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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 |