大整理: 仓库重组 + 新词表 + 新README
- 清理仓库: 删除所有废弃的generate_*脚本和旧版数据文件 - 目录重组: - data/ → 训练数据 - scripts/ → 数据生成脚本 - templates/ → Web UI - 重命名: - train_v2.py → train.py - vocab_v3.json → vocab.json (BPE, 6.8k tokens) - 更新 train.py 指向新词表路径 - 重写 README.md 完整文档 - 更新 .gitignore
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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小狸完整数据集训练 - 支持断点续训 + batch=4防OOM
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"""
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import sys, os
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sys.path.insert(0, '/workspace/xiaoli-llm-chat')
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sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
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sys.stderr = os.fdopen(sys.stderr.fileno(), 'w', buffering=1)
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import torch
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import torch.nn as nn
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import json
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import time
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import glob
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import random
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from torch.utils.data import Dataset, DataLoader
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from llm_chat_improved import (
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CharTokenizer, build_vocabulary, LLMModel, LLMChat,
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ConversationDataset, LLMTrainer, tokenizer_instance
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)
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from api_correction import batch_convert, batch_generate_multi_temp
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def dict_to_tuple(data_list):
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result = []
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for item in data_list:
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if isinstance(item, dict):
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q = item.get("question", "") or item.get("input", "") or item.get("prompt", "") or item.get("instruction", "")
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a = item.get("answer", "") or item.get("output", "") or item.get("response", "")
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if q and a:
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result.append((q, a))
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elif isinstance(item, (list, tuple)) and len(item) >= 2:
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result.append((str(item[0]), str(item[1])))
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return result
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def load_multiturn_data(path):
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with open(path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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single_turn = []
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for item in data:
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if isinstance(item, dict) and "conversations" in item:
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convs = item["conversations"]
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for i in range(0, len(convs) - 1, 2):
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if i + 1 < len(convs):
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c1, c2 = convs[i], convs[i+1]
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if isinstance(c1, dict):
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h = c1.get("value", "") or c1.get("content", "")
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a = c2.get("value", "") or c2.get("content", "")
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elif isinstance(c1, list) and len(c1) >= 2:
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h, a = str(c1[1]), str(c2[1])
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else:
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continue
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if h and a:
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single_turn.append((h, a))
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return single_turn
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def find_latest_checkpoint(model_dir):
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"""查找最新的checkpoint，返回 (路径, epoch数) 或 (None, 0)"""
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checkpoints = glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_epoch_*.pth'))
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if not checkpoints:
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checkpoints = glob.glob(os.path.join(model_dir, 'llm_model_v2_epoch_*.pth'))
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if not checkpoints:
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return None, 0
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best_path = None
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best_epoch = 0
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for path in checkpoints:
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import re
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nums = [int(s) for s in re.findall(r'\d+', os.path.basename(path).replace('.pth',''))]
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if nums:
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epoch = max(nums)
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if epoch > best_epoch:
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best_epoch = epoch
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best_path = path
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return best_path, best_epoch
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def migrate_weights(old_sd, new_sd_init, old_d_model, new_d_model,
