def main_v1(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 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)
num_sentences = 0
data_queue = Queue()
message_queue = JoinableQueue()
p_list = []
for i in range(args.postprocess_workers):
p = PostProcess(args, task, data_queue, message_queue)
p_list.append(p)
p.start()
io_process = IOProcess(args, task, message_queue)
io_process.start()
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for sample in t:
cpu_sample = sample
if 'net_input' not in sample:
continue
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()
try:
hypos = task.inference_step(generator, models, sample,
prefix_tokens)
except:
logging.exception(sys.exc_info()[0])
for p in p_list:
p.terminate()
io_process.terminate()
data_queue.close()
message_queue.close()
sys.exit(1)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
hypos = [h[:args.nbest] for h in hypos]
hypos = move_to_cpu(hypos) if use_cuda else hypos
data_queue.put((cpu_sample, hypos))
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += cpu_sample['nsentences']
data_queue.put(GENERATE_FINISHED)
for p in p_list:
p.join()
sent_throught = num_sentences / gen_timer.sum if num_sentences > 0 else 0
tokens_throught = 1. / gen_timer.avg if num_sentences > 0 else 0
message_queue.put(
'| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
. # pylint: disable=line-too-long
format(num_sentences, gen_timer.n, gen_timer.sum, sent_throught,
tokens_throught))
message_queue.put(GENERATE_FINISHED)
io_process.join()
return
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
# Setup task, e.g., translation
task = tasks.setup_task(args)
# 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,
)
# 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])
if args.buffer_size > 1:
print('| Sentence buffer size:', args.buffer_size)
print('| Type the input sentence and press return:')
start_id = 0
for inputs in buffered_read(args.input, args.buffer_size):
results = []
for batch in make_batches(inputs, args, task, max_positions):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
}
translations = task.inference_step(generator, models, sample)
for i, (id,
hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
results.append((start_id + id, src_tokens_i, hypos))
# sort output to match input order
for id, src_tokens, hypos 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))
# 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,
)
print('H-{}\t{}\t{}'.format(id, hypo['score'], hypo_str))
print('P-{}\t{}'.format(
id, ' '.join(
map(lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist()))))
if args.print_alignment:
print('A-{}\t{}'.format(
id,
' '.join(map(lambda x: str(utils.item(x)),
alignment))))
# update running id counter
start_id += len(inputs)
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
# ========== for bartsv task, rebuild dictionary after model args are loaded ==========
# assert not hasattr(args, 'node_freq_min'), 'node_freq_min should be read from model args'
# args.node_freq_min = 5 # temporarily set before model loading, as this is needed in tasks.setup_task(args)
# =====================================================================================
# Load dataset splits
task = tasks.setup_task(args)
# Note: states are not needed since they will be provided by the state
# machine
task.load_dataset(args.gen_subset, state_machine=False)
# 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))
try:
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=task,
)
except:
# NOTE this is for "bartsv" models when default "args.node_freq_min" (5) is not equal to the model
# when loading model with the above task there will be an error when building the model with the task's
# target vocabulary, which would be of different size
# TODO better handle these cases (without sacrificing compatibility with other model archs)
models, _model_args = checkpoint_utils.load_model_ensemble(
args.path.split(':'),
arg_overrides=eval(args.model_overrides),
task=None,
)
# ========== for bartsv task, rebuild the dictionary based on model args ==========
if 'bartsv' in _model_args.arch and args.node_freq_min != _model_args.node_freq_min:
args.node_freq_min = _model_args.node_freq_min
# Load dataset splits
task = tasks.setup_task(args)
# Note: states are not needed since they will be provided by the state machine
task.load_dataset(args.gen_subset, state_machine=False)
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# ==================================================================================
# import pdb; pdb.set_trace()
# print(_model_args)
# ========== for previous model trained when new arguments were not there ==========
if not hasattr(_model_args, 'shift_pointer_value'):
_model_args.shift_pointer_value = 1
# ==================================================================================
# 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=None,
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,
# large_sent_first=False # not in fairseq
).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args, _model_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
examples = Examples(args.path, args.results_path, args.gen_subset,
args.nbest)
error_stats = {'num_sub_start': 0}
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:
raise Exception("Did not expect empty sample")
continue
prefix_tokens = None
if args.prefix_size > 0:
prefix_tokens = sample['target'][:, :args.prefix_size]
# breakpoint()
gen_timer.start()
hypos = task.inference_step(generator, models, sample, args,
prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
# breakpoint()
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)
# debug: '<<unk>>' is added to the dictionary
# if 'unk' in target_str:
# breakpoint()
# ==========> NOTE we do not really have the ground truth target (with the same alignments)
# target_str might have <unk> as the target dictionary is only built on training data
# but it doesn't matter. It should not affect the target dictionary!
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'].int().cpu() if hypo['alignment'] is not None else None,
# align_dict=align_dict,
# tgt_dict=tgt_dict,
# remove_bpe=args.remove_bpe,
# # FIXME: AMR specific
# split_token="\t",
# line_tokenizer=task.tokenize,
# )
if 'bartsv' in _model_args.arch:
if not tgt_dict[hypo['tokens'][0]].startswith(
tgt_dict.bpe.INIT):
error_stats['num_sub_start'] += 1
try:
actions_nopos, actions_pos, actions = post_process_action_pointer_prediction_bartsv(
hypo, tgt_dict)
except:
breakpoint()
else:
actions_nopos, actions_pos, actions = post_process_action_pointer_prediction(
hypo, tgt_dict)
# breakpoint()
if args.clean_arcs:
actions_nopos, actions_pos, actions, invalid_idx = clean_pointer_arcs(
actions_nopos, actions_pos, actions)
# TODO these are just dummy for the reference below to run
hypo_tokens = hypo['tokens'].int().cpu()
hypo_str = '/t'.join(actions)
alignment = None
# update the list of examples
examples.append({
'actions_nopos': actions_nopos,
'actions_pos': actions_pos,
'actions': actions,
'reference': target_str,
'src_str': src_str,
'sample_id': sample_id
})
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 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=False)
# NOTE do not modify the tgt dictionary with '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']
# Save examples to files
examples.save()
print('| Error case (handled by manual fix) statistics:')
print(error_stats)
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(cfg: UnsupGenerateConfig, model=None):
if (
cfg.fairseq.dataset.max_tokens is None
and cfg.fairseq.dataset.batch_size is None
):
cfg.fairseq.dataset.max_tokens = 1024000
use_cuda = torch.cuda.is_available() and not cfg.fairseq.common.cpu
task = tasks.setup_task(cfg.fairseq.task)
overrides = ast.literal_eval(cfg.fairseq.common_eval.model_overrides)
if cfg.fairseq.task._name == "gan_audio_pretraining_feats":
overrides["model"] = {
"blank_weight": cfg.blank_weight,
"blank_mode": cfg.blank_mode,
"blank_is_sil": cfg.sil_is_blank,
"no_softmax": True,
"segmentation": {
"type": "NONE",
},
}
else:
overrides["model"] = {
"blank_weight": cfg.blank_weight,
"blank_mode": cfg.blank_mode,
}
if model is None:
# Load ensemble
logger.info("| loading model(s) from {}".format(cfg.fairseq.common_eval.path))
models, saved_cfg = checkpoint_utils.load_model_ensemble(
cfg.fairseq.common_eval.path.split("\\"),
arg_overrides=overrides,
task=task,
suffix=cfg.fairseq.checkpoint.checkpoint_suffix,
strict=(cfg.fairseq.checkpoint.checkpoint_shard_count == 1),
num_shards=cfg.fairseq.checkpoint.checkpoint_shard_count,
)
optimize_models(cfg, use_cuda, models)
else:
models = [model]
saved_cfg = cfg.fairseq
with open_dict(saved_cfg.task):
saved_cfg.task.shuffle = False
saved_cfg.task.sort_by_length = False
gen_result = generate(cfg, models, saved_cfg, use_cuda)
wer = None
if gen_result.lengths_t > 0:
wer = gen_result.errs_t * 100.0 / gen_result.lengths_t
logger.info(f"WER: {wer}")
lm_ppl = float("inf")
if gen_result.lm_score_t != 0 and gen_result.lengths_hyp_t > 0:
hyp_len = gen_result.lengths_hyp_t
lm_ppl = math.pow(
10, -gen_result.lm_score_t / (hyp_len + gen_result.num_sentences)
)
logger.info(f"LM PPL: {lm_ppl}")
logger.info(
"| Processed {} sentences ({} tokens) in {:.1f}s ({:.2f}"
" sentences/s, {:.2f} tokens/s)".format(
gen_result.num_sentences,
gen_result.gen_timer.n,
gen_result.gen_timer.sum,
gen_result.num_sentences / gen_result.gen_timer.sum,
1.0 / gen_result.gen_timer.avg,
)
)
vt_diff = None
if gen_result.vt_length_t > 0:
vt_diff = gen_result.vt_err_t / gen_result.vt_length_t
vt_diff = max(cfg.min_vt_uer, vt_diff)
lm_ppl = max(cfg.min_lm_ppl, lm_ppl)
if not cfg.unsupervised_tuning == 0:
weighted_score = wer
else:
weighted_score = math.log(lm_ppl) * (vt_diff or 1.0)
res = (
f"| Generate {cfg.fairseq.dataset.gen_subset} with beam={cfg.beam}, "
f"lm_weight={cfg.kaldi_decoder_config.acoustic_scale if cfg.kaldi_decoder_config else cfg.lm_weight}, "
f"word_score={cfg.word_score}, sil_weight={cfg.sil_weight}, blank_weight={cfg.blank_weight}, "
f"WER: {wer}, LM_PPL: {lm_ppl}, num feats: {gen_result.num_feats}, "
f"length: {gen_result.lengths_hyp_t}, UER to viterbi: {(vt_diff or 0) * 100}, score: {weighted_score}"
)
logger.info(res)
# print(res)
return task, weighted_score
def build_model(cls, args, task):
"""Build a new model instance."""
