def build_embedding(dictionary, embed_dim, is_encoder, path=None):
if path is not None:
if path.startswith('elmo:'):
lm_path = path[5:]
task = LanguageModelingTask(args, dictionary, dictionary)
models, _ = utils.load_ensemble_for_inference(
[lm_path], task, {'remove_head': True})
assert len(
models
) == 1, 'ensembles are currently not supported for elmo embeddings'
embedder = ElmoTokenEmbedder(
models[0],
dictionary.eos(),
dictionary.pad(),
add_bos=is_encoder,
remove_bos=is_encoder,
combine_tower_states=is_encoder,
projection_dim=embed_dim,
add_final_predictive=is_encoder,
add_final_context=is_encoder)
return embedder, 1
elif path.startswith('bilm:'):
lm_path = path[5:]
task = LanguageModelingTask(args, dictionary, dictionary)
models, _ = utils.load_ensemble_for_inference(
[lm_path], task, {
'remove_head': True,
'dropout': args.bilm_model_dropout,
'attention_dropout': args.bilm_attention_dropout,
'relu_dropout': args.bilm_relu_dropout,
})
assert len(
models
) == 1, 'ensembles are currently not supported for elmo embeddings'
return BILMEmbedder(models[0], args, args.encoder_embed_dim) if is_encoder \
else LMEmbedder(models[0], args.decoder_embed_dim)
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = nn.Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
def __init__(self, parsed_args):
self.args = parsed_args
import_user_module(parsed_args)
assert parsed_args.path is not None, '--path required for evaluation'
#print(parsed_args)
self.use_cuda = torch.cuda.is_available() and not parsed_args.cpu
self.task = tasks.setup_task(parsed_args)
# Load ensemble
print('| loading model(s) from {}'.format(parsed_args.path))
self.models, args = utils.load_ensemble_for_inference(
parsed_args.path.split(':'), self.task, model_arg_overrides=eval(parsed_args.model_overrides),
)
print(self.models)
for model in self.models:
model.make_generation_fast_()
if self.use_cuda:
model.cuda()
for arg in vars(parsed_args).keys():
if arg not in {'self_target', 'future_target', 'past_target', 'tokens_per_sample',
'output_size_dictionary'}:
setattr(args, arg, getattr(parsed_args, arg))
self.task = tasks.setup_task(args)
self.gen_timer = StopwatchMeter()
self.scorer = SequenceScorer(self.task.target_dictionary)
def generate_score(args, task, dataset, lang_pair=None):
"""
Generation for single and multi model training
Args:
args: Command-line arguments.
task: FairseqTask object.
dataset: Dataset set object for a specific split for a specific model
lang_pair: Model key in a multi model object. Specify None in single
model set up
"""
models, _ = utils.load_ensemble_for_inference(args.path.split(":"), task)
if lang_pair and len(models) > 0 and isinstance(models[0],
FairseqMultiModel):
return _generate_score(
models=[multi_model.models[lang_pair] for multi_model in models],
args=args,
task=task,
dataset=dataset,
)
else:
return _generate_score(models=models,
args=args,
task=task,
dataset=dataset)
def load_model(self, args):
use_cuda = torch.cuda.is_available() and not args.cpu
task = tasks.setup_task(args)
logger.info('loading edit model from {}'.format(args.path))
models, model_args = utils.load_ensemble_for_inference(
args.path.split(':'), task)
return task, models[0], model_args
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = 512
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = 512
dictionary = task.source_dictionary
assert args.bert_path is not None
args.short_seq_prob = 0.0
task = BertTask(args, dictionary)
#HACK_PATH = "/checkpoint/wangalexc/fairseq/bert-pretrained/20190520/checkpoint_best.pt"
HACK_PATH = "/misc/vlgscratch4/BowmanGroup/awang/ckpts/fairseq/bert/best_pretrained_bert.pt"
models, _ = utils.load_ensemble_for_inference([HACK_PATH], task,
{'save_masks': False})
#models, _ = utils.load_ensemble_for_inference([args.bert_path], task, {'save_masks' : False})
assert len(
models
) == 1, 'ensembles are currently not supported for elmo embeddings'
model = models[0]
return FTSummerization(args, model)
def load_fairseq_lm_model_and_dict(checkpoint_path, data_path):
# Initialize model
parser = options.get_eval_lm_parser()
parsed_args = options.parse_args_and_arch(parser, ['--path', checkpoint_path, data_path])
task = tasks.setup_task(parsed_args)
models, _ = utils.load_ensemble_for_inference([checkpoint_path], task)
return models[0], task.dictionary
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)'
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 12000
if not args.quiet:
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
task = tasks.setup_task(args)
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task)
# Optimize ensemble for generation
for i, model in enumerate(models):
models[i].make_generation_fast_(beamable_mm_beam_size=None if args.no_beamable_mm else args.beam)
if args.fp16:
models[i].half()
if args.decode_source_file is not None:
print('| [decode] decode from file')
decode_from_file(models, task, args, use_cuda)
else:
print('| [decode] decode from dataset')
decode_from_dataset(models, task, args, use_cuda)
def main(parsed_args):
assert parsed_args.path is not None, '--path required for evaluation!'
