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import os
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.autograd import Function
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from torch.utils.cpp_extension import load
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module_path = os.path.dirname(__file__)
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fused = load(
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"fused",
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sources=[
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os.path.join(module_path, "fused_bias_act.cpp"),
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os.path.join(module_path, "fused_bias_act_kernel.cu"),
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],
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)
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class FusedLeakyReLUFunctionBackward(Function):
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@staticmethod
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def forward(ctx, grad_output, out, bias, negative_slope, scale):
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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empty = grad_output.new_empty(0)
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grad_input = fused.fused_bias_act(
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grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale
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)
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dim = [0]
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if grad_input.ndim > 2:
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dim += list(range(2, grad_input.ndim))
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if bias:
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grad_bias = grad_input.sum(dim).detach()
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else:
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grad_bias = empty
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return grad_input, grad_bias
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@staticmethod
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def backward(ctx, gradgrad_input, gradgrad_bias):
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out, = ctx.saved_tensors
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gradgrad_out = fused.fused_bias_act(
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gradgrad_input.contiguous(),
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gradgrad_bias,
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out,
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3,
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1,
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ctx.negative_slope,
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ctx.scale,
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)
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return gradgrad_out, None, None, None, None
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class FusedLeakyReLUFunction(Function):
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@staticmethod
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def forward(ctx, input, bias, negative_slope, scale):
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empty = input.new_empty(0)
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ctx.bias = bias is not None
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if bias is None:
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bias = empty
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out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
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ctx.save_for_backward(out)
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ctx.negative_slope = negative_slope
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ctx.scale = scale
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return out
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@staticmethod
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def backward(ctx, grad_output):
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out, = ctx.saved_tensors
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grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
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grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale
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)
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if not ctx.bias:
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grad_bias = None
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return grad_input, grad_bias, None, None
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
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super().__init__()
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if bias:
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self.bias = nn.Parameter(torch.zeros(channel))
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else:
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self.bias = None
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
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if input.device.type == "cpu":
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if bias is not None:
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rest_dim = [1] * (input.ndim - bias.ndim - 1)
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return (
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F.leaky_relu(
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input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
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)
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* scale
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)
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else:
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return F.leaky_relu(input, negative_slope=0.2) * scale
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else:
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return FusedLeakyReLUFunction.apply(
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input.contiguous(), bias, negative_slope, scale
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)
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