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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18735 ghimport-source-id: d81bef54dafd7167d2451250d7be478d3c013920 Reviewed By: cpuhrsch Differential Revision: D14851415 Pulled By: zou3519 fbshipit-source-id: cea6039e760ad571b90f0a536e420498f34be325
340 lines
16 KiB
Python
340 lines
16 KiB
Python
import torch
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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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from torch._jit_internal import weak_module, weak_script_method
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@weak_module
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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 given, pads the output with the embedding vector at :attr:`padding_idx`
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(initialized to zeros) whenever it encounters the index.
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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 (boolean, 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:`(*)`, 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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With :attr:`padding_idx` set, the embedding vector at
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:attr:`padding_idx` is initialized to all zeros. However, note that this
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vector can be modified afterwards, e.g., using a customized
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initialization method, and thus changing the vector used to pad the
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output. The gradient for this vector from :class:`~torch.nn.Embedding`
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is always zero.
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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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>>> 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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"""
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__constants__ = ['num_embeddings', 'embedding_dim', 'padding_idx', 'max_norm',
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'norm_type', 'scale_grad_by_freq', 'sparse', '_weight']
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def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
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max_norm=None, norm_type=2., scale_grad_by_freq=False,
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sparse=False, _weight=None):
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super(Embedding, self).__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.Tensor(num_embeddings, embedding_dim))
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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)
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self.sparse = sparse
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def reset_parameters(self):
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init.normal_(self.weight)
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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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@weak_script_method
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def forward(self, input):
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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):
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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"""Creates 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 (boolean, 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): See module initialization documentation.
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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 (boolean, 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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>>> 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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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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embedding.weight.requires_grad = not freeze
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return embedding
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@weak_module
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class EmbeddingBag(Module):
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r"""Computes sums or means of 'bags' of embeddings, without instantiating the
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intermediate embeddings.
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For bags of constant length and no :attr:`per_sample_weights`, this class
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* with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=0)``,
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* with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=0)``,
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* with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=0)``.
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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 (boolean, 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 (string, 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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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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Inputs: :attr:`input` (LongTensor), :attr:`offsets` (LongTensor, optional), and
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:attr:`per_index_weights` (Tensor, optional)
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- If :attr:`input` is 2D of shape `(B, N)`,
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it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and
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this will return ``B`` values aggregated in a way depending on the :attr:`mode`.
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:attr:`offsets` is ignored and required to be ``None`` in this case.
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- If :attr:`input` is 1D of shape `(N)`,
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it will be treated as a concatenation of multiple bags (sequences).
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:attr:`offsets` is required to be a 1D tensor containing the
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starting index positions of each bag in :attr:`input`. Therefore,
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for :attr:`offsets` of shape `(B)`, :attr:`input` will be viewed as
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having ``B`` bags. Empty bags (i.e., having 0-length) will have
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returned vectors filled by zeros.
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per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
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to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`
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must have exactly the same shape as input and is treated as having the same
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:attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.
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Output shape: `(B, embedding_dim)`
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Examples::
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>>> # an Embedding 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.LongTensor([1,2,4,5,4,3,2,9])
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>>> offsets = torch.LongTensor([0,4])
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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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"""
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__constants__ = ['num_embeddings, embedding_dim', 'max_norm', 'norm_type',
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'scale_grad_by_freq', 'mode', 'sparse', '_weight']
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def __init__(self, num_embeddings, embedding_dim,
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max_norm=None, norm_type=2., scale_grad_by_freq=False,
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mode='mean', sparse=False, _weight=None):
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super(EmbeddingBag, self).__init__()
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self.num_embeddings = num_embeddings
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self.embedding_dim = embedding_dim
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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.Tensor(num_embeddings, embedding_dim))
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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)
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self.mode = mode
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self.sparse = sparse
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def reset_parameters(self):
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init.normal_(self.weight)
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@weak_script_method
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def forward(self, input, offsets=None, per_sample_weights=None):
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# type: (Tensor, Optional[Tensor], Optional[Tensor]) -> Tensor
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return F.embedding_bag(input, self.weight, offsets,
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self.max_norm, self.norm_type,
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self.scale_grad_by_freq, self.mode, self.sparse,
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per_sample_weights)
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def extra_repr(self):
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s = '{num_embeddings}, {embedding_dim}'
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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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s += ', mode={mode}'
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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, max_norm=None,
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norm_type=2., scale_grad_by_freq=False,
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mode='mean', sparse=False):
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r"""Creates EmbeddingBag instance from given 2-dimensional FloatTensor.
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Args:
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embeddings (Tensor): FloatTensor containing weights for the EmbeddingBag.
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First dimension is being passed to EmbeddingBag as 'num_embeddings', second as 'embedding_dim'.
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freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.
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Equivalent to ``embeddingbag.weight.requires_grad = False``. Default: ``True``
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max_norm (float, optional): See module initialization documentation. Default: ``None``
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norm_type (float, optional): See module initialization documentation. Default ``2``.
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scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.
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mode (string, optional): See module initialization documentation. Default: ``"mean"``
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sparse (bool, optional): See module initialization documentation. Default: ``False``.
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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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>>> embeddingbag = nn.EmbeddingBag.from_pretrained(weight)
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>>> # Get embeddings for index 1
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>>> input = torch.LongTensor([[1, 0]])
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>>> embeddingbag(input)
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tensor([[ 2.5000, 3.7000, 4.6500]])
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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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embeddingbag = 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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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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mode=mode,
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sparse=sparse)
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embeddingbag.weight.requires_grad = not freeze
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return embeddingbag
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