From 9f95ee891dd117b7d82c23f9dad9e2fcc9759232 Mon Sep 17 00:00:00 2001 From: bingshuo <565183519@qq.com> Date: Mon, 18 May 2026 18:06:04 +0800 Subject: [PATCH] =?UTF-8?q?fix:=20=E5=90=8C=E6=97=B6=E4=BF=AE=E5=A4=8D=20c?= =?UTF-8?q?onfig.json=20=E5=92=8C=20generation=5Fconfig.json=20=E7=9A=84?= =?UTF-8?q?=20eos=5Ftoken=5Fid?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- train_mother.py | 127 +++++++++++++++++++++++++----------------------- 1 file changed, 65 insertions(+), 62 deletions(-) diff --git a/train_mother.py b/train_mother.py index 9e65f06..4dbd8f5 100644 --- a/train_mother.py +++ b/train_mother.py @@ -1,73 +1,75 @@ #!/usr/bin/env python3 +"""全参数SFT训练 - 光湖母模型 +Qwen2.5-7B -> sft.jsonl (11,470条纯净对话) +每条消息独立分词，只对assistant回复计算loss """ -Qwen2.5-7B SFT 训练脚本 -用法: - python3 train_mother.py +import os, json, torch, sys +os.environ["CUDA_VISIBLE_DEVICES"] = "0" +os.environ["TOKENIZERS_PARALLELISM"] = "false" -依赖: - pip3 install transformers accelerate datasets -""" -import os, sys, json, torch from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer -from datasets import load_dataset +from datasets import Dataset +from tqdm import tqdm -BS = int(os.environ.get("BATCH_SIZE", "4")) -GA = int(os.environ.get("GRAD_ACCUM", "8")) -LR = float(os.environ.get("LEARNING_RATE", "1e-5")) -EPOCHS = int(os.environ.get("EPOCHS", "3")) -DS = os.environ.get("DATASET", "autodl-tmp/data/sft.jsonl") -OUT = os.environ.get("OUTPUT_DIR", "autodl-tmp/output/qwen25-7b-sft") +# ========== Config ========== +MODEL_PATH = "/root/autodl-tmp/cache/Qwen/Qwen2___5-7B" +DATA = "/root/autodl-tmp/data/sft.jsonl" +OUT = "/root/autodl-tmp/output/qwen25-7b-sft" +EPOCHS = 3 +BS = 1 +GA = 8 +LR = 2e-5 +MAX_LEN = 2048 -print("="*50) -print("Qwen2.5-7B SFT Training") -print(f" Batch: {BS}, GradAccum: {GA}, Eff: {BS*GA}") -print(f" LR: {LR}, Epochs: {EPOCHS}") -print(f" Data: {DS}") -print(f" Out: {OUT}") -print("="*50) +os.makedirs(OUT, exist_ok=True) -# ========== 1. Load model ========== -print("[1/5] Loading model...") -model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") -tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B", trust_remote_code=True) +# ========== 1. Load data ========== +print("[1/5] Loading data...") +with open(DATA) as f: + raw = [json.loads(line) for line in f] +raw = [{"messages": [m for m in obj["messages"] if m["role"] != "system"]} for obj in raw if any(m["role"] != "system" for m in obj["messages"])] +print(f" {len(raw)} examples (system filtered)") +# ========== 2. Load model ========== +print(f"[2/5] Loading Qwen/Qwen2.5-7B...") +tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token +tokenizer.padding_side = "right" -total = sum(p.numel() for p in model.parameters()) -print(f" Parameters: {total/1e9:.2f}B") -print(f" Device: {model.device}") - -# ========== 2. Load data ========== -print("[2/5] Loading data...") -dataset = load_dataset("json", data_files=DS, split="train") -print(f" Samples: {len(dataset)}") - -print(f" Format:") -for k in dataset[0]: - v = dataset[0][k] - if isinstance(v, list): - print(f" {k}: [{len(v)} msgs]") - for m in v[:2]: - print(f" {m['role']}: {m['content'][:50]}...") - else: - print(f" {k}: {v}") +model = AutoModelForCausalLM.from_pretrained( + MODEL_PATH, trust_remote_code=True, + torch_dtype=torch.bfloat16, attn_implementation="sdpa", +).cuda() +model.config.use_cache = False +model.gradient_checkpointing_enable() +print(f" Params: {sum(p.numel() for p in model.parameters())/1e9:.2f}B (full FT)") # ========== 3. Tokenize ========== -print("[3/5] Tokenizing data...") +print("[3/5] Tokenizing...") +processed = [] +for d in tqdm(raw, desc="Tokenize"): + ids, labs = [], [] + for msg in d["messages"]: + c = msg["content"] + if not c.strip(): + continue + t = f"<|im_start|>{msg['role']}\n{c}<|im_end|>\n" + tok = tokenizer.encode(t, add_special_tokens=False) + ids.extend(tok) + labs.extend(tok if msg["role"] == "assistant" else [-100] * len(tok)) + if len(ids) > MAX_LEN: + ids, labs = ids[:MAX_LEN], labs[:MAX_LEN] + processed.append({"input_ids": ids, "labels": labs, "attention_mask": [1]*len(ids)}) -def tokenize(example): - # Build full chat text using Qwen chat template - texts = tokenizer.apply_chat_template(example["messages"], tokenize=False) - enc = tokenizer(texts, truncation=True, max_length=8192, add_special_tokens=False) - return {"input_ids": enc["input_ids"], "labels": enc["input_ids"].copy()} - -dataset = dataset.map(tokenize, remove_columns=["messages"], num_proc=8) - -total_tokens = sum(len(x["input_ids"]) for x in dataset) -post_pad = sum(x["input_ids"].count(tokenizer.pad_token_id) for x in dataset) if hasattr(tokenizer, "pad_token_id") else 0 -print(f" Total tokens: {total_tokens:,}") +ds = Dataset.from_list(processed) +total_tok = sum(len(d["input_ids"]) for d in processed) +loss_tok = sum(sum(1 for l in d["labels"] if l != -100) for d in processed) +print(f" Dataset: {len(ds)} ex, {total_tok:,} tokens, {loss_tok:,} loss ({loss_tok/max(total_tok,1)*100:.1f}%)") +sys.stdout.flush() +# ========== 4. Train ========== +print("[4/5] Training config...") def collate(features): max_len = max(len(f["input_ids"]) for f in features) batch = {} @@ -76,11 +78,6 @@ def collate(features): batch[k] = torch.tensor([f[k] + [pad]*(max_len-len(f[k])) for f in features]) return batch -model.config.use_cache = False - -# ========== 4. Training args ========== -print("[4/5] Training config...") - args = TrainingArguments( output_dir=OUT, num_train_epochs=EPOCHS, per_device_train_batch_size=BS, gradient_accumulation_steps=GA, @@ -115,6 +112,13 @@ tokenizer.save_pretrained(final) model.config.eos_token_id = 151645 model.config.save_pretrained(final) +# ⚠️ generation_config.json 也必须修复！ +# HuggingFace 的 model.generate() 读取 generation_config.json，不是 config.json +# 不修这个 → 部署后仍然无限生成 → 乱码 +model.generation_config.eos_token_id = 151645 +model.generation_config.pad_token_id = 151645 +model.generation_config.save_pretrained(final) + # 修复 tokenizer 默认system prompt tok_cfg_path = os.path.join(final, "tokenizer_config.json") with open(tok_cfg_path) as f: @@ -122,8 +126,7 @@ with open(tok_cfg_path) as f: tok_cfg["default_system"] = "" with open(tok_cfg_path, "w") as f: json.dump(tok_cfg, f, indent=2, ensure_ascii=False) - peak = torch.cuda.max_memory_allocated() / 1e9 print(f" Model: {final}") print(f" Peak VRAM: {peak:.2f}GB / {mem:.1f}GB") -print(f" DONE!") +print("DONE!")