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Source code for torch.nn.modules.activation

import warnings
from typing import Tuple, Optional

import torch
from torch import Tensor
from .linear import _LinearWithBias
from torch.nn.init import xavier_uniform_
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F


class Threshold(Module):
    r"""Thresholds each element of the input Tensor.

    Threshold is defined as:

    .. math::
        y =
        \begin{cases}
        x, &\text{ if } x > \text{threshold} \\
        \text{value}, &\text{ otherwise }
        \end{cases}

    Args:
        threshold: The value to threshold at
        value: The value to replace with
        inplace: can optionally do the operation in-place. Default: ``False``

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

    Examples::

        >>> m = nn.Threshold(0.1, 20)
        >>> input = torch.randn(2)
        >>> output = m(input)
    """
    __constants__ = ['threshold', 'value', 'inplace']

    threshold: float
    value: float
    inplace: bool

    def __init__(self, threshold: float, value: float, inplace: bool = False) -> None:
        super(Threshold, self).__init__()
        self.threshold = threshold
        self.value = value
        self.inplace = inplace
        # TODO: check in THNN (if inplace == True, then assert value <= threshold)

    def forward(self, input: Tensor) -> Tensor:
        return F.threshold(input, self.threshold, self.value, self.inplace)

    def extra_repr(self):
        inplace_str = ', inplace=True' if self.inplace else ''
        return 'threshold={}, value={}{}'.format(
            self.threshold, self.value, inplace_str
        )


class ReLU(Module):
    r"""Applies the rectified linear unit function element-wise:

    :math:`\text{ReLU}(x) = (x)^+ = \max(0, x)`

    Args:
        inplace: can optionally do the operation in-place. Default: ``False``

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

    .. image:: ../scripts/activation_images/ReLU.png

    Examples::

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


      An implementation of CReLU - https://arxiv.org/abs/1603.05201

        >>> m = nn.ReLU()
        >>> input = torch.randn(2).unsqueeze(0)
        >>> output = torch.cat((m(input),m(-input)))
    """
    __constants__ = ['inplace']
    inplace: bool

    def __init__(self, inplace: bool = False):
        super(ReLU, self).__init__()
        self.inplace = inplace

    def forward(self, input: Tensor) -> Tensor:
        return F.relu(input, inplace=self.inplace)

    def extra_repr(self) -> str:
        inplace_str = 'inplace=True' if self.inplace else ''
        return inplace_str


class RReLU(Module):
    r"""Applies the randomized leaky rectified liner unit function, element-wise,
    as described in the paper:

    `Empirical Evaluation of Rectified Activations in Convolutional Network`_.

    The function is defined as:

    .. math::
        \text{RReLU}(x) =
        \begin{cases}
            x & \text{if } x \geq 0 \\
            ax & \text{ otherwise }
        \end{cases}

    where :math:`a` is randomly sampled from uniform distribution
    :math:`\mathcal{U}(\text{lower}, \text{upper})`.

     See: https://arxiv.org/pdf/1505.00853.pdf

    Args:
        lower: lower bound of the uniform distribution. Default: :math:`\frac{1}{8}`
        upper: upper bound of the uniform distribution. Default: :math:`\frac{1}{3}`
        inplace: can optionally do the operation in-place. Default: ``False``

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

    Examples::

        >>> m = nn.RReLU(0.1, 0.3)
        >>> input = torch.randn(2)
        >>> output = m(input)

    .. _`Empirical Evaluation of Rectified Activations in Convolutional Network`:
        https://arxiv.org/abs/1505.00853
    """
    __constants__ = ['lower', 'upper', 'inplace']

    lower: float
    upper: float
    inplace: bool

    def __init__(
        self,
        lower: float = 1. / 8,
        upper: float = 1. / 3,
        inplace: bool = False
    ):
        super(RReLU, self).__init__()
        self.lower = lower
        self.upper = upper
        self.inplace = inplace

    def forward(self, input: Tensor) -> Tensor:
        return F.rrelu(input, self.lower, self.upper, self.training, self.inplace)

    def extra_repr(self):
        inplace_str = ', inplace=True' if self.inplace else ''
        return 'lower={}, upper={}{}'.format(self.lower, self.upper, inplace_str)


