def test_decode_infer_sample(self):
"""Tests infer_sample
"""
decoder = TransformerDecoder(vocab_size=self._vocab_size,
output_layer=self._output_layer)
helper = tx_helper.SampleEmbeddingHelper(self._embedding_fn,
self._start_tokens,
self._end_token)
outputs, length = decoder(
memory=self._memory,
memory_sequence_length=self._memory_sequence_length,
memory_attention_bias=None,
inputs=None,
helper=helper,
max_decoding_length=self._max_decode_len,
mode=tf.estimator.ModeKeys.PREDICT)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_ = sess.run(outputs)
self.assertIsInstance(outputs_, TransformerDecoderOutput)
def _build(
self, # pylint: disable=arguments-differ, too-many-statements
decoding_strategy='train_greedy',
inputs=None,
memory=None,
memory_sequence_length=None,
memory_attention_bias=None,
beam_width=None,
length_penalty=0.,
start_tokens=None,
end_token=None,
context=None,
context_sequence_length=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
embedding=None,
helper=None,
mode=None):
"""Performs decoding.
The interface is mostly the same with that of RNN decoders
(see :meth:`~texar.tf.modules.RNNDecoderBase._build`). The main difference
is that, here, `sequence_length` is not needed, and continuation
generation is additionally supported.
The function provides **3 ways** to specify the decoding method, with
varying flexibility:
1. The :attr:`decoding_strategy` argument.
- **"train_greedy"**: decoding in teacher-forcing fashion (i.e.,
feeding ground truth to decode the next step), and for each step
sample is obtained by taking the `argmax` of logits.
Argument :attr:`inputs` is required for this strategy.
- **"infer_greedy"**: decoding in inference fashion (i.e., feeding
`generated` sample to decode the next step), and for each step
sample is obtained by taking the `argmax` of logits.
Arguments :attr:`(start_tokens, end_token)` are
required for this strategy, and argument
:attr:`max_decoding_length` is optional.
- **"infer_sample"**: decoding in inference fashion, and for each
step sample is obtained by `random sampling` from the logits.
Arguments :attr:`(start_tokens, end_token)` are required for this
strategy, and argument :attr:`max_decoding_length` is optional.
This argument is used only when arguments :attr:`helper` and
:attr:`beam_width` are both `None`.
2. The :attr:`helper` argument: An instance of subclass of
:class:`texar.tf.modules.Helper`.
This provides a superset of decoding strategies than above.
The interface is the same as in RNN decoders.
Please refer to :meth:`texar.tf.modules.RNNDecoderBase._build` for
detailed usage and examples.
Note that, here, though using a
:class:`~texar.tf.modules.TrainingHelper` corresponds to the
"train_greedy" strategy above and will get the same output results,
the implementation is *slower* than
directly setting `decoding_strategy="train_greedy"`.
Argument :attr:`max_decoding_length` is optional.
3. **Beam search**: set :attr:`beam_width` to use beam search decoding.
Arguments :attr:`(start_tokens, end_token)` are required,
and argument :attr:`max_decoding_length` is optional.
Args:
memory (optional): The memory to attend, e.g., the output of an RNN
encoder. A Tensor of shape `[batch_size, memory_max_time, dim]`.
memory_sequence_length (optional): A Tensor of shape `[batch_size]`
containing the sequence lengths for the batch entries in
memory. Used to create attention bias of
:attr:`memory_attention_bias` is not given. Ignored if
`memory_attention_bias` is provided.
memory_attention_bias (optional): A Tensor of shape
`[batch_size, num_heads, memory_max_time, dim]`.
An attention bias typically sets the value of a padding
position to a large negative value for masking. If not given,
:attr:`memory_sequence_length` is used to automatically
create an attention bias.
inputs (optional): Input tensor for teacher forcing decoding, of
shape `[batch_size, target_max_time, emb_dim]` containing the
target sequence word embeddings.
Used when :attr:`decoding_strategy` is set to "train_greedy".
decoding_strategy (str): A string specifying the decoding
strategy, including "train_greedy", "infer_greedy",
"infer_sample".
