mirror of
https://github.com/jingyaogong/minimind.git
synced 2026-04-25 08:48:16 +08:00
627 lines
25 KiB
Python
627 lines
25 KiB
Python
import os
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import sys
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import subprocess
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import threading
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import json
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import socket
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import atexit
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import signal
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from flask import Flask, render_template, request, jsonify, redirect, url_for
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import time
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import psutil
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# 尝试导入torch来检测GPU
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try:
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import torch
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HAS_TORCH = True
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# 检测可用的GPU数量和设备信息
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if torch.cuda.is_available():
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GPU_COUNT = torch.cuda.device_count()
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# 获取GPU设备名称
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GPU_NAMES = [torch.cuda.get_device_name(i) for i in range(GPU_COUNT)]
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else:
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GPU_COUNT = 0
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GPU_NAMES = []
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except ImportError:
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HAS_TORCH = False
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GPU_COUNT = 0
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GPU_NAMES = []
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# 训练方式支持检测
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def get_supported_training_methods():
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"""获取当前环境支持的训练方法"""
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methods = {
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'pretrain': True, # 预训练总是支持
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'sft': True, # SFT总是支持
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'lora': True, # LoRA总是支持
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'dpo': True, # DPO总是支持
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'multi_gpu': HAS_TORCH and GPU_COUNT > 1 # 多GPU训练需要PyTorch和多个GPU
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}
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return methods
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# 获取当前环境支持的训练方法
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SUPPORTED_METHODS = get_supported_training_methods()
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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app = Flask(__name__, template_folder='templates', static_folder='static')
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# 存储训练进程的信息
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training_processes = {}
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# 进程信息持久化文件
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PROCESSES_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'training_processes.json')
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# PID文件
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PID_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'train_web_ui.pid')
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# 启动训练进程
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def start_training_process(train_type, params):
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# 获取脚本所在目录的绝对路径
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# 使用详细的时间戳作为进程ID和日志文件名
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process_id = time.strftime('%Y%m%d_%H%M%S')
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# 构建logfile目录的绝对路径
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log_dir = os.path.join(script_dir, '../logfile')
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log_dir = os.path.abspath(log_dir)
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log_file = os.path.join(log_dir, f"train_{train_type}_{process_id}.log")
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# 确保日志目录存在
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os.makedirs(log_dir, exist_ok=True)
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# 获取GPU数量参数，如果存在且大于1，则使用torchrun启动多卡训练
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gpu_num = int(params.get('gpu_num', 0)) if 'gpu_num' in params else 0
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use_torchrun = HAS_TORCH and GPU_COUNT > 0 and gpu_num > 1
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# 构建命令
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if train_type == 'pretrain':
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script_path = '../trainer/train_pretrain.py'
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if use_torchrun:
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cmd = ['torchrun', '--nproc_per_node', str(gpu_num), script_path]
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else:
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cmd = [sys.executable, script_path]
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if 'save_weight' in params:
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cmd.extend(['--save_weight', params['save_weight']])
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elif train_type == 'sft':
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script_path = '../trainer/train_full_sft.py'
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if use_torchrun:
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cmd = ['torchrun', '--nproc_per_node', str(gpu_num), script_path]
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else:
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cmd = [sys.executable, script_path]
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if 'save_weight' in params:
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cmd.extend(['--save_weight', params['save_weight']])
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elif train_type == 'lora':
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script_path = '../trainer/train_lora.py'
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if use_torchrun:
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cmd = ['torchrun', '--nproc_per_node', str(gpu_num), script_path]
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else:
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cmd = [sys.executable, script_path]
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if 'lora_name' in params:
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cmd.extend(['--lora_name', params['lora_name']])
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elif train_type == 'dpo':
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script_path = '../trainer/train_dpo.py'
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if use_torchrun:
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cmd = ['torchrun', '--nproc_per_node', str(gpu_num), script_path]
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else:
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cmd = [sys.executable, script_path]
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# 添加DPO特定参数
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if 'beta' in params and params['beta']:
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cmd.extend(['--beta', params['beta']])
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if 'accumulation_steps' in params and params['accumulation_steps']:
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cmd.extend(['--accumulation_steps', params['accumulation_steps']])
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if 'grad_clip' in params and params['grad_clip']:
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cmd.extend(['--grad_clip', params['grad_clip']])
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elif train_type == 'ppo':
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script_path = '../trainer/train_ppo.py'
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if use_torchrun:
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cmd = ['torchrun', '--nproc_per_node', str(gpu_num), script_path]
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else:
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cmd = [sys.executable, script_path]
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# 添加PPO特定参数
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if 'clip_epsilon' in params and params['clip_epsilon']:
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cmd.extend(['--clip_epsilon', params['clip_epsilon']])
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if 'vf_coef' in params and params['vf_coef']:
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cmd.extend(['--vf_coef', params['vf_coef']])
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if 'kl_coef' in params and params['kl_coef']:
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cmd.extend(['--kl_coef', params['kl_coef']])
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if 'reasoning' in params and params['reasoning']:
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cmd.extend(['--reasoning', params['reasoning']])
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if 'update_old_actor_freq' in params and params['update_old_actor_freq']:
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cmd.extend(['--update_old_actor_freq', params['update_old_actor_freq']])
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if 'reward_model_path' in params and params['reward_model_path']:
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cmd.extend(['--reward_model_path', params['reward_model_path']])
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else:
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return None
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# 添加通用参数
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for key, value in params.items():
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# 跳过特殊参数和DPO、PPO特有参数，以及gpu_num参数（因为已经在torchrun命令中使用）
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# 对于PPO训练，跳过--from_weight参数
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if key in ['train_type', 'save_weight', 'lora_name', 'train_monitor', 'beta', 'accumulation_steps', 'grad_clip', 'gpu_num', 'clip_epsilon', 'vf_coef', 'kl_coef', 'reasoning', 'update_old_actor_freq', 'reward_model_path'] or (train_type == 'ppo' and key == 'from_weight'):
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continue
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# 对于from_resume参数，需要正确传递参数值
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elif key == 'from_resume':
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# 确保传递参数名和参数值
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cmd.extend([f'--{key}', str(value)])
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else:
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# 确保log_interval和save_interval参数正确传递
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cmd.extend([f'--{key}', str(value)])
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# 单独处理训练监控参数，确保它不会被错误地添加值
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if 'train_monitor' in params:
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if params['train_monitor'] == 'wandb' or params['train_monitor'] == 'swanlab':
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cmd.append('--use_wandb') # 对于wandb和swanlab，只添加标志，不添加值
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if params['train_monitor'] == 'wandb':
