#!/bin/bash #SBATCH --job-name=ckpts #SBATCH --ntasks=1 # number of MP tasks #SBATCH --nodes=1 #SBATCH --cpus-per-task=40 # number of cores per tasks #SBATCH --hint=nomultithread # we get physical cores not logical #SBATCH --time=20:00:00 # maximum execution time (HH:MM:SS) #SBATCH --output=%x-%j.out # output file name #SBATCH --account=ajs@cpu #SBATCH --partition=cpu_p1 set -x -e source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0 export HF_DATASETS_OFFLINE=1 export TRANSFORMERS_OFFLINE=1 conda activate muennighoffmodelconv CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/tasky #CKPT_PATH=$six_ALL_CCFRSCRATCH/checkpoints/tr13f-6B3-ml-t0/checkpoints/p31lossseq CKPTS=( global_step250 global_step500 global_step750 global_step1000 global_step1250 ) EXAMPLE_CKPT=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/6b3t0/tr13f-6b3-ml-t0-lmtoks341b-t0toks13b-xp3capmixnewcodelonglossseq DUMP_PATH=$six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/6b3t0 OUT_PREFIX=tasky_ #OUT_PREFIX=p31lossseq TP=1 ### CONVERT ### for i in {0..6}; do CKPT=${CKPTS[$i]} echo "$i" echo "Running $CKPT" OUTPUTCKPT=$DUMP_PATH/"$OUT_PREFIX$CKPT" python $six_ALL_CCFRSCRATCH/commun/experiments/muennighoff/bloomckpt/transformers_clone/src/transformers/models/bloom/convert_bloom_original_checkpoint_to_pytorch.py --pytorch_dump_folder_path $OUTPUTCKPT --bloom_checkpoint_path $CKPT_PATH/$CKPT --pretraining_tp $TP --bloom_config_file $EXAMPLE_CKPT/config.json # Copy tokenizer.json etc cp -r $EXAMPLE_CKPT/*.json $OUTPUTCKPT/ eval_script="./eval_$i.slurm" cat < $eval_script #!/bin/bash #SBATCH --job-name=evaluate_t0 #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 --constraint=a100 #SBATCH --time 5:00:00 # maximum execution time (HH:MM:SS) #SBATCH --output=%x-%j.out # output file name #SBATCH --account=ajs@a100 #SBATCH --array=0-168 set -x -e source $six_ALL_CCFRWORK/start-py38-pt111 conda activate thomas_t_zero_evaluation CHECKPOINT_PATH=$OUTPUTCKPT WORKDIR=/gpfswork/rech/six/commun/code/tr13f-6B3-ml-t0 pushd "\$WORKDIR" OUTPUT_DIR="\$CHECKPOINT_PATH/evaluation" mkdir -p "\$OUTPUT_DIR" # Validation DATASETS_AND_CONFIGS_VAL=( head_qa,en,en,"multiple_choice_q_and_a_index_with_context_en",validation head_qa,en,en,"multiple_choice_q_and_a_en",validation head_qa,en,en,"multiple_choice_q_and_a_index_en",validation head_qa,en,en,"multiple_choice_a_and_q_with_context_en",validation head_qa,en,en,"multiple_choice_a_and_q_en",validation head_qa,es,en,"multiple_choice_q_and_a_index_with_context_en",validation head_qa,es,en,"multiple_choice_q_and_a_en",validation head_qa,es,en,"multiple_choice_q_and_a_index_en",validation head_qa,es,en,"multiple_choice_a_and_q_with_context_en",validation head_qa,es,en,"multiple_choice_a_and_q_en",validation climate_fever,None,None,"first_evidence_and_claim_itemization",test climate_fever,None,None,"claim_and_all_supporting_evidences",test climate_fever,None,None,"fifth_evidence_and_claim_itemization",test climate_fever,None,None,"third_evidence_claim_pair",test climate_fever,None,None,"second_evidence_and_claim_itemization",test codah,codah,None,"interrogative_instruction_after_sentence_and_choices",train codah,codah,None,"affirmative_instruction_before_sentence_and_choices",train codah,codah,None,"affirmative_instruction_after_sentence_and_choices",train aqua_rat,raw,None,"select_the_best_option",validation aqua_rat,raw,None,"answer_quiz",validation aqua_rat,raw,None,"Answer questions from options",validation commonsense_qa,None,None,"answer_given_question_without_options",validation commonsense_qa,None,None,"question_answering",validation commonsense_qa,None,None,"most_suitable_answer",validation amazon_reviews_multi,en,en,"prompt_title_to_star",validation