mirror of
https://github.com/huggingface/transformers.git
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610 lines
24 KiB
Python
610 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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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"""BERT finetuning runner."""
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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 csv
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import os
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import logging
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import argparse
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import random
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import numpy as np
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from tqdm import tqdm, trange
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import torch
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from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
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from torch.utils.data.distributed import DistributedSampler
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import tokenization_pytorch
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from modeling_pytorch import BertConfig, BertForSequenceClassification
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from optimization_pytorch import BERTAdam
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logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
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datefmt = '%m/%d/%Y %H:%M:%S',
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level = logging.INFO)
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logger = logging.getLogger(__name__)
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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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def __init__(self, guid, text_a, text_b=None, label=None):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.label = label
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, input_ids, input_mask, segment_ids, label_id):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_id = label_id
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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raise NotImplementedError()
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def get_labels(self):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with open(input_file, "r") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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lines.append(line)
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return lines
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class MrpcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization_pytorch.convert_to_unicode(line[3])
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text_b = tokenization_pytorch.convert_to_unicode(line[4])
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label = tokenization_pytorch.convert_to_unicode(line[0])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class MnliProcessor(DataProcessor):
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"""Processor for the MultiNLI data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
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"dev_matched")
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, tokenization_pytorch.convert_to_unicode(line[0]))
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text_a = tokenization_pytorch.convert_to_unicode(line[8])
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text_b = tokenization_pytorch.convert_to_unicode(line[9])
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label = tokenization_pytorch.convert_to_unicode(line[-1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class ColaProcessor(DataProcessor):
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"""Processor for the CoLA data set (GLUE version)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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guid = "%s-%s" % (set_type, i)
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text_a = tokenization_pytorch.convert_to_unicode(line[3])
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label = tokenization_pytorch.convert_to_unicode(line[1])
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
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return examples
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def convert_examples_to_features(examples, label_list, max_seq_length,
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tokenizer):
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"""Loads a data file into a list of `InputBatch`s."""
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label_map = {}
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for (i, label) in enumerate(label_list):
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label_map[label] = i
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features = []
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for (ex_index, example) in enumerate(examples):
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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if example.text_b:
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tokens_b = tokenizer.tokenize(example.text_b)
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if tokens_b:
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# Modifies `tokens_a` and `tokens_b` in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP] with "- 3"
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_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[0:(max_seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambigiously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens = []
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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in tokens_a:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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if tokens_b:
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for token in tokens_b:
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tokens.append(token)
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence 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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segment_ids.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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assert len(segment_ids) == max_seq_length
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label_id = label_map[example.label]
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if ex_index < 5:
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logger.info("*** Example ***")
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logger.info("guid: %s" % (example.guid))
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logger.info("tokens: %s" % " ".join(
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[tokenization_pytorch.printable_text(x) for x in tokens]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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logger.info("label: %s (id = %d)" % (example.label, label_id))
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features.append(
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InputFeatures(
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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label_id=label_id))
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return features
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def _truncate_seq_pair(tokens_a, tokens_b, max_length):
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"""Truncates a sequence pair in place to the maximum length."""
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# This is a simple heuristic which will always truncate the longer sequence
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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tokens_b.pop()
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def input_fn_builder(features, seq_length, train_batch_size):
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# TODO: delete
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"""Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
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all_input_ids = [f.input_ids for feature in features]
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all_input_mask = [f.input_mask for feature in features]
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all_segment_ids = [f.segment_ids for feature in features]
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all_label_ids = [f.label_id for feature in features]
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# for feature in features:
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# all_input_ids.append(feature.input_ids)
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# all_input_mask.append(feature.input_mask)
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# all_segment_ids.append(feature.segment_ids)
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# all_label_ids.append(feature.label_id)
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input_ids_tensor = torch.tensor(all_input_ids, dtype=torch.Long)
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input_mask_tensor = torch.tensor(all_input_mask, dtype=torch.Long)
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segment_tensor = torch.tensor(all_segment_ids, dtype=torch.Long)
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label_tensor = torch.tensor(all_label_ids, dtype=torch.Long)
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train_data = TensorDataset(input_ids_tensor, input_mask_tensor,
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segment_tensor, label_tensor)
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if args.local_rank == -1:
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train_sampler = RandomSampler(train_data)
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else:
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train_sampler = DistributedSampler(train_data)
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train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=train_batch_size)
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return train_dataloader
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def accuracy(out, labels):
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outputs = np.argmax(out, axis=1)
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return np.sum(outputs==labels)
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def main():
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parser = argparse.ArgumentParser()
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## Required parameters
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parser.add_argument("--data_dir",
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default=None,
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type=str,
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required=True,
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help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
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parser.add_argument("--bert_config_file",
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default=None,
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type=str,
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required=True,
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help="The config json file corresponding to the pre-trained BERT model. \n"
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"This specifies the model architecture.")
