543 lines
24 KiB
Python
543 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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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"""BERT finetuning runner."""
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import pandas as pd
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import logging
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import os
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import argparse
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import random
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from tqdm import tqdm, trange
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import numpy as np
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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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from pytorch_pretrained_bert.tokenization import BertTokenizer
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from pytorch_pretrained_bert.modeling import BertForMultipleChoice
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from pytorch_pretrained_bert.optimization import BertAdam
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from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
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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 SwagExample(object):
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"""A single training/test example for the SWAG dataset."""
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def __init__(self,
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swag_id,
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context_sentence,
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start_ending,
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ending_0,
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ending_1,
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ending_2,
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ending_3,
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label = None):
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self.swag_id = swag_id
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self.context_sentence = context_sentence
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self.start_ending = start_ending
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self.endings = [
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ending_0,
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ending_1,
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ending_2,
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ending_3,
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]
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self.label = label
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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l = [
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f"swag_id: {self.swag_id}",
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f"context_sentence: {self.context_sentence}",
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f"start_ending: {self.start_ending}",
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f"ending_0: {self.endings[0]}",
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f"ending_1: {self.endings[1]}",
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f"ending_2: {self.endings[2]}",
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f"ending_3: {self.endings[3]}",
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]
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if self.label is not None:
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l.append(f"label: {self.label}")
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return ", ".join(l)
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class InputFeatures(object):
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def __init__(self,
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example_id,
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choices_features,
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label
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):
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self.example_id = example_id
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self.choices_features = [
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{
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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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}
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for _, input_ids, input_mask, segment_ids in choices_features
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]
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self.label = label
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def read_swag_examples(input_file, is_training):
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input_df = pd.read_csv(input_file)
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if is_training and 'label' not in input_df.columns:
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raise ValueError(
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"For training, the input file must contain a label column.")
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examples = [
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SwagExample(
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swag_id = row['fold-ind'],
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context_sentence = row['sent1'],
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start_ending = row['sent2'], # in the swag dataset, the
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# common beginning of each
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# choice is stored in "sent2".
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ending_0 = row['ending0'],
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ending_1 = row['ending1'],
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ending_2 = row['ending2'],
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ending_3 = row['ending3'],
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label = row['label'] if is_training else None
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) for _, row in input_df.iterrows()
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]
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return examples
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def convert_examples_to_features(examples, tokenizer, max_seq_length,
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is_training):
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"""Loads a data file into a list of `InputBatch`s."""
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# Swag is a multiple choice task. To perform this task using Bert,
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# we will use the formatting proposed in "Improving Language
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# Understanding by Generative Pre-Training" and suggested by
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# @jacobdevlin-google in this issue
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# https://github.com/google-research/bert/issues/38.
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#
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# Each choice will correspond to a sample on which we run the
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# inference. For a given Swag example, we will create the 4
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# following inputs:
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# - [CLS] context [SEP] choice_1 [SEP]
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# - [CLS] context [SEP] choice_2 [SEP]
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# - [CLS] context [SEP] choice_3 [SEP]
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# - [CLS] context [SEP] choice_4 [SEP]
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# The model will output a single value for each input. To get the
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# final decision of the model, we will run a softmax over these 4
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# outputs.
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features = []
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for example_index, example in enumerate(examples):
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context_tokens = tokenizer.tokenize(example.context_sentence)
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start_ending_tokens = tokenizer.tokenize(example.start_ending)
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choices_features = []
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for ending_index, ending in enumerate(example.endings):
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# We create a copy of the context tokens in order to be
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# able to shrink it according to ending_tokens
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context_tokens_choice = context_tokens[:]
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ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
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# Modifies `context_tokens_choice` and `ending_tokens` in
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# place so that the total length is less than the
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# specified length. Account for [CLS], [SEP], [SEP] with
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# "- 3"
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_truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
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tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
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segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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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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padding = [0] * (max_seq_length - len(input_ids))
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input_ids += padding
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input_mask += padding
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segment_ids += padding
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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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choices_features.append((tokens, input_ids, input_mask, segment_ids))
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label = example.label
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if example_index < 5:
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logger.info("*** Example ***")
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logger.info(f"swag_id: {example.swag_id}")
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for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
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logger.info(f"choice: {choice_idx}")
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logger.info(f"tokens: {' '.join(tokens)}")
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logger.info(f"input_ids: {' '.join(map(str, input_ids))}")
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logger.info(f"input_mask: {' '.join(map(str, input_mask))}")
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logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}")
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if is_training:
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logger.info(f"label: {label}")
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features.append(
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InputFeatures(
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example_id = example.swag_id,
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choices_features = choices_features,
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label = label
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)
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)
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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 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 select_field(features, field):
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return [
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[
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choice[field]
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for choice in feature.choices_features
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]
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for feature in features
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]
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def copy_optimizer_params_to_model(named_params_model, named_params_optimizer):
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""" Utility function for optimize_on_cpu and 16-bits training.
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Copy the parameters optimized on CPU/RAM back to the model on GPU
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"""
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for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
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if name_opti != name_model:
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logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
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raise ValueError
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param_model.data.copy_(param_opti.data)
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def set_optimizer_params_grad(named_params_optimizer, named_params_model, test_nan=False):
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""" Utility function for optimize_on_cpu and 16-bits training.
