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dropout.py
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dropout.py
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# CREDITS: This comes almost as-is from the Triton dropout tutorial
# https://raw.githubusercontent.com/openai/triton/master/python/tutorials/04-low-memory-dropout.py
from typing import Optional
import torch
import triton
from torch.cuda.amp import custom_bwd, custom_fwd
from xformers.components.activations import Activation, build_activation
from xformers.triton.k_activations import (
get_triton_activation_bwd_kernel,
get_triton_activation_kernel,
)
from xformers.triton.k_dropout import k_dropout_bw, k_dropout_fw
from xformers.triton.sum_strided import sum_2d_dim_0
# Helper to handle the SPMD launch grid and error cases
class _dropout(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx, x, p, bias, activation, activation_grad, trainable_bias):
# Soft-flatten an hypothetical 3rd dimension
x_ = x.reshape(-1, x.shape[-1]).contiguous()
y = torch.empty_like(x_)
M, N = x_.shape
assert bias is None or (bias.dtype == x.dtype and bias.shape[0] == N)
# Generate one seed per sample
# seed max is int32 max for positive numbers: 2**16
# FIXME: adjust the number of seeds needed
seeds = torch.randint(65536, (N,), device=x.device).to(torch.int32)
def grid(meta):
return (
triton.cdiv(M, meta["BLOCK_M"] * 4),
triton.cdiv(N, meta["BLOCK_N"]),
)
GROUP_M = 16
BLOCK_M = GROUP_M // 4
# fmt: off
k_dropout_fw[grid](
y, x_,
bias if bias is not None else x_,
seeds,
y.stride(0),
M, N,
p,
USE_BIAS=bias is not None,
ACTIVATION=activation,
BLOCK_M=BLOCK_M
)
# fmt: on
if activation is not None:
ctx.save_for_backward(seeds, bias, x)
else:
ctx.save_for_backward(seeds, bias, None)
ctx.trainable_bias = bias is not None and trainable_bias
ctx.activation_grad = activation_grad
ctx.p = p
return y.reshape_as(x)
@staticmethod
@custom_bwd
def backward(ctx, grad_out):
(seeds, bias, inputs) = ctx.saved_tensors
# Soft-flatten an hypothetical 3rd dimension
grad_out_ = grad_out.reshape(-1, grad_out.shape[-1]).contiguous()
grad_in = torch.empty_like(grad_out_)
M, N = grad_out_.shape
# Optional inputs to compute the activation contribution to the gradient
assert inputs is not None or ctx.activation_grad is None
if inputs is None:
inputs = grad_out_
elif inputs.ndim > 2:
inputs = inputs.reshape(-1, N)
GROUP_M = 16 if M > 512 else M // 4
BLOCK_M = GROUP_M // 4
N_BLOCKS_M = triton.cdiv(M, GROUP_M)
if ctx.trainable_bias:
grad_bias = torch.empty(
(
N_BLOCKS_M,
N,
),
device=grad_in.device,
dtype=grad_in.dtype,
)
else:
grad_bias = grad_in # will not be used
def grid(meta):
return (
triton.cdiv(M, meta["BLOCK_M"] * 4),
triton.cdiv(N, meta["BLOCK_N"]),
)
# fmt: off
k_dropout_bw[grid](
grad_in, grad_bias, grad_out_,
inputs, bias if bias is not None else inputs,
seeds,
grad_out_.stride(0), inputs.stride(0),
M, N,
ctx.p,
USE_BIAS=bias is not None,
ACTIVATION_GRAD=ctx.activation_grad,
TRAINABLE_BIAS=ctx.trainable_bias,
BLOCK_M=BLOCK_M
)
# fmt: on
return (
grad_in.reshape_as(grad_out),
None,
sum_2d_dim_0(grad_bias) if ctx.trainable_bias else None,
None,
None,
None,
)
def dropout(
x: torch.Tensor,
p: float,
bias: Optional[torch.Tensor] = None,
activation: Optional[Activation] = None,
):
"""
Apply dropout on the input tensor.
Optionally add a bias, the computation will be fused.
"""
# Micro optim, skip dropout
if p == 0.0 and activation is None:
return x + bias if bias is not None else x
act_kernel = get_triton_activation_kernel(activation)
act_grad_kernel = get_triton_activation_bwd_kernel(activation)
return _dropout.apply(
x,
p,
bias,
act_kernel,
act_grad_kernel,
bias is not None and bias.requires_grad,
)
class FusedDropoutBias(torch.nn.Module):
def __init__(
self,
p: float,
bias_shape: Optional[int],
activation: Optional[Activation] = None,
) -> None:
super().__init__()
self.p = p
self.activation_type = activation
self.bias = (
torch.zeros(bias_shape, requires_grad=True)
if bias_shape is not None
else None
)
self.activation = get_triton_activation_kernel(activation)
self.activation_grad = get_triton_activation_bwd_kernel(activation)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Convenience, catch a possible type or device mismatch
if self.bias is not None: # type: ignore
self.bias = self.bias.to(dtype=x.dtype, device=x.device) # type: ignore
# Catch a non-cuda setup, fallback to pytorch
if not x.is_cuda:
activation = build_activation(self.activation_type)
x = x + self.bias if self.bias is not None else x
x = activation(x)
return torch.nn.functional.dropout(x, self.p)
# The normal, Triton-backed path
p = self.p if self.training else 0.0
return _dropout.apply(
x, p, self.bias, self.activation, self.activation_grad, True
)