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# ALBERT (ALBERT: A Lite BERT for Self-supervised Learning of Language Representations)
**WARNING**: This directory is deprecated.
See `nlp/docs/MODEL_GARDEN.md` for the new ALBERT implementation.
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# ALBERT (ALBERT: A Lite BERT for Self-supervised Learning of Language Representations)
**WARNING**: We are on the way to deprecate this directory.
We will add documentation in `nlp/docs` to use the new code in `nlp/modeling`.
The academic paper which describes ALBERT in detail and provides full results on
a number of tasks can be found here: https://arxiv.org/abs/1909.11942.
This repository contains TensorFlow 2.x implementation for ALBERT.
## Contents
* [Contents](#contents)
* [Pre-trained Models](#pre-trained-models)
* [Restoring from Checkpoints](#restoring-from-checkpoints)
* [Set Up](#set-up)
* [Process Datasets](#process-datasets)
* [Fine-tuning with BERT](#fine-tuning-with-bert)
* [Cloud GPUs and TPUs](#cloud-gpus-and-tpus)
* [Sentence and Sentence-pair Classification Tasks](#sentence-and-sentence-pair-classification-tasks)
* [SQuAD 1.1](#squad-1.1)
## Pre-trained Models
We released both checkpoints and tf.hub modules as the pretrained models for
fine-tuning. They are TF 2.x compatible and are converted from the ALBERT v2
checkpoints released in TF 1.x official ALBERT repository
[google-research/albert](https://github.com/google-research/albert)
in order to keep consistent with ALBERT paper.
Our current released checkpoints are exactly the same as TF 1.x official ALBERT
repository.
### Access to Pretrained Checkpoints
Pretrained checkpoints can be found in the following links:
**Note: We implemented ALBERT using Keras functional-style networks in [nlp/modeling](../modeling).
ALBERT V2 models compatible with TF 2.x checkpoints are:**
* **[`ALBERT V2 Base`](https://storage.googleapis.com/cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base.tar.gz)**:
12-layer, 768-hidden, 12-heads, 12M parameters
* **[`ALBERT V2 Large`](https://storage.googleapis.com/cloud-tpu-checkpoints/albert/checkpoints/albert_v2_large.tar.gz)**:
24-layer, 1024-hidden, 16-heads, 18M parameters
* **[`ALBERT V2 XLarge`](https://storage.googleapis.com/cloud-tpu-checkpoints/albert/checkpoints/albert_v2_xlarge.tar.gz)**:
24-layer, 2048-hidden, 32-heads, 60M parameters
* **[`ALBERT V2 XXLarge`](https://storage.googleapis.com/cloud-tpu-checkpoints/albert/checkpoints/albert_v2_xxlarge.tar.gz)**:
12-layer, 4096-hidden, 64-heads, 235M parameters
We recommend to host checkpoints on Google Cloud storage buckets when you use
Cloud GPU/TPU.
### Restoring from Checkpoints
`tf.train.Checkpoint` is used to manage model checkpoints in TF 2. To restore
weights from provided pre-trained checkpoints, you can use the following code:
```python
init_checkpoint='the pretrained model checkpoint path.'
model=tf.keras.Model() # Bert pre-trained model as feature extractor.
checkpoint = tf.train.Checkpoint(model=model)
checkpoint.restore(init_checkpoint)
```
Checkpoints featuring native serialized Keras models
(i.e. model.load()/load_weights()) will be available soon.
