def generate(
self,
src: batchers.Batch,
normalize_scores: bool = False) -> Sequence[sent.ReadableSentence]:
if not batchers.is_batched(src):
src = batchers.mark_as_batch([src])
assert src.batch_size() == 1, "batch size > 1 not properly tested"
batch_size, encodings, outputs, seq_len = self._encode_src(src)
best_words, best_scores = self.scorer.best_k(
outputs, k=1, normalize_scores=normalize_scores)
best_words = best_words[0, :]
score = np.sum(best_scores, axis=1)
outputs = [
sent.SimpleSentence(
words=best_words,
idx=src[0].idx,
vocab=self.trg_vocab if hasattr(self, "trg_vocab") else None,
output_procs=self.trg_reader.output_procs,
score=score)
]
return outputs
def generate(
self,
src: batchers.Batch,
forced_trg_ids: Sequence[numbers.Integral] = None,
normalize_scores: bool = False) -> Sequence[sent.ReadableSentence]:
if not batchers.is_batched(src):
src = batchers.mark_as_batch([src])
if forced_trg_ids:
forced_trg_ids = batchers.mark_as_batch([forced_trg_ids])
assert src.batch_size() == 1, "batch size > 1 not properly tested"
batch_size, encodings, outputs, seq_len = self._encode_src(src)
score_expr = self.scorer.calc_log_softmax(
outputs) if normalize_scores else self.scorer.calc_scores(outputs)
scores = score_expr.npvalue() # vocab_size x seq_len
if forced_trg_ids:
output_actions = forced_trg_ids
else:
output_actions = [np.argmax(scores[:, j]) for j in range(seq_len)]
score = np.sum([scores[output_actions[j], j] for j in range(seq_len)])
outputs = [
sent.SimpleSentence(
words=output_actions,
idx=src[0].idx,
vocab=self.trg_vocab if hasattr(self, "trg_vocab") else None,
output_procs=self.trg_reader.output_procs,
score=score)
]
return outputs
def _generate_one_batch(self,
generator: 'models.GeneratorModel',
batcher: Optional[batchers.Batcher] = None,
src_batch: batchers.Batch = None,
max_src_len: Optional[int] = None,
fp: TextIO = None):
"""
Generate outputs for a single batch and write them to the output file.
"""
batch_size = len(src_batch)
src_batches = batcher.pack(src_batch, None)
src_batch = src_batches[0]
src_len = src_batch.sent_len()
if max_src_len is not None and src_len > max_src_len:
output_txt = "\n".join([NO_DECODING_ATTEMPTED] * batch_size)
fp.write(f"{output_txt}\n")
else:
with utils.ReportOnException({
"src": src_batch,
"graph": utils.print_cg_conditional
}):
dy.renew_cg(immediate_compute=settings.IMMEDIATE_COMPUTE,
check_validity=settings.CHECK_VALIDITY)
outputs = self.generate_one(generator, src_batch)
if self.reporter: self._create_sent_report()
for i in range(len(outputs)):
output_txt = outputs[i].sent_str(
custom_output_procs=self.post_processor)
fp.write(f"{output_txt}\n")
def _forced_decode_one_batch(self,
generator: 'models.GeneratorModel',
batcher: Optional[batchers.Batcher] = None,
src_batch: batchers.Batch = None,
ref_batch: batchers.Batch = None,
assert_scores: batchers.Batch = None,
max_src_len: Optional[int] = None):
"""
Performs forced decoding for a single batch.
"""
batch_size = len(src_batch)
src_batches, ref_batches = batcher.pack(src_batch, ref_batch)
src_batch = src_batches[0]
src_len = src_batch.sent_len()
if max_src_len is None or src_len <= max_src_len is not None and src_len > max_src_len:
with utils.ReportOnException({
"src": src_batch,
"graph": utils.print_cg_conditional
}):
tt.reset_graph()
outputs = self.generate_one(generator, src_batch)
if self.reporter: self._create_sent_report()
for i in range(len(outputs)):
if assert_scores is not None:
# If debugging forced decoding, make sure it matches
assert batch_size == len(
outputs
), "debug forced decoding not supported with nbest inference"
if (abs(outputs[i].score - assert_scores[i]) /
abs(assert_scores[i])) > 1e-5:
raise ValueError(
f'Forced decoding score {outputs[i].score} and loss {assert_scores[i]} do not match at '
f'sentence {i}')
def _generate_one_batch(self,
generator: 'models.GeneratorModel',
batcher: Optional[batchers.Batcher] = None,
src_batch: batchers.Batch = None,
max_src_len: Optional[int] = None,
fp: TextIO = None):
"""
Generate outputs for a single batch and write them to the output file.
