Spaces:
Build error
Build error
File size: 6,379 Bytes
7143243 |
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
|