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- # Copyright (c) Meta Platforms, Inc. and affiliates
- """
- These are functions that should simply be applied to both mask and data.
- Take select or stack as an example. This operation can be applied to
- both the mask and data of a MaskedTensor and the result wrapped into
- a new MaskedTensor as a result.
- """
- import torch
- from .core import _map_mt_args_kwargs, _wrap_result
- __all__ = [] # type: ignore[var-annotated]
- PASSTHROUGH_FNS = [
- torch.ops.aten.select,
- torch.ops.aten.transpose,
- torch.ops.aten.split,
- torch.ops.aten.t,
- torch.ops.aten.slice,
- torch.ops.aten.slice_backward,
- torch.ops.aten.select_backward,
- torch.ops.aten.index,
- torch.ops.aten.expand,
- torch.ops.aten.view,
- torch.ops.aten._unsafe_view,
- torch.ops.aten._reshape_alias,
- torch.ops.aten.cat,
- torch.ops.aten.unsqueeze,
- ]
- def _is_pass_through_fn(fn):
- return fn in PASSTHROUGH_FNS
- def _apply_pass_through_fn(fn, *args, **kwargs):
- data_args, data_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_data())
- result_data = fn(*data_args, **data_kwargs)
- mask_args, mask_kwargs = _map_mt_args_kwargs(args, kwargs, lambda x: x.get_mask())
- result_mask = fn(*mask_args, **mask_kwargs)
- return _wrap_result(result_data, result_mask)
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