def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
net_output = model(**sample['net_input'])
loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce)
sample_size = sample['target'].size(
0) if self.args.sentence_avg else sample['ntokens']
# Only evalute bleu if not training
if model.training:
bleu_stats = utils.get_zero_bleu_stats()
else:
use_cuda = torch.cuda.is_available() and not self.args.cpu
if self.args.sacrebleu:
if hasattr(self.args, 'lowercase'):
scorer = bleu.SacrebleuScorer(
lowercase=self.args.lowercase)
else:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(self.task.target_dictionary.pad(),
self.task.target_dictionary.eos(),
self.task.target_dictionary.unk())
gen_timer = StopwatchMeter()
wps_meter = TimeMeter()
utils.run_inference_on_sample(sample=sample,
use_cuda=use_cuda,
args=self.args,
gen_timer=gen_timer,
task=self.task,
generator=self.generator,
model=model,
tgt_dict=self.task.target_dictionary,
align_dict=None,
subset=None,
src_dict=self.task.source_dictionary,
scorer=scorer,
wps_meter=wps_meter)
result_string = scorer.result_string()
bleu_stats = utils.BleuStatistics(correct=result_string.counts,
total=result_string.totals,
sys_len=result_string.sys_len,
ref_len=result_string.ref_len)
logging_output = {
'loss': utils.item(loss.data) if reduce else loss.data,
'ntokens': sample['ntokens'],
'nsentences': sample['target'].size(0),
'sample_size': sample_size,
'bleu': bleu_stats,
}
return loss, sample_size, logging_output
def __init__(self, args, task):
super().__init__(task = task)
self.args = args
self.generator = SimpleSequenceGenerator(beam=args.scst_beam,
penalty=args.scst_penalty,
max_pos=args.max_target_positions,
eos_index=task.target_dictionary.eos_index)
# Needed for decoding model output to string
self.conf_tokenizer = encoders.build_tokenizer(args)
self.conf_decoder = encoders.build_bpe(args)
self.target_dict = task.target_dictionary
# Tokenizer needed for computing CIDEr scores
self.tokenizer = encoders.build_tokenizer(args)
self.bpe = encoders.build_bpe(args)
self.scorer = bleu.SacrebleuScorer()
self.pad_idx = task.target_dictionary.pad()
def __init__(self, args, task, data_queue, message_queue):
"""
Handle detokenize and belu score computation
Args:
args (Namespace): paramerter for model and generation
task (fairseq.tasks.fairseq_task.Fairseq):
use to load dict for detokenize
data_queue (multiprocessing.Queue):
queue store tensor data for detokenize
message_queue (multiprocessing.Queue): queue store output
"""
super(PostProcess, self).__init__()
# Set dictionaries
try:
self.src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
self.src_dict = None
self.tgt_dict = task.target_dictionary
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(args.replace_unk)
# Generate and compute BLEU score
if args.sacrebleu:
self.scorer = bleu.SacrebleuScorer()
else:
self.scorer = bleu.Scorer(self.tgt_dict.pad(), self.tgt_dict.eos(),
self.tgt_dict.unk())
self.args = args
self.task = task
self.data_queue = data_queue
self.message_queue = message_queue
if args.decode_hypothesis:
self.tokenizer = encoders.build_tokenizer(args)
self.bpe = encoders.build_bpe(args)
def __init__(self, args, task, message_queue):
"""
Process to handle IO and compute metrics
Args:
args (Namespace): paramerter for model and generation
task (fairseq.tasks.fairseq_task.Fairseq):
use to load dict for detokenize
message_queue (multiprocessing.Queue): queue store output
"""
super(IOProcess, self).__init__()
self.tgt_dict = task.target_dictionary
# Generate and compute BLEU score
if args.sacrebleu:
self.scorer = bleu.SacrebleuScorer()
else:
self.scorer = bleu.Scorer(self.tgt_dict.pad(), self.tgt_dict.eos(),
self.tgt_dict.unk())
self.args = args
self.message_queue = message_queue
self.has_target = False
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
#pickle.dump(src_dict, open("./translations/seethetensors/src_dict.pkl", "bw") )
#pickle.dump(tgt_dict, open("./translations/seethetensors/tgt_dict.pkl", "bw") )
#print("* args.remove_bpe : ", args.remove_bpe)
#bpe_symbol = args.remove_bpe
#pickle.dump(bpe_symbol, open("./translations/seethetensors/bpe_symbol.pkl", "bw") )
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
bert_ratio=args.bert_ratio if args.change_ratio else None,
encoder_ratio=args.encoder_ratio if args.change_ratio else None,
geargs=args,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
#pickle.dump(align_dict, open("./translations/seethetensors/align_dict.pkl", "bw"))
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
resdict = {}
#hypo_strings = [] ##!!!
#src_strings = [] ##!!!
results_path = args.results_path
stamp = str(time.time())
resfp = results_path + "/" + args.gen_subset + "." + stamp + ".gen_sparql.json"
#resfp_ = results_path+"/"+args.gen_subset+"."+stamp+".txt"
#sampfp = results_path+"/"+args.gen_subset+"."+stamp+".sampleids.txt"
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
#open(sampfp, "w", encoding="UTF-8").writeline(str(sample['id'].tolist()))
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
#src_strings.append(src_str) ##!!!
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for i, hypo in enumerate(
hypos[i][: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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
#hypo_strings.append(hypo_str) ##!!!
resdict[str(int(sample_id) + 1)] = {
"sparql": interprete(hypo_str),
"en": src_str
} ##!!!
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
# Score only the top hypothesis
if has_target and 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
"""
with open(resfp, "a", encoding="UTF-8") as restore :
for gen_str in hypo_strings:
restore.write(gen_str+" \n")
restore.close()
with open(resfp_, "a", encoding="UTF-8") as res_tore :
for src_str in src_strings:
res_tore.write(src_str+" \n")
res_tore.close()"""
with open(resfp, "w", encoding="UTF-8") as restore:
json.dump(resdict, restore, ensure_ascii=False, indent=4)
restore.close()
return scorer
def infer_onebyone(args, models, task, input):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
input = ' '.join([i for i in input])
src_tokens = tgt_dict.encode_line(input).type(torch.LongTensor)
input_sample = {
'id': torch.Tensor([0]),
'nsentences': 1,
'ntokens': len(src_tokens),
'net_input': {
'src_tokens': src_tokens.unsqueeze(0),
'src_lengths': torch.tensor([len(src_tokens)]),
'prev_output_tokens': torch.tensor([[tgt_dict.eos()]])
},
'target': src_tokens.unsqueeze(0),
}
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
# Optimize ensemble for generation
# 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)
# Load dataset (possibly sharded)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
wps_meter = TimeMeter()
sample = input_sample
sample = utils.move_to_cuda(sample) if use_cuda else sample
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
#pdb.set_trace()
hypos = task.inference_step(generator, models, sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(sample['net_input']['src_tokens'][i, :],
tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
output = ''.join(hypo_str.split(' '))
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])))
if args.print_step:
print('I-{}\t{}'.format(sample_id, hypo['steps']))
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(target_str,
add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
return scorer, output
def validate_translation(args, trainer, task, epoch_itr, generator):
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
models = [trainer.get_model()]
bleu_dict = {key: None for key in task.eval_lang_pairs}
# Generate and compute BLEU score
if args.sacrebleu:
scorer_dict = {
key: bleu.SacrebleuScorer()
for key in task.eval_lang_pairs
}
else:
scorer_dict = {
key: bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
for key in task.eval_lang_pairs
}
itr = task.get_batch_iterator(
dataset=task.dataset('valid'),
max_tokens=args.max_tokens_valid,
max_sentences=args.max_sentences_valid,
max_positions=utils.resolve_max_positions(
task.max_positions(),
trainer.get_model().max_positions(),
),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=args.required_batch_size_multiple,
seed=args.seed,
num_shards=args.distributed_world_size,
shard_id=args.distributed_rank,
num_workers=args.num_workers,
noskip=True,
).next_epoch_itr(shuffle=False)
progress = progress_bar.build_progress_bar(args,
itr,
epoch_itr.epoch,
prefix='translate subset',
no_progress_bar='simple')
num_sentences = 0
has_target = True
#with progress_bar.build_progress_bar(args, itr) as t:
for samples in progress:
if torch.cuda.is_available() and not args.cpu:
samples = utils.move_to_cuda(samples)
#if 'net_input' not in samples:
# continue
prefix_tokens = None
for key, sample in samples.items():
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Remove padding
if args.sde:
src_tokens = target_tokens
else:
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :],
tgt_dict.pad())
