def setup_task(cls, args, **kwargs):
assert pytorch_translate_data.is_multilingual(
args
), "Must set `--task pytorch_translate_multilingual` for multilingual training"
args.left_pad_source = options.eval_bool(args.left_pad_source)
def load_dicts(langs, paths):
dicts = OrderedDict()
for lang, dict_path in zip(langs, paths):
d = pytorch_translate_dictionary.Dictionary.load(dict_path)
dicts[lang] = d
print(f"| [{lang}] dictionary: {len(d)} types")
return dicts
if not hasattr(args, "multiling_source_vocab_file"):
args.multiling_encoder_lang = args.multiling_source_lang
args.multiling_source_vocab_file = [args.source_vocab_file]
if not hasattr(args, "multiling_target_vocab_file"):
args.multiling_decoder_lang = args.multiling_target_lang
args.multiling_target_vocab_file = [args.target_vocab_file]
# Load dictionaries
src_dicts = load_dicts(
args.multiling_encoder_lang, args.multiling_source_vocab_file
)
tgt_dicts = load_dicts(
args.multiling_decoder_lang, args.multiling_target_vocab_file
)
return cls(args, src_dicts, tgt_dicts)
def setup_task(cls, args, **kwargs):
args.left_pad_source = options.eval_bool(args.left_pad_source)
assert not pytorch_translate_data.is_multilingual(
args
), "Must set `--task pytorch_translate_multilingual` for multilingual training"
# Load dictionaries
source_dict = pytorch_translate_dictionary.Dictionary.load(
args.source_vocab_file)
target_dict = pytorch_translate_dictionary.Dictionary.load(
args.target_vocab_file)
source_lang = args.source_lang or "src"
target_lang = args.target_lang or "tgt"
print(f"| [{source_lang}] dictionary: {len(source_dict)} types")
print(f"| [{target_lang}] dictionary: {len(target_dict)} types")
use_char_source = (
(args.char_source_vocab_file != "")
or (getattr(args, "arch", "") == "char_source")
or (getattr(args, "arch", "") == "char_source_transformer")
or getattr(args, "arch", "") == "char_source_hybrid")
if use_char_source:
char_source_dict = pytorch_translate_dictionary.Dictionary.load(
args.char_source_vocab_file)
# this attribute is used for CharSourceModel construction
args.char_source_dict_size = len(char_source_dict)
else:
char_source_dict = None
return cls(args, source_dict, target_dict, char_source_dict)
def preprocess_corpora(args, dictionary_cls=Dictionary):
if (args.train_source_binary_path is not None
and args.train_target_binary_path is not None):
if isinstance(
utils.maybe_parse_collection_argument(
args.train_source_binary_path), str) and isinstance(
utils.maybe_parse_collection_argument(
args.train_target_binary_path), str):
args.train_source_binary_path = maybe_generate_temp_file_path(
args.train_source_binary_path)
args.train_target_binary_path = maybe_generate_temp_file_path(
args.train_target_binary_path)
args.eval_source_binary_path = maybe_generate_temp_file_path(
args.eval_source_binary_path)
args.eval_target_binary_path = maybe_generate_temp_file_path(
args.eval_target_binary_path)
# Additional text preprocessing options could be added here before
# binarizing.
if pytorch_translate_data.is_multilingual(args):
preprocess_corpora_multilingual(args)
elif pytorch_translate_data.is_multilingual_many_to_one(args):
preprocess_corpora_multilingual_many_to_one(args, dictionary_cls)
elif pytorch_translate_data.is_latent_variable(args):
preprocess_corpora_latent_variable(args)
else:
# Vocabs are built before preprocessing because we might need to use
# both monolingual and bilingual corpora sources to build the vocab
# (in the case of semisupervised training)
source_dict, char_source_dict, target_dict = build_vocabs(
args=args, dictionary_cls=dictionary_cls)
preprocess_bilingual_corpora(
args=args,
source_dict=source_dict,
char_source_dict=char_source_dict,
target_dict=target_dict,
)
# Binarize additional monolingual corpora for the semisupervised translation
# task
if (args.task == constants.SEMI_SUPERVISED_TASK
or args.task == constants.DENOISING_AUTOENCODER_TASK):
args.train_mono_source_binary_path = maybe_generate_temp_file_path(
output_path=getattr(args, "train_mono_source_binary_path",
None))
args.train_mono_target_binary_path = maybe_generate_temp_file_path(
output_path=getattr(args, "train_mono_target_binary_path",
None))
preprocess_monolingual_corpora(
args,
source_dict=source_dict,
char_source_dict=char_source_dict,
target_dict=target_dict,
)
def generate(args):
pytorch_translate_options.print_args(args)
models, model_args, task = pytorch_translate_utils.load_diverse_ensemble_for_inference(
args.path.split(":")
)
args.source_lang = model_args[0].source_lang
args.target_lang = model_args[0].target_lang
append_eos_to_source = model_args[0].append_eos_to_source
reverse_source = model_args[0].reverse_source
