import torch
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
[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.
sparse (boolean, optional): if ``True``, gradient w.r.t. 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)
Shape:
- Input: LongTensor `(N, W)`, N = mini-batch, W = number of indices to extract per mini-batch
- Output: `(N, W, embedding_dim)`
Notes:
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`)
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)
(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 (2,4,3)]
>>> # example with padding_idx
>>> embedding = nn.Embedding(10, 3, padding_idx=0)
>>> input = torch.LongTensor([[0,2,0,5]])
>>> embedding(input)
(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 (1,4,3)]
"""
def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
max_norm=None, norm_type=2, scale_grad_by_freq=False,
sparse=False, _weight=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.sparse = sparse
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):
return F.embedding(
input, self.weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
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__)
[docs] @classmethod
def from_pretrained(cls, embeddings, freeze=True):
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``
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)
4.0000 5.1000 6.3000
[torch.FloatTensor of size (1,3)]
"""
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)
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,
* nn.EmbeddingBag with `mode=sum` is equivalent to nn.Embedding followed by `torch.sum(dim=1)`
* with `mode=mean` is equivalent to nn.Embedding followed by `torch.mean(dim=1)`
However, nn.EmbeddingBag is much more time and memory efficient than using a chain of these
operations.
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, 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 dictionary.
mode (string, optional): 'sum' | 'mean'. Specifies the way to reduce the bag. Default: 'mean'
sparse (boolean, optional): if ``True``, gradient w.r.t. 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)
Inputs: input, offsets
- **input** (``N`` or ``B x N``): LongTensor containing the indices of the embeddings
to extract. When `input` is 1D Tensor of shape `N`,
an `offsets` Tensor is given, that contains the
starting position of each new sequence in the
mini-batch.
- **offsets** (``B`` or ``None``): LongTensor containing the starting positions of
each sample in a mini-batch of variable length
sequences. If `input` is 2D (``B x N``), then offsets
does not need to be given, as the `input` is
treated as a mini-batch of fixed length sequences
of length `N` each.
Shape:
- Input: LongTensor `N`, N = number of embeddings to extract
(or) LongTensor ``B x N``, B = number of sequences in mini-batch,
N = number of embeddings per sequence
- Offsets: LongTensor `B`, B = number of bags. The values are the
offsets in `input` for each bag, i.e. the cumsum of lengths.
Offsets is not given if Input is 2D ``B x N`` Tensor,
the input is considered to be of fixed-length sequences
- Output: `(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)
-0.7296 -4.6926 0.3295
-0.5186 -0.5631 -0.2792
[torch.FloatTensor of size (2,3)]
"""
def __init__(self, num_embeddings, embedding_dim,
max_norm=None, norm_type=2, scale_grad_by_freq=False,
mode='mean', sparse=False):
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
self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim))
self.mode = mode
self.sparse = sparse
self.reset_parameters()
def reset_parameters(self):
self.weight.data.normal_(0, 1)
def forward(self, input, offsets=None):
return F.embedding_bag(self.weight, input, offsets,
self.max_norm, self.norm_type,
self.scale_grad_by_freq, self.mode, self.sparse)
def __repr__(self):
s = '{name}({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}'
s += ')'
return s.format(name=self.__class__.__name__, **self.__dict__)
# TODO: SparseLinear