| 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)
|
|
|