Different arguments are required based on the
strategy. See above for details. Ignored if
:attr:`beam_width` or :attr:`helper` is set.
beam_width (int): Set to use beam search. If given,
:attr:`decoding_strategy` is ignored.
length_penalty (float): Length penalty coefficient used in beam
search decoding. Refer to https://arxiv.org/abs/1609.08144
for more details.
It Should be larger if longer sentences are wanted.
start_tokens (optional): An int Tensor of shape `[batch_size]`,
containing the start tokens.
Used when :attr:`decoding_strategy` = "infer_greedy" or
"infer_sample", or :attr:`beam_width` is set.
Ignored when context is set.
end_token (optional): An int 0D Tensor, the token that marks end
of decoding.
Used when :attr:`decoding_strategy` = "infer_greedy" or
"infer_sample", or :attr:`beam_width` is set.
context (optional): An int Tensor of shape `[batch_size, length]`,
containing the starting tokens for decoding.
If context is set, the start_tokens will be ignored.
context_sequence_length (optional): specify the length of context.
softmax_temperature (optional): A float 0D Tensor, value to divide
the logits by before computing the softmax. Larger values
(above 1.0) result in more random samples. Must > 0. If `None`,
1.0 is used.
Used when :attr:`decoding_strategy` = "infer_sample"`.
max_decoding_length (optional): An int scalar Tensor indicating
the maximum allowed number of decoding steps.
If `None` (default), use "max_decoding_length" defined in
:attr:`hparams`. Ignored in "train_greedy" decoding.
impute_finished (bool): If `True`, then states for batch
entries which are marked as finished get copied through and
the corresponding outputs get zeroed out. This causes some
slowdown at each time step, but ensures that the final state
and outputs have the correct values and that backprop ignores
time steps that were marked as finished. Ignored in
"train_greedy" decoding.
embedding (optional): Embedding used when
"infer_greedy" or "infer_sample" `decoding_strategy`, or
beam search, is used. This can be
a callable or the `params` argument for
:tf_main:`embedding_lookup <nn/embedding_lookup>`.
If a callable, it can take a vector tensor of token `ids`,
or take two arguments (`ids`, `times`), where `ids`
is a vector tensor of token ids, and `times` is a vector tensor
of time steps (i.e., position ids). The latter case can be used
when attr:`embedding` is a combination of word embedding and
position embedding.
helper (optional): An instance of
:tf_main:`Helper <contrib/seq2seq/Helper>` that defines the
decoding strategy. If given, :attr:`decoding_strategy` is
ignored.
mode (optional): A tensor taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. Controls dropout mode.
If `None` (default), :func:`texar.tf.global_mode`
is used.
Returns:
- For **"train_greedy"** decoding, returns an instance of \
:class:`~texar.tf.modules.TransformerDecoderOutput` which contains\
`sample_id` and `logits`.
- For **"infer_greedy"** and **"infer_sample"** decoding or\
decoding with :attr:`helper`, returns\
a tuple `(outputs, sequence_lengths)`, where `outputs` is an \
instance of :class:`~texar.tf.modules.TransformerDecoderOutput` as\
in "train_greedy", and `sequence_lengths` is a Tensor of shape\
`[batch_size]` containing the length of each sample.
- For **beam search** decoding, returns a `dict` containing keys\
"sample_id" and "log_prob".
- **"sample_id"** is an int Tensor of shape \
`[batch_size, max_time, beam_width]` containing generated\
token indexes. `sample_id[:,:,0]` is the highest-probable \
sample.
- **"log_prob"** is a float Tensor of shape \
`[batch_size, beam_width]` containing the log probability \
of each sequence sample.
"""
if memory is not None:
if memory_attention_bias is None:
if memory_sequence_length is None:
raise ValueError("`memory_sequence_length` is required if "
"`memory_attention_bias` is not given.")