# Either retrieve the original sentences or regenerate them from tokens.
#if src_dict is not None:
# src_str = src_dict.string(src_tokens, args.remove_bpe)
#else:
# src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
#if not args.quiet:
# if src_dict is not None:
# print('S-{}\t{}'.format(sample_id, src_str))
# if has_target:
# print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str="",
alignment=hypo['alignment'].int().cpu()
if hypo['alignment'] is not None else None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
#if not args.quiet:
# print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
# print('P-{}\t{}'.format(
# sample_id,
# ' '.join(map(
# lambda x: '{:.4f}'.format(x),
# hypo['positional_scores'].tolist(),
# ))
# ))
# if args.print_alignment:
# print('A-{}\t{}'.format(
# sample_id,
# ' '.join(map(lambda x: str(utils.item(x)), alignment))
# ))
# Score only the top hypothesis
if has_target and j == 0:
if args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer_dict[key], 'add_string'):
scorer_dict[key].add_string(target_str, hypo_str)
else:
scorer_dict[key].add(target_tokens, hypo_tokens)
num_sentences += sample['nsentences']
for key, scorer in scorer_dict.items():
bleu_dict[key] = scorer.score()
return bleu_dict
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
args.path.split(':'), task, model_arg_overrides=eval(args.model_overrides),
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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)
# Initialize generator
gen_timer = StopwatchMeter()
if args.score_reference:
translator = SequenceScorer(models, task.target_dictionary)
else:
translator = SequenceGenerator(
models, task.target_dictionary, beam_size=args.beam, minlen=args.min_len,
stop_early=(not args.no_early_stop), normalize_scores=(not args.unnormalized),
len_penalty=args.lenpen, unk_penalty=args.unkpen,
sampling=args.sampling, sampling_topk=args.sampling_topk, sampling_temperature=args.sampling_temperature,
diverse_beam_groups=args.diverse_beam_groups, diverse_beam_strength=args.diverse_beam_strength,
match_source_len=args.match_source_len, no_repeat_ngram_size=args.no_repeat_ngram_size,
)
if use_cuda:
translator.cuda()
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
if args.score_reference:
translations = translator.score_batched_itr(t, cuda=use_cuda, timer=gen_timer)
else:
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=args.prefix_size,
)
wps_meter = TimeMeter()
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
has_target = target_tokens is not None
target_tokens = target_tokens.int().cpu() if has_target else None
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(args.gen_subset).tgt.get_original_text(sample_id)
else:
src_str = src_dict.string(src_tokens, args.remove_bpe)
if has_target:
target_str = tgt_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# 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() if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
print('P-{}\t{}'.format(
sample_id,
' '.join(map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))
))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id,
' '.join(map(lambda x: str(utils.item(x)), alignment))
))
# Score only the top hypothesis
if has_target and 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 = tokenizer.Tokenizer.tokenize(
target_str, tgt_dict, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
num_sentences += 1
print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
def _generate_score(models, args, task, dataset, optimize=True):
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
if optimize:
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 = get_eval_itr(args, models, task, dataset)
oracle_scorer = None
if args.report_oracle_bleu:
oracle_scorer = bleu.Scorer(dst_dict.pad(), dst_dict.eos(),
dst_dict.unk())
rescoring_model = rescoring.setup_rescoring(args)
rescoring_scorer = None
if rescoring_model:
rescoring_scorer = bleu.Scorer(dst_dict.pad(), dst_dict.eos(),
dst_dict.unk())
num_sentences = 0
translation_samples = []
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, rescoring_model):
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)
if rescoring_scorer is not None:
rescoring_scorer.add(trans_info.target_tokens,
trans_info.hypo_tokens_after_rescoring)
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
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 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()}"
)
if rescoring_scorer is not None:
print(
f"| Rescoring BLEU (top hypo in beam after rescoring):{rescoring_scorer.result_string()}"
)
return scorer, num_sentences, gen_timer, translation_samples
def main(args):
assert args.path is not None, "--path required for generation!"
assert (not args.sampling or args.nbest
== args.beam), "--sampling requires --nbest to be equal to --beam"
assert (args.replace_unk is None or args.raw_text
), "--replace-unk requires a raw text dataset (--raw-text)"
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, "source_dictionary", None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print("| loading model(s) from {}".format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(":"),
arg_overrides=eval(args.model_overrides),
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if "net_input" not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample["target"][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample["id"].tolist()):
has_target = sample["target"] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample["net_input"]["src_tokens"][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = (utils.strip_pad(
sample["target"][i, :], tgt_dict.pad()).int().cpu())
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print("S-{}\t{}".format(sample_id, src_str))
if has_target:
print("T-{}\t{}".format(sample_id, target_str))
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo["tokens"].int().cpu(),
src_str=src_str,
alignment=hypo["alignment"],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print("H-{}\t{}\t{}".format(sample_id, hypo["score"],
hypo_str))
print("P-{}\t{}".format(
sample_id,
" ".join(
map(
lambda x: "{:.4f}".format(x),
hypo["positional_scores"].tolist(),
)),
))
if args.print_alignment:
print("A-{}\t{}".format(
sample_id,
" ".join([
"{}-{}".format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
]),
))
if args.print_step:
print("I-{}\t{}".format(sample_id, hypo["steps"]))
if getattr(args, "retain_iter_history", False):
print("\n".join([
"E-{}_{}\t{}".format(
sample_id,
step,
utils.post_process_prediction(
h["tokens"].int().cpu(),
src_str,
None,
None,
tgt_dict,
None,
)[1],
) for step, h in enumerate(hypo["history"])
]))
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, "add_string"):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({"wps": round(wps_meter.avg)})
num_sentences += sample["nsentences"]
print(
"| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)"
.format(
num_sentences,
gen_timer.n,
gen_timer.sum,
num_sentences / gen_timer.sum,
1.0 / gen_timer.avg,
))
if has_target:
print("| Generate {} with beam={}: {}".format(args.gen_subset,
args.beam,
scorer.result_string()))
return scorer
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.dataset_impl == 'raw', \
'--replace-unk requires a raw text dataset (--dataset-impl=raw)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=args.model_overrides,
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
if args.decoding_path is not None:
src_sents = [[] for _ in range(5000000)]
tgt_sents = [[] for _ in range(5000000)]
hyp_sents = [[] for _ in range(5000000)]
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])))
if args.print_step:
print('I-{}\t{}'.format(sample_id, hypo['steps']))
if getattr(args, 'retain_iter_history', False):
print("\n".join([
'E-{}_{}\t{}'.format(
sample_id, step,
utils.post_process_prediction(
h['tokens'].int().cpu(), src_str, None,
None, tgt_dict, None)[1])
for step, h in enumerate(hypo['history'])
]))
if args.decoding_path is not None:
src_sents[int(sample_id)].append(src_str)
tgt_sents[int(sample_id)].append(target_str)
hyp_sents[int(sample_id)].append(hypo_str)
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
if args.decoding_path is not None:
with open(os.path.join(args.decoding_path, 'source.txt'),
'w',
encoding='utf-8') as f:
for sents in src_sents:
if len(sents) == 0:
continue
for sent in sents:
f.write(sent + '\n')
with open(os.path.join(args.decoding_path, 'target.txt'),
'w',
encoding='utf-8') as f:
for sents in tgt_sents:
if len(sents) == 0:
continue
for sent in sents:
f.write(sent + '\n')
with open(os.path.join(args.decoding_path, 'decoding.txt'),
'w',
encoding='utf-8') as f:
for sents in hyp_sents:
if len(sents) == 0:
continue
for sent in sents:
f.write(sent + '\n')
if len(list(args.num_ref.values())) == 1:
num_ref = int(list(args.num_ref.values())[0])
else:
raise NotImplementedError
ref_path = []
if num_ref == 1:
ref_path.append(
os.path.join(args.valid_decoding_path,
args.gen_subset + '.tok.' + args.target_lang))
else:
for i in range(num_ref):
ref_path.append(
os.path.join(
args.valid_decoding_path,
args.gen_subset + '.tok.' + args.target_lang + str(i)))
decoding_path = os.path.join(args.decoding_path, 'decoding.txt')
#with open(decoding_path) as out_file:
# out_file.seek(0)
# subprocess.call(
# 'perl %s/multi-bleu.perl %s' % (args.multi_bleu_path, ' '.join(ref_path)),
# stdin=out_file, shell=True)
return scorer
def main(args):
utils.import_user_module(args)
if args.buffer_size < 1:
args.buffer_size = 1
if args.max_tokens is None and args.max_sentences is None:
args.max_sentences = 1
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert not args.max_sentences or args.max_sentences <= args.buffer_size, \
'--max-sentences/--batch-size cannot be larger than --buffer-size'
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
use_ctc_loss = True if args.criterion == 'ctc_loss' else False
# Setup task, e.g., image captioning
task = tasks.setup_task(args)
# Load dataset split
task.load_dataset(args.gen_subset, combine=True, epoch=0)
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
model_paths = args.path.split(':')
models, _model_args = checkpoint_utils.load_model_ensemble(
model_paths,
arg_overrides=eval(args.model_overrides),
task=task,
)
# Set dictionaries
tgt_dict = task.target_dictionary
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
stats = collections.OrderedDict()
num_sentences = 0
num_correct = 0
has_target = True
with progress_bar.build_progress_bar(
args, itr,
prefix='inference on \'{}\' subset'.format(args.gen_subset),
no_progress_bar='simple',
) as progress:
wps_meter = TimeMeter()
for sample in progress:
sample = utils.move_to_cuda(sample) if use_cuda else sample
gen_timer.start()
hypos = task.inference_step(generator, models, sample)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
target_tokens = None
if has_target:
if use_ctc_loss:
target_tokens = sample['target'][i]
target_str = tgt_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
else:
# Remove padding
target_tokens = utils.strip_pad(sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Regenerate original sentences from tokens.
