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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129765 Approved by: https://github.com/ezyang
77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
# mypy: ignore-errors
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import inspect
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import itertools
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from . import _funcs_impl, _reductions_impl
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from ._normalizations import normalizer
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# _funcs_impl.py contains functions which mimic NumPy's eponymous equivalents,
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# and consume/return PyTorch tensors/dtypes.
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# They are also type annotated.
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# Pull these functions from _funcs_impl and decorate them with @normalizer, which
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# - Converts any input `np.ndarray`, `torch._numpy.ndarray`, list of lists, Python scalars, etc into a `torch.Tensor`.
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# - Maps NumPy dtypes to PyTorch dtypes
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# - If the input to the `axis` kwarg is an ndarray, it maps it into a tuple
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# - Implements the semantics for the `out=` arg
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# - Wraps back the outputs into `torch._numpy.ndarrays`
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def _public_functions(mod):
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def is_public_function(f):
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return inspect.isfunction(f) and not f.__name__.startswith("_")
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return inspect.getmembers(mod, is_public_function)
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# We fill in __all__ in the loop below
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__all__ = []
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# decorate implementer functions with argument normalizers and export to the top namespace
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for name, func in itertools.chain(
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_public_functions(_funcs_impl), _public_functions(_reductions_impl)
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):
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if name in ["percentile", "quantile", "median"]:
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decorated = normalizer(func, promote_scalar_result=True)
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elif name == "einsum":
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# normalized manually
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decorated = func
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else:
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decorated = normalizer(func)
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decorated.__qualname__ = name
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decorated.__name__ = name
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vars()[name] = decorated
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__all__.append(name)
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"""
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Vendored objects from numpy.lib.index_tricks
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"""
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class IndexExpression:
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"""
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Written by Konrad Hinsen <hinsen@cnrs-orleans.fr>
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last revision: 1999-7-23
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Cosmetic changes by T. Oliphant 2001
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"""
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def __init__(self, maketuple):
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self.maketuple = maketuple
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def __getitem__(self, item):
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if self.maketuple and not isinstance(item, tuple):
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return (item,)
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else:
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return item
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index_exp = IndexExpression(maketuple=True)
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s_ = IndexExpression(maketuple=False)
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__all__ += ["index_exp", "s_"]
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