# Copyright 2019 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Various embedders.
"""
from typing import Optional
import torch
from torch.nn import functional as F
from texar.torch.modules.embedders import embedder_utils
from texar.torch.modules.embedders.embedder_base import (
EmbedderBase, EmbeddingDropout)
__all__ = [
"WordEmbedder",
]
[docs]class WordEmbedder(EmbedderBase):
r"""Simple word embedder that maps indexes into embeddings. The indexes
can be soft (e.g., distributions over vocabulary).
Either :attr:`init_value` or :attr:`vocab_size` is required. If both are
given, there must be ``init_value.shape[0]==vocab_size``.
Args:
init_value (optional): A Tensor or numpy array that contains the
initial value of embeddings. It is typically of shape
``[vocab_size] + embedding-dim``. Embeddings can have dimensionality
> 1.
If `None`, embedding is initialized as specified in
``hparams["initializer"]``. Otherwise, the
``"initializer"`` and ``"dim"`` hyperparameters in :attr:`hparams`
are ignored.
vocab_size (int, optional): The vocabulary size. Required if
:attr:`init_value` is not given.
hparams (dict, optional): Embedder hyperparameters. Missing
hyperparameters will be set to default values. See
:meth:`default_hparams` for the hyperparameter structure and
default values.
See :meth:`forward` for the inputs and outputs of the embedder.
Example:
.. code-block:: python
ids = torch.empty([32, 10]).uniform_(to=10).type(torch.int64).
soft_ids = torch.empty([32, 10, 100]).uniform_()
embedder = WordEmbedder(vocab_size=100, hparams={'dim': 256})
ids_emb = embedder(ids=ids) # shape: [32, 10, 256]
soft_ids_emb = embedder(soft_ids=soft_ids) # shape: [32, 10, 256]
.. code-block:: python
# Use with Texar data module
hparams={
'dataset': {
'embedding_init': {'file': 'word2vec.txt'}
...
},
}
data = MonoTextData(data_params)
iterator = DataIterator(data)
batch = next(iter(iterator))
# Use data vocab size
embedder_1 = WordEmbedder(vocab_size=data.vocab.size)
emb_1 = embedder_1(batch['text_ids'])
# Use pre-trained embedding
embedder_2 = WordEmbedder(init_value=data.embedding_init_value)
emb_2 = embedder_2(batch['text_ids'])
.. document private functions
"""
def __init__(self, init_value: Optional[torch.Tensor] = None,
vocab_size: Optional[int] = None, hparams=None):
if init_value is None and vocab_size is None:
raise ValueError(
"Either `init_value` or `vocab_size` is required.")
super().__init__(init_value=init_value,
num_embeds=vocab_size, hparams=hparams)
if vocab_size is None:
self._vocab_size = self._num_embeds
else:
self._vocab_size = vocab_size
if self._vocab_size != self._num_embeds:
raise ValueError(
f"vocab_size must equal to init_value.shape[0]. "
f"Got {self._vocab_size} and {self._num_embeds}")
self._dropout_layer = EmbeddingDropout(self._hparams.dropout_rate)
[docs] @staticmethod
def default_hparams():
r"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
"dim": 100,
"dropout_rate": 0,
"dropout_strategy": 'element',
"initializer": {
"type": "random_uniform_initializer",
"kwargs": {
"minval": -0.1,
"maxval": 0.1,
"seed": None
}
},
"trainable": True,
"name": "word_embedder",
}
Here:
`"dim"`: int or list
Embedding dimension. Can be a list of integers to yield embeddings
with dimensionality > 1.
Ignored if :attr:`init_value` is given to the embedder constructor.
`"dropout_rate"`: float
The dropout rate between 0 and 1. For example, ``dropout_rate=0.1``
would zero out 10% of the embeddings. Set to 0 to disable dropout.
`"dropout_strategy"`: str
The dropout strategy. Can be one of the following
- ``"element"``: The regular strategy that drops individual elements
in the embedding vectors.