"""
batch_size = len(src_batch)
src_batches = batcher.pack(src_batch, None)
src_batch = src_batches[0]
src_len = src_batch.sent_len()
if max_src_len is not None and src_len > max_src_len:
output_txt = "\n".join([NO_DECODING_ATTEMPTED] * batch_size)
fp.write(f"{output_txt}\n")
else:
with utils.ReportOnException({
"src": src_batch,
"graph": utils.print_cg_conditional
}):
tt.reset_graph()
with torch.no_grad(
) if xnmt.backend_torch else utils.dummy_context_mgr():
outputs = self.generate_one(generator, src_batch)
if self.reporter: self._create_sent_report()
for i in range(len(outputs)):
output_txt = outputs[i].sent_str(
custom_output_procs=self.post_processor)
fp.write(f"{output_txt}\n")
def generate_search_output(self,
src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy,
forced_trg_ids: batchers.Batch=None) -> List[search_strategies.SearchOutput]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
forced_trg_ids: The target IDs to generate if performing forced decoding
Returns:
A list of search outputs including scores, etc.
"""
if src.batch_size()!=1:
raise NotImplementedError("batched decoding not implemented for DefaultTranslator. "
"Specify inference batcher with batch size 1.")
event_trigger.start_sent(src)
all_src = src
if isinstance(src, batchers.CompoundBatch): src = src.batches[0]
# Generating outputs
cur_forced_trg = None
src_sent = src[0]#checkme
sent_mask = None
if src.mask: sent_mask = batchers.Mask(np_arr=src.mask.np_arr[0:1])
sent_batch = batchers.mark_as_batch([sent], mask=sent_mask)
# Encode the sentence
initial_state = self._encode_src(all_src)
if forced_trg_ids is not None: cur_forced_trg = forced_trg_ids[0]
search_outputs = search_strategy.generate_output(self, initial_state,
src_length=[src_sent.sent_len()],
forced_trg_ids=cur_forced_trg)
return search_outputs
def generate_search_output(
self, src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy
) -> List[search_strategies.SearchOutput]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
Returns:
A list of search outputs including scores, etc.
"""
if src.batch_size() != 1:
raise NotImplementedError(
"batched decoding not implemented for DefaultTranslator. "
"Specify inference batcher with batch size 1.")
if isinstance(src, batchers.CompoundBatch):
src = src.batches[0]
search_outputs = search_strategy.generate_output(
self, self._initial_state(src), src_length=src.sent_len())
return search_outputs
def generate(
self,
src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy,
forced_trg_ids: batchers.Batch = None) -> Sequence[sent.Sentence]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
forced_trg_ids: The target IDs to generate if performing forced decoding
Returns:
A list of search outputs including scores, etc.
