forked from mindspore-Ecosystem/mindspore
202 lines
8.3 KiB
Python
202 lines
8.3 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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"""Create training instances for Transformer."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import collections
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import logging
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import numpy as np
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import src.tokenization as tokenization
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from mindspore.mindrecord import FileWriter
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class SampleInstance():
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"""A single sample instance (sentence pair)."""
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def __init__(self, source_sos_tokens, source_eos_tokens, target_sos_tokens, target_eos_tokens):
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self.source_sos_tokens = source_sos_tokens
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self.source_eos_tokens = source_eos_tokens
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self.target_sos_tokens = target_sos_tokens
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self.target_eos_tokens = target_eos_tokens
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def __str__(self):
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s = ""
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s += "source sos tokens: %s\n" % (" ".join(
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[tokenization.convert_to_printable(x) for x in self.source_sos_tokens]))
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s += "source eos tokens: %s\n" % (" ".join(
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[tokenization.convert_to_printable(x) for x in self.source_eos_tokens]))
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s += "target sos tokens: %s\n" % (" ".join(
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[tokenization.convert_to_printable(x) for x in self.target_sos_tokens]))
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s += "target eos tokens: %s\n" % (" ".join(
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[tokenization.convert_to_printable(x) for x in self.target_eos_tokens]))
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s += "\n"
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return s
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def __repr__(self):
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return self.__str__()
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def write_instance_to_file(writer, instance, tokenizer, max_seq_length):
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"""Create files from `SampleInstance`s."""
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def _convert_ids_and_mask(input_tokens):
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input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
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input_mask = [1] * len(input_ids)
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assert len(input_ids) <= max_seq_length
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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return input_ids, input_mask
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source_sos_ids, source_sos_mask = _convert_ids_and_mask(instance.source_sos_tokens)
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source_eos_ids, source_eos_mask = _convert_ids_and_mask(instance.source_eos_tokens)
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target_sos_ids, target_sos_mask = _convert_ids_and_mask(instance.target_sos_tokens)
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target_eos_ids, target_eos_mask = _convert_ids_and_mask(instance.target_eos_tokens)
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features = collections.OrderedDict()
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features["source_sos_ids"] = np.asarray(source_sos_ids)
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features["source_sos_mask"] = np.asarray(source_sos_mask)
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features["source_eos_ids"] = np.asarray(source_eos_ids)
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features["source_eos_mask"] = np.asarray(source_eos_mask)
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features["target_sos_ids"] = np.asarray(target_sos_ids)
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features["target_sos_mask"] = np.asarray(target_sos_mask)
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features["target_eos_ids"] = np.asarray(target_eos_ids)
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features["target_eos_mask"] = np.asarray(target_eos_mask)
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writer.write_raw_data([features])
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return features
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def create_training_instance(source_words, target_words, max_seq_length, clip_to_max_len):
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"""Creates `SampleInstance`s for a single sentence pair."""
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EOS = "</s>"
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SOS = "<s>"
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if len(source_words) >= max_seq_length or len(target_words) >= max_seq_length:
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if clip_to_max_len:
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print("####lalalal")
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source_words = source_words[:min([len(source_words, max_seq_length-1)])]
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target_words = target_words[:min([len(target_words, max_seq_length-1)])]
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else:
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return None
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source_sos_tokens = [SOS] + source_words
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source_eos_tokens = source_words + [EOS]
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target_sos_tokens = [SOS] + target_words
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target_eos_tokens = target_words + [EOS]
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instance = SampleInstance(
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source_sos_tokens=source_sos_tokens,
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source_eos_tokens=source_eos_tokens,
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target_sos_tokens=target_sos_tokens,
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target_eos_tokens=target_eos_tokens)
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return instance
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input_file", type=str, required=True,
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help='Input raw text file (or comma-separated list of files).')
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parser.add_argument("--output_file", type=str, required=True, help='Output MindRecord file.')
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parser.add_argument("--num_splits", type=int, default=16,
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help='The MindRecord file will be split into the number of partition.')
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parser.add_argument("--vocab_file", type=str, required=True,
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help='The vocabulary file that the Transformer model was trained on.')
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parser.add_argument("--clip_to_max_len", type=bool, default=False,
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help='clip sequences to maximum sequence length.')
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parser.add_argument("--max_seq_length", type=int, default=128, help='Maximum sequence length.')
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args = parser.parse_args()
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tokenizer = tokenization.WhiteSpaceTokenizer(vocab_file=args.vocab_file)
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input_files = []
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for input_pattern in args.input_file.split(","):
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input_files.append(input_pattern)
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logging.info("*** Reading from input files ***")
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for input_file in input_files:
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logging.info(" %s", input_file)
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output_file = args.output_file
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logging.info("*** Writing to output files ***")
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logging.info(" %s", output_file)
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writer = FileWriter(output_file, args.num_splits)
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data_schema = {"source_sos_ids": {"type": "int64", "shape": [-1]},
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"source_sos_mask": {"type": "int64", "shape": [-1]},
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"source_eos_ids": {"type": "int64", "shape": [-1]},
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"source_eos_mask": {"type": "int64", "shape": [-1]},
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"target_sos_ids": {"type": "int64", "shape": [-1]},
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"target_sos_mask": {"type": "int64", "shape": [-1]},
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"target_eos_ids": {"type": "int64", "shape": [-1]},
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"target_eos_mask": {"type": "int64", "shape": [-1]}
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}
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writer.add_schema(data_schema, "tranformer hisi")
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total_written = 0
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total_read = 0
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for input_file in input_files:
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logging.info("*** Reading from %s ***", input_file)
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with open(input_file, "r") as reader:
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while True:
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line = tokenization.convert_to_unicode(reader.readline())
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if not line:
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break
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total_read += 1
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if total_read % 100000 == 0:
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logging.info("%d ...", total_read)
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source_line, target_line = line.strip().split("\t")
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source_tokens = tokenizer.tokenize(source_line)
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target_tokens = tokenizer.tokenize(target_line)
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if len(source_tokens) >= args.max_seq_length or len(target_tokens) >= args.max_seq_length:
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logging.info("ignore long sentence!")
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continue
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instance = create_training_instance(source_tokens, target_tokens, args.max_seq_length,
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clip_to_max_len=args.clip_to_max_len)
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if instance is None:
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continue
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features = write_instance_to_file(writer, instance, tokenizer, args.max_seq_length)
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total_written += 1
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if total_written <= 20:
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logging.info("*** Example ***")
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logging.info("source tokens: %s", " ".join(
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[tokenization.convert_to_printable(x) for x in instance.source_eos_tokens]))
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logging.info("target tokens: %s", " ".join(
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[tokenization.convert_to_printable(x) for x in instance.target_sos_tokens]))
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for feature_name in features.keys():
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feature = features[feature_name]
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logging.info("%s: %s", feature_name, feature)
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writer.commit()
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logging.info("Wrote %d total instances", total_written)
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if __name__ == "__main__":
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main()
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