NMT结构图：（具体结构图）
LSTM基础知识
nmt_model.py：
参考文章：LSTM输出结构描述
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
CS224N 2020-21: Homework 4
nmt_model.py: NMT Model
Pencheng Yin <pcyin@cs.cmu.edu>
Sahil Chopra <schopra8@stanford.edu>
Vera Lin <veralin@stanford.edu>
"""
from collections import namedtuple
import sys
from typing import List, Tuple, Dict, Set, Union
import torch
import torch.nn as nn
import torch.nn.utils
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
from model_embeddings import ModelEmbeddings
Hypothesis = namedtuple('Hypothesis', ['value', 'score'])
class NMT(nn.Module):
""" Simple Neural Machine Translation Model:
- Bidrectional LSTM Encoder
- Unidirection LSTM Decoder
- Global Attention Model (Luong, et al. 2015)
"""
def __init__(self, embed_size, hidden_size, vocab, dropout_rate=0.2):
""" Init NMT Model.
@param embed_size (int): Embedding size (dimensionality)
@param hidden_size (int): Hidden Size, the size of hidden states (dimensionality)
@param vocab (Vocab): Vocabulary object containing src and tgt languages
See vocab.py for documentation.
@param dropout_rate (float): Dropout probability, for attention
"""
super(NMT, self).__init__()
self.model_embeddings = ModelEmbeddings(embed_size, vocab)
self.hidden_size = hidden_size
self.dropout_rate = dropout_rate
self.vocab = vocab
# default values
self.encoder = None
self.decoder = None
self.h_projection = None
self.c_projection = None
self.att_projection = None
self.combined_output_projection = None
self.target_vocab_projection = None
self.dropout = None
# For sanity check only, not relevant to implementation
self.gen_sanity_check = False
self.counter = 0
### YOUR CODE HERE (~8 Lines)
### TODO - Initialize the following variables:
### self.encoder (Bidirectional LSTM with bias)
### self.decoder (LSTM Cell with bias)
### self.h_projection (Linear Layer with no bias), called W_{h} in the PDF.
### self.c_projection (Linear Layer with no bias), called W_{c} in the PDF.
### self.att_projection (Linear Layer with no bias), called W_{attProj} in the PDF.
### self.combined_output_projection (Linear Layer with no bias), called W_{u} in the PDF.
### self.target_vocab_projection (Linear Layer with no bias), called W_{vocab} in the PDF.
### self.dropout (Dropout Layer)
###
### Use the following docs to properly initialize these variables:
### LSTM:
### https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM
### LSTM Cell:
### https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell
### Linear Layer:
### https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
### Dropout Layer:
### https://pytorch.org/docs/stable/nn.html#torch.nn.Dropout
self.encoder = nn.LSTM(input_size=embed_size, hidden_size=hidden_size, bias=True, bidirectional=True)
self.decoder = nn.LSTMCell(input_size=embed_size + hidden_size, hidden_size=hidden_size, bias=True)
self.h_projection = nn.Linear(in_features=2 * hidden_size, out_features=hidden_size, bias=False)
self.c_projection = nn.Linear(in_features=2 * hidden_size, out_features=hidden_size, bias=False)
self.att_projection = nn.Linear(in_features=2 * hidden_size, out_features=hidden_size, bias=False)
self.combined_output_projection = nn.Linear(in_features=3 * hidden_size, out_features=hidden_size, bias=False)
self.target_vocab_projection = nn.Linear(in_features=hidden_size, out_features=len(self.vocab.tgt), bias=False)
self.dropout = nn.Dropout(p=dropout_rate)
### END YOUR CODE
def forward(self, source: List[List[str]], target: List[List[str]]) -> torch.Tensor:
""" Take a mini-batch of source and target sentences, compute the log-likelihood of
target sentences under the language models learned by the NMT system.
@param source (List[List[str]]): list of source sentence tokens
@param target (List[List[str]]): list of target sentence tokens, wrapped by `<s>` and `</s>`
@returns scores (Tensor): a variable/tensor of shape (b, ) representing the
log-likelihood of generating the gold-standard target sentence for
each example in the input batch. Here b = batch size.
