88 lines
3.5 KiB
Bash
88 lines
3.5 KiB
Bash
#!/bin/bash
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#SBATCH --exclusive
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#SBATCH --mem=0
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#SBATCH --overcommit
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# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
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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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set -eux
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# Docker image resulting from bash scripts/docker/build.sh
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readonly docker_image="gitlab-master.nvidia.com/dl/joc/electra_tf2:keras_mp_20.07_clean_up"
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# Location of dataset for phase 1 amd phase 2
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readonly datadir="/lustre/fsw/joc-luna/sharatht/electra_tf2_data/"
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readonly mounts=".:/workspace/electra,${datadir}:/workspace/electra/data"
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DGXSYSTEM=DGXA100
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cluster="selene"
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if [[ "${DGXSYSTEM}" == DGX2* ]]; then
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cluster='circe'
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fi
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if [[ "${DGXSYSTEM}" == DGXA100* ]]; then
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cluster='selene'
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fi
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BIND_CMD="./scripts/bind.sh --cpu=exclusive --ib=single --cluster=$cluster -- "
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BATCHSIZE=${BATCHSIZE:-16}
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PHASE=${PHASE:-1}
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LR=${LR:-3e-3}
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STEPS=${STEPS:-57450}
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WARMUP=${WARMUP:-3750}
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GRAD_ACCUM_STEPS=${GRAD_ACCUM_STEPS:-1}
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b1=${b1:-"0.878"}
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b2=${b2:-"0.974"}
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decay=${decay:-"0.5"}
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end_lr=${end_lr:-"0.0"}
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skip_adaptive=${skip_adaptive:-"yes"}
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model_count=${model_count:-1}
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skip_flag=""
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if [ "$skip_adaptive" = "yes" ] ; then
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skip_flag=" --skip_adaptive"
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fi
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ckpt_STEPS=$(awk -v a=$STEPS 'BEGIN { print a / 10}')
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if [ "$PHASE" = "1" ] ; then
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LAUNCH_CMD="$BIND_CMD python run_pretraining.py \
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--model_name='electra_keras_mp_base_lamb_48x8x${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}' \
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--pretrain_tfrecords='/workspace/electra/data/tfrecord_lower_case_1_seq_len_128_random_seed_12345/books_wiki_en_corpus/train/pretrain_data*' \
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--num_train_steps=$STEPS \
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--num_warmup_steps=$WARMUP \
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--disc_weight=50.0 \
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--generator_hidden_size=0.3333333 \
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--learning_rate=$LR \
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--train_batch_size=$BATCHSIZE \
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--max_seq_length=128 --log_freq=10 \
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--save_checkpoints_steps=$ckpt_STEPS \
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--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 "
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else
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LAUNCH_CMD="$BIND_CMD python run_pretraining.py \
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--model_name='electra_keras_mp_base_lamb_48x8x176x1_p1_skip_adaptive_yes_LR_6e-3_WARMUP_2000_STEPS_10000_b1_0.878_b2_0.974_decay_0.5_end_lr_0.0_${model_count}' \
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--pretrain_tfrecords='/workspace/electra/data/tfrecord_lower_case_1_seq_len_512_random_seed_12345/books_wiki_en_corpus/train/pretrain_data*' \
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--num_train_steps=$STEPS \
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--num_warmup_steps=$WARMUP \
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--disc_weight=50.0 \
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--generator_hidden_size=0.3333333 \
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--learning_rate=$LR \
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--train_batch_size=$BATCHSIZE \
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--max_seq_length=512 --log_freq=10 \
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--restore_checkpoint --phase2 \
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--save_checkpoints_steps=$ckpt_STEPS \
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--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 "
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fi;
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srun --mpi=pmi2 -l --container-image="${docker_image}" --container-mounts="${mounts}" bash -c "${LAUNCH_CMD}"
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