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Running
on
T4
import os | |
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
from torch.autograd import Function | |
from torch.utils.cpp_extension import load | |
module_path = os.path.dirname(__file__) | |
fused = load( | |
"fused", | |
sources=[ | |
os.path.join(module_path, "fused_bias_act.cpp"), | |
os.path.join(module_path, "fused_bias_act_kernel.cu"), | |
], | |
) | |
class FusedLeakyReLUFunctionBackward(Function): | |
def forward(ctx, grad_output, out, bias, negative_slope, scale): | |
ctx.save_for_backward(out) | |
ctx.negative_slope = negative_slope | |
ctx.scale = scale | |
empty = grad_output.new_empty(0) | |
grad_input = fused.fused_bias_act( | |
grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale | |
) | |
dim = [0] | |
if grad_input.ndim > 2: | |
dim += list(range(2, grad_input.ndim)) | |
if bias: | |
grad_bias = grad_input.sum(dim).detach() | |
else: | |
grad_bias = empty | |
return grad_input, grad_bias | |
def backward(ctx, gradgrad_input, gradgrad_bias): | |
out, = ctx.saved_tensors | |
gradgrad_out = fused.fused_bias_act( | |
gradgrad_input.contiguous(), gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale | |
) | |
return gradgrad_out, None, None, None, None | |
class FusedLeakyReLUFunction(Function): | |
def forward(ctx, input, bias, negative_slope, scale): | |
empty = input.new_empty(0) | |
ctx.bias = bias is not None | |
if bias is None: | |
bias = empty | |
out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) | |
ctx.save_for_backward(out) | |
ctx.negative_slope = negative_slope | |
ctx.scale = scale | |
return out | |
def backward(ctx, grad_output): | |
out, = ctx.saved_tensors | |
grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( | |
grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale | |
) | |
if not ctx.bias: | |
grad_bias = None | |
return grad_input, grad_bias, None, None | |
class FusedLeakyReLU(nn.Module): | |
def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5): | |
super().__init__() | |
if bias: | |
self.bias = nn.Parameter(torch.zeros(channel)) | |
else: | |
self.bias = None | |
self.negative_slope = negative_slope | |
self.scale = scale | |
def forward(self, input): | |
return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) | |
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5): | |
if input.device.type == "cpu": | |
if bias is not None: | |
rest_dim = [1] * (input.ndim - bias.ndim - 1) | |
return ( | |
F.leaky_relu( | |
input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2 | |
) | |
* scale | |
) | |
else: | |
return F.leaky_relu(input, negative_slope=0.2) * scale | |
else: | |
return FusedLeakyReLUFunction.apply(input.contiguous(), bias, negative_slope, scale) | |