MLPY/Lib/site-packages/torch/nn/modules/sparse.py

from typing import Optional

import torch
from torch import Tensor
from torch.nn.parameter import Parameter

from .module import Module
from .. import functional as F
from .. import init

__all__ = ['Embedding', 'EmbeddingBag']

class Embedding(Module):
    r"""A simple lookup table that stores embeddings of a fixed dictionary and size.



    This module is often used to store word embeddings and retrieve them using indices.

    The input to the module is a list of indices, and the output is the corresponding

    word embeddings.



    Args:

        num_embeddings (int): size of the dictionary of embeddings

        embedding_dim (int): the size of each embedding vector

        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;

                                     therefore, the embedding vector at :attr:`padding_idx` is not updated during training,

                                     i.e. it remains as a fixed "pad". For a newly constructed Embedding,

                                     the embedding vector at :attr:`padding_idx` will default to all zeros,

                                     but can be updated to another value to be used as the padding vector.

        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`

                                    is renormalized to have norm :attr:`max_norm`.

        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.

        scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse of frequency of

                                                the words in the mini-batch. Default ``False``.

        sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.

                                 See Notes for more details regarding sparse gradients.



    Attributes:

        weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)

                         initialized from :math:`\mathcal{N}(0, 1)`



    Shape:

        - Input: :math:`(*)`, IntTensor or LongTensor of arbitrary shape containing the indices to extract

        - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`



    .. note::

        Keep in mind that only a limited number of optimizers support

        sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),

        :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)



    .. note::

        When :attr:`max_norm` is not ``None``, :class:`Embedding`'s forward method will modify the

        :attr:`weight` tensor in-place. Since tensors needed for gradient computations cannot be

        modified in-place, performing a differentiable operation on ``Embedding.weight`` before

        calling :class:`Embedding`'s forward method requires cloning ``Embedding.weight`` when

        :attr:`max_norm` is not ``None``. For example::



            n, d, m = 3, 5, 7

            embedding = nn.Embedding(n, d, max_norm=True)

            W = torch.randn((m, d), requires_grad=True)

            idx = torch.tensor([1, 2])

            a = embedding.weight.clone() @ W.t()  # weight must be cloned for this to be differentiable

            b = embedding(idx) @ W.t()  # modifies weight in-place

            out = (a.unsqueeze(0) + b.unsqueeze(1))

            loss = out.sigmoid().prod()

            loss.backward()



    Examples::



        >>> # an Embedding module containing 10 tensors of size 3

        >>> embedding = nn.Embedding(10, 3)

        >>> # a batch of 2 samples of 4 indices each

        >>> input = torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]])

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")

        >>> embedding(input)

        tensor([[[-0.0251, -1.6902,  0.7172],

                 [-0.6431,  0.0748,  0.6969],

                 [ 1.4970,  1.3448, -0.9685],

                 [-0.3677, -2.7265, -0.1685]],



                [[ 1.4970,  1.3448, -0.9685],

                 [ 0.4362, -0.4004,  0.9400],

                 [-0.6431,  0.0748,  0.6969],

                 [ 0.9124, -2.3616,  1.1151]]])





        >>> # example with padding_idx

        >>> embedding = nn.Embedding(10, 3, padding_idx=0)

        >>> input = torch.LongTensor([[0, 2, 0, 5]])

        >>> embedding(input)

        tensor([[[ 0.0000,  0.0000,  0.0000],

                 [ 0.1535, -2.0309,  0.9315],

                 [ 0.0000,  0.0000,  0.0000],

                 [-0.1655,  0.9897,  0.0635]]])



        >>> # example of changing `pad` vector

        >>> padding_idx = 0

        >>> embedding = nn.Embedding(3, 3, padding_idx=padding_idx)

        >>> embedding.weight

        Parameter containing:

        tensor([[ 0.0000,  0.0000,  0.0000],

                [-0.7895, -0.7089, -0.0364],

                [ 0.6778,  0.5803,  0.2678]], requires_grad=True)

        >>> with torch.no_grad():

        ...     embedding.weight[padding_idx] = torch.ones(3)