[docs]class Hardtanh(Module): r"""Applies the HardTanh function element-wise HardTanh is defined as: .. math:: \text{HardTanh}(x) = \begin{cases} 1 & \text{ if } x > 1 \\ -1 & \text{ if } x < -1 \\ x & \text{ otherwise } \\ \end{cases} The range of the linear region :math:`[-1, 1]` can be adjusted using :attr:`min_val` and :attr:`max_val`. Args: min_val: minimum value of the linear region range. Default: -1 max_val: maximum value of the linear region range. Default: 1 inplace: can optionally do the operation in-place. Default: ``False`` Keyword arguments :attr:`min_value` and :attr:`max_value` have been deprecated in favor of :attr:`min_val` and :attr:`max_val`. Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Hardtanh.png Examples:: >>> m = nn.Hardtanh(-2, 2) >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['min_val', 'max_val', 'inplace'] min_val: float max_val: float inplace: bool def __init__( self, min_val: float = -1., max_val: float = 1., inplace: bool = False, min_value: Optional[float] = None, max_value: Optional[float] = None ) -> None: super(Hardtanh, self).__init__() if min_value is not None: warnings.warn("keyword argument min_value is deprecated and rename to min_val") min_val = min_value if max_value is not None: warnings.warn("keyword argument max_value is deprecated and rename to max_val") max_val = max_value self.min_val = min_val self.max_val = max_val self.inplace = inplace assert self.max_val > self.min_val def forward(self, input: Tensor) -> Tensor: return F.hardtanh(input, self.min_val, self.max_val, self.inplace) def extra_repr(self) -> str: inplace_str = ', inplace=True' if self.inplace else '' return 'min_val={}, max_val={}{}'.format( self.min_val, self.max_val, inplace_str )
class ReLU6(Hardtanh): r"""Applies the element-wise function: .. math:: \text{ReLU6}(x) = \min(\max(0,x), 6) Args: inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/ReLU6.png Examples:: >>> m = nn.ReLU6() >>> input = torch.randn(2) >>> output = m(input) """ def __init__(self, inplace: bool = False): super(ReLU6, self).__init__(0., 6., inplace) def extra_repr(self) -> str: inplace_str = 'inplace=True' if self.inplace else '' return inplace_str class Sigmoid(Module): r"""Applies the element-wise function: .. math:: \text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)} Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Sigmoid.png Examples:: >>> m = nn.Sigmoid() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return torch.sigmoid(input)
[docs]class Hardsigmoid(Module): r"""Applies the element-wise function: .. math:: \text{Hardsigmoid}(x) = \begin{cases} 0 & \text{if~} x \le -3, \\ 1 & \text{if~} x \ge +3, \\ x / 6 + 1 / 2 & \text{otherwise} \end{cases} Args: inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Examples:: >>> m = nn.Hardsigmoid() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['inplace'] inplace: bool def __init__(self, inplace : bool = False) -> None: super(Hardsigmoid, self).__init__() self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.hardsigmoid(input, self.inplace)
class Tanh(Module): r"""Applies the element-wise function: .. math:: \text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)} Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Tanh.png Examples:: >>> m = nn.Tanh() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return torch.tanh(input) class SiLU(Module): r"""Applies the silu function, element-wise. .. math:: \text{silu}(x) = x * \sigma(x), \text{where } \sigma(x) \text{ is the logistic sigmoid.} .. note:: See `Gaussian Error Linear Units (GELUs) <https://arxiv.org/abs/1606.08415>`_ where the SiLU (Sigmoid Linear Unit) was originally coined, and see `Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning <https://arxiv.org/abs/1702.03118>`_ and `Swish: a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941v1>`_ where the SiLU was experimented with later. Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Examples:: >>> m = nn.SiLU() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['inplace'] inplace: bool def __init__(self, inplace: bool = False): super(SiLU, self).__init__() self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.silu(input, inplace=self.inplace) def extra_repr(self) -> str: inplace_str = 'inplace=True' if self.inplace else '' return inplace_str
[docs]class Hardswish(Module): r"""Applies the hardswish function, element-wise, as described in the paper: `Searching for MobileNetV3`_. .. math:: \text{Hardswish}(x) = \begin{cases} 0 & \text{if~} x \le -3, \\ x & \text{if~} x \ge +3, \\ x \cdot (x + 3) /6 & \text{otherwise} \end{cases} Args: inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Examples:: >>> m = nn.Hardswish() >>> input = torch.randn(2) >>> output = m(input) .. _`Searching for MobileNetV3`: https://arxiv.org/abs/1905.02244 """ __constants__ = ['inplace'] inplace: bool def __init__(self, inplace : bool = False) -> None: super(Hardswish, self).__init__() self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.hardswish(input, self.inplace)