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old_n_layers, new_n_layers, old_d_ff, new_d_ff,
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old_vocab_size, new_vocab_size):
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"""权重扩展迁移：将旧模型权重扩展到新架构
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支持 d_model / n_layers / d_ff / vocab_size 全部变化
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"""
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import torch
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new_sd = {}
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old_keys = list(old_sd.keys())
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new_keys = list(new_sd_init.keys())
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for key in new_keys:
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if 'freq' in key:
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# RoPE频率表，新模型自动生成，跳过
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new_sd[key] = new_sd_init[key].clone()
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continue
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# 解析层索引（如 transformer_blocks.3.attention.wq.weight）
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old_key = key
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new_w = new_sd_init[key].clone()
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old_w_shape = None
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if key in old_sd:
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old_w = old_sd[key]
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old_w_shape = old_w.shape
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new_shape = new_w.shape
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# 1. 严格匹配 = 直接复制
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if old_w.shape == new_shape:
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new_w = old_w.clone()
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# 2. 二维权重（线性层 + embedding）
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elif old_w.dim() == 2:
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o0, o1 = old_w.shape
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n0, n1 = new_shape
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# embedding.weight 和 fc_out.weight: 词汇表维度变化
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if 'embedding' in key and old_vocab_size != new_vocab_size:
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# 词汇表扩展：复制旧词嵌入，新词随机
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min_v = min(old_vocab_size, new_vocab_size)
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min_d = min(o1, n1)
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new_w[:min_v, :min_d] = old_w[:min_v, :min_d]
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print(f" 迁移 {key}: ({o0},{o1})→({n0},{n1}) vocab扩展")
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elif 'fc_out' in key and old_vocab_size != new_vocab_size:
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min_vocab = min(old_vocab_size, new_vocab_size)
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min_feat = min(o1, n1)
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new_w[:min_vocab, :min_feat] = old_w[:min_vocab, :min_feat]
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print(f" 迁移 {key}: ({o0},{o1})→({n0},{n1}) vocab扩展")
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elif o0 <= n0 and o1 <= n1:
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# 权重扩展（old 是 new 的子集，放在左上角）
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new_w[:o0, :o1] = old_w
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if o0 < n0 or o1 < n1:
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print(f" 扩展 {key}: ({o0},{o1})→({n0},{n1})")
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elif n0 <= o0 and n1 <= o1:
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# 权重缩小（new 是 old 的子集，取左上角）
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new_w = old_w[:n0, :n1].clone()
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print(f" 缩小 {key}: ({o0},{o1})→({n0},{n1})")
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else:
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# 特殊形状 → 尽量复制重合部分
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min0 = min(o0, n0)
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min1 = min(o1, n1)
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new_w[:min0, :min1] = old_w[:min0, :min1]
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print(f" 部分迁移 {key}: ({o0},{o1})→({n0},{n1})")
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# 3. 一维权重（LayerNorm, bias等）
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elif old_w.dim() == 1:
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o0 = old_w.shape[0]
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n0 = new_w.shape[0]
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min_d = min(o0, n0)
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new_w[:min_d] = old_w[:min_d]
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if o0 != n0:
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print(f" 迁移 {key}: ({o0})→({n0}) norm扩展")
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else:
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new_w = old_w.clone()
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else:
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# 旧模型中没有这个key（可能是不存在的层）
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# 检查是否是额外的新层
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# 如 transformer_blocks.6.xxx → 旧模型只有0-5
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new_sd[key] = new_w # 保持随机初始化
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continue
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new_sd[key] = new_w
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return new_sd
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def main():
