Merge pull request #2046 from jplu/tf2-ner-example
Add NER TF2 example.
This commit is contained in:
Thomas Wolf
@@ -467,7 +467,8 @@ Training with the previously defined hyper-parameters yields the following resul
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## Named Entity Recognition
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Based on the script [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py).
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Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) for Pytorch and
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[`run_tf_ner.py`(https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py)] for Tensorflow 2.
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This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
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Details and results for the fine-tuning provided by @stefan-it.
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@@ -512,7 +513,7 @@ The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so
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cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
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```
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### Training
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### Prepare the run
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Additional environment variables must be set:
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@@ -524,6 +525,8 @@ export SAVE_STEPS=750
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export SEED=1
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```
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### Run the Pytorch version
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To start training, just run:
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```bash
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@@ -544,7 +547,7 @@ python3 run_ner.py --data_dir ./ \
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If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
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### Evaluation
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#### Evaluation
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Evaluation on development dataset outputs the following for our example:
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@@ -566,7 +569,7 @@ On the test dataset the following results could be achieved:
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10/04/2019 00:42:42 - INFO - __main__ - recall = 0.8624150210424085
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```
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### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
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#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
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Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
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@@ -576,6 +579,72 @@ Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) a
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| `roberta-large` | 95.96 | 91.87
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| `distilbert-base-uncased` | 94.34 | 90.32
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### Run the Tensorflow 2 version
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To start training, just run:
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```bash
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python3 run_tf_ner.py --data_dir ./ \
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--model_type bert \
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--labels ./labels.txt \
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--model_name_or_path $BERT_MODEL \
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--output_dir $OUTPUT_DIR \
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--max_seq_length $MAX_LENGTH \
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--num_train_epochs $NUM_EPOCHS \
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--per_device_train_batch_size $BATCH_SIZE \
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--save_steps $SAVE_STEPS \
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--seed $SEED \
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--do_train \
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--do_eval \
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--do_predict
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```
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Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
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#### Evaluation
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Evaluation on development dataset outputs the following for our example:
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```bash
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precision recall f1-score support
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LOCderiv 0.7619 0.6154 0.6809 52
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PERpart 0.8724 0.8997 0.8858 4057
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OTHpart 0.9360 0.9466 0.9413 711
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ORGpart 0.7015 0.6989 0.7002 269
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LOCpart 0.7668 0.8488 0.8057 496
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LOC 0.8745 0.9191 0.8963 235
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ORGderiv 0.7723 0.8571 0.8125 91
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OTHderiv 0.4800 0.6667 0.5581 18
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OTH 0.5789 0.6875 0.6286 16
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PERderiv 0.5385 0.3889 0.4516 18
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PER 0.5000 0.5000 0.5000 2
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ORG 0.0000 0.0000 0.0000 3
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micro avg 0.8574 0.8862 0.8715 5968
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macro avg 0.8575 0.8862 0.8713 5968
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```
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On the test dataset the following results could be achieved:
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```bash
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precision recall f1-score support
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PERpart 0.8847 0.8944 0.8896 9397
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OTHpart 0.9376 0.9353 0.9365 1639
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ORGpart 0.7307 0.7044 0.7173 697
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LOC 0.9133 0.9394 0.9262 561
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LOCpart 0.8058 0.8157 0.8107 1150
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ORG 0.0000 0.0000 0.0000 8
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OTHderiv 0.5882 0.4762 0.5263 42
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PERderiv 0.6571 0.5227 0.5823 44
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OTH 0.4906 0.6667 0.5652 39
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ORGderiv 0.7016 0.7791 0.7383 172
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LOCderiv 0.8256 0.6514 0.7282 109
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PER 0.0000 0.0000 0.0000 11
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micro avg 0.8722 0.8774 0.8748 13869
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macro avg 0.8712 0.8774 0.8740 13869
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```
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## Abstractive summarization
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Based on the script
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615
examples/run_tf_ner.py
Normal file
615
examples/run_tf_ner.py
Normal file
@@ -0,0 +1,615 @@
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# coding=utf-8
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import datetime
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import os
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import math
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import glob
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import re
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import tensorflow as tf
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import collections
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import numpy as np
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from seqeval import metrics
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import _pickle as pickle
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from absl import logging
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from transformers import TF2_WEIGHTS_NAME, BertConfig, BertTokenizer, TFBertForTokenClassification
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from transformers import RobertaConfig, RobertaTokenizer, TFRobertaForTokenClassification
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from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForTokenClassification
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from transformers import create_optimizer, GradientAccumulator
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from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
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from fastprogress import master_bar, progress_bar
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from absl import flags
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from absl import app
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ALL_MODELS = sum(
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(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)),
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())
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MODEL_CLASSES = {
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"bert": (BertConfig, TFBertForTokenClassification, BertTokenizer),
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"roberta": (RobertaConfig, TFRobertaForTokenClassification, RobertaTokenizer),
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"distilbert": (DistilBertConfig, TFDistilBertForTokenClassification, DistilBertTokenizer)
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}
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flags.DEFINE_string(
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"data_dir", None,
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"The input data dir. Should contain the .conll files (or other data files) "
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"for the task.")
