mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
9bce208dfb
This is a lot of files changed! Don't panic! Here's how it works: * Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file. * When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded. * The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors. * Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list. * Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves. * torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state. * There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many. In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file. The codemod was done with this script authored by GPT-4: ``` import glob exclude_patterns = [ ... ] for pattern in exclude_patterns: for filepath in glob.glob(pattern, recursive=True): if filepath.endswith('.py'): with open(filepath, 'r+') as f: content = f.read() f.seek(0, 0) f.write('# mypy: ignore-errors\n\n' + content) ``` Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414 Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
131 lines
2.7 KiB
Python
131 lines
2.7 KiB
Python
# mypy: ignore-errors
|
|
|
|
from __future__ import annotations
|
|
|
|
import functools
|
|
|
|
import torch
|
|
|
|
from . import _dtypes_impl, _util
|
|
from ._normalizations import ArrayLike, normalizer
|
|
|
|
|
|
def upcast(func):
|
|
"""NumPy fft casts inputs to 64 bit and *returns 64-bit results*."""
|
|
|
|
@functools.wraps(func)
|
|
def wrapped(tensor, *args, **kwds):
|
|
target_dtype = (
|
|
_dtypes_impl.default_dtypes().complex_dtype
|
|
if tensor.is_complex()
|
|
else _dtypes_impl.default_dtypes().float_dtype
|
|
)
|
|
tensor = _util.cast_if_needed(tensor, target_dtype)
|
|
return func(tensor, *args, **kwds)
|
|
|
|
return wrapped
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def fft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.fft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def ifft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.ifft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def rfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.rfft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def irfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.irfft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def fftn(a: ArrayLike, s=None, axes=None, norm=None):
|
|
return torch.fft.fftn(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def ifftn(a: ArrayLike, s=None, axes=None, norm=None):
|
|
return torch.fft.ifftn(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def rfftn(a: ArrayLike, s=None, axes=None, norm=None):
|
|
return torch.fft.rfftn(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def irfftn(a: ArrayLike, s=None, axes=None, norm=None):
|
|
return torch.fft.irfftn(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def fft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
|
return torch.fft.fft2(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def ifft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
|
return torch.fft.ifft2(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def rfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
|
return torch.fft.rfft2(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def irfft2(a: ArrayLike, s=None, axes=(-2, -1), norm=None):
|
|
return torch.fft.irfft2(a, s, dim=axes, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def hfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.hfft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
@upcast
|
|
def ihfft(a: ArrayLike, n=None, axis=-1, norm=None):
|
|
return torch.fft.ihfft(a, n, dim=axis, norm=norm)
|
|
|
|
|
|
@normalizer
|
|
def fftfreq(n, d=1.0):
|
|
return torch.fft.fftfreq(n, d)
|
|
|
|
|
|
@normalizer
|
|
def rfftfreq(n, d=1.0):
|
|
return torch.fft.rfftfreq(n, d)
|
|
|
|
|
|
@normalizer
|
|
def fftshift(x: ArrayLike, axes=None):
|
|
return torch.fft.fftshift(x, axes)
|
|
|
|
|
|
@normalizer
|
|
def ifftshift(x: ArrayLike, axes=None):
|
|
return torch.fft.ifftshift(x, axes)
|