Skip to content

Added OpInfo gradcheck_wrapper to replace output_func #54914

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 2 commits into from
Closed

Added OpInfo gradcheck_wrapper to replace output_func #54914

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
17b059d
Added OpInfo field gradcheck_wrapper to replace output_func
heitorschueroff Mar 29, 2021
e8d36c8
Passing sampe.output_process_fn_grad to jit test
heitorschueroff Mar 30, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 12 additions & 12 deletions test/test_ops.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -104,21 +104,15 @@ def _check_helper(self, device, dtype, op, variant, check):

samples = op.sample_inputs(device, dtype, requires_grad=True)
for sample in samples:
if sample.output_process_fn_grad is not None:
out_fn = sample.output_process_fn_grad

def variant_out_fn(*args, **kwargs):
return out_fn(variant(*args, **kwargs))
else:
variant_out_fn = variant

def fn(*inputs):
# Pack input back into TensorList since we splat it when passing to gradcheck
if is_iterable_of_tensors(sample.input):
n = len(sample.input)
inputs = (inputs[:n], *inputs[n:])
output = variant_out_fn(*inputs, **sample.kwargs)
return op.output_func(output)
output = op.gradcheck_wrapper(variant, *inputs, **sample.kwargs)
if sample.output_process_fn_grad is not None:
return sample.output_process_fn_grad(output)
return output

# Gradcheck does not support TensorList so we splat it with the remaining args
gradcheck_args = (sample.input,) if isinstance(sample.input, torch.Tensor) else tuple(sample.input)
Expand Down Expand Up @@ -316,10 +310,16 @@ def test_variant_consistency_jit(self, device, dtype, op):
# Check scripted forward, grad, and grad grad
script_fn = create_script_fn(self, name, func_type)

def out_fn(output):
# Processes the output for autograd
if sample.output_process_fn_grad is not None:
return sample.output_process_fn_grad(output)
return output

check_against_reference(self,
script_fn,
func,
op.output_func,
out_fn,
(sample.input,) + sample.args,
sample.kwargs,
no_grad=not _requires_grad)
Expand All @@ -329,7 +329,7 @@ def test_variant_consistency_jit(self, device, dtype, op):
check_against_reference(self,
traced_fn,
func,
op.output_func,
out_fn,
(sample.input,) + sample.args,
sample.kwargs,
no_grad=not _requires_grad)
Expand Down
Loading