        >>> embedding.weight

        Parameter containing:

        tensor([[ 1.0000,  1.0000,  1.0000],

                [-0.7895, -0.7089, -0.0364],

                [ 0.6778,  0.5803,  0.2678]], requires_grad=True)

    """

    __constants__ = ['num_embeddings', 'embedding_dim', 'padding_idx', 'max_norm',
                     'norm_type', 'scale_grad_by_freq', 'sparse']

    num_embeddings: int
    embedding_dim: int
    padding_idx: Optional[int]
    max_norm: Optional[float]
    norm_type: float
    scale_grad_by_freq: bool
    weight: Tensor
    freeze: bool
    sparse: bool

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,

                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,

                 sparse: bool = False, _weight: Optional[Tensor] = None, _freeze: bool = False,

                 device=None, dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
            elif padding_idx < 0:
                assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
                padding_idx = self.num_embeddings + padding_idx
        self.padding_idx = padding_idx
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        if _weight is None:
            self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs),
                                    requires_grad=not _freeze)
            self.reset_parameters()
        else:
            assert list(_weight.shape) == [num_embeddings, embedding_dim], \
                'Shape of weight does not match num_embeddings and embedding_dim'
            self.weight = Parameter(_weight, requires_grad=not _freeze)

        self.sparse = sparse

    def reset_parameters(self) -> None:
        init.normal_(self.weight)
        self._fill_padding_idx_with_zero()

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input: Tensor) -> Tensor:
        return F.embedding(
            input, self.weight, self.padding_idx, self.max_norm,
            self.norm_type, self.scale_grad_by_freq, self.sparse)

    def extra_repr(self) -> str:
        s = '{num_embeddings}, {embedding_dim}'
        if self.padding_idx is not None:
            s += ', padding_idx={padding_idx}'
        if self.max_norm is not None:
            s += ', max_norm={max_norm}'
        if self.norm_type != 2:
            s += ', norm_type={norm_type}'
        if self.scale_grad_by_freq is not False:
            s += ', scale_grad_by_freq={scale_grad_by_freq}'
        if self.sparse is not False:
            s += ', sparse=True'
        return s.format(**self.__dict__)

    @classmethod
    def from_pretrained(cls, embeddings, freeze=True, padding_idx=None,

                        max_norm=None, norm_type=2., scale_grad_by_freq=False,

                        sparse=False):
        r"""Create Embedding instance from given 2-dimensional FloatTensor.



        Args:

            embeddings (Tensor): FloatTensor containing weights for the Embedding.

                First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.

            freeze (bool, optional): If ``True``, the tensor does not get updated in the learning process.

                Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``

            padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;

                                         therefore, the embedding vector at :attr:`padding_idx` is not updated during training,

                                         i.e. it remains as a fixed "pad".

            max_norm (float, optional): See module initialization documentation.

            norm_type (float, optional): See module initialization documentation. Default ``2``.

            scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.

            sparse (bool, optional): See module initialization documentation.



        Examples::



            >>> # FloatTensor containing pretrained weights

            >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])

            >>> embedding = nn.Embedding.from_pretrained(weight)

            >>> # Get embeddings for index 1

            >>> input = torch.LongTensor([1])

            >>> # xdoctest: +IGNORE_WANT("non-deterministic")

            >>> embedding(input)

            tensor([[ 4.0000,  5.1000,  6.3000]])

        """
        assert embeddings.dim() == 2, \
            'Embeddings parameter is expected to be 2-dimensional'
        rows, cols = embeddings.shape
        embedding = cls(
            num_embeddings=rows,
            embedding_dim=cols,
            _weight=embeddings,
            _freeze=freeze,
            padding_idx=padding_idx,
            max_norm=max_norm,
            norm_type=norm_type,
            scale_grad_by_freq=scale_grad_by_freq,
            sparse=sparse)
        return embedding


class EmbeddingBag(Module):
    r"""Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.



    For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,

    and with 2D inputs, this class



        * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,

        * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,

        * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.



    However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these

    operations.



