fairseq入门教程

前言：该教程利用fairseq增加一个新的FaiseqEncoderDecoderModel，该新模型利用LSTM来encodes一个sentence输入句；接着把最后的hidden state传给第二个LSTM，用于decodes出target sentence目标句。

教程包括：

a.编写Encoder和Decoder，分别用于encode、decode输入句与目标句；

b.注册一个适用于Command-line Tools的新模型；

c.使用现有的command-line tools来训练这个模型；

d.使用Incremental decoding，让修改Decoder更加迅速敏捷making generation faster；

1.搭建Encoder和Decoder

利用FairseqEncoder和FairDecoder接口来搭建自己的Encoder和Decoder，这两个接口继承torch.nn.Module，因此FairseqEncoder和FairseqDecoder能够使用传统的PyTorch Modules来进行编写。

Encoder

该encoder会在输入句中嵌入tokens，然后传入torch.nn.LSTM并且返回最终的隐藏状态hidden state。创建的encoder保存为fairseq/models/simple_lstm.py。

import torch.nn as nn
from fairseq import utils
from fairseq.models import FairseqEncoder

class SimpleLSTMEncoder(FairseqEncoder):

    def __init__(
        self, args, dictionary, embed_dim=128, hidden_dim=128, dropout=0.1,
    ):
        super().__init__(dictionary)
        self.args = args

        # Our encoder will embed the inputs before feeding them to the LSTM.
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)

        # We'll use a single-layer, unidirectional LSTM for simplicity.
        self.lstm = nn.LSTM(
            input_size=embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
            batch_first=True,
        )

    def forward(self, src_tokens, src_lengths):
        # The inputs to the ``forward()`` function are determined by the
        # Task, and in particular the ``'net_input'`` key in each
        # mini-batch. We discuss Tasks in the next tutorial, but for now just
        # know that *src_tokens* has shape `(batch, src_len)` and *src_lengths*
        # has shape `(batch)`.

        # Note that the source is typically padded on the left. This can be
        # configured by adding the `--left-pad-source "False"` command-line
        # argument, but here we'll make the Encoder handle either kind of
        # padding by converting everything to be right-padded.
        if self.args.left_pad_source:
            # Convert left-padding to right-padding.
            src_tokens = utils.convert_padding_direction(
                src_tokens,
                padding_idx=self.dictionary.pad(),
                left_to_right=True
            )

        # Embed the source.
        x = self.embed_tokens(src_tokens)

        # Apply dropout.
        x = self.dropout(x)

        # Pack the sequence into a PackedSequence object to feed to the LSTM.
        x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True)

        # Get the output from the LSTM.
        _outputs, (final_hidden, _final_cell) = self.lstm(x)

        # Return the Encoder's output. This can be any object and will be
        # passed directly to the Decoder.
        return {
            # this will have shape `(bsz, hidden_dim)`
            'final_hidden': final_hidden.squeeze(0),
        }

    # Encoders are required to implement this method so that we can rearrange
    # the order of the batch elements during inference (e.g., beam search).
    def reorder_encoder_out(self, encoder_out, new_order):
        """
        Reorder encoder output according to `new_order`.

        Args:
            encoder_out: output from the ``forward()`` method
            new_order (LongTensor): desired order

        Returns:
            `encoder_out` rearranged according to `new_order`
        """
        final_hidden = encoder_out['final_hidden']
        return {
            'final_hidden': final_hidden.index_select(0, new_order),
        }

Decoder

基于Encoder的最终hidden state和嵌入的目标单词target word，利用Decoder来预测下一个word，有时候也叫teacher forcing。具体来说，使用torch.nn.LSTM来生成一个隐藏状态hidden state序列，之后通过函数映射到输出的词汇中用于预测每一个目标词target word。

import torch
from fairseq.models import FairseqDecoder

class SimpleLSTMDecoder(FairseqDecoder):

    def __init__(
        self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128,
        dropout=0.1,
    ):
        super().__init__(dictionary)