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flags.DEFINE_string(
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"model_type", None,
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"Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
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flags.DEFINE_string(
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"model_name_or_path", None,
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"Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
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flags.DEFINE_string(
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"output_dir", None,
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"The output directory where the model checkpoints will be written.")
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flags.DEFINE_string(
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"labels", "",
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"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.")
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flags.DEFINE_string(
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"config_name", "",
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"Pretrained config name or path if not the same as model_name")
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flags.DEFINE_string(
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"tokenizer_name", "",
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"Pretrained tokenizer name or path if not the same as model_name")
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flags.DEFINE_string(
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"cache_dir", "",
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"Where do you want to store the pre-trained models downloaded from s3")
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flags.DEFINE_integer(
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"max_seq_length", 128,
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"The maximum total input sentence length after tokenization. "
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"Sequences longer than this will be truncated, sequences shorter "
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"will be padded.")
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flags.DEFINE_string(
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"tpu", None,
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"The Cloud TPU to use for training. This should be either the name "
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"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
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"url.")
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flags.DEFINE_integer(
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"num_tpu_cores", 8,
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"Total number of TPU cores to use.")
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flags.DEFINE_boolean(
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"do_train", False,
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"Whether to run training.")
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flags.DEFINE_boolean(
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"do_eval", False,
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"Whether to run eval on the dev set.")
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flags.DEFINE_boolean(
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"do_predict", False,
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"Whether to run predictions on the test set.")
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flags.DEFINE_boolean(
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"evaluate_during_training", False,
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"Whether to run evaluation during training at each logging step.")
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flags.DEFINE_boolean(
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"do_lower_case", False,
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"Set this flag if you are using an uncased model.")
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flags.DEFINE_integer(
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"per_device_train_batch_size", 8,
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"Batch size per GPU/CPU/TPU for training.")
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flags.DEFINE_integer(
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"per_device_eval_batch_size", 8,
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"Batch size per GPU/CPU/TPU for evaluation.")
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flags.DEFINE_integer(
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"gradient_accumulation_steps", 1,
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"Number of updates steps to accumulate before performing a backward/update pass.")
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flags.DEFINE_float(
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"learning_rate", 5e-5,
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"The initial learning rate for Adam.")
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flags.DEFINE_float(
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"weight_decay", 0.0,
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"Weight decay if we apply some.")
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flags.DEFINE_float(
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"adam_epsilon", 1e-8,
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"Epsilon for Adam optimizer.")
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flags.DEFINE_float(
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"max_grad_norm", 1.0,
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"Max gradient norm.")
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flags.DEFINE_integer(
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"num_train_epochs", 3,
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"Total number of training epochs to perform.")
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flags.DEFINE_integer(
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"max_steps", -1,
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"If > 0: set total number of training steps to perform. Override num_train_epochs.")
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flags.DEFINE_integer(
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"warmup_steps", 0,
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"Linear warmup over warmup_steps.")
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flags.DEFINE_integer(
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"logging_steps", 50,
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"Log every X updates steps.")
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flags.DEFINE_integer(
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"save_steps", 50,
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"Save checkpoint every X updates steps.")