max_source_positions = getattr(
args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
)
max_target_positions = getattr(
args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim,
)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim
):
raise ValueError(
"--share-decoder-input-output-embed requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
out_channels = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int
)
kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int
)
strides = speech_utils.eval_str_nested_list_or_tuple(
args.encoder_conv_strides, type=int
)
logger.info(
"input feature dimension: {}, channels: {}".format(
task.feat_dim, task.feat_in_channels
)
)
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = (
ConvBNReLU(
out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels,
)
if out_channels is not None
else None
)
rnn_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
rnn_encoder_input_size = (rnn_encoder_input_size + s - 1) // s
rnn_encoder_input_size *= out_channels[-1]
else:
rnn_encoder_input_size = task.feat_dim
if args.encoder_multilayer_rnn_as_single_module and args.encoder_rnn_residual:
args.encoder_rnn_residual = False
logger.info(
"--encoder-rnn-residual is set to False when --encoder-multilayer-rnn-as-single-module=True"
)
scheduled_sampling_rate_scheduler = ScheduledSamplingRateScheduler(
args.scheduled_sampling_probs,
args.start_scheduled_sampling_epoch,
)
encoder = SpeechLSTMEncoder(
pre_encoder=conv_layers,
input_size=rnn_encoder_input_size,
hidden_size=args.encoder_rnn_hidden_size,
num_layers=args.encoder_rnn_layers,
dropout_in=args.encoder_rnn_dropout_in,
dropout_out=args.encoder_rnn_dropout_out,
bidirectional=args.encoder_rnn_bidirectional,
residual=args.encoder_rnn_residual,
src_bucketed=(getattr(task.cfg, "num_batch_buckets", 0) > 0),
max_source_positions=max_source_positions,
multilayer_rnn_as_single_module=args.encoder_multilayer_rnn_as_single_module,
)
decoder = SpeechLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
encoder_output_units=encoder.output_units,
attn_type=args.attention_type,
attn_dim=args.attention_dim,
need_attn=args.need_attention,
residual=args.decoder_rnn_residual,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion_name == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
scheduled_sampling_rate_scheduler=scheduled_sampling_rate_scheduler,
)
pretrained_lm = None
if args.pretrained_lm_checkpoint:
logger.info(
"loading pretrained LM from {}".format(args.pretrained_lm_checkpoint)
)
pretrained_lm = checkpoint_utils.load_model_ensemble(
args.pretrained_lm_checkpoint, task=task
)[0][0]
pretrained_lm.make_generation_fast_()
# freeze pretrained model
for param in pretrained_lm.parameters():
param.requires_grad = False
return cls(encoder, decoder, pretrained_lm)
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
parser = options.get_eval_lm_parser()
args = options.parse_args_and_arch(parser)
utils.import_user_module(args)
assert args.max_tokens is not None or args.max_sentences is not None, \
'Must specify batch size either with --max-tokens or --max-sentences'
use_cuda = torch.cuda.is_available() and not args.cpu
# Print args
print(args)
# Setup task, e.g., translation, language modeling, etc.
task_tone_recovery = tasks.setup_task(args)
# Load model
print('| loading model from {}'.format(checkpoint_path))
models, _model_args = checkpoint_utils.load_model_ensemble(
[checkpoint_path], task=task_tone_recovery)
model = models[0]
model.eval()
if use_cuda:
model.cuda()
print(model)
# infer batch
# with open('./data-bin/src-testx-w.txt') as file_test:
# lines = file_test.read().split('\n')
#
# with open('./data-bin/tgt-testx-w.txt', 'w', encoding='utf-8') as file_tgt:
# batch_size = 100
#
#
# def make_batch(items, group_size):
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,
)
torch.manual_seed(args.seed)
# Optimize ensemble for generation
for model in models:
if use_cuda:
model.cuda()
config = utils.get_subtransformer_config(args)
model.set_sample_config(config)
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()
print(model, file=sys.stderr)
print(args.path, file=sys.stderr)
# 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)
num_sentences = 0
has_target = True
decoder_times_all = []
input_len_all = []
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, decoder_times = task.inference_step(generator, models,
sample, prefix_tokens)
input_len_all.append(
np.mean(sample['net_input']['src_lengths'].cpu().numpy()))
print(decoder_times)
decoder_times_all.append(decoder_times)
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'].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))))
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
def main(args):
ckpt = torch.load(args.checkpoint)
ckpt['args'].path = args.checkpoint
task = tasks.setup_task(ckpt['args'])
models, _model_args = checkpoint_utils.load_model_ensemble(
ckpt['args'].path.split(os.pathsep),
arg_overrides=eval("{}"),
task=task,
)
model = models[0]
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
tokenizer = encoders.build_tokenizer(ckpt['args'])
bpe = encoders.build_bpe(ckpt['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
max_positions = utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]
)
start_id = 0
outputs = {
"id": [],
"enc_ctrl_feat": [],
"dec_ctrl_feat": [],
}
ctrl_enc = getattr(model.encoder, 'module_ctrl', None)
ctrl_dec = getattr(model.decoder, 'module_ctrl', None)
if ctrl_enc is not None:
outputs['enc_ctrl_pred'] = []
if ctrl_dec is not None:
outputs['dec_ctrl_pred'] = []
for inputs in buffered_read(args.input_file, 100):
results = []
with torch.no_grad():
for batch in make_batches(inputs, ckpt['args'], task, max_positions, encode_fn):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
#if use_cuda:
# src_tokens = src_tokens.cuda()
# src_lengths = src_lengths.cuda()
bsz = src_tokens.size(0)
mask = src_tokens.eq(
model.encoder.padding_idx).view(bsz, -1, 1)
emb_out, _ = model.encoder.forward_embedding(src_tokens)
if ctrl_enc is not None:
enc_ctrl_feat = ctrl_enc.extract_features(
emb_out.clone(),
padding_mask=mask)
enc_ctrl_out = ctrl_enc(
emb_out.clone(),
'validation',
padding_mask=mask)
enc_out = model.encoder.forward(src_tokens, src_lengths)
enc_out = enc_out.encoder_out.transpose(0, 1)
if ctrl_dec is not None:
dec_ctrl_feat = ctrl_dec.extract_features(
enc_out.clone(),
padding_mask=mask)
dec_ctrl_out = ctrl_dec(
enc_out.clone(),
'validation',
padding_mask=mask
)
for i, idx in enumerate(batch.ids.tolist()):
outputs['id'].append(idx)
outputs['enc_ctrl_feat'].append(enc_ctrl_feat[i].numpy())
outputs['dec_ctrl_feat'].append(dec_ctrl_feat[i].numpy())
if ctrl_enc is not None:
outputs['enc_ctrl_pred'].append(
enc_ctrl_out.ctrl_prediction[i].numpy())
if ctrl_dec is not None:
outputs['dec_ctrl_pred'].append(
dec_ctrl_out.ctrl_prediction[i].numpy())
for k in outputs:
outputs[k] = np.stack(outputs[k], 0)
if ctrl_enc is not None:
outputs['enc_ctrl_proj'] = ctrl_enc.out_proj.weight.detach().numpy()
if ctrl_dec is not None:
outputs['dec_ctrl_proj'] = ctrl_dec.out_proj.weight.detach().numpy()
np.savez(args.output_file, **outputs)
def main(parsed_args):
assert parsed_args.path is not None, "--path required for evaluation!"
