def step(self, helper: Helper, time: int,
inputs: torch.Tensor, state: Optional[Cache]) \
-> Tuple[TransformerDecoderOutput, Cache,
torch.Tensor, torch.ByteTensor]:
assert state is not None
outputs, state = self._inputs_to_outputs(inputs, state)
sample_ids = helper.sample(time=time, outputs=outputs)
if self._state_context is not None:
assert self._state_context_sequence_length is not None
sample_ids = torch.where(
self._state_context_sequence_length > time,
self._state_context[:, time], sample_ids)
if time + 1 == self._state_max_decoding_length:
# Maximum decoding length reached, mark all batches as finished.
# This requires special handling because performing lookup on
# position embeddings with `time + 1` may result in IndexError.
finished = torch.ones_like(sample_ids, dtype=torch_bool)
# Since `next_inputs` will not be used, simply create a null tensor.
next_inputs = torch.empty(0)
else:
finished, next_inputs = helper.next_inputs(self.embed_tokens, time,
outputs, sample_ids)
next_state = state
outputs = TransformerDecoderOutput(logits=outputs,
sample_id=sample_ids)
return outputs, next_state, next_inputs, finished
def step(self,
helper: Helper,
time: int,
inputs: torch.Tensor,
state: Optional[State]) \
-> Tuple[Output, Optional[State], torch.Tensor, torch.ByteTensor]:
self._state_previous_inputs.append(inputs)
if self._state_recompute_memory:
net_output, memory = self._forward(
two_stream=True,
**self._create_input(
self._state_previous_inputs[-self._state_cache_len:]))
else:
assert state is not None
net_output, memory = self._forward(
memory=state, cache_len=self._state_cache_len, two_stream=True,
**self._create_input(self._state_previous_inputs[-1:]))
assert memory is not None
# Omit memory for the dummy token.
memory = [mem[:, :-1] for mem in memory]
logits = F.linear(net_output, self.word_embed.weight, self.lm_bias)
logits = logits[:, -1]
sample_ids = helper.sample(time=time, outputs=logits)
(finished, next_inputs) = helper.next_inputs(
self.embed_tokens, time, logits, sample_ids)
outputs = XLNetDecoderOutput(logits=logits, sample_id=sample_ids)
return outputs, memory, next_inputs, finished
def step(self, helper: Helper, time: int, inputs: torch.Tensor,
state: Optional[HiddenState]) \
-> Tuple[BasicRNNDecoderOutput, HiddenState]:
cell_outputs, cell_state = self._cell(inputs, state)
logits = self._output_layer(cell_outputs)
sample_ids = helper.sample(time=time, outputs=logits)
next_state = cell_state
outputs = BasicRNNDecoderOutput(logits, sample_ids, cell_outputs)
return outputs, next_state
def step(self, helper: Helper, time: int, inputs: torch.Tensor,
state: Optional[Cache]) -> \
Tuple[TransformerDecoderOutput, Cache]:
assert state is not None
outputs, state = self._inputs_to_outputs(inputs, state)
sample_ids = helper.sample(time=time, outputs=outputs)
if self._state_context is not None:
assert self._state_context_sequence_length is not None
sample_ids = torch.where(
self._state_context_sequence_length > time,
self._state_context[:, time], sample_ids)
next_state = state
outputs = TransformerDecoderOutput(logits=outputs,
sample_id=sample_ids)
return outputs, next_state
def step(self, helper: Helper, time: int, inputs: torch.Tensor,
state: Optional[AttentionWrapperState]) -> \
Tuple[AttentionRNNDecoderOutput, AttentionWrapperState]:
wrapper_outputs, wrapper_state = self._cell(
inputs, state, self.memory, self.memory_sequence_length)
# Essentially the same as in BasicRNNDecoder.step()
logits = self._output_layer(wrapper_outputs)
sample_ids = helper.sample(time=time, outputs=logits)
attention_scores = wrapper_state.alignments
attention_context = wrapper_state.attention
outputs = AttentionRNNDecoderOutput(logits, sample_ids,
wrapper_outputs, attention_scores,
attention_context)
next_state = wrapper_state
return outputs, next_state