, XBLOCK
=512, num_warps=8, num_stages=1)
buf28 = extern_kernels.convolution(buf27, primals_24, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf28, (4, 64, 64, 64), (262144, 4096, 64, 1))
buf29 = empty_strided_cuda((4, 64, 66, 66), (278784, 4356, 66, 1),
torch.float32)
triton_poi_fused_convolution_reflection_pad2d_relu_13[grid(1115136)](
buf28, primals_25, buf29, 1115136, XBLOCK=1024, num_warps=4,
num_stages=1)
buf30 = extern_kernels.convolution(buf29, primals_26, stride=(1, 1),
padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf30, (4, 3, 64, 64), (12288, 4096, 64, 1))
buf31 = buf30
del buf30
triton_poi_fused_convolution_14[grid(49152)](buf31, primals_27,
49152, XBLOCK=512, num_warps=4, num_stages=1)
del primals_27
buf32 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_15[grid(1048576)](
buf28, primals_25, buf32, 1048576, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf28
del primals_25
buf33 = empty_strided_cuda((4, 64, 32, 32), (65536, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_16[grid(262144)](
buf24, primals_23, buf33, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf24
del primals_23
buf34 = empty_strided_cuda((4, 128, 32, 32), (131072, 1024, 32, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_17[grid(524288)](
buf22, primals_21, buf34, 524288, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf22
del primals_21
buf35 = empty_strided_cuda((4, 128, 16, 16), (32768, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_18[grid(131072)](
buf19, primals_19, buf35, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf19
del primals_19
buf36 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)](
buf17, primals_17, buf36, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf17
del primals_17
buf37 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)](
buf15, primals_15, buf37, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf15
del primals_15
buf38 = empty_strided_cuda((4, 256, 16, 16), (65536, 256, 16, 1),
torch.bool)
triton_poi_fused_convolution_relu_threshold_backward_19[grid(262144)](
buf13, primals_13, buf38, 262144, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf13
del primals_13
buf39 = empty_strided_cuda((4, 256, 8, 8), (16384, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_20[grid(65536)](
buf10, primals_11, buf39, 65536, XBLOCK=512, num_warps=4,
num_stages=1)
del buf10
del primals_11
buf40 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)](
buf8, primals_9, buf40, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf8
del primals_9
buf41 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)](
buf6, primals_7, buf41, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf6
del primals_7
buf42 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
.bool)
triton_poi_fused_convolution_relu_threshold_backward_21[grid(131072)](
buf4, primals_5, buf42, 131072, XBLOCK=1024, num_warps=4,
num_stages=1)
del buf4
del primals_5
buf43 = empty_strided_cuda((4, 512, 4, 4), (8192, 16, 4, 1), torch.bool
)
triton_poi_fused_convolution_relu_threshold_backward_22[grid(32768)](
buf1, primals_3, buf43, 32768, XBLOCK=128, num_warps=4,
num_stages=1)
del buf1
del primals_3
return (buf31, primals_2, primals_4, primals_6, primals_8, primals_10,
primals_12, primals_14, primals_16, primals_18, primals_20,
primals_22, primals_24, primals_26, buf0, buf2, buf3, buf5, buf7,
buf9, buf11, buf12, buf14, buf16, buf18, buf20, buf21, buf23, buf25,
buf26, buf27, buf29, buf32, buf33, buf34, buf35, buf36, buf37,
buf38, buf39, buf40, buf41, buf42, buf43)
class decoder5New(nn.Module):
def __init__(self):
super(decoder5New, self).__init__()
self.reflecPad15 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv15 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu15 = nn.ReLU(inplace=True)
self.unpool = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad16 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv16 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu16 = nn.ReLU(inplace=True)
self.reflecPad17 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv17 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu17 = nn.ReLU(inplace=True)
self.reflecPad18 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv18 = nn.Conv2d(512, 512, 3, 1, 0)
self.relu18 = nn.ReLU(inplace=True)
self.reflecPad19 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv19 = nn.Conv2d(512, 256, 3, 1, 0)
self.relu19 = nn.ReLU(inplace=True)
self.unpool2 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad20 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv20 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu20 = nn.ReLU(inplace=True)
self.reflecPad21 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv21 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu21 = nn.ReLU(inplace=True)
self.reflecPad22 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv22 = nn.Conv2d(256, 256, 3, 1, 0)
self.relu22 = nn.ReLU(inplace=True)
self.reflecPad23 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv23 = nn.Conv2d(256, 128, 3, 1, 0)
self.relu23 = nn.ReLU(inplace=True)
self.unpool3 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad24 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv24 = nn.Conv2d(128, 128, 3, 1, 0)
self.relu24 = nn.ReLU(inplace=True)
self.reflecPad25 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv25 = nn.Conv2d(128, 64, 3, 1, 0)
self.relu25 = nn.ReLU(inplace=True)
self.unpool4 = nn.UpsamplingNearest2d(scale_factor=2)
self.reflecPad26 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv26 = nn.Conv2d(64, 64, 3, 1, 0)
self.relu26 = nn.ReLU(inplace=True)
self.reflecPad27 = nn.ReflectionPad2d((1, 1, 1, 1))
self.conv27 = nn.Conv2d(64, 3, 3, 1, 0)
def forward(self, input_0):
primals_2 = self.conv15.weight
primals_3 = self.conv15.bias
primals_4 = self.conv16.weight
primals_5 = self.conv16.bias
primals_6 = self.conv17.weight
primals_7 = self.conv17.bias
primals_8 = self.conv18.weight
primals_9 = self.conv18.bias
primals_10 = self.conv19.weight
primals_11 = self.conv19.bias
primals_12 = self.conv20.weight
primals_13 = self.conv20.bias
primals_14 = self.conv21.weight
primals_15 = self.conv21.bias
primals_16 = self.conv22.weight
primals_17 = self.conv22.bias
primals_18 = self.conv23.weight
primals_19 = self.conv23.bias
primals_20 = self.conv24.weight
primals_21 = self.conv24.bias
primals_22 = self.conv25.weight
primals_23 = self.conv25.bias
primals_24 = self.conv26.weight
primals_25 = self.conv26.bias
primals_26 = self.conv27.weight
primals_27 = self.conv27.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8, primals_9,
primals_10, primals_11, primals_12, primals_13, primals_14,
primals_15, primals_16, primals_17, primals_18, primals_19,
primals_20, primals_21, primals_22, primals_23, primals_24,
primals_25, primals_26, primals_27])
return output[0]
|
Holmes-Alan/RefVAE
|
decoder5
| false | 8,334 |
[
"MIT"
] | 13 |
836b8f1168f1b0f923b609a48e202ace7806f79c
|
https://github.com/Holmes-Alan/RefVAE/tree/836b8f1168f1b0f923b609a48e202ace7806f79c
|
LxmertAttentionOutput
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/3u/c3umavnvadlllbskntmuqkfyjjumyvn7wgx526np6gyluppsa6uh.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
# add => add
# Graph fragment:
# %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_4), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + (x2), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jp/cjpjmhgkplwuotx62zhekpk2imaqte5voaejxj736wmajstebemq.py
# Topologically Sorted Source Nodes: [hidden_states_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# hidden_states_2 => add_1, rsqrt, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-12), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-12
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + (x0), tmp8, xmask)
tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/t6/ct6xarlcswgrblnv4kneomy44vcs3xqdpm4brv5kqkxweah6agd4.py
# Topologically Sorted Source Nodes: [hidden_states_2], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
# hidden_states_2 => add_1, add_2, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-12), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_5), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_6), kwargs = {})
triton_poi_fused_native_layer_norm_2 = async_compile.triton('triton_poi_fused_native_layer_norm_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused_add_0.run(buf1, primals_2, primals_4, 256, grid=grid(256), stream=stream0)
del primals_2
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
# Topologically Sorted Source Nodes: [hidden_states_2], Original ATen: [aten.native_layer_norm]
triton_poi_fused_native_layer_norm_1.run(buf1, buf2, buf3, 64, grid=grid(64), stream=stream0)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [hidden_states_2], Original ATen: [aten.native_layer_norm]
triton_poi_fused_native_layer_norm_2.run(buf1, buf2, buf3, primals_5, primals_6, buf4, 256, grid=grid(256), stream=stream0)
del buf2
del buf3
del primals_6
return (buf4, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class LxmertAttentionOutput(nn.Module):
def __init__(self, hidden_size, hidden_dropout_prob):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12)
self.dropout = nn.Dropout(hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4, 'hidden_dropout_prob': 0.5}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr1 + x2, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tl.store(in_out_ptr0 + x2, tmp4, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1,
xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp7 = 4.0
tmp8 = tmp6 / tmp7
tmp9 = tmp0 - tmp8
tmp10 = tmp9 * tmp9
tmp11 = tmp1 - tmp8
tmp12 = tmp11 * tmp11
tmp13 = tmp10 + tmp12
tmp14 = tmp3 - tmp8
tmp15 = tmp14 * tmp14
tmp16 = tmp13 + tmp15
tmp17 = tmp5 - tmp8
tmp18 = tmp17 * tmp17
tmp19 = tmp16 + tmp18
tmp20 = tmp19 / tmp7
tmp21 = 1e-12
tmp22 = tmp20 + tmp21
tmp23 = libdevice.rsqrt(tmp22)
tl.store(out_ptr0 + x0, tmp8, xmask)
tl.store(out_ptr1 + x0, tmp23, xmask)
@triton.jit
def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 - tmp1
tmp4 = tmp2 * tmp3
tmp6 = tmp4 * tmp5
tmp8 = tmp6 + tmp7
tl.store(out_ptr0 + x2, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
get_raw_stream(0)
triton_poi_fused_add_0[grid(256)](buf1, primals_2, primals_4, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused_native_layer_norm_1[grid(64)](buf1, buf2, buf3, 64,
