def beam_search_step(self,
input,
state,
beam_size,
attention_construct_fn=None,
attention_keys=None,
attention_values=None,
input_text=None):
output, state = self.cell(input, state)
#TODO: this step cause.. attenion decode each step after initalization still need input_text feed
#will this case attention_keys and attention_values to be recompute(means redo encoding process) each step?
#can we avoid this? seems not better method,
#if enocding is slow may be feed attention_keys, attention_values each step
if attention_construct_fn is not None:
output = attention_construct_fn(output, attention_keys,
attention_values)
logits = tf.nn.xw_plus_b(output, self.w, self.v)
logprobs = tf.nn.log_softmax(logits)
if input_text is not None:
logprobs = melt.gather_cols(logprobs, tf.to_int32(input_text))
top_logprobs, top_ids = tf.nn.top_k(logprobs, beam_size)
#------too slow... for transfering large data between py and c++ cost a lot!
#top_logprobs, top_ids = tf.nn.top_k(logprobs, self.vocab_size)
if input_text is not None:
top_ids = tf.nn.embedding_lookup(input_text, top_ids)
return output, state, top_logprobs, top_ids
def beam_search_step(self,
input,
state,
cell,
beam_size,
attention_construct_fn=None,
input_text=None):
output, state = cell(input, state)
if hasattr(state, 'alignments'):
tf.add_to_collection('attention_alignments', state.alignments)
tf.add_to_collection('beam_search_alignments',
tf.get_collection('attention_alignments')[-1])
#TODO: this step cause.. attenion decode each step after initalization still need input_text feed
#will this case attention_keys and attention_values to be recompute(means redo encoding process) each step?
#can we avoid this? seems no better method,
#if enocding is slow may be feed attention_keys, attention_values each step
if not FLAGS.decode_use_alignment:
if FLAGS.gen_only:
output_fn = self.output_fn
logits = output_fn(output)
else:
indices = melt.batch_values_to_indices(tf.to_int32(input_text))
batch_size = melt.get_batch_size(input)
if FLAGS.copy_only:
output_fn_ = self.copy_output_fn
else:
output_fn_ = self.gen_copy_output_fn
output_fn = lambda cell_output, cell_state: output_fn_(
indices, batch_size, cell_output, cell_state)
logits = output_fn(output, state)
if FLAGS.gen_copy_switch and FLAGS.switch_after_softmax:
logprobs = tf.log(logits)
else:
logprobs = tf.nn.log_softmax(logits)
if FLAGS.decode_copy:
logprobs = melt.gather_cols(logprobs, tf.to_int32(input_text))
else:
logits = state.alignments
logits = scores[:, :tf.shape(input_text)[-1]]
logprobs = tf.nn.log_softmax(logits)
top_logprobs, top_ids = tf.nn.top_k(logprobs, beam_size)
#------too slow... for transfering large data between py and c++ cost a lot!
#top_logprobs, top_ids = tf.nn.top_k(logprobs, self.vocab_size)
if input_text is not None and FLAGS.decode_copy:
top_ids = tf.nn.embedding_lookup(input_text, top_ids)
if hasattr(state, 'cell_state'):
state = state.cell_state
return output, state, top_logprobs, top_ids