""" 这个页面的代码大量参考了 allenNLP """ from typing import Optional, Tuple, List, Callable import os import torch import torch.nn as nn import torch.nn.functional as F from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence from ...core.vocabulary import Vocabulary import json import pickle from ..utils import get_dropout_mask import codecs class LstmCellWithProjection(torch.nn.Module): """ An LSTM with Recurrent Dropout and a projected and clipped hidden state and memory. Note: this implementation is slower than the native Pytorch LSTM because it cannot make use of CUDNN optimizations for stacked RNNs due to and variational dropout and the custom nature of the cell state. Parameters ---------- input_size : ``int``, required. The dimension of the inputs to the LSTM. hidden_size : ``int``, required. The dimension of the outputs of the LSTM. cell_size : ``int``, required. The dimension of the memory cell used for the LSTM. go_forward: ``bool``, optional (default = True) The direction in which the LSTM is applied to the sequence. Forwards by default, or backwards if False. recurrent_dropout_probability: ``float``, optional (default = 0.0) The dropout probability to be used in a dropout scheme as stated in `A Theoretically Grounded Application of Dropout in Recurrent Neural Networks `_ . Implementation wise, this simply applies a fixed dropout mask per sequence to the recurrent connection of the LSTM. state_projection_clip_value: ``float``, optional, (default = None) The magnitude with which to clip the hidden_state after projecting it. memory_cell_clip_value: ``float``, optional, (default = None) The magnitude with which to clip the memory cell. Returns ------- output_accumulator : ``torch.FloatTensor`` The outputs of the LSTM for each timestep. A tensor of shape (batch_size, max_timesteps, hidden_size) where for a given batch element, all outputs past the sequence length for that batch are zero tensors. final_state: ``Tuple[torch.FloatTensor, torch.FloatTensor]`` The final (state, memory) states of the LSTM, with shape (1, batch_size, hidden_size) and (1, batch_size, cell_size) respectively. The first dimension is 1 in order to match the Pytorch API for returning stacked LSTM states. """ def __init__(self, input_size: int, hidden_size: int, cell_size: int, go_forward: bool = True, recurrent_dropout_probability: float = 0.0, memory_cell_clip_value: Optional[float] = None, state_projection_clip_value: Optional[float] = None) -> None: super(LstmCellWithProjection, self).__init__() # Required to be wrapped with a :class:`PytorchSeq2SeqWrapper`. self.input_size = input_size self.hidden_size = hidden_size self.cell_size = cell_size self.go_forward = go_forward self.state_projection_clip_value = state_projection_clip_value self.memory_cell_clip_value = memory_cell_clip_value self.recurrent_dropout_probability = recurrent_dropout_probability # We do the projections for all the gates all at once. self.input_linearity = torch.nn.Linear(input_size, 4 * cell_size, bias=False) self.state_linearity = torch.nn.Linear(hidden_size, 4 * cell_size, bias=True) # Additional projection matrix for making the hidden state smaller. self.state_projection = torch.nn.Linear(cell_size, hidden_size, bias=False) self.reset_parameters() def reset_parameters(self): # Use sensible default initializations for parameters. nn.init.orthogonal_(self.input_linearity.weight.data) nn.init.orthogonal_(self.state_linearity.weight.data) self.state_linearity.bias.data.fill_(0.0) # Initialize forget gate biases to 1.0 as per An Empirical # Exploration of Recurrent Network Architectures, (Jozefowicz, 2015). self.state_linearity.bias.data[self.cell_size:2 * self.cell_size].fill_(1.0) def forward(self, # pylint: disable=arguments-differ inputs: torch.FloatTensor, batch_lengths: List[int], initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None): """ Parameters ---------- inputs : ``torch.FloatTensor``, required. A tensor of shape (batch_size, num_timesteps, input_size) to apply the LSTM