utils.import_user_module(parsed_args)
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 = checkpoint_utils.load_model_ensemble(
parsed_args.path.split(":"),
arg_overrides=eval(parsed_args.model_overrides),
task=task,
)
for arg in vars(parsed_args).keys():
if arg not in {
"self_target",
"future_target",
"past_target",
"tokens_per_sample",
"output_size_dictionary",
"add_bos_token",
}:
setattr(args, arg, getattr(parsed_args, arg))
# reduce tokens per sample by the required context window size
args.tokens_per_sample -= args.context_window
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args.gen_subset)
dataset = task.dataset(args.gen_subset)
if args.context_window > 0:
dataset = LMContextWindowDataset(
dataset=dataset,
tokens_per_sample=args.tokens_per_sample,
context_window=args.context_window,
pad_idx=task.source_dictionary.pad(),
)
print("| {} {} {} examples".format(args.data, args.gen_subset, len(dataset)))
# 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()
if use_cuda:
model.cuda()
assert len(models) > 0
print(
"num. model params: {}".format(sum(p.numel() for p in models[0].parameters()))
)
itr = task.get_batch_iterator(
dataset=dataset,
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]
),
ignore_invalid_inputs=True,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
gen_timer = StopwatchMeter()
scorer = SequenceScorer(task.target_dictionary, args.softmax_batch)
score_sum = 0.0
count = 0
if args.remove_bpe is not None:
if args.remove_bpe == "sentencepiece":
raise NotImplementedError
else:
bpe_cont = args.remove_bpe.rstrip()
bpe_toks = set(
i
for i in range(len(task.source_dictionary))
if task.source_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:
wps_meter = TimeMeter()
for sample in t:
if "net_input" not in sample:
continue
sample = utils.move_to_cuda(sample) if use_cuda else sample
gen_timer.start()
hypos = scorer.generate(models, sample)
gen_timer.stop(sample["ntokens"])
for i, hypos_i in enumerate(hypos):
hypo = hypos_i[0]
sample_id = sample["id"][i]
tokens = hypo["tokens"]
tgt_len = tokens.numel()
pos_scores = hypo["positional_scores"].float()
if args.add_bos_token:
assert hypo["tokens"][0].item() == task.target_dictionary.bos()
tokens = tokens[1:]
pos_scores = pos_scores[1:]
skipped_toks = 0
if bpe_toks is not None:
for i in range(tgt_len - 1):
if 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(tokens[inf_scores.nonzero()]),
)
pos_scores = pos_scores[(~inf_scores).nonzero()]
score_sum += pos_scores.sum().cpu()
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(tokens)):
w_ind = tokens[i].item()
w += task.source_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()))
next_prob = None
ind = i + 1
while ind < len(tokens):
if pos_scores[ind].item() != 0:
next_prob = pos_scores[ind]
break
ind += 1
word_stats.setdefault(w, WordStat(w, is_bpe)).add(
pos_scores[i].item(), next_prob
)
is_bpe = False
w = ""
if args.output_word_probs:
print(
str(int(sample_id))
+ " "
+ (
"\t".join(
"{} [{:2f}]".format(x[0], x[1]) for x in word_prob
)
)
)
wps_meter.update(sample["ntokens"])
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, 1.0 / 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):
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)
write_json(vars(args), 'generate-args.json')
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
num_exact_match_sentences = 0 # Exact match
num_oracle_exact_match_sentences = 0 # Exact match in nbest
predictions_out = open('generate-predictions.jsonl', 'w')
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()
prediction = {
'sample_id': sample_id,
}
# 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))
prediction['source_str'] = src_str
if has_target:
print('T-{}\t{}'.format(sample_id, target_str))
prediction['target_str'] = target_str
# Process top predictions
prediction['hypotheses'] = hypotheses = []
exact_match = False
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'].int().cpu() if hypo['alignment'] is not None else None,
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
exact_match = (target_str == hypo_str)
hypotheses.append({
'score': hypo['score'],
'str': hypo_str,
'exact_match': exact_match,
})
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 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)
prediction['exact_match'] = len(hypotheses) > 0 and hypotheses[0]['exact_match']
prediction['oracle_exact_match'] = any(h['exact_match'] for h in hypotheses)
if prediction['exact_match']:
num_exact_match_sentences += 1
if prediction['oracle_exact_match']:
num_oracle_exact_match_sentences += 1
print(json.dumps(prediction), file=predictions_out)
wps_meter.update(num_generated_tokens)
t.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
predictions_out.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()))
stats = {
'num_sentences': num_sentences,
'tokens_per_second': 1. / gen_timer.avg,
'exact_match': 1. * num_exact_match_sentences / num_sentences,
'oracle_exact_match': 1. * num_oracle_exact_match_sentences / num_sentences,
'bleu': scorer.score(),
}
write_json(stats, 'generate-stats.json')
return scorer
def main(parsed_args):
assert parsed_args.path is not None, '--path required for evaluation!'
utils.import_user_module(parsed_args)
logger.info(parsed_args)
use_cuda = torch.cuda.is_available() and not parsed_args.cpu
task = tasks.setup_task(parsed_args)
# Load ensemble
logger.info('loading model(s) from {}'.format(parsed_args.path))
models, args = checkpoint_utils.load_model_ensemble(
parsed_args.path.split(os.pathsep),
arg_overrides=eval(parsed_args.model_overrides),
task=task,
)
for arg in vars(parsed_args).keys():
if arg not in {
'self_target',
'future_target',
'past_target',
'tokens_per_sample',
'output_size_dictionary',
'add_bos_token',
}:
setattr(args, arg, getattr(parsed_args, arg))
# reduce tokens per sample by the required context window size
args.tokens_per_sample -= args.context_window
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args.gen_subset)
dataset = task.dataset(args.gen_subset)
if args.context_window > 0:
dataset = LMContextWindowDataset(
dataset=dataset,
tokens_per_sample=args.tokens_per_sample,
context_window=args.context_window,
pad_idx=task.source_dictionary.pad(),
)
logger.info('{} {} {} examples'.format(args.data, args.gen_subset,
len(dataset)))
# 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()
if use_cuda:
model.cuda()
assert len(models) > 0
logger.info('num. model params: {}'.format(
sum(p.numel() for p in models[0].parameters())))
itr = task.get_batch_iterator(
dataset=dataset,
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]),
ignore_invalid_inputs=True,
num_shards=args.num_shards,
shard_id=args.shard_id,
num_workers=args.num_workers,
).next_epoch_itr(shuffle=False)
gen_timer = StopwatchMeter()
scorer = SequenceScorer(task.target_dictionary,
args.softmax_batch,
args=args)
score_sum = 0.
count = 0
if args.remove_bpe is not None:
if args.remove_bpe == 'sentencepiece':
raise NotImplementedError
else:
bpe_cont = args.remove_bpe.rstrip()
bpe_toks = {
i
for i in range(len(task.source_dictionary))
if task.source_dictionary[i].endswith(bpe_cont)
}
bpe_len = len(bpe_cont)
else:
bpe_toks = None
bpe_len = 0
word_stats = dict()
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)
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
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
for ex_i, sample in enumerate(t):
if 'net_input' not in sample:
continue
sample = utils.move_to_cuda(sample) if use_cuda else sample
gen_timer.start()
if args.knnlm:
hypos = scorer.generate(models, sample, knn_dstore=knn_dstore)
else:
hypos = scorer.generate(models, sample)
gen_timer.stop(sample['ntokens'])
for i, hypos_i in enumerate(hypos):
hypo = hypos_i[0]
if args.save_knnlm_dstore:
shape = hypo['dstore_keys'].shape
if shape[0] == args.tokens_per_sample:
if dstore_idx + shape[0] > args.dstore_size:
shape = [args.dstore_size - dstore_idx]
hypo['dstore_keys'] = hypo[
'dstore_keys'][:shape[0]]
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]
else:
print('Skipping this one with shape', shape)
sample_id = sample['id'][i]
tokens = hypo['tokens']
tgt_len = tokens.numel()
pos_scores = hypo['positional_scores'].float()
if args.add_bos_token:
assert hypo['tokens'][0].item(
) == task.target_dictionary.bos()
tokens = tokens[1:]
pos_scores = pos_scores[1:]
skipped_toks = 0
if bpe_toks is not None:
for i in range(tgt_len - 1):
if 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():
# logger.info(
# 'skipping tokens with inf scores:',
# task.target_dictionary.string(tokens[inf_scores.nonzero()])
# )
# pos_scores = pos_scores[(~inf_scores).nonzero()]
score_sum += pos_scores.sum().cpu()
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(tokens)):
w_ind = tokens[i].item()
w += task.source_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()))
next_prob = None
ind = i + 1
while ind < len(tokens):
if pos_scores[ind].item() != 0:
next_prob = pos_scores[ind]
break
ind += 1
word_stats.setdefault(w, WordStat(w, is_bpe)).add(
pos_scores[i].item(), next_prob)
is_bpe = False
w = ''
if args.output_word_probs:
logger.info(
str(int(sample_id)) + " " +
('\t'.join('{} [{:2f}]'.format(x[0], x[1])
for x in word_prob)))
wps_meter.update(sample['ntokens'])
t.log({'wps': round(wps_meter.avg)})
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)
avg_nll_loss = -score_sum / count / math.log(2) # convert to base 2
logger.info('Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(
gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
logger.info('Loss (base 2): {:.4f}, Perplexity: {:.2f}'.format(
avg_nll_loss, 2**avg_nll_loss))
if args.output_word_stats:
for ws in sorted(word_stats.values(),
key=lambda x: x.count,
reverse=True):
logger.info(ws)
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.batch_size is None:
args.batch_size = 1
assert (not args.batch_size or args.batch_size <= args.buffer_size
), "--batch-size cannot be larger than --buffer-size"
logger.info(args)
# Fix seed for stochastic decoding
if args.seed is not None and not args.no_seed_provided:
np.random.seed(args.seed)
utils.set_torch_seed(args.seed)