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print("=" * 60)
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print("小狸训练 - 从头训练版 v2 (13.2M架构)")
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print("=" * 60)
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device = 'cpu'
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model_dir = '/workspace/xiaoli-llm-chat'
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# 加载数据 - 从合并文件加载
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merged_path = os.path.join(model_dir, 'xiaoli_merged_data_v2.json')
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if not os.path.exists(merged_path):
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print("❌ 未找到合并数据文件 xiaoli_merged_data.json")
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print("请先运行合并脚本")
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return
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with open(merged_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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unique = dict_to_tuple(data)
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print(f"加载合并数据: {len(unique)}条")
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# ====== 划分训练/验证集（保存到独立文件，避免问题重复问题 ======
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val_split = 0.1
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train_path = os.path.join(model_dir, 'xiaoli_train_v2.json')
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val_path = os.path.join(model_dir, 'xiaoli_valid_v2.json')
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if os.path.exists(train_path) and os.path.exists(val_path):
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# 从独立文件加载（精确的position-based划分）
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with open(train_path, 'r', encoding='utf-8') as f:
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train_set = dict_to_tuple(json.load(f))
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with open(val_path, 'r', encoding='utf-8') as f:
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val_set = dict_to_tuple(json.load(f))
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# 检查是否有新数据（来自外部脚本）
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train_qs = set(q for q, _ in train_set)
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val_qs = set(q for q, _ in val_set)
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new_entries = [(q, a) for q, a in unique if q not in train_qs and q not in val_qs]
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if new_entries:
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train_set.extend(new_entries)
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with open(train_path, 'w', encoding='utf-8') as f:
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json.dump([{"question": q, "answer": a} for q, a in train_set], f, ensure_ascii=False, indent=2)
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print(f" 新增外部数据: {len(new_entries)}条 → 加入训练集")
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print(f" 训练集: {len(train_set)}条 | 验证集: {len(val_set)}条 (来自文件)")
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else:
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# 首次划分：随机打乱后按位置切分
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shuffled = unique.copy()
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random.seed(42)
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random.shuffle(shuffled)
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split_idx = int(len(shuffled) * val_split)
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val_set = shuffled[:split_idx]
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train_set = shuffled[split_idx:]
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# 保存两份独立文件
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with open(train_path, 'w', encoding='utf-8') as f:
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json.dump([{"question": q, "answer": a} for q, a in train_set], f, ensure_ascii=False, indent=2)
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with open(val_path, 'w', encoding='utf-8') as f:
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json.dump([{"question": q, "answer": a} for q, a in val_set], f, ensure_ascii=False, indent=2)
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print(f" 首次划分: 训练集{len(train_set)}条 + 验证集{len(val_set)}条 (已保存)")
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print(f" 训练集: {len(train_set)}条 | 验证集: {len(val_set)}条")
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# ====== 加载固定vocab ======
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vocab_path = os.path.join(model_dir, 'vocab.json')
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if not os.path.exists(vocab_path):
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print("❌ 未找到 vocab.json，请先运行BPE词表生成脚本")
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return
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with open(vocab_path, 'r', encoding='utf-8') as f:
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vocab = json.load(f)
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print(f"词汇表: {len(vocab)} (BPE, 来自 vocab.json)")
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# 同步tokenizer（新字符映射为UNK）
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tokenizer_instance.char_to_id = vocab.copy()
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tokenizer_instance.id_to_char = {v: k for k, v in vocab.items()}
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tokenizer_instance._sorted_vocab_cache = None
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for attr in ['_multi_trie', '_multi_keys', '_single_map']:
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if hasattr(tokenizer_instance, attr):
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delattr(tokenizer_instance, attr)
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# ====== 加载蒸馏问题(v3 + 真题) ======
