def test_sequence_mask(self):
r"""Tests :meth:`texar.torch.utils.sequence_mask`.
"""
mask1 = utils.sequence_mask([1, 3, 2], 5).numpy()
expected1 = np.asarray([[True, False, False, False, False],
[True, True, True, False, False],
[True, True, False, False, False]])
np.testing.assert_array_equal(mask1, expected1)
mask2 = utils.sequence_mask(torch.tensor([[1, 3], [2, 0]]))
expected2 = np.asarray([[[True, False, False], [True, True, True]],
[[True, True, False], [False, False, False]]])
np.testing.assert_array_equal(mask2, expected2)
def mask_sequences(sequence: Union[torch.Tensor, List[int]],
sequence_length: Union[torch.LongTensor, List[int]],
dtype: Optional[torch.dtype] = None,
time_major: bool = False) -> torch.Tensor:
r"""Masks out sequence entries that are beyond the respective sequence
lengths. Masks along the time dimension.
:attr:`sequence` and :attr:`sequence_length` can either be python
arrays or Tensors, respectively. If both are Python arrays (or None), the
return will be a Python array as well.
Args:
sequence: A Tensor or Python array of sequence values.
If ``time_major==False`` (default), this must be a Tensor of shape
``[batch_size, max_time, ...]``. The batch and time dimension is
exchanged if ``time_major==True``.
sequence_length: A Tensor or python array of shape ``[batch_size]``.
Time steps beyond the respective sequence lengths will be
made zero.
dtype (dtype): Type of :attr:`sequence`. If `None`, infer from
:attr:`sequence` automatically.
time_major (bool): The shape format of the inputs. If `True`,
:attr:`sequence` must have shape
``[max_time, batch_size, ...]``.
If `False` (default), :attr:`sequence` must have
shape ``[batch_size, max_time, ...]``.
Returns:
The masked sequence, i.e., a Tensor or python array of the same shape
as :attr:`sequence` but with masked-out entries (set to zero).
If both :attr:`sequence` and :attr:`sequence_length` are python
arrays, the returned value is a python array as well.
"""
if not torch.is_tensor(sequence):
sequence = torch.tensor(sequence, dtype=dtype)
sequence: torch.Tensor
rank = sequence.dim()
if rank < 2:
raise ValueError("`sequence` must be 2D or higher order.")
if time_major:
sequence = transpose_batch_time(sequence)
max_time = sequence.size(1)
if dtype is None:
dtype = sequence.dtype
mask = utils.sequence_mask(sequence_length, max_time, dtype=dtype)
mask = mask.view(*mask.size(), *([1] * (rank - 2)))
sequence = sequence * mask
if time_major:
sequence = transpose_batch_time(sequence)
return sequence
def maybe_mask_score(score: torch.Tensor,
score_mask_value: torch.Tensor,
memory_sequence_length: Optional[torch.LongTensor]) \
-> torch.Tensor:
r"""Mask the attention score based on the masks."""
if memory_sequence_length is None:
return score
for memory_sequence_length_value in memory_sequence_length:
if memory_sequence_length_value <= 0:
raise ValueError(
"All values in memory_sequence_length must be greater "
"than zero.")
score_mask = sequence_mask(memory_sequence_length, max_len=score.shape[1])
score_mask_values = score_mask_value * torch.ones_like(score)
return torch.where(score_mask, score, score_mask_values)
def prepare_memory(memory: torch.Tensor,
memory_sequence_length: Optional[torch.LongTensor]) \
-> torch.Tensor:
r"""Convert to tensor and possibly mask ``memory``.
Args:
memory: tensor, shaped ``[batch_size, max_time, ...]``.
memory_sequence_length: integer tensor, shaped ``[batch_size]``.
Returns:
A (possibly masked), new ``memory``.
Raises:
ValueError: if ``memory`` and ``memory_sequence_length`` do not have
the same ``batch_size``.
