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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 | |
| import warnings | |
| module_path = os.path.dirname(os.path.abspath(__file__)) | |
| try: | |
| fused = load( | |
| "fused", | |
| sources=[ | |
| os.path.join(module_path, "fused_bias_act.cpp"), | |
| os.path.join(module_path, "fused_bias_act_kernel.cu"), | |
| ], | |
| ) | |
| except: | |
| warnings.warn( | |
| f"(This is not error) Switch to native implementation" | |
| ) | |
| fused = None | |
| 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 | |
| ) | |
| class FusedLeakyReLU_Native(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_native(input, self.bias, self.negative_slope, self.scale) | |
| def fused_leaky_relu_native(input, bias, negative_slope=0.2, scale=2 ** 0.5): | |
| return scale * F.leaky_relu(input + bias.view((1, -1) + (1,) * (len(input.shape) - 2)), negative_slope=negative_slope) | |