target_str = tgt_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
if has_target:
print('\nT-{}\t{}'.format(sample_id, target_str))
# Process top predictions
hypo = hypos[i][0]
hypo_tokens = hypo['tokens'] if use_ctc_loss else hypo['tokens'].int().cpu()
hypo_str = tgt_dict.string(hypo_tokens, args.remove_bpe, escape_unk=True)
alignment = hypo['alignment'].int().cpu() if hypo['alignment'] is not None else None
if hypo_str == target_str:
num_correct += 1
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo_str, hypo['score']))
print('P-{}\t{}'.format(
sample_id,
' '.join(map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
)) if not use_ctc_loss else None
))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id,
' '.join(map(lambda x: str(utils.item(x)), alignment))
))
# Score only the top hypothesis
if has_target:
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
num_sentences += sample['nsentences']
stats['wps'] = round(wps_meter.avg)
stats['acc'] = num_correct / num_sentences
progress.log(stats, tag='accuracy')
progress.print(stats, tag='accuracy')
print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
return scorer
def main(args):
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=os.environ.get('LOGLEVEL', 'INFO').upper(),
stream=sys.stdout,
)
logger = logging.getLogger('fairseq_cli.generate')
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
#print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
logger.info('loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
)
mose_en = MosesDetokenizer(lang='en')
mose_de = MosesDetokenizer(lang='de')
if args.bert_model_path :
bert_tokenizer = BertTokenizer.from_pretrained(args.bert_model_path)
bert_model = BertModel.from_pretrained(args.bert_model_path)
bert_model.cuda()
bert_model.eval()
bert_de_tokenizer = BertTokenizer.from_pretrained(args.bert_german_path)
bert_de_model = BertModel.from_pretrained(args.bert_german_path)
bert_de_model.cuda()
bert_de_model.eval()
# 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=args.print_alignment,
)
model.eval()
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
batch_len = len(task.dataset(args.gen_subset))
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
#Bert model
#bert_model = bert_as_lm.Bert_score(torch.cuda.current_device())
#mose_de = MosesDetokenizer(lang='en')
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
total_pearson = []
total_bert_pearson = []
random_list = []
bert_bleu_equal = 0
sents_num = 0
if args.gen_subset == 'train':
random_list = [i for i in range(0, batch_len)]
random.shuffle(random_list)
random_list = random_list[:1000]
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
selected = []
if random_list:
for i in sample['id']:
selected.append(True) if i in random_list else selected.append(False)
selected = torch.nonzero(torch.tensor(selected).ne(0)).squeeze(-1)
if len(selected) == 0:
continue
for item in sample.keys():
if item == 'nsentences' or item== 'ntokens':
continue
elif item == 'net_input':
for input in sample[item].keys():
sample[item][input] = sample[item][input].index_select(0,selected)
else: sample[item] = sample[item].index_select(0,selected)
sample['nsentences'] = len(selected)
sample['ntokens'] = torch.LongTensor([s.ne(1).long().sum() for s in sample['target']]).sum().item()
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
sents_num += 1
if random_list and sample_id not in random_list:
continue
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
src_splittokens = src_str.split(' ')
src_str_for_bert = mose_de.detokenize(src_splittokens)
src_ids, src_bert = \
get_bert_out(src_str_for_bert, bert_de_tokenizer, bert_de_model)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
print ("---------------{}--------------".format(sents_num))
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str), file=sys.stdout)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str), file=sys.stdout)
# Process top predictions
probs = []
bleu_score = []
cands = []
sents_bert_score = []
detoken_cands =[]
temp_cand_tokens = []
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
score = hypo['score'] / math.log(2) # convert to base 2
probs.append(score)
single_score = sacrebleu.corpus_bleu([hypo_str],[[target_str]], use_effective_order=True, tokenize="none")
bleu_score.append(single_score.score)
cands.append(hypo_str)
temp_cand_tokens.append(hypo['tokens'].cpu())
hypo_splittokens = hypo_str.split(' ')
detoken_hypo = mose_de.detokenize(hypo_splittokens)
detoken_cands.append(detoken_hypo)
#print (cands)
cand_ids, cand_bert = get_bert_out(detoken_cands, bert_tokenizer, bert_model)
src_hidden = models[0].decoder.through_ffnet(src_bert[:,0,:],'src')
cand_hidden = models[0].decoder.through_ffnet(cand_bert[:,0,:],'cand')
cand_net_vocab = models[0].decoder.through_ffnet(cand_bert)
cand_lprob = \
models[0].get_normalized_probs([cand_net_vocab],log_probs=True).view(-1,cand_net_vocab.size(-1))
#compute similarity
cos_sim = F.cosine_similarity(
src_hidden.repeat(args.nbest,1), cand_hidden, dim=1, eps=1e-6) #[beam]
cos_sim = torch.log((cos_sim + 1)/2)
cand_probs = cand_lprob.gather(dim=-1,index=cand_ids.view(-1,1))
cand_loss = cand_probs.view(-1,cand_ids.size(-1)).sum(dim=1)
cand_loss = cand_loss/cand_ids.ne(0).sum(dim=1).float()
#print (cand_loss)
total_score = cos_sim + 0.1*cand_loss
#print (total_score)
net_pos = torch.argmax(total_score).item()
pearson = 0
#if args.nbest > 20:
np_prob = np.array(probs)
np_bleu = np.array(bleu_score)
pearson = np.corrcoef(np_prob, np_bleu)[0][1]
if not np.isnan(pearson):
total_pearson.append(pearson)
#else:
# print ("cands:", cands, file=sys.stdout)
# print ("probs:", np_prob, file=sys.stdout)
# print ("bleus:", np_bleu, file=sys.stdout)
bleu_pos = bleu_score.index(max(bleu_score))
print ("-----bleu choice: {} bleu:{:.3f} pos: {}".format(
cands[bleu_pos], bleu_score[bleu_pos], bleu_pos+1),file=sys.stdout)
pos = probs.index(max(probs))
print ("-----prob choice: {} bleu:{:.3f} pos: {}".format(
cands[pos], bleu_score[pos],pos+1),file=sys.stdout)
print ("-----net choice: {} bleu:{:.3f} pos: {} score:{:.3f}".format(
cands[net_pos], bleu_score[net_pos],net_pos+1, total_score[net_pos]),file=sys.stdout)
'''
np_bert = np.array(sents_bert_score)
bert_bleu_pearson = np.corrcoef(np_bert, np_bleu)[0][1]
if not np.isnan(bert_bleu_pearson):
total_bert_pearson.append(bert_bleu_pearson)
bert_pos = sents_bert_score.index(min(sents_bert_score))
print('*****{} bert choice: {}\tprob:{:.3f}\tbleu:{:.3f}\tbertscore:{:.3f}\tposition:{}\tprob_bleu_pearson:{:.3f} bert_bleu_p: {:.3f} '. \
format(sample_id, cands[bert_pos], probs[bert_pos], bleu_score[bert_pos],
sents_bert_score[bert_pos], bert_pos+1, pearson, bert_bleu_pearson), file=sys.stdout)
'''
if args.usebleu:
final_hypo = cands[bleu_pos]
elif args.usebert:
final_hypo = cands[net_pos]
else: final_hypo = cands[pos]
scorer.add_string(target_str, final_hypo)
print ('H choice use bleu: {} usebert: {}'.format(args.usebleu, args.usebert))
if has_target and sents_num % 800 == 0:
print('Generate {} with beam={}: {}\t{}'.format(args.gen_subset, args.beam,
scorer.result_string(),
sents_num,file=sys.stdout))
wps_meter.update(num_generated_tokens)
#t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
logger.info('Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('Generate {} with beam={}: {}\n --- prob&bleu pearson: {:.4f} ---'.format(
args.gen_subset, args.beam, scorer.result_string(),
sum(total_pearson)/len(total_pearson)), file=sys.stdout)
return scorer
def main(args, override_args=None):
utils.import_user_module(args)
use_cuda = torch.cuda.is_available() and not args.cpu
eval_task = args.eval_task
model, task, criterion, generator, model_args = set_up_model(
args, args.vqvae_path, override_args)
if eval_task == 'sampling':
assert args.prior_path is not None
prior_model, prior_task, prior_criterion, prior_generator, prior_args = set_up_model(
args, args.prior_path, None)
dictionary = task.dictionary
if eval_task == 'code_extract':
fopt = io.open(os.path.join(args.results_path,
args.gen_subset + ".codes"),
"w",
encoding='utf-8')
elif eval_task == 'reconstruct':
if args.sampling:
prefix = ".sample"
else:
prefix = ".bs"
if args.code_extract_strategy is not None:
prefix = prefix + '.' + args.code_extract_strategy
else:
prefix += '.orig.sampling'
if args.prefix_num > 0:
prefix = prefix + ".prefix_{}".format(args.prefix_num)
fopt = io.open(os.path.join(
args.results_path, args.gen_subset + prefix + ".reconstruction"),
"w",
encoding='utf-8')
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(dictionary.pad(), dictionary.eos(),
dictionary.unk())
elif eval_task == 'sampling':
fopt = io.open(os.path.join(args.results_path,
args.gen_subset + ".samples"),
"w",
encoding='utf-8')
else:
raise ValueError
# Initialize generator
gen_timer = StopwatchMeter()
num_sentences = 0
generate_id = 0
if eval_task != 'sampling':
# Load valid dataset (we load training data below, based on the latest checkpoint)
for subset in args.gen_subset.split(','):
try:
task.load_dataset(subset, combine=False, epoch=args.shard_id)
dataset = task.dataset(subset)
except KeyError:
raise Exception('Cannot find dataset: ' + subset)
# Initialize data iterator
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(),
),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=args.required_batch_size_multiple,
seed=args.seed,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
progress = progress_bar.build_progress_bar(
args,
itr,
prefix='valid on \'{}\' subset'.format(subset),
no_progress_bar='simple')
log_outputs = []
wps_meter = TimeMeter()
all_codes = set()
all_stats = []
for jj, sample in enumerate(progress):
sample = utils.move_to_cuda(sample) if use_cuda else sample
log_output = {'sample_size': sample['target'].size(0)}
log_outputs.append(log_output)
num_sentences += sample['nsentences']
if eval_task == 'code_extract':
codes = task.extract_codes(sample, model)
if args.gen_subset == 'valid':
all_codes.update(torch.unique(codes).tolist())
codes = codes.cpu().numpy()
elif eval_task == 'reconstruct':
prefix_tokens = None if args.prefix_num == 0 else sample[
'target'][:, :args.prefix_num]
gen_timer.start()
hypos, codes, stats = task.reconstruct(
sample,
model,
generator,
prefix_tokens,
extract_mode=args.code_extract_strategy)
if isinstance(codes, list):
codes, topk = codes
else:
topk = None
if 'avg_topp' in stats:
all_stats.append(stats)
all_codes.update(torch.unique(codes).tolist())
num_generated_tokens = sum(
len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
wps_meter.update(num_generated_tokens)
progress.log({'wps': round(wps_meter.avg)})
else:
raise NotImplementedError
progress.log(log_output, step=jj)
for i, sample_id in enumerate(sample['id'].tolist()):
tokens = utils.strip_pad(sample['target'][i, :],
dictionary.pad())
