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Source code for torch.nn.modules.sparse

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


[docs]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 given, pads the output with the embedding vector at :attr:`padding_idx` (initialized to zeros) whenever it encounters the index. 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 (boolean, 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:: With :attr:`padding_idx` set, the embedding vector at :attr:`padding_idx` is initialized to all zeros. However, note that this vector can be modified afterwards, e.g., using a customized initialization method, and thus changing the vector used to pad the output. The gradient for this vector from :class:`~torch.nn.Embedding` is always zero. .. 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]]) >>> 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]]]) """ __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 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) -> None: super(Embedding, self).__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.Tensor(num_embeddings, embedding_dim)) 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._fill_padding_idx_with_zero() 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__)
[docs] @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"""Creates 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 (boolean, 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): See module initialization documentation. max_norm (float, optional): See module initialization documentation. norm_type (float, optional): See module initialization documentation. Default ``2``. scale_grad_by_freq (boolean, 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]) >>> 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, padding_idx=padding_idx, max_norm=max_norm, norm_type=norm_type, scale_grad_by_freq=scale_grad_by_freq, sparse=sparse) embedding.weight.requires_grad = not freeze return embedding
[docs]class EmbeddingBag(Module): r"""Computes sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. For bags of constant length and no :attr:`per_sample_weights` and 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 (boolean, 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 (string, 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. Attributes: weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` initialized from :math:`\mathcal{N}(0, 1)`. Inputs: :attr:`input` (IntTensor or LongTensor), :attr:`offsets` (IntTensor or LongTensor, optional), and :attr:`per_index_weights` (Tensor, optional) - :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. 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'``. Output shape: `(B, embedding_dim)` Examples:: >>> # an Embedding 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.LongTensor([1,2,4,5,4,3,2,9]) >>> offsets = torch.LongTensor([0,4]) >>> embedding_sum(input, offsets) tensor([[-0.8861, -5.4350, -0.0523], [ 1.1306, -2.5798, -1.0044]]) """ __constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type', 'scale_grad_by_freq', 'mode', 'sparse', 'include_last_offset'] 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 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) -> None: super(EmbeddingBag, self).__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 _weight is None: self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim)) 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) def forward(self, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) -> Tensor: 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) 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}' return s.format(**self.__dict__)
[docs] @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) -> 'EmbeddingBag': r"""Creates 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 (boolean, 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 (boolean, optional): See module initialization documentation. Default ``False``. mode (string, 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``. 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]]) >>> 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) embeddingbag.weight.requires_grad = not freeze return embeddingbag

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