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generate.py
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#!/usr/bin/env python3 -u
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import torch
from fairseq import bleu, data, options, progress_bar, tokenizer, utils
from fairseq.meters import StopwatchMeter, TimeMeter
from fairseq.sequence_generator import SequenceGenerator
from fairseq.sequence_scorer import SequenceScorer
def main(args):
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
if hasattr(torch, 'set_grad_enabled'):
torch.set_grad_enabled(False)
# Load dataset
if args.replace_unk is None:
dataset = data.load_dataset(
args.data,
[args.gen_subset],
args.source_lang,
args.target_lang,
)
else:
dataset = data.load_raw_text_dataset(
args.data,
[args.gen_subset],
args.source_lang,
args.target_lang,
)
if args.source_lang is None or args.target_lang is None:
# record inferred languages in args
args.source_lang, args.target_lang = dataset.src, dataset.dst
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, _ = utils.load_ensemble_for_inference(args.path, dataset.src_dict, dataset.dst_dict)
print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam,
)
# 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)
max_positions = min(model.max_encoder_positions() for model in models)
itr = dataset.eval_dataloader(
args.gen_subset,
max_sentences=args.max_sentences,
max_positions=max_positions,
skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test,
)
if args.num_shards > 1:
if args.shard_id < 0 or args.shard_id >= args.num_shards:
raise ValueError('--shard-id must be between 0 and num_shards')
itr = data.sharded_iterator(itr, args.num_shards, args.shard_id)
# Initialize generator
gen_timer = StopwatchMeter()
if args.score_reference:
translator = SequenceScorer(models)
else:
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.cuda()
# Generate and compute BLEU score
scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
num_sentences = 0
with progress_bar.build_progress_bar(args, itr) as t:
if args.score_reference:
translations = translator.score_batched_itr(t, cuda=use_cuda, timer=gen_timer)
else:
translations = translator.generate_batched_itr(
t, maxlen_a=args.max_len_a, maxlen_b=args.max_len_b,
cuda=use_cuda, timer=gen_timer)
wps_meter = TimeMeter()
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
target_tokens = target_tokens.int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = dataset.splits[args.gen_subset].src.get_original_text(sample_id)
target_str = dataset.splits[args.gen_subset].dst.get_original_text(sample_id)
else:
src_str = dataset.src_dict.string(src_tokens, args.remove_bpe)
target_str = dataset.dst_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str))
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for i, hypo in enumerate(hypos[:min(len(hypos), args.nbest)]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dataset.dst_dict,
remove_bpe=args.remove_bpe,
)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
print('P-{}\t{}'.format(
sample_id,
' '.join(map(
lambda x: '{:.4f}'.format(x),
hypo['positional_scores'].tolist(),
))
))
print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))
# Score only the top hypothesis
if i == 0:
if align_dict is not None or args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tokenizer.Tokenizer.tokenize(
target_str, dataset.dst_dict, add_if_not_exist=True)
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
num_sentences += 1
print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
if __name__ == '__main__':
parser = options.get_generation_parser()
args = parser.parse_args()
main(args)