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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
86 lines
1.8 KiB
Python
86 lines
1.8 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,
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arctan2,
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bitwise_and,
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bitwise_left_shift as left_shift,
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bitwise_or,
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bitwise_right_shift as right_shift,
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bitwise_xor,
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copysign,
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divide,
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eq as equal,
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float_power,
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floor_divide,
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fmax,
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fmin,
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fmod,
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gcd,
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greater,
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greater_equal,
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heaviside,
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hypot,
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lcm,
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ldexp,
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less,
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less_equal,
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logaddexp,
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logaddexp2,
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logical_and,
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logical_or,
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logical_xor,
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maximum,
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minimum,
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multiply,
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nextafter,
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not_equal,
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pow as power,
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remainder,
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remainder as mod,
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subtract,
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true_divide,
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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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