### Access to Pretrained hub modules.
Pretrained tf.hub modules in TF 2.x SavedModel format can be found in the
following links:
* **[`ALBERT V2 Base`](https://tfhub.dev/tensorflow/albert_en_base/1)**:
12-layer, 768-hidden, 12-heads, 12M parameters
* **[`ALBERT V2 Large`](https://tfhub.dev/tensorflow/albert_en_large/1)**:
24-layer, 1024-hidden, 16-heads, 18M parameters
* **[`ALBERT V2 XLarge`](https://tfhub.dev/tensorflow/albert_en_xlarge/1)**:
24-layer, 2048-hidden, 32-heads, 60M parameters
* **[`ALBERT V2 XXLarge`](https://tfhub.dev/tensorflow/albert_en_xxlarge/1)**:
12-layer, 4096-hidden, 64-heads, 235M parameters
## Set Up
```shell
export PYTHONPATH="$PYTHONPATH:/path/to/models"
```
Install `tf-nightly` to get latest updates:
```shell
pip install tf-nightly-gpu
```
With TPU, GPU support is not necessary. First, you need to create a `tf-nightly`
TPU with [ctpu tool](https://github.com/tensorflow/tpu/tree/master/tools/ctpu):
```shell
ctpu up -name <instance name> --tf-version=”nightly”
```
Second, you need to install TF 2 `tf-nightly` on your VM:
```shell
pip install tf-nightly
```
Warning: More details TPU-specific set-up instructions and tutorial should come
along with official TF 2.x release for TPU. Note that this repo is not
officially supported by Google Cloud TPU team yet until TF 2.1 released.
## Process Datasets
### Pre-training
Pre-train ALBERT using TF2.x will come soon.
For now, please use [ALBERT research repo](https://github.com/google-research/ALBERT)
to pretrain the model and convert the checkpoint to TF2.x compatible ones using
[tf2_albert_encoder_checkpoint_converter.py](tf2_albert_encoder_checkpoint_converter.py).
### Fine-tuning
To prepare the fine-tuning data for final model training, use the
[`../data/create_finetuning_data.py`](../data/create_finetuning_data.py) script.
Note that different from BERT models that use word piece tokenzer,
ALBERT models employ sentence piece tokenizer. So the FLAG tokenizer_impl has
to be set to 'sentence_piece'.
Resulting datasets in `tf_record` format and training meta data should be later
passed to training or evaluation scripts. The task-specific arguments are
described in following sections:
* GLUE
Users can download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.
```shell
export GLUE_DIR=~/glue
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export TASK_NAME=MNLI
export OUTPUT_DIR=gs://some_bucket/datasets
python ../data/create_finetuning_data.py \
--input_data_dir=${GLUE_DIR}/${TASK_NAME}/ \
--sp_model_file=${ALBERT_DIR}/30k-clean.model \
--train_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_train.tf_record \
--eval_data_output_path=${OUTPUT_DIR}/${TASK_NAME}_eval.tf_record \
--meta_data_file_path=${OUTPUT_DIR}/${TASK_NAME}_meta_data \
--fine_tuning_task_type=classification --max_seq_length=128 \
--classification_task_name=${TASK_NAME} \
--tokenization=SentencePiece
```
* SQUAD
The [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/) contains
detailed information about the SQuAD datasets and evaluation.
The necessary files can be found here:
* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
* [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
* [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
* [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)
```shell
export SQUAD_DIR=~/squad
export SQUAD_VERSION=v1.1
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export OUTPUT_DIR=gs://some_bucket/datasets
python ../data/create_finetuning_data.py \
--squad_data_file=${SQUAD_DIR}/train-${SQUAD_VERSION}.json \
--sp_model_file=${ALBERT_DIR}/30k-clean.model \
--train_data_output_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
--meta_data_file_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_meta_data \
--fine_tuning_task_type=squad --max_seq_length=384 \
--tokenization=SentencePiece
```
## Fine-tuning with ALBERT
### Cloud GPUs and TPUs
* Cloud Storage
The unzipped pre-trained model files can also be found in the Google Cloud
Storage folder `gs://cloud-tpu-checkpoints/albert/checkpoints`. For example:
```shell
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export MODEL_DIR=gs://some_bucket/my_output_dir
```
Currently, users are able to access to `tf-nightly` TPUs and the following TPU
script should run with `tf-nightly`.
* GPU -> TPU
Just add the following flags to `run_classifier.py` or `run_squad.py`:
```shell
--distribution_strategy=tpu
--tpu=grpc://${TPU_IP_ADDRESS}:8470
```
### Sentence and Sentence-pair Classification Tasks
This example code fine-tunes `albert_v2_base` on the Microsoft Research
Paraphrase Corpus (MRPC) corpus, which only contains 3,600 examples and can
fine-tune in a few minutes on most GPUs.
