wehub-resource-sync
55 lines
1.5 KiB
Bash
55 lines
1.5 KiB
Bash
#!/usr/bin/env bash
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set -x
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set -e
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DIR="$( cd "$( dirname "$0" )" && cd .. && pwd )"
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echo "working directory: ${DIR}"
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if [ -z "$OUTPUT_DIR" ]; then
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OUTPUT_DIR="${DIR}/checkpoint/rlm_$(date +%F-%H%M.%S)"
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fi
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if [ -z "$DATA_DIR" ]; then
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DATA_DIR="${DIR}/data/msmarco_bm25_official/"
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fi
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mkdir -p "${OUTPUT_DIR}"
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PROC_PER_NODE=$(nvidia-smi --list-gpus | wc -l)
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# python -u -m torch.distributed.launch --nproc_per_node ${PROC_PER_NODE} src/train_rlm.py \
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deepspeed src/train_rlm.py --deepspeed ds_config.json \
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--model_name_or_path bert-base-uncased \
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--per_device_train_batch_size 64 \
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--per_device_eval_batch_size 64 \
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--gradient_accumulation_steps 4 \
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--seed 45678 \
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--do_train \
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--do_eval \
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--fp16 \
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--train_file "${DATA_DIR}/passages.jsonl.gz" \
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--rlm_max_length 144 \
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--rlm_encoder_mask_prob 0.3 \
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--rlm_decoder_mask_prob 0.5 \
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--rlm_generator_model_name google/electra-base-generator \
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--rlm_freeze_generator True \
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--rlm_generator_mlm_weight 0.2 \
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--all_use_mask_token True \
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--dataloader_num_workers 1 \
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--max_steps 80000 \
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--learning_rate 3e-4 \
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--warmup_steps 4000 \
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--weight_decay 0.0 \
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--remove_unused_columns False \
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--logging_steps 50 \
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--report_to none \
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--output_dir "${OUTPUT_DIR}" \
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--save_total_limit 20 \
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--save_strategy steps \
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--save_steps 10000 \
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--evaluation_strategy steps \
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--eval_steps 10000 \
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--data_dir "${DATA_DIR}" \
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--overwrite_output_dir \
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--disable_tqdm True "$@"
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