enc_padding = 1 - tf.sequence_mask(memory_sequence_length,
shape_list(memory)[1],
dtype=tf.float32)
memory_attention_bias = attn.attention_bias_ignore_padding(
enc_padding)
# record the context, which will be used in step function
# for dynamic_decode
if context is not None:
start_tokens = context[:, 0]
self.context = context[:, 1:]
self.context_sequence_length = context_sequence_length - 1
else:
self.context = None
self.embedding = embedding
if helper is None and beam_width is None and \
decoding_strategy == 'train_greedy': # Teacher-forcing
decoder_self_attention_bias = (attn.attention_bias_lower_triangle(
shape_list(inputs)[1]))
decoder_output = self._self_attention_stack(
inputs,
memory,
decoder_self_attention_bias=decoder_self_attention_bias,
memory_attention_bias=memory_attention_bias,
cache=None,
mode=mode)
logits = self._output_layer(decoder_output)
preds = tf.cast(tf.argmax(logits, axis=-1), tf.int32)
rets = TransformerDecoderOutput(logits=logits, sample_id=preds)
else:
if max_decoding_length is None:
max_decoding_length = self._hparams.max_decoding_length
self.max_decoding_length = max_decoding_length
if beam_width is None: # Inference-like decoding
# Prepare helper
if helper is None:
if decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token,
softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(
decoding_strategy))
self._helper = helper
self._cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=False)
if context is not None:
self.context = tf.pad(self.context, [[
0, 0
], [0, max_decoding_length - shape_list(self.context)[1]]])
outputs, _, sequence_lengths = dynamic_decode(
decoder=self,
impute_finished=impute_finished,
maximum_iterations=max_decoding_length,
output_time_major=False,
scope=self.variable_scope)
if context is not None:
# Here the length of sample_id will be larger than that
# of logit by 1, because there will be a additional
# start_token in the returned sample_id.
# the start_id should be the first token of the
# given context
outputs = TransformerDecoderOutput(
logits=outputs.logits,
sample_id=tf.concat([
tf.expand_dims(start_tokens, 1), outputs.sample_id
],
axis=1))
sequence_lengths = sequence_lengths + 1
rets = outputs, sequence_lengths
else: # Beam-search decoding
# Ignore `decoding_strategy`; Assume `helper` is not set
if helper is not None:
raise ValueError("Must not set 'beam_width' and 'helper' "
"simultaneously.")
_batch_size = shape_list(start_tokens)[0]
self._cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=True,
batch_size=_batch_size)
# The output format is different when running beam search
sample_id, log_prob = self._beam_decode(
start_tokens,
end_token,
beam_width=beam_width,
length_penalty=length_penalty,
decode_length=max_decoding_length,
)
rets = {'sample_id': sample_id, 'log_prob': log_prob}
if not self._built:
self._add_internal_trainable_variables()
self._built = True
return rets
def _build(self,
decoding_strategy="train_greedy",
initial_state=None,
inputs=None,
memory=None,
sequence_length=None,
embedding=None,
start_tokens=None,
end_token=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
output_time_major=False,
input_time_major=False,
helper=None,
mode=None,
**kwargs):
# Memory
for _mechanism in self._cell._attention_mechanisms:
_mechanism.initialize_memory(memory)
# Helper
if helper is not None:
pass
elif decoding_strategy is not None:
if decoding_strategy == "train_greedy":
helper = rnn_decoder_helpers._get_training_helper(
inputs, sequence_length, embedding, input_time_major)
elif decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token, softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(decoding_strategy))
else:
if is_train_mode_py(mode):
kwargs_ = copy.copy(self._hparams.helper_train.kwargs.todict())
helper_type = self._hparams.helper_train.type
else:
kwargs_ = copy.copy(self._hparams.helper_infer.kwargs.todict())
helper_type = self._hparams.helper_infer.type
kwargs_.update({
"inputs": inputs,
"sequence_length": sequence_length,
"time_major": input_time_major,
"embedding": embedding,
"start_tokens": start_tokens,
"end_token": end_token,
"softmax_temperature": softmax_temperature})
kwargs_.update(kwargs)
helper = rnn_decoder_helpers.get_helper(helper_type, **kwargs_)
self._helper = helper
# Initial state
if initial_state is not None:
self._initial_state = initial_state
else:
self._initial_state = self.zero_state(
batch_size=self.batch_size, dtype=tf.float32)
# Maximum decoding length
max_l = max_decoding_length
if max_l is None:
max_l_train = self._hparams.max_decoding_length_train
if max_l_train is None:
max_l_train = utils.MAX_SEQ_LENGTH
max_l_infer = self._hparams.max_decoding_length_infer
if max_l_infer is None:
max_l_infer = utils.MAX_SEQ_LENGTH
max_l = tf.cond(is_train_mode(mode),
lambda: max_l_train, lambda: max_l_infer)
self.max_decoding_length = max_l
# Decode
outputs, final_state, sequence_lengths = dynamic_decode(
decoder=self, impute_finished=impute_finished,
maximum_iterations=max_l, output_time_major=output_time_major)
if not self._built:
self._add_internal_trainable_variables()
