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https://github.com/zebrajr/pytorch.git
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76a283db40
* Fix some minor errors in existing docs. * Fix Convolution and Pooling docs in torch.nn.functional * Cleaned up torch.nn.functional docs * Address @SsnL 's comments * Add multiplication sign missing in docs * Fix more typos, and clear some warnings * Change infinity symbol in LPPool2d * Revert some changes in torch.nn.functional * Few more minor changes
254 lines
11 KiB
Python
254 lines
11 KiB
Python
import torch
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from torch.autograd import Variable
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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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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 zeros whenever it encounters the index.
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max_norm (float, optional): If given, will renormalize the embeddings to always have a norm lesser than this
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norm_type (float, optional): The p of the p-norm to compute for the max_norm option
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scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of
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the words in the mini-batch.
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sparse (boolean, optional): if ``True``, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for
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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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Shape:
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- Input: LongTensor `(N, W)`, N = mini-batch, W = number of indices to extract per mini-batch
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- Output: `(N, W, embedding_dim)`
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Notes:
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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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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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(0 ,.,.) =
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-1.0822 1.2522 0.2434
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0.8393 -0.6062 -0.3348
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0.6597 0.0350 0.0837
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0.5521 0.9447 0.0498
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(1 ,.,.) =
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0.6597 0.0350 0.0837
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-0.1527 0.0877 0.4260
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0.8393 -0.6062 -0.3348
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-0.8738 -0.9054 0.4281
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[torch.FloatTensor of size (2,4,3)]
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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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(0 ,.,.) =
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0.0000 0.0000 0.0000
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0.3452 0.4937 -0.9361
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0.0000 0.0000 0.0000
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0.0706 -2.1962 -0.6276
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[torch.FloatTensor of size (1,4,3)]
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"""
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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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self.weight.data.normal_(0, 1)
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if self.padding_idx is not None:
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self.weight.data[self.padding_idx].fill_(0)
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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 __repr__(self):
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s = '{name}({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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s += ')'
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return s.format(name=self.__class__.__name__, **self.__dict__)
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@classmethod
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def from_pretrained(cls, embeddings, freeze=True):
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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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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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4.0000 5.1000 6.3000
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[torch.FloatTensor of size (1,3)]
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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(num_embeddings=rows, embedding_dim=cols, _weight=embeddings)
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embedding.weight.requires_grad = not freeze
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return embedding
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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,
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* nn.EmbeddingBag with `mode=sum` is equivalent to nn.Embedding followed by `torch.sum(dim=1)`
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* with `mode=mean` is equivalent to nn.Embedding followed by `torch.mean(dim=1)`
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However, nn.EmbeddingBag is much more time and memory efficient than using a chain of these
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operations.
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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, will renormalize the embeddings to always have a norm lesser than this
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norm_type (float, optional): The p of the p-norm to compute for the max_norm option
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scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of
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the words in the dictionary.
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mode (string, optional): 'sum' | 'mean'. Specifies the way to reduce the bag. Default: 'mean'
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sparse (boolean, optional): if ``True``, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for
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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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Inputs: input, offsets
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- **input** (``N`` or ``B x N``): LongTensor containing the indices of the embeddings
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to extract. When `input` is 1D Tensor of shape `N`,
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an `offsets` Tensor is given, that contains the
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starting position of each new sequence in the
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mini-batch.
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- **offsets** (``B`` or ``None``): LongTensor containing the starting positions of
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each sample in a mini-batch of variable length
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sequences. If `input` is 2D (``B x N``), then offsets
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does not need to be given, as the `input` is
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treated as a mini-batch of fixed length sequences
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of length `N` each.
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Shape:
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- Input: LongTensor `N`, N = number of embeddings to extract
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(or) LongTensor ``B x N``, B = number of sequences in mini-batch,
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N = number of embeddings per sequence
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- Offsets: LongTensor `B`, B = number of bags. The values are the
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offsets in `input` for each bag, i.e. the cumsum of lengths.
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Offsets is not given if Input is 2D ``B x N`` Tensor,
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the input is considered to be of fixed-length sequences
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- Output: `(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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-0.7296 -4.6926 0.3295
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-0.5186 -0.5631 -0.2792
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[torch.FloatTensor of size (2,3)]
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"""
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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):
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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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self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim))
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self.mode = mode
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self.sparse = sparse
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self.reset_parameters()
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def reset_parameters(self):
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self.weight.data.normal_(0, 1)
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def forward(self, input, offsets=None):
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return F.embedding_bag(self.weight, input, 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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def __repr__(self):
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s = '{name}({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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s += ')'
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return s.format(name=self.__class__.__name__, **self.__dict__)
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# TODO: SparseLinear
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