XBLOCK=64, num_warps=1, num_stages=1)
buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_native_layer_norm_2[grid(256)](buf1, buf2, buf3,
primals_5, primals_6, buf4, 256, XBLOCK=256, num_warps=4,
num_stages=1)
del buf2
del buf3
del primals_6
return buf4, primals_5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
), buf1
class LxmertAttentionOutputNew(nn.Module):
def __init__(self, hidden_size, hidden_dropout_prob):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12)
self.dropout = nn.Dropout(hidden_dropout_prob)
def forward(self, input_0, input_1):
primals_1 = self.dense.weight
primals_2 = self.dense.bias
primals_5 = self.LayerNorm.weight
primals_6 = self.LayerNorm.bias
primals_3 = input_0
primals_4 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
IsmaelElsharkawi/new_pororo_repo
|
LxmertAttentionOutput
| false | 8,335 |
[
"MIT"
] | 19 |
4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
MixActiv
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/n4/cn4kucsnq6o64ew45q53ccxrjlmmahtbxepryte7g6utb5mdv4jq.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# x => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sin, %tanh, %exp, %relu], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 16) % 4
x0 = xindex % 16
x2 = (xindex // 64)
x3 = xindex
tmp0 = x1
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + (64*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tl_math.sin(tmp5)
tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
tmp8 = tl.where(tmp4, tmp6, tmp7)
tmp9 = tmp0 >= tmp3
tmp10 = tl.full([1], 2, tl.int64)
tmp11 = tmp0 < tmp10
tmp12 = tmp9 & tmp11
tmp13 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
tmp14 = libdevice.tanh(tmp13)
tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
tmp16 = tl.where(tmp12, tmp14, tmp15)
tmp17 = tmp0 >= tmp10
tmp18 = tl.full([1], 3, tl.int64)
tmp19 = tmp0 < tmp18
tmp20 = tmp17 & tmp19
tmp21 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
tmp22 = 0.0
tmp23 = tmp21 - tmp22
tmp24 = tmp23 * tmp23
tmp25 = -tmp24
tmp26 = 0.5
tmp27 = tmp25 * tmp26
tmp28 = tl_math.exp(tmp27)
tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
tmp30 = tl.where(tmp20, tmp28, tmp29)
tmp31 = tmp0 >= tmp18
tmp32 = tl.full([1], 4, tl.int64)
tmp33 = tmp0 < tmp32
tmp34 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), tmp31 & xmask, eviction_policy='evict_last', other=0.0)
tmp35 = tl.full([1], 0, tl.int32)
tmp36 = triton_helpers.maximum(tmp35, tmp34)
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp31, tmp36, tmp37)
tmp39 = tl.where(tmp20, tmp30, tmp38)
tmp40 = tl.where(tmp12, tmp16, tmp39)
tmp41 = tl.where(tmp4, tmp8, tmp40)
tl.store(out_ptr0 + (x3), tmp41, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch as th
from torch import nn
def gauss(x, mean=0, std=1):
return th.exp(-(x - mean) ** 2 / (2 * std ** 2))
class MixActiv(nn.Module):
def __init__(self):
super().__init__()
self.activations = th.sin, th.tanh, gauss, th.relu
self.n_activs = len(self.activations)
def forward(self, x):
n_chan = x.shape[1]
chans_per_activ = n_chan / self.n_activs
chan_i = 0
xs = []
for i, activ in enumerate(self.activations):
xs.append(activ(x[:, int(chan_i):int(chan_i + chans_per_activ),
:, :]))
chan_i += chans_per_activ
x = th.cat(xs, axis=1)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch as th
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 16 % 4
x0 = xindex % 16
x2 = xindex // 64
x3 = xindex
tmp0 = x1
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 1, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (x0 + 64 * x2), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl_math.sin(tmp5)
tmp7 = tl.full(tmp6.shape, 0.0, tmp6.dtype)
tmp8 = tl.where(tmp4, tmp6, tmp7)
tmp9 = tmp0 >= tmp3
tmp10 = tl.full([1], 2, tl.int64)
tmp11 = tmp0 < tmp10
tmp12 = tmp9 & tmp11
tmp13 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), tmp12 & xmask,
eviction_policy='evict_last', other=0.0)
tmp14 = libdevice.tanh(tmp13)
tmp15 = tl.full(tmp14.shape, 0.0, tmp14.dtype)
tmp16 = tl.where(tmp12, tmp14, tmp15)
tmp17 = tmp0 >= tmp10
tmp18 = tl.full([1], 3, tl.int64)
tmp19 = tmp0 < tmp18
tmp20 = tmp17 & tmp19
tmp21 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp22 = 0.0
tmp23 = tmp21 - tmp22
tmp24 = tmp23 * tmp23
tmp25 = -tmp24
tmp26 = 0.5
tmp27 = tmp25 * tmp26
tmp28 = tl_math.exp(tmp27)
tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
tmp30 = tl.where(tmp20, tmp28, tmp29)
tmp31 = tmp0 >= tmp18
tl.full([1], 4, tl.int64)
tmp34 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), tmp31 & xmask,
eviction_policy='evict_last', other=0.0)
tmp35 = tl.full([1], 0, tl.int32)
tmp36 = triton_helpers.maximum(tmp35, tmp34)
tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
tmp38 = tl.where(tmp31, tmp36, tmp37)
tmp39 = tl.where(tmp20, tmp30, tmp38)
tmp40 = tl.where(tmp12, tmp16, tmp39)
tmp41 = tl.where(tmp4, tmp8, tmp40)
tl.store(out_ptr0 + x3, tmp41, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del arg0_1
return buf0,
def gauss(x, mean=0, std=1):
return th.exp(-(x - mean) ** 2 / (2 * std ** 2))
class MixActivNew(nn.Module):
def __init__(self):
super().__init__()
self.activations = th.sin, th.tanh, gauss, th.relu
self.n_activs = len(self.activations)
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
JiangZehua/control-pcgrl
|
MixActiv
| false | 8,336 |
[
"MIT"
] | 15 |
e4fd1bf9670e5855f04941ebca34170517c451b4
|
https://github.com/JiangZehua/control-pcgrl/tree/e4fd1bf9670e5855f04941ebca34170517c451b4
|
ClassPredictor
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/u2/cu2beycg2t2ghizs6f4qom7bxbxmajhdaakuyq6y2korxywhp6ba.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# log_softmax => amax, sub
# Graph fragment:
# %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [1], True), kwargs = {})
# %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/rz/crzznyrp25xw7vat6ii2ydzqrgg4pltpke5lcsjsywnjqglf2sli.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
# log_softmax => exp, log, sub_1, sum_1
# Graph fragment:
# %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = (xindex // 64)
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp5 + tmp7
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tl_math.log(tmp11)
tmp13 = tmp0 - tmp12
tl.store(out_ptr0 + (x3), tmp13, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [class_logits], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
stream0 = get_raw_stream(0)
triton_poi_fused__log_softmax_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
triton_poi_fused__log_softmax_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
del buf1
return (buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class ClassPredictor(nn.Module):
def __init__(self, nz_feat, max_object_classes):
super(ClassPredictor, self).__init__()
self.predictor = nn.Linear(nz_feat, max_object_classes)
def forward(self, feats):
class_logits = self.predictor(feats)
return torch.nn.functional.log_softmax(class_logits)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nz_feat': 4, 'max_object_classes': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x0 = xindex % 16
x2 = xindex // 64
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
'evict_last')
tmp2 = tl_math.exp(tmp1)
tmp4 = tl_math.exp(tmp3)
tmp5 = tmp2 + tmp4
tmp7 = tl_math.exp(tmp6)
tmp8 = tmp5 + tmp7
tmp10 = tl_math.exp(tmp9)
tmp11 = tmp8 + tmp10
tmp12 = tl_math.log(tmp11)
tmp13 = tmp0 - tmp12
tl.store(out_ptr0 + x3, tmp13, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused__log_softmax_0[grid(256)](buf0, buf1, 256, XBLOCK=
128, num_warps=4, num_stages=1)
buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
triton_poi_fused__log_softmax_1[grid(256)](buf1, buf2, 256, XBLOCK=
128, num_warps=4, num_stages=1)
del buf1
return buf2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2
class ClassPredictorNew(nn.Module):
def __init__(self, nz_feat, max_object_classes):
super(ClassPredictorNew, self).__init__()
self.predictor = nn.Linear(nz_feat, max_object_classes)
def forward(self, input_0):
primals_1 = self.predictor.weight
primals_2 = self.predictor.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JasonQSY/Associative3D
|
ClassPredictor
| false | 8,337 |
[
"MIT"
] | 25 |
c50818b593ec48c38ed7ee3e109c23531089da32
|
https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32
|
InnerProductDecoder
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/pl/cpltqkfxcnv7w6fd4f2fn4b26fopcoerdukw34ixpnlhrqm5wtta.py
# Topologically Sorted Source Nodes: [adj], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
# adj => sigmoid
# Graph fragment:
# %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mm,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = tl.sigmoid(tmp0)
tl.store(in_out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0)
del arg0_1
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [adj], Original ATen: [aten.sigmoid]
stream0 = get_raw_stream(0)
triton_poi_fused_sigmoid_0.run(buf1, 16, grid=grid(16), stream=stream0)
return (buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.functional as F
import torch.nn.modules.loss
from scipy.sparse import *
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)``
Returns
-------
output: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied.
"""
if drop_prob == 0 or drop_prob is None or not training:
return x
sz = list(x.size())
for i in shared_axes:
sz[i] = 1
mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob)
mask = mask.expand_as(x)
return x * mask
class InnerProductDecoder(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoder, self).__init__()
self.dropout = dropout
self.act = act
def forward(self, z):
z = F.dropout(z, self.dropout, training=self.training)
adj = self.act(torch.mm(z, z.t()))
return adj
def get_inputs():
return [torch.rand([4, 4])]
def get_init_inputs():
return [[], {'dropout': 0.5}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tl.store(in_out_ptr0 + x0, tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4),
0), out=buf0)
del arg0_1
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_sigmoid_0[grid(16)](buf1, 16, XBLOCK=16, num_warps
=1, num_stages=1)
return buf1,
def dropout(x, drop_prob, shared_axes=[], training=False):
"""
Apply dropout to input tensor.
Parameters
----------
input_tensor: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)``
Returns
-------
output: ``torch.FloatTensor``
A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied.