origin_string = dictionary.string(
tokens, bpe_symbol=args.remove_bpe, escape_unk=True)
if len(tokens) <= 1:
continue
bpe_string = dictionary.string(tokens,
bpe_symbol=None,
escape_unk=True)
fopt.write('T-ori-{}\t{}\n'.format(sample_id,
origin_string))
fopt.write('T-bpe-{}\t{}\n'.format(sample_id, bpe_string))
if eval_task == 'code_extract':
code = codes[i]
fopt.write('C-{}\t{}\n'.format(
sample_id, ' '.join([
"c-{}".format(x) for x in code.tolist()
if x != -1
])))
elif eval_task == 'reconstruct':
for j, hypo in enumerate(hypos[i][:args.nbest]):
code = codes[i]
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu()
[:len(tokens) - 1],
src_str="",
alignment=hypo['alignment'],
align_dict=None,
tgt_dict=dictionary,
remove_bpe=args.remove_bpe,
)
_, hypo_str_bpe, _ = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu()
[:len(tokens) - 1],
src_str="",
alignment=hypo['alignment'],
align_dict=None,
tgt_dict=dictionary,
remove_bpe=None,
)
fopt.write('H-bpe-{}\t{}\t{}\n'.format(
sample_id, hypo['score'], hypo_str_bpe))
fopt.write('H-{}\t{}\t{}\n'.format(
sample_id, hypo['score'], hypo_str))
code_str = ""
if topk is not None:
prob_str = ""
sent_topk = topk[i] / topk[i].sum(1).unsqueeze(
1)
for ii, token_code in enumerate(code):
code_str += " ".join([
"c{}-{}".format(ii, kk)
for kk in token_code if kk != -1
]) + ' '
if topk is not None:
prob_str += " ".join([
"p{}-{:.2f}".format(ii, kk.item())
for kk, mm in zip(
sent_topk[ii], token_code)
if mm != -1
]) + ' '
fopt.write('C-{}\t{}\n'.format(
sample_id, code_str))
if topk is not None:
fopt.write('K-{}\n'.format(prob_str))
if hypo['attention'] is not None:
hypo_attn = hypo['attention'].cpu().numpy(
) # src_len x tgt_len
entropy, max_idx = compute_attn(hypo_attn)
baseline_entropy = hypo_attn.shape[
0] * math.log(1. / hypo_attn.shape[0]
) * 1.0 / hypo_attn.shape[0]
fopt.write(
'A-entropy-baseline-{:.2f}\n'.format(
baseline_entropy))
fopt.write('A-entropy-{}\n'.format(" ".join(
["%.2f" % e for e in entropy])))
fopt.write('A-max-attn-pos-{}\n'.format(
" ".join([str(kk) for kk in max_idx])))
fopt.write('\n')
if args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
tokens = dictionary.encode_line(
origin_string, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(origin_string, hypo_str)
else:
scorer.add(tokens, hypo_tokens)
else:
raise NotImplementedError
generate_id += len(sample['id'])
if generate_id % 1000 == 0:
print("Processed {} lines!".format(i))
progress.print(log_outputs[0], tag=subset, step=i)
print("Total unique active codes = {}".format(len(all_codes)))
if len(all_stats) > 0:
avg_topp = sum([stat['avg_topp']
for stat in all_stats]) * 1. / len(all_stats)
max_topp = sum([stat['max_topp']
for stat in all_stats]) * 1. / len(all_stats)
min_topp = sum([stat['min_topp']
for stat in all_stats]) * 1. / len(all_stats)
print(
'Max #codes in top 0.9 = {}, min #codes in top 0.9 = {}, avg #codes in top 0.9 = {}'
.format(max_topp, min_topp, avg_topp))
if eval_task == 'reconstruct':
print(
'| Reconstructed {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
print('| Reconstruct {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
else:
batch_size = 3072 // args.max_len_b
gen_epochs = args.num_samples // batch_size
latent_dictionary = prior_task.dictionary
latent_dictionary_size = len(latent_dictionary)
for ii in range(gen_epochs):
dummy_tokens = torch.ones(batch_size, args.max_len_b).long().cuda()
dummy_lengths = (torch.ones(args.max_len_b) *
args.max_len_b).long().cuda()
dummy_samples = {
'net_input': {
'src_tokens': dummy_tokens,
'src_lengths': dummy_lengths,
},
'target': dummy_tokens
}
prefix_tokens = None
code_hypos = prior_task.inference_step(prior_generator,
[prior_model],
dummy_samples,
prefix_tokens)
list_predictions = []
for jj in range(batch_size):
code_hypo = code_hypos[jj][0] # best output
latent_hypo_tokens, latent_hypo_str, _ = utils.post_process_prediction(
hypo_tokens=code_hypo['tokens'].int().cpu(),
src_str="",
alignment=code_hypo['alignment'],
align_dict=None,
tgt_dict=latent_dictionary,
remove_bpe=None,
)
# should have no pad and eos
list_predictions.append(
torch.LongTensor([
int(ss) for ss in latent_hypo_str.strip().split()
]).cuda())
merged_codes = data_utils.collate_tokens(
list_predictions,
latent_dictionary_size,
left_pad=False,
)
code_masks = merged_codes.eq(latent_dictionary_size)
merged_codes = merged_codes.masked_fill_(code_masks, 0)
hypos, _, _ = task.sampling(dummy_samples, merged_codes,
code_masks, model, generator)
for tt in range(len(hypos)):
hypo = hypos[tt][0]
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str="",
alignment=hypo['alignment'],
align_dict=None,
tgt_dict=dictionary,
remove_bpe=args.remove_bpe,
)
fopt.write('C-{}\t{}\n'.format(
generate_id, " ".join([
"c-%d" % kk for kk in list_predictions[tt] if kk != -1
])))
fopt.write('H-{}\t{}\t{}\n'.format(generate_id, hypo['score'],
hypo_str))
generate_id += 1
if generate_id % 1000 == 0:
print("Sampled {} sentences!".format(generate_id))
fopt.close()
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
model.decoder.alignment_layer = args.alignment_layer
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
if args.print_vanilla_alignment:
import string
punc = string.punctuation
src_punc_tokens = [
w for w in range(len(src_dict)) if src_dict[w] in punc
]
tgt_punc_tokens = [
w for w in range(len(tgt_dict)) if tgt_dict[w] in punc
]
else:
src_punc_tokens = None
import time
print('start time is :', time.strftime("%Y-%m-%d %X"))
with progress_bar.build_progress_bar(args, itr) as t:
if args.decoding_path is not None:
align_sents = [[] for _ in range(4000000)]
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
if args.print_vanilla_alignment:
if args.set_shift:
alignments = utils.extract_soft_alignment(
sample,
models[0],
src_punc_tokens,
tgt_punc_tokens,
alignment_task=args.alignment_task)
else:
alignments = utils.extract_soft_alignment_noshift(
sample,
models[0],
src_punc_tokens,
tgt_punc_tokens,
alignment_task=args.alignment_task)
else:
alignments = None
for sample_id in sample['id'].tolist():
if args.print_vanilla_alignment and args.decoding_path is not None:
align_sents[int(sample_id)].append(
alignments[int(sample_id)])
print('end time is :', time.strftime("%Y-%m-%d %X"))
if args.decoding_path is not None and args.print_vanilla_alignment:
with open(
os.path.join(
args.decoding_path,
f'{args.gen_subset}.{args.source_lang}2{args.target_lang}.align'
), 'w') as f:
for sents in align_sents:
if len(sents) == 0:
continue
for sent in sents:
f.write(str(sent) + '\n')
print("finished ...")
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
# print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
)
trained_epoch = checkpoint_utils.get_checkpoint_epoch(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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
# itr = task.get_batch_iterator(
# dataset=task.dataset(args.gen_subset),
# 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=args.required_batch_size_multiple,
# num_shards=args.num_shards,
# shard_id=args.shard_id,
# num_workers=args.num_workers,
# ).next_epoch_itr(shuffle=False)
# we modify to use the max_positions only from the task and not the model.
# the reason is that we keep a low max positions while training transformer
# to handle large batches, but we need to disable this while testing to get
# metrics evaluated on full dev/test set.
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
max_tokens=args.max_tokens,
max_sentences=args.max_sentences,
max_positions=task.max_positions(),
ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
# em_scorer = bleu.EmScorer()
all_metrics = bleu.Metric()
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
all_preds = []
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
all_preds.append({
'id': sample_id,
'tgt_str': target_str,
'src_str': src_str,
'url': task.urls[sample_id]
})
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for i, hypo in enumerate(
hypos[i][: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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
# print('=========')
# print(hypo_tokens)
# print(hypo_str)
# print(align_dict)
if i == 0:
# get best hypothesis
all_preds[-1]['hypo_str'] = hypo_str
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
# Score only the top hypothesis
if has_target and 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
all_metrics.add_string(target_str, hypo_str)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
# we can dump the preds out in notebook format
preds_dir = dirname(args.path) + '/preds'
# sort them in order of index in dev/test set.
all_preds.sort(key=lambda x: int(x['id']))
log_preds_to_notebook(preds=all_preds, outdir=preds_dir)
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
print(args.path)
# compute, store, and print the metrics
all_metrics.compute_metrics(trained_epoch)
all_metrics.save(dirname(args.path) + '/metrics.json')
print('All metrics:')
print(all_metrics.result_string())
return all_metrics.get_metric('corpus_bleu'), all_metrics.get_metric('em')
def run_generation(ckpt, results, ents):
gen_parser = options.get_generation_parser()
args = options.parse_args_and_arch(gen_parser,
input_args=[
data_set, '--gen-subset', 'valid',
'--path', ckpt, '--beam', '10',
'--max-tokens', '4000',
'--sacrebleu', '--remove-bpe',
'--log-format', 'none'
])
use_cuda = torch.cuda.is_available() and not args.cpu
# if use_cuda:
# lock.acquire()
# torch.cuda.set_device(device_id)
# lock.release()
utils.import_user_module(args)
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
entropies = []
token_counts = []
with progress_bar.build_progress_bar(args, itr) as t:
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
if 'avg_ent' in sample:
entropies.append(sample['avg_ent'][0])
token_counts.append(sample['avg_ent'][1])
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
num_sentences += sample['nsentences']
results[ckpt] = scorer.score()
ents[ckpt] = sum(entropies) / sum(token_counts)
def __init__(self, lowercase=False):
self.scorer = bleu.SacrebleuScorer(lowercase=lowercase)
self.target_sum = []
self.hypo_sum = []
self.count = 0
def _main(args, output_file):
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
stream=output_file,
)
logger = logging.getLogger('fairseq_cli.generate')
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
logger.info('loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(os.pathsep),
arg_overrides=eval(args.model_overrides),
task=task,
)
args.vocab_size = len(tgt_dict)
for arg in vars(_model_args).keys():
if arg in {'decoder_embed_dim', 'vocab_size'}:
setattr(args, arg, getattr(_model_args, arg))
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
if args.knnlm and args.save_knnlm_dstore:
raise ValueError(
"Cannot use knnlm while trying to build the datastore!")
