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# Copyright (c) 2023, Tri Dao.
# Inspired by https://github.com/NVIDIA/apex/blob/master/apex/fused_dense/fused_dense.py
# We make it work with pytorch amp and with bfloat16.
# The TensorParallel linear modules are inspired by https://github.com/NVIDIA/apex/blob/master/apex/transformer/tensor_parallel/layers.py
from functools import partial
from typing import Optional
# import fused_dense_cuda # from apex
import fused_dense_lib as fused_dense_cuda
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
from torch.distributed import ProcessGroup
from flash_attn.ops.activations import gelu_bwd, relu_bwd, sqrelu_bwd, sqrelu_fwd
from flash_attn.utils.distributed import (
all_gather_raw,
all_reduce,
all_reduce_raw,
reduce_scatter,
reduce_scatter_raw,
)
class FusedDenseFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx, x, weight, bias, return_residual=False, process_group=None, sequence_parallel=True
):
"""
If process_group is not None and sequence_parallel=True, we're doing Tensor Parallel
with sequence parallelism: we do an all_gather_raw of x before doing the matmul.
"""
ctx.compute_weight_gradient = weight.requires_grad
ctx.return_residual = return_residual
ctx.process_group = process_group
ctx.sequence_parallel = sequence_parallel
if torch.is_autocast_enabled():
x = x.to(dtype=torch.get_autocast_gpu_dtype())
x = x.contiguous()
if process_group is not None and sequence_parallel:
# We want to kick off the all_gather early, before weight dtype conversion
total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
else:
total_x = x
if torch.is_autocast_enabled():
weight = weight.to(dtype=torch.get_autocast_gpu_dtype())
bias = bias.to(dtype=torch.get_autocast_gpu_dtype()) if bias is not None else None
weight = weight.contiguous()
if process_group is not None and sequence_parallel:
handle_x.wait()
batch_shape, n = total_x.shape[:-1], total_x.shape[-1]
batch_dim = batch_shape.numel()
# https://github.com/pytorch/pytorch/blob/5b51849b48a7dbccd297286cc0110def4706f9e7/aten/src/ATen/native/cuda/Blas.cpp#L174
if min(batch_dim, n, *weight.shape) > 65535 * 32:
raise RuntimeError("fused_dense only supports matrix dims <= 2M")
output = F.linear(total_x, weight, bias)
if ctx.compute_weight_gradient:
ctx.save_for_backward(x, weight)
else:
ctx.save_for_backward(weight)
return output if not return_residual else (output, x)
@staticmethod
@custom_bwd
def backward(ctx, grad_output, *args):
grad_output = grad_output.contiguous()
if ctx.return_residual:
(grad_input,) = args
grad_input = grad_input.contiguous()
process_group = ctx.process_group
sequence_parallel = ctx.sequence_parallel
if ctx.compute_weight_gradient:
x, weight = ctx.saved_tensors
if process_group is not None and sequence_parallel:
total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
else:
total_x = x
else:
(weight,) = ctx.saved_tensors
total_x = None
batch_shape = grad_output.shape[:-1]
batch_dim = batch_shape.numel()
grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
if ctx.needs_input_grad[0]:
if not ctx.return_residual:
grad_input = F.linear(grad_output, weight.t())
else:
grad_input = torch.addmm(
grad_input.reshape(batch_dim, grad_input.shape[-1]), grad_output, weight
)
grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
if process_group is not None:
reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
else:
grad_input = None
if ctx.needs_input_grad[1]:
assert ctx.compute_weight_gradient
if process_group is not None and sequence_parallel:
handle_x.wait()
grad_weight, grad_bias = fused_dense_cuda.linear_bias_wgrad(
total_x.reshape(batch_dim, total_x.shape[-1]), grad_output, ctx.needs_input_grad[2]
)
else:
grad_weight = None
grad_bias = grad_output if ctx.needs_input_grad[2] else None
if process_group is not None and ctx.needs_input_grad[0]:
handle_grad_input.wait()
return grad_input, grad_weight, grad_bias, None, None, None
def fused_dense_func(
x: Tensor,
weight: Tensor,
bias: Optional[Tensor] = None,
return_residual: bool = False,
process_group: Optional[ProcessGroup] = None,
sequence_parallel: bool = True,
):
dtype_eligible = x.dtype in [torch.float16, torch.bfloat16] or (
x.dtype == torch.float32 and torch.is_autocast_enabled()
)
if x.is_cuda and weight.is_cuda and (bias is None or bias.is_cuda) and dtype_eligible:
return FusedDenseFunc.apply(
x, weight, bias, return_residual, process_group, sequence_parallel
)
else:
assert process_group is None
out = F.linear(x, weight, bias)
return out if not return_residual else (out, x)
class FusedDense(nn.Linear):
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
return_residual: bool = False,
device=None,
dtype=None,
) -> None:
super().__init__(in_features, out_features, bias=bias, device=device, dtype=dtype)
self.return_residual = return_residual
def forward(self, x, process_group=None):
"""
If process_group is not None, we're doing Tensor Parallel with sequence parallelism:
we do an all_gather of x before doing the matmul.