class ELU(Module): r"""Applies the element-wise function: .. math:: \text{ELU}(x) = \begin{cases} x, & \text{ if } x > 0\\ \alpha * (\exp(x) - 1), & \text{ if } x \leq 0 \end{cases} Args: alpha: the :math:`\alpha` value for the ELU formulation. Default: 1.0 inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/ELU.png Examples:: >>> m = nn.ELU() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['alpha', 'inplace'] alpha: float inplace: bool def __init__(self, alpha: float = 1., inplace: bool = False) -> None: super(ELU, self).__init__() self.alpha = alpha self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.elu(input, self.alpha, self.inplace) def extra_repr(self) -> str: inplace_str = ', inplace=True' if self.inplace else '' return 'alpha={}{}'.format(self.alpha, inplace_str) class CELU(Module): r"""Applies the element-wise function: .. math:: \text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1)) More details can be found in the paper `Continuously Differentiable Exponential Linear Units`_ . Args: alpha: the :math:`\alpha` value for the CELU formulation. Default: 1.0 inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/CELU.png Examples:: >>> m = nn.CELU() >>> input = torch.randn(2) >>> output = m(input) .. _`Continuously Differentiable Exponential Linear Units`: https://arxiv.org/abs/1704.07483 """ __constants__ = ['alpha', 'inplace'] alpha: float inplace: bool def __init__(self, alpha: float = 1., inplace: bool = False) -> None: super(CELU, self).__init__() self.alpha = alpha self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.celu(input, self.alpha, self.inplace) def extra_repr(self) -> str: inplace_str = ', inplace=True' if self.inplace else '' return 'alpha={}{}'.format(self.alpha, inplace_str) class SELU(Module): r"""Applied element-wise, as: .. math:: \text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))) with :math:`\alpha = 1.6732632423543772848170429916717` and :math:`\text{scale} = 1.0507009873554804934193349852946`. More details can be found in the paper `Self-Normalizing Neural Networks`_ . Args: inplace (bool, optional): can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/SELU.png Examples:: >>> m = nn.SELU() >>> input = torch.randn(2) >>> output = m(input) .. _Self-Normalizing Neural Networks: https://arxiv.org/abs/1706.02515 """ __constants__ = ['inplace'] inplace: bool def __init__(self, inplace: bool = False) -> None: super(SELU, self).__init__() self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.selu(input, self.inplace) def extra_repr(self) -> str: inplace_str = 'inplace=True' if self.inplace else '' return inplace_str class GLU(Module): r"""Applies the gated linear unit function :math:`{GLU}(a, b)= a \otimes \sigma(b)` where :math:`a` is the first half of the input matrices and :math:`b` is the second half. Args: dim (int): the dimension on which to split the input. Default: -1 Shape: - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional dimensions - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2` Examples:: >>> m = nn.GLU() >>> input = torch.randn(4, 2) >>> output = m(input) """ __constants__ = ['dim'] dim: int def __init__(self, dim: int = -1) -> None: super(GLU, self).__init__() self.dim = dim def forward(self, input: Tensor) -> Tensor: return F.glu(input, self.dim) def extra_repr(self) -> str: return 'dim={}'.format(self.dim) 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. Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/GELU.png Examples:: >>> m = nn.GELU() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return F.gelu(input)
[docs]class Hardshrink(Module): r"""Applies the hard shrinkage function element-wise: .. math:: \text{HardShrink}(x) = \begin{cases} x, & \text{ if } x > \lambda \\ x, & \text{ if } x < -\lambda \\ 0, & \text{ otherwise } \end{cases} Args: lambd: the :math:`\lambda` value for the Hardshrink formulation. Default: 0.5 Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Hardshrink.png Examples:: >>> m = nn.Hardshrink() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['lambd'] lambd: float def __init__(self, lambd: float = 0.5) -> None: super(Hardshrink, self).__init__() self.lambd = lambd def forward(self, input: Tensor) -> Tensor: return F.hardshrink(input, self.lambd) def extra_repr(self) -> str: return '{}'.format(self.lambd)