"""
if (memory_sequence_length is not None
and not isinstance(memory_sequence_length, torch.Tensor)):
memory_sequence_length = torch.tensor(memory_sequence_length,
dtype=torch.long,
device=memory.device)
if memory_sequence_length is None:
seq_len_mask = None
else:
seq_len_mask = sequence_mask(memory_sequence_length,
max_len=memory.shape[1],
dtype=memory.dtype)
seq_len_batch_size = memory_sequence_length.shape[0]
# Mask the memory based on the memory mask.
rank = memory.dim()
m_batch_size = memory.shape[0]
if seq_len_mask is not None:
if seq_len_batch_size != m_batch_size:
raise ValueError("memory_sequence_length and memory tensor "
"batch sizes do not match.")
return memory * seq_len_mask.view(seq_len_mask.size() + (1, ) *
(rank - 2))
else:
return memory
def _discount_reward_tensor_1d(reward: torch.Tensor,
sequence_length: Optional[torch.LongTensor],
discount: float = 1.) -> torch.Tensor:
r"""Computes discounted reward.
Args:
reward: 1D Tensor with shape `[batch_size]`.
sequence_length: A Tensor of shape `[batch_size]`.
Time steps beyond the respective sequence lengths will be masked.
discount (float): A scalar. The discount factor.
Returns:
A 2D Tensor of the discounted reward.
"""
if sequence_length is None:
raise ValueError('sequence_length must not be `None` for 1D reward.')
if not isinstance(sequence_length, torch.Tensor):
sequence_length = torch.tensor(sequence_length,
dtype=torch.int64,
device=reward.device)
batch_size = reward.shape[0]
max_seq_length = torch.max(sequence_length)
dtype: torch.dtype = reward.dtype
if discount == 1.:
disc_reward = reward.unsqueeze(-1).expand(batch_size, max_seq_length)
else:
mask = sequence_mask(sequence_length, dtype=dtype)
mask = torch.cat((mask[:, 1:], torch.zeros_like(mask[:, -1:])), dim=1)
# Make each row = [discount, ..., discount, 1, ..., 1]
dmat = mask * discount + (1 - mask)
dmat = torch.flip(dmat, (1, ))
dmat = torch.cumprod(dmat, dim=1)
dmat = torch.flip(dmat, (1, ))
disc_reward = dmat * reward.unsqueeze(-1)
disc_reward = mask_sequences(disc_reward, sequence_length, dtype=dtype)
return disc_reward
def forward(self, # type: ignore
inputs: Optional[torch.Tensor] = None,
sequence_length: Optional[torch.LongTensor] = None,
memory: Optional[torch.Tensor] = None,
memory_sequence_length: Optional[torch.LongTensor] = None,
memory_attention_bias: Optional[torch.Tensor] = None,
context: Optional[torch.Tensor] = None,
context_sequence_length: Optional[torch.LongTensor] = None,
helper: Optional[Helper] = None,
decoding_strategy: str = 'train_greedy',
max_decoding_length: Optional[int] = None,
impute_finished: bool = False,
infer_mode: Optional[bool] = None,
beam_width: Optional[int] = None,
length_penalty: float = 0.,
**kwargs) \
-> Union[
TransformerDecoderOutput,
Tuple[TransformerDecoderOutput, torch.LongTensor],
Dict[str, torch.Tensor]]:
r"""Performs decoding.
The interface is very similar to that of RNN decoders
(:class:`~texar.torch.modules.RNNDecoderBase`). In particular,
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.
:attr:`sequence_length` is optional.
- **"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.torch.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.torch.modules.RNNDecoderBase.forward`
for detailed usage and examples.
Note that, here, though using a
:class:`~texar.torch.modules.TrainingHelper` corresponding to the
``"train_greedy"`` strategy above, the implementation is *slower*
than directly setting ``decoding_strategy="train_greedy"`` (though
output results are the same).
