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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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