def generate_batched_itr(self,
data_itr,
beam_size=None,
cuda=False,
timer=None):
"""Iterate over a batched dataset and yield individual translations.
Args:
cuda (bool, optional): use GPU for generation
timer (StopwatchMeter, optional): time generations
"""
for sample in data_itr:
s = utils.move_to_cuda(sample) if cuda else sample
if "net_input" not in s:
continue
input = s["net_input"]
# model.forward normally channels prev_output_tokens into the decoder
# separately, but SequenceGenerator directly calls model.encoder
encoder_input = {
k: v
for k, v in input.items() if k != "prev_output_tokens"
}
if timer is not None:
timer.start()
with torch.no_grad():
hypos = self.generate(encoder_input)
if timer is not None:
timer.stop(sum(len(h[0]["tokens"]) for h in hypos))
for i, id in enumerate(s["id"].data):
# remove padding
src = utils.strip_pad(input["src_tokens"].data[i, :], self.pad)
ref = (utils.strip_pad(s["target"].data[i, :], self.pad)
if s["target"] is not None else None)
yield id, src, ref, hypos[i]
def generate_batched_itr(
self,
data_itr,
maxlen_a=None,
maxlen_b=None,
cuda=False,
timer=None,
prefix_size=0,
):
"""Iterate over a batched dataset and yield individual translations.
Args:
maxlen_a/b: generate sequences of maximum length ax + b,
where x is the source sentence length.
cuda: use GPU for generation
timer: StopwatchMeter for timing generations.
"""
for sample in data_itr:
if "net_input" not in sample:
continue
if timer is not None:
timer.start()
with torch.no_grad():
hypos = self.generate(
self.models,
sample,
prefix_tokens=sample["target"][:, :prefix_size]
if prefix_size > 0
else None,
)
if timer is not None:
timer.stop(sample["ntokens"])
for i, id in enumerate(sample["id"]):
# remove padding
src = utils.strip_pad(sample["net_input"]["src_tokens"][i, :], self.pad)
ref = utils.strip_pad(sample["target"][i, :], self.pad)
yield id, src, ref, hypos[i]
def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
assert (hasattr(model.decoder, "adaptive_softmax")
and model.decoder.adaptive_softmax is not None)
adaptive_softmax = model.decoder.adaptive_softmax
net_output = model(**sample["net_input"])
orig_target = model.get_targets(sample, net_output)
nsentences = orig_target.size(0)
orig_target = orig_target.view(-1)
bsz = orig_target.size(0)
logits, target = adaptive_softmax(net_output[0], orig_target)
assert len(target) == len(logits)
loss = net_output[0].new(1 if reduce else bsz).zero_()
for i in range(len(target)):
if target[i] is not None:
assert target[i].min() >= 0 and target[i].max(
) <= logits[i].size(1)
loss += F.cross_entropy(
logits[i],
target[i],
ignore_index=self.padding_idx,
reduction="sum" if reduce else "none",
)
orig = utils.strip_pad(orig_target, self.padding_idx)
ntokens = orig.numel()
sample_size = sample["target"].size(
0) if self.sentence_avg else ntokens
logging_output = {
"loss": loss.data,
"ntokens": ntokens,
"nsentences": nsentences,
"sample_size": sample_size,
}
return loss, sample_size, logging_output
def _inference_with_bleu(self, generator, sample, model):
import sacrebleu
def decode(toks, escape_unk=False):
