| |
| """ |
| Generate SRT experiment scripts for new_gainlora. |
| Generates 6 scripts: |
| - T5-XL SuperNI order 1 (15 tasks) |
| - T5-XL SuperNI order 2 (15 tasks) |
| - T5-XL Long-Seq order 3 (15 tasks) |
| - T5-XL Long-Seq order 4 (15 tasks) |
| - Llama-3 8B SuperNI order 1 (15 tasks) |
| - Llama-3 8B SuperNI order 2 (15 tasks) |
| """ |
|
|
| import textwrap |
|
|
| |
|
|
| SUPERNI_ORDER1 = ( |
| "task1572_samsum_summary,task363_sst2_polarity_classification,task1290_xsum_summarization," |
| "task181_outcome_extraction,task002_quoref_answer_generation,task1510_evalution_relation_extraction," |
| "task639_multi_woz_user_utterance_generation,task1729_personachat_generate_next," |
| "task073_commonsenseqa_answer_generation,task1590_diplomacy_text_generation," |
| "task748_glucose_reverse_cause_event_detection,task511_reddit_tifu_long_text_summarization," |
| "task591_sciq_answer_generation,task1687_sentiment140_classification,task875_emotion_classification" |
| ) |
|
|
| SUPERNI_ORDER2 = ( |
| "task748_glucose_reverse_cause_event_detection,task073_commonsenseqa_answer_generation," |
| "task1590_diplomacy_text_generation,task639_multi_woz_user_utterance_generation," |
| "task1572_samsum_summary,task1687_sentiment140_classification,task591_sciq_answer_generation," |
| "task363_sst2_polarity_classification,task1510_evalution_relation_extraction," |
| "task1729_personachat_generate_next,task181_outcome_extraction," |
| "task511_reddit_tifu_long_text_summarization,task002_quoref_answer_generation," |
| "task1290_xsum_summarization,task875_emotion_classification" |
| ) |
|
|
| LONG_ORDER3 = ( |
| "yelp,amazon,mnli,cb,copa,qqp,rte,imdb,sst2,dbpedia,agnews,yahoo,multirc,boolq,wic" |
| ) |
|
|
| LONG_ORDER4 = ( |
| "mnli,cb,wic,copa,qqp,boolq,rte,imdb,yelp,amazon,sst2,dbpedia,agnews,multirc,yahoo" |
| ) |
|
|
| LONG_TASK_LIST = [ |
| "yelp", "amazon", "mnli", "cb", "copa", "qqp", "rte", |
| "imdb", "sst2", "dbpedia", "agnews", "yahoo", "multirc", "boolq", "wic" |
| ] |
|
|
| SUPERNI_TASK_LIST_ORDER1 = [ |
| "task1572_samsum_summary", "task363_sst2_polarity_classification", |
| "task1290_xsum_summarization", "task181_outcome_extraction", |
| "task002_quoref_answer_generation", "task1510_evalution_relation_extraction", |
| "task639_multi_woz_user_utterance_generation", "task1729_personachat_generate_next", |
| "task073_commonsenseqa_answer_generation", "task1590_diplomacy_text_generation", |
| "task748_glucose_reverse_cause_event_detection", "task511_reddit_tifu_long_text_summarization", |
| "task591_sciq_answer_generation", "task1687_sentiment140_classification", |
| "task875_emotion_classification" |
| ] |
|
|
| SUPERNI_TASK_LIST_ORDER2 = [ |
| "task748_glucose_reverse_cause_event_detection", "task073_commonsenseqa_answer_generation", |
| "task1590_diplomacy_text_generation", "task639_multi_woz_user_utterance_generation", |
| "task1572_samsum_summary", "task1687_sentiment140_classification", |
| "task591_sciq_answer_generation", "task363_sst2_polarity_classification", |
| "task1510_evalution_relation_extraction", "task1729_personachat_generate_next", |
