456 lines
23 KiB
Python
456 lines
23 KiB
Python
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from typing import Optional
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import torch
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from torch import Tensor
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from torch.nn.parameter import Parameter
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from .module import Module
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from .. import functional as F
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from .. import init
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__all__ = ['Embedding', 'EmbeddingBag']
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class Embedding(Module):
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r"""A simple lookup table that stores embeddings of a fixed dictionary and size.
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This module is often used to store word embeddings and retrieve them using indices.
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The input to the module is a list of indices, and the output is the corresponding
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word embeddings.
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Args:
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num_embeddings (int): size of the dictionary of embeddings
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embedding_dim (int): the size of each embedding vector
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padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
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therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
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i.e. it remains as a fixed "pad". For a newly constructed Embedding,
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the embedding vector at :attr:`padding_idx` will default to all zeros,
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but can be updated to another value to be used as the padding vector.
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max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
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is renormalized to have norm :attr:`max_norm`.
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norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
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scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse of frequency of
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the words in the mini-batch. Default ``False``.
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sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.
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See Notes for more details regarding sparse gradients.
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Attributes:
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weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
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initialized from :math:`\mathcal{N}(0, 1)`
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Shape:
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- Input: :math:`(*)`, IntTensor or LongTensor of arbitrary shape containing the indices to extract
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- Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`
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.. note::
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Keep in mind that only a limited number of optimizers support
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sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),
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:class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)
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.. note::
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When :attr:`max_norm` is not ``None``, :class:`Embedding`'s forward method will modify the
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:attr:`weight` tensor in-place. Since tensors needed for gradient computations cannot be
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modified in-place, performing a differentiable operation on ``Embedding.weight`` before
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calling :class:`Embedding`'s forward method requires cloning ``Embedding.weight`` when
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:attr:`max_norm` is not ``None``. For example::
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n, d, m = 3, 5, 7
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embedding = nn.Embedding(n, d, max_norm=True)
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W = torch.randn((m, d), requires_grad=True)
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idx = torch.tensor([1, 2])
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a = embedding.weight.clone() @ W.t() # weight must be cloned for this to be differentiable
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b = embedding(idx) @ W.t() # modifies weight in-place
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out = (a.unsqueeze(0) + b.unsqueeze(1))
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loss = out.sigmoid().prod()
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loss.backward()
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Examples::
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>>> # an Embedding module containing 10 tensors of size 3
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>>> embedding = nn.Embedding(10, 3)
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>>> # a batch of 2 samples of 4 indices each
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>>> input = torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]])
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> embedding(input)
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tensor([[[-0.0251, -1.6902, 0.7172],
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[-0.6431, 0.0748, 0.6969],
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[ 1.4970, 1.3448, -0.9685],
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[-0.3677, -2.7265, -0.1685]],
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[[ 1.4970, 1.3448, -0.9685],
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[ 0.4362, -0.4004, 0.9400],
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[-0.6431, 0.0748, 0.6969],
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[ 0.9124, -2.3616, 1.1151]]])
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>>> # example with padding_idx
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>>> embedding = nn.Embedding(10, 3, padding_idx=0)
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>>> input = torch.LongTensor([[0, 2, 0, 5]])
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>>> embedding(input)
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tensor([[[ 0.0000, 0.0000, 0.0000],
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[ 0.1535, -2.0309, 0.9315],
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[ 0.0000, 0.0000, 0.0000],
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[-0.1655, 0.9897, 0.0635]]])
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>>> # example of changing `pad` vector
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>>> padding_idx = 0
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>>> embedding = nn.Embedding(3, 3, padding_idx=padding_idx)
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>>> embedding.weight
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Parameter containing:
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tensor([[ 0.0000, 0.0000, 0.0000],
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[-0.7895, -0.7089, -0.0364],
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[ 0.6778, 0.5803, 0.2678]], requires_grad=True)
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>>> with torch.no_grad():
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... embedding.weight[padding_idx] = torch.ones(3)
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>>> embedding.weight
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Parameter containing:
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tensor([[ 1.0000, 1.0000, 1.0000],
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[-0.7895, -0.7089, -0.0364],
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[ 0.6778, 0.5803, 0.2678]], requires_grad=True)
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"""
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__constants__ = ['num_embeddings', 'embedding_dim', 'padding_idx', 'max_norm',
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'norm_type', 'scale_grad_by_freq', 'sparse']
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num_embeddings: int
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embedding_dim: int
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padding_idx: Optional[int]
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max_norm: Optional[float]
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norm_type: float
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scale_grad_by_freq: bool
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weight: Tensor
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freeze: bool
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sparse: bool
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def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
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max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
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sparse: bool = False, _weight: Optional[Tensor] = None, _freeze: bool = False,
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device=None, dtype=None) -> None:
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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self.num_embeddings = num_embeddings
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self.embedding_dim = embedding_dim
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if padding_idx is not None:
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if padding_idx > 0:
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assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
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elif padding_idx < 0:
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assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
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padding_idx = self.num_embeddings + padding_idx
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self.padding_idx = padding_idx
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self.max_norm = max_norm
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self.norm_type = norm_type
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self.scale_grad_by_freq = scale_grad_by_freq
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if _weight is None:
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self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs),
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requires_grad=not _freeze)
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self.reset_parameters()
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else:
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assert list(_weight.shape) == [num_embeddings, embedding_dim], \
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'Shape of weight does not match num_embeddings and embedding_dim'
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self.weight = Parameter(_weight, requires_grad=not _freeze)
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self.sparse = sparse
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def reset_parameters(self) -> None:
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init.normal_(self.weight)
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self._fill_padding_idx_with_zero()
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def _fill_padding_idx_with_zero(self) -> None:
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if self.padding_idx is not None:
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with torch.no_grad():
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self.weight[self.padding_idx].fill_(0)
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def forward(self, input: Tensor) -> Tensor:
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return F.embedding(
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input, self.weight, self.padding_idx, self.max_norm,
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self.norm_type, self.scale_grad_by_freq, self.sparse)
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def extra_repr(self) -> str:
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s = '{num_embeddings}, {embedding_dim}'
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if self.padding_idx is not None:
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s += ', padding_idx={padding_idx}'
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if self.max_norm is not None:
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s += ', max_norm={max_norm}'
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if self.norm_type != 2:
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s += ', norm_type={norm_type}'
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if self.scale_grad_by_freq is not False:
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s += ', scale_grad_by_freq={scale_grad_by_freq}'
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if self.sparse is not False:
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s += ', sparse=True'
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return s.format(**self.__dict__)
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@classmethod
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def from_pretrained(cls, embeddings, freeze=True, padding_idx=None,
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max_norm=None, norm_type=2., scale_grad_by_freq=False,
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sparse=False):
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r"""Create Embedding instance from given 2-dimensional FloatTensor.