print(parsed_args)
use_cuda = torch.cuda.is_available() and not parsed_args.cpu
task = tasks.setup_task(parsed_args)
# Load ensemble
print('| loading model(s) from {}'.format(parsed_args.path))
models, args = utils.load_ensemble_for_inference(
parsed_args.path.split(':'), task)
assert len(models) == 1
model = models[0]
if use_cuda:
model.cuda()
for arg in vars(parsed_args).keys():
setattr(args, arg, getattr(parsed_args, arg))
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset,
len(task.dataset(args.gen_subset))))
eval_dataset(task, model, task.dataset(args.gen_subset), args.out_file,
args.thresholds, args.compute_metrics, use_cuda)
def generate_score(args, dataset, dataset_split):
models, _ = utils.load_ensemble_for_inference(
args.path,
dataset.src_dict,
dataset.dst_dict,
)
return _generate_score(models, args, dataset, dataset_split)
def __init__(self, args):
super().__init__()
fairseq_pretrained_model = str(args.data) + "/" + str(
args.fairseq_model_name)
pre_task = tasks.setup_task(args)
print('| loading model from {}'.format(fairseq_pretrained_model))
self.dict_file = fairseq_pretrained_model + "/dict.txt"
models, self.model_args = utils.load_ensemble_for_inference(
[fairseq_pretrained_model], pre_task)
copy_args(args, self.model_args)
self.task = tasks.setup_task(self.model_args)
self.model = models[0]
self.decoder = self.model.decoder # <class 'fairseq.models.fconv.FConvDecoder'>
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
self.model.make_generation_fast_()
self.map_indices = None
self.pad = self.task.target_dictionary.pad
# add MASK to dictionary
self.mask_id = self.task.dictionary.add_symbol(MASK)
self.vocab = self.task.dictionary.symbols[:]
# reinitialize inverse vocab
self._init_inverse_vocab()
self.unk_index = self.inverse_vocab[FAIRSEQ_UNK]
def setup_model(args):
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('fairseq args: {}'.format(args))
# Setup task, e.g., translation
task = tasks.setup_task(args)
# Load ensemble
logger.info('| 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),
)
# Set 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,
)
translator = SequenceGenerator(
models,
tgt_dict,
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 torch.cuda.is_available() and not args.cpu:
translator.cuda()
logger.info('model has been read successfully!')
return models, task, tgt_dict, translator
def main(args):
assert args.path is not None, '--path required for evaluation!'
args.tokens_per_sample = getattr(args, 'tokens_per_sample', 1024)
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))))
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task)
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
for model in models:
model.make_generation_fast_()
if args.fp16:
model.half()
itr = data.EpochBatchIterator(
dataset=task.dataset(args.gen_subset),
max_tokens=args.max_tokens,
max_sentences=args.max_sentences or 4,
max_positions=model.max_positions(),
num_shards=args.num_shards,
shard_id=args.shard_id,
ignore_invalid_inputs=True,
).next_epoch_itr(shuffle=False)
gen_timer = StopwatchMeter()
scorer = SequenceScorer(models, task.target_dictionary)
if use_cuda:
scorer.cuda()
score_sum = 0.