        # Our decoder will embed the inputs before feeding them to the LSTM.
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)

        # We'll use a single-layer, unidirectional LSTM for simplicity.
        self.lstm = nn.LSTM(
            # For the first layer we'll concatenate the Encoder's final hidden
            # state with the embedded target tokens.
            input_size=encoder_hidden_dim + embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
        )

        # Define the output projection.
        self.output_projection = nn.Linear(hidden_dim, len(dictionary))

    # During training Decoders are expected to take the entire target sequence
    # (shifted right by one position) and produce logits over the vocabulary.
    # The *prev_output_tokens* tensor begins with the end-of-sentence symbol,
    # ``dictionary.eos()``, followed by the target sequence.
    def forward(self, prev_output_tokens, encoder_out):
        """
        Args:
            prev_output_tokens (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for teacher forcing
            encoder_out (Tensor, optional): output from the encoder, used for
                encoder-side attention

        Returns:
            tuple:
                - the last decoder layer's output of shape
                  `(batch, tgt_len, vocab)`
                - the last decoder layer's attention weights of shape
                  `(batch, tgt_len, src_len)`
        """
        bsz, tgt_len = prev_output_tokens.size()

        # Extract the final hidden state from the Encoder.
        final_encoder_hidden = encoder_out['final_hidden']

        # Embed the target sequence, which has been shifted right by one
        # position and now starts with the end-of-sentence symbol.
        x = self.embed_tokens(prev_output_tokens)

        # Apply dropout.
        x = self.dropout(x)

        # Concatenate the Encoder's final hidden state to *every* embedded
        # target token.
        x = torch.cat(
            [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)],
            dim=2,
        )

        # Using PackedSequence objects in the Decoder is harder than in the
        # Encoder, since the targets are not sorted in descending length order,
        # which is a requirement of ``pack_padded_sequence()``. Instead we'll
        # feed nn.LSTM directly.
        initial_state = (
            final_encoder_hidden.unsqueeze(0),  # hidden
            torch.zeros_like(final_encoder_hidden).unsqueeze(0),  # cell
        )
        output, _ = self.lstm(
            x.transpose(0, 1),  # convert to shape `(tgt_len, bsz, dim)`
            initial_state,
        )
        x = output.transpose(0, 1)  # convert to shape `(bsz, tgt_len, hidden)`

        # Project the outputs to the size of the vocabulary.
        x = self.output_projection(x)

        # Return the logits and ``None`` for the attention weights
        return x, None

2.注册模型（Registering the Model）

利用fairseq中的register_model()函数修饰器来注册模型。一旦模型注册成功，就可以使用Command-line Tools。

所有的注册模型必须实现BaseFairseqModel接口，对于sequence-to-sequence模型（如，任何模包含Encoder和Decoder的模型）需要使用FairseqEncoderDecoderModel接口。

创建SimpleLSTMModel类，在类中利用函数包装类wrapper class，命名该函数为simple_lstm。

from fairseq.models import FairseqEncoderDecoderModel, register_model

# Note: the register_model "decorator" should immediately precede the
# definition of the Model class.

@register_model('simple_lstm')
class SimpleLSTMModel(FairseqEncoderDecoderModel):

    @staticmethod
    def add_args(parser):
        # Models can override this method to add new command-line arguments.
        # Here we'll add some new command-line arguments to configure dropout
        # and the dimensionality of the embeddings and hidden states.
        parser.add_argument(
            '--encoder-embed-dim', type=int, metavar='N',
            help='dimensionality of the encoder embeddings',
        )
        parser.add_argument(
            '--encoder-hidden-dim', type=int, metavar='N',
            help='dimensionality of the encoder hidden state',
        )
        parser.add_argument(
            '--encoder-dropout', type=float, default=0.1,
            help='encoder dropout probability',
        )
        parser.add_argument(
            '--decoder-embed-dim', type=int, metavar='N',
            help='dimensionality of the decoder embeddings',
        )
        parser.add_argument(
            '--decoder-hidden-dim', type=int, metavar='N',
            help='dimensionality of the decoder hidden state',
        )
        parser.add_argument(
            '--decoder-dropout', type=float, default=0.1,
            help='decoder dropout probability',
        )

    @classmethod
    def build_model(cls, args, task):
        # Fairseq initializes models by calling the ``build_model()``
        # function. This provides more flexibility, since the returned model
        # instance can be of a different type than the one that was called.
        # In this case we'll just return a SimpleLSTMModel instance.