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cmd.extend(['--wandb_project', 'minimind_training'])
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# 创建日志文件
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with open(log_file, 'w') as f:
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f.write(f"开始训练 {train_type} 进程\n")
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f.write(f"命令: {' '.join(cmd)}\n\n")
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# 启动进程
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process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.STDOUT,
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text=True,
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cwd=os.path.dirname(os.path.abspath(__file__))
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)
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# 存储进程信息
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training_processes[process_id] = {
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'process': process,
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'train_type': train_type,
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'log_file': log_file,
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'start_time': time.strftime('%Y-%m-%d %H:%M:%S'),
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'running': True,
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'error': False,
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'train_monitor': params.get('train_monitor', 'none'), # 保存训练监控设置
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'swanlab_url': None,
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'next_line_is_swanlab_url': False
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}
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# 开始读取输出
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def read_output():
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try:
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while True:
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output = process.stdout.readline()
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if output == '' and process.poll() is not None:
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break
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if output:
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# 检查是否是swanlab链接的行
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output_stripped = output.strip()
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if training_processes[process_id]['next_line_is_swanlab_url']:
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# 保存swanlab链接
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training_processes[process_id]['swanlab_url'] = output_stripped
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training_processes[process_id]['next_line_is_swanlab_url'] = False
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elif 'swanlab: 🚀 View run at' in output_stripped:
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# 标记下一行是swanlab链接
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training_processes[process_id]['next_line_is_swanlab_url'] = True
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with open(log_file, 'a') as f:
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f.write(output)
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# 检查进程是否成功结束
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if process.returncode != 0:
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training_processes[process_id]['error'] = True
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finally:
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training_processes[process_id]['running'] = False
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# 启动线程读取输出
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threading.Thread(target=read_output, daemon=True).start()
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return process_id
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# Flask路由
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@app.route('/')
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def index():
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# 传递GPU信息到前端
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return render_template('index.html', has_gpu=HAS_TORCH and GPU_COUNT > 0, gpu_count=GPU_COUNT)
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@app.route('/train', methods=['POST'])
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def train():
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data = request.json
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train_type = data.get('train_type')
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# 移除不相关的参数
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params = data.copy()
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# 处理复选框参数
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if 'from_resume' not in params:
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params['from_resume'] = '0'
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# 启动训练进程
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process_id = start_training_process(train_type, params)
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if process_id:
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return jsonify({'success': True, 'process_id': process_id})
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else:
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return jsonify({'success': False, 'error': '无效的训练类型'})
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@app.route('/processes')
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def processes():
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result = []
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for process_id, info in training_processes.items():
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# 确定状态