"""
return fused_dense_func(
x,
self.weight,
self.bias,
return_residual=self.return_residual,
process_group=process_group,
)
class ColumnParallelLinear(nn.Linear):
def __init__(
self,
in_features: int,
out_features: int,
process_group: ProcessGroup,
bias: bool = True,
sequence_parallel=True,
multiple_of=1,
device=None,
dtype=None,
) -> None:
world_size = torch.distributed.get_world_size(process_group)
if out_features % multiple_of:
raise ValueError(f"out_features ({out_features}) must be a multiple of {multiple_of}")
multiple = out_features // multiple_of
# We want to split @multiple across world_size, but it could be an uneven split
div = multiple // world_size
mod = multiple % world_size
# The first @mod ranks get @div + 1 copies, the rest get @div copies
local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
super().__init__(
in_features, local_multiple * multiple_of, bias=bias, device=device, dtype=dtype
)
self.process_group = process_group
self.sequence_parallel = sequence_parallel
def forward(self, x):
# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
# we do an all_gather of x before doing the matmul.
# If not, then the input is already gathered.
return fused_dense_func(
x,
self.weight,
self.bias,
process_group=self.process_group,
sequence_parallel=self.sequence_parallel,
)
class RowParallelLinear(nn.Linear):
def __init__(
self,
in_features: int,
out_features: int,
process_group: ProcessGroup,
bias: bool = True,
sequence_parallel=True,
multiple_of=1,
device=None,
dtype=None,
) -> None:
world_size = torch.distributed.get_world_size(process_group)
rank = torch.distributed.get_rank(process_group)
if in_features % multiple_of:
raise ValueError(f"in_features ({in_features}) must be a multiple of {multiple_of}")
multiple = in_features // multiple_of
# We want to split @multiple across world_size, but it could be an uneven split
div = multiple // world_size
mod = multiple % world_size
# The first @mod ranks get @div + 1 copies, the rest get @div copies
local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
# Only rank 0 will have bias
super().__init__(
local_multiple * multiple_of,
out_features,
bias=bias and rank == 0,
device=device,
dtype=dtype,
)
self.process_group = process_group
self.sequence_parallel = sequence_parallel
def forward(self, x):
"""
We're doing Tensor Parallel with sequence parallelism: we do the matmul and then
a reduce_scatter of the result.
"""
out = fused_dense_func(x, self.weight, self.bias)
reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
return reduce_fn(out, self.process_group)
class FusedMLPFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx,
x,
weight1,
bias1,
weight2,
bias2,
activation="gelu_approx",
save_pre_act=True,
return_residual=False,
checkpoint_lvl=0,
heuristic=0,
process_group=None,
sequence_parallel=True,
):
"""
If process_group is not None and sequence_parallel=True, we're doing Tensor Parallel
with sequence parallelism: we do an all_gather of x before doing the matmul.
If sequence_parallel=False, then the input is already gathered.
