| import torch |
| from torch import nn |
| from torch.autograd import Function |
| from torch.nn import functional as F |
| from torch.utils.cpp_extension import load |
|
|
| fused = load( |
| 'fused', |
| sources=['models/op/fused_bias_act.cpp', 'models/op/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, 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, 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): |
| |
| if input.dtype == torch.float16: |
| bias = bias.half() |
|
|
| 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, bias, negative_slope, scale) |
|
|