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flags.DEFINE_boolean(
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"eval_all_checkpoints", False,
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"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
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flags.DEFINE_boolean(
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"no_cuda", False,
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"Avoid using CUDA when available")
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flags.DEFINE_boolean(
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"overwrite_output_dir", False,
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"Overwrite the content of the output directory")
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flags.DEFINE_boolean(
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"overwrite_cache", False,
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"Overwrite the cached training and evaluation sets")
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flags.DEFINE_integer(
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"seed", 42,
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"random seed for initialization")
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flags.DEFINE_boolean(
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"fp16", False,
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"Whether to use 16-bit (mixed) precision instead of 32-bit")
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flags.DEFINE_string(
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"gpus", "0",
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"Comma separated list of gpus devices. If only one, switch to single "
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"gpu strategy, if None takes all the gpus available.")
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def train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id):
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if args['max_steps'] > 0:
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num_train_steps = args['max_steps'] * args['gradient_accumulation_steps']
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args['num_train_epochs'] = 1
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else:
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num_train_steps = math.ceil(num_train_examples / train_batch_size) // args['gradient_accumulation_steps'] * args['num_train_epochs']
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writer = tf.summary.create_file_writer("/tmp/mylogs")
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with strategy.scope():
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loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
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optimizer = create_optimizer(args['learning_rate'], num_train_steps, args['warmup_steps'])
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if args['fp16']:
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optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(optimizer, 'dynamic')
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loss_metric = tf.keras.metrics.Mean(name='loss', dtype=tf.float32)
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gradient_accumulator = GradientAccumulator()
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logging.info("***** Running training *****")
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logging.info(" Num examples = %d", num_train_examples)
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logging.info(" Num Epochs = %d", args['num_train_epochs'])
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logging.info(" Instantaneous batch size per device = %d", args['per_device_train_batch_size'])
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logging.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
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train_batch_size * args['gradient_accumulation_steps'])
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logging.info(" Gradient Accumulation steps = %d", args['gradient_accumulation_steps'])
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logging.info(" Total training steps = %d", num_train_steps)
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model.summary()
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@tf.function
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def apply_gradients():
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grads_and_vars = []
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for gradient, variable in zip(gradient_accumulator.gradients, model.trainable_variables):
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if gradient is not None:
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scaled_gradient = gradient / (args['n_device'] * args['gradient_accumulation_steps'])
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grads_and_vars.append((scaled_gradient, variable))
