Create evalharness.sh

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