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,
suffix=getattr(args, "checkpoint_suffix", ""),
strict=(args.checkpoint_shard_count == 1),
num_shards=args.checkpoint_shard_count,
)
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Optimize ensemble for generation
for model in models:
if args.fp16:
model.half()
if use_cuda and not args.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(args)
# Initialize generator
generator = task.build_generator(models, args)
def encode_fn(x):
x = task.spm.encode(x, out_type=str)
x = segmenter_utils.to_character_sequence("".join(x))
return x
def decode_fn(chars, tags):
x = ""
for c, t in zip(chars.replace(" ", ""), tags.replace(" ", "")):
if t == "B":
x += " "
x += c
return x[1:]
max_positions = utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models])
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 inputs in buffered_read(args.input, args.buffer_size):
results = []
for batch in make_batches(inputs, args, task, max_positions,
encode_fn):
bsz = batch.src_tokens.size(0)
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
},
"src_chars": batch.src_chars,
}
segmentations = task.inference_step(generator, models, sample)
for i, (id,
hypos) in enumerate(zip(batch.ids.tolist(),
segmentations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
results.append((
start_id + id,
src_tokens_i,
batch.src_chars[i],
hypos,
))
# sort output to match input order
for id_, src_tokens, src_str, hypos in sorted(results,
key=lambda x: x[0]):
print("S-{}\t{}".format(id_, 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=None,
align_dict=None,
tgt_dict=tgt_dict,
)
detok_hypo_str = decode_fn(src_str, hypo_str)
score = hypo["score"] / math.log(2) # convert to base 2
# original hypothesis (after tokenization and BPE)
print("H-{}\t{}\t{}".format(id_, score, hypo_str))
# detokenized hypothesis
print("D-{}\t{}\t{}".format(id_, score, detok_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(),
)),
))
# update running id_ counter
start_id += len(inputs)
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.predict')
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.pred_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 TODO
for model in models:
if args.fp16:
model.half()
if use_cuda:
model.cuda()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.pred_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_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'),
)
# 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
model = models[0] ## Use first model only, TODO ensembles
y_true = []
y_pred = []
for sample in progress:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
for i, sample_id in enumerate(sample['id'].tolist()):
# Remove padding
src_tokens = utils.strip_pad(sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = utils.strip_pad(sample['target'][i, :], tgt_dict.pad()).int().cpu()
### Predict
_, _, y_true_, y_pred_ = task._predict_with_seqeval(sample, model) # get labels and predictions
y_true_ = y_true_[0]
y_pred_ = y_pred_[0]
y_true.append(y_true_) # append to all labels and predictions
y_pred.append(y_pred_)
if src_dict is not None:
src_str = src_dict.string(src_tokens, args.remove_bpe)
else:
src_str = ""
target_str = tgt_dict.string(
target_tokens,
args.remove_bpe,
escape_unk=True,
)
src_str = decode_fn(src_str)
target_str = decode_fn(target_str)
pred_str = ' '.join(y_pred_)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str), file=output_file)
print('P-{}\t{}'.format(sample_id, pred_str), file=output_file)
clf_report = classification_report(y_true, y_pred)
logger.info("Eval results on {} subset: \n\n{} ".format(args.pred_subset, clf_report))
def main(args, task=None, model_state=None):
check_args(args)
if args.max_tokens is None and args.batch_size is None:
args.max_tokens = 4000000
logger.info(args)
use_cuda = torch.cuda.is_available() and not args.cpu
logger.info("| decoding with criterion {}".format(args.criterion))
task = tasks.setup_task(args)
# Load ensemble
if args.load_emissions:
models, criterions = [], []
task.load_dataset(args.gen_subset)
else:
logger.info("| loading model(s) from {}".format(args.path))
models, saved_cfg = checkpoint_utils.load_model_ensemble(
utils.split_paths(args.path),
arg_overrides=ast.literal_eval(args.model_overrides),
task=task,
suffix=args.checkpoint_suffix,
strict=(args.checkpoint_shard_count == 1),
num_shards=args.checkpoint_shard_count,
state=model_state,
)
optimize_models(args, use_cuda, models)
task.load_dataset(args.gen_subset, task_cfg=saved_cfg.task)
# Set dictionary
tgt_dict = task.target_dictionary
logger.info("| {} {} {} examples".format(
args.data, args.gen_subset, len(task.dataset(args.gen_subset))))
# hack to pass transitions to W2lDecoder
if args.criterion == "asg_loss":
raise NotImplementedError("asg_loss is currently not supported")
# trans = criterions[0].asg.trans.data
# args.asg_transitions = torch.flatten(trans).tolist()
# Load dataset (possibly sharded)
itr = get_dataset_itr(args, task, models)
# Initialize generator
gen_timer = StopwatchMeter()
def build_generator(args):
w2l_decoder = getattr(args, "w2l_decoder", None)
if w2l_decoder == "viterbi":
from examples.speech_recognition.w2l_decoder import W2lViterbiDecoder
return W2lViterbiDecoder(args, task.target_dictionary)
elif w2l_decoder == "kenlm":
from examples.speech_recognition.w2l_decoder import W2lKenLMDecoder
return W2lKenLMDecoder(args, task.target_dictionary)
elif w2l_decoder == "fairseqlm":
from examples.speech_recognition.w2l_decoder import W2lFairseqLMDecoder
return W2lFairseqLMDecoder(args, task.target_dictionary)
else:
print(
"only flashlight decoders with (viterbi, kenlm, fairseqlm) options are supported at the moment"
)
# please do not touch this unless you test both generate.py and infer.py with audio_pretraining task
generator = build_generator(args)
if args.load_emissions:
generator = ExistingEmissionsDecoder(
generator, np.load(args.load_emissions, allow_pickle=True))
logger.info("loaded emissions from " + args.load_emissions)
num_sentences = 0
if args.results_path is not None and not os.path.exists(args.results_path):
os.makedirs(args.results_path)
max_source_pos = (utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models]), )
if max_source_pos is not None:
max_source_pos = max_source_pos[0]
if max_source_pos is not None:
max_source_pos = max_source_pos[0] - 1
if args.dump_emissions:
emissions = {}
if args.dump_features:
features = {}
models[0].bert.proj = None
else:
res_files = prepare_result_files(args)
errs_t = 0
lengths_t = 0
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.dump_emissions:
with torch.no_grad():
encoder_out = models[0](**sample["net_input"])
emm = models[0].get_normalized_probs(encoder_out,
log_probs=True)
emm = emm.transpose(0, 1).cpu().numpy()
for i, id in enumerate(sample["id"]):
emissions[id.item()] = emm[i]
continue
elif args.dump_features:
with torch.no_grad():
encoder_out = models[0](**sample["net_input"])
feat = encoder_out["encoder_out"].transpose(
0, 1).cpu().numpy()
for i, id in enumerate(sample["id"]):
padding = (encoder_out["encoder_padding_mask"][i].cpu(
).numpy() if encoder_out["encoder_padding_mask"]
is not None else None)
features[id.item()] = (feat[i], padding)
continue
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()):
speaker = None
# id = task.dataset(args.gen_subset).ids[int(sample_id)]
id = sample_id
toks = (sample["target"][i, :] if "target_label" not in sample
else sample["target_label"][i, :])
target_tokens = utils.strip_pad(toks,
tgt_dict.pad()).int().cpu()
# Process top predictions
errs, length = process_predictions(
args,
hypos[i],
None,
tgt_dict,
target_tokens,
res_files,
speaker,
id,
)
errs_t += errs
lengths_t += length
wps_meter.update(num_generated_tokens)
t.log({"wps": round(wps_meter.avg)})
num_sentences += (sample["nsentences"] if "nsentences" in sample
else sample["id"].numel())
wer = None
if args.dump_emissions:
emm_arr = []
for i in range(len(emissions)):
emm_arr.append(emissions[i])
np.save(args.dump_emissions, emm_arr)
logger.info(
f"saved {len(emissions)} emissions to {args.dump_emissions}")
elif args.dump_features:
feat_arr = []
for i in range(len(features)):
feat_arr.append(features[i])
np.save(args.dump_features, feat_arr)
logger.info(f"saved {len(features)} emissions to {args.dump_features}")
else:
if lengths_t > 0:
wer = errs_t * 100.0 / lengths_t
logger.info(f"WER: {wer}")
logger.info("| Processed {} 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,
))
logger.info("| Generate {} with beam={}".format(
args.gen_subset, args.beam))
return task, wer
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)'
args.beam=1
utils.import_user_module(args)
if args.max_tokens 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_positions=utils.resolve_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)
num_sentences = 0
source_sentences = []
shard_id = 0
all_avg_pool = None
encoder_has_langtok = (
hasattr(task.args, 'encoder_langtok')
and task.args.encoder_langtok is not None
and hasattr(task.args, 'lang_tok_replacing_bos_eos')
and not task.args.lang_tok_replacing_bos_eos
)
with progress_bar.build_progress_bar(args, itr) as t:
for sample in t:
if sample is None:
print("Skipping None")
continue
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]
with torch.no_grad():
avg_pool = get_avg_pool(
models, sample, prefix_tokens, src_dict,
args.remove_bpe,
has_langtok=encoder_has_langtok)
if all_avg_pool is not None:
all_avg_pool = np.concatenate((all_avg_pool, avg_pool))
else:
all_avg_pool = avg_pool
if not isinstance(sample['id'], list):
sample_ids = sample['id'].tolist()
else:
sample_ids = sample['id']
for i, sample_id in enumerate(sample_ids):
# Remove padding
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 align_dict is not None:
src_str = task.dataset(args.gen_subset).src.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 not args.quiet:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str))
source_sentences.append(f"{sample_id}\t{src_str}")
num_sentences += sample['nsentences']
if all_avg_pool.shape[0] >= 1000000:
