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scripts / evalharness.sh

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Create evalharness.sh

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	#!/bin/bash
	#SBATCH --job-name=run_evalharness-tr11f-6b3-ml
	#SBATCH --partition=gpu_p5
	#SBATCH --constraint=a100
	#SBATCH --nodes=1
	#SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
	#SBATCH --cpus-per-task=8 # number of cores per tasks
	#SBATCH --hint=nomultithread # we get physical cores not logical
	#SBATCH --gres=gpu:1 # number of gpus
	#SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS)
	#SBATCH --output=%x-%j.out # output file name
	#SBATCH --account=ajs@a100

	set -x -e

	source $six_ALL_CCFRWORK/start-py38-pt111

	echo "START TIME: $(date)"

	# a unique identifier for the current eval ideally correspnding to the modelname
	VARIANT="tr11f-6b3-ml-evalharness"


	CHECKPOINT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/tasky/global_step1000
	MEGATRON_DEEPSPEED_REPO=/gpfsssd/worksf/projects/rech/six/commun/code/eval/Megatron-DeepSpeed
	export HF_DATASETS_OFFLINE=1
	export TRANSFORMERS_OFFLINE=1

	export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models
	export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets
	export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules
	export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics

	cd $MEGATRON_DEEPSPEED_REPO

	TOKENIZER_NAME_OR_PATH=bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles

	PP_SIZE=1
	TP_SIZE=1
	SEQ_LEN=2048

	# different from the training MICRO_BATCH_SIZE - no optim memory, so can do bigger BS
	# make as big as it can fit into gpu w/o OOM, but not too close to 100%
	EVAL_MICRO_BATCH_SIZE=1

	#dummy arguments to make megatron happy.
	MEGATRON_REQUIRED_ARGS=" \
	--num-layers -1 \
	--hidden-size -1 \
	--num-attention-heads -1 \
	--seq-length -1 \
	--max-position-embeddings -1 \
	"


	ZERO_STAGE=0

	config_json="./ds_config.json"

	# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
	cat <<EOT > $config_json
	{
	"train_micro_batch_size_per_gpu": 1,
	"train_batch_size": 1,
	"gradient_clipping": 1.0,
	"zero_optimization": {
	"stage": $ZERO_STAGE
	},
	"bf16": {
	"enabled": false
	},
	"steps_per_print": 2000,
	"wall_clock_breakdown": false
	}
	EOT


	CMD="./tasks/eval_harness/evaluate.py \
	--load $CHECKPOINT_PATH \
	--results_path $VARIANT-results.json \
	--tensor-model-parallel-size $TP_SIZE \
	--pipeline-model-parallel-size $PP_SIZE \
	--tokenizer-type PretrainedFromHF \
	--tokenizer-name-or-path $TOKENIZER_NAME_OR_PATH \
	--micro-batch-size $EVAL_MICRO_BATCH_SIZE \
	--no-load-optim \
	--no-load-rng \
	--eval_fp32 \
	--inference \
	--seq-length $SEQ_LEN \
	--task_list arc_challenge,arc_easy,boolq,copa,headqa,hellaswag,lambada,logiqa,mathqa,mc_taco,mrpc,multirc,openbookqa,piqa,prost,pubmedqa,qnli,qqp,race,rte,sciq,sst,triviaqa,webqs,wic,winogrande,wnli,wsc \
	--deepspeed \
	--deepspeed_config ds_config.json \
	--intermed_results \
	--adaptive_seq_len \
	--micro_bs_multiplier 4 \
	$MEGATRON_REQUIRED_ARGS \
	"

	GPUS_PER_NODE=1
	NNODES=$SLURM_NNODES
	MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST \| head -n 1)
	MASTER_PORT=6000
	export LAUNCHER="python -u -m torch.distributed.run \
	--nproc_per_node $GPUS_PER_NODE \
	--nnodes $NNODES \
	--rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \
	--rdzv_backend c10d \
	--max_restarts 0 \
	--tee 3 \
	"

	export CUDA_LAUNCH_BLOCKING=1

	echo $LAUNCHER $CMD

	export PYTHONPATH=$MEGATRON_DEEPSPEED_REPO

	$LAUNCHER $CMD 2>&1 \| tee $VARIANT-eval-harness.log