Merge pull request #96 from rodgzilla/multiple-choice-code
BertForMultipleChoice and Swag dataset example.
This commit is contained in:
commit
ffe9075f48
48
README.md
48
README.md
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@ -52,8 +52,9 @@ This package comprises the following classes that can be imported in Python and
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- [`BertForNextSentencePrediction`](./pytorch_pretrained_bert/modeling.py#L752) - BERT Transformer with the pre-trained next sentence prediction classifier on top (**fully pre-trained**),
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- [`BertForPreTraining`](./pytorch_pretrained_bert/modeling.py#L620) - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (**fully pre-trained**),
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- [`BertForSequenceClassification`](./pytorch_pretrained_bert/modeling.py#L814) - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
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- [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
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- [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L946) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
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- [`BertForMultipleChoice`](./pytorch_pretrained_bert/modeling.py#L880) - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**, the sequence classification head **is only initialized and has to be trained**),
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- [`BertForTokenClassification`](./pytorch_pretrained_bert/modeling.py#L949) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**),
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- [`BertForQuestionAnswering`](./pytorch_pretrained_bert/modeling.py#L1015) - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**, the token classification head **is only initialized and has to be trained**).
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- Three tokenizers (in the [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) file):
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- `BasicTokenizer` - basic tokenization (punctuation splitting, lower casing, etc.),
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@ -68,10 +69,11 @@ This package comprises the following classes that can be imported in Python and
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The repository further comprises:
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- Three examples on how to use Bert (in the [`examples` folder](./examples)):
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- Four examples on how to use Bert (in the [`examples` folder](./examples)):
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- [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`,
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- [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task,
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- [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task.
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- [`run_swag.py`](./examples/run_swag.py) - Show how to fine-tune an instance of `BertForMultipleChoice` on Swag task.
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These examples are detailed in the [Examples](#examples) section of this readme.
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@ -278,13 +280,23 @@ The sequence-level classifier is a linear layer that takes as input the last hid
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An example on how to use this class is given in the [`run_classifier.py`](./examples/run_classifier.py) script which can be used to fine-tune a single sequence (or pair of sequence) classifier using BERT, for example for the MRPC task.
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#### 6. `BertForTokenClassification`
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#### 6. `BertForMultipleChoice`
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`BertForMultipleChoice` is a fine-tuning model that includes `BertModel` and a linear layer on top of the `BertModel`.
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The linear layer outputs a single value for each choice of a multiple choice problem, then all the outputs corresponding to an instance are passed through a softmax to get the model choice.
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This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38).
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An example on how to use this class is given in the [`run_swag.py`](./examples/run_swag.py) script which can be used to fine-tune a multiple choice classifier using BERT, for example for the Swag task.
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#### 7. `BertForTokenClassification`
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`BertForTokenClassification` is a fine-tuning model that includes `BertModel` and a token-level classifier on top of the `BertModel`.
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The token-level classifier is a linear layer that takes as input the last hidden state of the sequence.
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#### 7. `BertForQuestionAnswering`
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#### 8. `BertForQuestionAnswering`
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`BertForQuestionAnswering` is a fine-tuning model that includes `BertModel` with a token-level classifiers on top of the full sequence of last hidden states.
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@ -420,6 +432,32 @@ Training with the previous hyper-parameters gave us the following results:
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{"f1": 88.52381567990474, "exact_match": 81.22043519394512}
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```
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The data for Swag can be downloaded by cloning the following [repository](https://github.com/rowanz/swagaf)
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```shell
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export SWAG_DIR=/path/to/SWAG
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python run_swag.py \
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--bert_model bert-base-uncased \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR/data
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--train_batch_size 16 \
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--learning_rate 2e-5 \
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--num_train_epochs 3.0 \
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--max_seq_length 80 \
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--output_dir /tmp/swag_output/
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--gradient_accumulation_steps 4
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```
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Training with the previous hyper-parameters gave us the following results:
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```
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eval_accuracy = 0.8062081375587323
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eval_loss = 0.5966546792367169
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global_step = 13788
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loss = 0.06423990014260186
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```
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## Fine-tuning BERT-large on GPUs
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The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation.
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@ -0,0 +1,544 @@
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# 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 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 csv
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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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with open(input_file, 'r') as f:
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reader = csv.reader(f)
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lines = list(reader)
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if is_training and lines[0][-1] != 'label':
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raise ValueError(
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"For training, the input file must contain a label column."