    EmbeddingBag also supports per-sample weights as an argument to the forward

    pass. This scales the output of the Embedding before performing a weighted

    reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the

    only supported ``mode`` is ``"sum"``, which computes a weighted sum according to

    :attr:`per_sample_weights`.



    Args:

        num_embeddings (int): size of the dictionary of embeddings

        embedding_dim (int): the size of each embedding vector

        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`

                                    is renormalized to have norm :attr:`max_norm`.

        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.

        scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of

                                                the words in the mini-batch. Default ``False``.

                                                Note: this option is not supported when ``mode="max"``.

        mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.

                                 ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`

                                 into consideration. ``"mean"`` computes the average of the values

                                 in the bag, ``"max"`` computes the max value over each bag.

                                 Default: ``"mean"``

        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See

                                 Notes for more details regarding sparse gradients. Note: this option is not

                                 supported when ``mode="max"``.

        include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element

                                      is equivalent to the size of `indices`. This matches the CSR format.

        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the

                                     gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated

                                     during training, i.e. it remains as a fixed "pad". For a newly constructed

                                     EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all

                                     zeros, but can be updated to another value to be used as the padding vector.

                                     Note that the embedding vector at :attr:`padding_idx` is excluded from the

                                     reduction.



    Attributes:

        weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`

                         initialized from :math:`\mathcal{N}(0, 1)`.



    Examples::



        >>> # an EmbeddingBag module containing 10 tensors of size 3

        >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')

        >>> # a batch of 2 samples of 4 indices each

        >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)

        >>> offsets = torch.tensor([0, 4], dtype=torch.long)

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")

        >>> embedding_sum(input, offsets)

        tensor([[-0.8861, -5.4350, -0.0523],

                [ 1.1306, -2.5798, -1.0044]])



        >>> # Example with padding_idx

        >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)

        >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)

        >>> offsets = torch.tensor([0, 4], dtype=torch.long)

        >>> embedding_sum(input, offsets)

        tensor([[ 0.0000,  0.0000,  0.0000],

                [-0.7082,  3.2145, -2.6251]])



        >>> # An EmbeddingBag can be loaded from an Embedding like so

        >>> embedding = nn.Embedding(10, 3, padding_idx=2)

        >>> embedding_sum = nn.EmbeddingBag.from_pretrained(

                embedding.weight,

                padding_idx=embedding.padding_idx,

                mode='sum')

    """

    __constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type',
                     'scale_grad_by_freq', 'mode', 'sparse', 'include_last_offset',
                     'padding_idx']

    num_embeddings: int
    embedding_dim: int
    max_norm: Optional[float]
    norm_type: float
    scale_grad_by_freq: bool
    weight: Tensor
    mode: str
    sparse: bool
    include_last_offset: bool
    padding_idx: Optional[int]

    def __init__(self, num_embeddings: int, embedding_dim: int,

                 max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,

                 mode: str = 'mean', sparse: bool = False, _weight: Optional[Tensor] = None,

                 include_last_offset: bool = False, padding_idx: Optional[int] = None,

                 device=None, dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        super().__init__()
        self.num_embeddings = num_embeddings
        self.embedding_dim = embedding_dim
        self.max_norm = max_norm
        self.norm_type = norm_type
        self.scale_grad_by_freq = scale_grad_by_freq
        if padding_idx is not None:
            if padding_idx > 0:
                assert padding_idx < self.num_embeddings, 'padding_idx must be within num_embeddings'
            elif padding_idx < 0:
                assert padding_idx >= -self.num_embeddings, 'padding_idx must be within num_embeddings'
                padding_idx = self.num_embeddings + padding_idx
        self.padding_idx = padding_idx
        if _weight is None:
            self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs))
            self.reset_parameters()
        else:
            assert list(_weight.shape) == [num_embeddings, embedding_dim], \
                'Shape of weight does not match num_embeddings and embedding_dim'
            self.weight = Parameter(_weight)
        self.mode = mode
        self.sparse = sparse
        self.include_last_offset = include_last_offset

    def reset_parameters(self) -> None:
        init.normal_(self.weight)
        self._fill_padding_idx_with_zero()

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) -> Tensor:
        """Forward pass of EmbeddingBag.



