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# This is an example zero-shot eval script. Please first read the readme_evalharness.md under the ../MoE directory.
# CHECKPOINT_PATH=/blob/users/minjiaz/compression_library/checkpoint/125M10L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl_alpha-no_pp/global_step2000/
# CHECKPOINT_PATH=/blob/users/conglli/project/gpt3_with_pile/checkpoint/gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-64-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/global_step71000/
# CHECKPOINT_PATH=/blob/users/minjiaz/compression_library/checkpoint/125M12L_Compression_Test_INT8_64gpu_lr6e-5_tokens5.25B_nocl_alpha-no_pp/global_step5000/
CHECKPOINT_PATH=/blob/users/minjiaz/project/gpt3_distillation/checkpoint/gpt3-kd-test2-alpha1-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-15-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B/global_step71426/
CONFIG_PATH=ds_config_gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus--1-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-27638-token-60B.json
RESULT_PATH=gpt3-with-pile-0.125B-lr-2.4e-3-minlr-6.0e-5-bs-2048-gpus-128-zero-0-mp-1-pp-1-no_pp-cl-startseqlen-72-step-20728-token-45B_global_step81566.log
PP_SIZE=1
TP_SIZE=1
NO_PP="true"
EP_PARALLEL_SIZE=1
# Currently eval harness does not support data parallel
# However, for MoE models it's possible to enable a "fake data parallel"
# in order to load experts on multiple gpus. At the same time, it's not
# real data parallel because we load the same data on all gpus.
# On the other hand, it's better to use less number of gpus than training,
# to reduce communication overhead.
NUM_NODE=1
NUM_GPU_PER_NODE=1
# TASKS="lambada"
# WikiText-2, not used in GPT-3 paper but used in GPT-2 paper
TASKS="lambada,wikitext"
# Tasks that appeared in GPT-3 paper (sorted based on the order in paper), plus WikiText-2.
# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext"
# All tasks that confirmed to work, there are more tasks on https://github.com/EleutherAI/lm-evaluation-harness that we didn't test.
# TASKS="hellaswag,lambada,triviaqa,webqs,winogrande,piqa,arc_challenge,arc_easy,openbookqa,race,boolq,cb,copa,rte,wic,wsc,multirc,record,anli_r1,anli_r2,anli_r3,wikitext,logiqa,mathqa,mc_taco,mrpc,prost,pubmedqa,qnli,qqp,sciq,sst,wnli"
VOCAB_FILE=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-vocab.json
MERGE_FILE=/blob/data/the_pile_public_merged_nopreprocessing/gpt2-merges.txt
# export HF_DATASETS_OFFLINE=1
# Dummy arguments to make megatron happy. No need to configure them.
# The reason we don't need to configure them and many other arguments is
# because the eval framework will read the arguments from checkpoint file.
MEGATRON_REQUIRED_ARGS="\
--num-layers -1\
--hidden-size -1\
--num-attention-heads -1\
--seq-length -1 \
--max-position-embeddings -1
"
CMD="../../tasks/eval_harness/evaluate.py \
--load $CHECKPOINT_PATH\
--tensor-model-parallel-size $TP_SIZE \
--pipeline-model-parallel-size $PP_SIZE\
--moe-expert-parallel-size ${EP_PARALLEL_SIZE} \
--vocab-file $VOCAB_FILE\
--merge-file $MERGE_FILE\
--micro-batch-size 12\
--no-load-optim \
--no-load-rng \
--inference \
--disable-moe-token-dropping \
--tokenizer-type GPT2BPETokenizer \
--adaptive_seq_len\
--eval_fp32\
--task_list $TASKS\
--results_path $RESULT_PATH \
--deepspeed \
--deepspeed_config $CONFIG_PATH \
$MEGATRON_REQUIRED_ARGS\
"
if [[ "${NO_PP}" = "true" ]]; then
CMD="${CMD} \
--no-pipeline-parallel"
fi
LAUNCHER="deepspeed --num_nodes $NUM_NODE --num_gpus $NUM_GPU_PER_NODE"
$LAUNCHER $CMD |