def setUp(self):
super().setUp()
self.metric = BLEU(ngram_weights=(0.5, 0.5), exclude_indices={0})
class VAE(Model):
"""
This ``VAE`` class is a :class:`Model` which implements a simple VAE as first described
in https://arxiv.org/pdf/1511.06349.pdf (Bowman et al., 2015).
Parameters
----------
vocab : ``Vocabulary``, required
Vocabulary containing source and target vocabularies. They may be under the same namespace
(`tokens`) or the target tokens can have a different namespace, in which case it needs to
be specified as `target_namespace`.
variational_encoder : ``VariationalEncoder``, required
The encoder model of which to pass the source tokens
decoder : ``Model``, required
The variational decoder model of which to pass the the latent variable
latent_dim : ``int``, required
The dimention of the latent, z vector. This is not necessarily the same size as the encoder
output dim
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
Used to initialize the model parameters.
"""
def __init__(
self,
vocab: Vocabulary,
variational_encoder: VariationalEncoder,
decoder: Decoder,
kl_weight: LossWeight,
temperature: float = 1.0,
initializer: InitializerApplicator = InitializerApplicator()
) -> None:
super(VAE, self).__init__(vocab)
self._encoder = variational_encoder
self._decoder = decoder
self._latent_dim = variational_encoder.latent_dim
self._encoder_output_dim = self._encoder.get_encoder_output_dim()
self._start_index = self.vocab.get_token_index(START_SYMBOL)
self._end_index = self.vocab.get_token_index(END_SYMBOL)
self._pad_index = self.vocab.get_token_index(self.vocab._padding_token) # pylint: disable=protected-access
self._bleu = BLEU(exclude_indices={
self._pad_index, self._end_index, self._start_index
})
self._kl_metric = Average()
self.kl_weight = kl_weight
self._temperature = temperature
initializer(self)
@overrides
def forward(
self, # type: ignore
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None
) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Make forward pass for both training/validation/test time.
"""
encoder_outs = self._encoder(source_tokens)
p_z = encoder_outs['prior']
q_z = encoder_outs['posterior']
kl_weight = self.kl_weight.get()
if self.training:
z = q_z.rsample()
self.kl_weight.step()
else:
z = self._encoder.reparametrize(p_z, q_z, self._temperature)
batch_size = z.size(0)
kld = kl_divergence(q_z, p_z).sum() / batch_size
self._kl_metric(kld)
output_dict = {'z': z, 'predictions': source_tokens['tokens']}
if not target_tokens:
return output_dict
# Do Decoding
output_dict.update(self._decoder(z, target_tokens))
rec_loss = output_dict['loss']
kl_loss = kld * kl_weight
output_dict['loss'] = rec_loss + kl_loss
if not self.training:
best_predictions = output_dict["predictions"]
self._bleu(best_predictions, target_tokens["tokens"])
return output_dict
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu and not self.training:
all_metrics.update(self._bleu.get_metric(reset=reset))
all_metrics.update({'klw': float(self.kl_weight.get())})
all_metrics.update(
{'kl': float(self._kl_metric.get_metric(reset=reset))})
return all_metrics
def generate(self, num_to_sample: int = 1):
cuda_device = self._get_prediction_device()
prior_mean = nn_util.move_to_device(
torch.zeros((num_to_sample, self._latent_dim)), cuda_device)
prior_stddev = torch.ones_like(prior_mean)
prior = Normal(prior_mean, prior_stddev)
latent = prior.sample()
generated = self._decoder.generate(latent)
return self.decode(generated)
@overrides
# simple_seq2seq's decode
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Finalize predictions.
This method overrides ``Model.decode``, which gets called after ``Model.forward``, at test
time, to finalize predictions. The logic for the decoder part of the encoder-decoder lives
within the ``forward`` method.
This method trims the output predictions to the first end symbol, replaces indices with
corresponding tokens, and adds a field called ``predicted_tokens`` to the ``output_dict``.
"""
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for indices in predicted_indices:
# Beam search gives us the top k results for each source sentence in the batch
# but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[:indices.index(self._end_index)]
predicted_tokens = [
self.vocab.get_token_from_index(x) for x in indices
]
all_predicted_tokens.append(predicted_tokens)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
class BleuTest(AllenNlpTestCase):
def setUp(self):
super().setUp()
self.metric = BLEU(ngram_weights=(0.5, 0.5), exclude_indices={0})
def test_get_valid_tokens_mask(self):
tensor = torch.tensor([[1, 2, 3, 0],
[0, 1, 1, 0]])
result = self.metric._get_valid_tokens_mask(tensor)
result = result.long().numpy()
check = np.array([[1, 1, 1, 0],
[0, 1, 1, 0]])
np.testing.assert_array_equal(result, check)
def test_ngrams(self):
tensor = torch.tensor([1, 2, 3, 1, 2, 0])
# Unigrams.
counts = Counter(self.metric._ngrams(tensor, 1))
unigram_check = {(1,): 2, (2,): 2, (3,): 1}
assert counts == unigram_check
# Bigrams.
counts = Counter(self.metric._ngrams(tensor, 2))
bigram_check = {(1, 2): 2, (2, 3): 1, (3, 1): 1}
assert counts == bigram_check
# Trigrams.
counts = Counter(self.metric._ngrams(tensor, 3))
trigram_check = {(1, 2, 3): 1, (2, 3, 1): 1, (3, 1, 2): 1}
assert counts == trigram_check
# ngram size too big, no ngrams produced.
counts = Counter(self.metric._ngrams(tensor, 7))
assert counts == {}
def test_bleu_computed_correctly(self):
self.metric.reset()
# shape: (batch_size, max_sequence_length)
predictions = torch.tensor([[1, 0, 0],
[1, 1, 0],
[1, 1, 1]])
# shape: (batch_size, max_gold_sequence_length)
gold_targets = torch.tensor([[2, 0, 0],
[1, 0, 0],
[1, 1, 2]])
self.metric(predictions, gold_targets)
assert self.metric._prediction_lengths == 6
assert self.metric._reference_lengths == 5
# Number of unigrams in predicted sentences that match gold sentences
# (but not more than maximum occurence of gold unigram within batch).
assert self.metric._precision_matches[1] == (
0 + # no matches in first sentence.
1 + # one clipped match in second sentence.
2 # two clipped matches in third sentence.
)
# Total number of predicted unigrams.
assert self.metric._precision_totals[1] == (
1 +
2 +
3
)
# Number of bigrams in predicted sentences that match gold sentences
# (but not more than maximum occurence of gold bigram within batch).
assert self.metric._precision_matches[2] == (
0 +
0 +
1
)
# Total number of predicted bigrams.
assert self.metric._precision_totals[2] == (
0 +
1 +
2
)
# Brevity penalty should be 1.0
assert self.metric._get_brevity_penalty() == 1.0
bleu = self.metric.get_metric(reset=True)["BLEU"]
check = math.exp(0.5 * (math.log(3) - math.log(6)) +
0.5 * (math.log(1) - math.log(3)))
np.testing.assert_approx_equal(bleu, check)
def test_bleu_computed_with_zero_counts(self):
self.metric.reset()
assert self.metric.get_metric()["BLEU"] == 0
def __init__(self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
max_decoding_steps: int,
attention: Attention,
schema_path: str = None,
missing_alignment_int: int = 0,
indexfield_padding_index: int = -1,
beam_size: int = None,
target_namespace: str = "tokens",
target_embedding_dim: int = None,
scheduled_sampling_ratio: float = 0.,
use_bleu: bool = True,
emb_dropout: float = 0.0,
dec_dropout: float = 0.0,
attn_loss_lambda: float = 0.5,
token_based_metric: Metric = None) -> None:
super(AttnSupSeq2Seq, self).__init__(vocab)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
self._indexfield_padding_index = indexfield_padding_index
self._missing_alignment_int = missing_alignment_int
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace) # pylint: disable=protected-access
self._bleu = BLEU(exclude_indices={
pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
if token_based_metric:
self._token_based_metric = token_based_metric
else:
self._token_based_metric = TokenSequenceAccuracy()
# log attention supervision CE loss as a metric
self._attn_sup_loss = Average()
self._sql_metrics = schema_path is not None
if self._sql_metrics:
# SQL specific metrics: match between the templates free of schema constants,
# and match between the schema constants
self._schema_free_match = GlobalTemplAccuracy(
schema_path=schema_path)
self._kb_match = KnowledgeBaseConstsAccuracy(
schema_path=schema_path)
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
# Dense embedding of source vocab tokens.
self._source_embedder = source_embedder
self._emb_dropout = Dropout(p=emb_dropout)
self._dec_dropout = Dropout(p=dec_dropout)
self._attn_loss_lambda = attn_loss_lambda
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._encoder = encoder
num_classes = self.vocab.get_vocab_size(self._target_namespace)
# Attention mechanism applied to the encoder output for each step.
self._attention = attention
self._attention._normalize = False
# Dense embedding of vocab words in the target space.
target_embedding_dim = target_embedding_dim or source_embedder.get_output_dim(
)
self._target_embedder = Embedding(num_classes, target_embedding_dim)
# Decoder output dim needs to be the same as the encoder output dim since we initialize the
# hidden state of the decoder with the final hidden state of the encoder.
self._encoder_output_dim = self._encoder.get_output_dim()
self._decoder_output_dim = self._encoder_output_dim
# A weighted average over encoder outputs will be concatenated to the previous target embedding
# to form the input to the decoder at each time step.
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
# We'll use an LSTM cell as the recurrent cell that produces a hidden state
# for the decoder at each time step.
# TODO (pradeep): Do not hardcode decoder cell type.
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_output_dim)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(self._decoder_output_dim,
num_classes)
class Bart(Model):
"""
BART model from the paper "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension" (https://arxiv.org/abs/1910.13461). The Bart model here uses a language
modeling head and thus can be used for text generation.
# Parameters
model_name : `str`, required
Name of the pre-trained BART model to use. Available options can be found in
`transformers.models.bart.modeling_bart.BART_PRETRAINED_MODEL_ARCHIVE_MAP`.
vocab : `Vocabulary`, required
Vocabulary containing source and target vocabularies.
beam_search : `Lazy[BeamSearch]`, optional (default = `Lazy(BeamSearch)`)
This is used to during inference to select the tokens of the decoded output sequence.
indexer : `PretrainedTransformerIndexer`, optional (default = `None`)
Indexer to be used for converting decoded sequences of ids to to sequences of tokens.
encoder : `Seq2SeqEncoder`, optional (default = `None`)
Encoder to used in BART. By default, the original BART encoder is used.
"""
def __init__(
self,
model_name: str,
vocab: Vocabulary,
beam_search: Lazy[BeamSearch] = Lazy(BeamSearch),
indexer: PretrainedTransformerIndexer = None,
encoder: Seq2SeqEncoder = None,
**kwargs,
):
super().__init__(vocab)
self.bart = BartForConditionalGeneration.from_pretrained(model_name)
self._indexer = indexer or PretrainedTransformerIndexer(model_name, namespace="tokens")
self._start_id = self.bart.config.bos_token_id # CLS
self._decoder_start_id = self.bart.config.decoder_start_token_id or self._start_id
self._end_id = self.bart.config.eos_token_id # SEP
self._pad_id = self.bart.config.pad_token_id # PAD
# At prediction time, we'll use a beam search to find the best target sequence.
# For backwards compatibility, check if beam_size or max_decoding_steps were passed in as
# kwargs. If so, update the BeamSearch object before constructing and raise a DeprecationWarning
deprecation_warning = (
"The parameter {} has been deprecated."
" Provide this parameter as argument to beam_search instead."
)
beam_search_extras = {}
if "beam_size" in kwargs:
beam_search_extras["beam_size"] = kwargs["beam_size"]
warnings.warn(deprecation_warning.format("beam_size"), DeprecationWarning)
if "max_decoding_steps" in kwargs:
beam_search_extras["max_steps"] = kwargs["max_decoding_steps"]
warnings.warn(deprecation_warning.format("max_decoding_steps"), DeprecationWarning)
self._beam_search = beam_search.construct(
end_index=self._end_id, vocab=self.vocab, **beam_search_extras
)
self._rouge = ROUGE(exclude_indices={self._start_id, self._pad_id, self._end_id})
self._bleu = BLEU(exclude_indices={self._start_id, self._pad_id, self._end_id})
# Replace bart encoder with given encoder. We need to extract the two embedding layers so that
# we can use them in the encoder wrapper
if encoder is not None:
assert (
encoder.get_input_dim() == encoder.get_output_dim() == self.bart.config.hidden_size
)
self.bart.model.encoder = _BartEncoderWrapper(
encoder,
self.bart.model.encoder.embed_tokens,
self.bart.model.encoder.embed_positions,
)
def forward(
self, source_tokens: TextFieldTensors, target_tokens: TextFieldTensors = None
) -> Dict[str, torch.Tensor]:
"""
Performs the forward step of Bart.
# Parameters
source_tokens : `TextFieldTensors`, required
The source tokens for the encoder. We assume they are stored under the `tokens` key.
target_tokens : `TextFieldTensors`, optional (default = `None`)
The target tokens for the decoder. We assume they are stored under the `tokens` key. If no target
tokens are given, the source tokens are shifted to the right by 1.
# Returns
`Dict[str, torch.Tensor]`
During training, this dictionary contains the `decoder_logits` of shape `(batch_size,
max_target_length, target_vocab_size)` and the `loss`. During inference, it contains `predictions`
of shape `(batch_size, max_decoding_steps)` and `log_probabilities` of shape `(batch_size,)`.
"""
inputs = source_tokens
targets = target_tokens
input_ids, input_mask = inputs["tokens"]["token_ids"], inputs["tokens"]["mask"]
outputs = {}
# If no targets are provided, then shift input to right by 1. Bart already does this internally
# but it does not use them for loss calculation.
if targets is not None:
target_ids, target_mask = targets["tokens"]["token_ids"], targets["tokens"]["mask"]
else:
target_ids = input_ids[:, 1:]
target_mask = input_mask[:, 1:]
if self.training:
bart_outputs = self.bart(
input_ids=input_ids,
attention_mask=input_mask,
decoder_input_ids=target_ids[:, :-1].contiguous(),
decoder_attention_mask=target_mask[:, :-1].contiguous(),
use_cache=False,
return_dict=True,
)
outputs["decoder_logits"] = bart_outputs.logits
# The BART paper mentions label smoothing of 0.1 for sequence generation tasks
outputs["loss"] = sequence_cross_entropy_with_logits(
bart_outputs.logits,
cast(torch.LongTensor, target_ids[:, 1:].contiguous()),
cast(torch.BoolTensor, target_mask[:, 1:].contiguous()),
label_smoothing=0.1,
average="token",
)
else:
# Use decoder start id and start of sentence to start decoder
initial_decoder_ids = torch.tensor(
[[self._decoder_start_id]],
dtype=input_ids.dtype,
device=input_ids.device,
).repeat(input_ids.shape[0], 1)
inital_state = {
"input_ids": input_ids,
"input_mask": input_mask,
}
beam_result = self._beam_search.search(
initial_decoder_ids, inital_state, self.take_step
)
predictions = beam_result[0]
max_pred_indices = (
beam_result[1].argmax(dim=-1).view(-1, 1, 1).expand(-1, -1, predictions.shape[-1])
)
predictions = predictions.gather(dim=1, index=max_pred_indices).squeeze(dim=1)
self._rouge(predictions, target_ids)
self._bleu(predictions, target_ids)
outputs["predictions"] = predictions
outputs["log_probabilities"] = (
beam_result[1].gather(dim=-1, index=max_pred_indices[..., 0]).squeeze(dim=-1)
)
self.make_output_human_readable(outputs)
return outputs
@staticmethod
def _decoder_cache_to_dict(decoder_cache: DecoderCacheType) -> Dict[str, torch.Tensor]:
cache_dict = {}
for layer_index, layer_cache in enumerate(decoder_cache):
# Each layer caches the key and value tensors for its self-attention and cross-attention.
# Hence the `layer_cache` tuple has 4 elements.
assert len(layer_cache) == 4
for tensor_index, tensor in enumerate(layer_cache):
key = f"decoder_cache_{layer_index}_{tensor_index}"
cache_dict[key] = tensor
return cache_dict
def _dict_to_decoder_cache(self, cache_dict: Dict[str, torch.Tensor]) -> DecoderCacheType:
decoder_cache = []
for layer_index in range(len(self.bart.model.decoder.layers)):
base_key = f"decoder_cache_{layer_index}_"
layer_cache = (
cache_dict[base_key + "0"],
cache_dict[base_key + "1"],
cache_dict[base_key + "2"],
cache_dict[base_key + "3"],
)
decoder_cache.append(layer_cache)
assert decoder_cache
return tuple(decoder_cache)
def take_step(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor], step: int
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take step during beam search.
