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ecadbd9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | #!/bin/bash
export SAMA_CONFIG=./config/sama_math_gemma9.yaml
export TOKENIZERS_PARALLELISM=true
# CUDA Include (/cuda.h)
CUDA_INCLUDE_PATH="/home/work/miniconda3/envs/allm/include"
export CPATH=$CPATH:$CUDA_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:$CUDA_INCLUDE_PATH
export WANDB_PROJECT="SAMA_MATH"
date +"%F %T"
# test
STEP=50
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=1e-3 --trainer_args.output_dir "./Gemma7B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 16 --sama_adapter.row_R 16 \
# --sama_adapter.num_unique_blocks_L 16 --sama_adapter.num_unique_blocks_R 16 \
# --trainer_args.num_train_epochs 16 --trainer_args.report_to none --trainer_args.eval_delay 200 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --sama_adapter.scaling 2
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 16 --sama_adapter.row_R 16 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 16 --sama_adapter.num_unique_blocks_R 16 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# date +"%F %T"
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 4 --sama_adapter.num_unique_blocks_R 4 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 8 --sama_adapter.row_R 8 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 4 --sama_adapter.num_unique_blocks_R 4 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 2
# date +"%F %T"
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 16 --sama_adapter.row_R 16 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 4 --sama_adapter.num_unique_blocks_R 4 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 4
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 32 --sama_adapter.row_R 32 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 16 --sama_adapter.num_unique_blocks_R 16 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 2
# date +"%F %T"
#
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 2 --sama_adapter.row_R 2 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 2
# date +"%F %T"
#
# wandb sync wandb/latest-run
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 16 --sama_adapter.row_R 16 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 16 --sama_adapter.num_unique_blocks_R 16 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 2 --sama_adapter.row_R 2 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 4 --sama_adapter.num_unique_blocks_R 4 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 2
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 1.4142
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 2.8284
# date +"%F %T"
# STEP=500
# accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
# --config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
# --trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
# --sama_adapter.col_L 4 --sama_adapter.row_R 4 \
# --trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
# --trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
# --sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
# --sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
# --data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
# --sama_adapter.scaling 4
# date +"%F %T"
STEP=500
accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
--config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
--trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
--sama_adapter.col_L 4 --sama_adapter.row_R 4 \
--trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
--trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
--sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
--sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
--data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
--sama_adapter.scaling 1
date +"%F %T"
STEP=500
accelerate launch --dynamo_backend=inductor --dynamo_mode=max-autotune --main_process_port 41353 -m src.math_train \
--config_path $SAMA_CONFIG --trainer_args.learning_rate=5e-4 --trainer_args.output_dir "./MGemma9B" \
--trainer_args.load_best_model_at_end False --trainer_args.save_strategy '"no"' \
--sama_adapter.col_L 2 --sama_adapter.row_R 2 \
--trainer_args.num_train_epochs 2 --trainer_args.report_to wandb \
--trainer_args.save_steps $STEP --trainer_args.eval_steps $STEP --trainer_args.logging_steps $STEP \
--sama_adapter.num_unique_blocks_L 2 --sama_adapter.num_unique_blocks_R 2 \
--sama_adapter.target_modules '["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj","up_proj","down_proj"]' \
--data.dataset_split train[:20000] --trainer_args.eval_delay 0 \
--sama_adapter.scaling 0.7071
date +"%F %T"
# bash scripts/math_mistral7_train.sh
|