        Args:

            input (Tensor): Tensor containing bags of indices into the embedding matrix.

            offsets (Tensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines

                the starting index position of each bag (sequence) in :attr:`input`.

            per_sample_weights (Tensor, optional): a tensor of float / double weights, or None

                to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`

                must have exactly the same shape as input and is treated as having the same

                :attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.



        Returns:

            Tensor output shape of `(B, embedding_dim)`.



        .. note::



            A few notes about ``input`` and ``offsets``:



            - :attr:`input` and :attr:`offsets` have to be of the same type, either int or long



            - If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)

              each of fixed length ``N``, and this will return ``B`` values aggregated in a way

              depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.



            - If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of

              multiple bags (sequences).  :attr:`offsets` is required to be a 1D tensor containing the

              starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets` of shape `(B)`,

              :attr:`input` will be viewed as having ``B`` bags. Empty bags (i.e., having 0-length) will have

              returned vectors filled by zeros.

        """
        return F.embedding_bag(input, self.weight, offsets,
                               self.max_norm, self.norm_type,
                               self.scale_grad_by_freq, self.mode, self.sparse,
                               per_sample_weights, self.include_last_offset,
                               self.padding_idx)

    def extra_repr(self) -> str:
        s = '{num_embeddings}, {embedding_dim}'
        if self.max_norm is not None:
            s += ', max_norm={max_norm}'
        if self.norm_type != 2:
            s += ', norm_type={norm_type}'
        if self.scale_grad_by_freq is not False:
            s += ', scale_grad_by_freq={scale_grad_by_freq}'
        s += ', mode={mode}'
        if self.padding_idx is not None:
            s += ', padding_idx={padding_idx}'
        return s.format(**{k: repr(v) for k, v in self.__dict__.items()})

    @classmethod
    def from_pretrained(cls, embeddings: Tensor, freeze: bool = True, max_norm: Optional[float] = None,

                        norm_type: float = 2., scale_grad_by_freq: bool = False,

                        mode: str = 'mean', sparse: bool = False, include_last_offset: bool = False,

                        padding_idx: Optional[int] = None) -> 'EmbeddingBag':
        r"""Create EmbeddingBag instance from given 2-dimensional FloatTensor.



        Args:

            embeddings (Tensor): FloatTensor containing weights for the EmbeddingBag.

                First dimension is being passed to EmbeddingBag as 'num_embeddings', second as 'embedding_dim'.

            freeze (bool, optional): If ``True``, the tensor does not get updated in the learning process.

                Equivalent to ``embeddingbag.weight.requires_grad = False``. Default: ``True``

            max_norm (float, optional): See module initialization documentation. Default: ``None``

            norm_type (float, optional): See module initialization documentation. Default ``2``.

            scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.

            mode (str, optional): See module initialization documentation. Default: ``"mean"``

            sparse (bool, optional): See module initialization documentation. Default: ``False``.

            include_last_offset (bool, optional): See module initialization documentation. Default: ``False``.

            padding_idx (int, optional): See module initialization documentation. Default: ``None``.



        Examples::



            >>> # FloatTensor containing pretrained weights

            >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])

            >>> embeddingbag = nn.EmbeddingBag.from_pretrained(weight)

            >>> # Get embeddings for index 1

            >>> input = torch.LongTensor([[1, 0]])

            >>> # xdoctest: +IGNORE_WANT("non-deterministic")

            >>> embeddingbag(input)

            tensor([[ 2.5000,  3.7000,  4.6500]])

        """
        assert embeddings.dim() == 2, \
            'Embeddings parameter is expected to be 2-dimensional'
        rows, cols = embeddings.shape
        embeddingbag = cls(
            num_embeddings=rows,
            embedding_dim=cols,
            _weight=embeddings,
            max_norm=max_norm,
            norm_type=norm_type,
            scale_grad_by_freq=scale_grad_by_freq,
            mode=mode,
            sparse=sparse,
            include_last_offset=include_last_offset,
            padding_idx=padding_idx)
        embeddingbag.weight.requires_grad = not freeze
        return embeddingbag