DeepLearningExamples/TensorFlow2/LanguageModeling/ELECTRA/run.sub

#!/bin/bash
#SBATCH --exclusive
#SBATCH --mem=0
#SBATCH --overcommit

# Copyright (c) 2020 NVIDIA CORPORATION. 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.

set -eux
# Docker image resulting from bash scripts/docker/build.sh
readonly docker_image="gitlab-master.nvidia.com/dl/joc/electra_tf2:keras_mp_20.07_clean_up"
# Location of dataset for phase 1 amd phase 2
readonly datadir="/lustre/fsw/joc-luna/sharatht/electra_tf2_data/"

readonly mounts=".:/workspace/electra,${datadir}:/workspace/electra/data"

DGXSYSTEM=DGXA100
cluster="selene"
if [[ "${DGXSYSTEM}" == DGX2* ]]; then
    cluster='circe'
fi
if [[ "${DGXSYSTEM}" == DGXA100* ]]; then
    cluster='selene'
fi

BIND_CMD="./scripts/bind.sh --cpu=exclusive --ib=single --cluster=$cluster -- "

BATCHSIZE=${BATCHSIZE:-16}
PHASE=${PHASE:-1}
LR=${LR:-3e-3}
STEPS=${STEPS:-57450}
WARMUP=${WARMUP:-3750}
GRAD_ACCUM_STEPS=${GRAD_ACCUM_STEPS:-1}
b1=${b1:-"0.878"}
b2=${b2:-"0.974"}
decay=${decay:-"0.5"}
end_lr=${end_lr:-"0.0"}
skip_adaptive=${skip_adaptive:-"yes"}
model_count=${model_count:-1}

skip_flag=""
if [ "$skip_adaptive" = "yes" ] ; then
    skip_flag=" --skip_adaptive"
fi

ckpt_STEPS=$(awk -v a=$STEPS 'BEGIN { print a / 10}')

if [ "$PHASE" = "1" ] ; then

LAUNCH_CMD="$BIND_CMD python run_pretraining.py \
    --model_name='electra_keras_mp_base_lamb_48x8x${BATCHSIZE}x${GRAD_ACCUM_STEPS}_p1_skip_adaptive_${skip_adaptive}_LR_${LR}_WARMUP_${WARMUP}_STEPS_${STEPS}_b1_${b1}_b2_${b2}_decay_${decay}_end_lr_${end_lr}_${model_count}' \
    --pretrain_tfrecords='/workspace/electra/data/tfrecord_lower_case_1_seq_len_128_random_seed_12345/books_wiki_en_corpus/train/pretrain_data*' \
    --num_train_steps=$STEPS \
    --num_warmup_steps=$WARMUP \
    --disc_weight=50.0 \
    --generator_hidden_size=0.3333333 \
    --learning_rate=$LR \
    --train_batch_size=$BATCHSIZE \
    --max_seq_length=128 --log_freq=10 \
    --save_checkpoints_steps=$ckpt_STEPS \
    --optimizer='lamb' $skip_flag --opt_beta_1=$b1 --opt_beta_2=$b2 --lr_decay_power=$decay --end_lr=$end_lr $skip_flag --gradient_accumulation_steps=$GRAD_ACCUM_STEPS --amp --xla "
else
LAUNCH_CMD="$BIND_CMD python run_pretraining.py \
    --model_name='electra_keras_mp_base_lamb_48x8x176x1_p1_skip_adaptive_yes_LR_6e-3_WARMUP_2000_STEPS_10000_b1_0.878_b2_0.974_decay_0.5_end_lr_0.0_${model_count}' \
    --pretrain_tfrecords='/workspace/electra/data/tfrecord_lower_case_1_seq_len_512_random_seed_12345/books_wiki_en_corpus/train/pretrain_data*' \
    --num_train_steps=$STEPS \
    --num_warmup_steps=$WARMUP \
    --disc_weight=50.0 \
    --generator_hidden_size=0.3333333 \
    --learning_rate=$LR \
    --train_batch_size=$BATCHSIZE \
    --max_seq_length=512 --log_freq=10 \
    --restore_checkpoint --phase2 \
    --save_checkpoints_steps=$ckpt_STEPS \
    --optimizer='lamb' $skip_flag --opt_beta_1=$b1 --opt_beta_2=$b2 --lr_decay_power=$decay --end_lr=$end_lr $skip_flag --gradient_accumulation_steps=$GRAD_ACCUM_STEPS --amp --xla "
fi;

srun --mpi=pmi2 -l --container-image="${docker_image}" --container-mounts="${mounts}" bash -c "${LAUNCH_CMD}"