# Add trainable variables of `self._cell` which may be
# constructed externally.
self._add_trainable_variable(
layers.get_rnn_cell_trainable_variables(self._cell))
if isinstance(self._output_layer, tf.layers.Layer):
self._add_trainable_variable(
self._output_layer.trainable_variables)
# Add trainable variables of `self._beam_search_rnn_cell` which
# may already be constructed and used.
if self._beam_search_cell is not None:
self._add_trainable_variable(
self._beam_search_cell.trainable_variables)
self._built = True
return outputs, final_state, sequence_lengths
def _build(self,
decoding_strategy="train_greedy",
initial_state=None,
inputs=None,
sequence_length=None,
embedding=None,
start_tokens=None,
end_token=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
output_time_major=False,
input_time_major=False,
helper=None,
mode=None,
**kwargs):
"""Performs decoding. This is a shared interface for both
:class:`~texar.tf.modules.BasicRNNDecoder` and
:class:`~texar.tf.modules.AttentionRNNDecoder`.
The function provides **3 ways** to specify the
decoding method, with varying flexibility:
1. The :attr:`decoding_strategy` argument: A string taking value of:
- **"train_greedy"**: decoding in teacher-forcing fashion \
(i.e., feeding \
`ground truth` to decode the next step), and each sample is \
obtained by taking the `argmax` of the RNN output logits. \
Arguments :attr:`(inputs, sequence_length, input_time_major)` \
are required for this strategy, and argument :attr:`embedding` \
is optional.
- **"infer_greedy"**: decoding in inference fashion (i.e., feeding \
the `generated` sample to decode the next step), and each sample\
is obtained by taking the `argmax` of the RNN output logits.\
Arguments :attr:`(embedding, start_tokens, end_token)` are \
required for this strategy, and argument \
:attr:`max_decoding_length` is optional.
- **"infer_sample"**: decoding in inference fashion, and each
sample is obtained by `random sampling` from the RNN output
distribution. Arguments \
:attr:`(embedding, start_tokens, end_token)` are \
required for this strategy, and argument \
:attr:`max_decoding_length` is optional.
This argument is used only when argument :attr:`helper` is `None`.
Example:
.. code-block:: python
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size)
# Teacher-forcing decoding
outputs_1, _, _ = decoder(
decoding_strategy='train_greedy',
inputs=embedder(data_batch['text_ids']),
sequence_length=data_batch['length']-1)
# Random sample decoding. Gets 100 sequence samples
outputs_2, _, sequence_length = decoder(
decoding_strategy='infer_sample',
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos.token_id,
embedding=embedder,
max_decoding_length=60)
2. The :attr:`helper` argument: An instance of subclass of \
:class:`texar.tf.modules.Helper`. This
provides a superset of decoding strategies than above, for example:
- :class:`~texar.tf.modules.TrainingHelper` corresponding to the \
"train_greedy" strategy.
- :class:`~texar.tf.modules.GreedyEmbeddingHelper` and \
:class:`~texar.tf.modules.SampleEmbeddingHelper` corresponding to \
the "infer_greedy" and "infer_sample", respectively.
- :class:`~texar.tf.modules.TopKSampleEmbeddingHelper` for Top-K \
sample decoding.
- :class:`ScheduledEmbeddingTrainingHelper` and \
:class:`ScheduledOutputTrainingHelper` for scheduled \
sampling.
- :class:`~texar.tf.modules.SoftmaxEmbeddingHelper` and \
:class:`~texar.tf.modules.GumbelSoftmaxEmbeddingHelper` for \
soft decoding and gradient backpropagation.
Helpers give the maximal flexibility of configuring the decoding\
strategy.