with open(f'{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}',
'w') as avg_pool_file:
all_avg_pool.tofile(avg_pool_file)
with open(f'{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}', 'w') as sentence_file:
sentence_file.writelines(f'{line}\n' for line in source_sentences)
all_avg_pool = None
source_sentences = []
shard_id += 1
if all_avg_pool is not None:
with open(f'{args.encoder_save_dir}/all_avg_pool.{args.source_lang}.{shard_id}',
'w') as avg_pool_file:
all_avg_pool.tofile(avg_pool_file)
with open(f'{args.encoder_save_dir}/sentences.{args.source_lang}.{shard_id}', 'w') as sentence_file:
sentence_file.writelines(f'{line}\n' for line in source_sentences)
return None
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("espresso.dump_posteriors")
print_options_meaning_changes(args, logger)
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 split
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# 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,
suffix=getattr(args, "checkpoint_suffix", ""),
)
# Load state prior for cross-entropy trained systems decoding
if args.state_prior_file is not None:
prior = torch.from_numpy(kaldi_io.read_vec_flt(args.state_prior_file))
else:
prior = []
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_()
if args.fp16:
model.half()
if use_cuda:
model.cuda()
if isinstance(prior, list) and getattr(model, "state_prior",
None) is not None:
prior.append(model.state_prior.unsqueeze(0))
if isinstance(prior, list) and len(prior) > 0:
prior = torch.cat(prior, 0).mean(0) # average priors across models
prior = prior / prior.sum() # re-normalize
elif isinstance(prior, list):
prior = None
if prior is not None:
if args.fp16:
prior = prior.half()
if use_cuda:
prior = prior.cuda()
log_prior = prior.log()
else:
log_prior = None
# 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() if hasattr(model, "encoder") else
(None, 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)
# Generate and dump
num_sentences = 0
chunk_width = getattr(task, "chunk_width", None)
lprobs_wspecifier = "ark:| copy-matrix ark:- ark:-"
with kaldi_io.open_or_fd(lprobs_wspecifier, "wb") as f:
if chunk_width is None: # normal dumping (i.e., no chunking)
for sample in progress:
sample = utils.move_to_cuda(sample) if use_cuda else sample
if "net_input" not in sample:
continue
gen_timer.start()
lprobs, padding_mask = task.inference_step(
generator, models, sample)
if log_prior is not None:
assert lprobs.size(-1) == log_prior.size(0)
lprobs = lprobs - log_prior
out_lengths = (~padding_mask).long().sum(
dim=1).cpu() if padding_mask is not None else None
num_processed_frames = sample["ntokens"]
gen_timer.stop(num_processed_frames)
num_sentences += sample["nsentences"]
if out_lengths is not None:
for i in range(sample["nsentences"]):
length = out_lengths[i]
kaldi_io.write_mat(f,
lprobs[i, :length, :].cpu().numpy(),
key=sample["utt_id"][i])
else:
for i in range(sample["nsentences"]):
kaldi_io.write_mat(f,
lprobs[i, :, :].cpu().numpy(),
key=sample["utt_id"][i])
else: # dumping chunks within the same utterance from left to right
for sample in progress: # sample is actually a list of batches
sample = utils.move_to_cuda(sample) if use_cuda else sample
utt_id = sample[0]["utt_id"]
id = sample[0]["id"]
whole_lprobs = None
for i, chunk_sample in enumerate(sample):
if "net_input" not in chunk_sample:
continue
assert chunk_sample["utt_id"] == utt_id and (
chunk_sample["id"] == id).all()
gen_timer.start()
lprobs, _ = task.inference_step(generator, models,
chunk_sample)
if log_prior is not None:
assert lprobs.size(-1) == log_prior.size(0)
lprobs = lprobs - log_prior
if whole_lprobs is None:
whole_lprobs = lprobs.cpu()
else:
whole_lprobs = torch.cat((whole_lprobs, lprobs.cpu()),
1)
num_processed_frames = chunk_sample["ntokens"]
gen_timer.stop(num_processed_frames)
if i == len(sample) - 1:
num_sentences += len(utt_id)
for j in range(len(utt_id)):
truncated_length = models[0].output_lengths(
task.dataset(args.gen_subset).src_sizes[id[j]]
) # length is after possible subsampling by the model
mat = whole_lprobs[j, :truncated_length, :]
kaldi_io.write_mat(f, mat.numpy(), key=utt_id[j])
logger.info(
"Dumped {} utterances ({} frames) in {:.1f}s ({:.2f} sentences/s, {:.2f} frames/s)"
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
return
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('fairnr_cli.render')
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)
# 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:
if args.fp16:
model.half()
if use_cuda:
model.cuda()
logging.info(model)
# 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,
seed=args.seed,
num_workers=args.num_workers).next_epoch_itr(shuffle=False)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
shard_id, world_size = args.distributed_rank, args.distributed_world_size
output_files = []
if generator.test_poses is not None:
total_frames = generator.test_poses.shape[0]
_frames = int(np.floor(total_frames / world_size))
step = shard_id * _frames
frames = _frames if shard_id < (world_size -
1) else total_frames - step
else:
step = shard_id * args.render_num_frames
frames = args.render_num_frames
with progress_bar.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
for i, sample in enumerate(t):
sample = utils.move_to_cuda(sample) if use_cuda else sample
gen_timer.start()
step, _output_files = task.inference_step(generator, models,
[sample, step, frames])
output_files += _output_files
gen_timer.stop(500)
wps_meter.update(500)
t.log({'wps': round(wps_meter.avg)})
timestamp = generator.save_images(
output_files,
steps='shard{}'.format(shard_id),
combine_output=args.render_combine_output)
# join videos from all GPUs and delete temp files
try:
timestamps = distributed_utils.all_gather_list(timestamp)
except:
timestamps = [timestamp]
if shard_id == 0:
generator.merge_videos(timestamps)
def main(parsed_args, **unused_kwargs):
assert parsed_args.path is not None, '--path required for evaluation!'
if torch.cuda.is_available() and not parsed_args.cpu:
torch.cuda.set_device(parsed_args.device_id)
utils.import_user_module(parsed_args)
logger.info(parsed_args)
use_cuda = torch.cuda.is_available() and not parsed_args.cpu
task = tasks.setup_task(parsed_args)
# Load ensemble
logger.info('loading model(s) from {}'.format(parsed_args.path))
models, args = checkpoint_utils.load_model_ensemble(
parsed_args.path.split(os.pathsep),
arg_overrides=eval(parsed_args.model_overrides),
task=task,
suffix=getattr(parsed_args, "checkpoint_suffix", ""),
)
for arg in vars(parsed_args).keys():
if arg not in {
'self_target',
'future_target',
'past_target',
'tokens_per_sample',
'output_size_dictionary',
'add_bos_token',
}:
setattr(args, arg, getattr(parsed_args, arg))
# reduce tokens per sample by the required context window size
args.tokens_per_sample -= args.context_window
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args.gen_subset)
dataset = task.dataset(args.gen_subset)
if args.context_window > 0:
dataset = LMContextWindowDataset(
dataset=dataset,
tokens_per_sample=args.tokens_per_sample,
context_window=args.context_window,
pad_idx=task.source_dictionary.pad(),
)
logger.info('{} {} {} examples'.format(args.data, args.gen_subset,
len(dataset)))
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
for model in models:
model.prepare_for_inference_(args)
if args.fp16:
model.half()
if use_cuda:
model.cuda()
assert len(models) > 0
logger.info('num. model params: {}'.format(
sum(p.numel() for p in models[0].parameters())))
itr = task.get_batch_iterator(
dataset=dataset,
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]),
ignore_invalid_inputs=True,
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'),
)
gen_timer = StopwatchMeter()
scorer = SequenceScorer(task.target_dictionary, args.softmax_batch)
score_sum = 0.
count = 0
if args.remove_bpe is not None:
if args.remove_bpe == 'sentencepiece':
raise NotImplementedError
else:
bpe_cont = args.remove_bpe.rstrip()
bpe_toks = {
i
for i in range(len(task.source_dictionary))
if task.source_dictionary[i].endswith(bpe_cont)
}
bpe_len = len(bpe_cont)
else:
bpe_toks = None
bpe_len = 0
word_stats = dict()
wps_meter = TimeMeter()
for sample in progress:
if 'net_input' not in sample:
continue
sample = utils.move_to_cuda(sample) if use_cuda else sample
gen_timer.start()
hypos = scorer.generate(models, sample)
gen_timer.stop(sample['ntokens'])
for i, hypos_i in enumerate(hypos):
hypo = hypos_i[0]
sample_id = sample['id'][i]
tokens = hypo['tokens']
tgt_len = tokens.numel()
pos_scores = hypo['positional_scores'].float()
if getattr(args, 'add_bos_token', False):
assert hypo['tokens'][0].item() == task.target_dictionary.bos()
tokens = tokens[1:]
pos_scores = pos_scores[1:]
skipped_toks = 0
if bpe_toks is not None:
for i in range(tgt_len - 1):
if 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():
logger.info(
'skipping tokens with inf scores:',
task.target_dictionary.string(
tokens[inf_scores.nonzero()]))
pos_scores = pos_scores[(~inf_scores).nonzero()]
score_sum += pos_scores.sum().cpu()
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(tokens)):
w_ind = tokens[i].item()
w += task.source_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()))
next_prob = None
ind = i + 1
while ind < len(tokens):
if pos_scores[ind].item() != 0:
next_prob = pos_scores[ind]
break
ind += 1
word_stats.setdefault(w, WordStat(w, is_bpe)).add(
pos_scores[i].item(), next_prob)
is_bpe = False
w = ''
if args.output_word_probs:
logger.info(
str(int(sample_id)) + " " +
('\t'.join('{} [{:2f}]'.format(x[0], x[1])
for x in word_prob)))
wps_meter.update(sample['ntokens'])
progress.log({'wps': round(wps_meter.avg)})
avg_nll_loss = -score_sum / count / math.log(2) # convert to base 2
logger.info('Evaluated {} tokens in {:.1f}s ({:.2f} tokens/s)'.format(
gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
logger.info('Loss (base 2): {:.4f}, Perplexity: {:.2f}'.format(
avg_nll_loss, 2**avg_nll_loss))
if args.output_word_stats:
for ws in sorted(word_stats.values(),
key=lambda x: x.count,
reverse=True):
logger.info(ws)
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 _main(cfg: DictConfig, output_file):
