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"""
Custom replacement for `torch.nn.functional.convNd` and `torch.nn.functional.conv_transposeNd`
that supports arbitrarily high order gradients with zero performance penalty.
Modified from https://github.com/NVlabs/stylegan3/blob/main/torch_utils/ops/conv2d_gradfix.py
"""
import contextlib
import warnings
from typing import Optional
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.nn import Conv2d, Conv3d
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
# pylint: disable=protected-access
# ----------------------------------------------------------------------------
enabled = False # Enable the custom op by setting this to true.
weight_gradients_disabled = (
False # Forcefully disable computation of gradients with respect to the weights.
)
@contextlib.contextmanager
def no_weight_gradients():
global weight_gradients_disabled
old = weight_gradients_disabled
weight_gradients_disabled = True
yield
weight_gradients_disabled = old
# ----------------------------------------------------------------------------
class GradFixConv2d(Conv2d):
def __init__(self, *args, use_gradfix: bool = False, **kwargs):
self.use_gradfix = use_gradfix
super().__init__(*args, **kwargs)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
conv_fn = F.conv2d if not self.use_gradfix else convNd
if self.padding_mode != "zeros":
return conv_fn(
F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight,
bias,
self.stride,
(0, 0),
self.dilation,
self.groups,
)
return conv_fn(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
def forward(
self, input: Tensor, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None
) -> Tensor:
weight = self.weight if weight is None else weight
bias = self.bias if bias is None else bias
return self._conv_forward(input, weight, bias)
class GradFixConv3d(Conv3d):
def __init__(self, *args, use_gradfix: bool = False, **kwargs):
self.use_gradfix = use_gradfix
super().__init__(*args, **kwargs)
def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
conv_fn = F.conv3d if not self.use_gradfix else convNd
if self.padding_mode != "zeros":
return conv_fn(
F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
weight,
bias,
self.stride,
(0, 0, 0),
self.dilation,
self.groups,
)
return conv_fn(input, weight, bias, self.stride, self.padding, self.dilation, self.groups)
def forward(
self, input: Tensor, weight: Optional[Tensor] = None, bias: Optional[Tensor] = None
) -> Tensor:
weight = self.weight if weight is None else weight
bias = self.bias if bias is None else bias
return self._conv_forward(input, weight, bias)
# ----------------------------------------------------------------------------
def convNd(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
N = weight.ndim - 2
if _should_use_custom_op(input):
return _conv_gradfix(
transpose=False,
weight_shape=weight.shape,
stride=stride,
padding=padding,
output_padding=0,
dilation=dilation,
groups=groups,
).apply(input, weight, bias)
return getattr(torch.nn.functional, f"conv{N}d")(
input=input,
weight=weight,
bias=bias,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
)
def conv_transposeNd(
input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1
):
N = weight.ndim - 2
if _should_use_custom_op(input):
return _conv_gradfix(
transpose=True,
weight_shape=weight.shape,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
).apply(input, weight, bias)
return getattr(torch.nn.functional, f"conv_transpose{N}d")(
input=input,
weight=weight,
bias=bias,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
)
# ----------------------------------------------------------------------------
def _should_use_custom_op(input):
assert isinstance(input, torch.Tensor)
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.", "1.9"]):
return True
if torch.__version__.startswith("2"):
return True
warnings.warn(
f"conv2d_gradfix not supported on PyTorch {torch.__version__}. "
f"Falling back to torch.nn.functional.conv2d()."
)
return False
def _tuple_of_ints(xs, ndim):
xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
assert len(xs) == ndim
assert all(isinstance(x, int) for x in xs)
return xs
# ----------------------------------------------------------------------------
_conv_gradfix_cache = dict()
def _conv_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
ndim = len(weight_shape) - 2
# Parse arguments.
weight_shape = tuple(weight_shape)
stride = _tuple_of_ints(stride, ndim)
padding = _tuple_of_ints(padding, ndim)
output_padding = _tuple_of_ints(output_padding, ndim)
dilation = _tuple_of_ints(dilation, ndim)
# Lookup from cache.
key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
if key in _conv_gradfix_cache:
return _conv_gradfix_cache[key]
# Validate arguments.
assert groups >= 1
assert all(stride[i] >= 1 for i in range(ndim))
assert all(padding[i] >= 0 for i in range(ndim))
assert all(dilation[i] >= 0 for i in range(ndim))
if not transpose:
assert all(output_padding[i] == 0 for i in range(ndim))
else: # transpose
assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
# Helpers.
common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
def calc_output_padding(input_shape, output_shape):
if transpose:
return [
0,
] * ndim
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)
]
# Forward & backward.
class ConvNd(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight, bias):
"""
input size: [B, C, ...]