# Parameters
last_predictions : `torch.Tensor`
The predicted token ids from the previous step. Shape: `(group_size,)`
state : `Dict[str, torch.Tensor]`
State required to generate next set of predictions
step : `int`
The time step in beam search decoding.
# Returns
`Tuple[torch.Tensor, Dict[str, torch.Tensor]]`
A tuple containing logits for the next tokens of shape `(group_size, target_vocab_size)` and
an updated state dictionary.
"""
if len(last_predictions.shape) == 1:
last_predictions = last_predictions.unsqueeze(-1)
decoder_cache = None
decoder_cache_dict = {
k: state[k].contiguous()
for k in state
if k not in {"input_ids", "input_mask", "encoder_states"}
}
if len(decoder_cache_dict) != 0:
decoder_cache = self._dict_to_decoder_cache(decoder_cache_dict)
encoder_outputs = (state["encoder_states"],) if "encoder_states" in state else None
outputs = self.bart(
input_ids=state["input_ids"] if encoder_outputs is None else None,
attention_mask=state["input_mask"],
encoder_outputs=encoder_outputs,
decoder_input_ids=last_predictions,
past_key_values=decoder_cache,
use_cache=True,
return_dict=True,
)
logits = outputs.logits[:, -1, :]
log_probabilities = F.log_softmax(logits, dim=-1)
decoder_cache = outputs.past_key_values
if decoder_cache is not None:
decoder_cache_dict = self._decoder_cache_to_dict(decoder_cache)
state.update(decoder_cache_dict)
state["encoder_states"] = outputs.encoder_last_hidden_state
return log_probabilities, state
def make_output_human_readable(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, Any]:
"""
# Parameters
output_dict : `Dict[str, torch.Tensor]`
A dictionary containing a batch of predictions with key `predictions`. The tensor should have
shape `(batch_size, max_sequence_length)`
# Returns
`Dict[str, Any]`
Original `output_dict` with an additional `predicted_tokens` key that maps to a list of lists of
tokens.
"""
predictions = output_dict["predictions"]
predicted_tokens = [None] * predictions.shape[0]
for i in range(predictions.shape[0]):
predicted_tokens[i] = self._indexer.indices_to_tokens(
{"token_ids": predictions[i].tolist()},
self.vocab,
)
output_dict["predicted_tokens"] = predicted_tokens # type: ignore
output_dict["predicted_text"] = self._indexer._tokenizer.batch_decode(
predictions.tolist(), skip_special_tokens=True
)
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
metrics: Dict[str, float] = {}
if not self.training:
metrics.update(self._rouge.get_metric(reset=reset))
metrics.update(self._bleu.get_metric(reset=reset))
return metrics
default_predictor = "seq2seq"
class MaskedCopyNet(Model):
def __init__(self,
vocab: Vocabulary,
embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
max_decoding_steps: int,
attention: Attention = None,
mask_embedder: TextFieldEmbedder = None,
mask_attention: Attention = None,
beam_size: int = None,
target_namespace: str = "tokens",
scheduled_sampling_ratio: float = 0.,
use_bleu: bool = True) -> None:
super().__init__(vocab)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL, self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL, self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token, self._target_namespace)
self._bleu = BLEU(exclude_indices={pad_index, self._end_index, self._start_index})
else:
self._bleu = None
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index, max_steps=max_decoding_steps, beam_size=beam_size)
# Dense embedding of source vocab tokens.
self._embedder = embedder
self._mask_embedder = mask_embedder
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._encoder = encoder
num_classes = self.vocab.get_vocab_size(self._target_namespace)
# Attention mechanism applied to the encoder output for each step.
self._attention = attention
self._mask_attention = mask_attention
# Dense embedding of vocab words in the target space.
target_embedding_dim = self._embedder.get_output_dim()
# Decoder output dim needs to be the same as the encoder output dim since we initialize the
# hidden state of the decoder with the final hidden state of the encoder.
self._encoder_output_dim = self._encoder.get_output_dim()
self._decoder_output_dim = self._encoder_output_dim
if self._attention:
# If using attention, a weighted average over encoder outputs will be concatenated
# to the previous target embedding to form the input to the decoder at each
# time step.
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
else:
# Otherwise, the input to the decoder is just the previous target embedding.
self._decoder_input_dim = target_embedding_dim
if self._mask_attention:
self._decoder_input_dim += self._mask_embedder.get_output_dim()
# We'll use an LSTM cell as the recurrent cell that produces a hidden state
# for the decoder at each time step.
self._decoder_cell = LSTMCell(self._decoder_input_dim, self._decoder_output_dim)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(self._decoder_output_dim, num_classes)
def take_step(self,
last_predictions: torch.Tensor,
state: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
# shape: (group_size, num_classes)
output_projections, state = self._prepare_output_projections(last_predictions, state)
# shape: (group_size, num_classes)
class_log_probabilities = F.log_softmax(output_projections, dim=-1)
return class_log_probabilities, state
@overrides
def forward(self, # type: ignore
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
mask_tokens: Dict[str, torch.LongTensor] = None,
**kwargs) -> Dict[str, torch.Tensor]:
del kwargs
assert mask_tokens is not None or self._mask_embedder is None, \
'You must pass `mask_tokens` when `mask_embedder` is not None'
state = self.encode(source_tokens, mask_tokens)
if target_tokens:
state = self.init_decoder_state(state)
output_dict = self._forward_loop(state, target_tokens)
else:
output_dict = {}
if not self.training:
state = self.init_decoder_state(state)
predictions = self.beam_search(state)
output_dict.update(predictions)
if target_tokens and self._bleu:
# shape: (batch_size, beam_size, max_sequence_length)
top_k_predictions = output_dict["predictions"]
# shape: (batch_size, max_predicted_sequence_length)
best_predictions = top_k_predictions[:, 0, :]
self._bleu(best_predictions, target_tokens["tokens"])
return output_dict
@overrides
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for i, indices in enumerate(predicted_indices):
curr_predictions = []
for ind in indices:
ind = list(ind)
# Collect indices till the first end_symbol
if self._end_index in ind:
ind = ind[:ind.index(self._end_index)]
predicted_tokens = [self.vocab.get_token_from_index(x, namespace=self._target_namespace)
for x in ind]
curr_predictions.append(predicted_tokens)
all_predicted_tokens.append(curr_predictions)
output_dict["predicted_tokens"] = all_predicted_tokens # [batch_size, k, num_decoding_steps]
return output_dict
def encode(self, source_tokens: Dict[str, torch.Tensor],
mask_tokens: Dict[str, torch.Tensor] = None) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length, encoder_input_dim)
embedded_input = self._embedder(source_tokens)
# shape: (batch_size, max_input_sequence_length)
source_mask = util.get_text_field_mask(source_tokens)
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = self._encoder(embedded_input, source_mask)
state = {
"source_mask": source_mask,
"encoder_outputs": encoder_outputs
}
if mask_tokens is not None and self._mask_embedder is not None:
embedded_input = self._mask_embedder(mask_tokens)
masker_mask = util.get_text_field_mask(mask_tokens)
state.update(
{
"mask_source_mask": masker_mask,
"mask_encoder_outputs": embedded_input
}
)
return state
def init_decoder_state(self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
batch_size = state["source_mask"].size(0)
# shape: (batch_size, encoder_output_dim)
final_encoder_output = util.get_final_encoder_states(
state["encoder_outputs"],
state["source_mask"],
self._encoder.is_bidirectional())
# Initialize the decoder hidden state with the final output of the encoder.
# shape: (batch_size, decoder_output_dim)
state["decoder_hidden"] = final_encoder_output
# shape: (batch_size, decoder_output_dim)
state["decoder_context"] = state["encoder_outputs"].new_zeros(batch_size, self._decoder_output_dim)
return state
def _forward_loop(self,
state: Dict[str, torch.Tensor],
target_tokens: Dict[str, torch.LongTensor] = None) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length)
source_mask = state["source_mask"]
batch_size = source_mask.size()[0]
if target_tokens:
# shape: (batch_size, max_target_sequence_length)
targets = target_tokens["tokens"]
_, target_sequence_length = targets.size()
# The last input from the target is either padding or the end symbol.
# Either way, we don't have to process it.
num_decoding_steps = target_sequence_length - 1
else:
num_decoding_steps = self._max_decoding_steps
# Initialize target predictions with the start index.
# shape: (batch_size,)
last_predictions = source_mask.new_full((batch_size,), fill_value=self._start_index)
step_logits: List[torch.Tensor] = []
step_predictions: List[torch.Tensor] = []
for timestep in range(num_decoding_steps):
if self.training and torch.rand(1).item() < self._scheduled_sampling_ratio:
# Use gold tokens at test time and at a rate of 1 - _scheduled_sampling_ratio
# during training.
# shape: (batch_size,)
input_choices = last_predictions
elif not target_tokens:
# shape: (batch_size,)
input_choices = last_predictions
else:
# shape: (batch_size,)
input_choices = targets[:, timestep]
# shape: (batch_size, num_classes)
output_projections, state = self._prepare_output_projections(input_choices, state)
# list of tensors, shape: (batch_size, 1, num_classes)
step_logits.append(output_projections.unsqueeze(1))
# shape: (batch_size, num_classes)
class_probabilities = F.softmax(output_projections, dim=-1)
# shape (predicted_classes): (batch_size,)
_, predicted_classes = torch.max(class_probabilities, 1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes
step_predictions.append(last_predictions.unsqueeze(1))
# shape: (batch_size, num_decoding_steps)
predictions = torch.cat(step_predictions, 1)
output_dict = {"predictions": predictions}
if target_tokens:
# shape: (batch_size, num_decoding_steps, num_classes)
logits = torch.cat(step_logits, 1)
# Compute loss.
target_mask = util.get_text_field_mask(target_tokens)
loss = self._get_loss(logits, targets, target_mask)
output_dict["loss"] = loss
return output_dict
def beam_search(self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
batch_size = state["source_mask"].size()[0]
start_predictions = state["source_mask"].new_full((batch_size,), fill_value=self._start_index)
# shape (all_top_k_predictions): (batch_size, beam_size, num_decoding_steps)
# shape (log_probabilities): (batch_size, beam_size)
all_top_k_predictions, log_probabilities = self._beam_search.search(
start_predictions, state, self.take_step)
output_dict = {
"class_log_probabilities": log_probabilities,
"predictions": all_top_k_predictions,
}
return output_dict
def _prepare_output_projections(self,
last_predictions: torch.Tensor,
state: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
# shape: (group_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (group_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (group_size, decoder_output_dim)
decoder_hidden = state["decoder_hidden"]
# shape: (group_size, decoder_output_dim)
decoder_context = state["decoder_context"]
# shape: (group_size, target_embedding_dim)
embedded_input = self._embedder({self._target_namespace: last_predictions})
if self._attention:
# shape: (group_size, encoder_output_dim)
attended_input = self._prepare_attended_input(decoder_hidden, encoder_outputs, source_mask)
# shape: (group_size, decoder_output_dim + target_embedding_dim)
decoder_input = torch.cat((attended_input, embedded_input), -1)
else:
# shape: (group_size, target_embedding_dim)
decoder_input = embedded_input
if self._mask_attention and self._mask_embedder:
mask_encoder_outputs = state["mask_encoder_outputs"]
mask_source_mask = state["mask_source_mask"]
mask_attended_input = self._prepare_mask_attended_input(
decoder_hidden,
mask_encoder_outputs,
mask_source_mask
)
decoder_input = torch.cat((decoder_input, mask_attended_input), -1)
# shape (decoder_hidden): (batch_size, decoder_output_dim)
# shape (decoder_context): (batch_size, decoder_output_dim)
decoder_hidden, decoder_context = self._decoder_cell(
decoder_input,
(decoder_hidden, decoder_context))
state["decoder_hidden"] = decoder_hidden
state["decoder_context"] = decoder_context
# shape: (group_size, num_classes)
output_projections = self._output_projection_layer(decoder_hidden)
return output_projections, state
def _prepare_attended_input(self,
decoder_hidden_state: torch.LongTensor = None,
encoder_outputs: torch.LongTensor = None,
encoder_outputs_mask: torch.LongTensor = None) -> torch.Tensor:
encoder_outputs_mask = encoder_outputs_mask.float()
input_weights = self._attention(decoder_hidden_state, encoder_outputs, encoder_outputs_mask)
attended_input = util.weighted_sum(encoder_outputs, input_weights)
return attended_input
def _prepare_mask_attended_input(self,
decoder_hidden_state: torch.LongTensor = None,
mask_encoder_outputs: torch.LongTensor = None,
mask_encoder_outputs_mask: torch.LongTensor = None) -> torch.Tensor:
encoder_outputs_mask = mask_encoder_outputs_mask.float()
input_weights = self._mask_attention(decoder_hidden_state, mask_encoder_outputs, encoder_outputs_mask)
attended_input = util.weighted_sum(mask_encoder_outputs, input_weights)
return attended_input
@staticmethod
def _get_loss(logits: torch.LongTensor,
targets: torch.LongTensor,
target_mask: torch.LongTensor) -> torch.Tensor:
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets[:, 1:].contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask[:, 1:].contiguous()
return util.sequence_cross_entropy_with_logits(logits, relevant_targets, relevant_mask)
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu and not self.training:
all_metrics.update(self._bleu.get_metric(reset=reset))
return all_metrics
class AttnSupSeq2Seq(Model):
"""
Adaptation of the ``SimpleSeq2Seq`` class in allennlp_models, with auxiliary attention-supervision loss
Parameters
----------
vocab : ``Vocabulary``, required
Vocabulary containing source and target vocabularies. They may be under the same namespace
(`tokens`) or the target tokens can have a different namespace, in which case it needs to
be specified as `target_namespace`.
source_embedder : ``TextFieldEmbedder``, required
Embedder for source side sequences
encoder : ``Seq2SeqEncoder``, required
The encoder of the "encoder/decoder" model
max_decoding_steps : ``int``
Maximum length of decoded sequences.
target_namespace : ``str``, optional (default = 'target_tokens')
If the target side vocabulary is different from the source side's, you need to specify the
target's namespace here. If not, we'll assume it is "tokens", which is also the default
choice for the source side, and this might cause them to share vocabularies.
target_embedding_dim : ``int``, optional (default = source_embedding_dim)
You can specify an embedding dimensionality for the target side. If not, we'll use the same
value as the source embedder's.
attention : ``Attention``, optional (default = None)
If you want to use attention to get a dynamic summary of the encoder outputs at each step
of decoding, this is the function used to compute similarity between the decoder hidden
state and encoder outputs.
attention_function: ``SimilarityFunction``, optional (default = None)
This is if you want to use the legacy implementation of attention. This will be deprecated
since it consumes more memory than the specialized attention modules.
beam_size : ``int``, optional (default = None)
Width of the beam for beam search. If not specified, greedy decoding is used.
scheduled_sampling_ratio : ``float``, optional (default = 0.)