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=output_file,
)
logger = logging.getLogger("fairseq_cli.generate")
utils.import_user_module(cfg.common)
if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
cfg.dataset.max_tokens = 12000
logger.info(cfg)
# Fix seed for stochastic decoding
if cfg.common.seed is not None and not cfg.generation.no_seed_provided:
np.random.seed(cfg.common.seed)
utils.set_torch_seed(cfg.common.seed)
use_cuda = torch.cuda.is_available() and not cfg.common.cpu
# Load dataset splits
task = tasks.setup_task(cfg.task)
task.load_dataset(cfg.dataset.gen_subset)
# Set dictionaries
try:
src_dict = getattr(task, "source_dictionary", None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
overrides = ast.literal_eval(cfg.common_eval.model_overrides)
# Load ensemble
logger.info("loading model(s) from {}".format(cfg.common_eval.path))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(cfg.common_eval.path),
arg_overrides=overrides,
task=task,
suffix=cfg.checkpoint.checkpoint_suffix,
strict=(cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=cfg.checkpoint.checkpoint_shard_count,
)
if cfg.generation.lm_path is not None:
overrides["data"] = cfg.task.data
try:
lms, _ = checkpoint_utils.load_model_ensemble(
[cfg.generation.lm_path], arg_overrides=overrides, task=None)
except:
logger.warning(
f"Failed to load language model! Please make sure that the language model dict is the same "
f"as target dict and is located in the data dir ({cfg.task.data})"
)
raise
assert len(lms) == 1
else:
lms = [None]
# Optimize ensemble for generation
for model in chain(models, lms):
if model is None:
continue
if cfg.common.fp16:
model.half()
if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(cfg)
# 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(cfg.generation.replace_unk)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(cfg.dataset.gen_subset),
max_tokens=cfg.dataset.max_tokens,
max_sentences=cfg.dataset.batch_size,
max_positions=utils.resolve_max_positions(
task.max_positions(), *[m.max_positions() for m in models]),
ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,
required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,
seed=cfg.common.seed,
num_shards=cfg.distributed_training.distributed_world_size,
shard_id=cfg.distributed_training.distributed_rank,
num_workers=cfg.dataset.num_workers,
data_buffer_size=cfg.dataset.data_buffer_size,
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=cfg.common.log_format,
log_interval=cfg.common.log_interval,
default_log_format=("tqdm"
if not cfg.common.no_progress_bar else "simple"),
)
# Initialize generator
gen_timer = StopwatchMeter()
extra_gen_cls_kwargs = {
"lm_model": lms[0],
"lm_weight": cfg.generation.lm_weight
}
generator = task.build_generator(models,
cfg.task,
extra_gen_cls_kwargs=extra_gen_cls_kwargs)
# Handle tokenization and BPE
tokenizer = encoders.build_tokenizer(cfg.tokenizer)
bpe = encoders.build_bpe(cfg.bpe)
def decode_fn(x):
if bpe is not None:
x = bpe.decode(x)
if tokenizer is not None:
x = tokenizer.decode(x)
return x
scorer = scoring.build_scorer(cfg.scoring, tgt_dict)
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 cfg.generation.prefix_size > 0:
prefix_tokens = sample["target"][:, :cfg.generation.prefix_size]
constraints = None
if "constraints" in sample:
constraints = sample["constraints"]
gen_timer.start()
hypos = task.inference_step(
generator,
models,
sample,
prefix_tokens=prefix_tokens,
constraints=constraints,
)
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
if "src_tokens" in sample["net_input"]:
src_tokens = utils.strip_pad(
sample["net_input"]["src_tokens"][i, :], tgt_dict.pad())
else:
src_tokens = None
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(
cfg.dataset.gen_subset).src.get_original_text(sample_id)
target_str = task.dataset(
cfg.dataset.gen_subset).tgt.get_original_text(sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens,
cfg.common_eval.post_process)
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(
target_tokens,
cfg.common_eval.post_process,
escape_unk=True,
extra_symbols_to_ignore=
get_symbols_to_strip_from_output(generator),
)
src_str = decode_fn(src_str)
if has_target:
target_str = decode_fn(target_str)
if not cfg.common_eval.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][:cfg.generation.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=cfg.common_eval.post_process,
extra_symbols_to_ignore=get_symbols_to_strip_from_output(
generator),
)
detok_hypo_str = decode_fn(hypo_str)
if not cfg.common_eval.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 cfg.generation.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 cfg.generation.print_step:
print(
"I-{}\t{}".format(sample_id, hypo["steps"]),
file=output_file,
)
if cfg.generation.retain_iter_history:
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 cfg.common_eval.post_process 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"]
if "nsentences" in sample else sample["id"].numel())
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,
1.0 / gen_timer.avg,
))
if has_target:
if cfg.bpe and not cfg.generation.sacrebleu:
if cfg.common_eval.post_process:
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"
)
# use print to be consistent with other main outputs: S-, H-, T-, D- and so on
print(
"Generate {} with beam={}: {}".format(cfg.dataset.gen_subset,
cfg.generation.beam,
scorer.result_string()),
file=output_file,
)
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'
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
generator = task.build_generator(models, 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
# 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]
)
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 inputs in buffered_read(args.input, args.buffer_size):
results = []
for batch in make_batches(inputs, args, task, max_positions, encode_fn):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
}
translations = task.inference_step(generator, models, sample)
for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
results.append((start_id + id, src_tokens_i, hypos))
# sort output to match input order
for id, src_tokens, hypos 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))
# 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'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
)
detok_hypo_str = decode_fn(hypo_str)
score = hypo['score'] / math.log(2) # convert to base 2
# original hypothesis (after tokenization and BPE)
print('H-{}\t{}\t{}'.format(id, score, hypo_str))
# detokenized hypothesis
print('D-{}\t{}\t{}'.format(id, score, detok_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
))
# update running id counter
start_id += len(inputs)
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('espresso.speech_recognize')
if output_file is not sys.stdout: # also print to stdout
logger.addHandler(logging.StreamHandler(sys.stdout))
print_options_meaning_changes(args, logger)
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 split
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionary
dictionary = 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,
)
for i, m in enumerate(models):
if hasattr(m, 'is_wordlm') and m.is_wordlm:
# assume subword LM comes before word LM
if isinstance(models[i - 1], FairseqLanguageModel):
models[i - 1] = MultiLevelLanguageModel(
m,
models[i - 1],
subwordlm_weight=args.subwordlm_weight,
oov_penalty=args.oov_penalty,
open_vocab=not args.disable_open_vocab,
)
del models[i]
logger.info('LM fusion with Multi-level LM')
else:
models[i] = TensorizedLookaheadLanguageModel(
m,
dictionary,
oov_penalty=args.oov_penalty,
open_vocab=not args.disable_open_vocab,
)
logger.info('LM fusion with Look-ahead Word LM')
# assume subword LM comes after E2E models
elif i == len(models) - 1 and isinstance(m, FairseqLanguageModel):
logger.info('LM fusion with Subword LM')
if args.lm_weight != 0.0:
logger.info('using LM fusion with lm-weight={:.2f}'.format(
args.lm_weight))
# 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() if hasattr(model, 'encoder') else
(None, 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
if args.match_source_len:
logger.warning(
'The option match_source_len is not applicable to speech recognition. Ignoring it.'
)
gen_timer = StopwatchMeter()
generator = task.build_generator(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 WER
scorer = wer.Scorer(dictionary, wer_output_filter=args.wer_output_filter)
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,
lm_weight=args.lm_weight,
)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
# obtain nonpad mask of encoder output to plot attentions
if args.print_alignment:
net_input = sample['net_input']
src_tokens = net_input['src_tokens']
output_lengths = models[0].encoder.output_lengths(
net_input['src_lengths'])
nonpad_idxs = sequence_mask(
output_lengths,
models[0].encoder.output_lengths(src_tokens.size(1)))
for i in range(len(sample['id'])):
has_target = sample['target'] is not None
utt_id = sample['utt_id'][i]
# Retrieve the original sentences
if has_target:
target_str = sample['target_raw_text'][i]
if not args.quiet:
detok_target_str = decode_fn(target_str)
print('T-{}\t{}'.format(utt_id, detok_target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_str = dictionary.string(
hypo['tokens'].int().cpu(),
bpe_symbol=None,
extra_symbols_to_ignore={dictionary.pad()},
) # not removing bpe at this point
detok_hypo_str = decode_fn(hypo_str)
if not args.quiet:
score = hypo['score'] / math.log(2) # convert to base 2
print('H-{}\t{}\t{}'.format(utt_id, detok_hypo_str, score),
file=output_file)
# Score and obtain attention only the top hypothesis
if j == 0:
# src_len x tgt_len
attention = hypo['attention'][nonpad_idxs[i]].float().cpu() \
if args.print_alignment and hypo['attention'] is not None else None
if args.print_alignment and attention is not None:
save_dir = os.path.join(args.results_path,
'attn_plots')
os.makedirs(save_dir, exist_ok=True)
plot_attention(attention, detok_hypo_str, utt_id,
save_dir)
scorer.add_prediction(utt_id, hypo_str)
if has_target:
scorer.add_evaluation(utt_id, target_str, hypo_str)
