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pytorch 1.11 support: don't use conv2d_gradfix on v1.11, port grid_sample_gradfix to the new API 

thanks @timothybrooks for the fix!

for #145
This commit is contained in:
Janne Hellsten 2022-04-22 17:35:34 +03:00
parent 69c7ef0fbd
commit 407db86e6f
2 changed files with 12 additions and 1 deletions
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5
torch_utils/ops/conv2d_gradfix.py
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@ -11,6 +11,7 @@ arbitrarily high order gradients with zero performance penalty."""
import contextlib
import torch
from pkg_resources import parse_version
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
@ -20,6 +21,7 @@ import torch
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.
_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
@contextlib.contextmanager
def no_weight_gradients(disable=True):
@ -48,6 +50,9 @@ def _should_use_custom_op(input):
assert isinstance(input, torch.Tensor)
if (not enabled) or (not torch.backends.cudnn.enabled):
return False
if _use_pytorch_1_11_api:
# The work-around code doesn't work on PyTorch 1.11.0 onwards
return False
if input.device.type != 'cuda':
return False
return True

8
torch_utils/ops/grid_sample_gradfix.py
View File

@ -12,6 +12,7 @@ Only works on 2D images and assumes
`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
import torch
from pkg_resources import parse_version
# pylint: disable=redefined-builtin
# pylint: disable=arguments-differ
@ -20,6 +21,7 @@ import torch
#----------------------------------------------------------------------------
enabled = False # Enable the custom op by setting this to true.
_use_pytorch_1_11_api = parse_version(torch.__version__) >= parse_version('1.11.0a') # Allow prerelease builds of 1.11
#----------------------------------------------------------------------------
@ -56,7 +58,11 @@ class _GridSample2dBackward(torch.autograd.Function):
@staticmethod
def forward(ctx, grad_output, input, grid):
op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
if _use_pytorch_1_11_api:
output_mask = (ctx.needs_input_grad[1], ctx.needs_input_grad[2])
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False, output_mask)
else:
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
ctx.save_for_backward(grid)
return grad_input, grad_grid