[docs]class LeakyReLU(Module): r"""Applies the element-wise function: .. math:: \text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x) or .. math:: \text{LeakyRELU}(x) = \begin{cases} x, & \text{ if } x \geq 0 \\ \text{negative\_slope} \times x, & \text{ otherwise } \end{cases} Args: negative_slope: Controls the angle of the negative slope. Default: 1e-2 inplace: can optionally do the operation in-place. Default: ``False`` Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/LeakyReLU.png Examples:: >>> m = nn.LeakyReLU(0.1) >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['inplace', 'negative_slope'] inplace: bool negative_slope: float def __init__(self, negative_slope: float = 1e-2, inplace: bool = False) -> None: super(LeakyReLU, self).__init__() self.negative_slope = negative_slope self.inplace = inplace def forward(self, input: Tensor) -> Tensor: return F.leaky_relu(input, self.negative_slope, self.inplace) def extra_repr(self) -> str: inplace_str = ', inplace=True' if self.inplace else '' return 'negative_slope={}{}'.format(self.negative_slope, inplace_str)
[docs]class LogSigmoid(Module): r"""Applies the element-wise function: .. math:: \text{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right) Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/LogSigmoid.png Examples:: >>> m = nn.LogSigmoid() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return F.logsigmoid(input)
class Softplus(Module): r"""Applies the element-wise function: .. math:: \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) SoftPlus is a smooth approximation to the ReLU function and can be used to constrain the output of a machine to always be positive. For numerical stability the implementation reverts to the linear function when :math:`input \times \beta > threshold`. Args: beta: the :math:`\beta` value for the Softplus formulation. Default: 1 threshold: values above this revert to a linear function. Default: 20 Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Softplus.png Examples:: >>> m = nn.Softplus() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['beta', 'threshold'] beta: int threshold: int def __init__(self, beta: int = 1, threshold: int = 20) -> None: super(Softplus, self).__init__() self.beta = beta self.threshold = threshold def forward(self, input: Tensor) -> Tensor: return F.softplus(input, self.beta, self.threshold) def extra_repr(self) -> str: return 'beta={}, threshold={}'.format(self.beta, self.threshold) class Softshrink(Module): r"""Applies the soft shrinkage function elementwise: .. math:: \text{SoftShrinkage}(x) = \begin{cases} x - \lambda, & \text{ if } x > \lambda \\ x + \lambda, & \text{ if } x < -\lambda \\ 0, & \text{ otherwise } \end{cases} Args: lambd: the :math:`\lambda` (must be no less than zero) value for the Softshrink formulation. Default: 0.5 Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Softshrink.png Examples:: >>> m = nn.Softshrink() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['lambd'] lambd: float def __init__(self, lambd: float = 0.5) -> None: super(Softshrink, self).__init__() self.lambd = lambd def forward(self, input: Tensor) -> Tensor: return F.softshrink(input, self.lambd) def extra_repr(self) -> str: return str(self.lambd)
[docs]class MultiheadAttention(Module): r"""Allows the model to jointly attend to information from different representation subspaces. See reference: Attention Is All You Need .. math:: \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) Args: embed_dim: total dimension of the model. num_heads: parallel attention heads. dropout: a Dropout layer on attn_output_weights. Default: 0.0. bias: add bias as module parameter. Default: True. add_bias_kv: add bias to the key and value sequences at dim=0. add_zero_attn: add a new batch of zeros to the key and value sequences at dim=1. kdim: total number of features in key. Default: None. vdim: total number of features in value. Default: None. Note: if kdim and vdim are None, they will be set to embed_dim such that query, key, and value have the same number of features. Examples:: >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) >>> attn_output, attn_output_weights = multihead_attn(query, key, value) """ bias_k: Optional[torch.Tensor] bias_v: Optional[torch.Tensor] def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None): super(MultiheadAttention, self).__init__() self.embed_dim = embed_dim self.kdim = kdim if kdim is not None else embed_dim self.vdim = vdim if vdim is not None else embed_dim self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" if self._qkv_same_embed_dim is False: self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) self.register_parameter('in_proj_weight', None) else: self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim)) self.register_parameter('q_proj_weight', None) self.register_parameter('k_proj_weight', None) self.register_parameter('v_proj_weight', None) if bias: self.in_proj_bias = Parameter(torch.empty(3 * embed_dim)) else: self.register_parameter('in_proj_bias', None) self.out_proj = _LinearWithBias(embed_dim, embed_dim) if add_bias_kv: self.bias_k = Parameter(torch.empty(1, 1, embed_dim)) self.bias_v = Parameter(torch.empty(1, 1, embed_dim)) else: self.bias_k = self.bias_v = None self.add_zero_attn = add_zero_attn self._reset_parameters() def _reset_parameters(self): if self._qkv_same_embed_dim: xavier_uniform_(self.in_proj_weight) else: xavier_uniform_(self.q_proj_weight) xavier_uniform_(self.k_proj_weight) xavier_uniform_(self.v_proj_weight) if self.in_proj_bias is not None: constant_(self.in_proj_bias, 0.) constant_(self.out_proj.bias, 0.) if self.bias_k is not None: xavier_normal_(self.bias_k) if self.bias_v is not None: xavier_normal_(self.bias_v) def __setstate__(self, state): # Support loading old MultiheadAttention checkpoints generated by v1.1.0 if '_qkv_same_embed_dim' not in state: state['_qkv_same_embed_dim'] = True super(MultiheadAttention, self).__setstate__(state)
[docs] def forward(self, query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None): # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]] r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for more details. key_padding_mask: if provided, specified padding elements in the key will be ignored by the attention. When given a binary mask and a value is True, the corresponding value on the attention layer will be ignored. When given a byte mask and a value is non-zero, the corresponding value on the attention layer will be ignored need_weights: output attn_output_weights. attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all the batches while a 3D mask allows to specify a different mask for the entries of each batch. Shape: - Inputs: - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is the embedding dimension. - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is the embedding dimension. - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is the embedding dimension. - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length. If a ByteTensor is provided, the non-zero positions will be ignored while the position with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length. 3D mask :math:`(N*\text{num_heads}, L, S)` where N is the batch size, L is the target sequence length, S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True`` is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor is provided, it will be added to the attention weight. - Outputs: - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is the embedding dimension. - attn_output_weights: :math:`(N, L, S)` where N is the batch size, L is the target sequence length, S is the source sequence length. """ if not self._qkv_same_embed_dim: return F.multi_head_attention_forward( query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, training=self.training, key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask, use_separate_proj_weight=True, q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, v_proj_weight=self.v_proj_weight) else: return F.multi_head_attention_forward( query, key, value, self.embed_dim, self.num_heads, self.in_proj_weight, self.in_proj_bias, self.bias_k, self.bias_v, self.add_zero_attn, self.dropout, self.out_proj.weight, self.out_proj.bias, training=self.training, key_padding_mask=key_padding_mask, need_weights=need_weights, attn_mask=attn_mask)
class PReLU(Module): r"""Applies the element-wise function: .. math:: \text{PReLU}(x) = \max(0,x) + a * \min(0,x) or .. math:: \text{PReLU}(x) = \begin{cases} x, & \text{ if } x \geq 0 \\ ax, & \text{ otherwise } \end{cases} Here :math:`a` is a learnable parameter. When called without arguments, `nn.PReLU()` uses a single parameter :math:`a` across all input channels. If called with `nn.PReLU(nChannels)`, a separate :math:`a` is used for each input channel. .. note:: weight decay should not be used when learning :math:`a` for good performance. .. note:: Channel dim is the 2nd dim of input. When input has dims < 2, then there is no channel dim and the number of channels = 1. Args: num_parameters (int): number of :math:`a` to learn. Although it takes an int as input, there is only two values are legitimate: 1, or the number of channels at input. Default: 1 init (float): the initial value of :math:`a`. Default: 0.25 Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input Attributes: weight (Tensor): the learnable weights of shape (:attr:`num_parameters`). .. image:: ../scripts/activation_images/PReLU.png Examples:: >>> m = nn.PReLU() >>> input = torch.randn(2) >>> output = m(input) """ __constants__ = ['num_parameters'] num_parameters: int def __init__(self, num_parameters: int = 1, init: float = 0.25) -> None: self.num_parameters = num_parameters super(PReLU, self).