| "task181_outcome_extraction", "task511_reddit_tifu_long_text_summarization", |
| "task002_quoref_answer_generation", "task1290_xsum_summarization", |
| "task875_emotion_classification" |
| ] |
|
|
| |
|
|
| def gen_t5_script(run_name, task_order, task_list, config_dir, gen_data_dir, metric): |
| assert len(task_list) == 15 |
| SRT_FLAGS = ( |
| "--use_srt_router --srt_shrink --srt_shrink_factor 0.1 " |
| "--srt_metric_mode hard --srt_max_emb_samples 500 --srt_skip_forward" |
| ) |
| prev_lora_chains = ",".join( |
| f"$BASE_OUT/outputs/{i+1}-{task_list[i]}/saved_weights" |
| for i in range(14) |
| ) |
| prev_lora_task2 = ",".join( |
| [f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights"] |
| ) |
|
|
| task_cmds = [] |
| for i, task in enumerate(task_list): |
| |
| if i == 0: |
| prev_lora = "" |
| prev_key = "" |
| load_trans = "" |
| elif i == 1: |
| prev_lora = ( |
| f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights" |
| ) |
| prev_key = ( |
| f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights/" |
| f"prompts_keys_till_now.pt" |
| ) |
| load_trans = ( |
| f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights/trans_input.pt" |
| ) |
| else: |
| prev_lora_list = ",".join( |
| f"$BASE_OUT/outputs/{j+1}-{task_list[j]}/saved_weights" |
| for j in range(i) |
| ) |
| prev_lora = prev_lora_list |
| prev_key = ( |
| f"$BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/" |
| f"prompts_keys_till_now.pt" |
| ) |
| load_trans = ( |
| f"$BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/trans_input.pt" |
| ) |
|
|
| |
| if i == 0: |
| srt_load = "" |
| else: |
| srt_load = f"--srt_load_path $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights" |
|
|
| if i == 0: |
| common = f"""\ |
| --data_dir CL_Benchmark \\ |
| --task_order $TASK_ORDER \\ |
| --task_config_dir {config_dir}/{task} \\ |
| --output_dir $BASE_OUT/outputs/{i+1}-{task} \\""" |
| else: |
| common = f"""\ |
| --data_dir CL_Benchmark \\ |
| --load_checkpoint_from $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/trans_input.pt \\ |
| --previous_lora_path {prev_lora} \\ |
| --previous_prompt_key_path $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/prompts_keys_till_now.pt \\ |
| --task_order $TASK_ORDER \\ |
| --gen_data_dir {gen_data_dir} \\ |
| --task_config_dir {config_dir}/{task} \\ |
| --output_dir $BASE_OUT/outputs/{i+1}-{task} \\""" |
|
|
| metric_key = f"eval_exact_match" if "classification" in task.lower() or "polarity" in task.lower() else f"eval_{metric}" |
|
|
| cmd = f"""\ |
| CUDA_VISIBLE_DEVICES=$GPU_IDS python src/run_t5.py \\ |
| --do_train \\ |
| --do_predict \\ |
| --predict_with_generate \\ |
| --model_name_or_path $MODEL_PATH \\ |
| {common} |
| --per_device_train_batch_size $BSZ \\ |
| --per_device_eval_batch_size $EVAL_BSZ \\ |
| --gradient_accumulation_steps $GA \\ |
| --learning_rate 0.0003 \\ |
| --num_train_epochs 100 \\ |
| --run_name $RUN_NAME \\ |
| --max_source_length 512 \\ |
| --max_target_length 50 \\ |
| --generation_max_length 50 \\ |
| --add_task_name False \\ |