At each timestep during training, we sample a random number between 0 and 1, and if it is
not less than this value, we use the ground truth labels for the whole batch. Else, we use
the predictions from the previous time step for the whole batch. If this value is 0.0
(default), this corresponds to teacher forcing, and if it is 1.0, it corresponds to not
using target side ground truth labels. See the following paper for more information:
`Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. Bengio et al.,
2015 <https://arxiv.org/abs/1506.03099>`_.
use_bleu : ``bool``, optional (default = True)
If True, the BLEU metric will be calculated during validation.
"""
def __init__(self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
max_decoding_steps: int,
attention: Attention,
schema_path: str = None,
missing_alignment_int: int = 0,
indexfield_padding_index: int = -1,
beam_size: int = None,
target_namespace: str = "tokens",
target_embedding_dim: int = None,
scheduled_sampling_ratio: float = 0.,
use_bleu: bool = True,
emb_dropout: float = 0.0,
dec_dropout: float = 0.0,
attn_loss_lambda: float = 0.5,
token_based_metric: Metric = None) -> None:
super(AttnSupSeq2Seq, self).__init__(vocab)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
self._indexfield_padding_index = indexfield_padding_index
self._missing_alignment_int = missing_alignment_int
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace) # pylint: disable=protected-access
self._bleu = BLEU(exclude_indices={
pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
if token_based_metric:
self._token_based_metric = token_based_metric
else:
self._token_based_metric = TokenSequenceAccuracy()
# log attention supervision CE loss as a metric
self._attn_sup_loss = Average()
self._sql_metrics = schema_path is not None
if self._sql_metrics:
# SQL specific metrics: match between the templates free of schema constants,
# and match between the schema constants
self._schema_free_match = GlobalTemplAccuracy(
schema_path=schema_path)
self._kb_match = KnowledgeBaseConstsAccuracy(
schema_path=schema_path)
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
# Dense embedding of source vocab tokens.
self._source_embedder = source_embedder
self._emb_dropout = Dropout(p=emb_dropout)
self._dec_dropout = Dropout(p=dec_dropout)
self._attn_loss_lambda = attn_loss_lambda
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._encoder = encoder
num_classes = self.vocab.get_vocab_size(self._target_namespace)
# Attention mechanism applied to the encoder output for each step.
self._attention = attention
self._attention._normalize = False
# Dense embedding of vocab words in the target space.
target_embedding_dim = target_embedding_dim or source_embedder.get_output_dim(
)
self._target_embedder = Embedding(num_classes, target_embedding_dim)
# Decoder output dim needs to be the same as the encoder output dim since we initialize the
# hidden state of the decoder with the final hidden state of the encoder.
self._encoder_output_dim = self._encoder.get_output_dim()
self._decoder_output_dim = self._encoder_output_dim
# A weighted average over encoder outputs will be concatenated to the previous target embedding
# to form the input to the decoder at each time step.
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
# We'll use an LSTM cell as the recurrent cell that produces a hidden state
# for the decoder at each time step.
# TODO (pradeep): Do not hardcode decoder cell type.
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_output_dim)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(self._decoder_output_dim,
num_classes)
def take_step(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take a decoding step. This is called by the beam search class.
Parameters
----------
last_predictions : ``torch.Tensor``
A tensor of shape ``(group_size,)``, which gives the indices of the predictions
during the last time step.
state : ``Dict[str, torch.Tensor]``
A dictionary of tensors that contain the current state information
needed to predict the next step, which includes the encoder outputs,
the source mask, and the decoder hidden state and context. Each of these
tensors has shape ``(group_size, *)``, where ``*`` can be any other number
of dimensions.
Returns
-------
Tuple[torch.Tensor, Dict[str, torch.Tensor]]
A tuple of ``(log_probabilities, updated_state)``, where ``log_probabilities``
is a tensor of shape ``(group_size, num_classes)`` containing the predicted
log probability of each class for the next step, for each item in the group,
while ``updated_state`` is a dictionary of tensors containing the encoder outputs,
source mask, and updated decoder hidden state and context.
Notes
-----
We treat the inputs as a batch, even though ``group_size`` is not necessarily
equal to ``batch_size``, since the group may contain multiple states
for each source sentence in the batch.
"""
# shape: (group_size, num_classes)
_, output_projections, state = self._prepare_output_projections(
last_predictions, state)
# shape: (group_size, num_classes)
class_log_probabilities = F.log_softmax(output_projections, dim=-1)
return class_log_probabilities, state
@overrides
def forward_on_instances(
self, instances: List[Instance]) -> List[Dict[str, numpy.ndarray]]:
"""
Takes a list of :class:`~allennlp.data.instance.Instance`s, converts that text into
arrays using this model's :class:`Vocabulary`, passes those arrays through
:func:`self.forward()` and :func:`self.decode()` (which by default does nothing)
and returns the result. Before returning the result, we convert any
``torch.Tensors`` into numpy arrays and separate the
batched output into a list of individual dicts per instance. Note that typically
this will be faster on a GPU (and conditionally, on a CPU) than repeated calls to
:func:`forward_on_instance`.
Parameters
----------
instances : List[Instance], required
The instances to run the model on.
cuda_device : int, required
The GPU device to use. -1 means use the CPU.
Returns
-------
A list of the models output for each instance.
"""
batch_size = len(instances)
with torch.no_grad():
cuda_device = self._get_prediction_device()
dataset = Batch(instances)
dataset.index_instances(self.vocab)
model_input = util.move_to_device(dataset.as_tensor_dict(),
cuda_device)
outputs = self.decode(self(**model_input))
instance_separated_output: List[Dict[str, numpy.ndarray]] = [
{} for _ in dataset.instances
]
for name, output in list(outputs.items()):
if isinstance(output, torch.Tensor):
# NOTE(markn): This is a hack because 0-dim pytorch tensors are not iterable.
# This occurs with batch size 1, because we still want to include the loss in that case.
if output.dim() == 0:
output = output.unsqueeze(0)
if output.size(0) != batch_size:
self._maybe_warn_for_unseparable_batches(name)
continue
output = output.detach().cpu().numpy()
elif len(output) != batch_size:
self._maybe_warn_for_unseparable_batches(name)
continue
for instance_output, batch_element in zip(
instance_separated_output, output):
instance_output[name] = batch_element
for instance_output, instance_input in zip(
instance_separated_output, instances):
for field in instance_input.fields:
try:
instance_output[field] = instance_input.fields[
field].tokens
except Exception as e:
continue
return instance_separated_output
@overrides
def forward(
self, # type: ignore
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
alignment_sequence: torch.Tensor = None
) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Make foward pass with decoder logic for producing the entire target sequence.
Parameters
----------
source_tokens : ``Dict[str, torch.LongTensor]``
The output of `TextField.as_array()` applied on the source `TextField`. This will be
passed through a `TextFieldEmbedder` and then through an encoder.
target_tokens : ``Dict[str, torch.LongTensor]``, optional (default = None)
Output of `Textfield.as_array()` applied on target `TextField`. We assume that the
target tokens are also represented as a `TextField`.
alignment_sequence : ``Dict[str, torch.LongTensor]``, optional (default = None)
Output of `Textfield.as_array()` applied on alignemnet `TextField`.
Returns
-------
Dict[str, torch.Tensor]
"""
state = self._encode(source_tokens)
if target_tokens:
state = self._init_decoder_state(state)
# Remove the trailing dimension (from ListField[ListField[IndexField]]).
alignment_sequence = alignment_sequence.squeeze(-1)
# The `_forward_loop` decodes the input sequence and computes the loss during training
# and validation.
output_dict = self._forward_loop(state, target_tokens,
alignment_sequence)
else:
output_dict = {}
if not self.training:
state = self._init_decoder_state(state)
predictions = self._forward_beam_search(state)
output_dict.update(predictions)
if target_tokens:
if self._bleu:
# shape: (batch_size, beam_size, max_sequence_length)
top_k_predictions = output_dict["predictions"]
# shape: (batch_size, max_predicted_sequence_length)
best_predictions = top_k_predictions[:, 0, :]
self._bleu(best_predictions, target_tokens["tokens"])
predicted_tokens = self.decode(output_dict)["predicted_tokens"]
target_tokens_str = self.decode_target_tokens(target_tokens)
if self._token_based_metric:
self._token_based_metric(predicted_tokens,
target_tokens_str)
if self._sql_metrics:
self._kb_match(predicted_tokens, target_tokens_str)
self._schema_free_match(predicted_tokens,
target_tokens_str)
# In case of attention coverage mechanism, reset the coverage vector after every batch...
try:
self._attention.reset_coverage_vector()
except Exception:
pass
return output_dict
def decode_target_tokens(self, target_tokens):
target_indices = target_tokens['tokens'].detach().cpu().numpy()
target_tokens_output = []
for i in range(target_indices.shape[0]):
cur_target_indices = target_indices[i]
cur_target_indices = list(cur_target_indices)
if self._end_index in cur_target_indices:
cur_target_indices = cur_target_indices[:cur_target_indices.
index(self._end_index)]
if self._start_index in cur_target_indices:
cur_target_indices = cur_target_indices[
cur_target_indices.index(self._start_index) + 1:]
target_tokens_str = [
self.vocab.get_token_from_index(
x, namespace=self._target_namespace)
for x in cur_target_indices
]
target_tokens_output.append(target_tokens_str)
return target_tokens_output
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Finalize predictions.
This method overrides ``Model.decode``, which gets called after ``Model.forward``, at test
time, to finalize predictions. The logic for the decoder part of the encoder-decoder lives
within the ``forward`` method.
This method trims the output predictions to the first end symbol, replaces indices with
corresponding tokens, and adds a field called ``predicted_tokens`` to the ``output_dict``.
"""
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for indices in predicted_indices:
# Beam search gives us the top k results for each source sentence in the batch
# but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[:indices.index(self._end_index)]
predicted_tokens = [
self.vocab.get_token_from_index(
x, namespace=self._target_namespace) for x in indices
]
all_predicted_tokens.append(predicted_tokens)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
def _encode(
self,
source_tokens: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length, encoder_input_dim)
embedded_input = self._source_embedder(source_tokens)
# shape: (batch_size, max_input_sequence_length)
source_mask = util.get_text_field_mask(source_tokens)
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = self._encoder(embedded_input, source_mask)
encoder_outputs = self._emb_dropout(encoder_outputs)
return {
"source_mask": source_mask,
"encoder_outputs": encoder_outputs,
}
def _init_decoder_state(
self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
batch_size = state["source_mask"].size(0)
# shape: (batch_size, encoder_output_dim)
final_encoder_output = util.get_final_encoder_states(
state["encoder_outputs"], state["source_mask"],
self._encoder.is_bidirectional())
# Initialize the decoder hidden state with the final output of the encoder.
# shape: (batch_size, decoder_output_dim)
state["decoder_hidden"] = final_encoder_output
# shape: (batch_size, decoder_output_dim)
state["decoder_context"] = state["encoder_outputs"].new_zeros(
batch_size, self._decoder_output_dim)
return state
def _forward_loop(
self,
state: Dict[str, torch.Tensor],
target_tokens: Dict[str, torch.LongTensor] = None,
alignment_sequence: torch.Tensor = None
) -> Dict[str, torch.Tensor]:
"""
Make forward pass during training or do greedy search during prediction.
Notes
-----
We really only use the predictions from the method to test that beam search
with a beam size of 1 gives the same results.
"""
# shape: (batch_size, max_input_sequence_length)
source_mask = state["source_mask"]
batch_size = source_mask.size()[0]
if target_tokens:
# shape: (batch_size, max_target_sequence_length)
targets = target_tokens["tokens"]
_, target_sequence_length = targets.size()
# The last input from the target is either padding or the end symbol.
# Either way, we don't have to process it.
num_decoding_steps = target_sequence_length - 1
else:
num_decoding_steps = self._max_decoding_steps
# Initialize target predictions with the start index.
# shape: (batch_size,)
last_predictions = source_mask.new_full((batch_size, ),
fill_value=self._start_index)
step_logits: List[torch.Tensor] = []
step_predictions: List[torch.Tensor] = []
step_attn_weights: List[torch.Tensor] = []
for timestep in range(num_decoding_steps):
if self.training and torch.rand(
1).item() < self._scheduled_sampling_ratio:
# Use gold tokens at test time and at a rate of 1 - _scheduled_sampling_ratio
# during training.
# shape: (batch_size,)
input_choices = last_predictions
elif not target_tokens:
# shape: (batch_size,)
input_choices = last_predictions
else:
# shape: (batch_size,)
input_choices = targets[:, timestep]
# shape: (batch_size, num_classes)
# shape: (batch_size, input_max_size)
input_weights, output_projections, state = self._prepare_output_projections(
input_choices, state)
step_attn_weights.append(input_weights.unsqueeze(1))
# list of tensors, shape: (batch_size, 1, num_classes)
step_logits.append(output_projections.unsqueeze(1))
# shape: (batch_size, num_classes)
class_probabilities = F.softmax(output_projections, dim=-1)
# shape (predicted_classes): (batch_size,)
_, predicted_classes = torch.max(class_probabilities, 1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes
step_predictions.append(last_predictions.unsqueeze(1))
# shape: (batch_size, num_decoding_steps)
predictions = torch.cat(step_predictions, 1)
# shape: (batch_size, num_decoding_steps, max_input_sequence_length)
attention_input_weights = torch.cat(step_attn_weights[:-1], 1)
output_dict = {
"predictions": predictions,
'attention_input_weights': attention_input_weights
}
if target_tokens:
# shape: (batch_size, num_decoding_steps, num_classes)
logits = torch.cat(step_logits, 1)
# shape: (batch_size, num_decoding_steps, max_input_sequence_length)
alignment_mask = self._get_alignment_mask(alignment_sequence)
# Compute loss.
target_mask = util.get_text_field_mask(target_tokens)
loss = self._get_loss(logits, targets, target_mask)
attn_sup_loss = self._get_attn_sup_loss(attention_input_weights,
alignment_mask,
alignment_sequence)
self._attn_sup_loss(attn_sup_loss.detach().cpu().item())
output_dict["loss"] = loss + self._attn_loss_lambda * attn_sup_loss
return output_dict
def _forward_beam_search(
self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Make forward pass during prediction using a beam search."""
batch_size = state["source_mask"].size()[0]
start_predictions = state["source_mask"].new_full(
(batch_size, ), fill_value=self._start_index)
# shape (all_top_k_predictions): (batch_size, beam_size, num_decoding_steps)
# shape (log_probabilities): (batch_size, beam_size)
all_top_k_predictions, log_probabilities = self._beam_search.search(
start_predictions, state, self.take_step)
output_dict = {
"class_log_probabilities": log_probabilities,
"predictions": all_top_k_predictions,
}
return output_dict
def _prepare_output_projections(self,
last_predictions: torch.Tensor,
state: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]: # pylint: disable=line-too-long
"""
Decode current state and last prediction to produce produce projections
into the target space, which can then be used to get probabilities of
each target token for the next step.
Inputs are the same as for `take_step()`.