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status = '运行中' if info['running'] else \
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'手动停止' if 'manually_stopped' in info and info['manually_stopped'] else \
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'出错' if info['error'] else '已完成'
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result.append({
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'id': process_id,
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'train_type': info['train_type'],
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'start_time': info['start_time'],
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'running': info['running'],
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'error': info['error'],
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'status': status,
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'train_monitor': info.get('train_monitor', 'none'), # 添加train_monitor字段
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'swanlab_url': info.get('swanlab_url') # 添加swanlab_url字段
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})
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return jsonify(result)
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@app.route('/logs/<process_id>')
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def logs(process_id):
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# 直接从本地logfile目录读取日志文件
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# 获取脚本所在目录的绝对路径
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# 构建logfile目录的绝对路径
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log_dir = os.path.join(script_dir, '../logfile')
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log_dir = os.path.abspath(log_dir)
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# 查找匹配的日志文件
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log_file = None
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if os.path.exists(log_dir):
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for filename in os.listdir(log_dir):
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if filename.endswith(f'{process_id}.log'):
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log_file = os.path.join(log_dir, filename)
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break
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if not log_file or not os.path.exists(log_file):
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return '日志文件不存在或已被删除'
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try:
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# 使用高效且健壮的方法读取文件的最后200行
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def read_last_n_lines(file_path, n=200):
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# 使用二进制模式读取文件，避免编码问题
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with open(file_path, 'rb') as f:
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# 获取文件大小
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f.seek(0, os.SEEK_END)
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file_size = f.tell()
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# 如果文件很小，直接读取整个文件
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if file_size < 1024 * 1024: # 小于1MB的文件直接读取
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f.seek(0)
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content = f.read()
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return process_content(content)
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# 对于大文件，使用缓冲读取末尾部分
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# 估计需要读取的字节数（假设每行平均100字节）
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buffer_size = n * 200 # 为了保险，读取更多字节
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# 定位到适当的位置
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position = max(0, file_size - buffer_size)
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f.seek(position)
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# 读取缓冲区内容
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buffer = f.read(file_size - position)
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# 处理缓冲区内容
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lines = process_content(buffer)
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# 确保我们获取到完整的行
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# 如果缓冲区不是从文件开头开始，第一个行可能不完整
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if position > 0:
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# 跳过第一个可能不完整的行
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if len(lines) > 1:
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lines = lines[1:]
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else:
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# 如果只有一行且不在文件开头，可能需要读取更多
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# 这里简单处理，直接读取整个文件（罕见情况）
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f.seek(0)
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content = f.read()
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lines = process_content(content)
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# 返回最后n行
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return lines[-n:] if len(lines) > n else lines
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def process_content(content):
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# 尝试多种编码方式解码内容
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encodings = ['utf-8', 'latin-1', 'gbk', 'gb2312']
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for encoding in encodings:
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try:
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text = content.decode(encoding)
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# 使用True参数保留换行符，确保行分隔符正确
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return text.splitlines(True)
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except UnicodeDecodeError:
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continue
