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#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Generate n-best translations using a trained model.
"""
from contextlib import redirect_stdout
import os
import subprocess
import rerank_utils
from examples.noisychannel import rerank_options
from fairseq import options
import generate
import preprocess
def gen_and_reprocess_nbest(args):
if args.score_dict_dir is None:
args.score_dict_dir = args.data
if args.prefix_len is not None:
assert args.right_to_left1 is False, "prefix length not compatible with right to left models"
assert args.right_to_left2 is False, "prefix length not compatible with right to left models"
if args.nbest_list is not None:
assert args.score_model2 is None
if args.backwards1:
scorer1_src = args.target_lang
scorer1_tgt = args.source_lang
else:
scorer1_src = args.source_lang
scorer1_tgt = args.target_lang
store_data = os.path.join(os.path.dirname(__file__))+"/rerank_data/"+args.data_dir_name
if not os.path.exists(store_data):
os.makedirs(store_data)
pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \
backwards_preprocessed_dir, lm_preprocessed_dir = \
rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset,
args.gen_model_name, args.shard_id, args.num_shards,
args.sampling, args.prefix_len, args.target_prefix_frac,
args.source_prefix_frac)
assert not (args.right_to_left1 and args.backwards1), "backwards right to left not supported"
assert not (args.right_to_left2 and args.backwards2), "backwards right to left not supported"
assert not (args.prefix_len is not None and args.target_prefix_frac is not None), \
"target prefix frac and target prefix len incompatible"
# make directory to store generation results
if not os.path.exists(pre_gen):
os.makedirs(pre_gen)
rerank1_is_gen = args.gen_model == args.score_model1 and args.source_prefix_frac is None
rerank2_is_gen = args.gen_model == args.score_model2 and args.source_prefix_frac is None
if args.nbest_list is not None:
rerank2_is_gen = True
# make directories to store preprossed nbest list for reranking
if not os.path.exists(left_to_right_preprocessed_dir):
os.makedirs(left_to_right_preprocessed_dir)
if not os.path.exists(right_to_left_preprocessed_dir):
os.makedirs(right_to_left_preprocessed_dir)
if not os.path.exists(lm_preprocessed_dir):
os.makedirs(lm_preprocessed_dir)
if not os.path.exists(backwards_preprocessed_dir):
os.makedirs(backwards_preprocessed_dir)
score1_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model1_name,
target_prefix_frac=args.target_prefix_frac,
source_prefix_frac=args.source_prefix_frac,
backwards=args.backwards1)
if args.score_model2 is not None:
score2_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.model2_name,
target_prefix_frac=args.target_prefix_frac,
source_prefix_frac=args.source_prefix_frac,
backwards=args.backwards2)
predictions_bpe_file = pre_gen+"/generate_output_bpe.txt"
using_nbest = args.nbest_list is not None
if using_nbest:
print("Using predefined n-best list from interactive.py")
predictions_bpe_file = args.nbest_list
else:
if not os.path.isfile(predictions_bpe_file):
print("STEP 1: generate predictions using the p(T|S) model with bpe")
print(args.data)
param1 = [args.data,
"--path", args.gen_model,
"--shard-id", str(args.shard_id),
"--num-shards", str(args.num_shards),
"--nbest", str(args.num_rescore),
"--batch-size", str(args.batch_size),
"--beam", str(args.num_rescore),
"--max-sentences", str(args.num_rescore),
"--gen-subset", args.gen_subset,
"--source-lang", args.source_lang,
"--target-lang", args.target_lang]
if args.sampling:
param1 += ["--sampling"]
gen_parser = options.get_generation_parser()
input_args = options.parse_args_and_arch(gen_parser, param1)
print(input_args)
with open(predictions_bpe_file, 'w') as f:
with redirect_stdout(f):
generate.main(input_args)
gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe,
nbest=using_nbest, prefix_len=args.prefix_len,
target_prefix_frac=args.target_prefix_frac)
if args.diff_bpe:
rerank_utils.write_reprocessed(gen_output.no_bpe_source, gen_output.no_bpe_hypo,
gen_output.no_bpe_target, pre_gen+"/source_gen_bpe."+args.source_lang,
pre_gen+"/target_gen_bpe."+args.target_lang,
pre_gen+"/reference_gen_bpe."+args.target_lang)
bitext_bpe = args.rescore_bpe_code
bpe_src_param = ["-c", bitext_bpe,
"--input", pre_gen+"/source_gen_bpe."+args.source_lang,
"--output", pre_gen+"/rescore_data."+args.source_lang]
bpe_tgt_param = ["-c", bitext_bpe,
"--input", pre_gen+"/target_gen_bpe."+args.target_lang,