"""
# shape: (group_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (group_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (group_size, decoder_output_dim)
decoder_hidden = state["decoder_hidden"]
# shape: (group_size, decoder_output_dim)
decoder_context = state["decoder_context"]
# shape: (group_size, target_embedding_dim)
embedded_input = self._target_embedder(last_predictions)
# shape: (group_size, encoder_output_dim)
attended_input, input_weights = self._prepare_attended_input(
decoder_hidden, encoder_outputs, source_mask)
# shape: (group_size, decoder_output_dim + target_embedding_dim)
decoder_input = torch.cat((attended_input, embedded_input), -1)
decoder_input = self._dec_dropout(decoder_input)
# shape (decoder_hidden): (batch_size, decoder_output_dim)
# shape (decoder_context): (batch_size, decoder_output_dim)
decoder_hidden, decoder_context = self._decoder_cell(
decoder_input, (decoder_hidden, decoder_context))
state["decoder_hidden"] = decoder_hidden
state["decoder_context"] = decoder_context
# shape: (group_size, num_classes)
output_projections = self._output_projection_layer(
self._dec_dropout(decoder_hidden))
return input_weights, output_projections, state
def _prepare_attended_input(
self,
decoder_hidden_state: torch.LongTensor = None,
encoder_outputs: torch.LongTensor = None,
encoder_outputs_mask: torch.LongTensor = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Apply attention over encoder outputs and decoder state."""
# Ensure mask is also a FloatTensor. Or else the multiplication within
# attention will complain.
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs_mask = encoder_outputs_mask.float()
# shape: (batch_size, max_input_sequence_length)
input_logits = self._attention(decoder_hidden_state, encoder_outputs,
encoder_outputs_mask)
# the attention mechanism returns the logits that are necessary for attention supervision loss,
# so we normalize it here
input_weights = masked_softmax(input_logits, encoder_outputs_mask)
# shape: (batch_size, encoder_output_dim)
attended_input = util.weighted_sum(encoder_outputs, input_weights)
return attended_input, input_logits
@staticmethod
def _get_attn_sup_loss(attn_weights: torch.Tensor,
alignment_mask: torch.Tensor,
alignment_sequence: torch.Tensor) -> torch.Tensor:
"""
Compute the attention supervision CE loss.
For each step, take the index of the aligned
"""
# shape: (batch_size, max_decoding_steps, max_input_seq_length
attn_weights = attn_weights.float()
alignment_sequence[alignment_sequence == -1] = 0
# for each attn_weights[batch_index, step_index, :] I want to choose the index of
# alignment_sequence[batch_index, step_index]
return util.sequence_cross_entropy_with_logits(attn_weights,
alignment_sequence,
alignment_mask)
def _get_alignment_mask(self, alignment_sequence):
"""
The alignment mask includes the target mask + mask on steps that don't have alignment
shape: batch_size, max_steps, max_input
"""
pad_mask = alignment_sequence != self._indexfield_padding_index
missing_mask = alignment_sequence != self._missing_alignment_int
return pad_mask * missing_mask
@staticmethod
def _get_loss(logits: torch.LongTensor, targets: torch.LongTensor,
target_mask: torch.LongTensor) -> torch.Tensor:
"""
Compute loss.
Takes logits (unnormalized outputs from the decoder) of size (batch_size,
num_decoding_steps, num_classes), target indices of size (batch_size, num_decoding_steps+1)
and corresponding masks of size (batch_size, num_decoding_steps+1) steps and computes cross
entropy loss while taking the mask into account.
The length of ``targets`` is expected to be greater than that of ``logits`` because the
decoder does not need to compute the output corresponding to the last timestep of
``targets``. This method aligns the inputs appropriately to compute the loss.
During training, we want the logit corresponding to timestep i to be similar to the target
token from timestep i + 1. That is, the targets should be shifted by one timestep for
appropriate comparison. Consider a single example where the target has 3 words, and
padding is to 7 tokens.
The complete sequence would correspond to <S> w1 w2 w3 <E> <P> <P>
and the mask would be 1 1 1 1 1 0 0
and let the logits be l1 l2 l3 l4 l5 l6
We actually need to compare:
the sequence w1 w2 w3 <E> <P> <P>
with masks 1 1 1 1 0 0
against l1 l2 l3 l4 l5 l6
(where the input was) <S> w1 w2 w3 <E> <P>
"""
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets[:, 1:].contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask[:, 1:].contiguous()
return util.sequence_cross_entropy_with_logits(logits,
relevant_targets,
relevant_mask)
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if not self.training:
if self._bleu:
all_metrics.update(self._bleu.get_metric(reset=reset))
all_metrics.update(
self._token_based_metric.get_metric(reset=reset))
if self._sql_metrics:
all_metrics.update(self._kb_match.get_metric(reset=reset))
all_metrics.update(
self._schema_free_match.get_metric(reset=reset))
all_metrics['attn_sup_loss'] = self._attn_sup_loss.get_metric(
reset=reset)
return all_metrics
class MyTransformer(Model):
def __init__(
self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
transformer: Dict,
max_decoding_steps: int,
target_namespace: str,
target_embedder: TextFieldEmbedder = None,
use_bleu: bool = True,
) -> None:
super().__init__(vocab)
self._target_namespace = target_namespace
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
self._pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace)
if use_bleu:
self._bleu = BLEU(exclude_indices={
self._pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
self._seq_acc = SequenceAccuracy()
self._max_decoding_steps = max_decoding_steps
self._source_embedder = source_embedder
self._ndim = transformer["d_model"]
self.pos_encoder = PositionalEncoding(self._ndim,
transformer["dropout"])
num_classes = self.vocab.get_vocab_size(self._target_namespace)
self._transformer = Transformer(**transformer)
self._transformer.apply(inplace_relu)
if target_embedder is None:
self._target_embedder = self._source_embedder
else:
self._target_embedder = target_embedder
self._output_projection_layer = Linear(self._ndim, num_classes)
def _get_mask(self, meta_data):
mask = torch.zeros(1, len(meta_data),
self.vocab.get_vocab_size(
self._target_namespace)).float()
for bidx, md in enumerate(meta_data):
for k, v in self.vocab._token_to_index[
self._target_namespace].items():
if 'position' in k and k not in md['avail_pos']:
mask[:, bidx, v] = float('-inf')
return mask
def generate_square_subsequent_mask(self, sz):
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
mask = mask.float().masked_fill(mask == False,
float('-inf')).masked_fill(
mask == True, float(0.0))
return mask
@overrides
def forward(
self,
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
meta_data: Any = None,
) -> Dict[str, torch.Tensor]:
src, src_key_padding_mask = self._encode(self._source_embedder,
source_tokens)
memory = self._transformer.encoder(
src, src_key_padding_mask=src_key_padding_mask)
if meta_data is not None:
target_vocab_mask = self._get_mask(meta_data)
target_vocab_mask = target_vocab_mask.to(memory.device)
else:
target_vocab_mask = None
output_dict = {}
targets = None
if target_tokens:
targets = target_tokens["tokens"][:, 1:]
target_mask = (util.get_text_field_mask({"tokens": targets}) == 1)
assert targets.size(1) <= self._max_decoding_steps
if self.training and target_tokens:
tgt, tgt_key_padding_mask = self._encode(
self._target_embedder,
{"tokens": target_tokens["tokens"][:, :-1]})
tgt_mask = self.generate_square_subsequent_mask(tgt.size(0)).to(
memory.device)
output = self._transformer.decoder(
tgt,
memory,
tgt_mask=tgt_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=src_key_padding_mask)
logits = self._output_projection_layer(output)
if target_vocab_mask is not None:
logits += target_vocab_mask
class_probabilities = F.softmax(logits.detach(), dim=-1)
_, predictions = torch.max(class_probabilities, -1)
logits = logits.transpose(0, 1)
loss = self._get_loss(logits, targets, target_mask)
output_dict["loss"] = loss
else:
assert self.training is False
output_dict["loss"] = torch.tensor(0.0).to(memory.device)
if targets is not None:
max_target_len = targets.size(1)
else:
max_target_len = None
predictions, class_probabilities = self._decoder_step_by_step(
memory,
src_key_padding_mask,
target_vocab_mask,
max_target_len=max_target_len)
predictions = predictions.transpose(0, 1)
output_dict["predictions"] = predictions
output_dict["class_probabilities"] = class_probabilities.transpose(
0, 1)
if target_tokens:
with torch.no_grad():
best_predictions = output_dict["predictions"]
if self._bleu:
self._bleu(best_predictions, targets)
batch_size = targets.size(0)
max_sz = max(best_predictions.size(1), targets.size(1),
target_mask.size(1))
best_predictions_ = torch.zeros(batch_size,
max_sz).to(memory.device)
best_predictions_[:, :best_predictions.
size(1)] = best_predictions
targets_ = torch.zeros(batch_size, max_sz).to(memory.device)
targets_[:, :targets.size(1)] = targets.cpu()
target_mask_ = torch.zeros(batch_size,
max_sz).to(memory.device)
target_mask_[:, :target_mask.size(1)] = target_mask
self._seq_acc(best_predictions_.unsqueeze(1), targets_,
target_mask_)
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
# shape: (batch_size, num_decoding_steps)
predicted_indices = predicted_indices.detach().cpu().numpy()
# class_probabilities = output_dict["class_probabilities"].detach().cpu()
# sample_predicted_indices = []
# for cp in class_probabilities:
# sample = torch.multinomial(cp, num_samples=1)
# sample_predicted_indices.append(sample)
# # shape: (batch_size, num_decoding_steps, num_samples)
# sample_predicted_indices = torch.stack(sample_predicted_indices)
all_predicted_tokens = []
for indices in predicted_indices:
# Beam search gives us the top k results for each source sentence in the batch
# but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[:indices.index(self._end_index)]
predicted_tokens = [
self.vocab.get_token_from_index(
x, namespace=self._target_namespace) for x in indices
]
all_predicted_tokens.append(predicted_tokens)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
def _encode(
self, embedder: TextFieldEmbedder,
tokens: Dict[str,
torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
src = embedder(tokens) * math.sqrt(self._ndim)
src = src.transpose(0, 1)
src = self.pos_encoder(src)
mask = util.get_text_field_mask(tokens)
mask = (mask == 0)
return src, mask
def _decoder_step_by_step(
self,
memory: torch.Tensor,
memory_key_padding_mask: torch.Tensor,
target_vocab_mask: torch.Tensor = None,
max_target_len: int = None) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = memory.size(1)
if getattr(self, "target_limit_decode_steps",
False) and max_target_len is not None:
num_decoding_steps = min(self._max_decoding_steps, max_target_len)
print('decoding steps: ', num_decoding_steps)
else:
num_decoding_steps = self._max_decoding_steps
last_predictions = memory.new_full(
(batch_size, ), fill_value=self._start_index).long()
step_predictions: List[torch.Tensor] = []
all_predicts = memory.new_full((batch_size, num_decoding_steps),
fill_value=0).long()
for timestep in range(num_decoding_steps):
all_predicts[:, timestep] = last_predictions
tgt, tgt_key_padding_mask = self._encode(
self._target_embedder,
{"tokens": all_predicts[:, :timestep + 1]})
tgt_mask = self.generate_square_subsequent_mask(timestep + 1).to(
memory.device)
output = self._transformer.decoder(
tgt,
memory,
tgt_mask=tgt_mask,
tgt_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask)
output_projections = self._output_projection_layer(output)
if target_vocab_mask is not None:
output_projections += target_vocab_mask
class_probabilities = F.softmax(output_projections, dim=-1)
_, predicted_classes = torch.max(class_probabilities, -1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes[timestep, :]
step_predictions.append(last_predictions)
if ((last_predictions == self._end_index) +
(last_predictions == self._pad_index)).all():
break
# shape: (num_decoding_steps, batch_size)
predictions = torch.stack(step_predictions)
return predictions, class_probabilities
@staticmethod
def _get_loss(logits: torch.FloatTensor, targets: torch.LongTensor,
target_mask: torch.FloatTensor) -> torch.Tensor:
logits = logits.contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets.contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask.contiguous()
return util.sequence_cross_entropy_with_logits(logits,
relevant_targets,
relevant_mask)
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu:
all_metrics.update(self._bleu.get_metric(reset=reset))
all_metrics['seq_acc'] = self._seq_acc.get_metric(reset=reset)
return all_metrics
def load_state_dict(self, state_dict, strict=True):
new_state_dict = {}
for k, v in state_dict.items():
if k.startswith('module.'):
new_state_dict[k[len('module.'):]] = v
else:
new_state_dict[k] = v
super(MyTransformer, self).load_state_dict(new_state_dict, strict)
class FactParaphraseSeq2Seq(Model):
"""
Given facts and dialog acts, it generates the paraphrased message.
TODO: add dialog & dialog acts history
This implementation is based off the default SimpleSeq2Seq model,
which takes a sequence, encodes it, and then uses the encoded
representations to decode another sequence.
"""
def __init__(
self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
source_encoder: Seq2SeqEncoder,
max_decoding_steps: int,
dialog_acts_encoder: FeedForward = None,
attention: Attention = None,
attention_function: SimilarityFunction = None,
n_dialog_acts: int = None,
beam_size: int = None,
target_namespace: str = "tokens",
target_embedding_dim: int = None,
scheduled_sampling_ratio: float = 0.0,
use_bleu: bool = True,
use_dialog_acts: bool = True,
regularizers: Optional[RegularizerApplicator] = None,
) -> None:
super().__init__(vocab, regularizers)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
# We need the start symbol to provide as the input at the first
# timestep of decoding, and end symbol as a way to indicate the end
# of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace)
self._bleu = BLEU(exclude_indices={
pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
# At prediction time, we use a beam search to find the most
# likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
# Dense embedding of source (Facts) vocab tokens.
self._source_embedder = source_embedder
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._source_encoder = source_encoder
if use_dialog_acts:
# Dense embedding of dialog acts.
da_embedding_dim = dialog_acts_encoder.get_input_dim()
self._dialog_acts_embedder = EmbeddingBag(n_dialog_acts,
da_embedding_dim)
# Encodes dialog acts
self._dialog_acts_encoder = dialog_acts_encoder
else:
self._dialog_acts_embedder = None
self._dialog_acts_encoder = None
num_classes = self.vocab.get_vocab_size(self._target_namespace)
# Attention mechanism applied to the encoder output for each step.
if attention:
if attention_function:
raise ConfigurationError(
"You can only specify an attention module or an "
"attention function, but not both.")
self._attention = attention
elif attention_function:
self._attention = LegacyAttention(attention_function)
else:
self._attention = None
# Dense embedding of vocab words in the target space.
target_embedding_dim = target_embedding_dim or source_embedder.get_output_dim(
)
self._target_embedder = Embedding(num_classes, target_embedding_dim)
# Decoder output dim needs to be the same as the encoder output dim
# since we initialize the hidden state of the decoder with the final
# hidden state of the encoder.
self._encoder_output_dim = self._source_encoder.get_output_dim()
if use_dialog_acts:
self._merge_encoder = Sequential(
Linear(
self._source_encoder.get_output_dim() +
self._dialog_acts_encoder.get_output_dim(),
self._encoder_output_dim,
))
self._decoder_output_dim = self._encoder_output_dim
if self._attention:
# If using attention, a weighted average over encoder outputs will
# be concatenated to the previous target embedding to form the input
# to the decoder at each time step.
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
else:
# Otherwise, the input to the decoder is just the previous target embedding.
self._decoder_input_dim = target_embedding_dim
# We'll use an LSTM cell as the recurrent cell that produces a hidden state
# for the decoder at each time step.
# TODO (pradeep): Do not hardcode decoder cell type.
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_output_dim)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(self._decoder_output_dim,
num_classes)
def take_step(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take a decoding step. This is called by the beam search class.