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# 如果所有编码都失败，使用错误替换模式
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text = content.decode('utf-8', errors='replace')
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return text.splitlines(True)
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# 读取最后200行
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last_200_lines = read_last_n_lines(log_file, 200)
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# 确保返回的内容顺序正确，并且不包含空行
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return ''.join(last_200_lines)
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except Exception as e:
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return f'读取日志失败: {str(e)}'
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@app.route('/logfiles')
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def get_logfiles():
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# 获取脚本所在目录的绝对路径
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# 构建logfile目录的绝对路径
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log_dir = os.path.join(script_dir, '../logfile')
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log_dir = os.path.abspath(log_dir)
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logfiles = []
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if os.path.exists(log_dir):
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for filename in os.listdir(log_dir):
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if filename.endswith('.log') and filename.startswith('train_'):
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file_path = os.path.join(log_dir, filename)
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try:
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modified_time = os.path.getmtime(file_path)
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formatted_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(modified_time))
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logfiles.append({
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'filename': filename,
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'modified_time': formatted_time,
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'size': os.path.getsize(file_path)
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})
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except Exception as e:
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continue
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# 按修改时间倒序排序，最新的在前面
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logfiles.sort(key=lambda x: x['modified_time'], reverse=True)
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return jsonify(logfiles)
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@app.route('/logfile-content/<filename>')
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def get_logfile_content(filename):
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# 安全检查：确保文件名不包含路径遍历字符
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if '..' in filename or '/' in filename or '\\' in filename:
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return jsonify({'error': 'Invalid filename'}), 400
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# 获取脚本所在目录的绝对路径
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# 构建logfile目录的绝对路径，train_web_ui.py在scripts目录下
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log_dir = os.path.join(script_dir, '../logfile')
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log_dir = os.path.abspath(log_dir)
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log_file = os.path.join(log_dir, filename)
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try:
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# 使用二进制模式读取文件，可以更可靠地保留原始换行符
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with open(log_file, 'rb') as f:
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content_bytes = f.read()
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# 尝试多种编码方式解码，确保正确处理换行符
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encodings = ['utf-8', 'latin-1', 'gbk', 'gb2312']
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content = None
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for encoding in encodings:
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try:
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# 解码文件内容，保留原始换行符
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content = content_bytes.decode(encoding)
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break
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except UnicodeDecodeError:
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continue
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# 如果所有编码都失败，使用errors='replace'参数处理不可解码的字符
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if content is None:
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content = content_bytes.decode('utf-8', errors='replace')
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# 确保返回的内容正确保留所有换行符
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return content
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except FileNotFoundError:
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return jsonify({'error': 'Log file not found'}), 404
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@app.route('/delete-logfile/<filename>', methods=['DELETE'])
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def delete_logfile(filename):
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# 获取脚本所在目录的绝对路径
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script_dir = os.path.dirname(os.path.abspath(__file__))
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||
# 构建logfile目录的绝对路径
|
||
log_dir = os.path.join(script_dir, '../logfile')
|
||
log_dir = os.path.abspath(log_dir)
|
||
|
||
# 安全检查：防止路径遍历攻击
|
||
if '..' in filename or '/' in filename or '\\' in filename:
|
||
return jsonify({'success': False, 'message': '非法的文件名'})
|
||
|
||
log_file = os.path.join(log_dir, filename)
|
||