"""
if drop_prob == 0 or drop_prob is None or not training:
return x
sz = list(x.size())
for i in shared_axes:
sz[i] = 1
mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob)
mask = mask.expand_as(x)
return x * mask
class InnerProductDecoderNew(nn.Module):
"""Decoder for using inner product for prediction."""
def __init__(self, dropout, act=torch.sigmoid):
super(InnerProductDecoderNew, self).__init__()
self.dropout = dropout
self.act = act
def forward(self, input_0):
arg0_1 = input_0
output = call([arg0_1])
return output[0]
|
IBM/graph4nlp
|
InnerProductDecoder
| false | 8,338 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
GRUStep
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zn/czn6cztle4peyy4pa7mkag53s34sjkn6wpenptu6ttfuvhgzzrup.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/v2/cv2nvpmbs6gsy6ntbexmair46kggypvepim4pba3uboqzgoqzs6s.py
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat_2 => cat_2
# Graph fragment:
# %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %primals_2], -1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[512],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tl.sigmoid(tmp5)
tmp7 = tl.load(in_ptr1 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp8 = tmp6 * tmp7
tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
tmp10 = tl.where(tmp4, tmp8, tmp9)
tmp11 = tmp0 >= tmp3
tmp12 = tl.full([1], 8, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tl.load(in_ptr2 + ((4*x1) + ((-4) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
tmp15 = tl.where(tmp4, tmp10, tmp14)
tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/tk/ctkzci6ghcuwrazh6nqzf7qcitrqfdh3tintiihoeybyv3ue4dbc.py
# Topologically Sorted Source Nodes: [z, t, sub, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.tanh, aten.rsub, aten.mul, aten.add]
# Source node to ATen node mapping:
# h_state => add
# mul_1 => mul_1
# mul_2 => mul_2
# sub => sub
# t => tanh
# z => sigmoid
# Graph fragment:
# %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
# %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_tanh_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp4 = tl.load(in_ptr1 + (x0), xmask)
tmp6 = tl.load(in_ptr2 + (x0), xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp5 = tmp3 * tmp4
tmp7 = libdevice.tanh(tmp6)
tmp8 = tmp1 * tmp7
tmp9 = tmp5 + tmp8
tl.store(out_ptr0 + (x0), tmp9, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4, 8), (8, 1))
assert_size_stride(primals_5, (4, 8), (8, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2)
del primals_4
buf3 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(buf2, primals_1, primals_2, buf3, 512, grid=grid(512), stream=stream0)
del primals_2
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
extern_kernels.mm(reinterpret_tensor(buf3, (64, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [z, t, sub, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.tanh, aten.rsub, aten.mul, aten.add]
triton_poi_fused_add_mul_rsub_sigmoid_tanh_2.run(buf1, primals_1, buf4, buf5, 256, grid=grid(256), stream=stream0)
return (buf5, primals_1, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), buf1, buf2, reinterpret_tensor(buf3, (64, 8), (8, 1), 0), buf4, primals_5, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class GRUStep(nn.Module):
def __init__(self, hidden_size, input_size):
super(GRUStep, self).__init__()
"""GRU module"""
self.linear_z = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
self.linear_r = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
self.linear_t = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
def forward(self, h_state, input):
z = torch.sigmoid(self.linear_z(torch.cat([h_state, input], -1)))
r = torch.sigmoid(self.linear_r(torch.cat([h_state, input], -1)))
t = torch.tanh(self.linear_t(torch.cat([r * h_state, input], -1)))
h_state = (1 - z) * h_state + z * t
return h_state
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4, 'input_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
eviction_policy='evict_last', other=0.0)
tmp10 = tl.where(tmp4, tmp5, tmp9)
tl.store(out_ptr0 + x2, tmp10, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
XBLOCK: tl.constexpr):
xnumel = 512
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = xindex // 8
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tl.sigmoid(tmp5)
tmp7 = tl.load(in_ptr1 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp8 = tmp6 * tmp7
tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype)
tmp10 = tl.where(tmp4, tmp8, tmp9)
tmp11 = tmp0 >= tmp3
tl.full([1], 8, tl.int64)
tmp14 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp11 & xmask,
eviction_policy='evict_last', other=0.0)
tmp15 = tl.where(tmp4, tmp10, tmp14)
tl.store(out_ptr0 + x2, tmp15, xmask)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp4 = tl.load(in_ptr1 + x0, xmask)
tmp6 = tl.load(in_ptr2 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp5 = tmp3 * tmp4
tmp7 = libdevice.tanh(tmp6)
tmp8 = tmp1 * tmp7
tmp9 = tmp5 + tmp8
tl.store(out_ptr0 + x0, tmp9, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 8), (8, 1))
assert_size_stride(primals_4, (4, 8), (8, 1))
assert_size_stride(primals_5, (4, 8), (8, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512,
XBLOCK=128, num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
del primals_3
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2)
del primals_4
buf3 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
triton_poi_fused_cat_1[grid(512)](buf2, primals_1, primals_2, buf3,
512, XBLOCK=256, num_warps=4, num_stages=1)
del primals_2
buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(buf3, (64, 8), (8, 1), 0),
reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf4)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_rsub_sigmoid_tanh_2[grid(256)](buf1,
primals_1, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf5, primals_1, reinterpret_tensor(buf0, (64, 8), (8, 1), 0
), buf1, buf2, reinterpret_tensor(buf3, (64, 8), (8, 1), 0
), buf4, primals_5
class GRUStepNew(nn.Module):
def __init__(self, hidden_size, input_size):
super(GRUStepNew, self).__init__()
"""GRU module"""
self.linear_z = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
self.linear_r = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
self.linear_t = nn.Linear(hidden_size + input_size, hidden_size,
bias=False)
def forward(self, input_0, input_1):
primals_3 = self.linear_z.weight
primals_4 = self.linear_r.weight
primals_5 = self.linear_t.weight
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
return output[0]
|
IBM/graph4nlp
|
GRUStep
| false | 8,339 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
ScalePredictor
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/u5/cu5ehbtptmf5lbkgqlds5ze7jeryauehrfuqvqo2dw2x2xml2snh.py
# Topologically Sorted Source Nodes: [scale, relu, scale_1], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# relu => relu
# scale => add
# scale_1 => add_1
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, 1), kwargs = {})
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, 1e-12), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 3
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 + tmp3
tmp5 = tl.full([1], 0, tl.int32)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = 1e-12
tmp8 = tmp6 + tmp7
tmp9 = 0.0
tmp10 = tmp6 <= tmp9
tl.store(out_ptr0 + (x2), tmp8, xmask)
tl.store(out_ptr1 + (x2), tmp10, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (3, 4), (4, 1))
assert_size_stride(primals_2, (3, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 3), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.bool)
# Topologically Sorted Source Nodes: [scale, relu, scale_1], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_add_relu_threshold_backward_0.run(buf0, primals_2, buf1, buf2, 192, grid=grid(192), stream=stream0)
del buf0
del primals_2
return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((3, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class ScalePredictor(nn.Module):
def __init__(self, nz):
super(ScalePredictor, self).__init__()
self.pred_layer = nn.Linear(nz, 3)
def forward(self, feat):
scale = self.pred_layer.forward(feat) + 1
scale = torch.nn.functional.relu(scale) + 1e-12
return scale
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'nz': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_relu_threshold_backward_0(in_ptr0, in_ptr1,
out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 3
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 + tmp3
tmp5 = tl.full([1], 0, tl.int32)
tmp6 = triton_helpers.maximum(tmp5, tmp4)
tmp7 = 1e-12
tmp8 = tmp6 + tmp7
tmp9 = 0.0
tmp10 = tmp6 <= tmp9
tl.store(out_ptr0 + x2, tmp8, xmask)
tl.store(out_ptr1 + x2, tmp10, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (3, 4), (4, 1))
assert_size_stride(primals_2, (3,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 3), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
buf2 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_add_relu_threshold_backward_0[grid(192)](buf0,
primals_2, buf1, buf2, 192, XBLOCK=256, num_warps=4, num_stages=1)
del buf0
del primals_2
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf2
class ScalePredictorNew(nn.Module):
def __init__(self, nz):
super(ScalePredictorNew, self).__init__()
self.pred_layer = nn.Linear(nz, 3)
def forward(self, input_0):
primals_1 = self.pred_layer.weight
primals_2 = self.pred_layer.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JasonQSY/Associative3D
|
ScalePredictor
| false | 8,340 |
[
"MIT"
] | 25 |
c50818b593ec48c38ed7ee3e109c23531089da32
|
https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32
|
CombinedTargetMSELoss
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ol/colmfwe4lafsy43a2dyxvb2ceedfskhqvsw7bxleqxlmrk3374od.py
# Topologically Sorted Source Nodes: [heatmap_pred_1, heatmap_gt_1, mse_loss, mul_2, loss, mul_3, mul_4, mse_loss_1, mul_5, loss_1, mul_6, mul_7, mse_loss_2, mul_8, loss_2, truediv, mul_9], Original ATen: [aten.mul, aten.mse_loss, aten.add, aten.div]
# Source node to ATen node mapping:
# heatmap_gt_1 => mul_1
# heatmap_pred_1 => mul
# loss => add
# loss_1 => add_1
# loss_2 => add_2
# mse_loss => mean, pow_1, sub
# mse_loss_1 => mean_1, pow_2, sub_1
# mse_loss_2 => mean_2, pow_3, sub_2
# mul_2 => mul_2
# mul_3 => mul_3
# mul_4 => mul_4
# mul_5 => mul_5
# mul_6 => mul_6
# mul_7 => mul_7
# mul_8 => mul_8
# mul_9 => mul_9
# truediv => div
# Graph fragment:
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, %select), kwargs = {})
# %mul_1 : [num_users=5] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_1, %select_1), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
# %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
# %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 0.5), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, 0.0), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %squeeze_2), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %squeeze_3), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_3, %mul_4), kwargs = {})
# %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
# %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_1, 0.5), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_5), kwargs = {})
# %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %squeeze_4), kwargs = {})
# %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %squeeze_5), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_6, %mul_7), kwargs = {})
# %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
# %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_3,), kwargs = {})
# %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_2, 0.5), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_8), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_2, 1), kwargs = {})
# %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, 1.0), kwargs = {})
triton_per_fused_add_div_mse_loss_mul_0 = async_compile.triton('triton_per_fused_add_div_mse_loss_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.persistent_reduction(