        # Initialize our Encoder and Decoder.
        encoder = SimpleLSTMEncoder(
            args=args,
            dictionary=task.source_dictionary,
            embed_dim=args.encoder_embed_dim,
            hidden_dim=args.encoder_hidden_dim,
            dropout=args.encoder_dropout,
        )
        decoder = SimpleLSTMDecoder(
            dictionary=task.target_dictionary,
            encoder_hidden_dim=args.encoder_hidden_dim,
            embed_dim=args.decoder_embed_dim,
            hidden_dim=args.decoder_hidden_dim,
            dropout=args.decoder_dropout,
        )
        model = SimpleLSTMModel(encoder, decoder)

        # Print the model architecture.
        print(model)

        return model

    # We could override the ``forward()`` if we wanted more control over how
    # the encoder and decoder interact, but it's not necessary for this
    # tutorial since we can inherit the default implementation provided by
    # the FairseqEncoderDecoderModel base class, which looks like:
    #
    # def forward(self, src_tokens, src_lengths, prev_output_tokens):
    #     encoder_out = self.encoder(src_tokens, src_lengths)
    #     decoder_out = self.decoder(prev_output_tokens, encoder_out)
    #     return decoder_out

最后利用这个configuration配置方式为模型定义一个architecture，这个定义结构的方式是通过register_model_architecture函数修饰器完成的。之后就可以使用--arch命令参数，如--arch tutorial_simple_lstm。

from fairseq.models import register_model_architecture

# The first argument to ``register_model_architecture()`` should be the name
# of the model we registered above (i.e., 'simple_lstm'). The function we
# register here should take a single argument *args* and modify it in-place
# to match the desired architecture.

@register_model_architecture('simple_lstm', 'tutorial_simple_lstm')
def tutorial_simple_lstm(args):
    # We use ``getattr()`` to prioritize arguments that are explicitly given
    # on the command-line, so that the defaults defined below are only used
    # when no other value has been specified.
    args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
    args.encoder_hidden_dim = getattr(args, 'encoder_hidden_dim', 256)
    args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
    args.decoder_hidden_dim = getattr(args, 'decoder_hidden_dim', 256)

3.模型训练

可以利用fairseq-train命令行工具Command-line tool来训练模型，并且确保你的新模型结构为（--arch tutorial_simple_lstm）

> fairseq-train data-bin/iwslt14.tokenized.de-en \
  --arch tutorial_simple_lstm \
  --encoder-dropout 0.2 --decoder-dropout 0.2 \
  --optimizer adam --lr 0.005 --lr-shrink 0.5 \
  --max-tokens 12000
(...)
| epoch 052 | loss 4.027 | ppl 16.30 | wps 420805 | ups 39.7 | wpb 9841 | bsz 400 | num_updates 20852 | lr 1.95313e-05 | gnorm 0.218 | clip 0% | oom 0 | wall 529 | train_wall 396
| epoch 052 | valid on 'valid' subset | valid_loss 4.74989 | valid_ppl 26.91 | num_updates 20852 | best 4.74954
The model files should appear in the checkpoints/ directory. While this model architecture is not

模型文件将存储到./checkpoints文件夹中。可以使用fairseq-generate命令在测试集上来计算BLEU score。

> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
(...)
| Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s)
| Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146)