over. batch_lengths : ``List[int]``, required. A list of length batch_size containing the lengths of the sequences in batch. initial_state : ``Tuple[torch.Tensor, torch.Tensor]``, optional, (default = None) A tuple (state, memory) representing the initial hidden state and memory of the LSTM. The ``state`` has shape (1, batch_size, hidden_size) and the ``memory`` has shape (1, batch_size, cell_size). Returns ------- output_accumulator : ``torch.FloatTensor`` The outputs of the LSTM for each timestep. A tensor of shape (batch_size, max_timesteps, hidden_size) where for a given batch element, all outputs past the sequence length for that batch are zero tensors. final_state : ``Tuple[``torch.FloatTensor, torch.FloatTensor]`` A tuple (state, memory) representing the initial hidden state and memory of the LSTM. The ``state`` has shape (1, batch_size, hidden_size) and the ``memory`` has shape (1, batch_size, cell_size). """ batch_size = inputs.size()[0] total_timesteps = inputs.size()[1] # We have to use this '.data.new().fill_' pattern to create tensors with the correct # type - forward has no knowledge of whether these are torch.Tensors or torch.cuda.Tensors. output_accumulator = inputs.data.new(batch_size, total_timesteps, self.hidden_size).fill_(0) if initial_state is None: full_batch_previous_memory = inputs.data.new(batch_size, self.cell_size).fill_(0) full_batch_previous_state = inputs.data.new(batch_size, self.hidden_size).fill_(0) else: full_batch_previous_state = initial_state[0].squeeze(0) full_batch_previous_memory = initial_state[1].squeeze(0) current_length_index = batch_size - 1 if self.go_forward else 0 if self.recurrent_dropout_probability > 0.0 and self.training: dropout_mask = get_dropout_mask(self.recurrent_dropout_probability, full_batch_previous_state) else: dropout_mask = None for timestep in range(total_timesteps): # The index depends on which end we start. index = timestep if self.go_forward else total_timesteps - timestep - 1 # What we are doing here is finding the index into the batch dimension # which we need to use for this timestep, because the sequences have # variable length, so once the index is greater than the length of this # particular batch sequence, we no longer need to do the computation for # this sequence. The key thing to recognise here is that the batch inputs # must be _ordered_ by length from longest (first in batch) to shortest # (last) so initially, we are going forwards with every sequence and as we # pass the index at which the shortest elements of the batch finish, # we stop picking them up for the computation. if self.go_forward: while batch_lengths[current_length_index] <= index: current_length_index -= 1 # If we're going backwards, we are _picking up_ more indices. else: # First conditional: Are we already at the maximum number of elements in the batch? # Second conditional: Does the next shortest sequence beyond the current batch # index require computation use this timestep? while current_length_index < (len(batch_lengths) - 1) and \ batch_lengths[current_length_index + 1] > index: current_length_index += 1 # Actually get the slices of the batch which we # need for the computation at this timestep. # shape (batch_size, cell_size) previous_memory = full_batch_previous_memory[0: current_length_index + 1].clone() # Shape (batch_size, hidden_size) previous_state = full_batch_previous_state[0: current_length_index + 1].clone() # Shape (batch_size, input_size) timestep_input = inputs[0: current_length_index + 1, index] # Do the projections for all the gates all at once. # Both have shape (batch_size, 4 * cell_size) projected_input = self.input_linearity(timestep_input) projected_state = self.state_linearity(previous_state) # Main LSTM equations using relevant chunks of the big linear # projections of the hidden state and inputs. input_gate = torch.sigmoid(projected_input[:, (0 * self.cell_size):(1 * self.cell_size)] + projected_state[:, (0 * self.cell_size):(1 * self.cell_size)]) forget_gate = torch.sigmoid(projected_input[:, (1 * self.cell_size):(2 * self.cell_size)] + projected_state[:, (1 * self.cell_size):(2 * self.cell_size)]) memory_init = torch.tanh(projected_input[:, (2 * self.cell_size):(3 * self.cell_size)] + projected_state[:, (2 * self.cell_size):(3 * self.cell_size)]) output_gate = torch.sigmoid(projected_input[:, (3 * self.cell_size):(4 * self.cell_size)] + projected_state[:, (3 * self.cell_size):(4 * self.cell_size)]) memory = input_gate * memory_init + forget_gate * previous_memory # Here is the non-standard part of this LSTM cell; first, we clip the # memory cell, then we project the output of the timestep to a smaller size # and again clip it. if self.memory_cell_clip_value: # pylint: disable=invalid-unary-operand-type memory = torch.clamp(memory, -self.memory_cell_clip_value, self.memory_cell_clip_value) # shape (current_length_index, cell_size) pre_projection_timestep_output = output_gate * torch.tanh(memory) # shape (current_length_index, hidden_size) timestep_output = self.state_projection(pre_projection_timestep_output) if self.state_projection_clip_value: # pylint: disable=invalid-unary-operand-type timestep_output = torch.clamp(timestep_output, -self.state_projection_clip_value, self.state_projection_clip_value) # Only do dropout if the dropout prob is > 0.0 and we are in training mode. if dropout_mask is not None: timestep_output = timestep_output * dropout_mask[0: current_length_index + 1] # We've been doing computation with less than the full batch, so here we create a new # variable for the the whole batch at this timestep and insert the result for the # relevant elements of the batch into it. full_batch_previous_memory = full_batch_previous_memory.data.clone() full_batch_previous_state = full_batch_previous_state.data.clone() full_batch_previous_memory[0:current_length_index + 1] = memory full_batch_previous_state[0:current_length_index + 1] = timestep_output output_accumulator[0:current_length_index + 1, index] = timestep_output # Mimic the pytorch API by returning state in the following shape: # (num_layers * num_directions, batch_size, ...). As this # LSTM cell cannot be stacked, the first dimension here is just 1. final_state = (full_batch_previous_state.unsqueeze(0), full_batch_previous_memory.unsqueeze(0)) return output_accumulator, final_state class LstmbiLm(nn.Module): def __init__(self, config): super(LstmbiLm, self).__init__() self.config = config self.encoder = nn.LSTM(self.config['lstm']['projection_dim'], self.config['lstm']['dim'], num_layers=self.config['lstm']['n_layers'], bidirectional=True, batch_first=True, dropout=self.config['dropout']) self.projection = nn.Linear(self.config['lstm']['dim'], self.config['lstm']['projection_dim'], bias=True) def forward(self, inputs, seq_len): sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) inputs = inputs[sort_idx] inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=self.batch_first) output, hx = self.encoder(inputs, None) # -> [N,L,C] output, _ = nn.utils.rnn.pad_packed_sequence(output, batch_first=self.batch_first) _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) output = output[unsort_idx] forward, backward = output.split(self.config['lstm']['dim'], 2) return torch.cat([self.projection(forward), self.projection(backward)], dim=2) class ElmobiLm(torch.nn.Module): def __init__(self, config): super(ElmobiLm, self).__init__() self.config = config input_size = config['lstm']['projection_dim'] hidden_size = config['lstm']['projection_dim'] cell_size = config['lstm']['dim'] num_layers = config['lstm']['n_layers'] memory_cell_clip_value = config['lstm']['cell_clip'] state_projection_clip_value = config['lstm']['proj_clip'] recurrent_dropout_probability = 0.0 self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.cell_size = cell_size forward_layers = [] backward_layers = [] lstm_input_size = input_size go_forward = True for layer_index in range(num_layers): forward_layer = LstmCellWithProjection(lstm_input_size, hidden_size, cell_size, go_forward, recurrent_dropout_probability, memory_cell_clip_value, state_projection_clip_value) backward_layer = LstmCellWithProjection(lstm_input_size, hidden_size, cell_size, not go_forward, recurrent_dropout_probability, memory_cell_clip_value, state_projection_clip_value) lstm_input_size = hidden_size self.add_module('forward_layer_{}'.format(layer_index), forward_layer) self.add_module('backward_layer_{}'.format(layer_index), backward_layer) forward_layers.append(forward_layer) backward_layers.append(backward_layer) self.forward_layers = forward_layers self.backward_layers = backward_layers def forward(self, inputs, seq_len): """ :param inputs: batch_size x max_len x embed_size :param seq_len: batch_size :return: torch.FloatTensor. num_layers x batch_size x max_len x hidden_size """ max_len = inputs.size(1) sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True) inputs = inputs[sort_idx] inputs = nn.utils.rnn.pack_padded_sequence(inputs, sort_lens, batch_first=True) output, _ = self._lstm_forward(inputs, None) _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False) output = output[:, unsort_idx] return output def _lstm_forward(self, inputs: PackedSequence, initial_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None) -> \ Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: """ Parameters ---------- inputs : ``PackedSequence``, required. A batch first ``PackedSequence`` to run the stacked LSTM over. initial_state : ``Tuple[torch.Tensor, torch.Tensor]``, optional, (default = None) A tuple (state, memory) representing the initial hidden state and memory of the LSTM, with shape (num_layers, batch_size, 2 * hidden_size) and (num_layers, batch_size, 2 * cell_size) respectively. Returns ------- output_sequence : ``torch.FloatTensor`` The encoded sequence of shape (num_layers, batch_size, sequence_length, hidden_size) final_states: ``Tuple[torch.FloatTensor, torch.FloatTensor]`` The per-layer final (state, memory) states of the LSTM, with shape (num_layers, batch_size, 2 * hidden_size) and (num_layers, batch_size, 2 * cell_size) respectively. The last dimension is duplicated because it contains the state/memory for both the forward and backward layers. """ if initial_state is None: hidden_states: List[Optional[Tuple[torch.Tensor, torch.Tensor]]] = [None] * len(self.forward_layers) elif initial_state[0].size()[0] != len(self.forward_layers): raise Exception("Initial states were passed to forward() but the number of " "initial states does not match the number of layers.") else: hidden_states = list(zip(initial_state[0].split(1, 0), initial_state[1].split(1, 0))) inputs, batch_lengths = pad_packed_sequence(inputs, batch_first=True) forward_output_sequence = inputs backward_output_sequence = inputs final_states = [] sequence_outputs = [] for layer_index, state in enumerate(hidden_states): forward_layer = getattr(self, 'forward_layer_{}'.format(layer_index)) backward_layer = getattr(self, 'backward_layer_{}'.format(layer_index)) forward_cache = forward_output_sequence backward_cache = backward_output_sequence if state is not None: forward_hidden_state, backward_hidden_state = state[0].split(self.hidden_size, 2) forward_memory_state, backward_memory_state = state[1].split(self.cell_size, 2) forward_state = (forward_hidden_state, forward_memory_state) backward_state = (backward_hidden_state, backward_memory_state) else: forward_state = None backward_state = None forward_output_sequence, forward_state = forward_layer(forward_output_sequence, batch_lengths, forward_state) backward_output_sequence, backward_state = backward_layer(backward_output_sequence, batch_lengths, backward_state) # Skip connections, just adding the input to the output. if layer_index != 0: forward_output_sequence += forward_cache backward_output_sequence += backward_cache sequence_outputs.append(torch.cat([forward_output_sequence, backward_output_sequence], -1)) # Append the state tuples in a list, so that we can return # the final states for all the layers. final_states.append((torch.cat([forward_state[0], backward_state[0]], -1), torch.cat([forward_state[1], backward_state[1]], -1))) stacked_sequence_outputs: torch.FloatTensor = torch.stack(sequence_outputs) # Stack the hidden state and memory for each layer in。