if args.knnlm:
knn_dstore = KNN_Dstore(args)
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
if args.save_knnlm_dstore:
print('keytype being saved:', args.knn_keytype)
if args.dstore_fp16:
print('Saving fp16')
dstore_keys = np.memmap(args.dstore_mmap + '_keys.npy',
dtype=np.float16,
mode='w+',
shape=(args.dstore_size,
args.decoder_embed_dim))
dstore_vals = np.memmap(args.dstore_mmap + '_vals.npy',
dtype=np.int16,
mode='w+',
shape=(args.dstore_size, 1))
else:
print('Saving fp32')
dstore_keys = np.memmap(args.dstore_mmap + '_keys.npy',
dtype=np.float32,
mode='w+',
shape=(args.dstore_size,
args.decoder_embed_dim))
dstore_vals = np.memmap(args.dstore_mmap + '_vals.npy',
dtype=np.int,
mode='w+',
shape=(args.dstore_size, 1))
dstore_idx = 0
if args.save_knnlm_dstore or args.knnlm:
# source_tokens_file = open(args.output_tokens_file_prefix + '.src' , 'w')
target_tokens_file = open(args.output_tokens_file_prefix + '.tgt', 'w')
# This is only for MT right now, use interactive.py for language modeling
assert task != 'language_modeling'
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
if args.knnlm:
hypos = task.inference_step(generator,
models,
sample,
prefix_tokens,
knn_dstore=knn_dstore)
else:
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
if args.save_knnlm_dstore:
for i, hypos_i in enumerate(hypos):
hypo = hypos_i[0]
shape = hypo['dstore_keys'].shape
if dstore_idx + shape[0] > args.dstore_size:
shape = [args.dstore_size - dstore_idx]
hypo['dstore_keys'] = hypo['dstore_keys'][:shape[0]]
# import pdb; pdb.set_trace()
# print(hypo)
if args.dstore_fp16:
dstore_keys[dstore_idx:shape[0] +
dstore_idx] = hypo['dstore_keys'].view(
-1, args.decoder_embed_dim).cpu(
).numpy().astype(np.float16)
dstore_vals[dstore_idx:shape[0] +
dstore_idx] = hypo['tokens'].view(
-1, 1).cpu().numpy().astype(np.int16)
else:
dstore_keys[dstore_idx:shape[0] +
dstore_idx] = hypo['dstore_keys'].view(
-1, args.decoder_embed_dim).cpu(
).numpy().astype(np.float32)
dstore_vals[dstore_idx:shape[0] +
dstore_idx] = hypo['tokens'].view(
-1, 1).cpu().numpy().astype(np.int)
dstore_idx += shape[0]
if args.save_knnlm_dstore or args.knnlm:
for i, hypos_i in enumerate(hypos):
hypo = hypos_i[0]
# dump the tokens to a file, used for analysis and interactive printing
# source_tokens = [task.source_dictionary[token] for token in hypo['source_tokens']]
# source_tokens_file.write('\n'.join(source_tokens) + '\n')
target_tokens = [
task.target_dictionary[token]
for token in hypo['tokens']
]
target_tokens_file.write('\n'.join(target_tokens) + '\n')
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str),
file=output_file)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
score = hypo['score'] / math.log(
2) # convert to base 2
print('H-{}\t{}\t{}'.format(sample_id, score,
hypo_str),
file=output_file)
print(
'P-{}\t{}'.format(
sample_id,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
# convert from base e to base 2
hypo['positional_scores'].div_(
math.log(2)).tolist(),
))),
file=output_file)
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])),
file=output_file)
if args.print_step:
print('I-{}\t{}'.format(sample_id, hypo['steps']),
file=output_file)
if getattr(args, 'retain_iter_history', False):
for step, h in enumerate(hypo['history']):
_, h_str, _ = utils.post_process_prediction(
hypo_tokens=h['tokens'].int().cpu(),
src_str=src_str,
alignment=None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=None,
)
print('E-{}_{}\t{}'.format(
sample_id, step, h_str),
file=output_file)
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
logger.info('NOTE: hypothesis and token scores are output in base 2')
logger.info(
'Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
logger.info('Generate {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
if args.save_knnlm_dstore:
print("dstore_idx", dstore_idx, "final shape", shape)
print("Keys", dstore_keys.shape, dstore_keys.dtype)
print("Vals", dstore_vals.shape, dstore_vals.dtype)
target_tokens_file.seek(0)
num_lines = len(target_tokens_file.readlines())
if dstore_idx != num_lines:
print(
'Warning: size of KNN datastore is {}, does not match number of lines in train tokens file which is {}'
.format(dstore_idx, num_lines))
if args.save_knnlm_dstore or args.knnlm:
# source_tokens_file.close()
target_tokens_file.close()
return scorer
def __init__(self):
self.correct, self.total, self.sys_len, self.ref_len = utils.get_zero_bleu_stats(
)
# TODO handle lowercase
self.scorer = bleu.SacrebleuScorer(lowercase=False)
def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile,
score_outfile, lm_outfile, fw_outfile, bw_outfile,
write_hypos, normalize):
print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c,
"target_outfile", target_outfile, "hypo_outfile", hypo_outfile,
"lm_outfile", lm_outfile)
gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(
args)
dict = dictionary.Dictionary()
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(dict.pad(), dict.eos(), dict.unk())
ordered_hypos = {}
ordered_targets = {}
ordered_scores = {}
ordered_lm = {}
ordered_fw = {}
ordered_bw = {}
for shard_id in range(len(bitext1_lst)):
bitext1 = bitext1_lst[shard_id]
bitext2 = bitext2_lst[shard_id]
gen_output = gen_output_lst[shard_id]
lm_res = lm_res_lst[shard_id]
total = len(bitext1.rescore_source.keys())
source_lst = []
hypo_lst = []
score_lst = []
lm_lst = []
fw_lst = []
bw_lst = []
reference_lst = []
j = 1
best_score = -math.inf
for i in range(total):
# length is measured in terms of words, not bpe tokens, since models may not share the same bpe
target_len = len(bitext1.rescore_hypo[i].split())
if lm_res is not None and i in lm_res.score:
lm_score = lm_res.score[i]
else:
lm_score = 0
if bitext2 is not None:
bitext2_score = bitext2.rescore_score[i]
bitext2_backwards = bitext2.backwards
else:
bitext2_score = None
bitext2_backwards = None
score = rerank_utils.get_score(a,
b,
c,
target_len,
bitext1.rescore_score[i],
bitext2_score,
lm_score=lm_score,
lenpen=lenpen,
src_len=bitext1.source_lengths[i],
tgt_len=bitext1.target_lengths[i],
bitext1_backwards=bitext1.backwards,
bitext2_backwards=bitext2_backwards,
normalize=normalize)
if score > best_score:
best_score = score
best_hypo = bitext1.rescore_hypo[i]
best_lm = lm_score
best_fw = bitext1.rescore_score[i]
best_bw = bitext2_score
if j == gen_output.num_hypos[i] or j == args.num_rescore:
j = 1
hypo_lst.append(best_hypo)
score_lst.append(best_score)
lm_lst.append(best_lm)
fw_lst.append(best_fw)
bw_lst.append(best_bw)
source_lst.append(bitext1.rescore_source[i])
reference_lst.append(bitext1.rescore_target[i])
best_score = -math.inf
best_hypo = ""
best_lm = -math.inf
else:
j += 1
gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
for key in range(len(gen_keys)):
if args.prefix_len is None:
assert hypo_lst[key] in gen_output.no_bpe_hypo[
gen_keys[key]], ("pred and rescore hypo mismatch: i: " +
str(key) + ", " + str(hypo_lst[key]) +
str(gen_keys[key]) +
str(gen_output.no_bpe_hypo[key]))
sys_tok = dict.encode_line(hypo_lst[key])
ref_tok = dict.encode_line(
gen_output.no_bpe_target[gen_keys[key]])
if args.sacrebleu:
scorer.add_string(gen_output.no_bpe_target[gen_keys[key]],
hypo_lst[key])
else:
scorer.add(ref_tok, sys_tok)
else:
full_hypo = rerank_utils.get_full_from_prefix(
hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]])
sys_tok = dict.encode_line(full_hypo)
ref_tok = dict.encode_line(
gen_output.no_bpe_target[gen_keys[key]])
if args.sacrebleu:
scorer.add_string(gen_output.no_bpe_target[gen_keys[key]],
hypo_lst[key])
else:
scorer.add(ref_tok, sys_tok)
# if only one set of hyper parameters is provided, write the predictions to a file
if write_hypos:
# recover the orinal ids from n best list generation
for key in range(len(gen_output.no_bpe_target)):
if args.prefix_len is None:
assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], \
"pred and rescore hypo mismatch:"+"i:"+str(key)+str(hypo_lst[key]) + str(gen_output.no_bpe_hypo[key])
ordered_hypos[gen_keys[key]] = hypo_lst[key]
ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
gen_keys[key]]
ordered_scores[gen_keys[key]] = score_lst[key]
ordered_lm[gen_keys[key]] = lm_lst[key]
ordered_fw[gen_keys[key]] = fw_lst[key]
ordered_bw[gen_keys[key]] = bw_lst[key]
else:
full_hypo = rerank_utils.get_full_from_prefix(
hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]])
ordered_hypos[gen_keys[key]] = full_hypo
ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
gen_keys[key]]
ordered_scores[gen_keys[key]] = score_lst[key]
ordered_lm[gen_keys[key]] = lm_lst[key]
ordered_fw[gen_keys[key]] = fw_lst[key]
ordered_bw[gen_keys[key]] = bw_lst[key]
# print("Target = " + ordered_targets[gen_keys[key]] + " Hypothesis = " + ordered_hypos[gen_keys[key]])
# write the hypos in the original order from nbest list generation
# print(ordered_hypos)
# print(len(ordered_hypos))
# print(ordered_scores)
# print(len(ordered_scores))
if args.num_shards == (len(bitext1_lst)):
with open(target_outfile, 'a') as t:
with open(hypo_outfile, 'a') as h:
with open(score_outfile, 'a') as s:
with open(lm_outfile, 'a') as l:
with open(fw_outfile, 'a') as f:
with open(bw_outfile, 'a') as b:
for key in range(len(ordered_hypos)):
t.write(ordered_targets[key])
h.write(ordered_hypos[key])
s.write(str(ordered_scores[key]) + "\n")
l.write(str(ordered_lm[key]) + "\n")
f.write(str(ordered_fw[key]) + "\n")
b.write(str(ordered_bw[key]) + "\n")
print(scorer)
res = scorer.result_string(4)
if write_hypos:
print(res)
if args.sacrebleu:
score = res.score
else:
score = rerank_utils.parse_bleu_scoring(res)
return score
def main(args):
utils.import_user_module(args)
if args.buffer_size < 1:
args.buffer_size = 1
if args.max_tokens is None and args.max_sentences is None:
args.max_sentences = 1
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert not args.max_sentences or args.max_sentences <= args.buffer_size, \
'--max-sentences/--batch-size cannot be larger than --buffer-size'
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Setup task, e.g., translation
task = tasks.setup_task(args)
# Load ensemble
logger.info('loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(os.pathsep),
arg_overrides=eval(args.model_overrides),
task=task,
)
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Handle tokenization and BPE
tokenizer = encoders.build_tokenizer(args)
bpe = encoders.build_bpe(args)
def encode_fn(x):
if tokenizer is not None:
x = tokenizer.encode(x)
if bpe is not None:
x = bpe.encode(x)
return x
def decode_fn(x):
if bpe is not None:
x = bpe.decode(x)
if tokenizer is not None:
x = tokenizer.decode(x)
return x
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
