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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, 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, 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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grad_bias = grad_input.sum(dim).detach() |
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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, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale |
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) |
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return gradgrad_out, 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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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.negative_slope, ctx.scale |
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) |
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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, negative_slope=0.2, scale=2 ** 0.5): |
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super().__init__() |
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self.bias = nn.Parameter(torch.zeros(channel)) |
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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, negative_slope=0.2, scale=2 ** 0.5): |
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if input.device.type == "cpu": |
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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 FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) |
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