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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):
@staticmethod
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
@staticmethod
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):
@staticmethod
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
@staticmethod
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)
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