weight size:
-> Conv: [C_out, C_in // groups, ...]
-> Transpose: [C_in, C_out // groups, ...]
"""
assert weight.shape == weight_shape
ctx.save_for_backward(input, weight)
# General case => cuDNN.
if transpose:
return getattr(torch.nn.functional, f"conv_transpose{ndim}d")(
input=input,
weight=weight.to(input.dtype),
bias=bias,
output_padding=output_padding,
**common_kwargs,
)
return getattr(torch.nn.functional, f"conv{ndim}d")(
input=input, weight=weight.to(input.dtype), bias=bias, **common_kwargs
)
@staticmethod
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
grad_input = None
grad_weight = None
grad_bias = None
if ctx.needs_input_grad[0]: # Input
p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
op = _conv_gradfix(
transpose=(not transpose),
weight_shape=weight_shape,
output_padding=p,
**common_kwargs,
)
grad_input = op.apply(grad_output, weight, None)
assert grad_input.shape == input.shape
if ctx.needs_input_grad[1] and not weight_gradients_disabled: # Weight
grad_weight = ConvNdGradWeight.apply(grad_output, input)
assert grad_weight.shape == weight_shape
if ctx.needs_input_grad[2]: # Bias
grad_bias = grad_output.transpose(0, 1).flatten(1).sum(1)
return grad_input, grad_weight, grad_bias
# Gradient with respect to the weights.
class ConvNdGradWeight(torch.autograd.Function):
@staticmethod
def forward(ctx, grad_output, input):
flags = [
torch.backends.cudnn.benchmark,
torch.backends.cudnn.deterministic,
torch.backends.cudnn.allow_tf32,
]
if torch.__version__.startswith("1"):
op = torch._C._jit_get_operation(
"aten::cudnn_convolution_backward_weight"
if not transpose
else "aten::cudnn_convolution_transpose_backward_weight"
)
grad_weight = op(
weight_shape,
grad_output,
input.to(grad_output.dtype),
padding,
stride,
dilation,
groups,
*flags,
)
elif torch.__version__.startswith("2"):
# https://github.com/pytorch/pytorch/issues/74437
op, _ = torch._C._jit_get_operation("aten::convolution_backward")
dummy_weight = torch.tensor(
0.0, dtype=grad_output.dtype, device=input.device
).expand(weight_shape)
grad_weight = op(
grad_output,
input.to(grad_output.dtype),
dummy_weight,
None,
stride,
padding,
dilation,
transpose,
(0,) * ndim,
groups,
[False, True, False],
)[1]
else:
raise NotImplementedError
assert grad_weight.shape == weight_shape
ctx.save_for_backward(grad_output, input)
return grad_weight
@staticmethod
def backward(ctx, grad2_grad_weight):
grad_output, input = ctx.saved_tensors
grad2_grad_output = None
grad2_input = None
if ctx.needs_input_grad[0]: # Grad of Weight
grad2_grad_output = ConvNd.apply(input, grad2_grad_weight, None)
assert grad2_grad_output.shape == grad_output.shape
if ctx.needs_input_grad[1]: # Input
p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
op = _conv_gradfix(
transpose=(not transpose),
weight_shape=weight_shape,
output_padding=p,
**common_kwargs,
)
grad2_input = op.apply(grad_output, grad2_grad_weight, None)
assert grad2_input.shape == input.shape
return grad2_grad_output, grad2_input
_conv_gradfix_cache[key] = ConvNd
return ConvNd
# ----------------------------------------------------------------------------
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