s = self.tgt_dict.string(
toks.int().cpu(),
self.cfg.eval_bleu_remove_bpe,
# The default unknown string in fairseq is `<unk>`, but
# this is tokenized by sacrebleu as `< unk >`, inflating
# BLEU scores. Instead, we use a somewhat more verbose
# alternative that is unlikely to appear in the real
# reference, but doesn't get split into multiple tokens.
unk_string=("UNKNOWNTOKENINREF"
if escape_unk else "UNKNOWNTOKENINHYP"),
)
if self.tokenizer:
s = self.tokenizer.decode(s)
return s
gen_out = self.inference_step(generator, [model],
sample,
prefix_tokens=None)
hyps, refs = [], []
for i in range(len(gen_out)):
hyps.append(decode(gen_out[i][0]["tokens"]))
refs.append(
decode(
utils.strip_pad(sample["target"][i], self.tgt_dict.pad()),
escape_unk=True, # don't count <unk> as matches to the hypo
))
if self.cfg.eval_bleu_print_samples:
logger.info("example hypothesis: " + hyps[0])
logger.info("example reference: " + refs[0])
if self.cfg.eval_tokenized_bleu:
return sacrebleu.corpus_bleu(hyps, [refs], tokenize="none")
else:
return sacrebleu.corpus_bleu(hyps, [refs])
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)
# 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)
# START ba_st_ch code
eval_file_name, checkpoint = get_evaluation_file_name(
str(cfg.common_eval.path))
print(eval_file_name)
eval_manifest = {c: [] for c in MANIFEST_COLUMNS}
eval_file_name = resource_path + eval_file_name
# END ba_st_ch code
# Load ensemble
logger.info("loading model(s) from {}".format(cfg.common_eval.path))
models, saved_cfg = 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,
)
# loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config
task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)
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.generation,
extra_gen_cls_kwargs=extra_gen_cls_kwargs)
# Handle tokenization and BPE
tokenizer = task.build_tokenizer(cfg.tokenizer)
bpe = task.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),
)
# START ba_st_ch code
print("-------------")
test_dataset = corpus_path + str(cfg.dataset.gen_subset) + ".tsv"
df = pd.read_table(test_dataset)
id = df.loc[[sample_id], ["id"]]
print(id)
id = str(id).partition('\n')[2]
print("id: ", id)
print("-------------")
# END ba_st_ch code
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)
# START ba_st_ch code
eval_manifest["id"].append(id)
eval_manifest["target_txt"].append(target_str)
eval_manifest["prediction"].append(detok_hypo_str)
# END ba_st_ch code
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 == "hard":
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_alignment == "soft":
print(
"A-{}\t{}".format(
sample_id,
" ".join([
",".join(src_probs)
for src_probs 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,
)
# START ba_st_ch code
bleu_score = scorer.result_string().rsplit(' = ')[1].split(" ")[0]
# print(bleu_score)
# print(float(bleu_score) / 2)
f.write(checkpoint + ";" + bleu_score + '\n')
generate_manifest(eval_manifest, eval_file_name)
# END ba_st_ch code
return scorer
def generate(self, models, sample, **kwargs):
"""Score a batch of translations."""
net_input = sample["net_input"]
def batch_for_softmax(dec_out, target):
# assumes decoder_out[0] is the only thing needed (may not be correct for future models!)
first, rest = dec_out[0], dec_out[1:]
bsz, tsz, dim = first.shape
if bsz * tsz < self.softmax_batch:
yield dec_out, target, True
else:
flat = first.contiguous().view(1, -1, dim)
flat_tgt = target.contiguous().view(flat.shape[:-1])
s = 0
while s < flat.size(1):
e = s + self.softmax_batch
yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False
s = e
def gather_target_probs(probs, target):
probs = probs.gather(
dim=2,
index=target.unsqueeze(-1),
)
return probs
orig_target = sample["target"]
# compute scores for each model in the ensemble
avg_probs = None
avg_attn = None
for model in models:
model.eval()
decoder_out = model(**net_input)
attn = decoder_out[1] if len(decoder_out) > 1 else None
if type(attn) is dict:
attn = attn.get("attn", None)
batched = batch_for_softmax(decoder_out, orig_target)
probs, idx = None, 0
for bd, tgt, is_single in batched:
sample["target"] = tgt
curr_prob = model.get_normalized_probs(
bd, log_probs=len(models) == 1, sample=sample
).data
if is_single:
probs = gather_target_probs(curr_prob, orig_target)
else:
if probs is None:
probs = curr_prob.new(orig_target.numel())
step = curr_prob.size(0) * curr_prob.size(1)
end = step + idx
tgt_probs = gather_target_probs(
curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt
)
probs[idx:end] = tgt_probs.view(-1)
idx = end
sample["target"] = orig_target
probs = probs.view(sample["target"].shape)
if avg_probs is None:
avg_probs = probs
else:
avg_probs.add_(probs)
if attn is not None:
if torch.is_tensor(attn):