"""
# shape: (group_size, num_classes)
output_projections, state = self._prepare_output_projections(
last_predictions, state)
# shape: (group_size, num_classes)
class_log_probabilities = F.log_softmax(output_projections, dim=-1)
return class_log_probabilities, state
@overrides
def forward(
self, # type: ignore
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
dialog_acts: Optional[torch.Tensor] = None,
sender: Optional[torch.Tensor] = None,
metadata: Optional[Dict] = None,
) -> Dict[str, torch.Tensor]:
"""
Make foward pass with decoder logic for producing the entire target sequence.
"""
source_state, dialog_acts_state = self._encode(source_tokens,
dialog_acts)
if target_tokens:
state = self._init_decoder_state(source_state, dialog_acts_state)
# The `_forward_loop` decodes the input sequence and
# computes the loss during training and validation.
output_dict = self._forward_loop(state, target_tokens)
else:
output_dict = {}
if not self.training:
state = self._init_decoder_state(source_state, dialog_acts_state)
predictions = self._forward_beam_search(state)
output_dict.update(predictions)
if target_tokens and self._bleu:
# shape: (batch_size, beam_size, max_sequence_length)
top_k_predictions = output_dict["predictions"]
# shape: (batch_size, max_predicted_sequence_length)
best_predictions = top_k_predictions[:, 0, :]
self._bleu(best_predictions, target_tokens["tokens"])
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Finalize predictions.
"""
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for indices in predicted_indices:
# Beam search gives us the top k results for each source sentence
# in the batch but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[:indices.index(self._end_index)]
predicted_tokens = [
self.vocab.get_token_from_index(
x, namespace=self._target_namespace) for x in indices
]
all_predicted_tokens.append(predicted_tokens)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
def _encode(
self,
source_tokens: Dict[str, torch.Tensor],
dialog_acts: torch.Tensor = None
) -> Tuple[Dict[str, torch.Tensor], torch.Tensor]:
# Encode source tokens
source_state = self._encode_source_tokens(source_tokens)
# Encode dialog acts
if self._dialog_acts_encoder:
dialog_acts_state = self._encode_dialog_acts(dialog_acts)
else:
dialog_acts_state = None
return (source_state, dialog_acts_state)
def _encode_source_tokens(
self,
source_tokens: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length, encoder_input_dim)
embedded_input = self._source_embedder(source_tokens)
# shape: (batch_size, max_input_sequence_length)
source_mask = util.get_text_field_mask(source_tokens)
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = self._source_encoder(embedded_input, source_mask)
return {"source_mask": source_mask, "encoder_outputs": encoder_outputs}
def _encode_dialog_acts(self, dialog_acts: torch.Tensor) -> torch.Tensor:
# shape: (batch_size, dialog_acts_embeddings_size)
embedded_dialog_acts = self._dialog_acts_embedder(dialog_acts)
# shape: (batch_size, dim_encoder)
dialog_acts_state = self._dialog_acts_encoder(embedded_dialog_acts)
return dialog_acts_state
def _init_decoder_state(
self,
source_state: Dict[str, torch.Tensor],
dialog_acts_state: torch.Tensor = None,
) -> Dict[str, torch.Tensor]:
batch_size = source_state["source_mask"].size(0)
# shape: (batch_size, encoder_output_dim)
final_encoder_output = util.get_final_encoder_states(
source_state["encoder_outputs"],
source_state["source_mask"],
self._source_encoder.is_bidirectional(),
)
# Condition the source tokens state with dialog acts state
if self._dialog_acts_encoder:
final_encoder_output = self._merge_encoder(
torch.cat([final_encoder_output, dialog_acts_state], dim=1))
# Initialize the decoder hidden state with the final output of the encoder.
# shape: (batch_size, decoder_output_dim)
source_state["decoder_hidden"] = final_encoder_output
# shape: (batch_size, decoder_output_dim)
source_state["decoder_context"] = source_state[
"encoder_outputs"].new_zeros(batch_size, self._decoder_output_dim)
return source_state
def _forward_loop(
self,
state: Dict[str, torch.Tensor],
target_tokens: Dict[str, torch.LongTensor] = None,
) -> Dict[str, torch.Tensor]:
"""
Make forward pass during training or do greedy search during prediction.
Notes
-----
We really only use the predictions from the method to test that beam search
with a beam size of 1 gives the same results.
"""
# shape: (batch_size, max_input_sequence_length)
source_mask = state["source_mask"]
batch_size = source_mask.size()[0]
if target_tokens:
# shape: (batch_size, max_target_sequence_length)
targets = target_tokens["tokens"]
_, target_sequence_length = targets.size()
# The last input from the target is either padding or the end symbol.
# Either way, we don't have to process it.
num_decoding_steps = target_sequence_length - 1
else:
num_decoding_steps = self._max_decoding_steps
# Initialize target predictions with the start index.
# shape: (batch_size,)
last_predictions = source_mask.new_full((batch_size, ),
fill_value=self._start_index)
step_logits: List[torch.Tensor] = []
step_predictions: List[torch.Tensor] = []
for timestep in range(num_decoding_steps):
if self.training and torch.rand(
1).item() < self._scheduled_sampling_ratio:
# Use gold tokens at test time and at a rate of
# 1 - _scheduled_sampling_ratio during training.
# shape: (batch_size,)
input_choices = last_predictions
elif not target_tokens:
# shape: (batch_size,)
input_choices = last_predictions
else:
# shape: (batch_size,)
input_choices = targets[:, timestep]
# shape: (batch_size, num_classes)
output_projections, state = self._prepare_output_projections(
input_choices, state)
# list of tensors, shape: (batch_size, 1, num_classes)
step_logits.append(output_projections.unsqueeze(1))
# shape: (batch_size, num_classes)
class_probabilities = F.softmax(output_projections, dim=-1)
# shape (predicted_classes): (batch_size,)
_, predicted_classes = torch.max(class_probabilities, 1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes
step_predictions.append(last_predictions.unsqueeze(1))
# shape: (batch_size, num_decoding_steps)
predictions = torch.cat(step_predictions, 1)
output_dict = {"predictions": predictions}
if target_tokens:
# shape: (batch_size, num_decoding_steps, num_classes)
logits = torch.cat(step_logits, 1)
# Compute loss.
target_mask = util.get_text_field_mask(target_tokens)
loss = self._get_loss(logits, targets, target_mask)
output_dict["loss"] = loss
return output_dict
def _forward_beam_search(
self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Make forward pass during prediction using a beam search."""
batch_size = state["source_mask"].size()[0]
start_predictions = state["source_mask"].new_full(
(batch_size, ), fill_value=self._start_index)
# shape (all_top_k_predictions): (batch_size, beam_size, num_decoding_steps)
# shape (log_probabilities): (batch_size, beam_size)
all_top_k_predictions, log_probabilities = self._beam_search.search(
start_predictions, state, self.take_step)
output_dict = {
"class_log_probabilities": log_probabilities,
"predictions": all_top_k_predictions,
}
return output_dict
def _prepare_output_projections(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Decode current state and last prediction to produce produce projections
into the target space, which can then be used to get probabilities of
each target token for the next step.
Inputs are the same as for `take_step()`.
"""
# shape: (group_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (group_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (group_size, decoder_output_dim)
decoder_hidden = state["decoder_hidden"]
# shape: (group_size, decoder_output_dim)
decoder_context = state["decoder_context"]
# shape: (group_size, target_embedding_dim)
embedded_input = self._target_embedder(last_predictions)
if self._attention:
# shape: (group_size, encoder_output_dim)
attended_input = self._prepare_attended_input(
decoder_hidden, encoder_outputs, source_mask)
# shape: (group_size, decoder_output_dim + target_embedding_dim)
decoder_input = torch.cat((attended_input, embedded_input), -1)
else:
# shape: (group_size, target_embedding_dim)
decoder_input = embedded_input
# shape (decoder_hidden): (batch_size, decoder_output_dim)
# shape (decoder_context): (batch_size, decoder_output_dim)
decoder_hidden, decoder_context = self._decoder_cell(
decoder_input, (decoder_hidden, decoder_context))
state["decoder_hidden"] = decoder_hidden
state["decoder_context"] = decoder_context
# shape: (group_size, num_classes)
output_projections = self._output_projection_layer(decoder_hidden)
return output_projections, state
def _prepare_attended_input(
self,
decoder_hidden_state: torch.LongTensor = None,
encoder_outputs: torch.LongTensor = None,
encoder_outputs_mask: torch.LongTensor = None,
) -> torch.Tensor:
"""Apply attention over encoder outputs and decoder state."""
# Ensure mask is also a FloatTensor. Or else the multiplication within
# attention will complain.
# shape: (batch_size, max_input_sequence_length)
encoder_outputs_mask = encoder_outputs_mask.float()
# shape: (batch_size, max_input_sequence_length)
input_weights = self._attention(decoder_hidden_state, encoder_outputs,
encoder_outputs_mask)
# shape: (batch_size, encoder_output_dim)
attended_input = util.weighted_sum(encoder_outputs, input_weights)
return attended_input
@staticmethod
def _get_loss(
logits: torch.LongTensor,
targets: torch.LongTensor,
target_mask: torch.LongTensor,
) -> torch.Tensor:
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets[:, 1:].contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask[:, 1:].contiguous()
return util.sequence_cross_entropy_with_logits(logits,
relevant_targets,
relevant_mask)
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu and not self.training:
all_metrics.update(self._bleu.get_metric(reset=reset))
return all_metrics
class LatentAignmentCTC(Model):
def __init__(
self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
upsample: torch.nn.Module = None,
net: Seq2SeqEncoder = None,
target_namespace: str = "target_tokens",
target_embedding_dim: int = None,
use_bleu: bool = True,
) -> None:
super(LatentAignmentCTC, self).__init__(vocab)
self._target_namespace = target_namespace
self._pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace)
self._blank_index = self.vocab.get_token_index(SPECIAL_BLANK_TOKEN,
self._target_namespace)
if use_bleu:
self._bleu = BLEU(exclude_indices={self._pad_index, self._blank_index})
else:
self._bleu = None
self._source_embedder = source_embedder
source_embedding_dim = source_embedder.get_output_dim()
self._upsample = upsample or LinearUpsample(source_embedding_dim, s = 3)
self._net = net or StackedSelfAttentionEncoder(input_dim = source_embedding_dim,
hidden_dim = 128,
projection_dim = 128,
feedforward_hidden_dim = 512,
num_layers = 4,
num_attention_heads = 4)
num_classes = self.vocab.get_vocab_size(self._target_namespace)
target_embedding_dim = self._net.get_output_dim()
self._output_projection = torch.nn.Linear(target_embedding_dim, num_classes)
@overrides
def forward(
self, # type: ignore
source_tokens: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length, encoder_input_dim)
embedded_input = self._source_embedder(source_tokens)
# shape: (batch_size, max_input_sequence_length)
source_mask = util.get_text_field_mask(source_tokens)
# source_upsampled : shape : (batch_size, max_input_sequence_length, encoder_input_dim * self.s)
# source_mask_upsampled : shape : (batch_size, max_input_sequence_length)
source_upsampled, source_mask_upsampled = self._upsample(embedded_input, source_mask)
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
net_output = self._net(source_upsampled, source_mask_upsampled)
output_dict = {"source_mask_upsampled": source_mask_upsampled, "net_output": net_output}
alignment_logits = self._output_projection(net_output)
output_dict["alignment_logits"] = alignment_logits
if target_tokens:
# Compute loss.
loss = self._get_loss(output_dict, target_tokens)
output_dict["loss"] = loss
if not self.training:
alignments = alignment_logits.detach().cpu().argmax(2)
predictions = self.beta_inverse(alignments)
output_dict["predictions"] = predictions
if target_tokens and self._bleu:
self._bleu(output_dict['predictions'], target_tokens["tokens"])
#output_dict = self.decode(output_dict)
#print(output_dict["predicted_tokens"])
return output_dict
# TODO: too cheap. need pallalel processing
def beta_inverse(self, a:torch.Tensor):
"""
a : size (batch, sequence)
"""
max_length = a.size(1)
outputs = []
for sequence in a.tolist():
output = []
for token in sequence:
if token == self._blank_index:
continue
elif len(output) == 0:
output.append(token)
continue
elif token == output[-1]:
continue
else:
output.append(token)
pad_list = [self._pad_index] * (max_length - len(output))
outputs.append(output + pad_list)
return torch.LongTensor(outputs)
# @staticmethod
def _get_loss(self,
output_dict: Dict[str, torch.Tensor], target_tokens: Dict[str, torch.LongTensor]) -> torch.Tensor:
targets = target_tokens["tokens"]
target_mask = util.get_text_field_mask(target_tokens)
# shape: (batch_size, input_length, target_size)
alignment_logits = output_dict["alignment_logits"]
# shape: (batch_size, input_length)
source_mask_upsampled = output_dict["source_mask_upsampled"]
#return util.sequence_cross_entropy_with_logits(alignment_logits, targets, source_mask_upsampled)
return sequence_ctc_loss_with_logits(alignment_logits, source_mask_upsampled, targets, target_mask, self._blank_index)
@overrides
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Finalize predictions.
"""
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for indices in predicted_indices:
indices = list(indices)
# remove pad
if self._pad_index in indices:
indices = indices[: indices.index(self._pad_index)]
# lookup
predicted_tokens = [
self.vocab.get_token_from_index(x, namespace=self._target_namespace)
for x in indices
]
all_predicted_tokens.append(predicted_tokens)
# provide "tokens" and "predicted_tokens" for output.
output_dict["predicted_tokens"] = all_predicted_tokens
del output_dict["alignment_logits"], output_dict['source_mask_upsampled'], output_dict['net_output']
return output_dict
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu and not self.training:
all_metrics.update(self._bleu.get_metric(reset=reset))
return all_metrics
class Bart(Model):
"""
BART model from the paper "BART: Denosing Sequence-to-Sequence Pre-training for Natural Language Generation,
Translation, and Comprehension" (https://arxiv.org/abs/1910.13461). The Bart model here uses a language
modeling head and thus can be used for text generation.
"""
def __init__(
self,
model_name: str,
vocab: Vocabulary,
indexer: PretrainedTransformerIndexer = None,
max_decoding_steps: int = 140,
beam_size: int = 4,
encoder: Seq2SeqEncoder = None,
):
"""
# Parameters
model_name : `str`, required
Name of the pre-trained BART model to use. Available options can be found in
`transformers.modeling_bart.BART_PRETRAINED_MODEL_ARCHIVE_MAP`.
vocab : `Vocabulary`, required
Vocabulary containing source and target vocabularies.
indexer : `PretrainedTransformerIndexer`, optional (default = `None`)
Indexer to be used for converting decoded sequences of ids to to sequences of tokens.
max_decoding_steps : `int`, optional (default = `128`)
Number of decoding steps during beam search.
beam_size : `int`, optional (default = `5`)
Number of beams to use in beam search. The default is from the BART paper.
encoder : `Seq2SeqEncoder`, optional (default = `None`)
Encoder to used in BART. By default, the original BART encoder is used.
