Add gelu fast activation

[xiezipeng-ML]

add gelu_fast activation.

[xiezipeng-ML]

[daquexian]

这个和 torch.nn.GELU(approximate='tanh') 是重复的吗 https://pytorch.org/docs/stable/generated/torch.nn.GELU.html

相关讨论：https://github.com/huggingface/transformers/issues/15397

[xiezipeng-ML]

这个和 torch.nn.GELU(approximate='tanh') 是重复的吗 https://pytorch.org/docs/stable/generated/torch.nn.GELU.html

相关讨论：huggingface/transformers#15397

大老师，这个和 torch.nn.GELU(approximate='tanh')不是重复的，但是都是gelu的近似，gelu的变种有点多：

class NewGELUActivation(nn.Module):
    """
    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
    the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
    """

    def forward(self, input: Tensor) -> Tensor:
        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))


class GELUActivation(nn.Module):
    """
    Original Implementation of the GELU activation function in Google BERT repo when initially created. For
    information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
    torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
    Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
    """

    def __init__(self, use_gelu_python: bool = False):
        super().__init__()
        if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.4") or use_gelu_python:
            self.act = self._gelu_python
        else:
            self.act = nn.functional.gelu

    def _gelu_python(self, input: Tensor) -> Tensor:
        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))

    def forward(self, input: Tensor) -> Tensor:
        return self.act(input)


class FastGELUActivation(nn.Module):
    """
    Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs
    """

    def forward(self, input: Tensor) -> Tensor:
        return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)))


class QuickGELUActivation(nn.Module):
    """
    Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
    """

    def forward(self, input: Tensor) -> Tensor:
        return input * torch.sigmoid(1.702 * input)

class ClippedGELUActivation(nn.Module):
    """
    Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
    it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to
    https://arxiv.org/abs/2004.09602.

    Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
    initially created.

    For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 +
    torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415
    """

    def __init__(self, min: float, max: float):
        if min > max:
            raise ValueError(f"min should be < max (got min: {min}, max: {max})")

        super().__init__()
        self.min = min
        self.max = max

    def forward(self, x: Tensor) -> Tensor:
        return torch.clip(gelu(x), self.min, self.max)

[daquexian]

从文档来看确实是和 torch.nn.GELU(approximate='tanh') 公式一样的。

oneflow 文档（渲染好像不太正确，以及混用了 input 和 x）：

pytorch 文档（pytorch 文档也没正确渲染 lol）：

oneflow 文档里 input 和 x 应该是同一个东西？把 input 换成 x 并放进括号里就和 pytorch 公式一样了。这么来看上面贴的 NewGELUActivation 和 FastGELUActivation 也是一样的。

[xiezipeng-ML]

根据大老师的建议，这个算子改成torch.nn.functional.gelu(x, approximate='fast')的形式