count = 0
with progress_bar.build_progress_bar(args, itr) as t:
results = scorer.score_batched_itr(t, cuda=use_cuda, timer=gen_timer)
wps_meter = TimeMeter()
for _, src_tokens, __, hypos in results:
for hypo in hypos:
pos_scores = hypo['positional_scores']
inf_scores = pos_scores.eq(float('inf')) | pos_scores.eq(float('-inf'))
if inf_scores.any():
print('| Skipping tokens with inf scores:',
task.target_dictionary.string(hypo['tokens'][inf_scores.nonzero()]))
pos_scores = pos_scores[(~inf_scores).nonzero()]
score_sum += pos_scores.sum()
count += pos_scores.numel()
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
avg_nll_loss = -score_sum / count
print('| Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Loss: {:.4f}, Perplexity: {:.2f}'.format(avg_nll_loss, np.exp(avg_nll_loss)))
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path', metavar='FILE', required=True, action='append',
help='path(s) to model file(s)')
options.add_dataset_args(parser)
options.add_generation_args(parser)
args = parser.parse_args()
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, model_args = utils.load_ensemble_for_inference(args.path, data_dir=args.data)
src_dict, dst_dict = models[0].src_dict, models[0].dst_dict
print('| [{}] dictionary: {} types'.format(model_args.source_lang, len(src_dict)))
print('| [{}] dictionary: {} types'.format(model_args.target_lang, len(dst_dict)))
# 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)
# Initialize generator
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.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)
print('| Type the input sentence and press return:')
for src_str in sys.stdin:
src_str = src_str.strip()
src_tokens = tokenizer.Tokenizer.tokenize(src_str, src_dict, add_if_not_exist=False).long()
if use_cuda:
src_tokens = src_tokens.cuda()
translations = translator.generate(Variable(src_tokens.view(1, -1)))
hypos = translations[0]
print('O\t{}'.format(src_str))
# Process top predictions
for hypo in hypos[:min(len(hypos), args.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dst_dict,
remove_bpe=args.remove_bpe)
print('H\t{}\t{}'.format(hypo['score'], hypo_str))
print('A\t{}'.format(' '.join(map(str, alignment))))
def main(args):
check_args(args)
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 30000
logger.info(args)
#use_cuda = torch.cuda.is_available() and not args.cpu
# use_cuda = False
# Load dataset splits
task = tasks.setup_task(args)
# Set dictionary
tgt_dict = task.target_dictionary
if args.ctc or args.rnnt:
tgt_dict.add_symbol("<ctc_blank>")
if args.ctc:
logger.info("| decoding a ctc model")
if args.rnnt:
logger.info("| decoding a rnnt model")
# Load ensemble
logger.info("| 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), # noqa
)
optimize_models(args, models)
# Initialize generator
generator = task.build_generator(args)
sp = spm.SentencePieceProcessor()
sp.Load(os.path.join(args.data, 'spm.model'))
# TODO: replace this
# path = '/Users/jamarshon/Downloads/snippet.mp3'
# path = '/Users/jamarshon/Downloads/hamlet.mp3'
path = '/home/aakashns/speech_transcribe/deepspeech.pytorch/data/an4_dataset/train/an4/wav/cen8-mwhw-b.wav'
if not os.path.exists(path):
raise FileNotFoundError("Audio file not found: {}".format(path))
waveform, sample_rate = torchaudio.load_wav(path)
waveform = waveform.mean(0, True)
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate,
new_freq=16000)(waveform)
# waveform = waveform[:, :16000*30]
# torchaudio.save('/Users/jamarshon/Downloads/hello.wav', waveform >> 16, 16000)
import time
print(sample_rate, waveform.shape)
start = time.time()
transcribe(waveform, args, task, generator, models, sp, tgt_dict)
end = time.time()
print(end - start)
def build_embedding(dictionary, embed_dim, path):
assert path is not None
args.short_seq_prob = 0.0
task = BertTask(args, dictionary)
models, _ = utils.load_ensemble_for_inference([path], task, {'remove_head': True, 'remove_pooled' : True, 'save_masks' : False})
assert len(models) == 1, 'ensembles are currently not supported for elmo embeddings'
pretrain_model = models[0]
return pretrain_model
def load_model(cls, path, cpu=False):
args = argparse.Namespace(data=os.path.dirname(path), path=path, cpu=cpu, task='language_modeling',
output_dictionary_size=-1, self_target=False, future_target=False, past_target=False)
use_cuda = torch.cuda.is_available() and not cpu
task = tasks.setup_task(args)
models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task)
d = task.target_dictionary
scorer = SequenceScorer(models, d)
return cls(task, scorer, use_cuda)
def load_model(self, args):
#args = argparse.Namespace(data=data_path, path=model_path, cpu=cpu, task='edit')
use_cuda = torch.cuda.is_available() and not args.cpu
task = tasks.setup_task(args)
logger.info('loading model from {}'.format(args.path))
overrides = {'encoder_embed_path': None, 'decoder_embed_path': None}
models, model_args = utils.load_ensemble_for_inference(
args.path.split(':'), task, overrides)
return task, models[0], model_args
def main(args):
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, model_args = utils.load_ensemble_for_inference(args.path, data_dir=args.data)
src_dict, dst_dict = models[0].src_dict, models[0].dst_dict
print('| [{}] dictionary: {} types'.format(model_args.source_lang, len(src_dict)))
print('| [{}] dictionary: {} types'.format(model_args.target_lang, len(dst_dict)))
# 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,
)
# Initialize generator
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.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)
print('| Type the input sentence and press return:')
for src_str in sys.stdin:
src_str = src_str.strip()
src_tokens = tokenizer.Tokenizer.tokenize(src_str, src_dict, add_if_not_exist=False).long()
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_tokens.new([src_tokens.numel()])
translations = translator.generate(
Variable(src_tokens.view(1, -1)),
Variable(src_lengths.view(-1)),
)
hypos = translations[0]
print('O\t{}'.format(src_str))
# Process top predictions
for hypo in hypos[:min(len(hypos), args.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dst_dict,
remove_bpe=args.remove_bpe,
)
print('H\t{}\t{}'.format(hypo['score'], hypo_str))
print('A\t{}'.format(' '.join(map(str, alignment))))
def generate(args):
assert_test_corpus_and_vocab_files_specified(args)
assert args.path is not None, '--path required for generation!'