4.让代码飞起来Making generation faster

虽然sequence-to-sequence 模型天生就很慢，但我们上面的实现也特别慢，因为它为每个输出标记重新计算整个Decoder隐藏状态序列(即，它是O(n^2))。我们可以通过缓存以前的隐藏状态来大大加快这个过程。

在fairseq中，这被称为增量译码incremental decoding。增量解码是推理时的一种特殊模式，其中Model只接收与前一个输出令牌(用于教师强制)对应的单个时间步输入，并且必须增量地产生下一个输出。因此，模型必须缓存序列所需的任何长期状态，如隐藏状态、卷积状态等。

为了实现增量解码，将修改模型来实现FairseqIncrementalDecoder接口。与标准的FairseqDecoder接口相比，增量解码器接口允许forward()方法接受一个额外的关键字参数(incremental_state)，该参数可用于跨时间步缓存状态。

用一个增量的替换的SimpleLSTMDecoder:

import torch
from fairseq.models import FairseqIncrementalDecoder

class SimpleLSTMDecoder(FairseqIncrementalDecoder):

    def __init__(
        self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128,
        dropout=0.1,
    ):
        # This remains the same as before.
        super().__init__(dictionary)
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)
        self.lstm = nn.LSTM(
            input_size=encoder_hidden_dim + embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
        )
        self.output_projection = nn.Linear(hidden_dim, len(dictionary))

    # We now take an additional kwarg (*incremental_state*) for caching the
    # previous hidden and cell states.
    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
        if incremental_state is not None:
            # If the *incremental_state* argument is not ``None`` then we are
            # in incremental inference mode. While *prev_output_tokens* will
            # still contain the entire decoded prefix, we will only use the
            # last step and assume that the rest of the state is cached.
            prev_output_tokens = prev_output_tokens[:, -1:]

        # This remains the same as before.
        bsz, tgt_len = prev_output_tokens.size()
        final_encoder_hidden = encoder_out['final_hidden']
        x = self.embed_tokens(prev_output_tokens)
        x = self.dropout(x)
        x = torch.cat(
            [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)],
            dim=2,
        )

        # We will now check the cache and load the cached previous hidden and
        # cell states, if they exist, otherwise we will initialize them to
        # zeros (as before). We will use the ``utils.get_incremental_state()``
        # and ``utils.set_incremental_state()`` helpers.
        initial_state = utils.get_incremental_state(
            self, incremental_state, 'prev_state',
        )
        if initial_state is None:
            # first time initialization, same as the original version
            initial_state = (
                final_encoder_hidden.unsqueeze(0),  # hidden
                torch.zeros_like(final_encoder_hidden).unsqueeze(0),  # cell
            )

        # Run one step of our LSTM.
        output, latest_state = self.lstm(x.transpose(0, 1), initial_state)

        # Update the cache with the latest hidden and cell states.
        utils.set_incremental_state(
            self, incremental_state, 'prev_state', latest_state,
        )

        # This remains the same as before
        x = output.transpose(0, 1)
        x = self.output_projection(x)
        return x, None

    # The ``FairseqIncrementalDecoder`` interface also requires implementing a
    # ``reorder_incremental_state()`` method, which is used during beam search
    # to select and reorder the incremental state.
    def reorder_incremental_state(self, incremental_state, new_order):
        # Load the cached state.
        prev_state = utils.get_incremental_state(
            self, incremental_state, 'prev_state',
        )

        # Reorder batches according to *new_order*.
        reordered_state = (
            prev_state[0].index_select(1, new_order),  # hidden
            prev_state[1].index_select(1, new_order),  # cell
        )

        # Update the cached state.
        utils.set_incremental_state(
            self, incremental_state, 'prev_state', reordered_state,
        )

最后，可以重新运行生成并观察加速情况:

# Before

> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
(...)
| Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s)
| Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146)

# After

> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
(...)
| Translated 6750 sentences (153132 tokens) in 5.5s (1225.54 sentences/s, 27802.94 tokens/s)
| Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146)