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:`Tensor` of shape
``[batch_size, memory_max_time, dim]``.
memory_sequence_length (optional): A :tensor:`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
:attr:`memory_attention_bias` is provided.
memory_attention_bias (optional): A :tensor:`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 tensors for teacher forcing decoding.
Used when :attr:`decoding_strategy` is set to
``"train_greedy"``, or when `hparams`-configured helper is used.
The attr:`inputs` is a :tensor:`LongTensor` used as index to
look up embeddings and feed in the decoder. For example, if
:attr:`embedder` is an instance of
:class:`~texar.torch.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 :tensor:`LongTensor` of shape
``[batch_size]``, containing the sequence length of
:attr:`inputs`. Tokens beyond the respective sequence length are
masked out.
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 desired.
context (optional): An :tensor:`LongTensor` of shape
``[batch_size, length]``, containing the starting tokens for
decoding. If context is set, ``start_tokens`` of the
:class:`~texar.torch.modules.Helper` will be ignored.
context_sequence_length (optional): Specify the length of context.
max_decoding_length (int, optional): 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.
helper (optional): An instance of
:class:`~texar.torch.modules.Helper`
that defines the decoding strategy. If given,
``decoding_strategy`` and helper configurations in
:attr:`hparams` are ignored.
infer_mode (optional): If not `None`, overrides mode given by
:attr:`self.training`.
Returns:
- For **"train_greedy"** decoding, returns an instance of
:class:`~texar.torch.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.torch.modules.TransformerDecoderOutput`
as in `"train_greedy"`, and ``sequence_lengths`` is a
:tensor:`LongTensor` 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 a :tensor:`LongTensor` of shape
``[batch_size, max_time, beam_width]`` containing generated
token indexes. ``sample_id[:,:,0]`` is the highest-probable
sample.
- ``"log_prob"`` is a :tensor:`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 - sequence_mask(memory_sequence_length,
memory.size(1),
dtype=torch.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:
if context_sequence_length is None:
raise ValueError("'context_sequence_length' must not be None"
"when 'context' is specified.")
self._state_context = context[:, 1:]
self._state_context_sequence_length = context_sequence_length - 1
else:
self._state_context = None
self._state_context_sequence_length = None
# Faster code path for teacher-forcing training
if (helper is None and beam_width is None
and decoding_strategy == 'train_greedy'):
if inputs is None:
raise ValueError(
"'input' must not be none "
"when using 'train_greedy' decoding strategy.")
times = torch.arange(inputs.size(1),
dtype=torch.long,
device=inputs.device)
times = times.unsqueeze(0).expand(inputs.size(0), -1)
inputs = self.embed_tokens(inputs, times)
if sequence_length is not None:
inputs = mask_sequences(inputs, sequence_length)
decoder_self_attention_bias = (attn.attention_bias_lower_triangle(
inputs.size(1)))
decoder_output = self._self_attention_stack(
inputs,
memory,
decoder_self_attention_bias,
memory_attention_bias,
cache=None)
logits = self._output_layer(decoder_output)
sample_id = torch.argmax(logits, dim=-1)
return TransformerDecoderOutput(logits, sample_id)
# Inference code path.
if max_decoding_length is None:
max_decoding_length = self._hparams.max_decoding_length
self._state_max_decoding_length = max_decoding_length
if beam_width is None or beam_width == 1: # Inference-like decoding
# Prepare helper
if helper is None:
kwargs.update(decoding_strategy=decoding_strategy)
if context is not None:
kwargs.update(start_tokens=context[:, 0])
helper = self._create_or_get_helper(infer_mode, **kwargs)
assert isinstance(helper, EmbeddingHelper)
self._state_cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=False,
batch_size=helper.batch_size)
if context is not None:
assert self._state_context is not None
pad_length = max_decoding_length - self._state_context.size(1)
if pad_length > 0:
self._state_context = torch.cat(
(self._state_context,
self._state_context.new_zeros(
self._state_context.size(0), pad_length)),
dim=1)
outputs, cache, sequence_lengths = self.dynamic_decode(
helper,
inputs=None,
sequence_length=None,
initial_state=None,
max_decoding_length=max_decoding_length,
impute_finished=impute_finished)
del cache # not used
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
start_tokens = context[:, 0]
outputs = TransformerDecoderOutput(
logits=outputs.logits,
sample_id=torch.cat(
[start_tokens.unsqueeze(1), outputs.sample_id], dim=1))