print(args)
if args.source_lang is None:
args.source_lang = 'src'
if args.target_lang is None:
args.target_lang = 'tgt'
src_dict = pytorch_translate_dictionary.Dictionary.load(
args.source_vocab_file,
)
dst_dict = pytorch_translate_dictionary.Dictionary.load(
args.target_vocab_file,
)
dataset = data.LanguageDatasets(
src=args.source_lang,
dst=args.target_lang,
src_dict=src_dict,
dst_dict=dst_dict,
)
models, model_args = utils.load_ensemble_for_inference(
args.path,
dataset.src_dict,
dataset.dst_dict,
)
dataset.splits[args.gen_subset] = pytorch_translate_data.make_language_pair_dataset(
source_file=args.source_text_file,
target_file=args.target_text_file,
source_dict=src_dict,
target_dict=dst_dict,
append_eos=model_args.append_eos_to_source,
reverse_source=model_args.reverse_source,
)
if args.source_lang is None or args.target_lang is None:
# record inferred languages in args
args.source_lang, args.target_lang = dataset.src, dataset.dst
print(f'| [{dataset.src}] dictionary: {dataset.src_dict} types')
print(f'| [{dataset.dst}] dictionary: {len(dataset.dst_dict)} types')
print(f'| {args.gen_subset} {len(dataset.splits[args.gen_subset])} examples')
scorer, num_sentences, gen_timer = _generate_score(
models=models,
args=args,
dataset=dataset,
dataset_split=args.gen_subset,
)
print(f'| Translated {num_sentences} sentences ({gen_timer.n} tokens) '
f'in {gen_timer.sum:.1f}s ({1. / gen_timer.avg:.2f} tokens/s)')
print(f'| Generate {args.gen_subset} with beam={args.beam}: '
f'{scorer.result_string()}')
return scorer.score()
def load_lm(lm_path, cpu=False):
# TODO: don't hardcode path
args = argparse.Namespace(data=lm_path, path=lm_path+'/wiki103.pt', cpu=cpu, task='language_modeling')
use_cuda = torch.cuda.is_available() and not args.cpu
task = tasks.setup_task(args)
print('| loading model(s) from {}'.format(args.path))
models, _ = utils.load_ensemble_for_inference(args.path.split(':'), task)
d = task.target_dictionary
scorer = SequenceScorer(models, d)
if use_cuda:
scorer.cuda()
return task, scorer
def main(args):
check_args(args)
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 30000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
# Set dictionary
tgt_dict = task.target_dictionary
if args.ctc or args.rnnt:
tgt_dict.add_symbol("<ctc_blank>")
if args.ctc:
logger.info("| decoding a ctc model")
if args.rnnt:
logger.info("| decoding a rnnt model")
# Load ensemble
logger.info("| 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), # noqa
)
optimize_models(args, use_cuda, models)
# Initialize generator
generator = task.build_generator(args)
sp = spm.SentencePieceProcessor()
sp.Load(os.path.join(args.data, 'spm.model'))
path = args.input_file
if not os.path.exists(path):
raise FileNotFoundError("Audio file not found: {}".format(path))
waveform, sample_rate = torchaudio.load_wav(path)
waveform = waveform.mean(0, True)
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate,
new_freq=16000)(waveform)
import time
print(sample_rate, waveform.shape)
start = time.time()
transcribe(waveform, args, task, generator, models, sp, tgt_dict)
end = time.time()
print(end - start)
def build_model(cls, args, task):
trained_encoder, trained_decoder = None, None
pretrained = eval(args.pretrained)
if pretrained:
print("| Loading pretrained model")
trained_model = utils.load_ensemble_for_inference(
# not actually for inference, but loads pretrained model parameters
filenames=[args.pretrained_checkpoint],
task=task,
)[0][0]
trained_decoder = list(trained_model.children())[1]
trained_encoder = list(trained_model.children())[0]
# freeze pretrained model
for param in trained_decoder.parameters():
param.requires_grad = False
for param in trained_encoder.parameters():
param.requires_grad = False
"""Build a new model instance."""