amazon_reviews_multi,en,en,"prompt_review_to_star",validation amazon_reviews_multi,en,en,"prompt_body_title_to_star",validation amazon_reviews_multi,zh,en,"prompt_title_to_star",validation amazon_reviews_multi,zh,en,"prompt_review_to_star",validation amazon_reviews_multi,zh,en,"prompt_body_title_to_star",validation amazon_reviews_multi,fr,en,"prompt_title_to_star",validation amazon_reviews_multi,fr,en,"prompt_review_to_star",validation amazon_reviews_multi,fr,en,"prompt_body_title_to_star",validation amazon_reviews_multi,es,en,"prompt_title_to_star",validation amazon_reviews_multi,es,en,"prompt_review_to_star",validation amazon_reviews_multi,es,en,"prompt_body_title_to_star",validation art,None,None,"choose_hypothesis_options",validation art,None,None,"choose_hypothesis_believable",validation art,None,None,"choose_hypothesis",validation art,None,None,"choose_hypothesis_desc",validation art,None,None,"choose_hypothesis_likely",validation banking77,None,None,"help_page_topic",test banking77,None,None,"direct_to_which_department",test banking77,None,None,"rephrase_as_banking_term",test blbooksgenre,title_genre_classifiction,None,"multi-choice",train blbooksgenre,title_genre_classifiction,None,"premise_context_first",train blbooksgenre,title_genre_classifiction,None,"classify",train blimp,adjunct_island,None,"grammatical_between_1_2",train blimp,adjunct_island,None,"grammatical_between_A_B",train blimp,adjunct_island,None,"grammatical_which_one_1_2",train blimp,adjunct_island,None,"single_sentence_bad_yes_no",train blimp,adjunct_island,None,"single_sentence_good_yes_no",train conv_ai_3,None,None,"clarification_needed",validation conv_ai_3,None,None,"score_give_number",validation conv_ai_3,None,None,"ambiguous",validation conv_ai_3,None,None,"directly_answer",validation conv_ai_3,None,None,"score_how_much",validation craigslist_bargains,None,None,"good deal for seller no list price implicit",validation craigslist_bargains,None,None,"good deal for seller no list price",validation craigslist_bargains,None,None,"good deal for seller",validation craigslist_bargains,None,None,"best deal",validation ecthr_cases,alleged-violation-prediction,None,"implicit_advice_number",validation ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_declaration_at_end",validation ecthr_cases,alleged-violation-prediction,None,"ecthr_alleged_articles_question_at_start",validation ecthr_cases,alleged-violation-prediction,None,"implicit_judgment_paragraph",validation ecthr_cases,alleged-violation-prediction,None,"confirm number of violated articles",validation emo,None,None,"persons_describe",validation emo,None,None,"final_message",validation emo,None,None,"what_emotion_do_you_think",validation emo,None,None,"emotional_state",validation emo,None,None,"dialogue_between",validation emotion,None,None,"choose_the_best_emotion_label",test emotion,None,None,"reply_with_emoation_label",test emotion,None,None,"answer_with_class_label",test emotion,None,None,"answer_question_with_emotion_label",test financial_phrasebank,sentences_allagree,None,"share_price_option",train financial_phrasebank,sentences_allagree,None,"sentiment",train financial_phrasebank,sentences_allagree,None,"word_comes_to_mind",train financial_phrasebank,sentences_allagree,None,"complementary_industries",train financial_phrasebank,sentences_allagree,None,"bullish_neutral_bearish",train glue,cola,None,"Make sense yes no",validation glue,cola,None,"is_this_correct",validation glue,cola,None,"editing",validation glue,cola,None,"Following sentence acceptable",validation glue,cola,None,"Previous sentence acceptable",validation glue,sst2,None,"positive negative after",validation glue,sst2,None,"review",validation