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distill_q_path = os.path.join(model_dir, 'exam_distill_questions.json')
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if not os.path.exists(distill_q_path):
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distill_q_path = os.path.join(model_dir, 'distill_questions_v3.json')
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if not os.path.exists(distill_q_path):
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distill_q_path = os.path.join(model_dir, 'distill_questions_v2.json')
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if os.path.exists(distill_q_path):
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with open(distill_q_path, 'r', encoding='utf-8') as f:
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distill_questions = json.load(f)
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print(f"蒸馏问题: {len(distill_questions)}道")
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else:
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distill_questions = []
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print("蒸馏问题: 无")
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config = {
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'batch_size': 4,
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'gradient_accumulation_steps': 2,
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'epochs': 200,
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'd_model': 320, 'n_heads': 8, 'n_layers': 6,
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'd_ff': 1536, 'max_length': 256, 'dropout': 0.1,
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'use_amp': False,
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}
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model = LLMModel(
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vocab_size=len(vocab), d_model=config['d_model'],
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n_heads=config['n_heads'], n_layers=config['n_layers'],
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d_ff=config['d_ff'], max_len=config['max_length'],
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dropout=config['dropout'],
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use_memory=False, use_thinking=False, use_moe=False,
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num_experts=1, top_k=1,
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)
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total_params = sum(p.numel() for p in model.parameters())
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print(f"参数量: {total_params:,} ({total_params/1e6:.2f}M)")
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# 训练集/验证集 DataLoader
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train_dataset = ConversationDataset(train_set, vocab, config['max_length'])
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train_loader = DataLoader(train_dataset, batch_size=config['batch_size'],
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shuffle=True, num_workers=0)
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print(f"训练批次: {len(train_loader)}")
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val_dataset = ConversationDataset(val_set, vocab, config['max_length'])
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val_loader = DataLoader(val_dataset, batch_size=config['batch_size'],
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shuffle=False, num_workers=0)
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print(f"验证批次: {len(val_loader)}")
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# 检查断点
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ckpt_path, start_epoch = find_latest_checkpoint(model_dir)
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if ckpt_path and start_epoch > 0:
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print(f"\n发现断点: epoch {start_epoch} ({ckpt_path})")
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checkpoint = torch.load(ckpt_path, map_location=device)
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old_cfg = checkpoint.get('model_config', {})
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old_d_model = old_cfg.get('d_model', 256)
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old_n_layers = old_cfg.get('n_layers', 6)
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old_d_ff = old_cfg.get('d_ff', 1024)
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# 检测架构变化，自动做权重扩展迁移
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new_sd_init = model.state_dict() # 随机初始化的新权重
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old_sd = checkpoint['model_state_dict']
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old_vocab_size = old_cfg.get('vocab_size', 0)
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new_vocab_size = len(vocab)
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# 如果架构没变化，直接加载
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if (old_d_model == config['d_model'] and old_n_layers == config['n_layers']
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and old_d_ff == config['d_ff'] and old_vocab_size == new_vocab_size):
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model.load_state_dict(old_sd)
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print("✅ 架构一致，直接加载权重")
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else:
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print(f" 架构变化: d_model={old_d_model}→{config['d_model']}, "
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f"layers={old_n_layers}→{config['n_layers']}, "
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f"d_ff={old_d_ff}→{config['d_ff']}, "
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f"vocab={old_vocab_size}→{new_vocab_size}")
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print(" 执行权重扩展迁移（宽度扩展 + 深度复制）...")
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new_sd = migrate_weights(old_sd, new_sd_init, old_d_model, config['d_model'],