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Args:
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embeddings (Tensor): FloatTensor containing weights for the Embedding.
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First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.
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freeze (bool, optional): If ``True``, the tensor does not get updated in the learning process.
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Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``
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padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
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therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
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i.e. it remains as a fixed "pad".
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max_norm (float, optional): See module initialization documentation.
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norm_type (float, optional): See module initialization documentation. Default ``2``.
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scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.
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sparse (bool, optional): See module initialization documentation.
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Examples::
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>>> # FloatTensor containing pretrained weights
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>>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
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>>> embedding = nn.Embedding.from_pretrained(weight)
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>>> # Get embeddings for index 1
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>>> input = torch.LongTensor([1])
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> embedding(input)
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tensor([[ 4.0000, 5.1000, 6.3000]])
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"""
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assert embeddings.dim() == 2, \
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'Embeddings parameter is expected to be 2-dimensional'
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rows, cols = embeddings.shape
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embedding = cls(
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num_embeddings=rows,
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embedding_dim=cols,
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_weight=embeddings,
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_freeze=freeze,
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padding_idx=padding_idx,
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max_norm=max_norm,
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norm_type=norm_type,
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scale_grad_by_freq=scale_grad_by_freq,
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sparse=sparse)
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return embedding
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class EmbeddingBag(Module):
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r"""Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
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For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
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and with 2D inputs, this class
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* with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
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* with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
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* with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
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However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
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operations.
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EmbeddingBag also supports per-sample weights as an argument to the forward
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pass. This scales the output of the Embedding before performing a weighted
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reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
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only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
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:attr:`per_sample_weights`.
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Args:
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num_embeddings (int): size of the dictionary of embeddings
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embedding_dim (int): the size of each embedding vector
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max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
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is renormalized to have norm :attr:`max_norm`.
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norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
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scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of
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the words in the mini-batch. Default ``False``.
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Note: this option is not supported when ``mode="max"``.
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mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
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``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
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into consideration. ``"mean"`` computes the average of the values
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in the bag, ``"max"`` computes the max value over each bag.
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Default: ``"mean"``
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sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
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Notes for more details regarding sparse gradients. Note: this option is not
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supported when ``mode="max"``.
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include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
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is equivalent to the size of `indices`. This matches the CSR format.
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padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
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gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
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during training, i.e. it remains as a fixed "pad". For a newly constructed
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EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all
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zeros, but can be updated to another value to be used as the padding vector.
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Note that the embedding vector at :attr:`padding_idx` is excluded from the
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reduction.
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Attributes:
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weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
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initialized from :math:`\mathcal{N}(0, 1)`.
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Examples::
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>>> # an EmbeddingBag module containing 10 tensors of size 3
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>>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
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>>> # a batch of 2 samples of 4 indices each
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>>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
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>>> offsets = torch.tensor([0, 4], dtype=torch.long)
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> embedding_sum(input, offsets)
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tensor([[-0.8861, -5.4350, -0.0523],
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[ 1.1306, -2.5798, -1.0044]])
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>>> # Example with padding_idx
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>>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
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>>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
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>>> offsets = torch.tensor([0, 4], dtype=torch.long)
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>>> embedding_sum(input, offsets)
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tensor([[ 0.0000, 0.0000, 0.0000],
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[-0.7082, 3.2145, -2.6251]])
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>>> # An EmbeddingBag can be loaded from an Embedding like so
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>>> embedding = nn.Embedding(10, 3, padding_idx=2)
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>>> embedding_sum = nn.EmbeddingBag.from_pretrained(
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embedding.weight,
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padding_idx=embedding.padding_idx,
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mode='sum')
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"""
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__constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type',
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'scale_grad_by_freq', 'mode', 'sparse', 'include_last_offset',
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'padding_idx']
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num_embeddings: int
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embedding_dim: int
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max_norm: Optional[float]
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norm_type: float
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scale_grad_by_freq: bool
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weight: Tensor
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mode: str
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sparse: bool
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include_last_offset: bool
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padding_idx: Optional[int]
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||
|
|
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||
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|
def __init__(self, num_embeddings: int, embedding_dim: int,
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|
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'
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||
|
|
elif padding_idx < 0:
|
||
|
|
assert padding_idx >= -self.num_embeddings, 'padding_idx must be within num_embeddings'
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||
|
|
padding_idx = self.num_embeddings + padding_idx
|
||
|
|
self.padding_idx = padding_idx
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||
|
|
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
|