Tutorial: Simple LSTM
=====================
In this tutorial we will extend fairseq by adding a new
:class:`~fairseq.models.FairseqEncoderDecoderModel` that encodes a source
sentence with an LSTM and then passes the final hidden state to a second LSTM
that decodes the target sentence (without attention).
This tutorial covers:
1. **Writing an Encoder and Decoder** to encode/decode the source/target
sentence, respectively.
2. **Registering a new Model** so that it can be used with the existing
:ref:`Command-line tools`.
3. **Training the Model** using the existing command-line tools.
4. **Making generation faster** by modifying the Decoder to use
:ref:`Incremental decoding`.
1. Building an Encoder and Decoder
----------------------------------
In this section we'll define a simple LSTM Encoder and Decoder. All Encoders
should implement the :class:`~fairseq.models.FairseqEncoder` interface and
Decoders should implement the :class:`~fairseq.models.FairseqDecoder` interface.
These interfaces themselves extend :class:`torch.nn.Module`, so FairseqEncoders
and FairseqDecoders can be written and used in the same ways as ordinary PyTorch
Modules.
Encoder
~~~~~~~
Our Encoder will embed the tokens in the source sentence, feed them to a
:class:`torch.nn.LSTM` and return the final hidden state. To create our encoder
save the following in a new file named :file:`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
~~~~~~~
Our Decoder will predict the next word, conditioned on the Encoder's final
hidden state and an embedded representation of the previous target word -- which
is sometimes called *teacher forcing*. More specifically, we'll use a
:class:`torch.nn.LSTM` to produce a sequence of hidden states that we'll project
to the size of the output vocabulary to predict each 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. Registering the Model
------------------------
Now that we've defined our Encoder and Decoder we must *register* our model with
fairseq using the :func:`~fairseq.models.register_model` function decorator.
Once the model is registered we'll be able to use it with the existing
:ref:`Command-line Tools`.
All registered models must implement the
:class:`~fairseq.models.BaseFairseqModel` interface. For sequence-to-sequence
models (i.e., any model with a single Encoder and Decoder), we can instead
implement the :class:`~fairseq.models.FairseqEncoderDecoderModel` interface.
Create a small wrapper class in the same file and register it in fairseq with
the name ``'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
Finally let's define a *named architecture* with the configuration for our
model. This is done with the :func:`~fairseq.models.register_model_architecture`
function decorator. Thereafter this named architecture can be used with the
``--arch`` command-line argument, e.g., ``--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. Training the Model
---------------------
Now we're ready to train the model. We can use the existing :ref:`fairseq-train`
command-line tool for this, making sure to specify our new Model architecture
(``--arch tutorial_simple_lstm``).
.. note::
Make sure you've already preprocessed the data from the IWSLT example in the
:file:`examples/translation/` directory.
.. code-block:: console
> 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 :file:`checkpoints/` directory. While this
model architecture is not very good, we can use the :ref:`fairseq-generate` script to
generate translations and compute our BLEU score over the test set:
.. code-block:: console
> 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. Making generation faster
---------------------------
While autoregressive generation from sequence-to-sequence models is inherently
slow, our implementation above is especially slow because it recomputes the
entire sequence of Decoder hidden states for every output token (i.e., it is
``O(n^2)``). We can make this significantly faster by instead caching the
previous hidden states.
In fairseq this is called :ref:`Incremental decoding`. Incremental decoding is a
special mode at inference time where the Model only receives a single timestep
of input corresponding to the immediately previous output token (for teacher
forcing) and must produce the next output incrementally. Thus the model must
cache any long-term state that is needed about the sequence, e.g., hidden
states, convolutional states, etc.
To implement incremental decoding we will modify our model to implement the
:class:`~fairseq.models.FairseqIncrementalDecoder` interface. Compared to the
standard :class:`~fairseq.models.FairseqDecoder` interface, the incremental
decoder interface allows ``forward()`` methods to take an extra keyword argument
(*incremental_state*) that can be used to cache state across time-steps.
Let's replace our ``SimpleLSTMDecoder`` with an incremental one::
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,
)
Finally, we can rerun generation and observe the speedup:
.. code-block:: console
# 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)