44 lines
1.2 KiB
Python
44 lines
1.2 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__ = ["geglu"]
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class GegluFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input):
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output_shape = list(input.shape)
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output_shape[-1] = output_shape[-1] // 2
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output = input.new_empty(output_shape)
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ops.train.geglu_training_forward(input, output)
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ctx.save_for_backward(input)
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return output
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@staticmethod
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def backward(ctx: FunctionCtx, grad_output):
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input = ctx.saved_tensors[0]
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grad_input = torch.empty_like(input)
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ops.train.geglu_training_backward(input, grad_output, grad_input)
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return grad_input
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def geglu(input: "torch.Tensor"):
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"""
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等价实现：
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def ref_gelu_and_mul(x: torch.Tensor) -> torch.Tensor:
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dtype = x.dtype
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x = x.float()
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x1, x2 = x.chunk(chunks=2, dim=-1)
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res = NNF.gelu(x2) * x1
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return res.to(dtype)
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Args:
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input: dtype:[torch.float, torch.half, torch.bfloat16]
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Returns:
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output: (....,input.shape[-1] //2), dtype:[torch.float, torch.half, torch.bfloat16]
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"""
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return GegluFunction.apply(input)
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