size_hints=[1, 4],
reduction_hint=ReductionHint.INNER,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mse_loss_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 3, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_div_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
xnumel = 1
rnumel = 4
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
roffset = 0
rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 - tmp4
tmp6 = tmp5 * tmp5
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp11 = tmp4 * tmp10
tmp13 = tmp4 * tmp12
tmp14 = tmp11 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tmp20 = tmp4 * tmp19
tmp22 = tmp4 * tmp21
tmp23 = tmp20 - tmp22
tmp24 = tmp23 * tmp23
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tmp27 = tl.sum(tmp25, 1)[:, None]
tmp28 = 4.0
tmp29 = tmp9 / tmp28
tmp30 = 0.5
tmp31 = tmp29 * tmp30
tmp32 = 0.0
tmp33 = tmp31 + tmp32
tmp34 = tmp18 / tmp28
tmp35 = tmp34 * tmp30
tmp36 = tmp33 + tmp35
tmp37 = tmp27 / tmp28
tmp38 = tmp37 * tmp30
tmp39 = tmp36 + tmp38
tmp40 = 1.0
tmp41 = tmp39 * tmp40
tmp42 = tmp41 * tmp40
tl.debug_barrier()
tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp42, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
assert_size_stride(arg2_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [heatmap_pred_1, heatmap_gt_1, mse_loss, mul_2, loss, mul_3, mul_4, mse_loss_1, mul_5, loss_1, mul_6, mul_7, mse_loss_2, mul_8, loss_2, truediv, mul_9], Original ATen: [aten.mul, aten.mse_loss, aten.add, aten.div]
stream0 = get_raw_stream(0)
triton_per_fused_add_div_mse_loss_mul_0.run(buf3, arg0_1, arg2_1, arg1_1, 1, 4, grid=grid(1), stream=stream0)
del arg0_1
del arg1_1
del arg2_1
return (buf3, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1, arg2_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
class CombinedTargetMSELoss(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self, use_target_weight, loss_weight=1.0):
super().__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
def forward(self, output, target, target_weight):
batch_size = output.size(0)
num_channels = output.size(1)
heatmaps_pred = output.reshape((batch_size, num_channels, -1)).split(
1, 1)
heatmaps_gt = target.reshape((batch_size, num_channels, -1)).split(1, 1
)
loss = 0.0
num_joints = num_channels // 3
for idx in range(num_joints):
heatmap_pred = heatmaps_pred[idx * 3].squeeze()
heatmap_gt = heatmaps_gt[idx * 3].squeeze()
offset_x_pred = heatmaps_pred[idx * 3 + 1].squeeze()
offset_x_gt = heatmaps_gt[idx * 3 + 1].squeeze()
offset_y_pred = heatmaps_pred[idx * 3 + 2].squeeze()
offset_y_gt = heatmaps_gt[idx * 3 + 2].squeeze()
if self.use_target_weight:
heatmap_pred = heatmap_pred * target_weight[:, idx]
heatmap_gt = heatmap_gt * target_weight[:, idx]
loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt)
loss += 0.5 * self.criterion(heatmap_gt * offset_x_pred,
heatmap_gt * offset_x_gt)
loss += 0.5 * self.criterion(heatmap_gt * offset_y_pred,
heatmap_gt * offset_y_gt)
return loss / num_joints * self.loss_weight
def get_inputs():
return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4])]
def get_init_inputs():
return [[], {'use_target_weight': 4}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_per_fused_add_div_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
RBLOCK: tl.constexpr = 4
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:, None]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
rindex = tl.arange(0, RBLOCK)[None, :]
tl.full([XBLOCK, RBLOCK], True, tl.int1)
r0 = rindex
tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
tmp19 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp21 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
tmp2 = tmp0 * tmp1
tmp4 = tmp3 * tmp1
tmp5 = tmp2 - tmp4
tmp6 = tmp5 * tmp5
tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
tmp9 = tl.sum(tmp7, 1)[:, None]
tmp11 = tmp4 * tmp10
tmp13 = tmp4 * tmp12
tmp14 = tmp11 - tmp13
tmp15 = tmp14 * tmp14
tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
tmp18 = tl.sum(tmp16, 1)[:, None]
tmp20 = tmp4 * tmp19
tmp22 = tmp4 * tmp21
tmp23 = tmp20 - tmp22
tmp24 = tmp23 * tmp23
tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
tmp27 = tl.sum(tmp25, 1)[:, None]
tmp28 = 4.0
tmp29 = tmp9 / tmp28
tmp30 = 0.5
tmp31 = tmp29 * tmp30
tmp32 = 0.0
tmp33 = tmp31 + tmp32
tmp34 = tmp18 / tmp28
tmp35 = tmp34 * tmp30
tmp36 = tmp33 + tmp35
tmp37 = tmp27 / tmp28
tmp38 = tmp37 * tmp30
tmp39 = tmp36 + tmp38
tmp40 = 1.0
tmp41 = tmp39 * tmp40
tmp42 = tmp41 * tmp40
tl.debug_barrier()
tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp42, None)
def call(args):
arg0_1, arg1_1, arg2_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4), (4, 1))
assert_size_stride(arg1_1, (4, 4), (4, 1))
assert_size_stride(arg2_1, (4, 4), (4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((), (), torch.float32)
buf3 = buf0
del buf0
get_raw_stream(0)
triton_per_fused_add_div_mse_loss_mul_0[grid(1)](buf3, arg0_1,
arg2_1, arg1_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
del arg0_1
del arg1_1
del arg2_1
return buf3,
class CombinedTargetMSELossNew(nn.Module):
"""MSE loss for combined target.
CombinedTarget: The combination of classification target
(response map) and regression target (offset map).
Paper ref: Huang et al. The Devil is in the Details: Delving into
Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
Args:
use_target_weight (bool): Option to use weighted MSE loss.
Different joint types may have different target weights.
loss_weight (float): Weight of the loss. Default: 1.0.
"""
def __init__(self, use_target_weight, loss_weight=1.0):
super().__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_target_weight = use_target_weight
self.loss_weight = loss_weight
def forward(self, input_0, input_1, input_2):
arg0_1 = input_0
arg1_1 = input_1
arg2_1 = input_2
output = call([arg0_1, arg1_1, arg2_1])
return output[0]
|
Jackqu/mmpose
|
CombinedTargetMSELoss
| false | 8,341 |
[
"Apache-2.0"
] | 38 |
ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533
|
https://github.com/Jackqu/mmpose/tree/ad8acc5ff5da7993c6befdc4b1ced2c2ecb64533
|
RelativeScalePredictor
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/u3/cu3ktlckx5vzc2zy4gn3jcyq7ltb45i6fifudrpuos2dve4t2z5e.py
# Topologically Sorted Source Nodes: [predictions, relu, predictions_1, log], Original ATen: [aten.add, aten.relu, aten.log]
# Source node to ATen node mapping:
# log => log
# predictions => add
# predictions_1 => add_1
# relu => relu
# Graph fragment:
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, 1), kwargs = {})
# %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, 1e-12), kwargs = {})
# %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_log_relu_0 = async_compile.triton('triton_poi_fused_add_log_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_log_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_log_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp1 = 1.0
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 1e-12
tmp6 = tmp4 + tmp5
tmp7 = tl_math.log(tmp6)
tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [predictions, relu, predictions_1, log], Original ATen: [aten.add, aten.relu, aten.log]
stream0 = get_raw_stream(0)
triton_poi_fused_add_log_relu_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
class RelativeScalePredictor(nn.Module):
def __init__(self, in_size, out_size):
super(RelativeScalePredictor, self).__init__()
self.predictor = nn.Linear(in_size, out_size)
def forward(self, feat):
predictions = self.predictor.forward(feat) + 1
predictions = torch.nn.functional.relu(predictions) + 1e-12
return predictions.log()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_size': 4, 'out_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_add_log_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp1 = 1.0
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 1e-12
tmp6 = tmp4 + tmp5
tmp7 = tl_math.log(tmp6)
tl.store(out_ptr0 + x0, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64,
4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
), alpha=1, beta=1, out=buf0)
del primals_1
del primals_2
buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_add_log_relu_0[grid(256)](buf0, buf1, 256, XBLOCK=
256, num_warps=4, num_stages=1)
return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0
class RelativeScalePredictorNew(nn.Module):
def __init__(self, in_size, out_size):
super(RelativeScalePredictorNew, self).__init__()
self.predictor = nn.Linear(in_size, out_size)
def forward(self, input_0):
primals_1 = self.predictor.weight
primals_2 = self.predictor.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JasonQSY/Associative3D
|
RelativeScalePredictor
| false | 8,342 |
[
"MIT"
] | 25 |
c50818b593ec48c38ed7ee3e109c23531089da32
|
https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32
|
UpSample
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/ih/cih4w6b2mrjbbxovfv46parmaquaixdhrkycdu2ng42mhzij6o5m.py
# Topologically Sorted Source Nodes: [up_x], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten._unsafe_index, aten.sub, aten.add]
# Source node to ATen node mapping:
# up_x => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_2, add_3, add_4, clamp_max_2, clamp_max_3, clamp_min, clamp_min_2, clamp_min_3, convert_element_type, convert_element_type_1, convert_element_type_3, iota, mul, mul_2, mul_3, mul_4, sub, sub_1, sub_2, sub_3, sub_4
# Graph fragment:
# %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
# %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, 1.0), kwargs = {})
# %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 0.0), kwargs = {})
# %convert_element_type_1 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
# %convert_element_type_3 : [num_users=4] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%clamp_min, torch.int64), kwargs = {})
# %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_2, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {})
# %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_2, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {})
# %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_2, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {})
# %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_2, [None, None, %clamp_max, %clamp_max_1]), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {})
# %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {})
# %clamp_max_2 : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %clamp_max_2), kwargs = {})
# %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {})
# %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {})
# %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %clamp_max_2), kwargs = {})
# %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {})
# %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %convert_element_type_1), kwargs = {})
# %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_3, 0.0), kwargs = {})
# %clamp_max_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_3, 1.0), kwargs = {})
# %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %add_2), kwargs = {})
# %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_3), kwargs = {})
# %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_4), kwargs = {})
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0 = async_compile.triton('triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4) % 4
x0 = xindex % 4
x2 = (xindex // 16)
x6 = xindex
x4 = (xindex // 48)
x7 = xindex % 48
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 3, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tmp11 = x0