encoder = FConvEncoder(
task.source_dictionary,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
attention=eval(args.encoder_attention),
attention_nheads=args.encoder_attention_nheads,
mask_future_timesteps=(not args.not_mask_encoder_future_timesteps)
)
decoder = FConvDecoder(
task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
selfattention=eval(args.self_attention),
attention_nheads=args.multihead_attention_nheads,
selfattention_nheads=args.multihead_self_attention_nheads,
project_input=eval(args.project_input),
gated_attention=eval(args.gated_attention),
downsample=eval(args.downsample),
pretrained=pretrained,
trained_decoder=trained_decoder
)
model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
return model
def load_model_asr(args):
# Load ensemble
logger.info("| loading model(s) from {}".format(args.path))
models_asr, _model_args = utils.load_ensemble_for_inference(
args.path.split(":"), task, model_arg_overrides={})
optimize_models_asr(args, models_asr)
# Initialize generator
generator = task.build_generator(args)
sp = spm.SentencePieceProcessor()
sp.Load(os.path.join(args.data, 'spm.model'))
return models_asr, sp, generator
def build_model(cls, args, task):
trained_encoder, trained_decoder = None, None
pretrained = eval(args.pretrained)
if pretrained:
print("| loading pretrained model")
trained_model = utils.load_ensemble_for_inference(
# not actually for inference, but loads pretrained model parameters
filenames=[args.pretrained_checkpoint],
task=task,
)[0][0]
trained_decoder = list(trained_model.children())[1]
trained_encoder = list(trained_model.children())[0]
# freeze pretrained model
for param in trained_decoder.parameters():
param.requires_grad = False
for param in trained_encoder.parameters():
param.requires_grad = False
"""Build a new model instance."""
encoder = FConvEncoder(
task.source_dictionary,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
attention=eval(args.encoder_attention),
attention_nheads=args.encoder_attention_nheads
)
decoder = FConvDecoder(
task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
selfattention=eval(args.self_attention),
attention_nheads=args.multihead_attention_nheads,
selfattention_nheads=args.multihead_self_attention_nheads,
project_input=eval(args.project_input),
gated_attention=eval(args.gated_attention),
downsample=eval(args.downsample),
pretrained=pretrained,
trained_decoder=trained_decoder
)
model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
return model
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
dictionary = task.dictionary
assert args.bert_path is not None
args.short_seq_prob = 0.0
task = BertTask(args, dictionary)
models, _ = utils.load_ensemble_for_inference([args.bert_path], task, {
'remove_head': True, 'remove_pooled' : True, 'save_masks' : False
})
assert len(models) == 1, 'ensembles are currently not supported for elmo embeddings'
model = models[0]
return FinetuningSquad(args, model)
def calculate_bleu_on_subset(args, task, epoch_str: str, offset, dataset_split):
# This is a trick to have generate use max_sentences_valid
max_sentences_train = args.max_sentences
args.max_sentences = args.max_sentences_valid
datasets = []
lang_pairs = []
"""
In multi model training set up, evaluate one model at a time with
corresponding dataset
lang_pair is passed to identify model to be used for generation
"""
if isinstance(task, PytorchTranslateSemiSupervised) or isinstance(
task, DualLearningTask
):
for key, dataset in task.datasets[dataset_split].datasets.items():
datasets.append(dataset)
lang_pairs.append(key)
else:
datasets = [task.dataset(dataset_split)]
lang_pairs = [None]
score_aggregator_fn = (
task.score_aggregator if hasattr(task, "score_aggregator") else sum
)
scores = []
ensemble_models, _ = utils.load_ensemble_for_inference(args.path.split(":"), task)
for dataset, lang_pair in zip(datasets, lang_pairs):
# Generate score
scorer, num_sentences, gen_timer, translation_samples = generate.generate_score(
args=args,
task=task,
dataset=dataset,
models=ensemble_models,
lang_pair=lang_pair,
)
scores.append(scorer.score())
print(
f"| epoch {epoch_str} | offset {offset} "
f"| Eval on {dataset_split} {lang_pair if lang_pair else ''} subset "
f"with beam={args.beam}: {scorer.result_string()}. "
f"Generated {num_sentences} sentences ({gen_timer.n} tokens) "
f"in {gen_timer.sum:.1f}s ({1. / gen_timer.avg:.2f} tokens/s).",
flush=True,
)
# Set max_sentences to its original value
args.max_sentences = max_sentences_train
return score_aggregator_fn(scores), translation_samples
def main(args):
"""1 - Load Fairseq models in conventional Fairseq way."""
# Setup task, e.g., translation
task = tasks.setup_task(args)
# Load ensemble
model_paths = args.path.split(':')
models, _ = utils.load_ensemble_for_inference(model_paths,
task,
model_arg_overrides=eval(
args.model_overrides))
# Set dictionaries
src_dict = task.source_dictionary
model = models[0]
"""2 - Added src code by am-i-compositional team."""
