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| #!/usr/bin/env python | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import argparse | |
| import contextlib | |
| import sys | |
| from collections import Counter | |
| from multiprocessing import Pool | |
| from fairseq.data.encoders.gpt2_bpe import get_encoder | |
| def main(): | |
| """ | |
| Helper script to encode raw text with the GPT-2 BPE using multiple processes. | |
| The encoder.json and vocab.bpe files can be obtained here: | |
| - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json | |
| - https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe | |
| """ | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| "--encoder-json", | |
| help="path to encoder.json", | |
| ) | |
| parser.add_argument( | |
| "--vocab-bpe", | |
| type=str, | |
| help="path to vocab.bpe", | |
| ) | |
| parser.add_argument( | |
| "--inputs", | |
| nargs="+", | |
| default=["-"], | |
| help="input files to filter/encode", | |
| ) | |
| parser.add_argument( | |
| "--outputs", | |
| nargs="+", | |
| default=["-"], | |
| help="path to save encoded outputs", | |
| ) | |
| parser.add_argument( | |
| "--keep-empty", | |
| action="store_true", | |
| help="keep empty lines", | |
| ) | |
| parser.add_argument("--workers", type=int, default=20) | |
| args = parser.parse_args() | |
| assert len(args.inputs) == len( | |
| args.outputs | |
| ), "number of input and output paths should match" | |
| with contextlib.ExitStack() as stack: | |
| inputs = [ | |
| stack.enter_context(open(input, "r", encoding="utf-8")) | |
| if input != "-" | |
| else sys.stdin | |
| for input in args.inputs | |
| ] | |
| outputs = [ | |
| stack.enter_context(open(output, "w", encoding="utf-8")) | |
| if output != "-" | |
| else sys.stdout | |
| for output in args.outputs | |
| ] | |
| encoder = MultiprocessingEncoder(args) | |
| pool = Pool(args.workers, initializer=encoder.initializer) | |
| encoded_lines = pool.imap(encoder.encode_lines, zip(*inputs), 100) | |
| stats = Counter() | |
| for i, (filt, enc_lines) in enumerate(encoded_lines, start=1): | |
| if filt == "PASS": | |
| for enc_line, output_h in zip(enc_lines, outputs): | |
| print(enc_line, file=output_h) | |
| else: | |
| stats["num_filtered_" + filt] += 1 | |
| if i % 10000 == 0: | |
| print("processed {} lines".format(i), file=sys.stderr) | |
| for k, v in stats.most_common(): | |
| print("[{}] filtered {} lines".format(k, v), file=sys.stderr) | |
| class MultiprocessingEncoder(object): | |
| def __init__(self, args): | |
| self.args = args | |
| def initializer(self): | |
| global bpe | |
| bpe = get_encoder(self.args.encoder_json, self.args.vocab_bpe) | |
| def encode(self, line): | |
| global bpe | |
| ids = bpe.encode(line) | |
| return list(map(str, ids)) | |
| def decode(self, tokens): | |
| global bpe | |
| return bpe.decode(tokens) | |
| def encode_lines(self, lines): | |
| """ | |
| Encode a set of lines. All lines will be encoded together. | |
| """ | |
| enc_lines = [] | |
| for line in lines: | |
| line = line.strip() | |
| if len(line) == 0 and not self.args.keep_empty: | |
| return ["EMPTY", None] | |
| tokens = self.encode(line) | |
| enc_lines.append(" ".join(tokens)) | |
| return ["PASS", enc_lines] | |
| def decode_lines(self, lines): | |
| dec_lines = [] | |
| for line in lines: | |
| tokens = map(int, line.strip().split()) | |
| dec_lines.append(self.decode(tokens)) | |
| return ["PASS", dec_lines] | |
| if __name__ == "__main__": | |
| main() | |