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else:
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grads_and_vars.append((gradient, variable))
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optimizer.apply_gradients(grads_and_vars, args['max_grad_norm'])
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gradient_accumulator.reset()
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@tf.function
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def train_step(train_features, train_labels):
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def step_fn(train_features, train_labels):
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inputs = {'attention_mask': train_features['input_mask'], 'training': True}
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if args['model_type'] != "distilbert":
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inputs["token_type_ids"] = train_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None
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with tf.GradientTape() as tape:
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logits = model(train_features['input_ids'], **inputs)[0]
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logits = tf.reshape(logits, (-1, len(labels) + 1))
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active_loss = tf.reshape(train_features['input_mask'], (-1,))
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active_logits = tf.boolean_mask(logits, active_loss)
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train_labels = tf.reshape(train_labels, (-1,))
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active_labels = tf.boolean_mask(train_labels, active_loss)
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cross_entropy = loss_fct(active_labels, active_logits)
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loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size)
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grads = tape.gradient(loss, model.trainable_variables)
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gradient_accumulator(grads)
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return cross_entropy
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per_example_losses = strategy.experimental_run_v2(step_fn, args=(train_features, train_labels))
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mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN, per_example_losses, axis=0)
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return mean_loss
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current_time = datetime.datetime.now()
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train_iterator = master_bar(range(args['num_train_epochs']))
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global_step = 0
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logging_loss = 0.0
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for epoch in train_iterator:
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epoch_iterator = progress_bar(train_dataset, total=num_train_steps, parent=train_iterator, display=args['n_device'] > 1)
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step = 1
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with strategy.scope():
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for train_features, train_labels in epoch_iterator:
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loss = train_step(train_features, train_labels)
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if step % args['gradient_accumulation_steps'] == 0:
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strategy.experimental_run_v2(apply_gradients)
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loss_metric(loss)
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global_step += 1
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if args['logging_steps'] > 0 and global_step % args['logging_steps'] == 0:
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# Log metrics
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if args['n_device'] == 1 and args['evaluate_during_training']: # Only evaluate when single GPU otherwise metrics may not average well
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y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev")
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report = metrics.classification_report(y_true, y_pred, digits=4)
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logging.info("Eval at step " + str(global_step) + "\n" + report)
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logging.info("eval_loss: " + str(eval_loss))
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precision = metrics.precision_score(y_true, y_pred)
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recall = metrics.recall_score(y_true, y_pred)
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f1 = metrics.f1_score(y_true, y_pred)
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with writer.as_default():
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tf.summary.scalar("eval_loss", eval_loss, global_step)