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old_n_layers, config['n_layers'],
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old_d_ff, config['d_ff'],
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old_vocab_size, new_vocab_size)
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model.load_state_dict(new_sd)
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print(" ✅ 权重扩展迁移完成！")
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# 记录旧checkpoint已使用，从下一epoch开始
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start_epoch = start_epoch
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# 尝试加载优化器（仅当架构vocab完全一致时）
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if (old_d_model == config['d_model'] and old_n_layers == config['n_layers']
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and old_d_ff == config['d_ff'] and old_vocab_size == new_vocab_size
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and 'optimizer_state_dict' in checkpoint):
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try:
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trainer = LLMTrainer(model, device, use_amp=config['use_amp'])
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# 前向一步恢复优化器（不做完整epoch，太慢）
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batch = next(iter(train_loader))
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input_ids = batch['input_ids'].to(device)
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target_ids = batch['target_ids'].to(device)
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mask = model.generate_mask(input_ids.size(1)).to(device)
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outputs = model(input_ids, mask)
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loss = trainer.criterion(outputs.reshape(-1, outputs.size(-1)), target_ids.reshape(-1))
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loss.backward()
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trainer.optimizer.step()
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trainer.optimizer.zero_grad()
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# 加载优化器状态
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trainer.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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print("优化器状态已加载（1步热身）")
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except Exception as e:
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print(f"优化器加载失败（将使用新的）: {e}")
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print(f"\n开始训练: epoch {start_epoch + 1} -> {config['epochs']}")
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print("=" * 60)
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start = time.time()
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# 确保trainer已创建（可能因为架构变化没有在提前创建）
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if 'trainer' not in dir() or trainer is None:
|
||||
trainer = LLMTrainer(model, device, use_amp=config['use_amp'])
|
||||
|
||||
best_val_loss = float('inf')
|
||||
total_distill_added = 0
|
||||
for epoch in range(start_epoch + 1, config['epochs'] + 1):
|
||||
# ====== 每轮从train_path重建dataloader（蒸馏数据已追加至此）======
|
||||
with open(train_path, 'r', encoding='utf-8') as f:
|
||||
epoch_train = dict_to_tuple(json.load(f))
|
||||
print(f"\n 训练数据: {len(epoch_train)}条 (含{total_distill_added}条蒸馏新增)")
|
||||
|
||||
train_dataset = ConversationDataset(epoch_train, vocab, config['max_length'])
|
||||
train_loader = DataLoader(train_dataset, batch_size=config['batch_size'],
|
||||
shuffle=True, num_workers=0)
|
||||
|
||||
# ====== 训练 ======
|
||||
t0 = time.time()
|
||||
avg_loss = trainer.train_epoch(train_loader, epoch, vocab, config)
|
||||
dt = time.time() - t0
|
||||
|
||||
# ====== 验证 ======
|
||||
t1 = time.time()
|
||||
val_loss = trainer.evaluate(val_loader, config)
|
||||
dt_val = time.time() - t1
|
||||
|
||||
# 打印train/val loss
|
||||
marker = ""
|
||||
if val_loss < best_val_loss:
|
||||
best_val_loss = val_loss
|
||||
marker = " ★"
|
||||
best_path = os.path.join(model_dir, 'xiaoli_v2_best.pth')
|
||||
trainer.save_model(best_path, vocab, epoch)
|
||||
print(f"Epoch {epoch}/{config['epochs']} | Train Loss: {avg_loss:.4f} | Val Loss: {val_loss:.4f}{marker} | {dt:.0f}s+{dt_val:.0f}s")
|
||||
|
||||
# 每轮保存断点
|
||||
resume_path = os.path.join(model_dir, f'llm_model_v2_resume_epoch_{epoch}.pth')
|
||||
trainer.save_model(resume_path, vocab, epoch)
|
||||
if epoch % 20 == 0:
|
||||
save_path = os.path.join(model_dir, f'llm_model_v2_epoch_{epoch}.pth')
|
||||
trainer.save_model(save_path, vocab, epoch)
|
||||
print(f" 已保存正式checkpoint: epoch {epoch}")
|
||||
resume_files = sorted(glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_*.pth')))
|
||||
for old in resume_files[:-5]:
|
||||
try: os.remove(old)
|
||||
except: pass
|
||||
|
||||
# ====== 每轮末尾：在线蒸馏 ======
|
||||
if distill_questions:
|
||||
distill_t0 = time.time()
|
||||
try:
|
||||
print(f" 📡 在线蒸馏 ({len(distill_questions)}题 × 多种方式)...")
|
||||
chat = LLMChat(model, vocab, device, config['max_length'])
|
||||
all_new_pairs = []
|
||||
|
||||
# ====== Pass 1: 模型生成(temperature=0.15) → API转换 ======
|
||||
print(" [P1] 模型生成中...")
|
||||
model_answers = []
|
||||
for i, q in enumerate(distill_questions):
|
||||
ans = chat.generate_response(q, max_length=40, temperature=0.15)
|
||||
model_answers.append((q, ans))
|
||||
if (i + 1) % 100 == 0:
|
||||
print(f" P1: {i+1}/{len(distill_questions)}")
|
||||
p1_gen = time.time()
|
||||
print(f" [P1] 生成完毕 ({(p1_gen-distill_t0):.0f}s)，API转换中...")
|
||||
|
||||
def progress(done, total):
|
||||
if done % 50 == 0 or done == total:
|
||||
print(f" API: {done}/{total}", end="\r")
|
||||
|
||||
p1_results = batch_convert(model_answers, progress)
|
||||
print(f" API: {len(model_answers)}/{len(model_answers)}")
|
||||
all_new_pairs.extend(p1_results)
|
||||
print(f" [P1] +{len(p1_results)}条")
|
||||
|
||||
# ====== Pass 2: 模型高温生成(temperature=0.7) → 更多多样性 ======
|
||||
print(f" [P2] 模型高温生成中 (temp=0.7)...")