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Copy the gradient of the GPU parameters to the CPU/RAMM copy of the model
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"""
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is_nan = False
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for (name_opti, param_opti), (name_model, param_model) in zip(named_params_optimizer, named_params_model):
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if name_opti != name_model:
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logger.error("name_opti != name_model: {} {}".format(name_opti, name_model))
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raise ValueError
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if param_model.grad is not None:
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if test_nan and torch.isnan(param_model.grad).sum() > 0:
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is_nan = True
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if param_opti.grad is None:
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param_opti.grad = torch.nn.Parameter(param_opti.data.new().resize_(*param_opti.data.size()))
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param_opti.grad.data.copy_(param_model.grad.data)
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else:
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param_opti.grad = None
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return is_nan
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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 .csv files (or other data files) for the task.")
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parser.add_argument("--bert_model", default=None, type=str, required=True,
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help="Bert pre-trained model selected in the list: bert-base-uncased, "
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"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
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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("--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("--do_lower_case",
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default=False,
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action='store_true',
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help="Set this flag if you are using an uncased model.")
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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("--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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parser.add_argument('--gradient_accumulation_steps',
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.")
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parser.add_argument('--optimize_on_cpu',
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default=False,
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action='store_true',
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help="Whether to perform optimization and keep the optimizer averages on CPU")
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parser.add_argument('--fp16',
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default=False,
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action='store_true',
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help="Whether to use 16-bit float precision instead of 32-bit")
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parser.add_argument('--loss_scale',
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type=float, default=128,
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help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
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args = parser.parse_args()
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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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# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
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torch.distributed.init_process_group(backend='nccl')
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if args.fp16:
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logger.info("16-bits training currently not supported in distributed training")
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args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
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logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1))
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if args.gradient_accumulation_steps < 1:
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raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
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args.gradient_accumulation_steps))
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args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
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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:
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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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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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tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
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train_examples = None
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num_train_steps = None
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if args.do_train:
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train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
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num_train_steps = int(
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len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
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# Prepare model
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model = BertForMultipleChoice.from_pretrained(args.bert_model,
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cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
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num_choices = 4
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)
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if args.fp16:
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model.half()
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model.to(device)
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if args.local_rank != -1:
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model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
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output_device=args.local_rank)
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elif n_gpu > 1:
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model = torch.nn.DataParallel(model)
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# Prepare optimizer
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if args.fp16:
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param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \
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for n, param in model.named_parameters()]
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elif args.optimize_on_cpu:
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param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \
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for n, param in model.named_parameters()]
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else:
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param_optimizer = list(model.named_parameters())
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no_decay = ['bias', 'gamma', 'beta']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
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{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
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]
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t_total = num_train_steps
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if args.local_rank != -1:
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t_total = t_total // torch.distributed.get_world_size()
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optimizer = BertAdam(optimizer_grouped_parameters,
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lr=args.learning_rate,
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warmup=args.warmup_proportion,
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t_total=t_total)
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global_step = 0
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if args.do_train:
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train_features = convert_examples_to_features(
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train_examples, tokenizer, args.max_seq_length, True)
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logger.info("***** Running training *****")
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logger.info(" Num examples = %d", len(train_examples))
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logger.info(" Batch size = %d", args.train_batch_size)
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logger.info(" Num steps = %d", num_train_steps)
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all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
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all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
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all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
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all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
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train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
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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=args.train_batch_size)
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model.train()
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for _ in trange(int(args.num_train_epochs), desc="Epoch"):
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tr_loss = 0
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nb_tr_examples, nb_tr_steps = 0, 0
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for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
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batch = tuple(t.to(device) for t in batch)
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input_ids, input_mask, segment_ids, label_ids = batch
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loss = model(input_ids, segment_ids, input_mask, label_ids)
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if n_gpu > 1:
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loss = loss.mean() # mean() to average on multi-gpu.
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if args.fp16 and args.loss_scale != 1.0:
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# rescale loss for fp16 training
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# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
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|
loss = loss * args.loss_scale
|
|
if args.gradient_accumulation_steps > 1:
|
|
loss = loss / args.gradient_accumulation_steps
|
|
loss.backward()
|
|
tr_loss += loss.item()
|
|
nb_tr_examples += input_ids.size(0)
|
|
nb_tr_steps += 1
|
|
if (step + 1) % args.gradient_accumulation_steps == 0:
|
|
if args.fp16 or args.optimize_on_cpu:
|
|
if args.fp16 and args.loss_scale != 1.0:
|
|
# scale down gradients for fp16 training
|
|
for param in model.parameters():
|
|
if param.grad is not None:
|
|
param.grad.data = param.grad.data / args.loss_scale
|
|
is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
|
|
if is_nan:
|
|
logger.info("FP16 TRAINING: Nan in gradients, reducing loss scaling")
|
|
args.loss_scale = args.loss_scale / 2
|
|
model.zero_grad()
|
|
continue
|
|
optimizer.step()
|
|
copy_optimizer_params_to_model(model.named_parameters(), param_optimizer)
|
|
else:
|
|
optimizer.step()
|
|
model.zero_grad()
|
|
global_step += 1
|
|
|
|
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
|
eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
|
|
eval_features = convert_examples_to_features(
|
|
eval_examples, tokenizer, args.max_seq_length, True)
|
|
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(select_field(eval_features, 'input_ids'), dtype=torch.long)
|
|
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
|
|
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
|
|
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
|
|
# Run prediction for full data
|
|
eval_sampler = SequentialSampler(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, 0
|
|
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
label_ids = label_ids.to(device)
|
|
|
|
with torch.no_grad():
|
|
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
|
|
logits = model(input_ids, segment_ids, input_mask)
|
|
|
|
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
|
|
eval_accuracy = eval_accuracy / nb_eval_examples
|
|
|
|
result = {'eval_loss': eval_loss,
|
|
'eval_accuracy': eval_accuracy,
|
|
'global_step': global_step,
|
|
'loss': tr_loss/nb_tr_steps}
|
|
|
|
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()
|