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| import os | |
| import torch | |
| from torch import nn | |
| 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, 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, empty, out, 3, 1, negative_slope, scale | |
| ) | |
| dim = [0] | |
| if grad_input.ndim > 2: | |
| dim += list(range(2, grad_input.ndim)) | |
| grad_bias = grad_input.sum(dim).detach() | |
| return grad_input, grad_bias | |
| def backward(ctx, gradgrad_input, gradgrad_bias): | |
| out, = ctx.saved_tensors | |
| gradgrad_out = fused.fused_bias_act( | |
| gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale | |
| ) | |
| return gradgrad_out, None, None, None | |
| class FusedLeakyReLUFunction(Function): | |
| def forward(ctx, input, bias, negative_slope, scale): | |
| empty = input.new_empty(0) | |
| 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.negative_slope, ctx.scale | |
| ) | |
| return grad_input, grad_bias, None, None | |
| class FusedLeakyReLU(nn.Module): | |
| def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): | |
| super().__init__() | |
| self.bias = nn.Parameter(torch.zeros(channel)) | |
| 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, negative_slope=0.2, scale=2 ** 0.5): | |
| return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) | |