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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import List, Tuple
import torch
import torch.nn.functional as F
from fairseq.data import Dictionary
from torch import nn
CHAR_PAD_IDX = 0
CHAR_EOS_IDX = 257
logger = logging.getLogger(__name__)
class CharacterTokenEmbedder(torch.nn.Module):
def __init__(
self,
vocab: Dictionary,
filters: List[Tuple[int, int]],
char_embed_dim: int,
word_embed_dim: int,
highway_layers: int,
max_char_len: int = 50,
char_inputs: bool = False,
):
super(CharacterTokenEmbedder, self).__init__()
self.onnx_trace = False
self.embedding_dim = word_embed_dim
self.max_char_len = max_char_len
self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0)
self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim))
self.eos_idx, self.unk_idx = 0, 1
self.char_inputs = char_inputs
self.convolutions = nn.ModuleList()
for width, out_c in filters:
self.convolutions.append(
nn.Conv1d(char_embed_dim, out_c, kernel_size=width)
)
last_dim = sum(f[1] for f in filters)
self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None
self.projection = nn.Linear(last_dim, word_embed_dim)
assert (
vocab is not None or char_inputs
), "vocab must be set if not using char inputs"
self.vocab = None
if vocab is not None:
self.set_vocab(vocab, max_char_len)
self.reset_parameters()
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def set_vocab(self, vocab, max_char_len):
word_to_char = torch.LongTensor(len(vocab), max_char_len)
truncated = 0
for i in range(len(vocab)):
if i < vocab.nspecial:
char_idxs = [0] * max_char_len
else:
chars = vocab[i].encode()
# +1 for padding
char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars))
if len(char_idxs) > max_char_len:
truncated += 1
char_idxs = char_idxs[:max_char_len]
word_to_char[i] = torch.LongTensor(char_idxs)
if truncated > 0:
logger.info(
"truncated {} words longer than {} characters".format(
truncated, max_char_len
)
)
self.vocab = vocab
self.word_to_char = word_to_char
@property
def padding_idx(self):
return Dictionary().pad() if self.vocab is None else self.vocab.pad()
def reset_parameters(self):
nn.init.xavier_normal_(self.char_embeddings.weight)
nn.init.xavier_normal_(self.symbol_embeddings)
nn.init.xavier_uniform_(self.projection.weight)
nn.init.constant_(
self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0
)
nn.init.constant_(self.projection.bias, 0.0)
def forward(
self,
input: torch.Tensor,
):
if self.char_inputs:
chars = input.view(-1, self.max_char_len)
pads = chars[:, 0].eq(CHAR_PAD_IDX)
eos = chars[:, 0].eq(CHAR_EOS_IDX)
if eos.any():
if self.onnx_trace:
chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars)
else:
chars[eos] = 0
unk = None
else:
flat_words = input.view(-1)
chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as(
input
)
pads = flat_words.eq(self.vocab.pad())
eos = flat_words.eq(self.vocab.eos())
unk = flat_words.eq(self.vocab.unk())
word_embs = self._convolve(chars)
if self.onnx_trace:
if pads.any():
word_embs = torch.where(
pads.unsqueeze(1), word_embs.new_zeros(1), word_embs
)
if eos.any():
word_embs = torch.where(
eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs
)
if unk is not None and unk.any():
word_embs = torch.where(
unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs
)
else:
if pads.any():
word_embs[pads] = 0
if eos.any():
word_embs[eos] = self.symbol_embeddings[self.eos_idx]
if unk is not None and unk.any():
word_embs[unk] = self.symbol_embeddings[self.unk_idx]
return word_embs.view(input.size()[:2] + (-1,))
def _convolve(
self,
char_idxs: torch.Tensor,
):
char_embs = self.char_embeddings(char_idxs)
char_embs = char_embs.transpose(1, 2) # BTC -> BCT
conv_result = []
for conv in self.convolutions:
x = conv(char_embs)
x, _ = torch.max(x, -1)
x = F.relu(x)
conv_result.append(x)
x = torch.cat(conv_result, dim=-1)
if self.highway is not None:
x = self.highway(x)
x = self.projection(x)
return x
class Highway(torch.nn.Module):
"""
A `Highway layer <https://arxiv.org/abs/1505.00387>`_.
Adopted from the AllenNLP implementation.
"""
def __init__(self, input_dim: int, num_layers: int = 1):
super(Highway, self).__init__()
self.input_dim = input_dim
self.layers = nn.ModuleList(
[nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)]
)
self.activation = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
for layer in self.layers:
# As per comment in AllenNLP:
# We should bias the highway layer to just carry its input forward. We do that by
# setting the bias on `B(x)` to be positive, because that means `g` will be biased to
# be high, so we will carry the input forward. The bias on `B(x)` is the second half
# of the bias vector in each Linear layer.
nn.init.constant_(layer.bias[self.input_dim :], 1)
nn.init.constant_(layer.bias[: self.input_dim], 0)
nn.init.xavier_normal_(layer.weight)
def forward(self, x: torch.Tensor):
for layer in self.layers:
projection = layer(x)
proj_x, gate = projection.chunk(2, dim=-1)
proj_x = self.activation(proj_x)
gate = torch.sigmoid(gate)
x = gate * x + (gate.new_tensor([1]) - gate) * proj_x
return x
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