# 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)
max_positions = utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models])
num_sentences = 0
if args.buffer_size > 1:
logger.info('Sentence buffer size: %s', args.buffer_size)
logger.info('NOTE: hypothesis and token scores are output in base 2')
logger.info('Type the input sentence and press return:')
start_id = 0
for line in sys.stdin:
inputs = [line.strip()]
results = []
for batch in make_batches(inputs, args, task, max_positions,
encode_fn):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
tgt_tokens = batch.tgt_tokens
num_sentences += src_tokens[0].size(0)
if use_cuda:
if isinstance(src_tokens, list):
src_tokens = [tokens.cuda() for tokens in src_tokens]
src_lengths = [lengths.cuda() for lengths in src_lengths]
else:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
'target': tgt_tokens,
}
gen_timer.start()
translations = task.inference_step(generator, models, sample)
num_generated_tokens = sum(
len(h[0]['tokens']) for h in translations)
gen_timer.stop(num_generated_tokens)
for i, (id,
hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
tgt_tokens_i = None
if tgt_tokens is not None:
tgt_tokens_i = utils.strip_pad(tgt_tokens[i, :],
tgt_dict.pad()).int().cpu()
results.append(
(start_id + id, src_tokens_i, hypos, tgt_tokens_i))
# sort output to match input order
for id, src_tokens, hypos, tgt_tokens in sorted(results,
key=lambda x: x[0]):
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
print('S-{}\t{}'.format(id, src_str))
if tgt_tokens is not None:
tgt_str = tgt_dict.string(tgt_tokens,
args.remove_bpe,
escape_unk=True)
print('T-{}\t{}'.format(id, tgt_str))
# Process top predictions
for j, 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'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
hypo_str = decode_fn(hypo_str)
score = hypo['score'] / math.log(2) # convert to base 2
print('H-{}\t{}\t{}'.format(id, score, hypo_str))
print('P-{}\t{}'.format(
id,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
# convert from base e to base 2
hypo['positional_scores'].div_(math.log(2)
).tolist(),
))))
if args.print_alignment:
alignment_str = " ".join(
["{}-{}".format(src, tgt) for src, tgt in alignment])
print('A-{}\t{}'.format(id, alignment_str))
if args.print_step:
print('I-{}\t{}'.format(id, hypo['steps']))
print('O-{}\t{}'.format(id, hypo['num_ops']))
if getattr(args, 'retain_iter_history', False):
for step, h in enumerate(hypo['history']):
_, h_str, _ = utils.post_process_prediction(
hypo_tokens=h['tokens'].int().cpu(),
src_str=src_str,
alignment=None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=None,
)
print('E-{}_{}\t{}'.format(id, step, h_str))
# Score only the top hypothesis
if tgt_tokens is not None and j == 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
tgt_tokens = tgt_dict.encode_line(
tgt_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(tgt_str, hypo_str)
else:
scorer.add(tgt_tokens, hypo_tokens)
sys.stdout.flush()
# update running id counter
start_id += len(inputs)
logger.info(
'Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if args.has_target:
logger.info('Generate with beam={}: {}'.format(args.beam,
scorer.result_string()))
def _main(args, output_file):
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
stream=output_file,
)
logger = logging.getLogger('fairseq_cli.generate')
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
logger.info('loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(os.pathsep),
arg_overrides=eval(args.model_overrides),
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
from fairseq.sequence_scorer import SequenceScorer
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
avg_ranks = AverageMeter()
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
all_ents = []
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
if 'ents' in sample:
all_ents.extend(sample['ents'])
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str),
file=output_file)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
score = hypo['score'] / math.log(
2) # convert to base 2
print('H-{}\t{}\t{}'.format(sample_id, score,
hypo_str),
file=output_file)
print(
'P-{}\t{}'.format(
sample_id,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
# convert from base e to base 2
hypo['positional_scores'].div_(
math.log(2)).tolist(),
))),
file=output_file)
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])),
file=output_file)
if args.print_step:
print('I-{}\t{}'.format(sample_id, hypo['steps']),
file=output_file)
if getattr(args, 'retain_iter_history', False):
for step, h in enumerate(hypo['history']):
_, h_str, _ = utils.post_process_prediction(
hypo_tokens=h['tokens'].int().cpu(),
src_str=src_str,
alignment=None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=None,
)
print('E-{}_{}\t{}'.format(
sample_id, step, h_str),
file=output_file)
if getattr(args, 'score_reference', False):
print('R-{}\t{}'.format(
sample_id, '{:.4f}'.format(hypo['avg_ranks'])),
file=output_file)
# Score only the top hypothesis
if getattr(args, 'score_reference', False):
avg_ranks.update(hypo['avg_ranks'])
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
logger.info('NOTE: hypothesis and token scores are output in base 2')
logger.info(
'Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
logger.info('Generate {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
if getattr(args, 'score_reference', False):
logger.info('Average rank of reference={:.4f}, Entropy={:.4f}'.format(
avg_ranks.avg,
torch.cat(all_ents, dim=0).mean()))
return scorer
def eval(args, task, models, subset):
use_cuda = torch.cuda.is_available() and not args.cpu
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
results = []
hits = 0.
hit_vector = []
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
out_dict = {}
if not args.quiet:
if src_dict is not None:
# print('S-{}\t{}'.format(sample_id, src_str))
out_dict['source'] = src_str
if has_target:
# print('T-{}\t{}'.format(sample_id, target_str))
out_dict['gold_target'] = target_str
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
out_dict['pred_target'] = hypo_str
# print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
# print('P-{}\t{}'.format(
# sample_id,
# ' '.join(map(
# lambda x: '{:.4f}'.format(x),
# hypo['positional_scores'].tolist(),
# ))
# ))
# if args.print_alignment:
# print('A-{}\t{}'.format(
# sample_id,
# ' '.join(['{}-{}'.format(src_idx, tgt_idx) for src_idx, tgt_idx in alignment])
# ))
# if args.print_step:
# print('I-{}\t{}'.format(sample_id, hypo['steps']))
# if getattr(args, 'retain_iter_history', False):
# print("\n".join([
# 'E-{}_{}\t{}'.format(
# sample_id, step,
# utils.post_process_prediction(
# h['tokens'].int().cpu(),
# src_str, None, None, tgt_dict, None)[1])
# for step, h in enumerate(hypo['history'])]))
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
results.append(out_dict)
if out_dict['gold_target'] == out_dict['pred_target']:
hit_vector.append(1)
hits += 1
else:
hit_vector.append(0)
wps_meter.update(num_generated_tokens)
# t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
acc = hits / len(results)
print("Hit {}/{}. ACC: {}".format(hits, len(results), acc))
return acc, hit_vector
def _main(args, output_file):
logging.basicConfig(
format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
stream=output_file,
)
logger = logging.getLogger('fairseq_cli.generate')
utils.import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
logger.info('loading model(s) from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args.path),
arg_overrides=eval(args.model_overrides),
task=task,
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=args.log_format,
log_interval=args.log_interval,
default_log_format=('tqdm' if not args.no_progress_bar else 'none'),
)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(models, args)
# Handle tokenization and BPE
tokenizer = encoders.build_tokenizer(args)
bpe = encoders.build_bpe(args)
def decode_fn(x):
if bpe is not None:
x = bpe.decode(x)
if tokenizer is not None:
x = tokenizer.decode(x)
return x
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
wps_meter = TimeMeter()
for sample in progress:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True,
extra_symbols_to_ignore={
generator.eos,
})
src_str = decode_fn(src_str)
if has_target:
target_str = decode_fn(target_str)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str),
file=output_file)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
extra_symbols_to_ignore={
generator.eos,
})
detok_hypo_str = decode_fn(hypo_str)
if not args.quiet:
score = hypo['score'] / math.log(2) # convert to base 2
# original hypothesis (after tokenization and BPE)
print('H-{}\t{}\t{}'.format(sample_id, score, hypo_str),
file=output_file)
# detokenized hypothesis
print('D-{}\t{}\t{}'.format(sample_id, score,
detok_hypo_str),
file=output_file)
print(
'P-{}\t{}'.format(
sample_id,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
# convert from base e to base 2
hypo['positional_scores'].div_(math.log(2)
).tolist(),
))),
file=output_file)
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])),
file=output_file)
if args.print_step:
print('I-{}\t{}'.format(sample_id, hypo['steps']),
file=output_file)
if 'enc_selection' in hypo:
print('Menc-{}\t{}'.format(sample_id,
hypo['enc_selection']),
file=output_file)
if 'dec_selection' in hypo:
print('Mdec-{}\t{}'.format(sample_id,
hypo['dec_selection']),
file=output_file)
if args.print_attn_confidence:
print('C-{}\t{}'.format(sample_id,
hypo['enc_self_attn_conf']),
file=output_file)
if getattr(args, 'retain_iter_history', False):
for step, h in enumerate(hypo['history']):
_, h_str, _ = utils.post_process_prediction(
hypo_tokens=h['tokens'].int().cpu(),
src_str=src_str,
alignment=None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=None,
)
print('E-{}_{}\t{}'.format(sample_id, step, h_str),
file=output_file)
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
hypo_tokens = tgt_dict.encode_line(
detok_hypo_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, detok_hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
progress.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
logger.info('NOTE: hypothesis and token scores are output in base 2')
logger.info(
'Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
if args.bpe and not args.sacrebleu:
if args.remove_bpe:
logger.warning(
"BLEU score is being computed by splitting detokenized string on spaces, this is probably not what you want. Use --sacrebleu for standard 13a BLEU tokenization"
)
else:
logger.warning(
"If you are using BPE on the target side, the BLEU score is computed on BPE tokens, not on proper words. Use --sacrebleu for standard 13a BLEU tokenization"
)