| --add_dataset_name False \\ |
| --overwrite_output_dir \\ |
| --overwrite_cache \\ |
| --lr_scheduler_type constant \\ |
| --warmup_steps 0 \\ |
| --logging_strategy steps \\ |
| --logging_steps 10 \\ |
| --metric_for_best_model {metric_key} \\ |
| --evaluation_strategy steps \\ |
| --save_strategy steps \\ |
| --save_total_limit 1 \\ |
| --lora_r 8 \\ |
| --lora_alpha 32 \\ |
| --lora_dropout 0.0 \\ |
| --load_best_model_at_end \\ |
| --data_replay_freq -1 \\ |
| --replay_after_n_epoch 0 \\ |
| --kl_ratio 0.5 \\ |
| --attn_temperature 1 \\ |
| --mlp_hidden_dim 100 \\ |
| --model_name gainlora_inflora \\ |
| --threshold 0.995 \\ |
| --transthreshold 0.995 \\ |
| $FP16_FLAG \\ |
| $SRT_FLAGS \\ |
| {srt_load} |
| |
| rm -rf $BASE_OUT/outputs/{i+1}-{task}/checkpoint* |
| sleep 5""" |
| task_cmds.append(cmd) |
|
|
| return textwrap.dedent(f"""\ |
| #!/bin/bash |
| #SBATCH -J srt |
| #SBATCH -o srt-%j.out |
| #SBATCH -p compute |
| #SBATCH -N 1 |
| #SBATCH -t 80:00:00 |
| #SBATCH --mem 256G |
| #SBATCH --gres=gpu:a100-sxm4-80gb:1 |
| |
| export CUDA_DEVICE_ORDER="PCI_BUS_ID" |
| GPU_ID="${{1:-0}}" |
| MODEL_PATH="${{2:-google/flan-t5-xl}}" |
| |
| # ── GPU detection ──────────────────────────────────────────────────────────── |
| NUM_GPUS=$(nvidia-smi -L 2>/dev/null | wc -l) |
| GPU_MEM=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null | head -1) |
| : ${{GPU_MEM:=16000}}; : ${{NUM_GPUS:=1}} |
| |
| if [ "$GPU_MEM" -lt 20000 ]; then |
| IS_T4=1; GPU_MODE="t4_1gpu"; GPU_IDS="$GPU_ID"; FP16_FLAG="--gradient_checkpointing" |
| else |
| IS_T4=0; GPU_MODE="a100"; GPU_IDS="$GPU_ID"; FP16_FLAG="" |
| fi |
| |
| echo "[GPU] $GPU_MODE | CUDA_VISIBLE_DEVICES=$GPU_IDS | $MODEL_PATH" |
| echo "============================================================" |
| |
| if [ "$GPU_MODE" = "t4_1gpu" ]; then |
| BSZ=2; GA=8; EVAL_BSZ=8 |
| else |
| BSZ=2; GA=8; EVAL_BSZ=16 |
| fi |
| |
| RUN_NAME="{run_name}" |
| TASK_ORDER="{task_order}" |
| BASE_OUT="logs_and_outputs/$RUN_NAME" |
| SRT_FLAGS="--use_srt_router --srt_shrink --srt_shrink_factor 0.1 --srt_metric_mode hard --srt_max_emb_samples 500 --srt_skip_forward" |
| |
| """) + "\n\n".join(task_cmds) + f"""\ |
| |
| echo "[DONE] All 15 tasks complete. Run: python score.py $RUN_NAME $RUN_NAME" |
| """ |
|
|
|
|
| |
|
|
| def gen_llama_script(run_name, task_order, task_list, config_dir, gen_data_dir): |
| SRT_FLAGS = ( |
| "--use_srt_router --srt_shrink --srt_shrink_factor 0.1 " |
| "--srt_metric_mode hard --srt_max_emb_samples 500 --srt_skip_forward" |
| ) |
|
|
| task_cmds = [] |
| for i, task in enumerate(task_list): |
| if i == 0: |
| prev_lora = "" |
| prev_key = "" |
| load_trans = "" |
| elif i == 1: |
| prev_lora = f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights" |
| prev_key = f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights/prompts_keys_till_now.pt" |
| load_trans = f"$BASE_OUT/outputs/1-{task_list[0]}/saved_weights/trans_input.pt" |
| else: |
| prev_lora = ",".join( |
| f"$BASE_OUT/outputs/{j+1}-{task_list[j]}/saved_weights" |
| for j in range(i) |
| ) |