"--output", pre_gen+"/rescore_data."+args.target_lang]
subprocess.call(["python",
os.path.join(os.path.dirname(__file__),
"subword-nmt/subword_nmt/apply_bpe.py")] + bpe_src_param,
shell=False)
subprocess.call(["python",
os.path.join(os.path.dirname(__file__),
"subword-nmt/subword_nmt/apply_bpe.py")] + bpe_tgt_param,
shell=False)
if (not os.path.isfile(score1_file) and not rerank1_is_gen) or \
(args.score_model2 is not None and not os.path.isfile(score2_file) and not rerank2_is_gen):
print("STEP 2: process the output of generate.py so we have clean text files with the translations")
rescore_file = "/rescore_data"
if args.prefix_len is not None:
prefix_len_rescore_file = rescore_file + "prefix"+str(args.prefix_len)
if args.target_prefix_frac is not None:
target_prefix_frac_rescore_file = rescore_file + "target_prefix_frac"+str(args.target_prefix_frac)
if args.source_prefix_frac is not None:
source_prefix_frac_rescore_file = rescore_file + "source_prefix_frac"+str(args.source_prefix_frac)
if not args.right_to_left1 or not args.right_to_left2:
if not args.diff_bpe:
rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
pre_gen+rescore_file+"."+args.source_lang,
pre_gen+rescore_file+"."+args.target_lang,
pre_gen+"/reference_file", bpe_symbol=args.remove_bpe)
if args.prefix_len is not None:
bw_rescore_file = prefix_len_rescore_file
rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
pre_gen+prefix_len_rescore_file+"."+args.source_lang,
pre_gen+prefix_len_rescore_file+"."+args.target_lang,
pre_gen+"/reference_file", prefix_len=args.prefix_len,
bpe_symbol=args.remove_bpe)
elif args.target_prefix_frac is not None:
bw_rescore_file = target_prefix_frac_rescore_file
rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
pre_gen+target_prefix_frac_rescore_file+"."+args.source_lang,
pre_gen+target_prefix_frac_rescore_file+"."+args.target_lang,
pre_gen+"/reference_file", bpe_symbol=args.remove_bpe,
target_prefix_frac=args.target_prefix_frac)
else:
bw_rescore_file = rescore_file
if args.source_prefix_frac is not None:
fw_rescore_file = source_prefix_frac_rescore_file
rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
pre_gen+source_prefix_frac_rescore_file+"."+args.source_lang,
pre_gen+source_prefix_frac_rescore_file+"."+args.target_lang,
pre_gen+"/reference_file", bpe_symbol=args.remove_bpe,
source_prefix_frac=args.source_prefix_frac)
else:
fw_rescore_file = rescore_file
if args.right_to_left1 or args.right_to_left2:
rerank_utils.write_reprocessed(gen_output.source, gen_output.hypo, gen_output.target,
pre_gen+"/right_to_left_rescore_data."+args.source_lang,
pre_gen+"/right_to_left_rescore_data."+args.target_lang,
pre_gen+"/right_to_left_reference_file",
right_to_left=True, bpe_symbol=args.remove_bpe)
print("STEP 3: binarize the translations")
if not args.right_to_left1 or args.score_model2 is not None and not args.right_to_left2 or not rerank1_is_gen:
if args.backwards1 or args.backwards2:
if args.backwards_score_dict_dir is not None:
bw_dict = args.backwards_score_dict_dir
else:
bw_dict = args.score_dict_dir
bw_preprocess_param = ["--source-lang", scorer1_src,
"--target-lang", scorer1_tgt,
"--trainpref", pre_gen+bw_rescore_file,
"--srcdict", bw_dict + "/dict." + scorer1_src + ".txt",
"--tgtdict", bw_dict + "/dict." + scorer1_tgt + ".txt",
"--destdir", backwards_preprocessed_dir]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(bw_preprocess_param)
preprocess.main(input_args)
preprocess_param = ["--source-lang", scorer1_src,
"--target-lang", scorer1_tgt,
"--trainpref", pre_gen+fw_rescore_file,
"--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt",
"--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt",
"--destdir", left_to_right_preprocessed_dir]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(preprocess_param)
preprocess.main(input_args)
if args.right_to_left1 or args.right_to_left2:
preprocess_param = ["--source-lang", scorer1_src,
"--target-lang", scorer1_tgt,
"--trainpref", pre_gen+"/right_to_left_rescore_data",
"--srcdict", args.score_dict_dir+"/dict."+scorer1_src+".txt",
"--tgtdict", args.score_dict_dir+"/dict."+scorer1_tgt+".txt",
"--destdir", right_to_left_preprocessed_dir]
preprocess_parser = options.get_preprocessing_parser()
input_args = preprocess_parser.parse_args(preprocess_param)
preprocess.main(input_args)
return gen_output
def cli_main():
parser = rerank_options.get_reranking_parser()
args = options.parse_args_and_arch(parser)
gen_and_reprocess_nbest(args)
if __name__ == '__main__':
cli_main()
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