Source code for torch.nn.modules.sparse

import torch
from torch.nn.parameter import Parameter

from .module import Module


[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 zeros whenever it encounters the index. max_norm (float, optional): If given, will renormalize the embeddings to always have a norm lesser than this norm_type (float, optional): The p of the p-norm to compute for the max_norm option scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of the words in the mini-batch. Attributes: weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) Shape: - Input: LongTensor `(N, W)`, N = mini-batch, W = number of indices to extract per mini-batch - Output: `(N, W, embedding_dim)` 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 = Variable(torch.LongTensor([[1,2,4,5],[4,3,2,9]])) >>> embedding(input) Variable containing: (0 ,.,.) = -1.0822 1.2522 0.2434 0.8393 -0.6062 -0.3348 0.6597 0.0350 0.0837 0.5521 0.9447 0.0498 (1 ,.,.) = 0.6597 0.0350 0.0837 -0.1527 0.0877 0.4260 0.8393 -0.6062 -0.3348 -0.8738 -0.9054 0.4281 [torch.FloatTensor of size 2x4x3] >>> # example with padding_idx >>> embedding = nn.Embedding(10, 3, padding_idx=0) >>> input = Variable(torch.LongTensor([[0,2,0,5]])) >>> embedding(input) Variable containing: (0 ,.,.) = 0.0000 0.0000 0.0000 0.3452 0.4937 -0.9361 0.0000 0.0000 0.0000 0.0706 -2.1962 -0.6276 [torch.FloatTensor of size 1x4x3] """ def __init__(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, sparse=False): super(Embedding, self).__init__() self.num_embeddings = num_embeddings self.embedding_dim = embedding_dim self.padding_idx = padding_idx self.max_norm = max_norm self.norm_type = norm_type self.scale_grad_by_freq = scale_grad_by_freq self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim)) self.sparse = sparse self.reset_parameters() def reset_parameters(self): self.weight.data.normal_(0, 1) if self.padding_idx is not None: self.weight.data[self.padding_idx].fill_(0) def forward(self, input): padding_idx = self.padding_idx if padding_idx is None: padding_idx = -1 return self._backend.Embedding( padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse )(input, self.weight) def __repr__(self): s = '{name}({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' s += ')' return s.format(name=self.__class__.__name__, **self.__dict__)
# TODO: SparseLinear