checkpoint_lvl:
0: no recomputation in the bwd
1: recompute gelu_out / relu_out in the bwd
2: recompute pre_act and gelu_out / relu_out in the bwd
"""
assert -1 <= heuristic <= 4
assert activation in ["gelu_approx", "relu", "sqrelu"]
if activation == "sqrelu":
assert heuristic == -1
if not save_pre_act:
checkpoint_lvl = 2
assert checkpoint_lvl in [0, 1, 2]
ctx.return_residual = return_residual
ctx.process_group = process_group
ctx.sequence_parallel = sequence_parallel
ctx.checkpoint_lvl = checkpoint_lvl
ctx.activation = activation
ctx.heuristic = heuristic
if torch.is_autocast_enabled():
x = x.to(dtype=torch.get_autocast_gpu_dtype())
x = x.contiguous()
if process_group is not None and sequence_parallel:
# We want to kick off the all_gather early, before weight dtype conversion
total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
else:
total_x = x
if torch.is_autocast_enabled():
dtype = torch.get_autocast_gpu_dtype()
weight1, weight2 = [a.to(dtype=dtype) for a in [weight1, weight2]]
bias1 = bias1.to(dtype=dtype) if bias1 is not None else None
bias2 = bias2.to(dtype=dtype) if bias2 is not None else None
weight1 = weight1.contiguous()
bias1 = bias1.contiguous() if bias1 is not None else None
weight2 = weight2.contiguous()
bias2 = bias2.contiguous() if bias2 is not None else None
if process_group is not None and sequence_parallel:
handle_x.wait()
batch_shape, n = total_x.shape[:-1], total_x.shape[-1]
batch_dim = batch_shape.numel()
# https://github.com/pytorch/pytorch/blob/5b51849b48a7dbccd297286cc0110def4706f9e7/aten/src/ATen/native/cuda/Blas.cpp#L174
if min(batch_dim, n, *weight1.shape, *weight2.shape) > 65535 * 32:
raise RuntimeError("fused_dense only supports matrix dims <= 2M")
if heuristic == -1:
pre_act = F.linear(total_x, weight1, bias1)
activation_fn = (
partial(F.gelu, approximate="tanh")
if activation == "gelu_approx"
else (sqrelu_fwd if activation == "sqrelu" else F.relu)
)
with torch.jit.fuser("fuser2"):
output1 = activation_fn(pre_act)
# This is before adding bias1
# pre_act = F.linear(total_x.reshape(batch_dim, n), weight1)
# with torch.jit.fuser('fuser2'):
# output1 = bias_gelu(pre_act, bias1)
else:
is_gelu = activation == "gelu_approx"
output1, *rest = fused_dense_cuda.linear_act_forward(
total_x.reshape(batch_dim, n), weight1, bias1, is_gelu, save_pre_act, heuristic
)
if save_pre_act:
pre_act = rest[0]
output2 = F.linear(output1, weight2, bias2)
if checkpoint_lvl == 0 or (checkpoint_lvl == 1 and activation == "relu"):
# For RELU the pre_act is very small (just a bit-mask) so we just save it
ctx.save_for_backward(x, weight1, weight2, pre_act, output1)
elif checkpoint_lvl == 1:
ctx.save_for_backward(x, weight1, weight2, pre_act)
elif checkpoint_lvl == 2:
ctx.save_for_backward(x, weight1, weight2, bias1)
output2 = output2.reshape(*batch_shape, output2.shape[-1])
return output2 if not return_residual else (output2, x)
@staticmethod
@custom_bwd
def backward(ctx, grad_output, *args):
grad_output = grad_output.contiguous()
checkpoint_lvl = ctx.checkpoint_lvl
activation = ctx.activation
activation_fn = (
partial(F.gelu, approximate="tanh")
if activation == "gelu_approx"
else (sqrelu_fwd if activation == "sqrelu" else F.relu)
)
if ctx.return_residual:
(grad_input,) = args
grad_input = grad_input.contiguous()
process_group = ctx.process_group
sequence_parallel = ctx.sequence_parallel
x, weight1, weight2, *rest = ctx.saved_tensors
if process_group is None or not sequence_parallel:
total_x = x
batch_shape = grad_output.shape[:-1]
batch_dim = batch_shape.numel()
if checkpoint_lvl in [0, 1]:
if process_group is not None and sequence_parallel:
total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
if checkpoint_lvl == 0 or (checkpoint_lvl == 1 and activation == "relu"):
pre_act, output1 = rest
elif checkpoint_lvl == 1:
(pre_act,) = rest
with torch.jit.fuser("fuser2"):
output1 = activation_fn(pre_act)
elif checkpoint_lvl == 2:
(bias1,) = rest
if process_group is not None and sequence_parallel:
total_x, _ = all_gather_raw(x, process_group)
if ctx.heuristic == -1:
pre_act = F.linear(total_x, weight1, bias1)
with torch.jit.fuser("fuser2"):
output1 = activation_fn(pre_act)
else:
output1, pre_act = fused_dense_cuda.linear_act_forward(
total_x.reshape(batch_dim, total_x.shape[-1]),
weight1,
bias1,
activation == "gelu_approx",
True,
ctx.heuristic,
)
grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