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp12 * tmp2
tmp14 = triton_helpers.maximum(tmp13, tmp4)
tmp15 = tmp14.to(tl.int32)
tmp16 = tl.load(in_ptr0 + (tmp15 + (4*tmp10) + (16*x2)), xmask, eviction_policy='evict_last')
tmp17 = tmp15 + tmp7
tmp18 = triton_helpers.minimum(tmp17, tmp9)
tmp19 = tl.load(in_ptr0 + (tmp18 + (4*tmp10) + (16*x2)), xmask, eviction_policy='evict_last')
tmp20 = tmp19 - tmp16
tmp21 = tmp15.to(tl.float32)
tmp22 = tmp14 - tmp21
tmp23 = triton_helpers.maximum(tmp22, tmp4)
tmp24 = triton_helpers.minimum(tmp23, tmp2)
tmp25 = tmp20 * tmp24
tmp26 = tmp16 + tmp25
tmp27 = tl.load(in_ptr0 + (tmp15 + (4*tmp6) + (16*x2)), xmask, eviction_policy='evict_last')
tmp28 = tl.load(in_ptr0 + (tmp18 + (4*tmp6) + (16*x2)), xmask, eviction_policy='evict_last')
tmp29 = tmp28 - tmp27
tmp30 = tmp29 * tmp24
tmp31 = tmp27 + tmp30
tmp32 = tmp26 - tmp31
tmp33 = tmp6.to(tl.float32)
tmp34 = tmp5 - tmp33
tmp35 = triton_helpers.maximum(tmp34, tmp4)
tmp36 = triton_helpers.minimum(tmp35, tmp2)
tmp37 = tmp32 * tmp36
tmp38 = tmp31 + tmp37
tl.store(out_ptr1 + (x7 + (64*x4)), tmp38, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/e7/ce7gaixaxazsnmiemxasy6wwaujjrirql44yimi7stimo4c5snvo.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%add_4, %primals_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 16
x1 = (xindex // 16)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tl.store(out_ptr0 + (x0 + (64*x1)), tmp0, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ia/ciaor2h3gtv7cwv5ai7n475zufoszj6e5b6e4gydoxiv75cf7f5c.py
# Topologically Sorted Source Nodes: [conv2d, leaky_relu], Original ATen: [aten.convolution, aten.leaky_relu]
# Source node to ATen node mapping:
# conv2d => convolution
# leaky_relu => gt, mul_5, where
# Graph fragment:
# %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%cat, %primals_3, %primals_4, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
# %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.2), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul_5), kwargs = {})
triton_poi_fused_convolution_leaky_relu_2 = async_compile.triton('triton_poi_fused_convolution_leaky_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_leaky_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = (xindex // 16) % 4
tmp0 = tl.load(in_ptr0 + (x3), xmask)
tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + (x3), tmp4, xmask)
tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (4, ), (1, ))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf1 = reinterpret_tensor(buf3, (4, 3, 4, 4), (64, 16, 4, 1), 0) # alias
# Topologically Sorted Source Nodes: [up_x], Original ATen: [aten.arange, aten._to_copy, aten.mul, aten.clamp, aten._unsafe_index, aten.sub, aten.add]
stream0 = get_raw_stream(0)
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0.run(primals_2, buf1, 192, grid=grid(192), stream=stream0)
del primals_2
buf2 = reinterpret_tensor(buf3, (4, 1, 4, 4), (64, 16, 4, 1), 48) # alias
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
triton_poi_fused_cat_1.run(primals_1, buf2, 64, grid=grid(64), stream=stream0)
del primals_1
# Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
buf4 = extern_kernels.convolution(buf3, primals_3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [conv2d, leaky_relu], Original ATen: [aten.convolution, aten.leaky_relu]
triton_poi_fused_convolution_leaky_relu_2.run(buf4, primals_4, buf5, buf6, 256, grid=grid(256), stream=stream0)
del primals_4
# Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
buf7 = extern_kernels.convolution(buf6, primals_5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf9 = buf4; del buf4 # reuse
# Topologically Sorted Source Nodes: [conv2d_1, leaky_relu_1], Original ATen: [aten.convolution, aten.leaky_relu]
triton_poi_fused_convolution_leaky_relu_2.run(buf7, primals_6, buf8, buf9, 256, grid=grid(256), stream=stream0)
del buf7
del primals_6
return (buf9, primals_3, primals_5, buf3, buf5, buf6, buf8, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leakyreluA = nn.LeakyReLU(0.2)
self.convB = nn.Conv2d(output_features, output_features,
kernel_size=3, stride=1, padding=1)
self.leakyreluB = nn.LeakyReLU(0.2)
def forward(self, x, concat_with):
up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size
(3)], mode='bilinear', align_corners=True)
return self.leakyreluB(self.convB(self.leakyreluA(self.convA(torch.
cat([up_x, concat_with], dim=1)))))
def get_inputs():
return [torch.rand([4, 3, 4, 4]), torch.rand([4, 1, 4, 4])]
def get_init_inputs():
return [[], {'skip_input': 4, 'output_features': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0(in_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 192
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4 % 4
x0 = xindex % 4
x2 = xindex // 16
x4 = xindex // 48
x7 = xindex % 48
tmp0 = x1
tmp1 = tmp0.to(tl.float32)
tmp2 = 1.0
tmp3 = tmp1 * tmp2
tmp4 = 0.0
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp6 = tmp5.to(tl.int32)
tmp7 = tl.full([1], 1, tl.int64)
tmp8 = tmp6 + tmp7
tmp9 = tl.full([1], 3, tl.int64)
tmp10 = triton_helpers.minimum(tmp8, tmp9)
tmp11 = x0
tmp12 = tmp11.to(tl.float32)
tmp13 = tmp12 * tmp2
tmp14 = triton_helpers.maximum(tmp13, tmp4)
tmp15 = tmp14.to(tl.int32)
tmp16 = tl.load(in_ptr0 + (tmp15 + 4 * tmp10 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp17 = tmp15 + tmp7
tmp18 = triton_helpers.minimum(tmp17, tmp9)
tmp19 = tl.load(in_ptr0 + (tmp18 + 4 * tmp10 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp20 = tmp19 - tmp16
tmp21 = tmp15.to(tl.float32)
tmp22 = tmp14 - tmp21
tmp23 = triton_helpers.maximum(tmp22, tmp4)
tmp24 = triton_helpers.minimum(tmp23, tmp2)
tmp25 = tmp20 * tmp24
tmp26 = tmp16 + tmp25
tmp27 = tl.load(in_ptr0 + (tmp15 + 4 * tmp6 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp28 = tl.load(in_ptr0 + (tmp18 + 4 * tmp6 + 16 * x2), xmask,
eviction_policy='evict_last')
tmp29 = tmp28 - tmp27
tmp30 = tmp29 * tmp24
tmp31 = tmp27 + tmp30
tmp32 = tmp26 - tmp31
tmp33 = tmp6.to(tl.float32)
tmp34 = tmp5 - tmp33
tmp35 = triton_helpers.maximum(tmp34, tmp4)
tmp36 = triton_helpers.minimum(tmp35, tmp2)
tmp37 = tmp32 * tmp36
tmp38 = tmp31 + tmp37
tl.store(out_ptr1 + (x7 + 64 * x4), tmp38, xmask)
@triton.jit
def triton_poi_fused_cat_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 16
x1 = xindex // 16
tmp0 = tl.load(in_ptr0 + x2, xmask)
tl.store(out_ptr0 + (x0 + 64 * x1), tmp0, xmask)
@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x3 = xindex
x1 = xindex // 16 % 4
tmp0 = tl.load(in_ptr0 + x3, xmask)
tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 0.0
tmp4 = tmp2 > tmp3
tmp5 = 0.2
tmp6 = tmp2 * tmp5
tmp7 = tl.where(tmp4, tmp2, tmp6)
tl.store(out_ptr0 + x3, tmp4, xmask)
tl.store(out_ptr1 + x3, tmp7, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
args.clear()
assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
assert_size_stride(primals_2, (4, 3, 4, 4), (48, 16, 4, 1))
assert_size_stride(primals_3, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_4, (4,), (1,))
assert_size_stride(primals_5, (4, 4, 3, 3), (36, 9, 3, 1))
assert_size_stride(primals_6, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
buf1 = reinterpret_tensor(buf3, (4, 3, 4, 4), (64, 16, 4, 1), 0)
get_raw_stream(0)
triton_poi_fused__to_copy__unsafe_index_add_arange_clamp_mul_sub_0[grid
(192)](primals_2, buf1, 192, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = reinterpret_tensor(buf3, (4, 1, 4, 4), (64, 16, 4, 1), 48)
triton_poi_fused_cat_1[grid(64)](primals_1, buf2, 64, XBLOCK=64,
num_warps=1, num_stages=1)
del primals_1
buf4 = extern_kernels.convolution(buf3, primals_3, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_convolution_leaky_relu_2[grid(256)](buf4,
primals_4, buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
del primals_4
buf7 = extern_kernels.convolution(buf6, primals_5, stride=(1, 1),
padding=(1, 1), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1))
buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
buf9 = buf4
del buf4
triton_poi_fused_convolution_leaky_relu_2[grid(256)](buf7,
primals_6, buf8, buf9, 256, XBLOCK=256, num_warps=4, num_stages=1)
del buf7
del primals_6
return buf9, primals_3, primals_5, buf3, buf5, buf6, buf8
class UpSampleNew(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSampleNew, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3,
stride=1, padding=1)
self.leakyreluA = nn.LeakyReLU(0.2)
self.convB = nn.Conv2d(output_features, output_features,
kernel_size=3, stride=1, padding=1)
self.leakyreluB = nn.LeakyReLU(0.2)
def forward(self, input_0, input_1):
primals_3 = self.convA.weight
primals_4 = self.convA.bias
primals_5 = self.convB.weight
primals_6 = self.convB.bias
primals_2 = input_0
primals_1 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6])
return output[0]
|
JanRocketMan/regression-prior-networks
|
UpSample
| false | 8,343 |
[
"MIT"
] | 24 |
3c8ffa758ee6eaa15b8afe31ac1c03f87bbf6a14
|
https://github.com/JanRocketMan/regression-prior-networks/tree/3c8ffa758ee6eaa15b8afe31ac1c03f87bbf6a14
|
BasicConv2d
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/x7/cx7rsytn4dw4f5pkseu4j6wzhfyvzgdf3yv7xxelzaio5syz5ta2.py
# Topologically Sorted Source Nodes: [leaky_relu], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
# Source node to ATen node mapping:
# leaky_relu => gt, mul, where
# Graph fragment:
# %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.01), kwargs = {})
# %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %convolution, %mul), kwargs = {})
# %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%where, 0), kwargs = {})
triton_poi_fused_leaky_relu_leaky_relu_backward_0 = async_compile.triton('triton_poi_fused_leaky_relu_leaky_relu_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_leaky_relu_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tmp6 = tmp5 > tmp1
tl.store(in_out_ptr0 + (x0), tmp5, xmask)
tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
buf1 = buf0; del buf0 # reuse
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
# Topologically Sorted Source Nodes: [leaky_relu], Original ATen: [aten.leaky_relu, aten.leaky_relu_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_leaky_relu_leaky_relu_backward_0.run(buf1, buf2, 16, grid=grid(16), stream=stream0)
return (buf1, primals_1, primals_2, buf2, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=
False, **kwargs)
def forward(self, x):
x = self.conv(x)
return F.leaky_relu(x, inplace=True)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_leaky_relu_leaky_relu_backward_0(in_out_ptr0, out_ptr0,
xnumel, XBLOCK: tl.constexpr):
xnumel = 16
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_out_ptr0 + x0, xmask)
tmp1 = 0.0
tmp2 = tmp0 > tmp1
tmp3 = 0.01
tmp4 = tmp0 * tmp3
tmp5 = tl.where(tmp2, tmp0, tmp4)
tmp6 = tmp5 > tmp1
tl.store(in_out_ptr0 + x0, tmp5, xmask)
tl.store(out_ptr0 + x0, tmp6, xmask)
def call(args):
primals_1, primals_2 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1,
1), padding=(0, 0), dilation=(1, 1), transposed=False,
output_padding=(0, 0), groups=1, bias=None)
assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
buf1 = buf0
del buf0
buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_leaky_relu_leaky_relu_backward_0[grid(16)](buf1,
buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
return buf1, primals_1, primals_2, buf2
class BasicConv2dNew(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super(BasicConv2dNew, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, bias=
False, **kwargs)
def forward(self, input_0):
primals_1 = self.conv.weight
primals_2 = input_0
output = call([primals_1, primals_2])
return output[0]
|
JinkaiZheng/TraND
|
BasicConv2d
| false | 8,344 |
[
"MIT"
] | 33 |
a8babc34073ee126789969bd97e149bae4015953
|