We use the `albert_v2_base` as an example throughout the
workflow.
```shell
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export MODEL_DIR=gs://some_bucket/my_output_dir
export GLUE_DIR=gs://some_bucket/datasets
export TASK=MRPC
python run_classifier.py \
--mode='train_and_eval' \
--input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
--train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
--eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
--bert_config_file=${ALBERT_DIR}/albert_config.json \
--init_checkpoint=${ALBERT_DIR}/bert_model.ckpt \
--train_batch_size=4 \
--eval_batch_size=4 \
--steps_per_loop=1 \
--learning_rate=2e-5 \
--num_train_epochs=3 \
--model_dir=${MODEL_DIR} \
--distribution_strategy=mirrored
```
Alternatively, instead of specifying `init_checkpoint`, you can specify
`hub_module_url` to employ a pretraind BERT hub module, e.g.,
` --hub_module_url=https://tfhub.dev/tensorflow/albert_en_base/1`.
To use TPU, you only need to switch distribution strategy type to `tpu` with TPU
information and use remote storage for model checkpoints.
```shell
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export TPU_IP_ADDRESS='???'
export MODEL_DIR=gs://some_bucket/my_output_dir
export GLUE_DIR=gs://some_bucket/datasets
python run_classifier.py \
--mode='train_and_eval' \
--input_meta_data_path=${GLUE_DIR}/${TASK}_meta_data \
--train_data_path=${GLUE_DIR}/${TASK}_train.tf_record \
--eval_data_path=${GLUE_DIR}/${TASK}_eval.tf_record \
--bert_config_file=$ALBERT_DIR/albert_config.json \
--init_checkpoint=$ALBERT_DIR/bert_model.ckpt \
--train_batch_size=32 \
--eval_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3 \
--model_dir=${MODEL_DIR} \
--distribution_strategy=tpu \
--tpu=grpc://${TPU_IP_ADDRESS}:8470
```
### SQuAD 1.1
The Stanford Question Answering Dataset (SQuAD) is a popular question answering
benchmark dataset. See more in [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/).
We use the `albert_v2_base` as an example throughout the
workflow.
```shell
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export SQUAD_DIR=gs://some_bucket/datasets
export MODEL_DIR=gs://some_bucket/my_output_dir
export SQUAD_VERSION=v1.1
python run_squad.py \
--input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
--train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
--predict_file=${SQUAD_DIR}/dev-v1.1.json \
--sp_model_file=${ALBERT_DIR}/30k-clean.model \
--bert_config_file=$ALBERT_DIR/albert_config.json \
--init_checkpoint=$ALBERT_DIR/bert_model.ckpt \
--train_batch_size=4 \
--predict_batch_size=4 \
--learning_rate=8e-5 \
--num_train_epochs=2 \
--model_dir=${MODEL_DIR} \
--distribution_strategy=mirrored
```
Similarily, you can replace `init_checkpoint` FLAGS with `hub_module_url` to
specify a hub module path.
To use TPU, you need switch distribution strategy type to `tpu` with TPU
information.
```shell
export ALBERT_DIR=gs://cloud-tpu-checkpoints/albert/checkpoints/albert_v2_base
export TPU_IP_ADDRESS='???'
export MODEL_DIR=gs://some_bucket/my_output_dir
export SQUAD_DIR=gs://some_bucket/datasets
export SQUAD_VERSION=v1.1
python run_squad.py \
--input_meta_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_meta_data \
--train_data_path=${SQUAD_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
--predict_file=${SQUAD_DIR}/dev-v1.1.json \
--sp_model_file=${ALBERT_DIR}/30k-clean.model \
--bert_config_file=$ALBERT_DIR/albert_config.json \
--init_checkpoint=$ALBERT_DIR/bert_model.ckpt \
--train_batch_size=32 \
--learning_rate=8e-5 \
--num_train_epochs=2 \
--model_dir=${MODEL_DIR} \
--distribution_strategy=tpu \
--tpu=grpc://${TPU_IP_ADDRESS}:8470
```
The dev set predictions will be saved into a file called predictions.json in the
model_dir:
```shell
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ./squad/predictions.json
```
official/nlp/albert/run_classifier.py deleted 100644 → 0
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ALBERT classification finetuning runner in tf2.x."""