output1 = output1.reshape(batch_dim, output1.shape[-1])
pre_act = pre_act.reshape(batch_dim, pre_act.shape[-1])
if ctx.needs_input_grad[3]:
grad_weight2, grad_bias2 = fused_dense_cuda.linear_bias_wgrad(
output1, grad_output, ctx.needs_input_grad[4]
)
else:
grad_weight2 = None
grad_bias2 = grad_output if ctx.needs_input_grad[4] else None
if ctx.heuristic == -1:
# grad_pre_act = matmul_dgelu(grad_output, weight2, pre_act)
grad_output1 = F.linear(grad_output, weight2.t())
activation_grad_fn = (
gelu_bwd
if activation == "gelu_approx"
else (sqrelu_bwd if activation == "sqrelu" else relu_bwd)
)
with torch.jit.fuser("fuser2"):
grad_pre_act = activation_grad_fn(grad_output1, pre_act)
else:
# The cublasLt epilogue has to compute both gelu/relu grad and bias grad, we can't
# just compute gelu/relu grad
grad_pre_act, grad_bias1 = fused_dense_cuda.bias_act_linear_dgrad_bgrad(
weight2, grad_output, pre_act, activation == "gelu_approx", ctx.heuristic
)
if not ctx.needs_input_grad[2]:
grad_bias1 = None
if ctx.needs_input_grad[0]:
if not ctx.return_residual:
grad_input = F.linear(grad_pre_act, weight1.t())
else:
grad_input = torch.addmm(
grad_input.reshape(batch_dim, grad_input.shape[-1]), grad_pre_act, weight1
)
grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
if process_group is not None:
reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
else:
grad_input = None
if ctx.heuristic == -1:
if ctx.needs_input_grad[1]:
if process_group is not None and sequence_parallel and checkpoint_lvl != 2:
handle_x.wait()
grad_weight1, grad_bias1 = fused_dense_cuda.linear_bias_wgrad(
total_x.reshape(batch_dim, total_x.shape[-1]),
grad_pre_act,
ctx.needs_input_grad[2],
)
else:
grad_weight1 = None
grad_bias1 = grad_pre_act if ctx.needs_input_grad[2] else None
else:
if ctx.needs_input_grad[1]:
if process_group is not None and sequence_parallel and checkpoint_lvl != 2:
handle_x.wait()
grad_weight1 = F.linear(
grad_pre_act.t(), total_x.reshape(batch_dim, total_x.shape[-1]).t()
)
else:
grad_weight1 = None
if process_group is not None and ctx.needs_input_grad[0]:
handle_grad_input.wait()
return (
grad_input,
grad_weight1,
grad_bias1,
grad_weight2,
grad_bias2,
None,
None,
None,
None,
None,
None,
None,
)
def fused_mlp_func(
x: Tensor,
weight1: Tensor,
weight2: Tensor,
bias1: Optional[Tensor] = None,
bias2: Optional[Tensor] = None,
activation: str = "gelu_approx",
save_pre_act: bool = True,
return_residual: bool = False,
checkpoint_lvl: int = 0,
heuristic: int = 0,
process_group: Optional[ProcessGroup] = None,
sequence_parallel: bool = True,
):
assert activation in ["gelu_approx", "relu", "sqrelu"]
dtype_eligible = x.dtype in [torch.float16, torch.bfloat16] or (
x.dtype == torch.float32 and torch.is_autocast_enabled()
)
# If we save pre-activation, dimension must be divisible by 128 (relu) or 8 (gelu)
dim_eligible = not save_pre_act or (x.shape[-1] % (128 if activation == "relu" else 8) == 0)
if (
x.is_cuda
and weight1.is_cuda
and weight2.is_cuda
and (bias1 is None or bias1.is_cuda)
and (bias2 is None or bias2.is_cuda)
and dtype_eligible
and dim_eligible
):
return FusedMLPFunc.apply(
x,
weight1,
bias1,
weight2,
bias2,
activation,
save_pre_act,
return_residual,
checkpoint_lvl,
heuristic,
process_group,
sequence_parallel,
)
else:
assert process_group is None
pre_act = F.linear(x, weight1, bias1)
activation_fn = (
partial(F.gelu, approximate="tanh")
if activation == "gelu_approx"
else partial(F.relu, inplace=True)
)
output1 = activation_fn(pre_act)
output2 = F.linear(output1, weight2, bias2)
return output2 if not return_residual else (output2, x)
class FusedMLP(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
bias1=True,
bias2=True,
activation="gelu_approx",
return_residual=False,
checkpoint_lvl=0,
heuristic="auto",
device=None,
dtype=None,
):
"""
If process_group is not None, we're doing Tensor Parallel with sequence parallelism:
we do an all_gather of x before doing the matmul, gelu, then matmul.
Finally we do a reduce_scatter of the output.
checkpoint_lvl (increasing lvl means slower but more memory saving):
0: no recomputation in the bwd
1: recompute gelu_out in the bwd
2: recompute pre_act and gelu_out in the bwd
heuristic:
-1: don't fuse gemm + gelu (separate kernel)
0..4: use this heuristic for the algo section in the fused gemm + gelu
'auto': heuristic will be picked automatically:
For CUDA >= 11.8, we set heuristic=0 for both fp16 and bf16 for best perf.
