48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
from typing import List, 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__ = [
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"gelu",
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]
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class GeluFunction(Function):
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@staticmethod
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def forward(
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ctx, input: torch.Tensor, in_place: bool = False, training: bool = False
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):
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if training:
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if in_place:
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ctx.save_for_backward(input.clone())
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else:
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ctx.save_for_backward(input)
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if in_place:
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return ops.train.gelu_forward(input, input)
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else:
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return ops.train.gelu_forward(input)
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@staticmethod
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def backward(ctx: FunctionCtx, grad_outputs):
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input = ctx.saved_tensors[0]
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grad_input = ops.train.gelu_backward(input, grad_outputs)
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return grad_input, None, None
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def gelu(
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input: torch.Tensor, in_place: bool = False, training: bool = False
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) -> torch.Tensor:
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"""
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等价实现：
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torch.nn.functional.gelu
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Args:
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input: dtype:[torch.float, torch.half, torch.bfloat16]
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in place: bool. Whether to operate directly on the original input data.
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Returns:
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output: dtype:[torch.float, torch.half, torch.bfloat16]
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"""
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return GeluFunction.apply(input, in_place, training)
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