import json
import os
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from official.common import distribute_utils
from official.nlp.albert import configs as albert_configs
from official.nlp.bert import bert_models
from official.nlp.bert import run_classifier as run_classifier_bert
FLAGS = flags.FLAGS
def predict(strategy, albert_config, input_meta_data, predict_input_fn):
"""Function outputs both the ground truth predictions as .tsv files."""
with strategy.scope():
classifier_model = bert_models.classifier_model(
albert_config, input_meta_data['num_labels'])[0]
checkpoint = tf.train.Checkpoint(model=classifier_model)
latest_checkpoint_file = (
FLAGS.predict_checkpoint_path or
tf.train.latest_checkpoint(FLAGS.model_dir))
assert latest_checkpoint_file
logging.info('Checkpoint file %s found and restoring from '
'checkpoint', latest_checkpoint_file)
checkpoint.restore(
latest_checkpoint_file).assert_existing_objects_matched()
preds, ground_truth = run_classifier_bert.get_predictions_and_labels(
strategy, classifier_model, predict_input_fn, return_probs=True)
output_predict_file = os.path.join(FLAGS.model_dir, 'test_results.tsv')
with tf.io.gfile.GFile(output_predict_file, 'w') as writer:
logging.info('***** Predict results *****')
for probabilities in preds:
output_line = '\t'.join(
str(class_probability)
for class_probability in probabilities) + '\n'
writer.write(output_line)
ground_truth_labels_file = os.path.join(FLAGS.model_dir,
'output_labels.tsv')
with tf.io.gfile.GFile(ground_truth_labels_file, 'w') as writer:
logging.info('***** Ground truth results *****')
for label in ground_truth:
output_line = '\t'.join(str(label)) + '\n'
writer.write(output_line)
return
def main(_):
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if not FLAGS.model_dir:
FLAGS.model_dir = '/tmp/bert20/'
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
tpu_address=FLAGS.tpu)
max_seq_length = input_meta_data['max_seq_length']
train_input_fn = run_classifier_bert.get_dataset_fn(
FLAGS.train_data_path,
max_seq_length,
FLAGS.train_batch_size,
is_training=True)
eval_input_fn = run_classifier_bert.get_dataset_fn(
FLAGS.eval_data_path,
max_seq_length,
FLAGS.eval_batch_size,
is_training=False)
albert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
if FLAGS.mode == 'train_and_eval':
run_classifier_bert.run_bert(strategy, input_meta_data, albert_config,
train_input_fn, eval_input_fn)
elif FLAGS.mode == 'predict':
predict(strategy, albert_config, input_meta_data, eval_input_fn)
else:
raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
return
if __name__ == '__main__':
flags.mark_flag_as_required('bert_config_file')
flags.mark_flag_as_required('input_meta_data_path')
flags.mark_flag_as_required('model_dir')
app.run(main)
official/nlp/albert/run_squad.py deleted 100644 → 0
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run ALBERT on SQuAD 1.1 and SQuAD 2.0 in TF 2.x."""
import json
import os
import time
# Import libraries
from absl import app
from absl import flags
from absl import logging
import tensorflow as tf
from official.common import distribute_utils
from official.nlp.albert import configs as albert_configs
from official.nlp.bert import run_squad_helper
from official.nlp.bert import tokenization
from official.nlp.data import squad_lib_sp
flags.DEFINE_string(
'sp_model_file', None,
'The path to the sentence piece model. Used by sentence piece tokenizer '
'employed by ALBERT.')
