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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
87 lines
2.4 KiB
Python
87 lines
2.4 KiB
Python
# mypy: ignore-errors
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"""Export torch work functions for binary ufuncs, rename/tweak to match numpy.
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This listing is further exported to public symbols in the `torch._numpy/_ufuncs.py` module.
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"""
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import torch
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from torch import ( # noqa: F401
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add, # noqa: F401
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arctan2, # noqa: F401
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bitwise_and, # noqa: F401
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bitwise_left_shift as left_shift, # noqa: F401
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bitwise_or, # noqa: F401
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bitwise_right_shift as right_shift, # noqa: F401
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bitwise_xor, # noqa: F401
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copysign, # noqa: F401
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divide, # noqa: F401
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eq as equal, # noqa: F401
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float_power, # noqa: F401
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floor_divide, # noqa: F401
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fmax, # noqa: F401
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fmin, # noqa: F401
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fmod, # noqa: F401
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gcd, # noqa: F401
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greater, # noqa: F401
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greater_equal, # noqa: F401
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heaviside, # noqa: F401
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hypot, # noqa: F401
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lcm, # noqa: F401
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ldexp, # noqa: F401
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less, # noqa: F401
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less_equal, # noqa: F401
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logaddexp, # noqa: F401
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logaddexp2, # noqa: F401
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logical_and, # noqa: F401
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logical_or, # noqa: F401
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logical_xor, # noqa: F401
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maximum, # noqa: F401
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minimum, # noqa: F401
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multiply, # noqa: F401
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nextafter, # noqa: F401
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not_equal, # noqa: F401
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pow as power, # noqa: F401
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remainder, # noqa: F401
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remainder as mod, # noqa: F401
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subtract, # noqa: F401
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true_divide, # noqa: F401
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)
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from . import _dtypes_impl, _util
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# work around torch limitations w.r.t. numpy
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def matmul(x, y):
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# work around:
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# - RuntimeError: expected scalar type Int but found Double
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# - RuntimeError: "addmm_impl_cpu_" not implemented for 'Bool'
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# - RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
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dtype = _dtypes_impl.result_type_impl(x, y)
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is_bool = dtype == torch.bool
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is_half = (x.dtype == torch.float16 or y.dtype == torch.float16) and (
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x.is_cpu or y.is_cpu
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)
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work_dtype = dtype
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if is_bool:
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work_dtype = torch.uint8
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if is_half:
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work_dtype = torch.float32
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x = _util.cast_if_needed(x, work_dtype)
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y = _util.cast_if_needed(y, work_dtype)
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result = torch.matmul(x, y)
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if work_dtype != dtype:
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result = result.to(dtype)
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return result
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# a stub implementation of divmod, should be improved after
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# https://github.com/pytorch/pytorch/issues/90820 is fixed in pytorch
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def divmod(x, y):
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return x // y, x % y
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