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tf.summary.scalar("precision", precision, global_step)
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tf.summary.scalar("recall", recall, global_step)
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tf.summary.scalar("f1", f1, global_step)
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lr = optimizer.learning_rate
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learning_rate = lr(step)
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with writer.as_default():
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tf.summary.scalar("lr", learning_rate, global_step)
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tf.summary.scalar("loss", (loss_metric.result() - logging_loss) / args['logging_steps'], global_step)
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logging_loss = loss_metric.result()
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with writer.as_default():
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tf.summary.scalar("loss", loss_metric.result(), step=step)
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if args['save_steps'] > 0 and global_step % args['save_steps'] == 0:
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# Save model checkpoint
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output_dir = os.path.join(args['output_dir'], "checkpoint-{}".format(global_step))
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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model.save_pretrained(output_dir)
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logging.info("Saving model checkpoint to %s", output_dir)
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train_iterator.child.comment = f'loss : {loss_metric.result()}'
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step += 1
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train_iterator.write(f'loss epoch {epoch + 1}: {loss_metric.result()}')
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loss_metric.reset_states()
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logging.info(" Training took time = {}".format(datetime.datetime.now() - current_time))
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def evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode):
|
||||
eval_batch_size = args['per_device_eval_batch_size'] * args['n_device']
|
||||
eval_dataset, size = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode=mode)
|
||||
eval_dataset = strategy.experimental_distribute_dataset(eval_dataset)
|
||||
preds = None
|
||||
num_eval_steps = math.ceil(size / eval_batch_size)
|
||||
master = master_bar(range(1))
|
||||
eval_iterator = progress_bar(eval_dataset, total=num_eval_steps, parent=master, display=args['n_device'] > 1)
|
||||
loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
|
||||
loss = 0.0
|
||||
|
||||
logging.info("***** Running evaluation *****")
|
||||
logging.info(" Num examples = %d", size)
|
||||
logging.info(" Batch size = %d", eval_batch_size)
|
||||
|
||||
for eval_features, eval_labels in eval_iterator:
|
||||
inputs = {'attention_mask': eval_features['input_mask'], 'training': False}
|
||||
|
||||
if args['model_type'] != "distilbert":
|
||||
inputs["token_type_ids"] = eval_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None
|
||||
|
||||
with strategy.scope():
|
||||
logits = model(eval_features['input_ids'], **inputs)[0]
|
||||
tmp_logits = tf.reshape(logits, (-1, len(labels) + 1))
|
||||
active_loss = tf.reshape(eval_features['input_mask'], (-1,))
|
||||
active_logits = tf.boolean_mask(tmp_logits, active_loss)
|
||||
tmp_eval_labels = tf.reshape(eval_labels, (-1,))
|
||||
active_labels = tf.boolean_mask(tmp_eval_labels, active_loss)
|
||||
cross_entropy = loss_fct(active_labels, active_logits)
|
||||
loss += tf.reduce_sum(cross_entropy) * (1.0 / eval_batch_size)
|
||||
|
||||
if preds is None:
|
||||
preds = logits.numpy()
|
||||
label_ids = eval_labels.numpy()
|
||||
else:
|
||||
preds = np.append(preds, logits.numpy(), axis=0)
|
||||
label_ids = np.append(label_ids, eval_labels.numpy(), axis=0)
|
||||
|
||||
preds = np.argmax(preds, axis=2)
|
||||
y_pred = [[] for _ in range(label_ids.shape[0])]
|
||||
y_true = [[] for _ in range(label_ids.shape[0])]
|
||||
loss = loss / num_eval_steps
|
||||
|
||||
for i in range(label_ids.shape[0]):
|
||||
for j in range(label_ids.shape[1]):
|
||||
if label_ids[i, j] != pad_token_label_id:
|
||||
y_pred[i].append(labels[preds[i, j] - 1])
|
||||
y_true[i].append(labels[label_ids[i, j] - 1])
|
||||
|
||||
return y_true, y_pred, loss.numpy()
|
||||
|
||||
|
||||
def load_cache(cached_file, max_seq_length):
|
||||
name_to_features = {
|
||||
"input_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
|
||||
"input_mask": tf.io.FixedLenFeature([max_seq_length], tf.int64),
|
||||
"segment_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
|
||||
"label_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
|
||||
}
|
||||
|
||||
def _decode_record(record):
|
||||