emb_dict = {}
distance_per_token = defaultdict(lambda: {"twin": 0, "other": []})
for i, embedding in enumerate(model.encoder.embed_tokens.weights):
emb_dict[src_dict.symbols[i]] = embedding
# For every adapted function, compute distance to its twin,
# and to all other functions
for f in ["repeat", "remove_second", "swap_first_last", "append"]:
functions = [
"repeat", "swap_first_last", "reverse", "echo", "copy", "shift",
"append", "prepend", "remove_first", "remove_second"
]
functions.remove(f)
for f2 in functions:
distance = spatial.distance.cosine(emb_dict[f], emb_dict[f2])
distance_per_token[f]["other"].append(distance)
twin = f + "_twin"
distance = spatial.distance.cosine(emb_dict[f], emb_dict[twin])
distance_per_token[f]["twin"] = distance
averaged = dict()
for token in distance_per_token:
distance_twin = distance_per_token[token]['twin']
distance_other = np.mean(distance_per_token[token]['other'])
print(f"{token} vs twin: {distance_twin:.3f}, " +
f"{token} vs other: {distance_other:.3f}")
averaged[token] = (distance_twin, distance_other)
return averaged
def __init__(self, model_path, dict_path):
parser = options.get_eval_lm_parser()
parsed_args = options.parse_args_and_arch(parser,
input_args=[None],
parse_known=True)[0]
parsed_args.path = model_path
parsed_args.dict = dict_path
parsed_args.max_sentence = 1
parsed_args.gen_subset = 'test'
parsed_args.raw_text = True
parsed_args.no_progress_bar = True
import_user_module(parsed_args)
print(parsed_args)
task = tasks.setup_task(parsed_args)
print('| loading model(s) from {}'.format(parsed_args.path))
models, args = utils.load_ensemble_for_inference(
parsed_args.path.split(':'),
task,
model_arg_overrides=eval(parsed_args.model_overrides),
)
for arg in vars(parsed_args).keys():
if arg not in {
'self_target', 'future_target', 'past_target',
'tokens_per_sample', 'output_size_dictionary'
}:
setattr(args, arg, getattr(parsed_args, arg))
task = tasks.setup_task(args)
self.use_cuda = torch.cuda.is_available() and not parsed_args.cpu
for model in models:
model.make_generation_fast_()
if self.use_cuda:
model.cuda()
assert len(models) > 0
scorer = SequenceScorer(task.target_dictionary)
self.args = args
self.task = task
self.models = models
self.scorer = scorer
def __init__(self, path, data, use_cpu=True):
# Create the language modeling task.
self.args = FluencyArgs(path, data)
self.task = tasks.setup_task(self.args)
self.use_cuda = torch.cuda.is_available and not use_cpu
# Load language model ensemble.
models, model_args = utils.load_ensemble_for_inference(self.args.path.split(':'), self.task)
self.models = models
self.model_args = model_args
# Optimize ensemble for generation.
for model in self.models:
model.make_generation_fast_()
if self.use_cuda and self.model_args.fp16:
model.half()
# Create the sequence scorer.
self.scorer = SequenceScorer(self.models, self.task.target_dictionary)
if self.use_cuda:
self.scorer.cuda()
def __init__(self, args, task):
super().__init__(args, task)
assert (args.teacher_path
), "Please specify at least one valid file for --teacher-path"
use_cuda = torch.cuda.is_available() and not self.args.cpu
# Load model ensemble from checkpoints
self.teacher_models, self.teacher_model_args = pytorch_translate_utils.load_ensemble_for_inference(
args.teacher_path.split(":"), task)
# Move models to device and to evaluation mode
if use_cuda:
for model in self.teacher_models:
model.cuda()
for model in self.teacher_models:
model.make_generation_fast_(beamable_mm_beam_size=None)
self.kd_weight = getattr(args, "kd_weight", 1)
if self.kd_weight < 0 or self.kd_weight > 1:
raise ValueError(
f"--kd-weight ({self.kd_weight}) must be in [0, 1]")
def load(model_names, use_cpu):
parser = options.get_generation_parser(interactive=True)
args = options.parse_args_and_arch(parser)
args.encoder_embed_path = None #'/data/wangzhe/SematicSeg/mlconvgec2018/models/embeddings/wiki_model.vec'
args.beam = 12
args.nbest = args.beam
args.num_workers = 12
args.data = '/data/wangzhe/SematicSeg/mlconvgec2018/models/data_bin'
args.s = 'src'
args.t = 'trg'
task = tasks.setup_task(args)
use_cuda = torch.cuda.is_available() and not use_cpu
#print('| loading model(s) from {}'.format(args.path))
models, _model_args = utils.load_ensemble_for_inference(
model_names, task, model_arg_overrides=eval(args.model_overrides))
# 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
generator = task.build_generator(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)
max_positions = utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models])
return models, generator, align_dict, max_positions, args, use_cuda, task, src_dict, tgt_dict
def main(args):
check_args(args)
import_user_module(args)
if args.max_tokens is None and args.max_sentences is None:
args.max_tokens = 30000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset splits
task = tasks.setup_task(args)
# Set dictionary
tgt_dict = task.target_dictionary
# Load ensemble
logger.info("| 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), # noqa
)
optimize_models(args, use_cuda, models)
# Initialize generator
generator = task.build_generator(args)
sp = spm.SentencePieceProcessor()
sp.Load(os.path.join(args.data, 'spm.model'))
print("READY!")