assert all(
a.append_eos_to_source == append_eos_to_source
and a.reverse_source == reverse_source
for a in model_args
)
if args.source_binary_file != "":
assert args.target_binary_file != ""
task.load_dataset(
args.gen_subset, args.source_binary_file, args.target_binary_file
)
elif pytorch_translate_data.is_multilingual(args):
task.set_encoder_langs(model_args[0].multiling_encoder_lang)
task.set_decoder_langs(model_args[0].multiling_decoder_lang)
task.load_dataset_from_text_multilingual(
args.gen_subset,
source_text_file=args.source_text_file[0],
target_text_file=args.target_text_file,
source_lang_id=task.get_encoder_lang_id(args.multiling_source_lang[0]),
target_lang_id=task.get_decoder_lang_id(args.multiling_target_lang[0]),
append_eos=append_eos_to_source,
reverse_source=reverse_source,
)
elif args.source_ensembling:
task.load_multisource_dataset_from_text(
args.gen_subset,
source_text_files=args.source_text_file,
target_text_file=args.target_text_file,
append_eos=append_eos_to_source,
reverse_source=reverse_source,
)
else:
task.load_dataset_from_text(
args.gen_subset,
source_text_file=args.source_text_file[0],
target_text_file=args.target_text_file,
append_eos=append_eos_to_source,
reverse_source=reverse_source,
)
lang_pair = None
if isinstance(task, PytorchTranslateSemiSupervised):
lang_pair = "src-tgt"
scorer, num_sentences, gen_timer, _ = generate_score(
args=args,
task=task,
dataset=task.dataset(args.gen_subset),
lang_pair=lang_pair,
models=models,
)
print(
f"| Translated {num_sentences} sentences ({gen_timer.n} tokens) "
f"in {gen_timer.sum:.1f}s ({1. / gen_timer.avg:.2f} tokens/s)"
)
print(
f"| Generate {args.gen_subset} with beam={args.beam}: "
f"{scorer.result_string()}"
)
return scorer.score()
def _iter_translations(args, task, dataset, translations, align_dict, rescorer):
"""Iterate over translations.
This is a generator function which wraps the beam-search sequence generator,
performing such work on the output as converting token indices to
strings, printing output where applicable (not args.quiet), collecting
oracle translations where applicable, and removing language-ID tokens
for multilingual translation.
Args:
args: Command-line arguments.
task: FairseqTask object.
dataset: Dataset set object for a specific split.
translations: Batched translation iterator, as returned by
SequenceGenerator.generate_batched_itr().
align_dict: Dictionary for UNK replacement.
Yields:
For each sentence in `translations`, yields a TranslationInfo.
"""
is_multilingual = pytorch_translate_data.is_multilingual(args)
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
target_tokens = target_tokens.int().cpu()
if is_multilingual:
src_lang_id = (
src_tokens[-1] - pytorch_translate_data.MULTILING_DIALECT_ID_OFFSET
)
target_lang_id = (
target_tokens[0] - pytorch_translate_data.MULTILING_DIALECT_ID_OFFSET
)
# remove language ID tokens
src_tokens = src_tokens[:-1]
target_tokens = target_tokens[1:]
# Select dictionaries
src_dict = task.source_dictionaries[task.get_encoder_lang_code(src_lang_id)]
target_dict = task.target_dictionaries[
task.get_decoder_lang_code(target_lang_id)
]
else:
src_dict = task.source_dictionary
target_dict = task.target_dictionary
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = dataset.src.get_original_text(sample_id)
target_str = dataset.tgt.get_original_text(sample_id)
else:
src_str = src_dict.string(src_tokens, args.remove_bpe)
target_str = target_dict.string(
target_tokens, args.remove_bpe, escape_unk=True
)
if not args.quiet:
print(f"S-{sample_id}\t{src_str}")
print(f"T-{sample_id}\t{target_str}")
# used for oracle evaluation (args.report_oracle_bleu)
best_hypo_tokens = None
best_hypo_score = 0
collect_oracle_hypos = args.report_oracle_bleu or (
args.output_hypos_binary_path and args.nbest > 0
)
# Process top predictions
for i, hypo in enumerate(hypos[: min(len(hypos), args.nbest)]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo["tokens"].int().cpu(),
src_str=src_str,
alignment=hypo["alignment"].int().cpu(),
align_dict=align_dict,
tgt_dict=task.target_dictionary,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print(f"H-{sample_id}\t{hypo['score']}\t{hypo_str}")
print(
"A-{}\t{}".format(
sample_id,
" ".join(map(lambda x: str(utils.item(x)), alignment)),
)
)
if collect_oracle_hypos:
score = smoothed_sentence_bleu(task, target_tokens, hypo_tokens)
if score > best_hypo_score:
best_hypo_tokens = hypo_tokens
best_hypo_score = score
if i == 0:
if align_dict is not None or args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement
# and/or without BPE
target_tokens = task.target_dictionary.encode_line(
target_str, add_if_not_exist=True
)
