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Add Neuron dtype metadata for unfold and AA upsample #97

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Add Neuron dtype metadata for unfold and AA upsample #97

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2 changes: 2 additions & 0 deletions torch/testing/_internal/common_methods_invocations.py
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Expand Up @@ -16320,6 +16320,7 @@ def sample_inputs_abs(op_info, device, dtype, requires_grad, op_kwargs=None, **k
OpInfo('nn.functional.unfold',
aten_name='im2col',
dtypes=floating_and_complex_types_and(torch.half, torch.bfloat16, torch.bool),
dtypesIf={'neuron': all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16)},

@AbigailFernandes AbigailFernandes Aug 13, 2026

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You cannot add anything neuron specific here - all of this code needs to be upstreamed to PyTorch

dtypesIfCUDA=floating_and_complex_types_and(torch.half, torch.bfloat16, torch.bool),
sample_inputs_func=sample_inputs_nn_unfold,
# Runs very slowly on slow gradcheck - alternatively reduce input sizes
Expand Down Expand Up @@ -16492,6 +16493,7 @@ def sample_inputs_abs(op_info, device, dtype, requires_grad, op_kwargs=None, **k
supports_forward_ad=True,
supports_fwgrad_bwgrad=True,
dtypes=floating_types_and(torch.uint8),
dtypesIf={'neuron': floating_types_and(torch.uint8, torch.half, torch.bfloat16)},

@AbigailFernandes AbigailFernandes Aug 13, 2026

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Same as above

@AbigailFernandes AbigailFernandes Aug 13, 2026

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Please write this in a way that can be upstreamed and also raise a corresponding PR to PyTorch

dtypesIfCUDA=floating_types_and(torch.half, torch.bfloat16),
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
sample_inputs_func=partial(sample_inputs_upsample_aa, 'bilinear'),
Expand Down