"""
super().__init__(vocab)
self.bart = BartForConditionalGeneration.from_pretrained(model_name)
self._indexer = indexer or PretrainedTransformerIndexer(
model_name, namespace="tokens")
self._start_id = self.bart.config.bos_token_id # CLS
self._decoder_start_id = self.bart.config.decoder_start_token_id or self._start_id
self._end_id = self.bart.config.eos_token_id # SEP
self._pad_id = self.bart.config.pad_token_id # PAD
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_id,
max_steps=max_decoding_steps,
beam_size=beam_size or 1)
self._rouge = ROUGE(
exclude_indices={self._start_id, self._pad_id, self._end_id})
self._bleu = BLEU(
exclude_indices={self._start_id, self._pad_id, self._end_id})
# Replace bart encoder with given encoder. We need to extract the two embedding layers so that
# we can use them in the encoder wrapper
if encoder is not None:
assert (encoder.get_input_dim() == encoder.get_output_dim() ==
self.bart.config.hidden_size)
self.bart.model.encoder = _BartEncoderWrapper(
encoder,
self.bart.model.encoder.embed_tokens,
self.bart.model.encoder.embed_positions,
)
@overrides
def forward(
self,
source_tokens: TextFieldTensors,
target_tokens: TextFieldTensors = None) -> Dict[str, torch.Tensor]:
"""
Performs the forward step of Bart.
# Parameters
source_tokens : `TextFieldTensors`, required
The source tokens for the encoder. We assume they are stored under the `tokens` key.
target_tokens : `TextFieldTensors`, optional (default = `None`)
The target tokens for the decoder. We assume they are stored under the `tokens` key. If no target
tokens are given, the source tokens are shifted to the right by 1.
# Returns
`Dict[str, torch.Tensor]`
During training, this dictionary contains the `decoder_logits` of shape `(batch_size,
max_target_length, target_vocab_size)` and the `loss`. During inference, it contains `predictions`
of shape `(batch_size, max_decoding_steps)` and `log_probabilities` of shape `(batch_size,)`.
"""
inputs = source_tokens
targets = target_tokens
input_ids, input_mask = inputs["tokens"]["token_ids"], inputs[
"tokens"]["mask"]
outputs = {}
# If no targets are provided, then shift input to right by 1. Bart already does this internally
# but it does not use them for loss calculation.
if targets is not None:
target_ids, target_mask = targets["tokens"]["token_ids"], targets[
"tokens"]["mask"]
else:
target_ids = input_ids[:, 1:]
target_mask = input_mask[:, 1:]
if self.training:
decoder_logits = self.bart(
input_ids=input_ids,
attention_mask=input_mask,
decoder_input_ids=target_ids[:, :-1].contiguous(),
decoder_attention_mask=target_mask[:, :-1].contiguous(),
use_cache=False,
)[0]
outputs["decoder_logits"] = decoder_logits
# The BART paper mentions label smoothing of 0.1 for sequence generation tasks
outputs["loss"] = sequence_cross_entropy_with_logits(
decoder_logits,
target_ids[:, 1:].contiguous(),
target_mask[:, 1:].contiguous(),
label_smoothing=0.1,
average="token",
)
else:
# Use decoder start id and start of sentence to start decoder
initial_decoder_ids = torch.tensor(
[[self._decoder_start_id, self._start_id]],
dtype=input_ids.dtype,
device=input_ids.device,
).repeat(input_ids.shape[0], 1)
inital_state = {
"input_ids": input_ids,
"input_mask": input_mask,
"encoder_states": None,
}
beam_result = self._beam_search.search(initial_decoder_ids,
inital_state,
self.take_step)
predictions = beam_result[0]
max_pred_indices = (beam_result[1].argmax(dim=-1).view(
-1, 1, 1).expand(-1, -1, predictions.shape[-1]))
predictions = predictions.gather(
dim=1, index=max_pred_indices).squeeze(dim=1)
self._rouge(predictions, target_ids)
self._bleu(predictions, target_ids)
outputs["predictions"] = predictions
outputs["log_probabilities"] = (beam_result[1].gather(
dim=-1, index=max_pred_indices[..., 0]).squeeze(dim=-1))
self.make_output_human_readable(outputs)
return outputs
@staticmethod
def _decoder_cache_to_dict(decoder_cache):
cache_dict = {}
for layer_index, layer_cache in enumerate(decoder_cache):
for attention_name, attention_cache in layer_cache.items():
for tensor_name, cache_value in attention_cache.items():
key = (layer_index, attention_name, tensor_name)
cache_dict[key] = cache_value
return cache_dict
@staticmethod
def _dict_to_decoder_cache(cache_dict):
decoder_cache = []
for key, cache_value in cache_dict.items():
# Split key and extract index and dict keys
layer_idx, attention_name, tensor_name = key
# Extend decoder_cache to fit layer_idx + 1 layers
decoder_cache = decoder_cache + [
{} for _ in range(layer_idx + 1 - len(decoder_cache))
]
cache = decoder_cache[layer_idx]
if attention_name not in cache:
cache[attention_name] = {}
assert tensor_name not in cache[attention_name]
cache[attention_name][tensor_name] = cache_value
return decoder_cache
def take_step(self, last_predictions: torch.Tensor,
state: Dict[str, torch.Tensor],
step: int) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take step during beam search.
# Parameters
last_predictions : `torch.Tensor`
The predicted token ids from the previous step. Shape: `(group_size,)`
state : `Dict[str, torch.Tensor]`
State required to generate next set of predictions
step : `int`
The time step in beam search decoding.
# Returns
`Tuple[torch.Tensor, Dict[str, torch.Tensor]]`
A tuple containing logits for the next tokens of shape `(group_size, target_vocab_size)` and
an updated state dictionary.
"""
if len(last_predictions.shape) == 1:
last_predictions = last_predictions.unsqueeze(-1)
# Only the last predictions are needed for the decoder, but we need to pad the decoder ids
# to not mess up the positional embeddings in the decoder.
padding_size = 0
if step > 0:
padding_size = step + 1
padding = torch.full(
(last_predictions.shape[0], padding_size),
self._pad_id,
dtype=last_predictions.dtype,
device=last_predictions.device,
)
last_predictions = torch.cat([padding, last_predictions], dim=-1)
decoder_cache = None
decoder_cache_dict = {
k: (state[k].contiguous() if state[k] is not None else None)
for k in state
if k not in {"input_ids", "input_mask", "encoder_states"}
}
if len(decoder_cache_dict) != 0:
decoder_cache = self._dict_to_decoder_cache(decoder_cache_dict)
log_probabilities = None
for i in range(padding_size, last_predictions.shape[1]):
encoder_outputs = ((state["encoder_states"], ) if
state["encoder_states"] is not None else None)
outputs = self.bart(
input_ids=state["input_ids"],
attention_mask=state["input_mask"],
encoder_outputs=encoder_outputs,
decoder_input_ids=last_predictions[:, :i + 1],
past_key_values=decoder_cache,
generation_mode=True,
use_cache=True,
)
decoder_log_probabilities = F.log_softmax(outputs[0][:, 0], dim=-1)
if log_probabilities is None:
log_probabilities = decoder_log_probabilities
else:
idx = last_predictions[:, i].view(-1, 1)
log_probabilities = decoder_log_probabilities + log_probabilities.gather(
dim=-1, index=idx)
decoder_cache = outputs[1]
state["encoder_states"] = outputs[2]
if decoder_cache is not None:
decoder_cache_dict = self._decoder_cache_to_dict(decoder_cache)
state.update(decoder_cache_dict)
return log_probabilities, state
@overrides
def make_output_human_readable(
self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, Any]:
"""
# Parameters
output_dict : `Dict[str, torch.Tensor]`
A dictionary containing a batch of predictions with key `predictions`. The tensor should have
shape `(batch_size, max_sequence_length)`
# Returns
`Dict[str, Any]`
Original `output_dict` with an additional `predicted_tokens` key that maps to a list of lists of
tokens.
"""
predictions = output_dict["predictions"]
predicted_tokens = [None] * predictions.shape[0]
for i in range(predictions.shape[0]):
predicted_tokens[i] = self._indexer.indices_to_tokens(
{"token_ids": predictions[0].tolist()}, self.vocab)
output_dict["predicted_tokens"] = predicted_tokens
return output_dict
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
metrics: Dict[str, float] = {}
if not self.training:
metrics.update(self._rouge.get_metric(reset=reset))
metrics.update(self._bleu.get_metric(reset=reset))
return metrics
def __init__(self,
vocab: Vocabulary,
bidaf_model: BidirectionalAttentionFlowModified,
source_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
attention: Attention,
beam_size: int,
max_decoding_steps: int,
target_embedding_dim: int = 30,
copy_token: str = "@COPY@",
source_namespace: str = "source_tokens",
target_namespace: str = "target_tokens",
tensor_based_metric: Metric = None,
token_based_metric: Metric = None,
initializer: InitializerApplicator = InitializerApplicator(),
dropout: float = 0.0,
pretrained_bidaf: bool = False) -> None:
super().__init__(vocab)
if pretrained_bidaf:
params = Params.from_file("./temp/bidaf_baseline/config.json")
vocab = Vocabulary.from_files("./temp/bidaf_baseline/vocabulary")
self.bidaf_model = Model.from_params(vocab=vocab,
params=params.pop('model'))
map_location = None if torch.cuda.is_available() else 'cpu'
with open("./temp/bidaf_baseline/best.th", 'rb') as f:
self.bidaf_model.load_state_dict(
torch.load(f, map_location=map_location))
else:
self.bidaf_model = bidaf_model
self._source_namespace = source_namespace
self._target_namespace = target_namespace
self._src_start_index = self.vocab.get_token_index(
START_SYMBOL, self._source_namespace)
self._src_end_index = self.vocab.get_token_index(
END_SYMBOL, self._source_namespace)
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
self._oov_index = self.vocab.get_token_index(self.vocab._oov_token,
self._target_namespace) # pylint: disable=protected-access
self._pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace) # pylint: disable=protected-access
self._copy_index = self.vocab.add_token_to_namespace(
copy_token, self._target_namespace)
self._tensor_based_metric = tensor_based_metric or \
BLEU(exclude_indices={self._pad_index, self._end_index, self._start_index})
self._token_based_metric = token_based_metric
self._target_vocab_size = self.vocab.get_vocab_size(
self._target_namespace)
# Encoding modules.
self._source_embedder = source_embedder
self._encoder = encoder
# Decoder output dim needs to be the same as the encoder output dim since we initialize the
# hidden state of the decoder with the final hidden state of the encoder.
# We arbitrarily set the decoder's input dimension to be the same as the output dimension.
self.encoder_output_dim = self._encoder.get_output_dim()
self.decoder_output_dim = self.encoder_output_dim
self.decoder_input_dim = self.decoder_output_dim
modeling_dim = self.bidaf_model._modeling_layer.get_output_dim()
self._init_decoder_projection = Linear(
self.encoder_output_dim + modeling_dim, self.decoder_output_dim)
target_vocab_size = self.vocab.get_vocab_size(self._target_namespace)
# The decoder input will be a function of the embedding of the previous predicted token,
# an attended encoder hidden state called the "attentive read", and another
# weighted sum of the encoder hidden state called the "selective read".
# While the weights for the attentive read are calculated by an `Attention` module,
# the weights for the selective read are simply the predicted probabilities
# corresponding to each token in the source sentence that matches the target
# token from the previous timestep.
self._target_embedder = Embedding(target_vocab_size,
target_embedding_dim)
self._attention = attention
self._input_projection_layer = Linear(
target_embedding_dim + self.encoder_output_dim * 2,
self.decoder_input_dim)
# We then run the projected decoder input through an LSTM cell to produce
# the next hidden state.
self._decoder_cell = LSTMCell(self.decoder_input_dim,
self.decoder_output_dim)
# We create a "generation" score for each token in the target vocab
# with a linear projection of the decoder hidden state.
self._output_generation_layer = Linear(self.decoder_output_dim,
target_vocab_size)
# We create a "copying" score for each source token by applying a non-linearity
# (tanh) to a linear projection of the encoded hidden state for that token,
# and then taking the dot product of the result with the decoder hidden state.
self._output_copying_layer = Linear(self.encoder_output_dim,
self.decoder_output_dim)
# At prediction time, we'll use a beam search to find the best target sequence.
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
if dropout > 0:
self._dropout = torch.nn.Dropout(p=dropout)
else:
self._dropout = lambda x: x
initializer(self)
def __init__(
self,
vocab: Vocabulary,
decoder_net: DecoderNet,
max_decoding_steps: int,
target_embedder: Embedding,
target_namespace: str = "tokens",
tie_output_embedding: bool = False,
scheduled_sampling_ratio: float = 0,
label_smoothing_ratio: Optional[float] = None,
beam_size: int = 4,
tensor_based_metric: Metric = None,
token_based_metric: Metric = None,
bleu_exclude_tokens: List = [],
) -> None:
super().__init__(target_embedder)
self._vocab = vocab
# Decodes the sequence of encoded hidden states into e new sequence of hidden states.
self._decoder_net = decoder_net
self._max_decoding_steps = max_decoding_steps
self._target_namespace = target_namespace
self._label_smoothing_ratio = label_smoothing_ratio
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self._vocab.get_token_index(
START_SYMBOL, self._target_namespace
)
self._end_index = self._vocab.get_token_index(
END_SYMBOL, self._target_namespace
)
self._beam_search = BeamSearch(
self._end_index, max_steps=max_decoding_steps, beam_size=beam_size
)
target_vocab_size = self._vocab.get_vocab_size(self._target_namespace)
if (
self.target_embedder.get_output_dim()
!= self._decoder_net.target_embedding_dim
):
raise ConfigurationError(
"Target Embedder output_dim doesn't match decoder module's input."
)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(
self._decoder_net.get_output_dim(), target_vocab_size
)
if tie_output_embedding:
if (
self._output_projection_layer.weight.shape
!= self.target_embedder.weight.shape
):
raise ConfigurationError(
"Can't tie embeddings with output linear layer, due to shape mismatch"
)
self._output_projection_layer.weight = self.target_embedder.weight
# These metrics will be updated during training and validation
if isinstance(tensor_based_metric, BLEU):
pad_index = self._vocab.get_token_index(
self._vocab._padding_token, self._target_namespace
)
new_exclude_indices = set([pad_index])
for token in bleu_exclude_tokens:
new_exclude_indices.add(
self._vocab.get_token_index(token, self._target_namespace)
)
new_exclude_indices.update(tensor_based_metric._exclude_indices)
logger.info(
f"Reconstruct BLEU to exclude indices {' '.join(map(str, new_exclude_indices))}"
)
self._tensor_based_metric = BLEU(
tensor_based_metric._ngram_weights, new_exclude_indices
)
else:
self._tensor_based_metric = tensor_based_metric
self._token_based_metric = token_based_metric
self._scheduled_sampling_ratio = scheduled_sampling_ratio
class BleuAutoRegressiveSeqDecoder(SeqDecoder):
"""
An autoregressive decoder that can be used for most seq2seq tasks.
Parameters
----------
vocab : ``Vocabulary``, required
Vocabulary containing source and target vocabularies. They may be under the same namespace
(`tokens`) or the target tokens can have a different namespace, in which case it needs to
be specified as `target_namespace`.
decoder_net : ``DecoderNet``, required
Module that contains implementation of neural network for decoding output elements
max_decoding_steps : ``int``, required
Maximum length of decoded sequences.
target_embedder : ``Embedding``, required
Embedder for target tokens.
target_namespace : ``str``, optional (default = 'tokens')
If the target side vocabulary is different from the source side's, you need to specify the
target's namespace here. If not, we'll assume it is "tokens", which is also the default
choice for the source side, and this might cause them to share vocabularies.
beam_size : ``int``, optional (default = 4)
Width of the beam for beam search.
tensor_based_metric : ``Metric``, optional (default = None)
A metric to track on validation data that takes raw tensors when its called.
This metric must accept two arguments when called: a batched tensor
of predicted token indices, and a batched tensor of gold token indices.
token_based_metric : ``Metric``, optional (default = None)
A metric to track on validation data that takes lists of lists of tokens
as input. This metric must accept two arguments when called, both
of type `List[List[str]]`. The first is a predicted sequence for each item
in the batch and the second is a gold sequence for each item in the batch.
scheduled_sampling_ratio : ``float`` optional (default = 0)
Defines ratio between teacher forced training and real output usage. If its zero
(teacher forcing only) and `decoder_net`supports parallel decoding, we get the output
predictions in a single forward pass of the `decoder_net`.