# The probs score for the hypo_str; whether it's normalized by
# sequence length or not depends on normalize_scores, which is
# set by arg.nonormalize.
# However, as I tried, whether normalize_scores is set or not,
# the returned scores are the same (to be investigated).
# Here, the probs are normalized by hypo length so the value
# is big enough to be used as weights for backtranslations in
# dual learning.
hypo_score = (
hypo["score"] / len(hypo_tokens) if len(hypo_tokens) > 0 else 0.0
)
top_hypo_tokens = hypo_tokens
if not collect_oracle_hypos:
best_hypo_tokens = top_hypo_tokens
# Strip expensive data from hypotheses before yielding
hypos_stripped = [
{k: v for k, v in hypo.items() if k in ["tokens", "score"]}
for hypo in hypos
]
yield TranslationInfo(
sample_id=sample_id,
src_tokens=src_tokens,
target_tokens=target_tokens,
hypo_tokens=top_hypo_tokens,
src_str=src_str,
target_str=target_str,
hypo_str=hypo_str,
hypo_score=hypo_score,
best_hypo_tokens=best_hypo_tokens,
hypos=hypos_stripped,
)
def _generate_score(models, args, task, dataset):
use_cuda = torch.cuda.is_available() and not args.cpu
# Load ensemble
if not args.quiet:
print("| loading model(s) from {}".format(", ".join(args.path.split(":"))))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
need_attn=True,
)
translator = build_sequence_generator(args, task, models)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.replace_unk)
# Keep track of translations
# Initialize with empty translations
# and zero probs scores
translated_sentences = [""] * len(dataset)
translated_scores = [0.0] * len(dataset)
collect_output_hypos = getattr(args, "output_hypos_binary_path", False)
if collect_output_hypos:
output_hypos_token_arrays = [None] * len(dataset)
# Generate and compute BLEU score
dst_dict = task.target_dictionary
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(dst_dict.pad(), dst_dict.eos(), dst_dict.unk())
itr = task.get_batch_iterator(
dataset=dataset,
max_tokens=args.max_tokens,
max_sentences=args.max_sentences,
max_positions=utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models]
),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=8,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
oracle_scorer = None
if args.report_oracle_bleu:
oracle_scorer = bleu.Scorer(dst_dict.pad(), dst_dict.eos(), dst_dict.unk())
rescorer = None
num_sentences = 0
translation_samples = []
translation_info_list = []
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
gen_timer = StopwatchMeter()
translations = translator.generate_batched_itr(
t,
maxlen_a=args.max_len_a,
maxlen_b=args.max_len_b,
cuda=use_cuda,
timer=gen_timer,
prefix_size=1 if pytorch_translate_data.is_multilingual(args) else 0,
)
for trans_info in _iter_translations(
args, task, dataset, translations, align_dict, rescorer
):
if hasattr(scorer, "add_string"):
scorer.add_string(trans_info.target_str, trans_info.hypo_str)
else:
scorer.add(trans_info.target_tokens, trans_info.hypo_tokens)
if oracle_scorer is not None:
oracle_scorer.add(trans_info.target_tokens, trans_info.best_hypo_tokens)
translated_sentences[trans_info.sample_id] = trans_info.hypo_str
translated_scores[trans_info.sample_id] = trans_info.hypo_score
if collect_output_hypos:
output_hypos_token_arrays[
trans_info.sample_id
] = trans_info.best_hypo_tokens
if args.translation_info_export_path is not None:
translation_info_list.append(
{
"src_tokens": trans_info.src_tokens,
"target_tokens": trans_info.target_tokens,
"hypos": trans_info.hypos,
}
)
translation_samples.append(
collections.OrderedDict(
{
"sample_id": trans_info.sample_id.item(),
"src_str": trans_info.src_str,
"target_str": trans_info.target_str,
"hypo_str": trans_info.hypo_str,
}
)
)
wps_meter.update(trans_info.src_tokens.size(0))
t.log({"wps": round(wps_meter.avg)})
num_sentences += 1
# If applicable, save collected hypothesis tokens to binary output file
if collect_output_hypos:
output_dataset = pytorch_translate_data.InMemoryNumpyDataset()
output_dataset.load_from_sequences(output_hypos_token_arrays)
output_dataset.save(args.output_hypos_binary_path)
if args.translation_info_export_path is not None:
f = open(args.translation_info_export_path, "wb")
pickle.dump(translation_info_list, f)
f.close()
# If applicable, save the translations to the output file
# For eg. external evaluation
if getattr(args, "translation_output_file", False):
with open(args.translation_output_file, "w") as out_file:
for hypo_str in translated_sentences:
print(hypo_str, file=out_file)
if getattr(args, "translation_probs_file", False):
with open(args.translation_probs_file, "w") as out_file:
for hypo_score in translated_scores:
print(np.exp(hypo_score), file=out_file)
if oracle_scorer is not None:
print(f"| Oracle BLEU (best hypo in beam): {oracle_scorer.result_string()}")
return scorer, num_sentences, gen_timer, translation_samples