| prev_key = f"$BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/prompts_keys_till_now.pt" |
| load_trans = f"$BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/trans_input.pt" |
|
|
| srt_load = "" if i == 0 else f"--srt_load_path $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights" |
|
|
| if i == 0: |
| common = f"""\ |
| --data_dir CL_Benchmark \\ |
| --task_order $TASK_ORDER \\ |
| --task_config_dir {config_dir}/{task} \\ |
| --output_dir $BASE_OUT/outputs/{i+1}-{task} \\""" |
| else: |
| common = f"""\ |
| --data_dir CL_Benchmark \\ |
| --load_checkpoint_from $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/trans_input.pt \\ |
| --previous_lora_path {prev_lora} \\ |
| --previous_prompt_key_path $BASE_OUT/outputs/{i}-{task_list[i-1]}/saved_weights/prompts_keys_till_now.pt \\ |
| --task_order $TASK_ORDER \\ |
| --gen_data_dir {gen_data_dir} \\ |
| --task_config_dir {config_dir}/{task} \\ |
| --output_dir $BASE_OUT/outputs/{i+1}-{task} \\""" |
|
|
| metric_key = "eval_exact_match" if "classification" in task.lower() or "polarity" in task.lower() else "eval_rougeL" |
|
|
| cmd = f"""\ |
| CUDA_VISIBLE_DEVICES=$GPU_IDS python src/run_llama.py \\ |
| --do_train \\ |
| --do_predict \\ |
| --predict_with_generate \\ |
| --model_name_or_path $MODEL_PATH \\ |
| {common} |
| --per_device_train_batch_size $BSZ \\ |
| --per_device_eval_batch_size $EVAL_BSZ \\ |
| --gradient_accumulation_steps $GA \\ |
| --learning_rate 0.0003 \\ |
| --num_train_epochs 100 \\ |
| --run_name $RUN_NAME \\ |
| --max_source_length 512 \\ |
| --max_target_length 50 \\ |
| --generation_max_length 50 \\ |
| --add_task_name False \\ |
| --add_dataset_name False \\ |
| --overwrite_output_dir \\ |
| --overwrite_cache \\ |
| --lr_scheduler_type constant \\ |
| --warmup_steps 0 \\ |
| --logging_strategy steps \\ |
| --logging_steps 10 \\ |
| --metric_for_best_model {metric_key} \\ |
| --evaluation_strategy steps \\ |
| --save_strategy steps \\ |
| --save_total_limit 1 \\ |
| --lora_r 8 \\ |
| --lora_alpha 32 \\ |
| --lora_dropout 0.0 \\ |
| --load_best_model_at_end \\ |
| --data_replay_freq -1 \\ |
| --replay_after_n_epoch 0 \\ |
| --kl_ratio 0.5 \\ |
| --attn_temperature 1 \\ |
| --mlp_hidden_dim 100 \\ |
| --model_name gainlora_inflora \\ |
| --threshold 0.995 \\ |
| --transthreshold 0.995 \\ |
| $FP16_FLAG \\ |
| $SRT_FLAGS \\ |
| {srt_load} |
| |
| rm -rf $BASE_OUT/outputs/{i+1}-{task}/checkpoint* |
| sleep 5""" |
| task_cmds.append(cmd) |
|
|
| return textwrap.dedent(f"""\ |
| #!/bin/bash |
| #SBATCH -J srt-llama |
| #SBATCH -o srt-llama-%j.out |
| #SBATCH -p compute |
| #SBATCH -N 1 |
| #SBATCH -t 80:00:00 |
| #SBATCH --mem 256G |
| #SBATCH --gres=gpu:a100-sxm4-80gb:1 |
| |
| export CUDA_DEVICE_ORDER="PCI_BUS_ID" |
| MODEL_PATH="${{1:-meta-llama/Meta-Llama-3-8B}}" |
| |
| NUM_GPUS=$(nvidia-smi -L 2>/dev/null | wc -l) |
| GPU_MEM=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits 2>/dev/null | head -1) |
| : ${{GPU_MEM:=16000}}; : ${{NUM_GPUS:=1}} |
| |
| if [ "$GPU_MEM" -lt 20000 ]; then |
| IS_T4=1; GPU_MODE="t4_1gpu"; GPU_IDS="$GPU_ID"; FP16_FLAG="--gradient_checkpointing" |