if os.path.exists(log_file) and os.path.isfile(log_file):
|
||
try:
|
||
os.remove(log_file)
|
||
return jsonify({'success': True, 'message': '日志文件删除成功'})
|
||
except Exception as e:
|
||
print(f"删除日志文件失败: {str(e)}")
|
||
return jsonify({'success': False, 'message': f'删除失败: {str(e)}'})
|
||
return jsonify({'success': False, 'message': '日志文件不存在'})
|
||
|
||
|
||
@app.route('/stop/<process_id>', methods=['POST'])
|
||
def stop(process_id):
|
||
if process_id in training_processes and training_processes[process_id]['running']:
|
||
process = training_processes[process_id]['process']
|
||
# 在Windows上使用terminate，在Unix上尝试优雅终止
|
||
try:
|
||
process.terminate()
|
||
# 等待进程结束
|
||
process.wait(timeout=5)
|
||
# 标记为手动停止
|
||
training_processes[process_id]['running'] = False
|
||
training_processes[process_id]['manually_stopped'] = True
|
||
except subprocess.TimeoutExpired:
|
||
# 如果超时，强制杀死
|
||
process.kill()
|
||
# 标记为手动停止
|
||
training_processes[process_id]['running'] = False
|
||
training_processes[process_id]['manually_stopped'] = True
|
||
return jsonify({'success': True})
|
||
return jsonify({'success': False})
|
||
|
||
@app.route('/delete/<process_id>', methods=['POST'])
|
||
def delete(process_id):
|
||
if process_id in training_processes:
|
||
# 确保进程已经停止
|
||
if training_processes[process_id]['running']:
|
||
# 如果进程还在运行，先停止它
|
||
try:
|
||
process = training_processes[process_id]['process']
|
||
process.terminate()
|
||
try:
|
||
process.wait(timeout=3)
|
||
except subprocess.TimeoutExpired:
|
||
process.kill()
|
||
except Exception as e:
|
||
print(f"停止进程失败: {str(e)}")
|
||
|
||
# 从进程字典中删除
|
||
del training_processes[process_id]
|
||
|
||
# 可选：删除对应的日志文件
|
||
try:
|
||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||
log_dir = os.path.join(script_dir, '../logfile')
|
||
log_dir = os.path.abspath(log_dir)
|
||
|
||
if os.path.exists(log_dir):
|
||
for filename in os.listdir(log_dir):
|
||
if filename.endswith(f'{process_id}.log'):
|
||
os.remove(os.path.join(log_dir, filename))
|
||
except Exception as e:
|
||
print(f"删除日志文件失败: {str(e)}")
|
||
|
||
return jsonify({'success': True})
|
||
return jsonify({'success': False})
|
||
|
||
def find_available_port(start_port=12581, max_attempts=100):
|
||
"""查找可用的端口号
|
||
|
||
Args:
|
||
start_port: 起始端口号
|
||
max_attempts: 最大尝试次数
|
||
|
||
Returns:
|
||
可用的端口号，如果没有找到则返回None
|
||
"""
|
||
for port in range(start_port, start_port + max_attempts):
|
||
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||
result = sock.connect_ex(('localhost', port))
|
||
sock.close()
|
||
if result != 0: # 端口可用
|
||
return port
|
||
return None
|
||
|
||
def save_processes_info():
|
||
"""保存训练进程信息到文件"""
|
||
try:
|
||
# 创建一个不包含进程对象的可序列化版本
|
||
serializable_processes = {}
|
||
for pid, info in training_processes.items():
|
||
serializable_processes[pid] = {
|
||
'pid': info.get('pid', info.get('process').pid) if isinstance(info.get('process'), subprocess.Popen) else info.get('pid'),
|
||
'train_type': info['train_type'],
|
||
'log_file': info['log_file'],
|
||
'start_time': info['start_time'],
|
||
'running': info['running'],
|
||
'error': info.get('error', False),
|
||
'manually_stopped': info.get('manually_stopped', False),
|
||
'train_monitor': info.get('train_monitor', 'none'), # 保存train_monitor
|
||
'swanlab_url': info.get('swanlab_url') # 保存swanlab_url
|
||
}
|
||
|
||
with open(PROCESSES_FILE, 'w', encoding='utf-8') as f:
|
||
json.dump(serializable_processes, f, ensure_ascii=False, indent=2)
|
||
except Exception as e:
|
||
print(f"保存进程信息失败: {str(e)}")
|
||
|
||
def load_processes_info():
|
||
"""从文件加载训练进程信息"""
|
||
global training_processes
|
||
try:
|
||
if os.path.exists(PROCESSES_FILE):
|
||
with open(PROCESSES_FILE, 'r', encoding='utf-8') as f:
|
||
loaded_processes = json.load(f)
|
||
|
||
# 检查每个进程是否还在运行
|
||
for pid, info in loaded_processes.items():
|
||
# 确保所有需要的字段都存在
|
||
if 'swanlab_url' not in info:
|
||
info['swanlab_url'] = None
|
||
if 'manually_stopped' not in info:
|
||
info['manually_stopped'] = False
|
||
if 'error' not in info:
|
||
info['error'] = False
|
||
if 'train_monitor' not in info:
|
||
info['train_monitor'] = 'none'
|
||
|
||
if info['running']:
|
||
try:
|
||
# 检查进程是否还在运行
|
||
proc = psutil.Process(info['pid'])
|
||
if proc.is_running() and proc.status() != 'zombie':
|
||
# 进程仍在运行，恢复信息
|
||
training_processes[pid] = info
|
||
else:
|
||
# 进程已停止
|
||
info['running'] = False
|
||
# 如果进程未被明确标记为完成或出错，则默认为手动停止
|
||
if not info['error']:
|
||
info['manually_stopped'] = True
|
||
training_processes[pid] = info
|
||
except (psutil.NoSuchProcess, psutil.AccessDenied):
|
||
# 进程不存在或无权限访问
|
||
info['running'] = False
|
||
# 如果进程未被明确标记为完成或出错，则默认为手动停止
|
||
if not info['error']:
|
||
info['manually_stopped'] = True
|
||
training_processes[pid] = info
|
||
else:
|
||
# 进程已停止，直接恢复
|
||
training_processes[pid] = info
|
||
except Exception as e:
|
||
print(f"加载进程信息失败: {str(e)}")
|
||
|
||
def handle_exit(signum, frame):
|
||
"""处理程序退出信号，保存进程信息"""
|
||
print("正在保存进程信息...")
|
||
save_processes_info()
|
||
# 删除PID文件
|
||
if os.path.exists(PID_FILE):
|
||
try:
|
||
os.remove(PID_FILE)
|
||
except:
|
||
pass
|
||
sys.exit(0)
|
||
|
||
# 注册退出处理器
|
||
signal.signal(signal.SIGINT, handle_exit) # Ctrl+C
|
||
if hasattr(signal, 'SIGTERM'):
|
||
signal.signal(signal.SIGTERM, handle_exit) # 终止信号
|
||
|
||
# 注册程序退出时的处理函数
|
||
atexit.register(save_processes_info)
|
||
|
||
if __name__ == '__main__':
|
||
# 加载已保存的进程信息
|
||
load_processes_info()
|
||
|
||
# 创建PID文件，用于标识web进程
|
||
with open(PID_FILE, 'w') as f:
|
||
f.write(str(os.getpid()))
|
||
|
||
# 尝试使用默认端口12581，如果被占用则自动寻找可用端口
|
||
port = find_available_port(12581)
|
||
if port is not None:
|
||
print(f"启动Flask服务器在 http://0.0.0.0:{port}")
|
||
print(f"使用nohup启动可保持服务持续运行: nohup python -u scripts/train_web_ui.py &")
|
||
# 使用0.0.0.0作为host以兼容VSCode的端口转发功能
|
||
app.run(host='0.0.0.0', port=port, debug=False) # 生产环境关闭debug
|
||
else:
|
||
print("无法找到可用的端口，请检查系统端口占用情况")
|
||
# 删除PID文件
|
||
if os.path.exists(PID_FILE):
|
||
try:
|
||
os.remove(PID_FILE)
|
||
except:
|
||
pass
|
||
sys.exit(1) |