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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
decoder5
# 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