example = tf.io.parse_single_example(record, name_to_features)
|
||||
features = {}
|
||||
features['input_ids'] = example['input_ids']
|
||||
features['input_mask'] = example['input_mask']
|
||||
features['segment_ids'] = example['segment_ids']
|
||||
|
||||
return features, example['label_ids']
|
||||
|
||||
d = tf.data.TFRecordDataset(cached_file)
|
||||
d = d.map(_decode_record, num_parallel_calls=4)
|
||||
count = d.reduce(0, lambda x, _: x + 1)
|
||||
|
||||
return d, count.numpy()
|
||||
|
||||
|
||||
def save_cache(features, cached_features_file):
|
||||
writer = tf.io.TFRecordWriter(cached_features_file)
|
||||
|
||||
for (ex_index, feature) in enumerate(features):
|
||||
if ex_index % 5000 == 0:
|
||||
logging.info("Writing example %d of %d" % (ex_index, len(features)))
|
||||
|
||||
def create_int_feature(values):
|
||||
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
|
||||
return f
|
||||
|
||||
record_feature = collections.OrderedDict()
|
||||
record_feature["input_ids"] = create_int_feature(feature.input_ids)
|
||||
record_feature["input_mask"] = create_int_feature(feature.input_mask)
|
||||
record_feature["segment_ids"] = create_int_feature(feature.segment_ids)
|
||||
record_feature["label_ids"] = create_int_feature(feature.label_ids)
|
||||
|
||||
tf_example = tf.train.Example(features=tf.train.Features(feature=record_feature))
|
||||
|
||||
writer.write(tf_example.SerializeToString())
|
||||
|
||||
writer.close()
|
||||
|
||||
|
||||
def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, batch_size, mode):
|
||||
drop_remainder = True if args['tpu'] or mode == 'train' else False
|
||||
|
||||
# Load data features from cache or dataset file
|
||||
cached_features_file = os.path.join(args['data_dir'], "cached_{}_{}_{}.tf_record".format(mode,
|
||||
list(filter(None, args['model_name_or_path'].split("/"))).pop(),
|
||||
str(args['max_seq_length'])))
|
||||
if os.path.exists(cached_features_file) and not args['overwrite_cache']:
|
||||
logging.info("Loading features from cached file %s", cached_features_file)
|
||||
dataset, size = load_cache(cached_features_file, args['max_seq_length'])
|
||||
else:
|
||||
logging.info("Creating features from dataset file at %s", args['data_dir'])
|
||||
examples = read_examples_from_file(args['data_dir'], mode)
|
||||
features = convert_examples_to_features(examples, labels, args['max_seq_length'], tokenizer,
|
||||
cls_token_at_end=bool(args['model_type'] in ["xlnet"]),
|
||||
# xlnet has a cls token at the end
|
||||
cls_token=tokenizer.cls_token,
|
||||
cls_token_segment_id=2 if args['model_type'] in ["xlnet"] else 0,
|
||||
sep_token=tokenizer.sep_token,
|
||||
sep_token_extra=bool(args['model_type'] in ["roberta"]),
|
||||
# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
|
||||
pad_on_left=bool(args['model_type'] in ["xlnet"]),
|
||||
# pad on the left for xlnet
|
||||
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
|
||||
pad_token_segment_id=4 if args['model_type'] in ["xlnet"] else 0,
|
||||
pad_token_label_id=pad_token_label_id
|
||||
)
|
||||
logging.info("Saving features into cached file %s", cached_features_file)
|
||||
save_cache(features, cached_features_file)
|
||||
dataset, size = load_cache(cached_features_file, args['max_seq_length'])
|
||||
|
||||
if mode == 'train':
|
||||
dataset = dataset.repeat()
|
||||
dataset = dataset.shuffle(buffer_size=8192, seed=args['seed'])
|
||||
|
||||
dataset = dataset.batch(batch_size, drop_remainder)
|
||||
dataset = dataset.prefetch(buffer_size=batch_size)
|
||||
|
||||
return dataset, size
|
||||
|
||||
|
||||
def main(_):
|
||||
logging.set_verbosity(logging.INFO)
|
||||
args = flags.FLAGS.flag_values_dict()
|
||||
|
||||
if os.path.exists(args['output_dir']) and os.listdir(
|
||||
args['output_dir']) and args['do_train'] and not args['overwrite_output_dir']:
|
||||
raise ValueError(
|
||||
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
|
||||
args['output_dir']))
|
||||
|
||||
if args['fp16']:
|
||||
tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
|
||||
|
||||
if args['tpu']:
|
||||
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=args['tpu'])
|
||||
tf.config.experimental_connect_to_cluster(resolver)
|
||||
tf.tpu.experimental.initialize_tpu_system(resolver)
|
||||
strategy = tf.distribute.experimental.TPUStrategy(resolver)
|
||||
args['n_device'] = args['num_tpu_cores']
|
||||
elif len(args['gpus'].split(',')) > 1:
|
||||
args['n_device'] = len([f"/gpu:{gpu}" for gpu in args['gpus'].split(',')])
|
||||
strategy = tf.distribute.MirroredStrategy(devices=[f"/gpu:{gpu}" for gpu in args['gpus'].split(',')])
|
||||
elif args['no_cuda']:
|
||||
args['n_device'] = 1
|
||||
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
|
||||
else:
|
||||
args['n_device'] = len(args['gpus'].split(','))
|
||||
strategy = tf.distribute.OneDeviceStrategy(device="/gpu:" + args['gpus'].split(',')[0])
|
||||
|
||||
logging.warning("n_device: %s, distributed training: %s, 16-bits training: %s",
|
||||
args['n_device'], bool(args['n_device'] > 1), args['fp16'])
|
||||
|
||||