- ``"item"``: Drops individual items (e.g., words) entirely. For
example, for the word sequence "the simpler the better", the
strategy can yield "_ simpler the better", where the first "the"
is dropped.
- ``"item_type"``: Drops item types (e.g., word types). For example,
for the above sequence, the strategy can yield "_ simpler _
better", where the word type "the" is dropped. The dropout will
never yield "_ simpler the better" as in the ``"item"`` strategy.
`"initializer"`: dict or None
Hyperparameters of the initializer for embedding values. See
:func:`~texar.torch.core.get_initializer` for the details. Ignored
if :attr:`init_value` is given to the embedder constructor.
`"trainable"`: bool
Whether the embedding parameters are trainable. If false, freeze the
embedding parameters.
`"name"`: str
Name of the embedding variable.
"""
hparams = embedder_utils.default_embedding_hparams()
hparams["name"] = "word_embedder"
return hparams
[docs] def forward(self, # type: ignore
ids: Optional[torch.LongTensor] = None,
soft_ids: Optional[torch.Tensor] = None,
**kwargs) -> torch.Tensor:
r"""Embeds (soft) ids.
Either :attr:`ids` or :attr:`soft_ids` must be given, and they
must not be given at the same time.
Args:
ids (optional): An integer tensor containing the ids to embed.
soft_ids (optional): A tensor of weights (probabilities) used to
mix the embedding vectors.
kwargs: Additional keyword arguments for
:torch_nn:`functional.embedding` besides :attr:`params` and
:attr:`ids`.
Returns:
If :attr:`ids` is given, returns a Tensor of shape
``list(ids.shape) + embedding-dim``. For example,
if ``list(ids.shape) == [batch_size, max_time]``
and ``list(embedding.shape) == [vocab_size, emb_dim]``, then the
return tensor has shape ``[batch_size, max_time, emb_dim]``.
If :attr:`soft_ids` is given, returns a Tensor of shape
``list(soft_ids.shape)[:-1] + embedding-dim``. For example,
if ``list(soft_ids.shape) == [batch_size, max_time, vocab_size]``
and ``list(embedding.shape) == [vocab_size, emb_dim]``, then the
return tensor has shape ``[batch_size, max_time, emb_dim]``.
"""
if ids is not None:
if soft_ids is not None:
raise ValueError(
'Must not specify `ids` and `soft_ids` at the same time.')
ids_rank = ids.dim()
elif soft_ids is not None:
ids_rank = soft_ids.dim() - 1
else:
raise ValueError('Either `ids` or `soft_ids` must be given.')
embedding = self._embedding
if self._hparams.dropout_strategy == 'item_type':
noise_shape = self._get_noise_shape(self._hparams.dropout_strategy)
embedding = self._dropout_layer(embedding, noise_shape)
if ids is not None:
outputs = F.embedding(ids, embedding, **kwargs)
else:
outputs = embedder_utils.soft_embedding_lookup(embedding, soft_ids)
if self._hparams.dropout_strategy != 'item_type':
noise_shape = self._get_noise_shape(
self._hparams.dropout_strategy,
ids_rank=ids_rank, dropout_input=outputs)
outputs = self._dropout_layer(outputs, noise_shape)
return outputs
@property
def embedding(self) -> torch.Tensor:
r"""The embedding tensor, of shape ``[vocab_size] + dim``.
"""
return self._embedding
@property
def dim(self) -> int:
r"""The embedding dimension.
"""
return self._dim
@property
def vocab_size(self) -> int:
r"""The vocabulary size.
"""
return self._vocab_size
@property
def num_embeddings(self) -> int:
r"""The vocabulary size. This interface matches
:torch_nn:`Embedding`.
"""
return self._vocab_size
@property
def output_size(self) -> int:
r"""The feature size of :meth:`forward` output. If the :attr:`dim`
hyperparameter is a ``list`` or ``tuple``, the feature size
equals its final dimension; otherwise, if :attr:`dim` is an
``int``, the feature size equals :attr:`dim`.
"""
if isinstance(self._dim, (list, tuple)):
return self._dim[-1]
else:
return self._dim