"""
# Compute sentence lengths
source_lengths = [len(s) for s in source]
# Convert list of lists into tensors
source_padded = self.vocab.src.to_input_tensor(source, device=self.device) # Tensor: (src_len, b)
target_padded = self.vocab.tgt.to_input_tensor(target, device=self.device) # Tensor: (tgt_len, b)
### Run the network forward:
### 1. Apply the encoder to `source_padded` by calling `self.encode()`
### 2. Generate sentence masks for `source_padded` by calling `self.generate_sent_masks()`
### 3. Apply the decoder to compute combined-output by calling `self.decode()`
### 4. Compute log probability distribution over the target vocabulary using the
### combined_outputs returned by the `self.decode()` function.
enc_hiddens, dec_init_state = self.encode(source_padded, source_lengths)
enc_masks = self.generate_sent_masks(enc_hiddens, source_lengths)
combined_outputs = self.decode(enc_hiddens, enc_masks, dec_init_state, target_padded)
P = F.log_softmax(self.target_vocab_projection(combined_outputs), dim=-1)
# Zero out, probabilities for which we have nothing in the target text
target_masks = (target_padded != self.vocab.tgt['<pad>']).float()
# Compute log probability of generating true target words
target_gold_words_log_prob = torch.gather(P, index=target_padded[1:].unsqueeze(-1), dim=-1).squeeze(-1) * target_masks[1:]
scores = target_gold_words_log_prob.sum(dim=0)
return scores
def encode(self, source_padded: torch.Tensor, source_lengths: List[int]) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
""" Apply the encoder to source sentences to obtain encoder hidden states.
Additionally, take the final states of the encoder and project them to obtain initial states for decoder.
@param source_padded (Tensor): Tensor of padded source sentences with shape (src_len, b), where
b = batch_size, src_len = maximum source sentence length. Note that
these have already been sorted in order of longest to shortest sentence.
@param source_lengths (List[int]): List of actual lengths for each of the source sentences in the batch
@returns enc_hiddens (Tensor): Tensor of hidden units with shape (b, src_len, h*2), where
b = batch size, src_len = maximum source sentence length, h = hidden size.
@returns dec_init_state (tuple(Tensor, Tensor)): Tuple of tensors representing the decoder's initial
hidden state and cell.
"""
enc_hiddens, dec_init_state = None, None
### YOUR CODE HERE (~ 8 Lines)
### TODO:
### 1. Construct Tensor `X` of source sentences with shape (src_len, b, e) using the source model embeddings.
### src_len = maximum source sentence length, b = batch size, e = embedding size. Note
### that there is no initial hidden state or cell for the decoder.
### 2. Compute `enc_hiddens`, `last_hidden`, `last_cell` by applying the encoder to `X`.
### - Before you can apply the encoder, you need to apply the `pack_padded_sequence` function to X.
### - After you apply the encoder, you need to apply the `pad_packed_sequence` function to enc_hiddens.
### - Note that the shape of the tensor returned by the encoder is (src_len, b, h*2) and we want to
### return a tensor of shape (b, src_len, h*2) as `enc_hiddens`.
### 3. Compute `dec_init_state` = (init_decoder_hidden, init_decoder_cell):
### - `init_decoder_hidden`:
### `last_hidden` is a tensor shape (2, b, h). The first dimension corresponds to forwards and backwards.
### Concatenate the forwards and backwards tensors to obtain a tensor shape (b, 2*h).
### Apply the h_projection layer to this in order to compute init_decoder_hidden.
### This is h_0^{dec} in the PDF. Here b = batch size, h = hidden size
### - `init_decoder_cell`:
### `last_cell` is a tensor shape (2, b, h). The first dimension corresponds to forwards and backwards.
### Concatenate the forwards and backwards tensors to obtain a tensor shape (b, 2*h).
### Apply the c_projection layer to this in order to compute init_decoder_cell.
### This is c_0^{dec} in the PDF. Here b = batch size, h = hidden size
###
### See the following docs, as you may need to use some of the following functions in your implementation:
### Pack the padded sequence X before passing to the encoder:
### https://pytorch.org/docs/stable/nn.html#torch.nn.utils.rnn.pack_padded_sequence
### Pad the packed sequence, enc_hiddens, returned by the encoder:
### https://pytorch.org/docs/stable/nn.html#torch.nn.utils.rnn.pad_packed_sequence
### Tensor Concatenation:
### https://pytorch.org/docs/stable/torch.html#torch.cat
### Tensor Permute:
### https://pytorch.org/docs/stable/tensors.html#torch.Tensor.permute
X = self.model_embeddings.source(source_padded)
X = pack_padded_sequence(X, lengths=torch.tensor(source_lengths))
enc_hiddens, (last_hidden, last_cell) = self.encoder(X)
enc_hiddens = pad_packed_sequence(enc_hiddens, batch_first=True)[0]
last_hidden = torch.cat((last_hidden[0], last_hidden[1]), dim=1)
init_decoder_hidden = self.h_projection(last_hidden)
last_cell = torch.cat((last_cell[0], last_cell[1]), dim=1)
init_decoder_cell = self.c_projection(last_cell)
dec_init_state = (init_decoder_hidden, init_decoder_cell)
### END YOUR CODE
return enc_hiddens, dec_init_state
def decode(self, enc_hiddens: torch.Tensor, enc_masks: torch.Tensor,
dec_init_state: Tuple[torch.Tensor, torch.Tensor], target_padded: torch.Tensor) -> torch.Tensor:
# Chop off the <END> token for max length sentences.
target_padded = target_padded[:-1]
# Initialize the decoder state (hidden and cell)
dec_state = dec_init_state
# Initialize previous combined output vector o_{t-1} as zero
batch_size = enc_hiddens.size(0)
o_prev = torch.zeros(batch_size, self.hidden_size, device=self.device)
# Initialize a list we will use to collect the combined output o_t on each step
combined_outputs = []
enc_hiddens_proj = self.att_projection(enc_hiddens)
Y = self.model_embeddings.target(target_padded)
for Y_t in torch.split(Y, split_size_or_sections=1, dim=0):
Y_t = torch.squeeze(Y_t, dim=0)
Ybar_t = torch.cat((Y_t, o_prev), dim=1)
next_dec_state, o_t, _ = self.step(Ybar_t, dec_state, enc_hiddens, enc_hiddens_proj, enc_masks)
combined_outputs.append(o_t)
o_prev = o_t
dec_state = next_dec_state
# Notice the corrected indentation here
combined_outputs = torch.stack(combined_outputs, dim=0)
### END YOUR CODE
return combined_outputs
def step(self, Ybar_t: torch.Tensor,
dec_state: Tuple[torch.Tensor, torch.Tensor],
enc_hiddens: torch.Tensor,
enc_hiddens_proj: torch.Tensor,
enc_masks: torch.Tensor) -> Tuple[Tuple, torch.Tensor, torch.Tensor]:
combined_output = None
# Decode the input based on the decoder's current state
dec_state = self.decoder(Ybar_t, dec_state)
dec_hidden, dec_cell = dec_state
# Compute the attention scores
e_t = torch.bmm(input=torch.unsqueeze(dec_hidden, 1), mat2=enc_hiddens_proj.permute(0, 2, 1))
e_t = torch.squeeze(e_t, dim=1)
# Apply attention mask if necessary
if enc_masks is not None:
e_t.data.masked_fill_(enc_masks.bool(), -float('inf'))
# Compute the attention weights
alpha_t = F.softmax(e_t, dim=1)
alpha_t = torch.unsqueeze(alpha_t, dim=1)
# Compute the context vector
a_t = torch.bmm(input=alpha_t, mat2=enc_hiddens)
a_t = torch.squeeze(a_t, dim=1)
# Combine the context vector and the decoder's hidden state
u_t = torch.cat((a_t, dec_hidden), dim=1)
# Project the combined vector
v_t = self.combined_output_projection(u_t)
# Apply dropout and nonlinearity
O_t = self.dropout(torch.tanh(v_t))
# Assign the combined output
combined_output = O_t
# Return the updated decoder state, the combined output, and the attention scores
return dec_state, combined_output, e_t
def generate_sent_masks(self, enc_hiddens: torch.Tensor, source_lengths: List[int]) -> torch.Tensor:
""" Generate sentence masks for encoder hidden states.
@param enc_hiddens (Tensor): encodings of shape (b, src_len, 2*h), where b = batch size,
src_len = max source length, h = hidden size.
@param source_lengths (List[int]): List of actual lengths for each of the sentences in the batch.
@returns enc_masks (Tensor): Tensor of sentence masks of shape (b, src_len),
where src_len = max source length, h = hidden size.
"""
enc_masks = torch.zeros(enc_hiddens.size(0), enc_hiddens.size(1), dtype=torch.float)
for e_id, src_len in enumerate(source_lengths):
enc_masks[e_id, src_len:] = 1
return enc_masks.to(self.device)
def beam_search(self, src_sent: List[str], beam_size: int=5, max_decoding_time_step: int=70) -> List[Hypothesis]:
""" Given a single source sentence, perform beam search, yielding translations in the target language.
