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