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ALBERT-TF2.0

ALBERT model Fine Tuning using TF2.0 [WIP][90%]

warning 🐞🐞🐞

Requirements

  • python3
  • pip install -r requirements.txt

Download ALBERT TF 2.0 weights

unzip the model inside repo.

Above weights does not contain the final layer in orginal model. Now can only be used for fine tuning downstream tasks.

Above weights are converted from tf_hub version 1 checkpoints. converted weights are tested with tf_hub module and produces idential results.

Download glue data

Download using the below cmd

python download_glue_data.py --data_dir glue_data --tasks all

Fine-tuning

To prepare the fine-tuning data for final model training, use the create_finetuning_data.py script. 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:

Creating finetuninig data

  • Example CoLA
export GLUE_DIR=glue_data/
export ALBERT_DIR=large/

export TASK_NAME=CoLA
export OUTPUT_DIR=cola_processed
mkdir $OUTPUT_DIR

python create_finetuning_data.py \
 --input_data_dir=${GLUE_DIR}/ \
 --spm_model_file=${ALBERT_DIR}/vocab/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}

Running classifier

export MODEL_DIR=CoLA_OUT
python run_classifer.py \
--train_data_path=${OUTPUT_DIR}/${TASK_NAME}_train.tf_record \
--eval_data_path=${OUTPUT_DIR}/${TASK_NAME}_eval.tf_record \
--input_meta_data_path=${OUTPUT_DIR}/${TASK_NAME}_meta_data \
--albert_config_file=${ALBERT_DIR}/config.json \
--task_name=${TASK_NAME} \
--spm_model_file=${ALBERT_DIR}/vocab/30k-clean.model \
--output_dir=${MODEL_DIR} \
--init_checkpoint=${ALBERT_DIR}/tf2_model.h5 \
--do_train \
--do_eval \
--train_batch_size=16 \
--learning_rate=1e-5

By default run_classifier will run 3 epochs. and evaluate on development set

Above cmd would result in dev set accuracy of 76.22 in CoLA task

The above code tested on TITAN RTX 24GB single GPU

Ignore

Below warning will be there at end of each epoch. Issue with training steps calcuation when tf.data provided to model.fit() Have no effect on model performance so ignore. Mostly will fixed in the next tf2 relase . Issue-link

2019-10-31 13:35:48.322897: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Out of range:
End of sequence
         [[{{node IteratorGetNext}}]]
         [[model_1/albert_model/word_embeddings/Shape/_10]]
2019-10-31 13:36:03.302722: W tensorflow/core/common_runtime/base_collective_executor.cc:216] BaseCollectiveExecutor::StartAbort Out of range:
End of sequence
         [[{{node IteratorGetNext}}]]
         [[IteratorGetNext/_4]]

SQuAD

Training Data Preparation

export SQUAD_DIR=SQuAD
export SQUAD_VERSION=v1.1
export ALBERT_DIR=large
export OUTPUT_DIR=squad_out_${SQUAD_VERSION}
mkdir $OUTPUT_DIR

python create_finetuning_data.py \
--squad_data_file=${SQUAD_DIR}/train-${SQUAD_VERSION}.json \
--spm_model_file=${ALBERT_DIR}/vocab/30k-clean.model  \
--train_data_output_path=${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

Running Model

python run_squad.py \
  --mode=train_and_predict \
  --input_meta_data_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_meta_data \
  --train_data_path=${OUTPUT_DIR}/squad_${SQUAD_VERSION}_train.tf_record \
  --predict_file=${SQUAD_DIR}/dev-${SQUAD_VERSION}.json \
  --albert_config_file=${ALBERT_DIR}/config.json \
  --init_checkpoint=${ALBERT_DIR}/tf2_model.h5 \
  --spm_model_file=${ALBERT_DIR}/vocab/30k-clean.model \
  --train_batch_size=48 \
  --predict_batch_size=48 \
  --learning_rate=1e-5 \
  --num_train_epochs=3 \
  --model_dir=${OUTPUT_DIR} \
  --strategy_type=mirror

Multi-GPU training

Use flag --strategy_type=mirror for Multi GPU training. Currently All the exsisting GPUs in the enviorment will be used.

References

lots of code in this repo are adpted from multiple repos. No reference are added now. Will add evevrything.