https://github.com/JinkaiZheng/TraND/tree/a8babc34073ee126789969bd97e149bae4015953
|
TransNonlinear
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/4g/c4guhk7x6skkidedvs2gxz2kcu6gb76l3ig5crjjvjtzvnjlhlte.py
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
# relu => relu
# Graph fragment:
# %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
# %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + (x2), tmp4, xmask)
tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/f6/cf6g5vjl6clpvfa2j7jw5adg3xchgkyal7cg5smxzk57hjhn3cgo.py
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# src => add
# src_1 => var_mean
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_3), kwargs = {})
# %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_1 = async_compile.triton('triton_poi_fused_add_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[64],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + (x0), tmp16, xmask)
tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/em/cem6vr7kjs5yrruf5v4ykxzmxb7usf5y77j2nupb2ytwzcqkihrt.py
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
# src => add
# src_1 => add_1, add_2, mul, mul_1, rsqrt, sub
# Graph fragment:
# %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_3), kwargs = {})
# %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
# %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
# %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
# %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_6), kwargs = {})
# %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_7), kwargs = {})
triton_poi_fused_add_native_layer_norm_2 = async_compile.triton('triton_poi_fused_add_native_layer_norm_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr1 + (x2), xmask)
tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, ), (1, ))
assert_size_stride(primals_7, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
# Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward]
stream0 = get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf6, 256, grid=grid(256), stream=stream0)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [src2], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_1.run(primals_3, buf2, buf3, buf4, 64, grid=grid(64), stream=stream0)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [src, src_1], Original ATen: [aten.add, aten.native_layer_norm]
triton_poi_fused_add_native_layer_norm_2.run(primals_3, buf2, buf3, buf4, primals_6, primals_7, buf5, 256, grid=grid(256), stream=stream0)
del buf3
del buf4
del primals_7
return (buf5, primals_3, primals_6, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf2, primals_4, buf6, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class TransNonlinear(nn.Module):
def __init__(self, d_model, dim_feedforward, dropout=0.1):
super().__init__()
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = nn.ReLU()
def forward(self, src):
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'d_model': 4, 'dim_feedforward': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x0 = xindex % 4
tmp0 = tl.load(in_out_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = tl.full([1], 0, tl.int32)
tmp4 = triton_helpers.maximum(tmp3, tmp2)
tmp5 = 0.0
tmp6 = tmp4 <= tmp5
tl.store(in_out_ptr0 + x2, tmp4, xmask)
tl.store(out_ptr0 + x2, tmp6, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_1(in_ptr0, in_ptr1, out_ptr0,
out_ptr1, xnumel, XBLOCK: tl.constexpr):
xnumel = 64
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
)
tmp2 = tmp0 + tmp1
tmp5 = tmp3 + tmp4
tmp6 = tmp2 + tmp5
tmp9 = tmp7 + tmp8
tmp10 = tmp6 + tmp9
tmp13 = tmp11 + tmp12
tmp14 = tmp10 + tmp13
tmp15 = 4.0
tmp16 = tmp14 / tmp15
tmp17 = tmp2 - tmp16
tmp18 = tmp17 * tmp17
tmp19 = tmp5 - tmp16
tmp20 = tmp19 * tmp19
tmp21 = tmp18 + tmp20
tmp22 = tmp9 - tmp16
tmp23 = tmp22 * tmp22
tmp24 = tmp21 + tmp23
tmp25 = tmp13 - tmp16
tmp26 = tmp25 * tmp25
tmp27 = tmp24 + tmp26
tmp28 = tmp27 / tmp15
tl.store(out_ptr0 + x0, tmp16, xmask)
tl.store(out_ptr1 + x0, tmp28, xmask)
@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2,
in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
x0 = xindex % 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr1 + x2, xmask)
tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp4 = tmp2 - tmp3
tmp6 = 1e-05
tmp7 = tmp5 + tmp6
tmp8 = libdevice.rsqrt(tmp7)
tmp9 = tmp4 * tmp8
tmp11 = tmp9 * tmp10
tmp13 = tmp11 + tmp12
tl.store(out_ptr0 + x2, tmp13, xmask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4,), (1,))
assert_size_stride(primals_7, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
del buf0
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
get_raw_stream(0)
triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
primals_2, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
del primals_2
buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
alpha=1, beta=1, out=buf2)
del primals_5
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
triton_poi_fused_add_native_layer_norm_1[grid(64)](primals_3, buf2,
buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_native_layer_norm_2[grid(256)](primals_3, buf2,
buf3, buf4, primals_6, primals_7, buf5, 256, XBLOCK=256,
num_warps=4, num_stages=1)
del buf3
del buf4
del primals_7
return buf5, primals_3, primals_6, reinterpret_tensor(buf1, (64, 4), (4,
1), 0), buf2, primals_4, buf6
class TransNonlinearNew(nn.Module):
def __init__(self, d_model, dim_feedforward, dropout=0.1):
super().__init__()
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = nn.ReLU()
def forward(self, input_0):
primals_1 = self.linear1.weight
primals_2 = self.linear1.bias
primals_4 = self.linear2.weight
primals_5 = self.linear2.bias
primals_6 = self.norm2.weight
primals_7 = self.norm2.bias
primals_3 = input_0
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7])
return output[0]
|
Jasonkks/PTTR
|
TransNonlinear
| false | 8,345 |
[
"Apache-2.0"
] | 14 |
11f664a7f1b2281293d82a5450fdd3d4bfa5883e
|
https://github.com/Jasonkks/PTTR/tree/11f664a7f1b2281293d82a5450fdd3d4bfa5883e
|
LxmertAttention
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/z7/cz7gvwpm6kooqnwr4vixflp6q5wq6isg5on5czrzmd63imnnltlp.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
# %mul_scalar : [num_users=1] = call_function[target=torch.ops.aten.mul.Scalar](args = (%permute_default, 1.0), kwargs = {})
# %clone_default : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_default,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/xv/cxv5zlyqldhwuctiaey5xrrtrmgro2ckmgkb3xaym5udlyzstvai.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
# %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_default_2, [-1], True), kwargs = {})
# %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_default_2, %amax_default), kwargs = {})
# %exp_default : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_tensor,), kwargs = {})
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = (xindex // 4)
tmp0 = tl.load(in_ptr0 + (x2), xmask)
tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/jt/cjteqzpjjfp5f57sg6ohk5xnzwbndntoiin2wxevaquyjslzne6f.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
# %sum_dim_int_list : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_default, [-1], True), kwargs = {})
# %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp_default, %sum_dim_int_list), kwargs = {})
# %eq_scalar : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view_default_2, -inf), kwargs = {})
# %logical_not_default : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%eq_scalar,), kwargs = {})
# %any_dim : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%logical_not_default, -1, True), kwargs = {})
# %logical_not_default_1 : [num_users=1] = call_function[target=torch.ops.aten.logical_not.default](args = (%any_dim,), kwargs = {})
# %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
# %where_self : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%logical_not_default_1, %full_default, %div_tensor), kwargs = {})
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = (xindex // 4)
x2 = xindex
tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp18 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp25 = tl.load(in_ptr1 + (x2), xmask)
tmp26 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
tmp29 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
tmp31 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
tmp1 = float("-inf")
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = (tmp4 != 0)
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = (tmp9 != 0)
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = (tmp15 != 0)
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = (tmp21 != 0)
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/ej/cejxfbejrrsaghh6aun6yxkqmy4riwh54nh4ajhql72uuev27cjd.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
# %clone_default_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_default_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.DEFAULT,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/5k/c5kufnc7mciff7by75wm2btl7xamphqljghinmvgmksxfleox4tp.py
# Topologically Sorted Source Nodes: [context_layer_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
# context_layer_1 => clone_4
# Graph fragment:
# %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[16, 4], tile_hint=TileHint.SQUARE,
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = (yindex // 4)
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4, ), (1, ))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4, ), (1, ))
assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
del primals_7
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
stream0 = get_raw_stream(0)
triton_poi_fused_0.run(buf0, primals_2, buf3, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_2
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0); del buf0 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
triton_poi_fused_0.run(buf1, primals_5, buf4, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_5
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
triton_poi_fused_1.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [], Original ATen: []
triton_poi_fused_2.run(buf5, buf6, buf7, 256, grid=grid(256), stream=stream0)
del buf5
del buf6
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf1 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
triton_poi_fused_3.run(buf2, primals_8, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
del primals_8
buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2 # reuse
# Topologically Sorted Source Nodes: [], Original ATen: []
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
# Topologically Sorted Source Nodes: [context_layer_1], Original ATen: [aten.clone]
triton_poi_fused_clone_4.run(buf9, buf10, 16, 4, grid=grid(16, 4), stream=stream0)
del buf9
return (reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
class LxmertAttention(nn.Module):
def __init__(self, hidden_size, num_attention_heads,
attention_probs_dropout_prob, ctx_dim=None):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention heads (%d)'
% (hidden_size, num_attention_heads))
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(hidden_size / num_attention_heads)
self.head_size = self.num_attention_heads * self.attention_head_size
if ctx_dim is None:
ctx_dim = hidden_size
self.query = nn.Linear(hidden_size, self.head_size)
self.key = nn.Linear(ctx_dim, self.head_size)
self.value = nn.Linear(ctx_dim, self.head_size)
self.dropout = nn.Dropout(attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, context, attention_mask=None,
output_attentions=False):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(context)
mixed_value_layer = self.value(context)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1,
-2))
attention_scores = attention_scores / math.sqrt(self.