logger.info('Generate {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
return scorer
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Set up functions for multiturn
def train_path(lang):
return "{}{}".format(args.trainpref, ("." + lang) if lang else "")
def file_name(prefix, lang):
fname = prefix
if lang is not None:
fname += ".{lang}".format(lang=lang)
return fname
def dest_path(prefix, lang):
return os.path.join(args.destdir, file_name(prefix, lang))
def dict_path(lang):
return dest_path("dict", lang) + ".txt"
def build_dictionary(filenames, src=False, tgt=False):
assert src ^ tgt
return task.build_dictionary(
filenames,
workers=args.workers,
threshold=args.thresholdsrc if src else args.thresholdtgt,
nwords=args.nwordssrc if src else args.nwordstgt,
padding_factor=args.padding_factor,
)
def make_binary_dataset(input_prefix, output_prefix, lang, num_workers):
dict = task.load_dictionary(dict_path(lang))
print("| [{}] Dictionary: {} types".format(lang, len(dict) - 1))
n_seq_tok = [0, 0]
replaced = Counter()
def merge_result(worker_result):
replaced.update(worker_result["replaced"])
n_seq_tok[0] += worker_result["nseq"]
n_seq_tok[1] += worker_result["ntok"]
input_file = "{}{}".format(input_prefix,
("." + lang) if lang is not None else "")
offsets = Tokenizer.find_offsets(input_file, num_workers)
pool = None
if num_workers > 1:
pool = Pool(processes=num_workers - 1)
for worker_id in range(1, num_workers):
prefix = "{}{}".format(output_prefix, worker_id)
pool.apply_async(
binarize,
(
args,
input_file,
dict,
prefix,
lang,
offsets[worker_id],
offsets[worker_id + 1],
),
callback=merge_result,
)
pool.close()
ds = indexed_dataset.IndexedDatasetBuilder(
dataset_dest_file(args, output_prefix, lang, "bin"))
merge_result(
Tokenizer.binarize(input_file,
dict,
lambda t: ds.add_item(t),
offset=0,
end=offsets[1]))
if num_workers > 1:
pool.join()
for worker_id in range(1, num_workers):
prefix = "{}{}".format(output_prefix, worker_id)
temp_file_path = dataset_dest_prefix(args, prefix, lang)
ds.merge_file_(temp_file_path)
os.remove(indexed_dataset.data_file_path(temp_file_path))
os.remove(indexed_dataset.index_file_path(temp_file_path))
ds.finalize(dataset_dest_file(args, output_prefix, lang, "idx"))
print("| [{}] {}: {} sents, {} tokens, {:.3}% replaced by {}".format(
lang,
input_file,
n_seq_tok[0],
n_seq_tok[1],
100 * sum(replaced.values()) / n_seq_tok[1],
dict.unk_word,
))
def make_dataset(input_prefix, output_prefix, lang, num_workers=1):
if args.output_format == "binary":
make_binary_dataset(input_prefix, output_prefix, lang, num_workers)
elif args.output_format == "raw":
# Copy original text file to destination folder
output_text_file = dest_path(
output_prefix +
".{}-{}".format(args.source_lang, args.target_lang),
lang,
)
shutil.copyfile(file_name(input_prefix, lang), output_text_file)
def make_all(lang):
if args.multiturnpref:
make_dataset(args.multiturnpref,
"test",
lang,
num_workers=args.workers)
# Load dataset splits
task = tasks.setup_task(args)
# Multiturn tracking: prompt in test set, turn in debate
turn = 0
prompt = 1
first_pass = True
while first_pass or args.multiturn:
if args.multiturn:
# Set up first turn
if turn == 0:
multiturn_file = "{}{}".format(args.multiturnpref,
("." + args.source_lang))
test_file = "{}{}".format(args.testpref,
("." + args.source_lang))
if args.interactive:
line = input('What subject would you like to debate?')
else:
with open(test_file, 'r', encoding='utf-8') as f:
for i in range(prompt):
line = f.readline()
with open(multiturn_file, 'w', encoding='utf-8') as f:
f.write(line)
prompt += 1
target = not args.only_source
assert (args.multiturnpref), "--multiturnpref must be set"
if args.joined_dictionary:
assert (
not args.srcdict or not args.tgtdict
), "cannot use both --srcdict and --tgtdict with --joined-dictionary"
if args.srcdict:
src_dict = task.load_dictionary(args.srcdict)
elif args.tgtdict:
src_dict = task.load_dictionary(args.tgtdict)
else:
assert (
args.trainpref
), "--trainpref must be set if --srcdict is not specified"
src_dict = build_dictionary(
{
train_path(lang)
for lang in [args.source_lang, args.target_lang]
},
src=True)
tgt_dict = src_dict
else:
if args.srcdict:
src_dict = task.load_dictionary(args.srcdict)
else:
assert (
args.trainpref
), "--trainpref must be set if --srcdict is not specified"
src_dict = build_dictionary([train_path(args.source_lang)],
src=True)
if target:
if args.tgtdict:
tgt_dict = task.load_dictionary(args.tgtdict)
else:
assert (
args.trainpref
), "--trainpref must be set if --tgtdict is not specified"
tgt_dict = build_dictionary([train_path(args.target_lang)],
tgt=True)
else:
tgt_dict = None
src_dict.save(dict_path(args.source_lang))
if target and tgt_dict is not None:
tgt_dict.save(dict_path(args.target_lang))
make_all(args.source_lang)
if target:
make_all(args.target_lang)
if first_pass:
print("| Wrote preprocessed data to {}".format(args.destdir))
print('| Generating multiturn debate')
task.load_dataset('test')
else:
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(
args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
if first_pass:
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
args.path.split(':'),
task,
model_arg_overrides=eval(args.model_overrides),
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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)
# Initialize generator
gen_timer = StopwatchMeter()
if args.score_reference:
translator = SequenceScorer(models, task.target_dictionary)
else:
translator = SequenceGenerator(
models,
task.target_dictionary,
beam_size=args.beam,
minlen=args.min_len,
stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized),
len_penalty=args.lenpen,
unk_penalty=args.unkpen,
sampling=args.sampling,
sampling_topk=args.sampling_topk,
sampling_temperature=args.sampling_temperature,
diverse_beam_groups=args.diverse_beam_groups,
diverse_beam_strength=args.diverse_beam_strength,
match_source_len=args.match_source_len,
no_repeat_ngram_size=args.no_repeat_ngram_size,
)
if use_cuda:
translator.cuda()
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(),
tgt_dict.unk())
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
if args.score_reference:
translations = translator.score_batched_itr(t,
cuda=use_cuda,
timer=gen_timer)
else:
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=args.prefix_size,
)
wps_meter = TimeMeter()
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
has_target = target_tokens is not None
target_tokens = target_tokens.int().cpu(
) if has_target else None
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
src_str = src_dict.string(src_tokens, args.remove_bpe)
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# 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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
if args.multiturn:
multiturn_file = "{}{}".format(
args.multiturnpref, ("." + args.source_lang))
output_file = "{}{}".format(args.outputpref,
("." + args.target_lang))
with open(multiturn_file, 'r', encoding='utf-8') as f:
line = f.readline()
if args.interactive:
interactive_response = input('Please respond:')
line += f' <EOA> {interactive_response}'
if turn < MAX_TURNS - 1:
with open(multiturn_file, 'w',
encoding='utf-8') as f:
f.write(f'{line[:-1]} <EOA> {hypo_str}')
turn += 1
elif turn == MAX_TURNS - 1:
with open(output_file, 'a', encoding='utf-8') as f:
f.write(f'{line[:-1]} <EOA> {hypo_str}\n')
turn = 0
# Score only the top hypothesis
if has_target and 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 = tokenizer.Tokenizer.tokenize(
target_str, tgt_dict, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
num_sentences += 1
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(
args.gen_subset, args.beam, scorer.result_string()))
first_pass = False
def _generate_score(models, args, task, dataset, modify_target_dict):
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)
print("seed number is" + str(args.max_examples_to_evaluate_seed))
if args.max_examples_to_evaluate > 0:
pytorch_translate_data.subsample_pair_dataset(
dataset, args.max_examples_to_evaluate, args.max_examples_to_evaluate_seed
)
# Keep track of translations
# Initialize with empty translations
# and zero probs scores
translated_sentences = [""] * len(dataset)
translated_scores = [0.0] * len(dataset)
hypos_list = []
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_many_to_one(args)
else 0,
)
for trans_info in _iter_translations(
args, task, dataset, translations, align_dict, rescorer, modify_target_dict
):
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)
if getattr(args, "translation_output_file", False):
translated_sentences[trans_info.sample_id] = trans_info.hypo_str
if getattr(args, "translation_probs_file", False):
translated_scores[trans_info.sample_id] = trans_info.hypo_score
if getattr(args, "hypotheses_export_path", False):
hypos_list.append(trans_info.hypos)
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:
# Strip expensive data from hypotheses before saving
hypos = [
{k: v for k, v in hypo.items() if k in ["tokens", "score"]}
for hypo in trans_info.hypos
]
# Make sure everything is on cpu before exporting
hypos = [
{"score": hypo["score"], "tokens": hypo["tokens"].cpu()}
for hypo in hypos
]
translation_info_list.append(
{
"src_tokens": trans_info.src_tokens.cpu(),
"target_tokens": trans_info.target_tokens,
"hypos": 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.InMemoryIndexedDataset()
output_dataset.load_from_sequences(output_hypos_token_arrays)
output_dataset.save(args.output_hypos_binary_path)
if args.output_source_binary_path:
dataset.src.save(args.output_source_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 and scores to the output files
# These two ouputs are used in dual learning for weighted backtranslation
if getattr(args, "translation_output_file", False) and getattr(
args, "translation_probs_file", False
):
with open(args.translation_output_file, "w") as translation_file, open(
args.translation_probs_file, "w"
) as score_file:
for hypo_str, hypo_score in zip(translated_sentences, translated_scores):
if len(hypo_str.strip()) > 0:
print(hypo_str, file=translation_file)
print(np.exp(hypo_score), file=score_file)
# For eg. external evaluation
if getattr(args, "hypotheses_export_path", False):
with open(args.hypotheses_export_path, "w") as out_file:
for hypos in hypos_list:
for hypo in hypos:
print(
task.tgt_dict.string(
hypo["tokens"], bpe_symbol=args.remove_bpe
),
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
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
import_user_module(args)
"""
MODIFIED: The GEC task uses token-labeled raw text datasets, which
require raw text to be used.