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)
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examples = [
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SwagExample(
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swag_id = line[2],
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context_sentence = line[4],
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start_ending = line[5], # 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 = line[7],
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ending_1 = line[8],
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ending_2 = line[9],
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ending_3 = line[10],
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label = int(line[11]) if is_training else None
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) for line in lines[1:] # we skip the line with the column names
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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")
|
||||
parser.add_argument('--seed',
|
||||
type=int,
|
||||
default=42,
|
||||
help="random seed for initialization")
|
||||
parser.add_argument('--gradient_accumulation_steps',
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of updates steps to accumulate before performing a backward/update pass.")
|
||||
parser.add_argument('--optimize_on_cpu',
|
||||
default=False,
|
||||
action='store_true',
|
||||
help="Whether to perform optimization and keep the optimizer averages on CPU")
|
||||
parser.add_argument('--fp16',
|
||||
default=False,
|
||||
action='store_true',
|
||||
help="Whether to use 16-bit float precision instead of 32-bit")
|
||||
parser.add_argument('--loss_scale',
|
||||
type=float, default=128,
|
||||
help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.local_rank == -1 or args.no_cuda:
|
||||
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
||||
n_gpu = torch.cuda.device_count()
|
||||
else:
|
||||
device = torch.device("cuda", args.local_rank)
|
||||
n_gpu = 1
|
||||
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
||||
torch.distributed.init_process_group(backend='nccl')
|
||||
if args.fp16:
|
||||
logger.info("16-bits training currently not supported in distributed training")
|
||||
args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
|
||||
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu, bool(args.local_rank != -1))
|
||||
|
||||
if args.gradient_accumulation_steps < 1:
|
||||
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
|
||||
args.gradient_accumulation_steps))
|
||||
|
||||
args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
|
||||
|
||||
random.seed(args.seed)
|
||||
np.random.seed(args.seed)
|
||||
torch.manual_seed(args.seed)
|
||||
if n_gpu > 0:
|
||||
torch.cuda.manual_seed_all(args.seed)
|
||||
|
||||
if not args.do_train and not args.do_eval:
|
||||
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
|
||||
|
||||
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
|
||||
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
|
||||
os.makedirs(args.output_dir, exist_ok=True)
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
|
||||
|
||||
train_examples = None
|
||||
num_train_steps = None
|
||||
if args.do_train:
|
||||
train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
|
||||
num_train_steps = int(
|
||||
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
|
||||
|
||||
# Prepare model
|
||||
model = BertForMultipleChoice.from_pretrained(args.bert_model,
|
||||
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
|
||||
num_choices = 4
|
||||
)
|
||||
if args.fp16:
|
||||
model.half()
|
||||
model.to(device)
|
||||
if args.local_rank != -1:
|
||||
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
|
||||
output_device=args.local_rank)
|
||||
elif n_gpu > 1:
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# Prepare optimizer
|
||||
if args.fp16:
|
||||
param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_()) \
|
||||
for n, param in model.named_parameters()]
|
||||
elif args.optimize_on_cpu:
|
||||
param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_()) \
|
||||
for n, param in model.named_parameters()]
|
||||
else:
|
||||
param_optimizer = list(model.named_parameters())
|
||||
no_decay = ['bias', 'gamma', 'beta']
|
||||
optimizer_grouped_parameters = [
|
||||
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
|
||||
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
|
||||
]
|
||||
t_total = num_train_steps
|
||||
if args.local_rank != -1:
|
||||
t_total = t_total // torch.distributed.get_world_size()
|
||||
optimizer = BertAdam(optimizer_grouped_parameters,
|
||||
lr=args.learning_rate,
|
||||
warmup=args.warmup_proportion,
|
||||
t_total=t_total)
|
||||
|
||||
global_step = 0
|
||||
if args.do_train:
|
||||
train_features = convert_examples_to_features(
|
||||
train_examples, tokenizer, args.max_seq_length, True)
|
||||
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(select_field(train_features, 'input_ids'), dtype=torch.long)
|
||||
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
|
||||
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
|
||||
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
|
||||
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
|
||||
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 _ in trange(int(args.num_train_epochs), desc="Epoch"):
|
||||
tr_loss = 0
|
||||
nb_tr_examples, nb_tr_steps = 0, 0
|
||||
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
|
||||
batch = tuple(t.to(device) for t in batch)
|
||||
input_ids, input_mask, segment_ids, label_ids = batch
|
||||
loss = model(input_ids, segment_ids, input_mask, label_ids)
|
||||
if n_gpu > 1:
|
||||
loss = loss.mean() # mean() to average on multi-gpu.