"""
assert src.batch_size() == 1
search_outputs = self.generate_search_output(src, search_strategy,
forced_trg_ids)
if isinstance(src, batchers.CompoundBatch): src = src.batches[0]
sorted_outputs = sorted(search_outputs,
key=lambda x: x.score[0],
reverse=True)
assert len(sorted_outputs) >= 1
outputs = []
for curr_output in sorted_outputs:
output_actions = [x for x in curr_output.word_ids[0]]
attentions = [x for x in curr_output.attentions[0]]
score = curr_output.score[0]
out_sent = sent.SimpleSentence(
idx=src[0].idx,
words=output_actions,
vocab=getattr(self.trg_reader, "vocab", None),
output_procs=self.trg_reader.output_procs,
score=score)
if len(sorted_outputs) == 1:
outputs.append(out_sent)
else:
outputs.append(
sent.NbestSentence(base_sent=out_sent,
nbest_id=src[0].idx))
if self.is_reporting():
attentions = np.concatenate([x.npvalue() for x in attentions],
axis=1)
self.report_sent_info({
"attentions": attentions,
"src": src[0],
"output": outputs[0]
})
return outputs
def _cut_or_pad_targets(self, seq_len: numbers.Integral,
trg: batchers.Batch) -> batchers.Batch:
old_mask = trg.mask
if len(trg[0]) > seq_len:
trunc_len = len(trg[0]) - seq_len
trg = batchers.mark_as_batch([
trg_sent.get_truncated_sent(trunc_len=trunc_len)
for trg_sent in trg
])
if old_mask:
trg.mask = batchers.Mask(
np_arr=old_mask.np_arr[:, :-trunc_len])
else:
pad_len = seq_len - len(trg[0])
trg = batchers.mark_as_batch([
trg_sent.create_padded_sent(pad_len=pad_len)
for trg_sent in trg
])
if old_mask:
trg.mask = np.pad(old_mask.np_arr,
pad_width=((0, 0), (0, pad_len)),
mode="constant",
constant_values=1)
return trg
def generate(
self, src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy
) -> Sequence[sent.Sentence]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
Returns:
A list of search outputs including scores, etc.
"""
assert src.batch_size() == 1
event_trigger.start_sent(src)
search_outputs = self.generate_search_output(src, search_strategy)
if isinstance(src, batchers.CompoundBatch): src = src.batches[0]
sorted_outputs = sorted(search_outputs,
key=lambda x: x.score[0],
reverse=True)
assert len(sorted_outputs) >= 1
outputs = []
for curr_output in sorted_outputs:
output_actions = [x for x in curr_output.word_ids[0]]
attentions = [x for x in curr_output.attentions[0]]
score = curr_output.score[0]
out_sent = self._emit_translation(src, output_actions, score)
if len(sorted_outputs) == 1:
outputs.append(out_sent)
else:
outputs.append(
sent.NbestSentence(base_sent=out_sent,
nbest_id=src[0].idx))
if self.is_reporting():
attentions = np.concatenate([x.npvalue() for x in attentions],
axis=1)
self.report_sent_info({
"attentions": attentions,
"src": src[0],
"output": outputs[0]
})
return outputs
def _generate_one_batch(self, generator: 'models.GeneratorModel',
batcher: Optional[batchers.Batcher] = None,
src_batch: batchers.Batch = None,
ref_batch: Optional[batchers.Batch] = None,
assert_scores: Optional[List[int]] = None,
max_src_len: Optional[int] = None,
fp: TextIO = None):
"""
Generate outputs for a single batch and write them to the output file.
"""
batch_size = len(src_batch)
if ref_batch[0] is not None:
src_batches, ref_batches = batcher.pack(src_batch, ref_batch)
ref_batch = ref_batches[0]
else:
src_batches = batcher.pack(src_batch, None)
ref_batch = None
src_batch = src_batches[0]
src_len = src_batch.sent_len()
if max_src_len is not None and src_len > max_src_len:
output_txt = "\n".join([NO_DECODING_ATTEMPTED] * batch_size)
fp.write(f"{output_txt}\n")
else:
with utils.ReportOnException({"src": src_batch, "graph": utils.print_cg_conditional}):
dy.renew_cg(immediate_compute=settings.IMMEDIATE_COMPUTE, check_validity=settings.CHECK_VALIDITY)
outputs = self.generate_one(generator, src_batch, ref_batch)
if self.reporter: self._create_sent_report()
for i in range(len(outputs)):
if assert_scores[0] is not None:
# If debugging forced decoding, make sure it matches
assert batch_size == len(outputs), "debug forced decoding not supported with nbest inference"
if (abs(outputs[i].score - assert_scores[i]) / abs(assert_scores[i])) > 1e-5:
raise ValueError(
f'Forced decoding score {outputs[i].score} and loss {assert_scores[i]} do not match at '
f'sentence {i}')
output_txt = outputs[i].sent_str(custom_output_procs=self.post_processor)
fp.write(f"{output_txt}\n")