sequence_lengths = sequence_lengths + 1
return outputs, sequence_lengths
else: # Beam-search decoding
# Ignore `decoding_strategy` and # assume `helper` is not set.
if helper is not None:
raise ValueError("Must not set 'beam_width' and 'helper' "
"simultaneously.")
if context is not None:
start_tokens = context[:, 0]
else:
if 'start_tokens' not in kwargs:
raise ValueError(
"'start_tokens' must be specified when using"
"beam search decoding.")
start_tokens = kwargs['start_tokens']
_batch_size = start_tokens.size(0)
self._state_cache = self._init_cache(memory,
memory_attention_bias,
beam_search_decoding=True,
batch_size=_batch_size)
end_token: int = kwargs.get('end_token') # type: ignore
# The output format is different when running beam search.
sample_id, log_prob = self.beam_decode(
start_tokens,
end_token,
embedding_fn=self.embed_tokens,
beam_width=beam_width,
length_penalty=length_penalty,
decode_length=max_decoding_length)
return {'sample_id': sample_id, 'log_prob': log_prob}
def forward(
self, # type: ignore
inputs: torch.Tensor,
sequence_length: torch.LongTensor) -> torch.Tensor:
r"""Encodes the inputs.
Args:
inputs: A 3D Tensor of shape ``[batch_size, max_time, dim]``,
containing the embedding of input sequences. Note that
the embedding dimension `dim` must equal "dim" in
:attr:`hparams`. The input embedding is typically an
aggregation of word embedding and position embedding.
sequence_length: A 1D :tensor:`LongTensor` of shape
``[batch_size]``. Input tokens beyond respective sequence
lengths are masked out automatically.
Returns:
A Tensor of shape ``[batch_size, max_time, dim]`` containing the
encoded vectors.
"""
# Multiply input embedding with the sqrt of its dimension for
# normalization
inputs_padding = 1 - sequence_mask(sequence_length,
inputs.size()[1]).float()
if self._hparams.use_bert_config:
ignore_padding = attn.attention_bias_ignore_padding(
inputs_padding, bias_value=-1e4)
else:
ignore_padding = attn.attention_bias_ignore_padding(inputs_padding)
encoder_self_attention_bias = ignore_padding
input_embedding = inputs
if self._hparams.use_bert_config:
x = self.input_normalizer(input_embedding)
x = self.embed_dropout(x)
else:
x = self.embed_dropout(input_embedding)
for i in range(self._hparams.num_blocks):
# trivial difference between BERT and original Transformer
if self._hparams.use_bert_config:
_queries_input = x
else:
_queries_input = self.self_attn_layer_norm[i](x)
attention_output = self.self_attns[i](
queries=_queries_input,
memory=_queries_input,
memory_attention_bias=encoder_self_attention_bias,
)
attention_output = self.residual_dropout(attention_output)
x = x + attention_output
poswise_network = self.poswise_networks[i]
poswise_normalizer = self.poswise_layer_norm[i]
if self._hparams.use_bert_config:
x = poswise_normalizer(x)
y = x
else:
y = poswise_normalizer(x)
original_shape = y.size()
y = y.view(-1, self._hparams.dim)
layer_output = poswise_network(y)
sub_output = self.residual_dropout(layer_output)
sub_output = sub_output.view(original_shape)
x = x + sub_output
if self._hparams.use_bert_config:
x = self.output_layer_norm[i](x)
if not self._hparams.use_bert_config:
x = self.final_layer_norm(x)
return x