34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
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from typing import Union
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import ixformer._C as ops
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import torch
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from torch.autograd.function import Function, FunctionCtx
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__all__ = ["softmax", "ref_softmax"]
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def ref_softmax(input: torch.Tensor, dim: int = None, _stacklevel: int = 3, dtype=None):
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out = torch.nn.functional.softmax(
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input, dim=dim, _stacklevel=_stacklevel, dtype=dtype
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)
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return out
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def softmax(input: torch.Tensor, dim=None, _stacklevel=3, dtype=None):
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"""
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Args:
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input: (...) torch.float16
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dim: int
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要进行softmax的维度,目前只支持最后一维， dim==-1 or dim == input.dim()-1
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_stacklevel: int
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这个参数只是为了与pytorch中对齐。 stacklevel is used in python to indicate warning mechanism how far up the stack it has to go to find the line that called the function which issued the warning.
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dtype: torch.float16
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Returns:
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Tensor: (...) torch.float16
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"""
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output = torch.empty_like(input)
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ops.infer.softmax(input, output, dim)
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output = output.to(dtype)
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return output
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