|
||||
if args.fp16 and args.loss_scale != 1.0:
|
||||
# rescale loss for fp16 training
|
||||
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
|
||||
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()
|
|
@ -1,7 +1,7 @@
|
|||
from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
|
||||
from .modeling import (BertConfig, BertModel, BertForPreTraining,
|
||||
BertForMaskedLM, BertForNextSentencePrediction,
|
||||
BertForSequenceClassification, BertForTokenClassification,
|
||||
BertForQuestionAnswering)
|
||||
BertForSequenceClassification, BertForMultipleChoice,
|
||||
BertForTokenClassification, BertForQuestionAnswering)
|
||||
from .optimization import BertAdam
|
||||
from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE
|
||||
|
|
|
@ -877,6 +877,75 @@ class BertForSequenceClassification(PreTrainedBertModel):
|
|||
return logits
|
||||
|
||||
|
||||
class BertForMultipleChoice(PreTrainedBertModel):
|
||||
"""BERT model for multiple choice tasks.
|
||||
This module is composed of the BERT model with a linear layer on top of
|
||||
the pooled output.
|
||||
|
||||
Params:
|
||||
`config`: a BertConfig class instance with the configuration to build a new model.
|
||||
`num_choices`: the number of classes for the classifier. Default = 2.
|
||||
|
||||
Inputs:
|
||||
`input_ids`: a torch.LongTensor of shape [batch_size, num_choices, sequence_length]
|
||||
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
|
||||
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
|
||||
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length]
|
||||
with the token types indices selected in [0, 1]. Type 0 corresponds to a `sentence A`
|
||||
and type 1 corresponds to a `sentence B` token (see BERT paper for more details).
|
||||
`attention_mask`: an optional torch.LongTensor of shape [batch_size, num_choices, sequence_length] with indices
|
||||
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
|
||||
input sequence length in the current batch. It's the mask that we typically use for attention when
|
||||
a batch has varying length sentences.
|
||||
`labels`: labels for the classification output: torch.LongTensor of shape [batch_size]
|
||||
with indices selected in [0, ..., num_choices].
|
||||
|
||||
Outputs:
|
||||
if `labels` is not `None`:
|
||||
Outputs the CrossEntropy classification loss of the output with the labels.
|
||||
if `labels` is `None`:
|
||||
Outputs the classification logits of shape [batch_size, num_labels].
|
||||
|
||||
Example usage:
|
||||
```python
|
||||
# Already been converted into WordPiece token ids
|
||||
input_ids = torch.LongTensor([[[31, 51, 99], [15, 5, 0]], [[12, 16, 42], [14, 28, 57]]])
|
||||
input_mask = torch.LongTensor([[[1, 1, 1], [1, 1, 0]],[[1,1,0], [1, 0, 0]]])
|
||||
token_type_ids = torch.LongTensor([[[0, 0, 1], [0, 1, 0]],[[0, 1, 1], [0, 0, 1]]])
|
||||
config = BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
|
||||
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
|
||||
|
||||
num_choices = 2
|
||||
|
||||
model = BertForMultipleChoice(config, num_choices)
|
||||
logits = model(input_ids, token_type_ids, input_mask)
|
||||
```
|
||||
"""
|
||||
def __init__(self, config, num_choices=2):
|
||||
super(BertForMultipleChoice, self).__init__(config)
|
||||
self.num_choices = num_choices
|
||||
self.bert = BertModel(config)
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.classifier = nn.Linear(config.hidden_size, 1)
|
||||
self.apply(self.init_bert_weights)
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None):
|
||||
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
|
||||
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1))
|
||||
_, pooled_output = self.bert(flat_input_ids, flat_token_type_ids, flat_attention_mask, output_all_encoded_layers=False)
|
||||
pooled_output = self.dropout(pooled_output)
|
||||
logits = self.classifier(pooled_output)
|
||||
reshaped_logits = logits.view(-1, self.num_choices)
|
||||
|
||||
if labels is not None:
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(reshaped_logits, labels)
|
||||
return loss
|
||||
else:
|
||||
return reshaped_logits
|
||||
|
||||
|
||||
class BertForTokenClassification(PreTrainedBertModel):
|
||||
"""BERT model for token-level classification.
|
||||
This module is composed of the BERT model with a linear layer on top of
|
||||
|
|
Loading…
Reference in New Issue