@param src_sent (List[str]): a single source sentence (words)
@param beam_size (int): beam size
@param max_decoding_time_step (int): maximum number of time steps to unroll the decoding RNN
@returns hypotheses (List[Hypothesis]): a list of hypothesis, each hypothesis has two fields:
value: List[str]: the decoded target sentence, represented as a list of words
score: float: the log-likelihood of the target sentence
"""
src_sents_var = self.vocab.src.to_input_tensor([src_sent], self.device)
src_encodings, dec_init_vec = self.encode(src_sents_var, [len(src_sent)])
src_encodings_att_linear = self.att_projection(src_encodings)
h_tm1 = dec_init_vec
att_tm1 = torch.zeros(1, self.hidden_size, device=self.device)
eos_id = self.vocab.tgt['</s>']
hypotheses = [['<s>']]
hyp_scores = torch.zeros(len(hypotheses), dtype=torch.float, device=self.device)
completed_hypotheses = []
t = 0
while len(completed_hypotheses) < beam_size and t < max_decoding_time_step:
t += 1
hyp_num = len(hypotheses)
exp_src_encodings = src_encodings.expand(hyp_num,
src_encodings.size(1),
src_encodings.size(2))
exp_src_encodings_att_linear = src_encodings_att_linear.expand(hyp_num,
src_encodings_att_linear.size(1),
src_encodings_att_linear.size(2))
y_tm1 = torch.tensor([self.vocab.tgt[hyp[-1]] for hyp in hypotheses], dtype=torch.long, device=self.device)
y_t_embed = self.model_embeddings.target(y_tm1)
x = torch.cat([y_t_embed, att_tm1], dim=-1)
(h_t, cell_t), att_t, _ = self.step(x, h_tm1,
exp_src_encodings, exp_src_encodings_att_linear, enc_masks=None)
# log probabilities over target words
log_p_t = F.log_softmax(self.target_vocab_projection(att_t), dim=-1)
live_hyp_num = beam_size - len(completed_hypotheses)
contiuating_hyp_scores = (hyp_scores.unsqueeze(1).expand_as(log_p_t) + log_p_t).view(-1)
top_cand_hyp_scores, top_cand_hyp_pos = torch.topk(contiuating_hyp_scores, k=live_hyp_num)
prev_hyp_ids = top_cand_hyp_pos // len(self.vocab.tgt)
hyp_word_ids = top_cand_hyp_pos % len(self.vocab.tgt)
new_hypotheses = []
live_hyp_ids = []
new_hyp_scores = []
for prev_hyp_id, hyp_word_id, cand_new_hyp_score in zip(prev_hyp_ids, hyp_word_ids, top_cand_hyp_scores):
prev_hyp_id = prev_hyp_id.item()
hyp_word_id = hyp_word_id.item()
cand_new_hyp_score = cand_new_hyp_score.item()
hyp_word = self.vocab.tgt.id2word[hyp_word_id]
new_hyp_sent = hypotheses[prev_hyp_id] + [hyp_word]
if hyp_word == '</s>':
completed_hypotheses.append(Hypothesis(value=new_hyp_sent[1:-1],
score=cand_new_hyp_score))
else:
new_hypotheses.append(new_hyp_sent)
live_hyp_ids.append(prev_hyp_id)
new_hyp_scores.append(cand_new_hyp_score)
if len(completed_hypotheses) == beam_size:
break
live_hyp_ids = torch.tensor(live_hyp_ids, dtype=torch.long, device=self.device)
h_tm1 = (h_t[live_hyp_ids], cell_t[live_hyp_ids])
att_tm1 = att_t[live_hyp_ids]
hypotheses = new_hypotheses
hyp_scores = torch.tensor(new_hyp_scores, dtype=torch.float, device=self.device)
if len(completed_hypotheses) == 0:
completed_hypotheses.append(Hypothesis(value=hypotheses[0][1:],
score=hyp_scores[0].item()))
completed_hypotheses.sort(key=lambda hyp: hyp.score, reverse=True)
return completed_hypotheses
@property
def device(self) -> torch.device:
""" Determine which device to place the Tensors upon, CPU or GPU.
"""
return self.model_embeddings.source.weight.device
@staticmethod
def load(model_path: str):
""" Load the model from a file.
@param model_path (str): path to model
"""
params = torch.load(model_path, map_location=lambda storage, loc: storage)
args = params['args']
model = NMT(vocab=params['vocab'], **args)
model.load_state_dict(params['state_dict'])
return model
def save(self, path: str):
""" Save the odel to a file.
@param path (str): path to the model
"""
print('save model parameters to [%s]' % path, file=sys.stderr)
params = {
'args': dict(embed_size=self.model_embeddings.embed_size, hidden_size=self.hidden_size, dropout_rate=self.dropout_rate),
'vocab': self.vocab,
'state_dict': self.state_dict()
}
torch.save(params, path)
model_embeddings.py：
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import torch.nn as nn
class ModelEmbeddings(nn.Module):
def __init__(self, embed_size, vocab):
super(ModelEmbeddings, self).__init__()
self.embed_size = embed_size
# default values
self.source = None
self.target = None
src_pad_token_idx = vocab.src['<pad>']
tgt_pad_token_idx = vocab.tgt['<pad>']
self.source = nn.Embedding(num_embeddings=len(vocab.src),
embedding_dim=self.embed_size,
padding_idx=src_pad_token_idx)
self.target = nn.Embedding(num_embeddings=len(vocab.tgt),
embedding_dim=self.embed_size,
padding_idx=tgt_pad_token_idx)
### END YOUR CODE