for (waveform, sample_rate) in get_microphone_chunks():
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate,
new_freq=16000)(
waveform.reshape(1, -1))
transcription = transcribe(waveform, args, task, generator, models, sp,
tgt_dict)
print("{}: {}".format(dt.datetime.now().strftime('%H:%M:%S'),
transcription[0][0]))
def model_fn(model_dir):
model_name = 'checkpoint_best.pt'
model_path = os.path.join(model_dir, model_name)
logger.info('Loading the model')
with open(model_path, 'rb') as f:
model_info = torch.load(f, map_location=torch.device('cpu'))
# Will be overidden by the model_info['args'] - need to keep for pre-trained models
parser = options.get_generation_parser(interactive=True)
# get args for FairSeq by converting the hyperparameters as if they were command-line arguments
argv_copy = copy.deepcopy(sys.argv)
# remove the modifications we did in the command-line arguments
sys.argv[1:] = ['--path', model_path, model_dir]
args = options.parse_args_and_arch(parser)
# restore previous command-line args
sys.argv = argv_copy
saved_args = model_info['args']
for key, value in vars(saved_args).items():
setattr(args, key, value)
args.data = [model_dir]
print(args)
# Setup task, e.g., translation
task = tasks.setup_task(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info('Current device: {}'.format(device))
model_paths = [os.path.join(model_dir, model_name)]
models, model_args = utils.load_ensemble_for_inference(model_paths, task, model_arg_overrides={})
# 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()
# Initialize generator
translator = SequenceGenerator(
models, tgt_dict, 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,
)
if device.type == 'cuda':
translator.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)
align_dict = utils.load_align_dict(None)
max_positions = utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]
)
return dict(
translator=translator,
task=task,
max_positions=max_positions,
align_dict=align_dict,
tgt_dict=tgt_dict,
args=args,
device=device,
)
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)'
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, _ = 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,
).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,
)
if use_cuda:
translator.cuda()
# Generate and compute BLEU score
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)
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 main(parsed_args):
assert parsed_args.path is not None, '--path required for evaluation!'
print(parsed_args)
use_cuda = torch.cuda.is_available() and not parsed_args.cpu
task = tasks.setup_task(parsed_args)
# Load ensemble
print('| loading model(s) from {}'.format(parsed_args.path))
models, args = utils.load_ensemble_for_inference(parsed_args.path.split(':'), task)
args.__dict__.update(parsed_args.__dict__)
print(args)
task.args = args
# Load dataset splits
task.load_dataset(args.gen_subset)
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
for model in models:
model.make_generation_fast_()
if args.fp16:
model.half()
assert len(models) > 0
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
max_tokens=args.max_tokens or 36000,
max_sentences=args.max_sentences,
max_positions=utils.resolve_max_positions(*[
model.max_positions() for model in models
]),
num_shards=args.num_shards,
shard_id=args.shard_id,
ignore_invalid_inputs=True,
).next_epoch_itr(shuffle=False)
gen_timer = StopwatchMeter()
scorer = SequenceScorer(models, task.target_dictionary)
if use_cuda:
scorer.cuda()
score_sum = 0.
count = 0
if args.remove_bpe is not None:
bpe_cont = args.remove_bpe.rstrip()
bpe_toks = set(i for i in range(len(task.dictionary)) if task.dictionary[i].endswith(bpe_cont))
bpe_len = len(bpe_cont)
else:
bpe_toks = None
bpe_len = 0
word_stats = dict()
with progress_bar.build_progress_bar(args, itr) as t:
results = scorer.score_batched_itr(t, cuda=use_cuda, timer=gen_timer)
wps_meter = TimeMeter()
for _, src_tokens, __, hypos in results:
for hypo in hypos:
pos_scores = hypo['positional_scores']
skipped_toks = 0
if bpe_toks is not None:
for i in range(len(hypo['tokens']) - 1):
if hypo['tokens'][i].item() in bpe_toks:
skipped_toks += 1
pos_scores[i + 1] += pos_scores[i]
pos_scores[i] = 0
inf_scores = pos_scores.eq(float('inf')) | pos_scores.eq(float('-inf'))
if inf_scores.any():
print('| Skipping tokens with inf scores:',
task.target_dictionary.string(hypo['tokens'][inf_scores.nonzero()]))
pos_scores = pos_scores[(~inf_scores).nonzero()]
score_sum += utils.item(pos_scores.sum())
count += pos_scores.numel() - skipped_toks
if args.output_word_probs or args.output_word_stats:
w = ''
word_prob = []
is_bpe = False
for i in range(len(hypo['tokens'])):
w_ind = hypo['tokens'][i].item()
w += task.dictionary[w_ind]
if bpe_toks is not None and w_ind in bpe_toks:
w = w[:-bpe_len]
is_bpe = True
else:
word_prob.append((w, pos_scores[i].item()))
word_stats.setdefault(w, WordStat(w, is_bpe)).add(pos_scores[i].item())
is_bpe = False
w = ''
if args.output_word_probs:
print('\t'.join('{} [{:2f}]'.format(x[0], x[1]) for x in word_prob))
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
avg_nll_loss = -score_sum / count
