LDLM · GELU · OpenModelAtlas

Latent decoder › 6 decoder blocks › MLP

GELU

no parameters

Activate each feature with the exact GELU.

{\htmlData{light=activated gelu}{g}}={\htmlData{light=up}{h}}\,\Phi({\htmlData{light=up}{h}})

in: $h$ [B batch, T positions, 3072 d_ff]
out: $g$ [B batch, T positions, 3072 d_ff]

The exact form, not the tanh approximation the denoiser’s MLP uses.

Explanation

$\Phi$ is the cumulative distribution function of the standard Gaussian.

Codeactivation.py 669–709

activation.py 669–709

class GELU(Module):
    r"""Applies the Gaussian Error Linear Units function.

    .. math:: \text{GELU}(x) = x * \Phi(x)

    where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution.

    When the approximate argument is 'tanh', Gelu is estimated with:

    .. math:: \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3)))

    Args:
        approximate (str, optional): the gelu approximation algorithm to use:
            ``'none'`` | ``'tanh'``. Default: ``'none'``

    Shape:
        - Input: :math:`(*)`, where :math:`*` means any number of dimensions.
        - Output: :math:`(*)`, same shape as the input.

    .. image:: ../scripts/activation_images/GELU.png

    Examples::

        >>> m = nn.GELU()
        >>> input = torch.randn(2)
        >>> output = m(input)
    """

    __constants__ = ['approximate']
    approximate: str

    def __init__(self, approximate: str = 'none') -> None:
        super().__init__()
        self.approximate = approximate

    def forward(self, input: Tensor) -> Tensor:
        return F.gelu(input, approximate=self.approximate)

    def extra_repr(self) -> str:
        return f'approximate={repr(self.approximate)}'

Sources2

Direct evidence

The Perceiver blocks’ MLP: LayerNorm, an affine layer, exact GELU, dropout, an affine layer.

The implementation, pinned at revision e18c42acdfb7, not yet public

Direct evidence

nn.GELU defaults to approximate=none and evaluates x times the standard Gaussian CDF.

torch-exact-gelu · pytorch/pytorch / torch/nn/modules/activation.py
Pinned revision ee1b6804381c ↗ · lines 669–709

Read code excerpt

class GELU(Module):
    r"""Applies the Gaussian Error Linear Units function.

    .. math:: \text{GELU}(x) = x * \Phi(x)

    where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution.

    When the approximate argument is 'tanh', Gelu is estimated with:

    .. math:: \text{GELU}(x) = 0.5 * x * (1 + \text{Tanh}(\sqrt{2 / \pi} * (x + 0.044715 * x^3)))

    Args:
        approximate (str, optional): the gelu approximation algorithm to use:
            ``'none'`` | ``'tanh'``. Default: ``'none'``

    Shape:
        - Input: :math:`(*)`, where :math:`*` means any number of dimensions.
        - Output: :math:`(*)`, same shape as the input.

    .. image:: ../scripts/activation_images/GELU.png

    Examples::

        >>> m = nn.GELU()
        >>> input = torch.randn(2)
        >>> output = m(input)
    """

    __constants__ = ['approximate']
    approximate: str

    def __init__(self, approximate: str = 'none') -> None:
        super().__init__()
        self.approximate = approximate

    def forward(self, input: Tensor) -> Tensor:
        return F.gelu(input, approximate=self.approximate)

    def extra_repr(self) -> str:
        return f'approximate={repr(self.approximate)}'

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