"""
def __init__(
self,
vocab: Vocabulary,
decoder_net: DecoderNet,
max_decoding_steps: int,
target_embedder: Embedding,
target_namespace: str = "tokens",
tie_output_embedding: bool = False,
scheduled_sampling_ratio: float = 0,
label_smoothing_ratio: Optional[float] = None,
beam_size: int = 4,
tensor_based_metric: Metric = None,
token_based_metric: Metric = None,
bleu_exclude_tokens: List = [],
) -> None:
super().__init__(target_embedder)
self._vocab = vocab
# Decodes the sequence of encoded hidden states into e new sequence of hidden states.
self._decoder_net = decoder_net
self._max_decoding_steps = max_decoding_steps
self._target_namespace = target_namespace
self._label_smoothing_ratio = label_smoothing_ratio
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self._vocab.get_token_index(
START_SYMBOL, self._target_namespace
)
self._end_index = self._vocab.get_token_index(
END_SYMBOL, self._target_namespace
)
self._beam_search = BeamSearch(
self._end_index, max_steps=max_decoding_steps, beam_size=beam_size
)
target_vocab_size = self._vocab.get_vocab_size(self._target_namespace)
if (
self.target_embedder.get_output_dim()
!= self._decoder_net.target_embedding_dim
):
raise ConfigurationError(
"Target Embedder output_dim doesn't match decoder module's input."
)
# We project the hidden state from the decoder into the output vocabulary space
# in order to get log probabilities of each target token, at each time step.
self._output_projection_layer = Linear(
self._decoder_net.get_output_dim(), target_vocab_size
)
if tie_output_embedding:
if (
self._output_projection_layer.weight.shape
!= self.target_embedder.weight.shape
):
raise ConfigurationError(
"Can't tie embeddings with output linear layer, due to shape mismatch"
)
self._output_projection_layer.weight = self.target_embedder.weight
# These metrics will be updated during training and validation
if isinstance(tensor_based_metric, BLEU):
pad_index = self._vocab.get_token_index(
self._vocab._padding_token, self._target_namespace
)
new_exclude_indices = set([pad_index])
for token in bleu_exclude_tokens:
new_exclude_indices.add(
self._vocab.get_token_index(token, self._target_namespace)
)
new_exclude_indices.update(tensor_based_metric._exclude_indices)
logger.info(
f"Reconstruct BLEU to exclude indices {' '.join(map(str, new_exclude_indices))}"
)
self._tensor_based_metric = BLEU(
tensor_based_metric._ngram_weights, new_exclude_indices
)
else:
self._tensor_based_metric = tensor_based_metric
self._token_based_metric = token_based_metric
self._scheduled_sampling_ratio = scheduled_sampling_ratio
def _forward_beam_search(
self, state: Dict[str, torch.Tensor]
) -> Dict[str, torch.Tensor]:
"""
Prepare inputs for the beam search, does beam search and returns beam search results.
"""
batch_size = state["source_mask"].size()[0]
start_predictions = state["source_mask"].new_full(
(batch_size,), fill_value=self._start_index
)
# shape (all_top_k_predictions): (batch_size, beam_size, num_decoding_steps)
# shape (log_probabilities): (batch_size, beam_size)
all_top_k_predictions, log_probabilities = self._beam_search.search(
start_predictions, state, self.take_step
)
output_dict = {
"class_log_probabilities": log_probabilities,
"predictions": all_top_k_predictions,
}
return output_dict
def _forward_loss(
self, state: Dict[str, torch.Tensor], target_tokens: Dict[str, torch.LongTensor]
) -> Dict[str, torch.Tensor]:
"""
Make forward pass during training or do greedy search during prediction.
Notes
-----
We really only use the predictions from the method to test that beam search
with a beam size of 1 gives the same results.
"""
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (batch_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (batch_size, max_target_sequence_length)
targets = target_tokens["tokens"]
# Prepare embeddings for targets. They will be used as gold embeddings during decoder training
# shape: (batch_size, max_target_sequence_length, embedding_dim)
target_embedding = self.target_embedder(targets)
# shape: (batch_size, max_target_batch_sequence_length)
target_mask = util.get_text_field_mask(target_tokens)
if self._scheduled_sampling_ratio == 0 and self._decoder_net.decodes_parallel:
_, decoder_output = self._decoder_net(
previous_state=state,
previous_steps_predictions=target_embedding[:, :-1, :],
encoder_outputs=encoder_outputs,
source_mask=source_mask,
previous_steps_mask=target_mask[:, :-1],
)
# shape: (group_size, max_target_sequence_length, num_classes)
logits = self._output_projection_layer(decoder_output)
else:
batch_size = source_mask.size()[0]
_, target_sequence_length = targets.size()
# The last input from the target is either padding or the end symbol.
# Either way, we don't have to process it.
num_decoding_steps = target_sequence_length - 1
# Initialize target predictions with the start index.
# shape: (batch_size,)
last_predictions = source_mask.new_full(
(batch_size,), fill_value=self._start_index
)
# shape: (steps, batch_size, target_embedding_dim)
steps_embeddings = torch.Tensor([])
step_logits: List[torch.Tensor] = []
for timestep in range(num_decoding_steps):
if (
self.training
and torch.rand(1).item() < self._scheduled_sampling_ratio
):
# Use gold tokens at test time and at a rate of 1 - _scheduled_sampling_ratio
# during training.
# shape: (batch_size, steps, target_embedding_dim)
state["previous_steps_predictions"] = steps_embeddings
# shape: (batch_size, )
effective_last_prediction = last_predictions
else:
# shape: (batch_size, )
effective_last_prediction = targets[:, timestep]
if timestep == 0:
state["previous_steps_predictions"] = torch.Tensor([])
else:
# shape: (batch_size, steps, target_embedding_dim)
state["previous_steps_predictions"] = target_embedding[
:, :timestep
]
# shape: (batch_size, num_classes)
output_projections, state = self._prepare_output_projections(
effective_last_prediction, state
)
# list of tensors, shape: (batch_size, 1, num_classes)
step_logits.append(output_projections.unsqueeze(1))
# shape (predicted_classes): (batch_size,)
_, predicted_classes = torch.max(output_projections, 1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes
# shape: (batch_size, 1, target_embedding_dim)
last_predictions_embeddings = self.target_embedder(
last_predictions
).unsqueeze(1)
# This step is required, since we want to keep up two different prediction history: gold and real
if steps_embeddings.shape[-1] == 0:
# There is no previous steps, except for start vectors in ``last_predictions``
# shape: (group_size, 1, target_embedding_dim)
steps_embeddings = last_predictions_embeddings
else:
# shape: (group_size, steps_count, target_embedding_dim)
steps_embeddings = torch.cat(
[steps_embeddings, last_predictions_embeddings], 1
)
# shape: (batch_size, num_decoding_steps, num_classes)
logits = torch.cat(step_logits, 1)
# Compute loss.
target_mask = util.get_text_field_mask(target_tokens)
loss = self._get_loss(logits, targets, target_mask)
# TODO: We will be using beam search to get predictions for validation, but if beam size in 1
# we could consider taking the last_predictions here and building step_predictions
# and use that instead of running beam search again, if performance in validation is taking a hit
output_dict = {"loss": loss}
return output_dict
def _prepare_output_projections(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Decode current state and last prediction to produce produce projections
into the target space, which can then be used to get probabilities of
each target token for the next step.
Inputs are the same as for `take_step()`.
"""
# shape: (group_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (group_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (group_size, steps_count, decoder_output_dim)
previous_steps_predictions = state.get("previous_steps_predictions")
# shape: (batch_size, 1, target_embedding_dim)
last_predictions_embeddings = self.target_embedder(last_predictions).unsqueeze(
1
)
if (
previous_steps_predictions is None
or previous_steps_predictions.shape[-1] == 0
):
# There is no previous steps, except for start vectors in ``last_predictions``
# shape: (group_size, 1, target_embedding_dim)
previous_steps_predictions = last_predictions_embeddings
else:
# shape: (group_size, steps_count, target_embedding_dim)
previous_steps_predictions = torch.cat(
[previous_steps_predictions, last_predictions_embeddings], 1
)
decoder_state, decoder_output = self._decoder_net(
previous_state=state,
encoder_outputs=encoder_outputs,
source_mask=source_mask,
previous_steps_predictions=previous_steps_predictions,
)
state["previous_steps_predictions"] = previous_steps_predictions
# Update state with new decoder state, override previous state
state.update(decoder_state)
if self._decoder_net.decodes_parallel:
decoder_output = decoder_output[:, -1, :]
# shape: (group_size, num_classes)
output_projections = self._output_projection_layer(decoder_output)
return output_projections, state
def _get_loss(
self,
logits: torch.LongTensor,
targets: torch.LongTensor,
target_mask: torch.LongTensor,
) -> torch.Tensor:
"""
Compute loss.
Takes logits (unnormalized outputs from the decoder) of size (batch_size,
num_decoding_steps, num_classes), target indices of size (batch_size, num_decoding_steps+1)
and corresponding masks of size (batch_size, num_decoding_steps+1) steps and computes cross
entropy loss while taking the mask into account.
The length of ``targets`` is expected to be greater than that of ``logits`` because the
decoder does not need to compute the output corresponding to the last timestep of
``targets``. This method aligns the inputs appropriately to compute the loss.
During training, we want the logit corresponding to timestep i to be similar to the target
token from timestep i + 1. That is, the targets should be shifted by one timestep for
appropriate comparison. Consider a single example where the target has 3 words, and
padding is to 7 tokens.
The complete sequence would correspond to <S> w1 w2 w3 <E> <P> <P>
and the mask would be 1 1 1 1 1 0 0
and let the logits be l1 l2 l3 l4 l5 l6
We actually need to compare:
the sequence w1 w2 w3 <E> <P> <P>
with masks 1 1 1 1 0 0
against l1 l2 l3 l4 l5 l6
(where the input was) <S> w1 w2 w3 <E> <P>
"""
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets[:, 1:].contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask[:, 1:].contiguous()
return util.sequence_cross_entropy_with_logits(
logits,
relevant_targets,
relevant_mask,
label_smoothing=self._label_smoothing_ratio,
)
def get_output_dim(self):
return self._decoder_net.get_output_dim()
def take_step(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take a decoding step. This is called by the beam search class.
Parameters
----------
last_predictions : ``torch.Tensor``
A tensor of shape ``(group_size,)``, which gives the indices of the predictions
during the last time step.
state : ``Dict[str, torch.Tensor]``
A dictionary of tensors that contain the current state information
needed to predict the next step, which includes the encoder outputs,
the source mask, and the decoder hidden state and context. Each of these
tensors has shape ``(group_size, *)``, where ``*`` can be any other number
of dimensions.
Returns
-------
Tuple[torch.Tensor, Dict[str, torch.Tensor]]
A tuple of ``(log_probabilities, updated_state)``, where ``log_probabilities``
is a tensor of shape ``(group_size, num_classes)`` containing the predicted
log probability of each class for the next step, for each item in the group,
while ``updated_state`` is a dictionary of tensors containing the encoder outputs,
source mask, and updated decoder hidden state and context.
Notes
-----
We treat the inputs as a batch, even though ``group_size`` is not necessarily
equal to ``batch_size``, since the group may contain multiple states
for each source sentence in the batch.
"""
# shape: (group_size, num_classes)
output_projections, state = self._prepare_output_projections(
last_predictions, state
)
# shape: (group_size, num_classes)
class_log_probabilities = F.log_softmax(output_projections, dim=-1)
return class_log_probabilities, state
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if not self.training:
if self._tensor_based_metric is not None:
all_metrics.update(
self._tensor_based_metric.get_metric(reset=reset) # type: ignore
)
if self._token_based_metric is not None:
all_metrics.update(self._token_based_metric.get_metric(reset=reset)) # type: ignore
return all_metrics
@overrides
def forward(
self,
encoder_out: Dict[str, torch.LongTensor],
target_tokens: Dict[str, torch.LongTensor] = None,
) -> Dict[str, torch.Tensor]:
state = encoder_out
if target_tokens:
decoder_init_state = self._decoder_net.init_decoder_state(state)
decoder_init_state.update(state)
output_dict = self._forward_loss(decoder_init_state, target_tokens)
else:
output_dict = {}
if not self.training:
decoder_init_state = self._decoder_net.init_decoder_state(state)
decoder_init_state.update(state)
predictions = self._forward_beam_search(decoder_init_state)
output_dict.update(predictions)
if target_tokens:
if self._tensor_based_metric is not None:
# shape: (batch_size, beam_size, max_sequence_length)
top_k_predictions = output_dict["predictions"]
# shape: (batch_size, max_predicted_sequence_length)
best_predictions = top_k_predictions[:, 0, :]
self._tensor_based_metric( # type: ignore
best_predictions, target_tokens["tokens"]
)
if self._token_based_metric is not None:
output_dict = self.post_process(output_dict)
predicted_tokens = output_dict["predicted_tokens"]
self._token_based_metric( # type: ignore
predicted_tokens,
self.indices_to_tokens(target_tokens["tokens"][:, 1:]),
)
return output_dict
@overrides
def post_process(
self, output_dict: Dict[str, torch.Tensor]
) -> Dict[str, torch.Tensor]:
"""
This method trims the output predictions to the first end symbol, replaces indices with
corresponding tokens, and adds a field called ``predicted_tokens`` to the ``output_dict``.