| else |
| IS_T4=0; GPU_MODE="a100"; GPU_IDS="$GPU_ID"; FP16_FLAG="" |
| fi |
| |
| echo "[GPU] $GPU_MODE | CUDA_VISIBLE_DEVICES=$GPU_IDS | $MODEL_PATH" |
| echo "============================================================" |
| |
| # Llama: smaller BSZ due to larger model |
| if [ "$GPU_MODE" = "t4_1gpu" ]; then |
| BSZ=1; GA=16; EVAL_BSZ=4 |
| else |
| BSZ=1; GA=16; EVAL_BSZ=8 |
| fi |
| |
| RUN_NAME="{run_name}" |
| TASK_ORDER="{task_order}" |
| BASE_OUT="logs_and_outputs/$RUN_NAME" |
| SRT_FLAGS="--use_srt_router --srt_shrink --srt_shrink_factor 0.1 --srt_metric_mode hard --srt_max_emb_samples 500 --srt_skip_forward" |
| |
| """) + "\n\n".join(task_cmds) + f"""\ |
| |
| echo "[DONE] All 15 tasks complete. Run: python score.py $RUN_NAME $RUN_NAME" |
| """ |
|
|
|
|
| |
|
|
| scripts = [ |
| ("gen_script_superni_order1_t5_srt.sh", |
| gen_t5_script( |
| "superni_order1_t5_srt", |
| SUPERNI_ORDER1, |
| SUPERNI_TASK_LIST_ORDER1, |
| "configs/gen_script_superni_order1_t5_configs", |
| "generated_data/lora_gen_superni_t5", |
| "rougeL")), |
| ("gen_script_superni_order2_t5_srt.sh", |
| gen_t5_script( |
| "superni_order2_t5_srt", |
| SUPERNI_ORDER2, |
| SUPERNI_TASK_LIST_ORDER2, |
| "configs/gen_script_superni_order2_t5_configs", |
| "generated_data/lora_gen_superni_t5", |
| "rougeL")), |
| ("gen_script_long_order3_t5_srt.sh", |
| gen_t5_script( |
| "long_order3_t5_srt", |
| LONG_ORDER3, |
| LONG_TASK_LIST, |
| "configs/gen_script_long_order3_t5_configs", |
| "generated_data/lora_gen_long_t5", |
| "rougeL")), |
| ("gen_script_long_order4_t5_srt.sh", |
| gen_t5_script( |
| "long_order4_t5_srt", |
| LONG_ORDER4, |
| LONG_TASK_LIST, |
| "configs/gen_script_long_order4_t5_configs", |
| "generated_data/lora_gen_long_t5", |
| "rougeL")), |
| ("gen_script_superni_order1_llama_srt.sh", |
| gen_llama_script( |
| "superni_order1_llama_srt", |
| SUPERNI_ORDER1, |
| SUPERNI_TASK_LIST_ORDER1, |
| "configs/gen_script_superni_order1_llama_configs", |
| "generated_data/lora_gen_superni_llama")), |
| ("gen_script_superni_order2_llama_srt.sh", |
| gen_llama_script( |
| "superni_order2_llama_srt", |
| SUPERNI_ORDER2, |
| SUPERNI_TASK_LIST_ORDER2, |
| "configs/gen_script_superni_order2_llama_configs", |
| "generated_data/lora_gen_superni_llama")), |
| ] |
|
|
| out_dir = "/Users/nnminh322/Desktop/personal/Continual/new_gainlora" |
| for name, content in scripts: |
| path = f"{out_dir}/{name}" |
| with open(path, "w") as f: |
| f.write(content) |
| import os |
| os.chmod(path, 0o755) |
| print(f"Generated: {name}") |
|
|
| print("\nUsage:") |
| print(" cd new_gainlora") |
| print(" bash gen_script_superni_order1_t5_srt.sh 0 google/flan-t5-xl") |
| print(" bash gen_script_superni_order2_t5_srt.sh 0 google/flan-t5-xl") |
| print(" bash gen_script_long_order3_t5_srt.sh 0 google/flan-t5-xl") |
| print(" bash gen_script_long_order4_t5_srt.sh 0 google/flan-t5-xl") |
| print(" bash gen_script_superni_order1_llama_srt.sh 0 meta-llama/Meta-Llama-3-8B") |
| print(" bash gen_script_superni_order2_llama_srt.sh 0 meta-llama/Meta-Llama-3-8B") |
|
|
