mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:34:18 +08:00
a9dca53438
RFC: https://github.com/pytorch/rfcs/pull/54 First commit is the contents of https://github.com/Quansight-Labs/numpy_pytorch_interop/ We have already been using this in core for the last few months as a external dependency. This PR pulls all these into core. In the next commits, I do a number of things in this order - Fix a few small issues - Make the tests that this PR adds pass - Bend backwards until lintrunner passes - Remove the optional dependency on `torch_np` and simply rely on the upstreamed code - Fix a number dynamo tests that were passing before (they were not tasting anything I think) and are not passing now. Missing from this PR (but not blocking): - Have a flag that deactivates tracing NumPy functions and simply breaks. There used to be one but after the merge stopped working and I removed it. @lezcano to investigate. - https://github.com/pytorch/pytorch/pull/106431#issuecomment-1667079543. @voznesenskym to submit a fix after we merge. All the tests in `tests/torch_np` take about 75s to run. This was a work by @ev-br, @rgommers @honno and I. I did not create this PR via ghstack (which would have been convenient) as this is a collaboration, and ghstack doesn't allow for shared contributions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/106211 Approved by: https://github.com/ezyang
74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
import inspect
|
|
import itertools
|
|
|
|
from . import _funcs_impl, _reductions_impl
|
|
from ._normalizations import normalizer
|
|
|
|
# _funcs_impl.py contains functions which mimic NumPy's eponymous equivalents,
|
|
# and consume/return PyTorch tensors/dtypes.
|
|
# They are also type annotated.
|
|
# Pull these functions from _funcs_impl and decorate them with @normalizer, which
|
|
# - Converts any input `np.ndarray`, `torch._numpy.ndarray`, list of lists, Python scalars, etc into a `torch.Tensor`.
|
|
# - Maps NumPy dtypes to PyTorch dtypes
|
|
# - If the input to the `axis` kwarg is an ndarray, it maps it into a tuple
|
|
# - Implements the semantics for the `out=` arg
|
|
# - Wraps back the outputs into `torch._numpy.ndarrays`
|
|
|
|
|
|
def _public_functions(mod):
|
|
def is_public_function(f):
|
|
return inspect.isfunction(f) and not f.__name__.startswith("_")
|
|
|
|
return inspect.getmembers(mod, is_public_function)
|
|
|
|
|
|
# We fill in __all__ in the loop below
|
|
__all__ = []
|
|
|
|
# decorate implementer functions with argument normalizers and export to the top namespace
|
|
for name, func in itertools.chain(
|
|
_public_functions(_funcs_impl), _public_functions(_reductions_impl)
|
|
):
|
|
if name in ["percentile", "quantile", "median"]:
|
|
decorated = normalizer(func, promote_scalar_result=True)
|
|
elif name == "einsum":
|
|
# normalized manually
|
|
decorated = func
|
|
else:
|
|
decorated = normalizer(func)
|
|
|
|
decorated.__qualname__ = name
|
|
decorated.__name__ = name
|
|
vars()[name] = decorated
|
|
__all__.append(name)
|
|
|
|
|
|
"""
|
|
Vendored objects from numpy.lib.index_tricks
|
|
"""
|
|
|
|
|
|
class IndexExpression:
|
|
"""
|
|
Written by Konrad Hinsen <hinsen@cnrs-orleans.fr>
|
|
last revision: 1999-7-23
|
|
|
|
Cosmetic changes by T. Oliphant 2001
|
|
"""
|
|
|
|
def __init__(self, maketuple):
|
|
self.maketuple = maketuple
|
|
|
|
def __getitem__(self, item):
|
|
if self.maketuple and not isinstance(item, tuple):
|
|
return (item,)
|
|
else:
|
|
return item
|
|
|
|
|
|
index_exp = IndexExpression(maketuple=True)
|
|
s_ = IndexExpression(maketuple=False)
|
|
|
|
|
|
__all__ += ["index_exp", "s_"]
|