Example:
.. code-block:: python
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size)
# Teacher-forcing decoding, same as above with
# `decoding_strategy='train_greedy'`
helper_1 = tx.modules.TrainingHelper(
inputs=embedders(data_batch['text_ids']),
sequence_length=data_batch['length']-1)
outputs_1, _, _ = decoder(helper=helper_1)
# Gumbel-softmax decoding
helper_2 = GumbelSoftmaxEmbeddingHelper(
embedding=embedder,
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos_token_id,
tau=0.1)
outputs_2, _, sequence_length = decoder(
max_decoding_length=60, helper=helper_2)
3. :attr:`hparams["helper_train"]` and :attr:`hparams["helper_infer"]`:\
Specifying the helper through hyperparameters. Train and infer \
strategy is toggled based on :attr:`mode`. Appriopriate arguments \
(e.g., :attr:`inputs`, :attr:`start_tokens`, etc) are selected to \
construct the helper. Additional arguments for helper constructor \
can be provided either through :attr:`**kwargs`, or through \
:attr:`hparams["helper_train/infer"]["kwargs"]`.
This means is used only when both :attr:`decoding_strategy` and \
:attr:`helper` are `None`.
Example:
.. code-block:: python
h = {
"helper_infer": {
"type": "GumbelSoftmaxEmbeddingHelper",
"kwargs": { "tau": 0.1 }
}
}
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size, hparams=h)
# Gumbel-softmax decoding
output, _, _ = decoder(
decoding_strategy=None, # Sets to None explicit
embedding=embedder,
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos_token_id,
max_decoding_length=60,
mode=tf.estimator.ModeKeys.PREDICT)
# PREDICT mode also shuts down dropout
Args:
decoding_strategy (str): A string specifying the decoding
strategy. Different arguments are required based on the
strategy.
Ignored if :attr:`helper` is given.
initial_state (optional): Initial state of decoding.
If `None` (default), zero state is used.
inputs (optional): Input tensors for teacher forcing decoding.
Used when `decoding_strategy` is set to "train_greedy", or
when `hparams`-configured helper is used.
- If :attr:`embedding` is `None`, `inputs` is directly \
fed to the decoder. E.g., in `"train_greedy"` strategy, \
`inputs` must be a 3D Tensor of shape \
`[batch_size, max_time, emb_dim]` (or \
`[max_time, batch_size, emb_dim]` if `input_time_major`==True).
- If `embedding` is given, `inputs` is used as index \
to look up embeddings and feed in the decoder. \
E.g., if `embedding` is an instance of \
:class:`~texar.tf.modules.WordEmbedder`, \
then :attr:`inputs` is usually a 2D int Tensor \
`[batch_size, max_time]` (or \
`[max_time, batch_size]` if `input_time_major`==True) \
containing the token indexes.
sequence_length (optional): A 1D int Tensor containing the
sequence length of :attr:`inputs`.
Used when `decoding_strategy="train_greedy"` or
`hparams`-configured helper is used.
embedding (optional): Embedding used when:
- "infer_greedy" or "infer_sample" `decoding_strategy` is \
used. This can be a callable or the `params` argument for \
:tf_main:`embedding_lookup <nn/embedding_lookup>`. \
If a callable, it can take a vector tensor of token `ids`, \
or take two arguments (`ids`, `times`), where `ids` \
is a vector tensor of token ids, and `times` is a vector tensor\
of time steps (i.e., position ids). The latter case can be used\
when attr:`embedding` is a combination of word embedding and\
position embedding. `embedding` is required in this case.
- "train_greedy" `decoding_strategy` is used.\
This can be a callable or the `params` argument for \
:tf_main:`embedding_lookup <nn/embedding_lookup>`. \
If a callable, it can take :attr:`inputs` and returns \
the input embedding. `embedding` is optional in this case.
start_tokens (optional): A int Tensor of shape `[batch_size]`,
the start tokens. Used when `decoding_strategy="infer_greedy"`
or `"infer_sample"`, or when the helper specified in `hparams`
is used.
Example:
.. code-block:: python
data = tx.data.MonoTextData(hparams)
iterator = DataIterator(data)
batch = iterator.get_next()
bos_token_id = data.vocab.bos_token_id
start_tokens=tf.ones_like(batch['length'])*bos_token_id
end_token (optional): A int 0D Tensor, the token that marks end
of decoding.