"""
assert args.raw_text, \
f"--raw-text option is required for copy-based generation."
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
args.path.split(':'),
task,
model_arg_overrides=eval(args.model_overrides),
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
has_copy_scores = True
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
"""
MODIFIED: Use copy scores to replace <unk>'s with raw source words.
use_copy_scores may be False with non-copy-based transformers that
only use edit labels (e.g., transformer_aux_el and transformer_el).
"""
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
use_copy_scores = hypos[0][0].get('copy_scores', None) is not None
if has_copy_scores and not use_copy_scores:
print("| generate_or_copy.py | INFO | "
"Model does not include copy scores. "
"Generating hypotheses without replacing UNKs.")
has_copy_scores = False
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
"""
MODIFIED: Replace <unk>s with raw source tokens.
This is analogous to the case where align_dict is provided
in the original generate.py.
"""
rawtext_dataset = task.dataset(args.gen_subset)
src_str = rawtext_dataset.src.get_original_text(sample_id)
tokenized_src_str = rawtext_dataset.src_dict.string(
src_tokens, bpe_symbol=args.remove_bpe)
target_str = rawtext_dataset.tgt.get_original_text(sample_id)
if not args.quiet:
if src_dict is not None:
# Raw source text
print('S-{}\t{}'.format(sample_id, src_str))
# Tokenized source text
print('K-{}\t{}'.format(sample_id, tokenized_src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for k, hypo in enumerate(
hypos[i][: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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
"""
MODIFIED: Replace predicted <unk>s with the source token
that received the highest score.
"""
raw_src_tokens = src_str.split()
final_hypo_tokens_str = []
for tgt_position, hypo_token in enumerate(hypo_tokens):
if use_copy_scores and hypo_token == tgt_dict.unk():
# See sequence_copygenerator.py#L292 for details.
copy_scores = hypo[
'copy_scores'][:, tgt_position].cpu()
assert len(copy_scores) - 1 == len(raw_src_tokens), \
f"length of copy scores do not match input source tokens " \
f"(copy_scores: {copy_scores}, raw_src_tokens: {raw_src_tokens})"
src_position = torch.argmax(copy_scores).item()
# Don't copy if attending to an EOS (not ideal).
if src_position == len(raw_src_tokens):
print("WARNING: copy score highest at EOS.")
else:
final_hypo_tokens_str.append(
raw_src_tokens[src_position])
print('U-{}\t{}\t{}'.format(
sample_id,
tgt_position,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
copy_scores.tolist(),
)),
))
else:
final_hypo_tokens_str.append(tgt_dict[hypo_token])
# Note: raw input tokens could be included here.
final_hypo_str = ' '.join([
token for token in final_hypo_tokens_str
if token != tgt_dict.eos_word
])
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
final_hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
# Score only the top hypothesis
if has_target and k == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
return scorer
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
tgt_file = None
hypo_file = None
if args.output_dir:
os.makedirs(args.output_dir, exist_ok=True)
tgt_fn = os.path.join(args.output_dir, 'gold')
hypo_fn = os.path.join(args.output_dir, 'candidate')
tgt_file = open(tgt_fn, 'w', encoding='utf-8')
hypo_file = open(hypo_fn, 'w', encoding='utf-8')
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
args.path.split(':'),
task,
model_arg_overrides=eval(args.model_overrides),
)
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(
sample_id, src_str.encode(encoding='utf-8')))
if has_target:
print('T-{}\t{}'.format(
sample_id, target_str.encode(encoding='utf-8')))
# Process top predictions
for j, hypo in enumerate(
hypos[i][:min(len(hypos[i]), 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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print('H-{}\t{}\t{}'.format(
sample_id, hypo['score'],
hypo_str.encode(encoding='utf-8')))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
# Score only the top hypothesis
if has_target and j == 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
if args.output_dir:
tgt_file.writelines(target_str + '\n')
hypo_file.writelines(hypo_str + '\n')
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
tgt_file.close()
hypo_file.close()
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
return scorer
def main(args):
assert args.path is not None, '--path required for generation!'
assert not args.sampling or args.nbest == args.beam, \
'--sampling requires --nbest to be equal to --beam'
assert args.replace_unk is None or args.raw_text, \
'--replace-unk requires a raw text dataset (--raw-text)'
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset, args=args)
print('| {} {} {} examples'.format(args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
args.unk_idx = task.src_dict.indices['<unk>']
args.dict_len = task.src_dict.indices.__len__()
if '[APPEND]' in task.src_dict.indices.keys():
args.APPEND_ID = task.src_dict.indices['[APPEND]']
print("[APPEND] ID: {}".format(args.APPEND_ID))
else:
args.APPEND_ID = -1
if '[SRC]' in task.src_dict.indices.keys():
args.SRC_ID = task.src_dict.indices['[SRC]']
print("[SRC] ID: {}".format(args.SRC_ID))
else:
args.SRC_ID = -1
if '[TGT]' in task.src_dict.indices.keys():
args.TGT_ID = task.src_dict.indices['[TGT]']
print("[TGT] ID: {}".format(args.TGT_ID))
else:
args.TGT_ID = -1
if '[SEP]' in task.src_dict.indices.keys():
args.SEP_ID = task.src_dict.indices['[SEP]']
print("[SEP] ID: {}".format(args.SEP_ID))
else:
args.SEP_ID = -1
if '</s>' in task.src_dict.indices.keys():
args.EOS_ID = task.src_dict.indices['</s>']
else:
args.EOD_ID = -1
if '<pad>' in task.src_dict.indices.keys():
args.PAD_ID = task.src_dict.indices['<pad>']
else:
args.PAD_ID = -1
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
args.path.split(':'),
task,
model_arg_overrides=eval(args.model_overrides),
)
_model_args.avgpen = args.avgpen
task.datasets['test'].args = _model_args
# 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=args.print_alignment,
)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# 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)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
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=args.required_batch_size_multiple,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
if args.sacrebleu:
scorer = bleu.SacrebleuScorer()
else:
scorer = bleu.Scorer(tgt_dict.pad(), tgt_dict.eos(), tgt_dict.unk())
num_sentences = 0
has_target = True
select_retrieve_tokens = []
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
trans_results = []
for sample in t:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
gen_timer.start()
hypos, encoder_outs = task.inference_step(generator, models,
sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens, retrieve_source_tokens, retrieve_target_tokens = sample[
'net_input']['src_tokens']
retrieve_tokens = list(
itertools.chain.from_iterable(
zip(retrieve_source_tokens, retrieve_target_tokens)))
retrieve_tokens = torch.cat(retrieve_tokens, dim=1)
all_tokens = torch.cat([src_tokens, retrieve_tokens], dim=1)
src_tokens = utils.strip_pad(all_tokens[i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(
sample['target'][i, :], tgt_dict.pad()).int().cpu()
#
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
args.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
if not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
# add select tokens
select_retrieve_tokens.append([
sample_id, src_str, target_str,
sample['predict_ground_truth'][i, :],
retrieve_tokens[i, :],
encoder_outs[0]['new_retrieve_tokens'][i, :],
utils.strip_pad(retrieve_tokens[i, :],
src_dict.pad()).tolist(),
utils.strip_pad(
encoder_outs[0]['new_retrieve_tokens'][i, :],
src_dict.pad()).tolist()
])
# Process top predictions
for i, hypo in enumerate(
hypos[i][: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()
if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
trans_results.append((sample_id, hypo_str))
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'],
hypo_str))
print('P-{}\t{}'.format(
sample_id, ' '.join(
map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))))
if args.print_alignment:
print('A-{}\t{}'.format(
sample_id, ' '.join(
map(lambda x: str(utils.item(x)),
alignment))))
# Score only the top hypothesis
if has_target and 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 = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
if hasattr(scorer, 'add_string'):
scorer.add_string(target_str, hypo_str)
else:
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
print(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset,
args.beam,
scorer.result_string()))
trans_results.sort(key=lambda key: key[0])
print("saving translation result to {}...".format(args.output))
with open(args.output, "w", encoding="utf-8") as w:
for item in trans_results:
w.write("{}\n".format(item[1].replace("<<unk>>", "")))
select_retrieve_tokens.sort(key=lambda key: key[0])
orig_retrieve_tokens_length = 0
select_retrieve_tokens_length = 0
correct_tokens = 0
with open(args.output + ".select", "w", encoding="utf-8") as w_select:
for item in select_retrieve_tokens:
sample_id, src_str, target_str, sample_predict_ground_truth, sample_orig_id, sample_select_retrieve_id, sample_orig_retrieve_tokens, sample_select_retrieve_tokens = item
retrieve_str = src_dict.string(sample_orig_retrieve_tokens,
args.remove_bpe)
select_str = src_dict.string(sample_select_retrieve_tokens,
args.remove_bpe)
w_select.write("{}\n{}\n{}\n{}\n\n".format(src_str, target_str,
retrieve_str,
select_str))
orig_retrieve_tokens_length += len(sample_orig_retrieve_tokens)
select_retrieve_tokens_length += len(sample_select_retrieve_tokens)
#calculate accuracy
correct_tokens += (
(sample_select_retrieve_id != _model_args.PAD_ID
).long() == sample_predict_ground_truth).masked_fill(
(sample_orig_id == _model_args.PAD_ID).byte(), 0).sum()
ratio = select_retrieve_tokens_length / float(orig_retrieve_tokens_length)
accuracy = correct_tokens.tolist() / float(orig_retrieve_tokens_length)
print("Selective Tokens: {}".format(ratio))
print("Correct Tokens: {}".format(accuracy))
with open("{}.RetrieveNMT.BLEU".format(args.output), "a",
encoding="utf-8") as w:
w.write(
'{}->{}: Generate {} with beam={} and lenpen={}: {};\tSelection Ratio: {};\tAccuracy:{}\n'
.format(args.source_lang, args.target_lang,
args.gen_subset, args.beam, args.lenpen,
scorer.result_string(), ratio, accuracy))
return scorer