__init__() self.weight = Parameter(torch.Tensor(num_parameters).fill_(init)) def forward(self, input: Tensor) -> Tensor: return F.prelu(input, self.weight) def extra_repr(self) -> str: return 'num_parameters={}'.format(self.num_parameters) class Softsign(Module): r"""Applies the element-wise function: .. math:: \text{SoftSign}(x) = \frac{x}{ 1 + |x|} Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Softsign.png Examples:: >>> m = nn.Softsign() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return F.softsign(input) class Tanhshrink(Module): r"""Applies the element-wise function: .. math:: \text{Tanhshrink}(x) = x - \tanh(x) Shape: - Input: :math:`(N, *)` where `*` means, any number of additional dimensions - Output: :math:`(N, *)`, same shape as the input .. image:: ../scripts/activation_images/Tanhshrink.png Examples:: >>> m = nn.Tanhshrink() >>> input = torch.randn(2) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: return F.tanhshrink(input) class Softmin(Module): r"""Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range `[0, 1]` and sum to 1. Softmin is defined as: .. math:: \text{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)} Shape: - Input: :math:`(*)` where `*` means, any number of additional dimensions - Output: :math:`(*)`, same shape as the input Arguments: dim (int): A dimension along which Softmin will be computed (so every slice along dim will sum to 1). Returns: a Tensor of the same dimension and shape as the input, with values in the range [0, 1] Examples:: >>> m = nn.Softmin() >>> input = torch.randn(2, 3) >>> output = m(input) """ __constants__ = ['dim'] dim: Optional[int] def __init__(self, dim: Optional[int] = None) -> None: super(Softmin, self).__init__() self.dim = dim def __setstate__(self, state): self.__dict__.update(state) if not hasattr(self, 'dim'): self.dim = None def forward(self, input: Tensor) -> Tensor: return F.softmin(input, self.dim, _stacklevel=5) def extra_repr(self): return 'dim={dim}'.format(dim=self.dim) class Softmax(Module): r"""Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1. Softmax is defined as: .. math:: \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} When the input Tensor is a sparse tensor then the unspecifed values are treated as ``-inf``. Shape: - Input: :math:`(*)` where `*` means, any number of additional dimensions - Output: :math:`(*)`, same shape as the input Returns: a Tensor of the same dimension and shape as the input with values in the range [0, 1] Arguments: dim (int): A dimension along which Softmax will be computed (so every slice along dim will sum to 1). .. note:: This module doesn't work directly with NLLLoss, which expects the Log to be computed between the Softmax and itself. Use `LogSoftmax` instead (it's faster and has better numerical properties). Examples:: >>> m = nn.Softmax(dim=1) >>> input = torch.randn(2, 3) >>> output = m(input) """ __constants__ = ['dim'] dim: Optional[int] def __init__(self, dim: Optional[int] = None) -> None: super(Softmax, self).__init__() self.dim = dim def __setstate__(self, state): self.__dict__.update(state) if not hasattr(self, 'dim'): self.dim = None def forward(self, input: Tensor) -> Tensor: return F.softmax(input, self.dim, _stacklevel=5) def extra_repr(self) -> str: return 'dim={dim}'.format(dim=self.dim) class Softmax2d(Module): r"""Applies SoftMax over features to each spatial location. When given an image of ``Channels x Height x Width``, it will apply `Softmax` to each location :math:`(Channels, h_i, w_j)` Shape: - Input: :math:`(N, C, H, W)` - Output: :math:`(N, C, H, W)` (same shape as input) Returns: a Tensor of the same dimension and shape as the input with values in the range [0, 1] Examples:: >>> m = nn.Softmax2d() >>> # you softmax over the 2nd dimension >>> input = torch.randn(2, 3, 12, 13) >>> output = m(input) """ def forward(self, input: Tensor) -> Tensor: assert input.dim() == 4, 'Softmax2d requires a 4D tensor as input' return F.softmax(input, 1, _stacklevel=5)
[docs]class LogSoftmax(Module): r"""Applies the :math:`\log(\text{Softmax}(x))` function to an n-dimensional input Tensor. The LogSoftmax formulation can be simplified as: .. math:: \text{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right) Shape: - Input: :math:`(*)` where `*` means, any number of additional dimensions - Output: :math:`(*)`, same shape as the input Arguments: dim (int): A dimension along which LogSoftmax will be computed. Returns: a Tensor of the same dimension and shape as the input with values in the range [-inf, 0) Examples:: >>> m = nn.LogSoftmax() >>> input = torch.randn(2, 3) >>> output = m(input) """ __constants__ = ['dim'] dim: Optional[int] def __init__(self, dim: Optional[int] = None) -> None: super(LogSoftmax, self).__init__() self.dim = dim def __setstate__(self, state): self.__dict__.update(state) if not hasattr(self, 'dim'): self.dim = None def forward(self, input: Tensor) -> Tensor: return F.log_softmax(input, self.dim, _stacklevel=5) def extra_repr(self): return 'dim={dim}'.format(dim=self.dim)

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