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(
'Recognized {} utterances ({} 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.print_alignment:
logger.info('Saved attention plots in ' + save_dir)
if has_target:
scorer.add_ordered_utt_list(task.datasets[args.gen_subset].tgt.utt_ids)
fn = 'decoded_char_results.txt'
with open(os.path.join(args.results_path, fn), 'w', encoding='utf-8') as f:
f.write(scorer.print_char_results())
logger.info('Decoded char results saved as ' + f.name)
fn = 'decoded_results.txt'
with open(os.path.join(args.results_path, fn), 'w', encoding='utf-8') as f:
f.write(scorer.print_results())
logger.info('Decoded results saved as ' + f.name)
if has_target:
header = 'Recognize {} with beam={}: '.format(args.gen_subset,
args.beam)
fn = 'wer'
with open(os.path.join(args.results_path, fn), 'w',
encoding='utf-8') as f:
res = 'WER={:.2f}%, Sub={:.2f}%, Ins={:.2f}%, Del={:.2f}%'.format(
*(scorer.wer()))
logger.info(header + res)
f.write(res + '\n')
logger.info('WER saved in ' + f.name)
fn = 'cer'
with open(os.path.join(args.results_path, fn), 'w',
encoding='utf-8') as f:
res = 'CER={:.2f}%, Sub={:.2f}%, Ins={:.2f}%, Del={:.2f}%'.format(
*(scorer.cer()))
logger.info(' ' * len(header) + res)
f.write(res + '\n')
logger.info('CER saved in ' + f.name)
fn = 'aligned_results.txt'
with open(os.path.join(args.results_path, fn), 'w',
encoding='utf-8') as f:
f.write(scorer.print_aligned_results())
logger.info('Aligned results saved as ' + f.name)
return scorer
def _main(args, output_file):
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=output_file,
)
logger = logging.getLogger("espresso.speech_recognize")
if output_file is not sys.stdout: # also print to stdout
logger.addHandler(logging.StreamHandler(sys.stdout))
print_options_meaning_changes(args, logger)
utils.import_user_module(args)
if args.max_tokens is None and args.batch_size is None:
args.max_tokens = 12000
logger.info(args)
# Fix seed for stochastic decoding
if args.seed is not None and not args.no_seed_provided:
np.random.seed(args.seed)
utils.set_torch_seed(args.seed)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset split
task = tasks.setup_task(args)
task.load_dataset(args.gen_subset)
# Set dictionary
dictionary = task.target_dictionary
overrides = ast.literal_eval(args.model_overrides)
# 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=overrides,
task=task,
suffix=getattr(args, "checkpoint_suffix", ""),
strict=(args.checkpoint_shard_count == 1),
num_shards=args.checkpoint_shard_count,
)
if args.lm_path is not None:
overrides["data"] = args.data
try:
lms, _ = checkpoint_utils.load_model_ensemble(
utils.split_paths(args.lm_path),
arg_overrides=overrides,
task=None,
)
except:
logger.warning(
f"Failed to load language model! Please make sure that the language model dict is the same "
f"as target dict and is located in the data dir ({args.data})")
raise
assert len(lms) == 1 or len(lms) == 2 # Multi-level LM expects two LMs
else:
lms = [None]
for i, m in enumerate(lms):
if m is None:
continue
if hasattr(m, "is_wordlm") and m.is_wordlm:
# assume subword LM comes before word LM
if i > 0 and isinstance(lms[i - 1], FairseqLanguageModel):
lms[i - 1] = MultiLevelLanguageModel(
m,
lms[i - 1],
subwordlm_weight=args.subwordlm_weight,
oov_penalty=args.oov_penalty,
open_vocab=not args.disable_open_vocab,
)
del lms[i]
logger.info("LM fusion with Multi-level LM")
else:
lms[i] = TensorizedLookaheadLanguageModel(
m,
dictionary,
oov_penalty=args.oov_penalty,
open_vocab=not args.disable_open_vocab,
)
logger.info("LM fusion with Look-ahead Word LM")
else:
assert isinstance(m, FairseqLanguageModel)
logger.info("LM fusion with Subword LM")
if args.lm_weight != 0.0:
logger.info("using LM fusion with lm-weight={:.2f}".format(
args.lm_weight))
# Optimize ensemble for generation
for model in chain(models, lms):
if model is None:
continue
if args.fp16:
model.half()
if use_cuda and not args.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(args)
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args.gen_subset),
max_tokens=args.max_tokens,
max_sentences=args.batch_size,
max_positions=utils.resolve_max_positions(
task.max_positions(), *[
model.max_positions() if hasattr(model, "encoder") else
(None, 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,
data_buffer_size=args.data_buffer_size,
).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
if args.match_source_len:
logger.warning(
"The option match_source_len is not applicable to speech recognition. Ignoring it."
)
gen_timer = StopwatchMeter()
extra_gen_cls_kwargs = {
"lm_model": lms[0],
"lm_weight": args.lm_weight,
"eos_factor": args.eos_factor,
}
args.score_reference = False # not applicable for ASR
temp_val = args.print_alignment
args.print_alignment = False # not applicable for ASR
generator = task.build_generator(models,
args,
extra_gen_cls_kwargs=extra_gen_cls_kwargs)
args.print_alignment = temp_val
# Handle tokenization and BPE
tokenizer = task.build_tokenizer(args)
bpe = task.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 WER
scorer = wer.Scorer(dictionary, wer_output_filter=args.wer_output_filter)
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]
constraints = None
if "constraints" in sample:
constraints = sample["constraints"]
gen_timer.start()
hypos = task.inference_step(generator,
models,
sample,
prefix_tokens=prefix_tokens,
constraints=constraints)
num_generated_tokens = sum(len(h[0]["tokens"]) for h in hypos)
gen_timer.stop(num_generated_tokens)
# obtain nonpad mask of encoder output to plot attentions
if args.print_alignment:
net_input = sample["net_input"]
src_tokens = net_input["src_tokens"]
output_lengths = models[0].encoder.output_lengths(
net_input["src_lengths"])
nonpad_idxs = sequence_mask(
output_lengths,
models[0].encoder.output_lengths(src_tokens.size(1)))
for i in range(len(sample["id"])):
has_target = sample["target"] is not None
utt_id = sample["utt_id"][i]
# Retrieve the original sentences
if has_target:
target_str = sample["target_raw_text"][i]
if not args.quiet:
detok_target_str = decode_fn(target_str)
print("T-{}\t{}".format(utt_id, detok_target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args.nbest]):
hypo_str = dictionary.string(
hypo["tokens"].int().cpu(),
bpe_symbol=None,
extra_symbols_to_ignore=get_symbols_to_strip_from_output(
generator),
) # not removing bpe at this point
detok_hypo_str = decode_fn(hypo_str)
if not args.quiet:
score = hypo["score"] / math.log(2) # convert to base 2
print("H-{}\t{}\t{}".format(utt_id, detok_hypo_str, score),
file=output_file)
# Score and obtain attention only the top hypothesis
if j == 0:
# src_len x tgt_len
attention = hypo["attention"][nonpad_idxs[i]].float().cpu() \
if args.print_alignment and hypo["attention"] is not None else None
if args.print_alignment and attention is not None:
save_dir = os.path.join(args.results_path,
"attn_plots")
os.makedirs(save_dir, exist_ok=True)
plot_attention(attention, detok_hypo_str, utt_id,
save_dir)
scorer.add_prediction(utt_id, hypo_str)
if has_target:
scorer.add_evaluation(utt_id, target_str, hypo_str)
wps_meter.update(num_generated_tokens)
progress.log({"wps": round(wps_meter.avg)})
num_sentences += sample[
"nsentences"] if "nsentences" in sample else sample["id"].numel()
logger.info("NOTE: hypothesis and token scores are output in base 2")
logger.info(
"Recognized {} utterances ({} 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.print_alignment:
logger.info("Saved attention plots in " + save_dir)
if has_target:
scorer.add_ordered_utt_list(task.datasets[args.gen_subset].tgt.utt_ids)
fn = "decoded_char_results.txt"
with open(os.path.join(args.results_path, fn), "w", encoding="utf-8") as f:
f.write(scorer.print_char_results())
logger.info("Decoded char results saved as " + f.name)
fn = "decoded_results.txt"
with open(os.path.join(args.results_path, fn), "w", encoding="utf-8") as f:
f.write(scorer.print_results())
logger.info("Decoded results saved as " + f.name)
if has_target:
header = "Recognize {} with beam={}: ".format(args.gen_subset,
args.beam)
fn = "wer"
with open(os.path.join(args.results_path, fn), "w",
encoding="utf-8") as f:
res = "WER={:.2f}%, Sub={:.2f}%, Ins={:.2f}%, Del={:.2f}%".format(
*(scorer.wer()))
logger.info(header + res)
f.write(res + "\n")
logger.info("WER saved in " + f.name)
fn = "cer"
with open(os.path.join(args.results_path, fn), "w",
encoding="utf-8") as f:
res = "CER={:.2f}%, Sub={:.2f}%, Ins={:.2f}%, Del={:.2f}%".format(
*(scorer.cer()))
logger.info(" " * len(header) + res)
f.write(res + "\n")
logger.info("CER saved in " + f.name)
fn = "aligned_results.txt"
with open(os.path.join(args.results_path, fn), "w",
encoding="utf-8") as f:
f.write(scorer.print_aligned_results())
logger.info("Aligned results saved as " + f.name)
return scorer
def main(args):
start_time = time.time()
total_translate_time = 0
utils.import_user_module(args)
if args.buffer_size < 1:
args.buffer_size = 1
if args.max_tokens is None and args.batch_size is None:
args.batch_size = 1
assert (not args.sampling or args.nbest
== args.beam), "--sampling requires --nbest to be equal to --beam"
assert (not args.batch_size or args.batch_size <= args.buffer_size
), "--batch-size cannot be larger than --buffer-size"
logger.info(args)
# Fix seed for stochastic decoding
if args.seed is not None and not args.no_seed_provided:
np.random.seed(args.seed)
utils.set_torch_seed(args.seed)
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,
suffix=getattr(args, "checkpoint_suffix", ""),
strict=(args.checkpoint_shard_count == 1),
num_shards=args.checkpoint_shard_count,
)
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Optimize ensemble for generation
for model in models:
if args.fp16:
model.half()
if use_cuda and not args.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(args)
# Initialize generator
generator = task.build_generator(models, 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
# 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])
if args.constraints:
logger.warning(
"NOTE: Constrained decoding currently assumes a shared subword vocabulary."