attention_head_size)
if attention_mask is not None:
attention_mask = attention_mask.unsqueeze(1).unsqueeze(1).repeat(
1, attention_scores.shape[1], attention_scores.shape[2], 1)
attention_scores.data.masked_fill_(attention_mask.data > 0, -
float('inf'))
attention_probs = nn.Softmax(dim=-1)(attention_scores)
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (
context_layer,)
return outputs
def get_inputs():
return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4, 'num_attention_heads': 4,
'attention_probs_dropout_prob': 0.5}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tmp3 = 1.0
tmp4 = tmp2 * tmp3
tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask)
@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x2 = xindex
x1 = xindex // 4
tmp0 = tl.load(in_ptr0 + x2, xmask)
tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
tmp3 = triton_helpers.maximum(tmp1, tmp2)
tmp5 = triton_helpers.maximum(tmp3, tmp4)
tmp7 = triton_helpers.maximum(tmp5, tmp6)
tmp8 = tmp0 - tmp7
tmp9 = tl_math.exp(tmp8)
tl.store(out_ptr0 + x2, tmp9, xmask)
@triton.jit
def triton_poi_fused_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr
):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x1 = xindex // 4
x2 = xindex
tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp25 = tl.load(in_ptr1 + x2, xmask)
tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
)
tmp1 = float('-inf')
tmp2 = tmp0 == tmp1
tmp3 = tmp2 == 0
tmp4 = tmp3.to(tl.int64)
tmp5 = tmp4 != 0
tmp7 = tmp6 == tmp1
tmp8 = tmp7 == 0
tmp9 = tmp8.to(tl.int64)
tmp10 = tmp9 != 0
tmp11 = tmp5 | tmp10
tmp13 = tmp12 == tmp1
tmp14 = tmp13 == 0
tmp15 = tmp14.to(tl.int64)
tmp16 = tmp15 != 0
tmp17 = tmp11 | tmp16
tmp19 = tmp18 == tmp1
tmp20 = tmp19 == 0
tmp21 = tmp20.to(tl.int64)
tmp22 = tmp21 != 0
tmp23 = tmp17 | tmp22
tmp24 = tmp23 == 0
tmp28 = tmp26 + tmp27
tmp30 = tmp28 + tmp29
tmp32 = tmp30 + tmp31
tmp33 = tmp25 / tmp32
tmp34 = 0.0
tmp35 = tl.where(tmp24, tmp34, tmp33)
tl.store(out_ptr0 + x2, tmp35, xmask)
@triton.jit
def triton_poi_fused_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK:
tl.constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
tmp2 = tmp0 + tmp1
tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)
@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
constexpr, XBLOCK: tl.constexpr):
ynumel = 16
xnumel = 4
yoffset = tl.program_id(1) * YBLOCK
yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
ymask = yindex < ynumel
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
xmask = xindex < xnumel
x2 = xindex
y0 = yindex % 4
y1 = yindex // 4
y3 = yindex
tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
eviction_policy='evict_last')
tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)
def call(args):
(primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
primals_7, primals_8) = args
args.clear()
assert_size_stride(primals_1, (4, 4), (4, 1))
assert_size_stride(primals_2, (4,), (1,))
assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_4, (4, 4), (4, 1))
assert_size_stride(primals_5, (4,), (1,))
assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_7, (4, 4), (4, 1))
assert_size_stride(primals_8, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
del primals_1
buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
del primals_4
buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
del primals_7
buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del primals_2
buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
del buf0
triton_poi_fused_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4,
XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
del primals_5
buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0),
0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_1[grid(256)](buf5, buf6, 256, XBLOCK=128,
num_warps=4, num_stages=1)
buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_2[grid(256)](buf5, buf6, buf7, 256, XBLOCK=128,
num_warps=4, num_stages=1)
del buf5
del buf6
buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
del buf1
triton_poi_fused_3[grid(16, 4)](buf2, primals_8, buf8, 16, 4,
XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
del primals_8
buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
del buf2
extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
triton_poi_fused_clone_4[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
YBLOCK=16, num_warps=1, num_stages=1)
del buf9
return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)
class LxmertAttentionNew(nn.Module):
def __init__(self, hidden_size, num_attention_heads,
attention_probs_dropout_prob, ctx_dim=None):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention heads (%d)'
% (hidden_size, num_attention_heads))
self.num_attention_heads = num_attention_heads
self.attention_head_size = int(hidden_size / num_attention_heads)
self.head_size = self.num_attention_heads * self.attention_head_size
if ctx_dim is None:
ctx_dim = hidden_size
self.query = nn.Linear(hidden_size, self.head_size)
self.key = nn.Linear(ctx_dim, self.head_size)
self.value = nn.Linear(ctx_dim, self.head_size)
self.dropout = nn.Dropout(attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.
attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, input_0, input_1):
primals_1 = self.query.weight
primals_2 = self.query.bias
primals_4 = self.key.weight
primals_5 = self.key.bias
primals_7 = self.value.weight
primals_8 = self.value.bias
primals_3 = input_0
primals_6 = input_1
output = call([primals_1, primals_2, primals_3, primals_4,
primals_5, primals_6, primals_7, primals_8])
return output[0]
|
IsmaelElsharkawi/new_pororo_repo
|
LxmertAttention
| false | 8,346 |
[
"MIT"
] | 19 |
4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
https://github.com/IsmaelElsharkawi/new_pororo_repo/tree/4617083b420615b8a3eb0f44d02e4e91a8f407f7
|
Downsample
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/fw/cfwssnitgm62jqeknau3aq6gsfkreol2cujcvnosgakcnrwlnlaj.py
# Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
# conv3d => convolution
# Graph fragment:
# %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze_1, %primals_2, %primals_3, [4, 4, 4], [0, 0, 0], [1, 1, 1], False, [0, 0, 0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = tl.full([XBLOCK], True, tl.int1)
tmp0 = tl.load(in_out_ptr0 + (0))
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr0 + (0))
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp4 = tmp1 + tmp3
tl.store(in_out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp4, None)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (1, 1, 4, 4, 4), (64, 64, 16, 4, 1))
assert_size_stride(primals_3, (1, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
# Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 1, 4, 4, 4), (64, 64, 16, 4, 1), 0), primals_2, stride=(4, 4, 4), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False, output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf0, (1, 1, 1, 1, 1), (1, 1, 1, 1, 1))
buf1 = buf0; del buf0 # reuse
# Topologically Sorted Source Nodes: [conv3d], Original ATen: [aten.convolution]
stream0 = get_raw_stream(0)
triton_poi_fused_convolution_0.run(buf1, primals_3, 1, grid=grid(1), stream=stream0)
del primals_3
return (reinterpret_tensor(buf1, (1, 1, 1), (1, 1, 1), 0), primals_2, reinterpret_tensor(primals_1, (1, 1, 4, 4, 4), (64, 64, 16, 4, 1), 0), )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((1, 1, 4, 4, 4), (64, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
class Downsample(torch.nn.Module):
def __init__(self, s, use_max=False, batch_mode=False):
super(Downsample, self).__init__()
self.batch_mode = batch_mode
if use_max:
layer = torch.nn.MaxPool3d(s, stride=s)
else:
layer = torch.nn.Conv3d(1, 1, s, stride=s)
layer.weight.data.fill_(1.0 / layer.weight.data.nelement())
layer.bias.data.fill_(0)
self.layer = layer
def forward(self, vol):
if self.batch_mode:
out_vol = self.layer.forward(vol)
else:
out_vol = self.layer.forward(torch.unsqueeze(torch.unsqueeze(
vol, 0), 0))[0, 0]
return out_vol
def get_inputs():
return [torch.rand([4, 4, 4])]
def get_init_inputs():
return [[], {'s': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
.constexpr):
xoffset = tl.program_id(0) * XBLOCK
xoffset + tl.arange(0, XBLOCK)[:]
tl.full([XBLOCK], True, tl.int1)
tmp0 = tl.load(in_out_ptr0 + 0)
tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
tmp2 = tl.load(in_ptr0 + 0)
tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
tmp4 = tmp1 + tmp3
tl.store(in_out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp4, None)
def call(args):
primals_1, primals_2, primals_3 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
assert_size_stride(primals_2, (1, 1, 4, 4, 4), (64, 64, 16, 4, 1))
assert_size_stride(primals_3, (1,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
1, 4, 4, 4), (64, 64, 16, 4, 1), 0), primals_2, stride=(4, 4, 4
), padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
output_padding=(0, 0, 0), groups=1, bias=None)
assert_size_stride(buf0, (1, 1, 1, 1, 1), (1, 1, 1, 1, 1))
buf1 = buf0
del buf0
get_raw_stream(0)
triton_poi_fused_convolution_0[grid(1)](buf1, primals_3, 1, XBLOCK=
1, num_warps=1, num_stages=1)
del primals_3
return reinterpret_tensor(buf1, (1, 1, 1), (1, 1, 1), 0
), primals_2, reinterpret_tensor(primals_1, (1, 1, 4, 4, 4), (64,
64, 16, 4, 1), 0)
class DownsampleNew(torch.nn.Module):
def __init__(self, s, use_max=False, batch_mode=False):
super(DownsampleNew, self).__init__()
self.batch_mode = batch_mode
if use_max:
layer = torch.nn.MaxPool3d(s, stride=s)
else:
layer = torch.nn.Conv3d(1, 1, s, stride=s)
layer.weight.data.fill_(1.0 / layer.weight.data.nelement())
layer.bias.data.fill_(0)
self.layer = layer
def forward(self, input_0):
primals_2 = self.layer.weight
primals_3 = self.layer.bias
primals_1 = input_0
output = call([primals_1, primals_2, primals_3])
return output[0]
|
JasonQSY/Associative3D
|
Downsample
| false | 8,347 |
[
"MIT"
] | 25 |
c50818b593ec48c38ed7ee3e109c23531089da32
|
https://github.com/JasonQSY/Associative3D/tree/c50818b593ec48c38ed7ee3e109c23531089da32
|
MeanEmbedding
|
# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/zb/czbetfjna34uhimvwpecyskd2qlmjyw7ruir3ke27hx3wpvnizlf.py
# Topologically Sorted Source Nodes: [sum_1, truediv], Original ATen: [aten.sum, aten.div]
# Source node to ATen node mapping:
# sum_1 => sum_1
# truediv => div
# Graph fragment:
# %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [-2]), kwargs = {})
# %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %arg1_1), kwargs = {})
triton_poi_fused_div_sum_0 = async_compile.triton('triton_poi_fused_div_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_div_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = (xindex // 4) % 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask, eviction_policy='evict_last')
tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
tmp7 = tl.load(in_ptr1 + (x3), xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 / tmp7
tl.store(out_ptr0 + (x3), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [sum_1, truediv], Original ATen: [aten.sum, aten.div]
stream0 = get_raw_stream(0)
triton_poi_fused_div_sum_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0)
del arg0_1
del arg1_1
return (buf0, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
fn = lambda: call([arg0_1, arg1_1])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class MeanEmbedding(nn.Module):
"""Mean embedding class.
