File size: 3,301 Bytes
983684c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
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)