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parser.add_argument("--task_name",
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default=None,
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type=str,
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required=True,
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help="The name of the task to train.")
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parser.add_argument("--vocab_file",
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default=None,
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type=str,
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required=True,
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help="The vocabulary file that the BERT model was trained on.")
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parser.add_argument("--output_dir",
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default=None,
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type=str,
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required=True,
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help="The output directory where the model checkpoints will be written.")
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## Other parameters
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parser.add_argument("--init_checkpoint",
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default=None,
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type=str,
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help="Initial checkpoint (usually from a pre-trained BERT model).")
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parser.add_argument("--do_lower_case",
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default=False,
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action='store_true',
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help="Whether to lower case the input text. Should be True for uncased models and False for cased models.")
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parser.add_argument("--max_seq_length",
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default=128,
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type=int,
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help="The maximum total input sequence length after WordPiece tokenization. \n"
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"Sequences longer than this will be truncated, and sequences shorter \n"
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"than this will be padded.")
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parser.add_argument("--do_train",
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default=False,
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action='store_true',
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help="Whether to run training.")
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parser.add_argument("--do_eval",
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default=False,
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action='store_true',
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help="Whether to run eval on the dev set.")
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parser.add_argument("--train_batch_size",
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default=32,
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type=int,
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help="Total batch size for training.")
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parser.add_argument("--eval_batch_size",
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default=8,
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type=int,
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help="Total batch size for eval.")
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parser.add_argument("--learning_rate",
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default=5e-5,
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type=float,
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help="The initial learning rate for Adam.")
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parser.add_argument("--num_train_epochs",
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default=3.0,
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type=float,
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help="Total number of training epochs to perform.")
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parser.add_argument("--warmup_proportion",
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default=0.1,
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type=float,
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help="Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10%% of training.")
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parser.add_argument("--save_checkpoints_steps",
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default=1000,
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type=int,
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help="How often to save the model checkpoint.")
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parser.add_argument("--no_cuda",
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default=False,
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action='store_true',
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help="Whether not to use CUDA when available")
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parser.add_argument("--local_rank",
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type=int,
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default=-1,
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help="local_rank for distributed training on gpus")
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parser.add_argument('--seed',
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type=int,
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default=42,
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help="random seed for initialization")
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args = parser.parse_args()
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processors = {
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"cola": ColaProcessor,
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"mnli": MnliProcessor,
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"mrpc": MrpcProcessor,
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}
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if args.local_rank == -1 or args.no_cuda:
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device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
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n_gpu = torch.cuda.device_count()
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else:
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device = torch.device("cuda", args.local_rank)
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n_gpu = 1
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# print("Initializing the distributed backend: NCCL")
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print("device", device, "n_gpu", n_gpu)
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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if n_gpu>0: torch.cuda.manual_seed_all(args.seed)
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if not args.do_train and not args.do_eval:
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raise ValueError("At least one of `do_train` or `do_eval` must be True.")