"""
predicted_indices = output_dict["predictions"]
all_predicted_tokens = self.indices_to_tokens(predicted_indices)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
def indices_to_tokens(self, batch_indeces: numpy.ndarray) -> List[List[str]]:
if not isinstance(batch_indeces, numpy.ndarray):
batch_indeces = batch_indeces.detach().cpu().numpy()
all_tokens = []
for indices in batch_indeces:
# Beam search gives us the top k results for each source sentence in the batch
# but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[: indices.index(self._end_index)]
tokens = [
self._vocab.get_token_from_index(x, namespace=self._target_namespace)
for x in indices
]
all_tokens.append(tokens)
return all_tokens
def __init__(
self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
target_embedder: TextFieldEmbedder,
source_encoder: Seq2SeqEncoder,
target_encoder: Seq2SeqEncoder,
#instance_reference_similarity_function:MatrixAttention,
#target_instance_ref_similarity_function:MatrixAttention,
max_decoding_steps: int,
attention: Attention = None,
s2s_attention: Attention = None,
t2t_attention: Attention = None,
beam_size: int = None,
target_namespace: str = "tokens",
scheduled_sampling_ratio: float = 0.,
use_bleu: bool = True) -> None:
super(AssociativeSeq2SeqChainedAttention, self).__init__(vocab)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace) # pylint: disable=protected-access
self._bleu = BLEU(exclude_indices={
pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
# Dense embedding of source vocab tokens.
self._source_embedder = source_embedder
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._source_encoder = source_encoder
self._target_encoder = target_encoder
self._encoder_output_dim = self._target_encoder.get_output_dim()
self._decoder_output_dim = self._encoder_output_dim
target_embedding_dim = target_embedder.get_output_dim()
if attention:
self._attention = attention
self._s2s_attention = s2s_attention
self._t2t_attention = t2t_attention
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
else:
raise NotImplementedError
num_classes = self.vocab.get_vocab_size(self._target_namespace)
self._target_embedder = target_embedder
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_output_dim)
self._output_projection_layer = Linear(self._decoder_output_dim,
num_classes)
class AssociativeSeq2SeqChainedAttention(Model):
def __init__(
self,
vocab: Vocabulary,
source_embedder: TextFieldEmbedder,
target_embedder: TextFieldEmbedder,
source_encoder: Seq2SeqEncoder,
target_encoder: Seq2SeqEncoder,
#instance_reference_similarity_function:MatrixAttention,
#target_instance_ref_similarity_function:MatrixAttention,
max_decoding_steps: int,
attention: Attention = None,
s2s_attention: Attention = None,
t2t_attention: Attention = None,
beam_size: int = None,
target_namespace: str = "tokens",
scheduled_sampling_ratio: float = 0.,
use_bleu: bool = True) -> None:
super(AssociativeSeq2SeqChainedAttention, self).__init__(vocab)
self._target_namespace = target_namespace
self._scheduled_sampling_ratio = scheduled_sampling_ratio
# We need the start symbol to provide as the input at the first timestep of decoding, and
# end symbol as a way to indicate the end of the decoded sequence.
self._start_index = self.vocab.get_token_index(START_SYMBOL,
self._target_namespace)
self._end_index = self.vocab.get_token_index(END_SYMBOL,
self._target_namespace)
if use_bleu:
pad_index = self.vocab.get_token_index(self.vocab._padding_token,
self._target_namespace) # pylint: disable=protected-access
self._bleu = BLEU(exclude_indices={
pad_index, self._end_index, self._start_index
})
else:
self._bleu = None
# At prediction time, we use a beam search to find the most likely sequence of target tokens.
beam_size = beam_size or 1
self._max_decoding_steps = max_decoding_steps
self._beam_search = BeamSearch(self._end_index,
max_steps=max_decoding_steps,
beam_size=beam_size)
# Dense embedding of source vocab tokens.
self._source_embedder = source_embedder
# Encodes the sequence of source embeddings into a sequence of hidden states.
self._source_encoder = source_encoder
self._target_encoder = target_encoder
self._encoder_output_dim = self._target_encoder.get_output_dim()
self._decoder_output_dim = self._encoder_output_dim
target_embedding_dim = target_embedder.get_output_dim()
if attention:
self._attention = attention
self._s2s_attention = s2s_attention
self._t2t_attention = t2t_attention
self._decoder_input_dim = self._decoder_output_dim + target_embedding_dim
else:
raise NotImplementedError
num_classes = self.vocab.get_vocab_size(self._target_namespace)
self._target_embedder = target_embedder
self._decoder_cell = LSTMCell(self._decoder_input_dim,
self._decoder_output_dim)
self._output_projection_layer = Linear(self._decoder_output_dim,
num_classes)
def take_step(
self, last_predictions: torch.Tensor, state: Dict[str, torch.Tensor]
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
"""
Take a decoding step. This is called by the beam search class.
Parameters
----------
last_predictions : ``torch.Tensor``
A tensor of shape ``(group_size,)``, which gives the indices of the predictions
during the last time step.
state : ``Dict[str, torch.Tensor]``
A dictionary of tensors that contain the current state information
needed to predict the next step, which includes the encoder outputs,
the source mask, and the decoder hidden state and context. Each of these
tensors has shape ``(group_size, *)``, where ``*`` can be any other number
of dimensions.
Returns
-------
Tuple[torch.Tensor, Dict[str, torch.Tensor]]
A tuple of ``(log_probabilities, updated_state)``, where ``log_probabilities``
is a tensor of shape ``(group_size, num_classes)`` containing the predicted
log probability of each class for the next step, for each item in the group,
while ``updated_state`` is a dictionary of tensors containing the encoder outputs,
source mask, and updated decoder hidden state and context.
Notes
-----
We treat the inputs as a batch, even though ``group_size`` is not necessarily
equal to ``batch_size``, since the group may contain multiple states
for each source sentence in the batch.
"""
# shape: (group_size, num_classes)
output_projections, state = self._prepare_output_projections(
last_predictions, state)
# shape: (group_size, num_classes)
class_log_probabilities = F.log_softmax(output_projections, dim=-1)
return class_log_probabilities, state
@overrides
def forward(
self, # type: ignore
ref_source_tokens: Dict[str, torch.LongTensor],
instance_source_tokens: Dict[str, torch.LongTensor],
ref_target_tokens: Dict[str, torch.LongTensor],
instance_target_tokens: Dict[str, torch.LongTensor] = None
) -> Dict[str, torch.Tensor]:
state = self._encode(ref_target_tokens, ref_source_tokens,
instance_source_tokens)
if instance_target_tokens:
state = self._init_decoder_state(state)
# The `_forward_loop` decodes the input sequence and computes the loss during training
# and validation.
output_dict = self._forward_loop(state, instance_target_tokens)
else:
output_dict = {}
if not self.training:
state = self._init_decoder_state(state)
predictions = self._forward_beam_search(state)
output_dict.update(predictions)
if instance_target_tokens and self._bleu:
# shape: (batch_size, beam_size, max_sequence_length)
top_k_predictions = output_dict["predictions"]
# shape: (batch_size, max_predicted_sequence_length)
best_predictions = top_k_predictions[:, 0, :]
self._bleu(best_predictions, instance_target_tokens["tokens"])
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Finalize predictions.
This method overrides ``Model.decode``, which gets called after ``Model.forward``, at test
time, to finalize predictions. The logic for the decoder part of the encoder-decoder lives
within the ``forward`` method.
This method trims the output predictions to the first end symbol, replaces indices with
corresponding tokens, and adds a field called ``predicted_tokens`` to the ``output_dict``.
"""
predicted_indices = output_dict["predictions"]
if not isinstance(predicted_indices, numpy.ndarray):
predicted_indices = predicted_indices.detach().cpu().numpy()
all_predicted_tokens = []
for indices in predicted_indices:
# Beam search gives us the top k results for each source sentence in the batch
# but we just want the single best.
if len(indices.shape) > 1:
indices = indices[0]
indices = list(indices)
# Collect indices till the first end_symbol
if self._end_index in indices:
indices = indices[:indices.index(self._end_index)]
predicted_tokens = [
self.vocab.get_token_from_index(
x, namespace=self._target_namespace) for x in indices
]
all_predicted_tokens.append(predicted_tokens)
output_dict["predicted_tokens"] = all_predicted_tokens
return output_dict
def _encode(
self, ref_target_tokens: Dict[str, torch.Tensor],
ref_source_tokens: Dict[str, torch.Tensor],
instance_source_tokens: Dict[str, torch.Tensor]
) -> Dict[str, torch.Tensor]:
# shape: (batch_size, max_input_sequence_length, encoder_input_dim)
embedded_ref_target = self._target_embedder(ref_target_tokens)
ref_target_mask = util.get_text_field_mask(ref_target_tokens)
encoded_ref_target = self._target_encoder(embedded_ref_target,
ref_target_mask)
embedded_ref_source = self._source_embedder(ref_source_tokens)
ref_source_mask = util.get_text_field_mask(ref_source_tokens)
encoded_ref_source = self._source_encoder(embedded_ref_source,
ref_source_mask)
embedded_instance_source = self._source_embedder(
instance_source_tokens)
instance_source_mask = util.get_text_field_mask(instance_source_tokens)
encoded_instance_source = self._source_encoder(
embedded_instance_source, instance_source_mask)
return {
"source_mask": ref_target_mask,
"encoder_outputs": encoded_ref_target,
"ref_source_mask": ref_source_mask,
"ref_source_encoded": embedded_ref_source,
"instance_source_mask": instance_source_mask,
"instance_source_encoded": embedded_instance_source
}
def _init_decoder_state(
self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
batch_size = state["source_mask"].size(0)
# shape: (batch_size, encoder_output_dim)
final_encoder_output = util.get_final_encoder_states(
state["encoder_outputs"], state["source_mask"],
self._target_encoder.is_bidirectional())
# Initialize the decoder hidden state with the final output of the encoder.
# shape: (batch_size, decoder_output_dim)
state["decoder_hidden"] = final_encoder_output
# shape: (batch_size, decoder_output_dim)
state["decoder_context"] = state["encoder_outputs"].new_zeros(
batch_size, self._decoder_output_dim)
return state
def _forward_loop(
self,
state: Dict[str, torch.Tensor],
target_tokens: Dict[str, torch.LongTensor] = None
) -> Dict[str, torch.Tensor]:
"""
Make forward pass during training or do greedy search during prediction.
Notes
-----
We really only use the predictions from the method to test that beam search
with a beam size of 1 gives the same results.
"""
# shape: (batch_size, max_input_sequence_length)
source_mask = state["source_mask"]
batch_size = source_mask.size()[0]
if target_tokens:
# shape: (batch_size, max_target_sequence_length)
targets = target_tokens["tokens"]
_, target_sequence_length = targets.size()
# The last input from the target is either padding or the end symbol.
# Either way, we don't have to process it.
num_decoding_steps = target_sequence_length - 1
else:
num_decoding_steps = self._max_decoding_steps
# Initialize target predictions with the start index.
# shape: (batch_size,)
last_predictions = source_mask.new_full((batch_size, ),
fill_value=self._start_index)
step_logits: List[torch.Tensor] = []
step_predictions: List[torch.Tensor] = []
for timestep in range(num_decoding_steps):
if self.training and torch.rand(
1).item() < self._scheduled_sampling_ratio:
# Use gold tokens at test time and at a rate of 1 - _scheduled_sampling_ratio
# during training.
# shape: (batch_size,)
input_choices = last_predictions
elif not target_tokens:
# shape: (batch_size,)
input_choices = last_predictions
else:
# shape: (batch_size,)
input_choices = targets[:, timestep]
# shape: (batch_size, num_classes)
output_projections, state = self._prepare_output_projections(
input_choices, state)
# list of tensors, shape: (batch_size, 1, num_classes)
step_logits.append(output_projections.unsqueeze(1))
# shape: (batch_size, num_classes)
class_probabilities = F.softmax(output_projections, dim=-1)
# shape (predicted_classes): (batch_size,)
_, predicted_classes = torch.max(class_probabilities, 1)
# shape (predicted_classes): (batch_size,)
last_predictions = predicted_classes
step_predictions.append(last_predictions.unsqueeze(1))
# shape: (batch_size, num_decoding_steps)
predictions = torch.cat(step_predictions, 1)
output_dict = {"predictions": predictions}
if target_tokens:
# shape: (batch_size, num_decoding_steps, num_classes)
logits = torch.cat(step_logits, 1)
# Compute loss.
target_mask = util.get_text_field_mask(target_tokens)
loss = self._get_loss(logits, targets, target_mask)
output_dict["loss"] = loss
return output_dict
def _forward_beam_search(
self, state: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""Make forward pass during prediction using a beam search."""
batch_size = state["source_mask"].size()[0]
start_predictions = state["source_mask"].new_full(
(batch_size, ), fill_value=self._start_index)
# shape (all_top_k_predictions): (batch_size, beam_size, num_decoding_steps)
# shape (log_probabilities): (batch_size, beam_size)
all_top_k_predictions, log_probabilities = self._beam_search.search(
start_predictions, state, self.take_step)
output_dict = {
"class_log_probabilities": log_probabilities,
"predictions": all_top_k_predictions,
}
return output_dict
def _prepare_output_projections(self,
last_predictions: torch.Tensor,
state: Dict[str, torch.Tensor]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: # pylint: disable=line-too-long
"""
Decode current state and last prediction to produce produce projections
into the target space, which can then be used to get probabilities of
each target token for the next step.
Inputs are the same as for `take_step()`.
"""
# shape: (group_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs = state["encoder_outputs"]
# shape: (group_size, max_input_sequence_length)
source_mask = state["source_mask"]
# shape: (group_size, decoder_output_dim)
decoder_hidden = state["decoder_hidden"]
# shape: (group_size, decoder_output_dim)
decoder_context = state["decoder_context"]
# shape: (group_size, target_embedding_dim)
embedded_input = self._target_embedder._token_embedders['tokens'](
last_predictions)
if self._attention:
# shape: (group_size, encoder_output_dim)
attended_input_source = self._prepare_attended_input(
decoder_hidden, state['encoder_outputs'], state['source_mask'],
self._t2t_attention)
attended_input_ref_source = self._prepare_attended_input(
attended_input_source, state['ref_source_encoded'],
state['ref_source_mask'], self._attention)
attended_input_instance_source = self._prepare_attended_input(
attended_input_ref_source, state['instance_source_encoded'],
state['instance_source_mask'], self._s2s_attention)
# shape: (group_size, decoder_output_dim + target_embedding_dim)
decoder_input = torch.cat(
(attended_input_instance_source, embedded_input), -1)
else:
raise NotImplementedError
# shape (decoder_hidden): (batch_size, decoder_output_dim)
# shape (decoder_context): (batch_size, decoder_output_dim)
decoder_hidden, decoder_context = self._decoder_cell(
decoder_input, (decoder_hidden, decoder_context))
state["decoder_hidden"] = decoder_hidden
state["decoder_context"] = decoder_context
# shape: (group_size, num_classes)
output_projections = self._output_projection_layer(decoder_hidden)
return output_projections, state
def _prepare_attended_input(self,
decoder_hidden_state: torch.LongTensor = None,
encoder_outputs: torch.LongTensor = None,
encoder_outputs_mask: torch.LongTensor = None,
attention=None) -> torch.Tensor:
"""Apply attention over encoder outputs and decoder state."""
# Ensure mask is also a FloatTensor. Or else the multiplication within
# attention will complain.
# shape: (batch_size, max_input_sequence_length, encoder_output_dim)
encoder_outputs_mask = encoder_outputs_mask.float()
# shape: (batch_size, max_input_sequence_length)
input_weights = attention(decoder_hidden_state, encoder_outputs,
encoder_outputs_mask)
# shape: (batch_size, encoder_output_dim)
attended_input = util.weighted_sum(encoder_outputs, input_weights)
return attended_input
@staticmethod
def _get_loss(logits: torch.LongTensor, targets: torch.LongTensor,
target_mask: torch.LongTensor) -> torch.Tensor:
"""
Compute loss.
Takes logits (unnormalized outputs from the decoder) of size (batch_size,
num_decoding_steps, num_classes), target indices of size (batch_size, num_decoding_steps+1)
and corresponding masks of size (batch_size, num_decoding_steps+1) steps and computes cross
entropy loss while taking the mask into account.
The length of ``targets`` is expected to be greater than that of ``logits`` because the
decoder does not need to compute the output corresponding to the last timestep of
``targets``. This method aligns the inputs appropriately to compute the loss.
During training, we want the logit corresponding to timestep i to be similar to the target
token from timestep i + 1. That is, the targets should be shifted by one timestep for
appropriate comparison. Consider a single example where the target has 3 words, and
padding is to 7 tokens.
The complete sequence would correspond to <S> w1 w2 w3 <E> <P> <P>
and the mask would be 1 1 1 1 1 0 0
and let the logits be l1 l2 l3 l4 l5 l6
We actually need to compare:
the sequence w1 w2 w3 <E> <P> <P>
with masks 1 1 1 1 0 0
against l1 l2 l3 l4 l5 l6
(where the input was) <S> w1 w2 w3 <E> <P>
"""
# shape: (batch_size, num_decoding_steps)
relevant_targets = targets[:, 1:].contiguous()
# shape: (batch_size, num_decoding_steps)
relevant_mask = target_mask[:, 1:].contiguous()
return util.sequence_cross_entropy_with_logits(logits,
relevant_targets,
relevant_mask)
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
all_metrics: Dict[str, float] = {}
if self._bleu and not self.training:
all_metrics.update(self._bleu.get_metric(reset=reset))
return all_metrics