)
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 inputs in buffered_read(args.input, args.buffer_size):
results = []
for batch in make_batches(inputs, args, task, max_positions,
encode_fn):
bsz = batch.src_tokens.size(0)
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
constraints = batch.constraints
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
if constraints is not None:
constraints = constraints.cuda()
sample = {
"net_input": {
"src_tokens": src_tokens,
"src_lengths": src_lengths,
},
}
translate_start_time = time.time()
translations = task.inference_step(generator,
models,
sample,
constraints=constraints)
translate_time = time.time() - translate_start_time
total_translate_time += translate_time
list_constraints = [[] for _ in range(bsz)]
if args.constraints:
list_constraints = [unpack_constraints(c) for c in constraints]
for i, (id,
hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
constraints = list_constraints[i]
results.append((
start_id + id,
src_tokens_i,
hypos,
{
"constraints": constraints,
"time": translate_time / len(translations),
},
))
# sort output to match input order
for id_, src_tokens, hypos, info 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))
print("W-{}\t{:.3f}\tseconds".format(id_, info["time"]))
for constraint in info["constraints"]:
print("C-{}\t{}".format(
id_, tgt_dict.string(constraint, args.remove_bpe)))
# 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"],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.remove_bpe,
extra_symbols_to_ignore=get_symbols_to_strip_from_output(
generator),
)
detok_hypo_str = decode_fn(hypo_str)
score = hypo["score"] / math.log(2) # convert to base 2
# original hypothesis (after tokenization and BPE)
print("H-{}\t{}\t{}".format(id_, score, hypo_str))
# detokenized hypothesis
print("D-{}\t{}\t{}".format(id_, score, detok_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))
# update running id_ counter
start_id += len(inputs)
logger.info("Total time: {:.3f} seconds; translation time: {:.3f}".format(
time.time() - start_time, total_translate_time))
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)
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
# 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:
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_selector(args)
num_sentences = 0
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
# 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
src_str = task.dataset(args.gen_subset).src.get_original_text_for_hie(sample_id)
target_str = task.dataset(args.gen_subset).tgt.get_original_text(sample_id)
def split_sentence(paragraph):
sentences = tokenizer.tokenize(paragraph)
return sentences
import numpy as np
def getstr(hyp, src):
srcs = split_sentence(src)
# srcs = src.split('##SENT###')
hyp = hyp.cpu().numpy()
index = np.argsort(-hyp)
res = []
token_len = 0
for ind in index:
if ind < len(srcs):
res.append(srcs[ind])
token_len += len(srcs[ind].split())
# if len(res) >= 5 and token_len > 200:
# break
if token_len > 300:
break
return ' '.join(res)
hypo_str = getstr(hypos[i], src_str)
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))
print('H-{}\t{}'.format(sample_id, hypo_str))
# 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))
return None
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)
try:
print(args.print_step)
except:
args.print_step = False # why do I need this???
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()
ngram_downweight_model = NgramDownweightModel.build_model(args, task)
models.append(ngram_downweight_model) # ensemble Prism multilingual NMT model and model to downweight n-grams
# 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 = [model.max_positions() for model in models]
fixed_max_positions = []
for x in max_positions:
try:
fixed_max_positions.append( (x[0], x[1]) )
except:
fixed_max_positions.append( (12345677, x) )
# 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(), *fixed_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
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. / gen_timer.avg))
if has_target:
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
return scorer
def __init__(self):
parser = options.get_interactive_generation_parser()
args = options.parse_args_and_arch(parser)
cfg = convert_namespace_to_omegaconf(args)
utils.import_user_module(cfg.common)
if cfg.interactive.buffer_size < 1:
cfg.interactive.buffer_size = 1
if cfg.dataset.max_tokens is None and cfg.dataset.batch_size is None:
cfg.dataset.batch_size = 1
assert (not cfg.generation.sampling
or cfg.generation.nbest == cfg.generation.beam
), "--sampling requires --nbest to be equal to --beam"
assert (not cfg.dataset.batch_size
or cfg.dataset.batch_size <= cfg.interactive.buffer_size
), "--batch-size cannot be larger than --buffer-size"
use_cuda = torch.cuda.is_available() and not cfg.common.cpu
# Setup task, e.g., translation
task = tasks.setup_task(cfg.task)
# Load ensemble
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(cfg.common_eval.path),
task=task,
suffix=cfg.checkpoint.checkpoint_suffix,
strict=(cfg.checkpoint.checkpoint_shard_count == 1),
num_shards=cfg.checkpoint.checkpoint_shard_count,
)
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Optimize ensemble for generation
for model in models:
if model is None:
continue
if cfg.common.fp16:
model.half()
if use_cuda and not cfg.distributed_training.pipeline_model_parallel:
model.cuda()
model.prepare_for_inference_(cfg)
# Initialize generator
generator = task.build_generator(models, cfg.generation)
# Handle tokenization and BPE
tokenizer = encoders.build_tokenizer(cfg.tokenizer)
bpe = encoders.build_bpe(cfg.bpe)
# 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(cfg.generation.replace_unk)
max_positions = utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models])
if cfg.interactive.buffer_size > 1:
logger.info("Sentence buffer size: %s",
cfg.interactive.buffer_size)
self.context = {
'bpe': bpe,
'tokenizer': tokenizer,
'cfg': cfg,
'task': task,
'max_positions': max_positions,
'use_cuda': use_cuda,
'generator': generator,
'models': models,
'src_dict': src_dict,
'tgt_dict': tgt_dict,
'align_dict': align_dict,
}
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
def load_pretrained_embedding_from_file(embed_path, dictionary,
embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path, task.target_dictionary,
args.decoder_embed_dim)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim):
raise ValueError(
'--share-decoder-input-output-embed requires '
'--decoder-embed-dim to match --decoder-out-embed-dim')
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
def eval_str_nested_list_or_tuple(x, type=int):
if x is None:
return None
if isinstance(x, str):
x = eval(x)
if isinstance(x, list):
return list(
map(lambda s: eval_str_nested_list_or_tuple(s, type), x))
elif isinstance(x, tuple):
return tuple(
map(lambda s: eval_str_nested_list_or_tuple(s, type), x))
else:
try:
return type(x)
except:
raise ValueError
out_channels = eval_str_nested_list_or_tuple(
args.encoder_conv_channels, type=int)
kernel_sizes = eval_str_nested_list_or_tuple(
args.encoder_conv_kernel_sizes, type=int)
strides = eval_str_nested_list_or_tuple(args.encoder_conv_strides,
type=int)
print('| input feature dimension: {}, channels: {}'.format(
task.feat_dim, task.feat_in_channels))
assert task.feat_dim % task.feat_in_channels == 0
conv_layers = ConvBNReLU(out_channels,
kernel_sizes,
strides,
in_channels=task.feat_in_channels
) if not out_channels is None else None
rnn_encoder_input_size = task.feat_dim // task.feat_in_channels
if conv_layers is not None:
for stride in strides:
if isinstance(stride, (list, tuple)):
assert len(stride) > 0
s = stride[1] if len(stride) > 1 else stride[0]
else:
assert isinstance(stride, int)
s = stride
rnn_encoder_input_size = (rnn_encoder_input_size + s - 1) // s
rnn_encoder_input_size *= out_channels[-1]
encoder = SpeechLSTMEncoder(
conv_layers_before=conv_layers,
input_size=rnn_encoder_input_size,
hidden_size=args.encoder_rnn_hidden_size,
num_layers=args.encoder_rnn_layers,
dropout_in=args.encoder_rnn_dropout_in,
dropout_out=args.encoder_rnn_dropout_out,
bidirectional=args.encoder_rnn_bidirectional,
residual=args.encoder_rnn_residual,
)
decoder = SpeechLSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
encoder_output_units=encoder.output_units,
attn_type=args.attention_type,
attn_dim=args.attention_dim,
need_attn=args.need_attention,
residual=args.decoder_rnn_residual,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(options.eval_str_list(
args.adaptive_softmax_cutoff, type=int) if args.criterion
== 'adaptive_loss' else None),
)
pretrained_lm = None
if args.pretrained_lm_checkpoint:
print('| loading pretrained LM from {}'.format(
args.pretrained_lm_checkpoint))
pretrained_lm = checkpoint_utils.load_model_ensemble(
args.pretrained_lm_checkpoint, task)[0][0]
pretrained_lm.make_generation_fast_()
# freeze pretrained model
for param in pretrained_lm.parameters():
param.requires_grad = False
return cls(encoder, decoder, pretrained_lm)
def main(args):
utils.import_user_module(args)
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,
)
for arg in vars(_model_args).keys():
'''if arg not in {
'self_target', 'future_target', 'past_target', 'tokens_per_sample',
'output_size_dictionary', 'add_bos_token',
}:'''
if arg in {'decoder_embed_dim', 'vocab_size'}:
setattr(args, arg, getattr(_model_args, arg))
logger.info(args)
if args.knnlm:
knn_dstore = KNN_Dstore(args)
print("Loading training tokens...")
with open(args.input_tokens_file) as infile:
train_tokens = infile.read().split()
print("TODO, REMOVE ME\n\n\n !!!!Skipping first training tokens...")
train_tokens = train_tokens[3072:] # TODO, remove this
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
# 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)
# 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
# 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])
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 inputs in buffered_read(args.input, args.buffer_size):
results = []
for batch in make_batches(inputs, args, task, max_positions,
encode_fn):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
if use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
}
if args.knnlm:
translations = task.inference_step(generator,
models,
sample,
None,
knn_dstore=knn_dstore)
else:
translations = task.inference_step(generator, models, sample,
None)
for i, (id,
hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
results.append((start_id + id, src_tokens_i, hypos))
# sort output to match input order
for id, src_tokens, hypos 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))
# Process top predictions
for hypo in hypos[:min(len(hypos), args.nbest)]:
assert hypo['dists_full'] != None
dists_full = hypo['dists_full'].float()
knns_full = hypo['knns_full']
word_tokens = [
task.target_dictionary[token] for token in hypo['tokens']
]
#assert len(yhat_scores.tolist()) == len(word_tokens)
# TODO, trim off padding when its batched
context_size = 20
num_neighbors = 10
print("Example:", " ".join(word_tokens))
print(dists_full)
best_dist_indices = np.argsort(
dists_full)[-num_neighbors:][::-1]
for j, neighbor_index in enumerate(best_dist_indices):
distance = dists_full[neighbor_index]
knn_index = knns_full[neighbor_index]
print(
"Best neighbor {} (distance {:.2f}):".format(
j, distance),
" ".join(train_tokens[knn_index -
context_size:knn_index]), "[[",
train_tokens[knn_index], "]]")
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))
# update running id counter
start_id += len(inputs)
