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