batch_run.sh

@@ -1 +0,0 @@
-bash run_once.sh lora_sft Baichuan-7B 4 50

gpu_status.py

@@ -1,17 +1,16 @@
 import json
-import sys
 import pynvml 
 import time
 import psutil
 
-UNIT = 1024 * 1024 * 1024
 
-def gpu_status(output_path = "./results/gpu_status", print_status = False):
+def main():
+    UNIT = 1024 * 1024 * 1024
+
     pynvml.nvmlInit()
     gpuDeviceCount = pynvml.nvmlDeviceGetCount()
     start_time = time.time()
-    first_loop = True
-    
+
     while time.time() - start_time < 3600 *24:
         # print(time.time() - start_time)
         all_gpu_status = []
@@ -44,25 +43,14 @@ def gpu_status(output_path = "./results/gpu_status", print_status = False):
             all_gpu_status = all_gpu_status,
             all_processes_status =  all_processes_status
         )
-
-        with open(f"{output_path}/gpu_status.json", "a", encoding="utf-8") as f:
+        formatted_time = time.strftime('%Y%m%d%H%M%S', time.localtime())
+        with open(f"./results/gpu_status/gpu_status_{formatted_time}.json", "a", encoding="utf-8") as f:
             f.write(json.dumps(logs) + "\n")
-        
-        if first_loop:
-            print("Start run gpu_status.py")
-            first_loop = False
-            
-        if print_status:
-            print(logs)
+        print(logs)
 
         time.sleep(60)
-        
     pynvml.nvmlShutdown()
 
-def main():
-    output_path = sys.argv[1]
-    print_status = sys.argv[2]
-    gpu_status(output_path, print_status)
 
 if __name__ == "__main__":
     main()

prepare_yaml_file.py

@@ -1,52 +0,0 @@
-import os
-import sys
-import time
-import yaml
-import json
-import pynvml 
-import time
-import psutil
-
-def main():
-    run_type = sys.argv[1]
-    model = sys.argv[2]
-    max_steps = sys.argv[3]
-    run_name = sys.argv[4]
-    output_dir = sys.argv[5]
-
-    if run_type == "lora_sft":
-        yaml_file = './results/lora_sft_template.yml'
-    elif run_type == "inference":
-        yaml_file = './results/predict_template.yml'
-      
-    if model == "9g-8B":
-        model_name_or_path = "../../models/sft_8b_v2"
-        template = ""
-    elif model == "Baichuan2-7B":
-        model_name_or_path = "../../models/Baichuan-7B"
-        template = "baichuan"
-    elif model == "ChatGLM2-6B":
-        model_name_or_path = "../../models/chatglm2-6b"
-        template = "chatglm2"
-    elif model == "Llama2-7B":
-        model_name_or_path = "../../models/llama-2-7b-ms"
-        template = "llama2"
-    elif model == "Qwen-7B":
-        model_name_or_path = "../../models/Qwen-7B"
-        template = "qwen"
-        
-    config = None
-    with open(yaml_file, 'r', encoding='utf-8') as f:
-        config = yaml.load(f.read(), Loader=yaml.FullLoader)
-        
-    config['model_name_or_path'] = model_name_or_path
-    config['template'] = template
-    config['output_dir'] = output_dir
-    if run_type == "lora_sft":
-        config['max_steps'] = max_steps
-
-    with open(f'{output_dir}/{run_name}.yml', 'w', encoding='utf-8') as f:
-        yaml.dump(data=config, stream=f, allow_unicode=True)
-
-if __name__ == "__main__":
-    main()

results/lora_sft_template.yaml

@@ -1,42 +0,0 @@
-### model
-model_name_or_path: ../../llm/baichuan
-
-### method
-stage: sft
-do_train: true
-finetuning_type: lora
-lora_target: all
-
-### dataset
-dataset: belle_1m
-template: baichuan
-cutoff_len: 1024
-max_samples: 10000
-overwrite_cache: true
-preprocessing_num_workers: 16
-
-### output
-output_dir: ./results/lora_sft_2/Baichuan2-7B/Baichuan2_lora_sft_1_single_step500
-logging_steps: 3
-save_steps: 500
-plot_loss: true
-overwrite_output_dir: true
-
-### train
-per_device_train_batch_size: 2
-gradient_accumulation_steps: 8
-learning_rate: 1.0e-4
-num_train_epochs: 10.0
-lr_scheduler_type: cosine
-warmup_ratio: 0.1
-bf16: true
-ddp_timeout: 180000000
-max_steps: 500
-include_num_input_tokens_seen: true
-include_tokens_per_second: true
-
-### eval
-val_size: 0.1
-per_device_eval_batch_size: 2
-eval_strategy: steps
-eval_steps: 500

results/predict_template.yaml

@@ -1,23 +0,0 @@
-### model
-model_name_or_path: ../../llm/baichuan
-
-### method
-do_predict: true
-
-### dataset
-eval_dataset: alpaca_gpt4_zh
-template: baichuan
-cutoff_len: 1024
-max_samples: 50
-overwrite_cache: true
-preprocessing_num_workers: 16
-include_tokens_per_second: true
-
-### output
-output_dir: ./results/inference/Baichuan2-7B/Baichuan2_predict_1
-overwrite_output_dir: true
-
-### eval
-per_device_eval_batch_size: 2
-predict_with_generate: true
-ddp_timeout: 180000000

run_once.sh

@@ -1,53 +0,0 @@
-run_type = $1
-model = $2
-gpu_cnt = $3
-max_steps = $4
-
-current_datetime=$(date +%Y%m%d%H%M%S)
-
-if [ "${run_type}" = "lora_sft" ]; then
-    run_name="${run_type}_${model}_${gpu_cnt}_gpu_${max_steps}_step_${current_datetime}"
-    
-else
-    run_name="${run_type}_${model}_${gpu_cnt}_gpu_${current_datetime}"
-fi
-
-output_dir ="./results/${run_name}"
-
-if [ ! -d "$output_dir" ]; then
-    mkdir -p "$output_dir"
-    echo "路径不存在，已创建: $output_dir"
-else
-    echo "路径已存在: $output_dir"
-fi
-
-echo "${run_type} ${model} ${gpu_cnt} ${max_steps} ${run_name} ${output_dir}"
-python prepare_yaml_file.py ${run_type} ${model} ${max_steps} ${run_name} ${output_dir}
-
-
-
-# export USE_MODELSCOPE_HUB=1
-
-# # 0 means not printing gpu status
-# python gpu_status.py ${output_dir} 0 &
-# gpu_status_pid=$!
-# echo "Start recording gpu status "
-
-
-# if [ "${gpu_cnt}" = "1" ]; then
-#    ASCEND_RT_VISIBLE_DEVICES=0 llamafactory-cli train ${output_dir}/${run_name}.yml \
-#    | tee ${output_dir}/log.txt" &
-#    train_pid=$!
-#    echo "Start train"
-# else
-#     FORCE_TORCHRUN=1 llamafactory-cli train ${output_dir}/${run_name}.yml \
-#    | tee ${output_dir}/log.txt" &
-#    train_pid=$!
-#    echo "Start train"
-# fi
-
-# wait $train_pid
-# echo "Train ended"
-# sleep 90
-# kill $gpu_status_pid
-# echo "Gpu status ended"