to 2 tensors of shape # (num_layers, batch_size, hidden_size) and (num_layers, batch_size, cell_size) # respectively. final_hidden_states, final_memory_states = zip(*final_states) final_state_tuple: Tuple[torch.FloatTensor, torch.FloatTensor] = (torch.cat(final_hidden_states, 0), torch.cat(final_memory_states, 0)) return stacked_sequence_outputs, final_state_tuple class ConvTokenEmbedder(nn.Module): def __init__(self, config, weight_file, word_emb_layer, char_emb_layer): super(ConvTokenEmbedder, self).__init__() self.weight_file = weight_file self.word_emb_layer = word_emb_layer self.char_emb_layer = char_emb_layer self.output_dim = config['lstm']['projection_dim'] self._options = config char_cnn_options = self._options['char_cnn'] if char_cnn_options['activation'] == 'tanh': self.activation = torch.tanh elif char_cnn_options['activation'] == 'relu': self.activation = torch.nn.functional.relu else: raise Exception("Unknown activation") if char_emb_layer is not None: self.char_conv = [] cnn_config = config['char_cnn'] filters = cnn_config['filters'] char_embed_dim = cnn_config['embedding']['dim'] convolutions = [] for i, (width, num) in enumerate(filters): conv = torch.nn.Conv1d( in_channels=char_embed_dim, out_channels=num, kernel_size=width, bias=True ) convolutions.append(conv) self.add_module('char_conv_{}'.format(i), conv) self._convolutions = convolutions n_filters = sum(f[1] for f in filters) n_highway = cnn_config['n_highway'] self._highways = Highway(n_filters, n_highway, activation=torch.nn.functional.relu) self._projection = torch.nn.Linear(n_filters, self.output_dim, bias=True) def forward(self, words, chars): """ :param words: :param chars: Tensor Shape ``(batch_size, sequence_length, 50)``: :return Tensor Shape ``(batch_size, sequence_length + 2, embedding_dim)`` : """ # the character id embedding # (batch_size * sequence_length, max_chars_per_token, embed_dim) # character_embedding = torch.nn.functional.embedding( # chars.view(-1, max_chars_per_token), # self._char_embedding_weights # ) batch_size, sequence_length, max_char_len = chars.size() character_embedding = self.char_emb_layer(chars).reshape(batch_size * sequence_length, max_char_len, -1) # run convolutions # (batch_size * sequence_length, embed_dim, max_chars_per_token) character_embedding = torch.transpose(character_embedding, 1, 2) convs = [] for i in range(len(self._convolutions)): conv = getattr(self, 'char_conv_{}'.format(i)) convolved = conv(character_embedding) # (batch_size * sequence_length, n_filters for this width) convolved, _ = torch.max(convolved, dim=-1) convolved = self.activation(convolved) convs.append(convolved) # (batch_size * sequence_length, n_filters) token_embedding = torch.cat(convs, dim=-1) # apply the highway layers (batch_size * sequence_length, n_filters) token_embedding = self._highways(token_embedding) # final projection (batch_size * sequence_length, embedding_dim) token_embedding = self._projection(token_embedding) # reshape to (batch_size, sequence_length+2, embedding_dim) return token_embedding.view(batch_size, sequence_length, -1) class Highway(torch.nn.Module): """ A `Highway layer `_ does a gated combination of a linear transformation and a non-linear transformation of its input. :math:`y = g * x + (1 - g) * f(A(x))`, where :math:`A` is a linear transformation, :math:`f` is an element-wise non-linearity, and :math:`g` is an element-wise gate, computed as :math:`sigmoid(B(x))`. This module will apply a fixed number of highway layers to its input, returning the final result. Parameters ---------- input_dim : ``int`` The dimensionality of :math:`x`. We assume the input has shape ``(batch_size, input_dim)``. num_layers : ``int``, optional (default=``1``) The number of highway layers to apply to the input. activation : ``Callable[[torch.Tensor], torch.Tensor]``, optional (default=``torch.nn.functional.relu``) The non-linearity to use in the highway layers. """ def __init__(self, input_dim: int, num_layers: int = 1, activation: Callable[[torch.Tensor], torch.Tensor] = torch.nn.functional.relu) -> None: super(Highway, self).