Used when `decoding_strategy="infer_greedy"` or
`"infer_sample"`, or when the helper specified in `hparams`
is used.
softmax_temperature (optional): A float 0D Tensor, value to divide
the logits by before computing the softmax. Larger values
(above 1.0) result in more random samples. Must > 0. If `None`,
1.0 is used.
Used when `decoding_strategy="infer_sample"`.
max_decoding_length: A int scalar Tensor indicating the maximum
allowed number of decoding steps. If `None` (default), either
`hparams["max_decoding_length_train"]` or
`hparams["max_decoding_length_infer"]` is used
according to :attr:`mode`.
impute_finished (bool): If `True`, then states for batch
entries which are marked as finished get copied through and
the corresponding outputs get zeroed out. This causes some
slowdown at each time step, but ensures that the final state
and outputs have the correct values and that backprop ignores
time steps that were marked as finished.
output_time_major (bool): If `True`, outputs are returned as
time major tensors. If `False` (default), outputs are returned
as batch major tensors.
input_time_major (optional): Whether the :attr:`inputs` tensor is
time major.
Used when `decoding_strategy="train_greedy"` or
`hparams`-configured helper is used.
helper (optional): An instance of
:class:`texar.tf.modules.Helper`
that defines the decoding strategy. If given,
`decoding_strategy`
and helper configs in :attr:`hparams` are ignored.
mode (str, optional): A string taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`. If
`TRAIN`, training related hyperparameters are used (e.g.,
`hparams['max_decoding_length_train']`), otherwise,
inference related hyperparameters are used (e.g.,
`hparams['max_decoding_length_infer']`).
If `None` (default), `TRAIN` mode is used.
**kwargs: Other keyword arguments for constructing helpers
defined by `hparams["helper_trainn"]` or
`hparams["helper_infer"]`.
Returns:
`(outputs, final_state, sequence_lengths)`, where
- **`outputs`**: an object containing the decoder output on all \
time steps.
- **`final_state`**: is the cell state of the final time step.
- **`sequence_lengths`**: is an int Tensor of shape `[batch_size]` \
containing the length of each sample.
"""
# Helper
if helper is not None:
pass
elif decoding_strategy is not None:
if decoding_strategy == "train_greedy":
helper = rnn_decoder_helpers._get_training_helper(
inputs, sequence_length, embedding, input_time_major)
elif decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token, softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(decoding_strategy))
else:
if is_train_mode_py(mode):
kwargs_ = copy.copy(self._hparams.helper_train.kwargs.todict())
helper_type = self._hparams.helper_train.type
else:
kwargs_ = copy.copy(self._hparams.helper_infer.kwargs.todict())
helper_type = self._hparams.helper_infer.type
kwargs_.update({
"inputs": inputs,
"sequence_length": sequence_length,
"time_major": input_time_major,
"embedding": embedding,
"start_tokens": start_tokens,
"end_token": end_token,
"softmax_temperature": softmax_temperature
})
kwargs_.update(kwargs)
helper = rnn_decoder_helpers.get_helper(helper_type, **kwargs_)
self._helper = helper
# Initial state
if initial_state is not None:
self._initial_state = initial_state
else:
self._initial_state = self.zero_state(batch_size=self.batch_size,
dtype=tf.float32)
# Maximum decoding length
max_l = max_decoding_length
if max_l is None:
max_l_train = self._hparams.max_decoding_length_train
if max_l_train is None:
max_l_train = utils.MAX_SEQ_LENGTH
max_l_infer = self._hparams.max_decoding_length_infer
if max_l_infer is None:
max_l_infer = utils.MAX_SEQ_LENGTH
max_l = tf.cond(is_train_mode(mode), lambda: max_l_train,
lambda: max_l_infer)
self.max_decoding_length = max_l
# Decode
outputs, final_state, sequence_lengths = dynamic_decode(
decoder=self,
impute_finished=impute_finished,
maximum_iterations=max_l,
output_time_major=output_time_major)
if not self._built:
self._add_internal_trainable_variables()