glue,sst2,None,"said",validation glue,sst2,None,"following positive negative",validation glue,sst2,None,"happy or mad",validation health_fact,None,None,"claim_veracity_classification_after_reading_I_believe",validation health_fact,None,None,"claim_explanation_classification",validation health_fact,None,None,"claim_veracity_classification_tell_me",validation hlgd,None,None,"is_same_event_with_time_interrogative_related",validation hlgd,None,None,"is_same_event_interrogative_talk",validation hlgd,None,None,"is_same_event_with_time_interrogative_talk",validation hlgd,None,None,"is_same_event_refer",validation hlgd,None,None,"is_same_event_editor_asks",validation hyperpartisan_news_detection,byarticle,None,"consider_does_it_follow_a_hyperpartisan_argumentation",train hyperpartisan_news_detection,byarticle,None,"follows_hyperpartisan_argumentation",train hyperpartisan_news_detection,byarticle,None,"consume_with_caution",train hyperpartisan_news_detection,byarticle,None,"extreme_left_wing_or_right_wing",train hyperpartisan_news_detection,byarticle,None,"consider_it_exhibits_extreme_one_sidedness",train liar,None,None,"Given statement guess category",validation lince,sa_spaeng,None,"original poster expressed sentiment",validation lince,sa_spaeng,None,"sentiment trying to express",validation lince,sa_spaeng,None,"express sentiment",validation lince,sa_spaeng,None,"negation template",validation lince,sa_spaeng,None,"the author seem",validation math_qa,None,None,"choose_correct_og",test math_qa,None,None,"pick_the_correct",test math_qa,None,None,"first_choice_then_problem",test math_qa,None,None,"problem_set_type",test math_qa,None,None,"gre_problem",test movie_rationales,None,None,"Standard binary sentiment analysis",validation movie_rationales,None,None,"Evidences sentiment classification",validation movie_rationales,None,None,"Evidences + review",validation movie_rationales,None,None,"Generate evidences and sentiment",validation mwsc,None,None,"in-the-sentence-question-first",validation mwsc,None,None,"what-think",validation mwsc,None,None,"in-the-sentence",validation mwsc,None,None,"options-or",validation mwsc,None,None,"is-correct",validation poem_sentiment,None,None,"positive_or_negative_sentiment_variation_2",validation poem_sentiment,None,None,"question_answer_format",validation poem_sentiment,None,None,"guess_sentiment_without_options_variation_1",validation poem_sentiment,None,None,"positive_or_negative_sentiment_variation_1",validation poem_sentiment,None,None,"most_appropriate_sentiment",validation onestop_english,None,None,"esl_context",train onestop_english,None,None,"ara_context",train onestop_english,None,None,"determine_reading_level_from_the_first_three_sentences",train onestop_english,None,None,"esl_variation",train onestop_english,None,None,"assess",train pubmed_qa,pqa_labeled,None,"Long Answer to Final Decision",train pubmed_qa,pqa_labeled,None,"Question Answering (Short)",train riddle_sense,None,None,"most_suitable_answer",validation riddle_sense,None,None,"answer_given_question_without_options",validation riddle_sense,None,None,"question_to_answer_index",validation riddle_sense,None,None,"question_answering",validation scicite,None,None,"Classify intent w/section (select choice)",validation scicite,None,None,"Classify intent (choices first)",validation scicite,None,None,"Classify intent (select choice)",validation scicite,None,None,"Classify intent",validation scicite,None,None,"can_describe",validation selqa,answer_selection_analysis,None,"is-he-talking-about",validation selqa,answer_selection_analysis,None,"would-make-sense-qu-rand",validation selqa,answer_selection_analysis,None,"make-sense-rand",validation