# More flags can be found in run_squad_helper.
run_squad_helper.define_common_squad_flags()
FLAGS = flags.FLAGS
def train_squad(strategy,
input_meta_data,
custom_callbacks=None,
run_eagerly=False):
"""Runs bert squad training."""
bert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
run_squad_helper.train_squad(strategy, input_meta_data, bert_config,
custom_callbacks, run_eagerly)
def predict_squad(strategy, input_meta_data):
"""Makes predictions for the squad dataset."""
bert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
tokenizer = tokenization.FullSentencePieceTokenizer(
sp_model_file=FLAGS.sp_model_file)
run_squad_helper.predict_squad(strategy, input_meta_data, tokenizer,
bert_config, squad_lib_sp)
def eval_squad(strategy, input_meta_data):
"""Evaluate on the squad dataset."""
bert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
tokenizer = tokenization.FullSentencePieceTokenizer(
sp_model_file=FLAGS.sp_model_file)
eval_metrics = run_squad_helper.eval_squad(
strategy, input_meta_data, tokenizer, bert_config, squad_lib_sp)
return eval_metrics
def export_squad(model_export_path, input_meta_data):
"""Exports a trained model as a `SavedModel` for inference.
Args:
model_export_path: a string specifying the path to the SavedModel directory.
input_meta_data: dictionary containing meta data about input and model.
Raises:
Export path is not specified, got an empty string or None.
"""
bert_config = albert_configs.AlbertConfig.from_json_file(
FLAGS.bert_config_file)
run_squad_helper.export_squad(model_export_path, input_meta_data, bert_config)
def main(_):
with tf.io.gfile.GFile(FLAGS.input_meta_data_path, 'rb') as reader:
input_meta_data = json.loads(reader.read().decode('utf-8'))
if FLAGS.mode == 'export_only':
export_squad(FLAGS.model_export_path, input_meta_data)
return
# Configures cluster spec for multi-worker distribution strategy.
if FLAGS.num_gpus > 0:
_ = distribute_utils.configure_cluster(FLAGS.worker_hosts, FLAGS.task_index)
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.distribution_strategy,
num_gpus=FLAGS.num_gpus,
all_reduce_alg=FLAGS.all_reduce_alg,
tpu_address=FLAGS.tpu)
if 'train' in FLAGS.mode:
train_squad(strategy, input_meta_data, run_eagerly=FLAGS.run_eagerly)
if 'predict' in FLAGS.mode:
predict_squad(strategy, input_meta_data)
if 'eval' in FLAGS.mode:
eval_metrics = eval_squad(strategy, input_meta_data)
f1_score = eval_metrics['final_f1']
logging.info('SQuAD eval F1-score: %f', f1_score)
summary_dir = os.path.join(FLAGS.model_dir, 'summaries', 'eval')
summary_writer = tf.summary.create_file_writer(summary_dir)
with summary_writer.as_default():
# TODO(lehou): write to the correct step number.
tf.summary.scalar('F1-score', f1_score, step=0)
summary_writer.flush()
# Also write eval_metrics to json file.
squad_lib_sp.write_to_json_files(
eval_metrics, os.path.join(summary_dir, 'eval_metrics.json'))
time.sleep(60)
if __name__ == '__main__':
flags.mark_flag_as_required('bert_config_file')
flags.mark_flag_as_required('model_dir')
app.run(main)
official/nlp/bert/bert_models.py
View file @ 0b6e571a
......@@ -17,9 +17,8 @@
import gin
import tensorflow as tf
import tensorflow_hub as hub
from official.legacy.nlp.albert import configs as albert_configs
from official.modeling import tf_utils
from official.nlp.albert import configs as albert_configs
from official.nlp.bert import configs
from official.nlp.modeling import models
from official.nlp.modeling import networks
......
official/nlp/tools/tf2_albert_encoder_checkpoint_converter.py
View file @ 0b6e571a
......@@ -23,8 +23,8 @@ from absl import app
from absl import flags
import tensorflow as tf
from official.legacy.nlp.albert import configs
from official.modeling import tf_utils
from official.nlp.albert import configs
from official.nlp.bert import tf1_checkpoint_converter_lib
from official.nlp.modeling import models
from official.nlp.modeling import networks
......
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