__init__() self._input_dim = input_dim self._layers = torch.nn.ModuleList([torch.nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)]) self._activation = activation for layer in self._layers: # 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, to we will carry the input forward. The bias on `B(x)` is the second half # of the bias vector in each Linear layer. layer.bias[input_dim:].data.fill_(1) def forward(self, inputs: torch.Tensor) -> torch.Tensor: # pylint: disable=arguments-differ current_input = inputs for layer in self._layers: projected_input = layer(current_input) linear_part = current_input # NOTE: if you modify this, think about whether you should modify the initialization # above, too. nonlinear_part = projected_input[:, (0 * self._input_dim):(1 * self._input_dim)] gate = projected_input[:, (1 * self._input_dim):(2 * self._input_dim)] nonlinear_part = self._activation(nonlinear_part) gate = torch.sigmoid(gate) current_input = gate * linear_part + (1 - gate) * nonlinear_part return current_input class _ElmoModel(nn.Module): """ 该Module是ElmoEmbedding中进行所有的heavy lifting的地方。做的工作，包括 (1) 根据配置，加载模型; (2) 根据vocab，对模型中的embedding进行调整. 并将其正确初始化 (3) 保存一个words与chars的对应转换，获取时自动进行相应的转换 (4) 设计一个保存token的embedding，允许缓存word的表示。 """ def __init__(self, model_dir: str, vocab: Vocabulary = None, cache_word_reprs: bool = False): super(_ElmoModel, self).__init__() self.model_dir = model_dir dir = os.walk(self.model_dir) config_file = None weight_file = None config_count = 0 weight_count = 0 for path, dir_list, file_list in dir: for file_name in file_list: if file_name.__contains__(".json"): config_file = file_name config_count += 1 elif file_name.__contains__(".pkl"): weight_file = file_name weight_count += 1 if config_count > 1 or weight_count > 1: raise Exception(f"Multiple config files(*.json) or weight files(*.hdf5) detected in {model_dir}.") elif config_count == 0 or weight_count == 0: raise Exception(f"No config file or weight file found in {model_dir}") config = json.load(open(os.path.join(model_dir, config_file), 'r')) self.weight_file = os.path.join(model_dir, weight_file) self.config = config OOV_TAG = '' PAD_TAG = '' BOS_TAG = '' EOS_TAG = '' BOW_TAG = '' EOW_TAG = '' # For the model trained with character-based word encoder. char_lexicon = {} with codecs.open(os.path.join(model_dir, 'char.dic'), 'r', encoding='utf-8') as fpi: for line in fpi: tokens = line.strip().split('\t') if len(tokens) == 1: tokens.insert(0, '\u3000') token, i = tokens char_lexicon[token] = int(i) # 做一些sanity check for special_word in [PAD_TAG, OOV_TAG, BOW_TAG, EOW_TAG]: assert special_word in char_lexicon, f"{special_word} not found in char.dic." # 从vocab中构建char_vocab char_vocab = Vocabulary(unknown=OOV_TAG, padding=PAD_TAG) # 需要保证与在里面 char_vocab.add_word_lst([BOW_TAG, EOW_TAG, BOS_TAG, EOS_TAG]) for word, index in vocab: char_vocab.add_word_lst(list(word)) self.bos_index, self.eos_index, self._pad_index = len(vocab), len(vocab) + 1, vocab.padding_idx # 根据char_lexicon调整, 多设置一位，是预留给word padding的(该位置的char表示为全0表示) char_emb_layer = nn.Embedding(len(char_vocab) + 1, int(config['char_cnn']['embedding']['dim']), padding_idx=len(char_vocab)) # 读入预训练权重 这里的elmo_model 包含char_cnn和 lstm 的 state_dict elmo_model = torch.load(os.path.join(self.model_dir, weight_file), map_location='cpu') char_embed_weights = elmo_model["char_cnn"]['char_emb_layer.weight'] found_char_count = 0 for char, index in char_vocab: # 调整character embedding if char in char_lexicon: index_in_pre = char_lexicon.get(char) found_char_count += 1 else: index_in_pre = char_lexicon[OOV_TAG] char_emb_layer.weight.data[index] = char_embed_weights[index_in_pre] print(f"{found_char_count} out of {len(char_vocab)} characters