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fa84ae26d6
This is the result of:
$ black --line-length 119 examples templates transformers utils hubconf.py setup.py
There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.
This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.
90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocessing script before distillation.
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"""
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import argparse
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import pickle
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import random
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import time
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import numpy as np
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from transformers import BertTokenizer, RobertaTokenizer, GPT2Tokenizer
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import logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
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)
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logger = logging.getLogger(__name__)
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def main():
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parser = argparse.ArgumentParser(
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description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."
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)
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parser.add_argument("--file_path", type=str, default="data/dump.txt", help="The path to the data.")
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parser.add_argument("--tokenizer_type", type=str, default="bert", choices=["bert", "roberta", "gpt2"])
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parser.add_argument("--tokenizer_name", type=str, default="bert-base-uncased", help="The tokenizer to use.")
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parser.add_argument("--dump_file", type=str, default="data/dump", help="The dump file prefix.")
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args = parser.parse_args()
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logger.info(f"Loading Tokenizer ({args.tokenizer_name})")
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if args.tokenizer_type == "bert":
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tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name)
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bos = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
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sep = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
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elif args.tokenizer_type == "roberta":
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tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
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bos = tokenizer.special_tokens_map["cls_token"] # `<s>`
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sep = tokenizer.special_tokens_map["sep_token"] # `</s>`
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elif args.tokenizer_type == "gpt2":
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tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_name)
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bos = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
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sep = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
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logger.info(f"Loading text from {args.file_path}")
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with open(args.file_path, "r", encoding="utf8") as fp:
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data = fp.readlines()
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logger.info(f"Start encoding")
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logger.info(f"{len(data)} examples to process.")
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rslt = []
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iter = 0
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interval = 10000
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start = time.time()
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for text in data:
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text = f"{bos} {text.strip()} {sep}"
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token_ids = tokenizer.encode(text, add_special_tokens=False)
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rslt.append(token_ids)
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iter += 1
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if iter % interval == 0:
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end = time.time()
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logger.info(f"{iter} examples processed. - {(end-start)/interval:.2f}s/expl")
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start = time.time()
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logger.info("Finished binarization")
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logger.info(f"{len(data)} examples processed.")
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dp_file = f"{args.dump_file}.{args.tokenizer_name}.pickle"
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rslt_ = [np.uint16(d) for d in rslt]
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random.shuffle(rslt_)
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logger.info(f"Dump to {dp_file}")
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with open(dp_file, "wb") as handle:
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pickle.dump(rslt_, handle, protocol=pickle.HIGHEST_PROTOCOL)
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if __name__ == "__main__":
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main()
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