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bert_config = BertConfig.from_json_file(args.bert_config_file)
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if args.max_seq_length > bert_config.max_position_embeddings:
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raise ValueError(
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"Cannot use sequence length {} because the BERT model was only trained up to sequence length {}".format(
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args.max_seq_length, bert_config.max_position_embeddings))
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if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
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raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
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os.makedirs(args.output_dir, exist_ok=True)
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task_name = args.task_name.lower()
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if task_name not in processors:
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raise ValueError("Task not found: %s" % (task_name))
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processor = processors[task_name]()
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label_list = processor.get_labels()
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tokenizer = tokenization_pytorch.FullTokenizer(
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vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
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train_examples = None
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num_train_steps = None
|
|
if args.do_train:
|
|
train_examples = processor.get_train_examples(args.data_dir)
|
|
num_train_steps = int(
|
|
len(train_examples) / args.train_batch_size * args.num_train_epochs)
|
|
|
|
model = BertForSequenceClassification(bert_config, len(label_list))
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|
if args.init_checkpoint is not None:
|
|
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
|
|
model.to(device)
|
|
|
|
if n_gpu > 1:
|
|
model = torch.nn.DataParallel(model)
|
|
|
|
no_decay = ['bias', 'gamma', 'beta']
|
|
optimizer_parameters = [
|
|
{'params': [p for n, p in model.named_parameters() if n not in no_decay], 'weight_decay_rate': 0.01},
|
|
{'params': [p for n, p in model.named_parameters() if n in no_decay], 'weight_decay_rate': 0.0}
|
|
]
|
|
|
|
optimizer = BERTAdam(optimizer_parameters,
|
|
lr=args.learning_rate,
|
|
warmup=args.warmup_proportion,
|
|
t_total=num_train_steps)
|
|
|
|
global_step = 0
|
|
if args.do_train:
|
|
train_features = convert_examples_to_features(
|
|
train_examples, label_list, args.max_seq_length, tokenizer)
|
|
logger.info("***** Running training *****")
|
|
logger.info(" Num examples = %d", len(train_examples))
|
|
logger.info(" Batch size = %d", args.train_batch_size)
|
|
logger.info(" Num steps = %d", num_train_steps)
|
|
|
|
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
|
|
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
|
|
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
|
|
|
|
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
|
if args.local_rank == -1:
|
|
train_sampler = RandomSampler(train_data)
|
|
else:
|
|
train_sampler = DistributedSampler(train_data)
|
|
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
|
|
|
|
model.train()
|
|
|
|
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
|
|
tr_loss = 0
|
|
nb_tr_examples, nb_tr_steps = 0, 0
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(train_dataloader, desc="Iteration"):
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.float().to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
label_ids = label_ids.to(device)
|
|
|
|
loss, _ = model(input_ids, segment_ids, input_mask, label_ids)
|
|
loss = loss.mean() # sum() is to account for multi-gpu support.
|
|
tr_loss += loss.item()
|
|
nb_tr_examples += input_ids.size(0)
|
|
model.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
global_step += 1
|
|
nb_tr_steps += 1
|
|
|
|
if args.do_eval:
|
|
eval_examples = processor.get_dev_examples(args.data_dir)
|
|
eval_features = convert_examples_to_features(
|
|
eval_examples, label_list, args.max_seq_length, tokenizer)
|
|
|
|
logger.info("***** Running evaluation *****")
|
|
logger.info(" Num examples = %d", len(eval_examples))
|
|
logger.info(" Batch size = %d", args.eval_batch_size)
|
|
|
|
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
|
|
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
|
|
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
|
|
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
|
if args.local_rank == -1:
|
|
eval_sampler = SequentialSampler(eval_data)
|
|
else:
|
|
eval_sampler = DistributedSampler(eval_data)
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
|
|
|
model.eval()
|
|
eval_loss, eval_accuracy = 0, 0
|
|
nb_eval_steps, nb_eval_examples = 0
|
|
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.float().to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
label_ids = label_ids.to(device)
|
|
|
|
tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids)
|
|
|
|
logits = logits.detach().cpu().numpy()
|
|
label_ids = label_ids.to('cpu').numpy()
|
|
tmp_eval_accuracy = accuracy(logits, label_ids)
|
|
|
|
eval_loss += tmp_eval_loss.mean().item()
|
|
eval_accuracy += tmp_eval_accuracy
|
|
|
|
nb_eval_examples += input_ids.size(0)
|
|
nb_eval_steps += 1
|
|
|
|
eval_loss = eval_loss / nb_eval_steps #len(eval_dataloader)
|
|
eval_accuracy = eval_accuracy / nb_eval_examples #len(eval_dataloader)
|
|
|
|
result = {'eval_loss': eval_loss,
|
|
'eval_accuracy': eval_accuracy,
|
|
'global_step': global_step,
|
|
'loss': tr_loss/nb_tr_steps}#'loss': loss.item()}
|
|
|
|
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
|
with open(output_eval_file, "w") as writer:
|
|
logger.info("***** Eval results *****")
|
|
for key in sorted(result.keys()):
|
|
logger.info(" %s = %s", key, str(result[key]))
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
|
|
|
if __name__ == "__main__":
|
|
main()
|