print('| Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Loss: {:.4f}, Perplexity: {:.2f}'.format(avg_nll_loss, np.exp(avg_nll_loss)))
if args.output_word_stats:
for ws in sorted(word_stats.values(), key=lambda x: x.count, reverse=True):
print(ws)
def main(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
# Setup task, e.g., translation
task = tasks.setup_task(args)
# Load ensemble
print('| loading model(s) from {}'.format(args.path))
model_paths = args.path.split(':')
models, model_args = utils.load_ensemble_for_inference(model_paths, task, model_arg_overrides=eval(args.model_overrides))
# 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()
# Initialize generator
translator = SequenceGenerator(
models, tgt_dict, 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,
)
if use_cuda:
translator.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)
def make_result(src_str, hypos):
result = Translation(
src_str='O\t{}'.format(src_str),
hypos=[],
pos_scores=[],
alignments=[],
)
# Process top predictions
for hypo in 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,
)
result.hypos.append('H\t{}\t{}'.format(hypo['score'], hypo_str))
result.pos_scores.append('P\t{}'.format(
' '.join(map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))
))
result.alignments.append(
'A\t{}'.format(' '.join(map(lambda x: str(utils.item(x)), alignment)))
if args.print_alignment else None
)
return result
def process_batch(batch):
tokens = batch.tokens
lengths = batch.lengths
if use_cuda:
tokens = tokens.cuda()
lengths = lengths.cuda()
translations = translator.generate(
tokens,
lengths,
maxlen=int(args.max_len_a * tokens.size(1) + args.max_len_b),
)
return [make_result(batch.srcs[i], t) for i, t in enumerate(translations)]
max_positions = utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]
)
if args.buffer_size > 1:
print('| Sentence buffer size:', args.buffer_size)
print('| Type the input sentence and press return:')
for inputs in buffered_read(args.buffer_size):
indices = []
results = []
for batch, batch_indices in make_batches(inputs, args, task, max_positions):
indices.extend(batch_indices)
results += process_batch(batch)
for i in np.argsort(indices):
result = results[i]
print(result.src_str)
for hypo, pos_scores, align in zip(result.hypos, result.pos_scores, result.alignments):
print(hypo)
print(pos_scores)
if align is not None:
print(align)
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path', metavar='FILE', required=True, action='append',
help='path(s) to model file(s)')
dataset_args = options.add_dataset_args(parser)
dataset_args.add_argument('--batch-size', default=32, type=int, metavar='N',
help='batch size')
dataset_args.add_argument('--gen-subset', default='test', metavar='SPLIT',
help='data subset to generate (train, valid, test)')
options.add_generation_args(parser)
args = parser.parse_args()
if args.no_progress_bar and args.log_format is None:
args.log_format = 'none'
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset
if args.replace_unk is None:
dataset = data.load_dataset(args.data, [args.gen_subset], args.source_lang, args.target_lang)
else:
dataset = data.load_raw_text_dataset(args.data, [args.gen_subset], args.source_lang, args.target_lang)
if args.source_lang is None or args.target_lang is None:
# record inferred languages in args
args.source_lang, args.target_lang = dataset.src, dataset.dst
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, _ = utils.load_ensemble_for_inference(args.path, dataset.src_dict, dataset.dst_dict)
print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))
# 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)
# Initialize generator
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.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)
# Generate and compute BLEU score
scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
max_positions = min(model.max_encoder_positions() for model in models)
itr = dataset.eval_dataloader(
args.gen_subset, max_sentences=args.batch_size, max_positions=max_positions,
skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test)
num_sentences = 0
with utils.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_device=0 if use_cuda else None, timer=gen_timer)
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
target_tokens = target_tokens.int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = dataset.splits[args.gen_subset].src.get_original_text(sample_id)
target_str = dataset.splits[args.gen_subset].dst.get_original_text(sample_id)
else:
src_str = dataset.src_dict.string(src_tokens, args.remove_bpe)
target_str = dataset.dst_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str))
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(),
align_dict=align_dict,
dst_dict=dataset.dst_dict,
remove_bpe=args.remove_bpe)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))
# Score only the top hypothesis
if i == 0:
if align_dict is not None or args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tokenizer.Tokenizer.tokenize(target_str,
dataset.dst_dict,
add_if_not_exist=True)
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} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