For CUDA <= 11.7, we set heuristic=1 for fp16 and heuristic=-1 for bf16.
For H100, we set heuristic=-1 for both fp16 and bf16 as the fused cuBlasLt implementation
is slower than the unfused version.
return_residual: whether to return the input x along with the output. This is for
performance reason: for post-norm architecture, returning the input allows us
to fuse the backward of nn.Linear with the residual connection.
"""
assert checkpoint_lvl in [0, 1, 2]
assert activation in ["gelu_approx", "relu", "sqrelu"]
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features * 4
self.activation = activation
self.return_residual = return_residual
self.checkpoint_lvl = checkpoint_lvl
self.heuristic = heuristic if activation != "sqrelu" else -1
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias1, **factory_kwargs)
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2, **factory_kwargs)
def forward(self, x, process_group=None):
dtype = x.dtype if not torch.is_autocast_enabled() else torch.get_autocast_gpu_dtype()
if self.heuristic == "auto":
if self.activation == "gelu_approx":
if torch.cuda.get_device_capability("cuda") == (9, 0):
heuristic = -1
else:
cuda_ver = tuple(map(int, torch.version.cuda.split(".")))
heuristic = 0 if cuda_ver >= (11, 8) else (1 if dtype == torch.float16 else -1)
else:
heuristic = 0
else:
heuristic = self.heuristic
out = fused_mlp_func(
x,
self.fc1.weight,
self.fc2.weight,
self.fc1.bias,
self.fc2.bias,
activation=self.activation,
save_pre_act=self.training,
return_residual=self.return_residual,
checkpoint_lvl=self.checkpoint_lvl,
heuristic=heuristic,
process_group=process_group,
)
if self.return_residual:
out, x = out
if process_group is not None:
out = reduce_scatter(out, process_group)
return out if not self.return_residual else (out, x)
class ParallelFusedMLP(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
activation="gelu_approx",
process_group: ProcessGroup = None,
bias1=True,
bias2=True,
sequence_parallel=True,
checkpoint_lvl=0,
heuristic="auto",
device=None,
dtype=None,
):
"""
process_group is required. We're doing Tensor Parallel with sequence parallelism:
we do an all_gather of x before doing the matmul, gelu, then matmul.
Finally we do a reduce_scatter of the output.
checkpoint_lvl (increasing lvl means slower but more memory saving):
0: no recomputation in the bwd
1: recompute gelu_out in the bwd
2: recompute pre_act and gelu_out in the bwd
heuristic:
-1: don't fuse gemm + gelu (separate kernel)
0..4: use this heuristic for the algo section in the fused gemm + gelu
'auto': heuristic will be picked automatically:
For CUDA >= 11.8, we set heuristic=0 for both fp16 and bf16 for best perf.
For CUDA <= 11.7, we set heuristic=1 for fp16 and heuristic=-1 for bf16.
"""
assert checkpoint_lvl in [0, 1, 2]
assert activation in ["gelu_approx", "relu", "sqrelu"]
assert process_group is not None
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features * 4
self.activation = activation
self.process_group = process_group
self.sequence_parallel = sequence_parallel
self.checkpoint_lvl = checkpoint_lvl
self.heuristic = heuristic if activation != "sqrelu" else -1
self.fc1 = ColumnParallelLinear(
in_features, hidden_features, process_group, bias=bias1, **factory_kwargs
)
self.fc2 = RowParallelLinear(
hidden_features, out_features, process_group, bias=bias2, **factory_kwargs
)
def forward(self, x):
dtype = x.dtype if not torch.is_autocast_enabled() else torch.get_autocast_gpu_dtype()
if self.heuristic == "auto":
if self.activation == "gelu_approx":
cuda_ver = tuple(map(int, torch.version.cuda.split(".")))
heuristic = 0 if cuda_ver >= (11, 8) else (1 if dtype == torch.float16 else -1)
else:
heuristic = 0
else:
heuristic = self.heuristic
out = fused_mlp_func(
x,
self.fc1.weight,
self.fc2.weight,
self.fc1.bias,
self.fc2.bias,
activation=self.activation,
save_pre_act=self.training,
checkpoint_lvl=self.checkpoint_lvl,
heuristic=heuristic,
process_group=self.process_group,
sequence_parallel=self.sequence_parallel,
)
reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
return reduce_fn(out, self.process_group)
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