were found in pretrained elmo embedding.") # 生成words到chars的映射 max_chars = config['char_cnn']['max_characters_per_token'] self.words_to_chars_embedding = nn.Parameter(torch.full((len(vocab) + 2, max_chars), fill_value=len(char_vocab), dtype=torch.long), requires_grad=False) for word, index in list(iter(vocab)) + [(BOS_TAG, len(vocab)), (EOS_TAG, len(vocab) + 1)]: if len(word) + 2 > max_chars: word = word[:max_chars - 2] if index == self._pad_index: continue elif word == BOS_TAG or word == EOS_TAG: char_ids = [char_vocab.to_index(BOW_TAG)] + [char_vocab.to_index(word)] + [ char_vocab.to_index(EOW_TAG)] char_ids += [char_vocab.to_index(PAD_TAG)] * (max_chars - len(char_ids)) else: char_ids = [char_vocab.to_index(BOW_TAG)] + [char_vocab.to_index(c) for c in word] + [ char_vocab.to_index(EOW_TAG)] char_ids += [char_vocab.to_index(PAD_TAG)] * (max_chars - len(char_ids)) self.words_to_chars_embedding[index] = torch.LongTensor(char_ids) self.char_vocab = char_vocab self.token_embedder = ConvTokenEmbedder( config, self.weight_file, None, char_emb_layer) elmo_model["char_cnn"]['char_emb_layer.weight'] = char_emb_layer.weight self.token_embedder.load_state_dict(elmo_model["char_cnn"]) self.output_dim = config['lstm']['projection_dim'] # lstm encoder self.encoder = ElmobiLm(config) self.encoder.load_state_dict(elmo_model["lstm"]) if cache_word_reprs: if config['char_cnn']['embedding']['dim'] > 0: # 只有在使用了chars的情况下有用 print("Start to generate cache word representations.") batch_size = 320 # bos eos word_size = self.words_to_chars_embedding.size(0) num_batches = word_size // batch_size + \ int(word_size % batch_size != 0) self.cached_word_embedding = nn.Embedding(word_size, config['lstm']['projection_dim']) with torch.no_grad(): for i in range(num_batches): words = torch.arange(i * batch_size, min((i + 1) * batch_size, word_size)).long() chars = self.words_to_chars_embedding[words].unsqueeze(1) # batch_size x 1 x max_chars word_reprs = self.token_embedder(words.unsqueeze(1), chars).detach() # batch_size x 1 x config['encoder']['projection_dim'] self.cached_word_embedding.weight.data[words] = word_reprs.squeeze(1) print("Finish generating cached word representations. Going to delete the character encoder.") del self.token_embedder, self.words_to_chars_embedding else: print("There is no need to cache word representations, since no character information is used.") def forward(self, words): """ :param words: batch_size x max_len :return: num_layers x batch_size x max_len x hidden_size """ # 扩展, batch_size, max_len = words.size() expanded_words = words.new_zeros(batch_size, max_len + 2) # 因为pad一定为0， seq_len = words.ne(self._pad_index).sum(dim=-1) expanded_words[:, 1:-1] = words expanded_words[:, 0].fill_(self.bos_index) expanded_words[torch.arange(batch_size).to(words), seq_len + 1] = self.eos_index seq_len = seq_len + 2 zero_tensor = expanded_words.new_zeros(expanded_words.shape) mask = (expanded_words == zero_tensor).unsqueeze(-1) if hasattr(self, 'cached_word_embedding'): token_embedding = self.cached_word_embedding(expanded_words) else: if hasattr(self, 'words_to_chars_embedding'): chars = self.words_to_chars_embedding[expanded_words] else: chars = None token_embedding = self.token_embedder(expanded_words, chars) # batch_size x max_len x embed_dim encoder_output = self.encoder(token_embedding, seq_len) if encoder_output.size(2) < max_len + 2: num_layers, _, output_len, hidden_size = encoder_output.size() dummy_tensor = encoder_output.new_zeros(num_layers, batch_size, max_len + 2 - output_len, hidden_size) encoder_output = torch.cat((encoder_output, dummy_tensor), 2) sz = encoder_output.size() # 2, batch_size, max_len, hidden_size token_embedding = token_embedding.masked_fill(mask, 0) token_embedding = torch.cat((token_embedding, token_embedding), dim=2).view(1, sz[1], sz[2], sz[3]) encoder_output = torch.cat((token_embedding, encoder_output), dim=0) # 删除, . 这里没有精确地删除，但应该也不会影响最后的结果了。 encoder_output = encoder_output[:, :, 1:-1] return encoder_output