# Add trainable variables of `self._cell` which may be
# constructed externally.
self._add_trainable_variable(
layers.get_rnn_cell_trainable_variables(self._cell))
if isinstance(self._output_layer, tf.layers.Layer):
self._add_trainable_variable(
self._output_layer.trainable_variables)
# Add trainable variables of `self._beam_search_rnn_cell` which
# may already be constructed and used.
if self._beam_search_cell is not None:
self._add_trainable_variable(
self._beam_search_cell.trainable_variables)
self._built = True
return outputs, final_state, sequence_lengths
def _build(
self, # pylint: disable=arguments-differ, too-many-statements
decoding_strategy='train_greedy',
inputs=None,
adjs=None,
memory=None,
memory_sequence_length=None,
memory_attention_bias=None,
beam_width=None,
length_penalty=0.,
start_tokens=None,
end_token=None,
context=None,
context_sequence_length=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
embedding=None,
helper=None,
mode=None):
"""Performs decoding.
See 'texar.tf.modules.decoders.transformer_decoders.TransformerDecoder' for details
adjs: A 3D Tensor of shape `[batch_size, max_time, max_time]`,
containing the adjacency matrices of input sequences
"""
# Get adjacency masks from adjs
self.adj_masks = 1 - tf.cast(tf.equal(adjs, 0), dtype=tf.float32)
if memory is not None:
if memory_attention_bias is None:
if memory_sequence_length is None:
raise ValueError("`memory_sequence_length` is required if "
"`memory_attention_bias` is not given.")
enc_padding = 1 - tf.sequence_mask(memory_sequence_length,
shape_list(memory)[1],
dtype=tf.float32)
memory_attention_bias = attn.attention_bias_ignore_padding(
enc_padding)
# record the context, which will be used in step function
# for dynamic_decode
if context is not None:
start_tokens = context[:, 0]
self.context = context[:, 1:]
self.context_sequence_length = context_sequence_length - 1
else:
self.context = None
self.embedding = embedding
if helper is None and beam_width is None and \
decoding_strategy == 'train_greedy': # Teacher-forcing
decoder_self_attention_bias = (attn.attention_bias_lower_triangle(
shape_list(inputs)[1]))
decoder_output = self._self_attention_stack(
inputs,
memory,
decoder_self_attention_bias=decoder_self_attention_bias,
memory_attention_bias=memory_attention_bias,
cache=None,
mode=mode)
logits = self._output_layer(decoder_output)
preds = tf.to_int32(tf.argmax(logits, axis=-1))
rets = TransformerDecoderOutput(logits=logits, sample_id=preds)
else:
if max_decoding_length is None:
max_decoding_length = self._hparams.max_decoding_length
self.max_decoding_length = max_decoding_length
if beam_width is None: # Inference-like decoding
# Prepare helper
if helper is None:
if decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token,
softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(
decoding_strategy))
self._helper = helper
self._cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=False)
if context is not None:
self.context = tf.pad(self.context, [[
0, 0
], [0, max_decoding_length - shape_list(self.context)[1]]])
outputs, _, sequence_lengths = dynamic_decode(
decoder=self,
impute_finished=impute_finished,
maximum_iterations=max_decoding_length,
output_time_major=False,
scope=self.variable_scope)
if context is not None:
# Here the length of sample_id will be larger than that
# of logit by 1, because there will be a additional
# start_token in the returned sample_id.
# the start_id should be the first token of the
# given context
outputs = TransformerDecoderOutput(
logits=outputs.logits,
sample_id=tf.concat([
tf.expand_dims(start_tokens, 1), outputs.sample_id
],
axis=1))
sequence_lengths = sequence_lengths + 1
rets = outputs, sequence_lengths
else: # Beam-search decoding
# Ignore `decoding_strategy`; Assume `helper` is not set
if helper is not None:
raise ValueError("Must not set 'beam_width' and 'helper' "
"simultaneously.")
_batch_size = shape_list(start_tokens)[0]
self._cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=True,
batch_size=_batch_size)
# The output format is different when running beam search
sample_id, log_prob = self._beam_decode(
start_tokens,
end_token,
beam_width=beam_width,
length_penalty=length_penalty,
decode_length=max_decoding_length,
)
rets = {'sample_id': sample_id, 'log_prob': log_prob}
if not self._built:
self._add_internal_trainable_variables()
self._built = True
return rets