selqa,answer_selection_analysis,None,"which-answer-1st-vs-random",validation snips_built_in_intents,None,None,"voice_intent",train snips_built_in_intents,None,None,"categorize_query",train snips_built_in_intents,None,None,"intent_query",train snips_built_in_intents,None,None,"categorize_query_brief",train snips_built_in_intents,None,None,"query_intent",train ) DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS_VAL[\$SLURM_ARRAY_TASK_ID]}" echo "\$ARGUMENT" # Run T0 evaluation # For PrefixLM add --prefixlm IFS=',' read dataset_name dataset_config_name template_config_name template_name split <<< "\${DATASET_AND_CONFIG}" python t-zero/evaluation/run_eval.py \ --dataset_name "\$dataset_name" \ --dataset_config_name "\$dataset_config_name" \ --template_config_name "\$template_config_name" \ --template_name "\$template_name" \ --split "\$split" \ --model_name_or_path "\$CHECKPOINT_PATH" \ --output_dir "\$OUTPUT_DIR" \ --per_device_eval_batch_size 4 \ --max_length 2048 \ --dtype float16 EOT sbatch $eval_script lm_eval_script="./lm_eval_$i.slurm" cat < $lm_eval_script #!/bin/bash #SBATCH --job-name=lmeval #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 --constraint=a100 #SBATCH --time 20:00:00 # maximum execution time (HH:MM:SS) #SBATCH --output=%x-%j.out # output file name #SBATCH --account=ajs@a100 #SBATCH --array=0-12 set -x -e source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0 conda activate muennighofflmevalgen echo "START TIME: $(date)" # defining the right environment variables export TRANSFORMERS_CACHE=$six_ALL_CCFRWORK/models export HF_DATASETS_CACHE=$six_ALL_CCFRWORK/datasets export HF_MODULES_CACHE=$six_ALL_CCFRWORK/modules export HF_METRICS_CACHE=$six_ALL_CCFRWORK/metrics export HF_DATASETS_OFFLINE=1 export TRANSFORMERS_OFFLINE=1 export TOKENIZERS_PARALLELISM=false # Converted transformer checkpoint MODEL_CKPT=$OUTPUTCKPT cd /gpfsscratch/rech/six/commun/experiments/muennighoff/lm-evaluation-harness DATASETS_AND_CONFIGS=( wmt14_fr_en,fr-en,"version-en-fr-target" wmt14_fr_en,fr-en,"a_good_translation-en-fr-target" wmt14_fr_en,fr-en,"a_good_translation-en-fr-source+target" wmt14_fr_en,fr-en,"xglm-en-fr-target" wmt14_fr_en,fr-en,"gpt3-en-fr" wmt14_fr_en,fr-en,"version-fr-en-target" wmt14_fr_en,fr-en,"a_good_translation-fr-en-target" wmt14_fr_en,fr-en,"a_good_translation-fr-en-source+target" wmt14_fr_en,fr-en,"xglm-fr-en-target" wmt14_fr_en,fr-en,"gpt3-fr-en" wmt14_hi_en,hi-en,"version-en-hi-target" wmt14_hi_en,hi-en,"a_good_translation-en-hi-target" wmt14_hi_en,hi-en,"a_good_translation-en-hi-source+target" wmt14_hi_en,hi-en,"xglm-en-hi-target" wmt14_hi_en,hi-en,"gpt-3-en-hi-target" wmt14_hi_en,hi-en,"version-hi-en-target" wmt14_hi_en,hi-en,"a_good_translation-hi-en-target" wmt14_hi_en,hi-en,"a_good_translation-hi-en-source+target" wmt14_hi_en,hi-en,"xglm-hi-en-target" wmt14_hi_en,hi-en,"gpt-3-hi-en-target" mlsum_es,"es","layman_summ_es" mlsum_es,"es","palm_prompt" mlsum_es,"es","summarise_this_in_es_few_sentences" ) DATASET_AND_CONFIG="\${DATASETS_AND_CONFIGS[\$SLURM_ARRAY_TASK_ID]}" echo "\$ARGUMENT" IFS=',' read dataset_name lang template_name <<< "\${DATASET_AND_CONFIG}" # Use this fork of lm-eval: https://github.com/bigscience-workshop/lm-evaluation-harness/pull/109 python main.py \ --model_api_name 'hf-causal' \ --model_args "pretrained=\$MODEL_CKPT,use_accelerate=True,tokenizer=\$MODEL_CKPT,dtype=float16" \ --device cuda \ --batch_size 16 \ --no_tracking \ --task_name "\$dataset_name" \ --template_names "\$template_name" \ --bootstrap_iters 10 \ --limit 3000 mkdir -p "$OUTPUTCKPT/evaluation/\$dataset_name" mv "outputs/*$CKPT*\$dataset_name*" "$OUTPUTCKPT/evaluation/\$dataset_name/" echo "END TIME: $(date)" EOT sbatch $lm_eval_script done