|
||||
model_answers2 = []
|
||||
for i, q in enumerate(distill_questions):
|
||||
ans = chat.generate_response(q, max_length=40, temperature=0.7)
|
||||
model_answers2.append((q, ans))
|
||||
if (i + 1) % 100 == 0:
|
||||
print(f" P2: {i+1}/{len(distill_questions)}")
|
||||
p2_gen = time.time()
|
||||
print(f" [P2] 生成完毕 ({(p2_gen-p1_gen):.0f}s)，API转换中...")
|
||||
|
||||
p2_results = batch_convert(model_answers2, progress)
|
||||
print(f" API: {len(model_answers2)}/{len(model_answers2)}")
|
||||
all_new_pairs.extend(p2_results)
|
||||
print(f" [P2] +{len(p2_results)}条")
|
||||
|
||||
# ====== Pass 3: API直接生成多样回答(temp=1.0) ======
|
||||
print(f" [P3] API直接生成 (temp=1.0, 全部直答)...")
|
||||
p3_t0 = time.time()
|
||||
p3_results = batch_generate_multi_temp(
|
||||
distill_questions,
|
||||
temperatures=[1.0],
|
||||
max_workers=8
|
||||
)
|
||||
all_new_pairs.extend(p3_results)
|
||||
print(f" [P3] +{len(p3_results)}条")
|
||||
|
||||
# ====== 合并去重后追加 ======
|
||||
if all_new_pairs:
|
||||
with open(train_path, 'r', encoding='utf-8') as f:
|
||||
cur_train = json.load(f)
|
||||
existing_pairs = set((item.get("question",""), item.get("answer","")) for item in cur_train)
|
||||
truly_new = [item for item in all_new_pairs
|
||||
if (item["question"], item["answer"]) not in existing_pairs]
|
||||
|
||||
if truly_new:
|
||||
cur_train.extend(truly_new)
|
||||
with open(train_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur_train, f, ensure_ascii=False, indent=2)
|
||||
# 同步到merged
|
||||
with open(merged_path, 'r', encoding='utf-8') as f:
|
||||
cur_merged = json.load(f)
|
||||
cur_merged.extend(truly_new)
|
||||
with open(merged_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur_merged, f, ensure_ascii=False, indent=2)
|
||||
total_distill_added += len(truly_new)
|
||||
distill_total = time.time() - distill_t0
|
||||
print(f" ✅ 蒸馏完成: +{len(truly_new)}条 (新增{len(set(q for q,a in [(i['question'],i['answer']) for i in truly_new]))}道独特问题) | 总耗时{distill_total:.0f}s")
|
||||
else:
|
||||
print(f" ℹ️ 蒸馏完成: 本轮无新内容（全部已存在）({time.time()-distill_t0:.0f}s)")
|
||||
else:
|
||||
print(f" ℹ️ 蒸馏完成: 无有效转换 ({time.time()-distill_t0:.0f}s)")
|
||||
except Exception as e:
|
||||
print(f" ⚠️ 蒸馏异常: {e}")
|
||||
|
||||
# 保存最终模型
|
||||
trainer.save_model(os.path.join(model_dir, 'xiaoli_v2_final.pth'), vocab, config['epochs'])
|
||||
|
||||
# 清理所有resume文件
|
||||
for f in glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_*.pth')):
|
||||
try:
|
||||
os.remove(f)
|
||||
except:
|
||||
pass
|
||||
|
||||
total = time.time() - start
|
||||
print(f"\n训练完成！总耗时: {total/60:.1f}分钟")
|
||||
|
||||
# 测试
|
||||
print("\n测试:")
|
||||
chat = LLMChat(model, vocab, device, config['max_length'])
|
||||
for q in ["你好", "你是谁", "你叫什么名字", "你好可爱", "我喜欢你", "1+1等于几"]:
|
||||
r = chat.generate_response(q, max_length=60, temperature=0.8)
|
||||
print(f" Q: {q}")
|
||||
print(f" A: {r}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user