"""
def __init__(self):
super(MeanEmbedding, self).__init__()
def forward(self, emb, len_):
"""Compute average embeddings.
Parameters
----------
emb : torch.Tensor
The input embedding tensor.
len_ : torch.Tensor
The sequence length tensor.
Returns
-------
torch.Tensor
The average embedding tensor.
"""
return torch.sum(emb, dim=-2) / len_
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
|
import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.jit
def triton_poi_fused_div_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 4
x1 = xindex // 4 % 16
x3 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask, eviction_policy=
'evict_last')
tmp7 = tl.load(in_ptr1 + x3, xmask)
tmp2 = tmp0 + tmp1
tmp4 = tmp2 + tmp3
tmp6 = tmp4 + tmp5
tmp8 = tmp6 / tmp7
tl.store(out_ptr0 + x3, tmp8, xmask)
def call(args):
arg0_1, arg1_1 = args
args.clear()
assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
get_raw_stream(0)
triton_poi_fused_div_sum_0[grid(256)](arg0_1, arg1_1, buf0, 256,
XBLOCK=128, num_warps=4, num_stages=1)
del arg0_1
del arg1_1
return buf0,
class MeanEmbeddingNew(nn.Module):
"""Mean embedding class.
"""
def __init__(self):
super(MeanEmbeddingNew, self).__init__()
def forward(self, input_0, input_1):
arg0_1 = input_0
arg1_1 = input_1
output = call([arg0_1, arg1_1])
return output[0]
|
IBM/graph4nlp
|
MeanEmbedding
| false | 8,348 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
GatedFusion
|
# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()
# kernel path: runs/run_shard_1/inductor_cache/de/cdetncp5zzmwnnlv7eq3yvp4mepirr2fuirlmhbaa7iwepo7xhp3.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
# cat => cat
# Graph fragment:
# %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2, %mul, %sub], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[1024],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = (xindex // 16)
x2 = xindex
tmp0 = x0
tmp1 = tl.full([1], 0, tl.int64)
tmp2 = tmp0 >= tmp1
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp16 = tl.load(in_ptr1 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
tmp17 = tmp15 * tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp14, tmp17, tmp18)
tmp20 = tmp0 >= tmp12
tmp21 = tl.full([1], 16, tl.int64)
tmp22 = tmp0 < tmp21
tmp23 = tl.load(in_ptr0 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
tmp24 = tl.load(in_ptr1 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
tmp25 = tmp23 - tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp20, tmp25, tmp26)
tmp28 = tl.where(tmp14, tmp19, tmp27)
tmp29 = tl.where(tmp9, tmp10, tmp28)
tmp30 = tl.where(tmp4, tmp5, tmp29)
tl.store(out_ptr0 + (x2), tmp30, xmask)
''', device_str='cuda')
# kernel path: runs/run_shard_1/inductor_cache/kz/ckzzds5c5eawy76zxgqfbyjmeyq47dejygk2dxpkeqei26lynkz5.py
# Topologically Sorted Source Nodes: [z, sub_1, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add]
# Source node to ATen node mapping:
# h_state => add
# mul_1 => mul_1
# mul_2 => mul_2
# sub_1 => sub_1
# z => sigmoid
# Graph fragment:
# %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
# %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
# %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %primals_1), kwargs = {})
# %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %primals_2), kwargs = {})
# %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
triton_poi_fused_add_mul_rsub_sigmoid_1 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties
@triton_heuristics.pointwise(
size_hints=[256],
filename=__file__,
triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + (x0), xmask)
tmp4 = tl.load(in_ptr1 + (x0), xmask)
tmp6 = tl.load(in_ptr2 + (x0), xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp5 = tmp3 * tmp4
tmp7 = tmp1 * tmp6
tmp8 = tmp5 + tmp7
tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')
async_compile.wait(globals())
del async_compile
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 16), (16, 1))
assert_size_stride(primals_4, (4, ), (1, ))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
stream0 = get_raw_stream(0)
triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 1024, grid=grid(1024), stream=stream0)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 16), (16, 1), 0), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
# Topologically Sorted Source Nodes: [z, sub_1, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.rsub, aten.mul, aten.add]
triton_poi_fused_add_mul_rsub_sigmoid_1.run(buf1, primals_1, primals_2, buf2, 256, grid=grid(256), stream=stream0)
return (buf2, primals_1, primals_2, reinterpret_tensor(buf0, (64, 16), (16, 1), 0), buf1, )
def benchmark_compiled_module(times=10, repeat=10):
from torch._dynamo.testing import rand_strided
from torch._inductor.utils import print_performance
primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
from torch._inductor.wrapper_benchmark import compiled_module_main
compiled_module_main('None', benchmark_compiled_module)
|
import torch
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
class GatedFusion(nn.Module):
def __init__(self, hidden_size):
super(GatedFusion, self).__init__()
"""GatedFusion module"""
self.fc_z = nn.Linear(4 * hidden_size, hidden_size, bias=True)
def forward(self, h_state, input):
z = torch.sigmoid(self.fc_z(torch.cat([h_state, input, h_state *
input, h_state - input], -1)))
h_state = (1 - z) * h_state + z * input
return h_state
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {'hidden_size': 4}]
|
import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.nn.modules.loss
from scipy.sparse import *
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
constexpr):
xnumel = 1024
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 16
x1 = xindex // 16
x2 = xindex
tmp0 = x0
tl.full([1], 0, tl.int64)
tmp3 = tl.full([1], 4, tl.int64)
tmp4 = tmp0 < tmp3
tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
'evict_last', other=0.0)
tmp6 = tmp0 >= tmp3
tmp7 = tl.full([1], 8, tl.int64)
tmp8 = tmp0 < tmp7
tmp9 = tmp6 & tmp8
tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
eviction_policy='evict_last', other=0.0)
tmp11 = tmp0 >= tmp7
tmp12 = tl.full([1], 12, tl.int64)
tmp13 = tmp0 < tmp12
tmp14 = tmp11 & tmp13
tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
eviction_policy='evict_last', other=0.0)
tmp17 = tmp15 * tmp16
tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
tmp19 = tl.where(tmp14, tmp17, tmp18)
tmp20 = tmp0 >= tmp12
tl.full([1], 16, tl.int64)
tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp24 = tl.load(in_ptr1 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
eviction_policy='evict_last', other=0.0)
tmp25 = tmp23 - tmp24
tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
tmp27 = tl.where(tmp20, tmp25, tmp26)
tmp28 = tl.where(tmp14, tmp19, tmp27)
tmp29 = tl.where(tmp9, tmp10, tmp28)
tmp30 = tl.where(tmp4, tmp5, tmp29)
tl.store(out_ptr0 + x2, tmp30, xmask)
@triton.jit
def triton_poi_fused_add_mul_rsub_sigmoid_1(in_ptr0, in_ptr1, in_ptr2,
out_ptr0, xnumel, XBLOCK: tl.constexpr):
xnumel = 256
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex
tmp0 = tl.load(in_ptr0 + x0, xmask)
tmp4 = tl.load(in_ptr1 + x0, xmask)
tmp6 = tl.load(in_ptr2 + x0, xmask)
tmp1 = tl.sigmoid(tmp0)
tmp2 = 1.0
tmp3 = tmp2 - tmp1
tmp5 = tmp3 * tmp4
tmp7 = tmp1 * tmp6
tmp8 = tmp5 + tmp7
tl.store(out_ptr0 + x0, tmp8, xmask)
def call(args):
primals_1, primals_2, primals_3, primals_4 = args
args.clear()
assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
assert_size_stride(primals_3, (4, 16), (16, 1))
assert_size_stride(primals_4, (4,), (1,))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
buf0 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
float32)
get_raw_stream(0)
triton_poi_fused_cat_0[grid(1024)](primals_1, primals_2, buf0, 1024,
XBLOCK=128, num_warps=4, num_stages=1)
buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 16),
(16, 1), 0), reinterpret_tensor(primals_3, (16, 4), (1, 16), 0),
alpha=1, beta=1, out=buf1)
del primals_3
del primals_4
buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
triton_poi_fused_add_mul_rsub_sigmoid_1[grid(256)](buf1, primals_1,
primals_2, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
return buf2, primals_1, primals_2, reinterpret_tensor(buf0, (64, 16), (
16, 1), 0), buf1
class GatedFusionNew(nn.Module):
def __init__(self, hidden_size):
super(GatedFusionNew, self).__init__()
"""GatedFusion module"""
self.fc_z = nn.Linear(4 * hidden_size, hidden_size, bias=True)
def forward(self, input_0, input_1):
primals_3 = self.fc_z.weight
primals_4 = self.fc_z.bias
primals_1 = input_0
primals_2 = input_1
output = call([primals_1, primals_2, primals_3, primals_4])
return output[0]
|
IBM/graph4nlp
|
GatedFusion
| false | 8,349 |
[
"Apache-2.0"
] | 18 |
a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
https://github.com/IBM/graph4nlp/tree/a9bf20b23fa1ec368d9bd40cc8c557f86a9f8297
|
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