attn = attn.data
else:
attn = attn[0]
if avg_attn is None:
avg_attn = attn
else:
avg_attn.add_(attn)
if len(models) > 1:
avg_probs.div_(len(models))
avg_probs.log_()
if avg_attn is not None:
avg_attn.div_(len(models))
bsz = avg_probs.size(0)
hypos = []
start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz
for i in range(bsz):
# remove padding from ref
ref = (
utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad)
if sample["target"] is not None
else None
)
tgt_len = ref.numel()
avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len]
score_i = avg_probs_i.sum() / tgt_len
if avg_attn is not None:
avg_attn_i = avg_attn[i]
if self.compute_alignment:
alignment = utils.extract_hard_alignment(
avg_attn_i,
sample["net_input"]["src_tokens"][i],
sample["target"][i],
self.pad,
self.eos,
)
else:
alignment = None
else:
avg_attn_i = alignment = None
hypos.append(
[
{
"tokens": ref,
"score": score_i,
"attention": avg_attn_i,
"alignment": alignment,
"positional_scores": avg_probs_i,
}
]
)
return hypos
def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
lm_logits, output_metadata = model(**sample["net_input"])
# reshape lm_logits from (N,T,C) to (N*T,C)
lm_logits = lm_logits.view(-1, lm_logits.size(-1))
lm_targets = sample["lm_target"].view(-1)
lm_loss = compute_cross_entropy_loss(lm_logits, lm_targets,
self.padding_idx)
# compute the number of tokens for which loss is computed. This is used
# to normalize the loss
ntokens = utils.strip_pad(lm_targets, self.padding_idx).numel()
loss = lm_loss / ntokens
nsentences = sample["nsentences"]
# nsentences = 0
# Compute sentence loss if masked_lm_only is False
sentence_loss = None
if not self.masked_lm_only:
sentence_logits = output_metadata["sentence_logits"]
sentence_targets = sample["sentence_target"].view(-1)
# This needs to be recomputed due to some differences between
# TokenBlock and BlockPair dataset. This can be resolved with a
# refactor of BERTModel which we will do in the future.
# TODO: Remove this after refactor of BERTModel
nsentences = sentence_targets.size(0)
# Check for logits being none which can happen when remove_heads
# is set to true in the BERT model. Ideally we should set
# masked_lm_only to true in this case, but that requires some
# refactor in the BERT model.
if sentence_logits is not None:
sentence_loss = compute_cross_entropy_loss(
sentence_logits, sentence_targets)
loss += self.nsp_loss_weight * (sentence_loss / nsentences)
# NOTE: as we are summing up per token mlm loss and per sentence nsp loss
# we don't need to use sample_size as denominator for the gradient
# here sample_size is just used for logging
sample_size = 1
logging_output = {
"loss":
utils.item(loss.data) if reduce else loss.data,
"lm_loss":
utils.item(lm_loss.data) if reduce else lm_loss.data,
# sentence loss is not always computed
"sentence_loss":
((utils.item(sentence_loss.data) if reduce else sentence_loss.data)
if sentence_loss is not None else 0.0),
"ntokens":
ntokens,
"nsentences":
nsentences,
"sample_size":
sample_size,
}
return loss, sample_size, logging_output
def main(cfg: FairseqConfig):
if isinstance(cfg, Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
start_time = time.time()
total_translate_time = 0
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"
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
# Setup task, e.g., translation
task = tasks.setup_task(cfg.task)
# Load ensemble
overrides = ast.literal_eval(cfg.common_eval.model_overrides)
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,
)
# 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 = task.build_tokenizer(cfg.tokenizer)
bpe = task.build_bpe(cfg.bpe)
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(cfg.generation.replace_unk)
max_positions = utils.resolve_max_positions(
task.max_positions(), *[model.max_positions() for model in models]
)
if cfg.generation.constraints:
logger.warning(
"NOTE: Constrained decoding currently assumes a shared subword vocabulary."
)
if cfg.interactive.buffer_size > 1:
logger.info("Sentence buffer size: %s", cfg.interactive.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(cfg.interactive.input, cfg.interactive.buffer_size):
results = []
for batch in make_batches(inputs, cfg, 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 cfg.generation.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]):
src_str = ''
if src_dict is not None:
src_str = src_dict.string(src_tokens, cfg.common_eval.post_process)
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, cfg.common_eval.post_process)
)
)
# Process top predictions
for hypo in hypos[: min(len(hypos), 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)
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 cfg.generation.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
)
)