labels = get_labels(args['labels'])
|
||||
num_labels = len(labels) + 1
|
||||
pad_token_label_id = 0
|
||||
config_class, model_class, tokenizer_class = MODEL_CLASSES[args['model_type']]
|
||||
config = config_class.from_pretrained(args['config_name'] if args['config_name'] else args['model_name_or_path'],
|
||||
num_labels=num_labels,
|
||||
cache_dir=args['cache_dir'] if args['cache_dir'] else None)
|
||||
|
||||
logging.info("Training/evaluation parameters %s", args)
|
||||
|
||||
# Training
|
||||
if args['do_train']:
|
||||
tokenizer = tokenizer_class.from_pretrained(args['tokenizer_name'] if args['tokenizer_name'] else args['model_name_or_path'],
|
||||
do_lower_case=args['do_lower_case'],
|
||||
cache_dir=args['cache_dir'] if args['cache_dir'] else None)
|
||||
|
||||
with strategy.scope():
|
||||
model = model_class.from_pretrained(args['model_name_or_path'],
|
||||
from_pt=bool(".bin" in args['model_name_or_path']),
|
||||
config=config,
|
||||
cache_dir=args['cache_dir'] if args['cache_dir'] else None)
|
||||
model.layers[-1].activation = tf.keras.activations.softmax
|
||||
|
||||
train_batch_size = args['per_device_train_batch_size'] * args['n_device']
|
||||
train_dataset, num_train_examples = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, train_batch_size, mode="train")
|
||||
train_dataset = strategy.experimental_distribute_dataset(train_dataset)
|
||||
train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id)
|
||||
|
||||
if not os.path.exists(args['output_dir']):
|
||||
os.makedirs(args['output_dir'])
|
||||
|
||||
logging.info("Saving model to %s", args['output_dir'])
|
||||
|
||||
model.save_pretrained(args['output_dir'])
|
||||
tokenizer.save_pretrained(args['output_dir'])
|
||||
|
||||
# Evaluation
|
||||
if args['do_eval']:
|
||||
tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case'])
|
||||
checkpoints = []
|
||||
results = []
|
||||
|
||||
if args['eval_all_checkpoints']:
|
||||
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args['output_dir'] + "/**/" + TF2_WEIGHTS_NAME, recursive=True), key=lambda f: int(''.join(filter(str.isdigit, f)) or -1)))
|
||||
|
||||
logging.info("Evaluate the following checkpoints: %s", checkpoints)
|
||||
|
||||
if len(checkpoints) == 0:
|
||||
checkpoints.append(args['output_dir'])
|
||||
|
||||
for checkpoint in checkpoints:
|
||||
global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final"
|
||||
|
||||
with strategy.scope():
|
||||
model = model_class.from_pretrained(checkpoint)
|
||||
|
||||
y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev")
|
||||
report = metrics.classification_report(y_true, y_pred, digits=4)
|
||||
|
||||
if global_step:
|
||||
results.append({global_step + "_report": report, global_step + "_loss": eval_loss})
|
||||
|
||||
output_eval_file = os.path.join(args['output_dir'], "eval_results.txt")
|
||||
|
||||
with tf.io.gfile.GFile(output_eval_file, "w") as writer:
|
||||
for res in results:
|
||||
for key, val in res.items():
|
||||
if "loss" in key:
|
||||
logging.info(key + " = " + str(val))
|
||||
writer.write(key + " = " + str(val))
|
||||
writer.write("\n")
|
||||
else:
|
||||
logging.info(key)
|
||||
logging.info("\n" + report)
|
||||
writer.write(key + "\n")
|
||||
writer.write(report)
|
||||
writer.write("\n")
|
||||
|
||||
if args['do_predict']:
|
||||
tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case'])
|
||||
model = model_class.from_pretrained(args['output_dir'])
|
||||
eval_batch_size = args['per_device_eval_batch_size'] * args['n_device']
|
||||
predict_dataset, _ = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode="test")
|
||||
y_true, y_pred, pred_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="test")
|
||||
output_test_results_file = os.path.join(args['output_dir'], "test_results.txt")
|
||||
output_test_predictions_file = os.path.join(args['output_dir'], "test_predictions.txt")
|
||||
report = metrics.classification_report(y_true, y_pred, digits=4)
|
||||
|
||||
with tf.io.gfile.GFile(output_test_results_file, "w") as writer:
|
||||
report = metrics.classification_report(y_true, y_pred, digits=4)
|
||||
|
||||
logging.info("\n" + report)
|
||||
|
||||
writer.write(report)
|
||||
writer.write("\n\nloss = " + str(pred_loss))
|
||||
|
||||
with tf.io.gfile.GFile(output_test_predictions_file, "w") as writer:
|
||||
with tf.io.gfile.GFile(os.path.join(args['data_dir'], "test.txt"), "r") as f:
|
||||
example_id = 0
|
||||
|
||||
for line in f:
|
||||
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
|
||||
writer.write(line)
|
||||
|
||||
if not y_pred[example_id]:
|
||||
example_id += 1
|
||||
elif y_pred[example_id]:
|
||||
output_line = line.split()[0] + " " + y_pred[example_id].pop(0) + "\n"
|
||||
writer.write(output_line)
|
||||
else:
|
||||
logging.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
flags.mark_flag_as_required("data_dir")
|
||||
flags.mark_flag_as_required("output_dir")
|
||||
flags.mark_flag_as_required("model_name_or_path")
|
||||
flags.mark_flag_as_required("model_type")
|
||||
app.run(main)
|
||||
Reference in New Issue
Block a user