File size: 6,379 Bytes
d73a79d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
import contextlib
import warnings

import torch
from torch import autograd
from torch.nn import functional as F

enabled = True
weight_gradients_disabled = False


@contextlib.contextmanager
def no_weight_gradients():
    global weight_gradients_disabled

    old = weight_gradients_disabled
    weight_gradients_disabled = True
    yield
    weight_gradients_disabled = old


def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
    if could_use_op(input):
        return conv2d_gradfix(
            transpose=False,
            weight_shape=weight.shape,
            stride=stride,
            padding=padding,
            output_padding=0,
            dilation=dilation,
            groups=groups,
        ).apply(input, weight, bias)

    return F.conv2d(
        input=input,
        weight=weight,
        bias=bias,
        stride=stride,
        padding=padding,
        dilation=dilation,
        groups=groups,
    )


def conv_transpose2d(
    input,
    weight,
    bias=None,
    stride=1,
    padding=0,
    output_padding=0,
    groups=1,
    dilation=1,
):
    if could_use_op(input):
        return conv2d_gradfix(
            transpose=True,
            weight_shape=weight.shape,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            groups=groups,
            dilation=dilation,
        ).apply(input, weight, bias)

    return F.conv_transpose2d(
        input=input,
        weight=weight,
        bias=bias,
        stride=stride,
        padding=padding,
        output_padding=output_padding,
        dilation=dilation,
        groups=groups,
    )


def could_use_op(input):
    if (not enabled) or (not torch.backends.cudnn.enabled):
        return False

    if input.device.type != "cuda":
        return False

    if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
        return True

    warnings.warn(
        f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
    )

    return False


def ensure_tuple(xs, ndim):
    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim

    return xs


conv2d_gradfix_cache = dict()


def conv2d_gradfix(
    transpose, weight_shape, stride, padding, output_padding, dilation, groups
):
    ndim = 2
    weight_shape = tuple(weight_shape)
    stride = ensure_tuple(stride, ndim)
    padding = ensure_tuple(padding, ndim)
    output_padding = ensure_tuple(output_padding, ndim)
    dilation = ensure_tuple(dilation, ndim)

    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
    if key in conv2d_gradfix_cache:
        return conv2d_gradfix_cache[key]

    common_kwargs = dict(
        stride=stride, padding=padding, dilation=dilation, groups=groups
    )

    def calc_output_padding(input_shape, output_shape):
        if transpose:
            return [0, 0]

        return [
            input_shape[i + 2]
            - (output_shape[i + 2] - 1) * stride[i]
            - (1 - 2 * padding[i])
            - dilation[i] * (weight_shape[i + 2] - 1)
            for i in range(ndim)
        ]

    class Conv2d(autograd.Function):
        @staticmethod
        def forward(ctx, input, weight, bias):
            if not transpose:
                out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)

            else:
                out = F.conv_transpose2d(
                    input=input,
                    weight=weight,
                    bias=bias,
                    output_padding=output_padding,
                    **common_kwargs,
                )

            ctx.save_for_backward(input, weight)

            return out

        @staticmethod
        def backward(ctx, grad_output):
            input, weight = ctx.saved_tensors
            grad_input, grad_weight, grad_bias = None, None, None

            if ctx.needs_input_grad[0]:
                p = calc_output_padding(
                    input_shape=input.shape, output_shape=grad_output.shape
                )
                grad_input = conv2d_gradfix(
                    transpose=(not transpose),
                    weight_shape=weight_shape,
                    output_padding=p,
                    **common_kwargs,
                ).apply(grad_output, weight, None)

            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
                grad_weight = Conv2dGradWeight.apply(grad_output, input)

            if ctx.needs_input_grad[2]:
                grad_bias = grad_output.sum((0, 2, 3))

            return grad_input, grad_weight, grad_bias

    class Conv2dGradWeight(autograd.Function):
        @staticmethod
        def forward(ctx, grad_output, input):
            op = torch._C._jit_get_operation(
                "aten::cudnn_convolution_backward_weight"
                if not transpose
                else "aten::cudnn_convolution_transpose_backward_weight"
            )
            flags = [
                torch.backends.cudnn.benchmark,
                torch.backends.cudnn.deterministic,
                torch.backends.cudnn.allow_tf32,
            ]
            grad_weight = op(
                weight_shape,
                grad_output,
                input,
                padding,
                stride,
                dilation,
                groups,
                *flags,
            )
            ctx.save_for_backward(grad_output, input)

            return grad_weight

        @staticmethod
        def backward(ctx, grad_grad_weight):
            grad_output, input = ctx.saved_tensors
            grad_grad_output, grad_grad_input = None, None

            if ctx.needs_input_grad[0]:
                grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)

            if ctx.needs_input_grad[1]:
                p = calc_output_padding(
                    input_shape=input.shape, output_shape=grad_output.shape
                )
                grad_grad_input = conv2d_gradfix(
                    transpose=(not transpose),
                    weight_shape=weight_shape,
                    output_padding=p,
                    **common_kwargs,
                ).apply(grad_output, grad_grad_weight, None)

            return grad_grad_output, grad_grad_input

    conv2d_gradfix_cache[key] = Conv2d

    return Conv2d