130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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Qwen2.5-7B SFT 训练脚本
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用法:
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python3 train_mother.py
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依赖:
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pip3 install transformers accelerate datasets
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"""
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import os, sys, json, torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
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from datasets import load_dataset
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BS = int(os.environ.get("BATCH_SIZE", "4"))
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GA = int(os.environ.get("GRAD_ACCUM", "8"))
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LR = float(os.environ.get("LEARNING_RATE", "1e-5"))
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EPOCHS = int(os.environ.get("EPOCHS", "3"))
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DS = os.environ.get("DATASET", "autodl-tmp/data/sft.jsonl")
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OUT = os.environ.get("OUTPUT_DIR", "autodl-tmp/output/qwen25-7b-sft")
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print("="*50)
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print("Qwen2.5-7B SFT Training")
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print(f" Batch: {BS}, GradAccum: {GA}, Eff: {BS*GA}")
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print(f" LR: {LR}, Epochs: {EPOCHS}")
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print(f" Data: {DS}")
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print(f" Out: {OUT}")
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print("="*50)
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# ========== 1. Load model ==========
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print("[1/5] Loading model...")
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B", trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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total = sum(p.numel() for p in model.parameters())
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print(f" Parameters: {total/1e9:.2f}B")
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print(f" Device: {model.device}")
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# ========== 2. Load data ==========
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print("[2/5] Loading data...")
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dataset = load_dataset("json", data_files=DS, split="train")
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print(f" Samples: {len(dataset)}")
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print(f" Format:")
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for k in dataset[0]:
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v = dataset[0][k]
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if isinstance(v, list):
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print(f" {k}: [{len(v)} msgs]")
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for m in v[:2]:
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print(f" {m['role']}: {m['content'][:50]}...")
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else:
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print(f" {k}: {v}")
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# ========== 3. Tokenize ==========
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print("[3/5] Tokenizing data...")
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def tokenize(example):
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# Build full chat text using Qwen chat template
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texts = tokenizer.apply_chat_template(example["messages"], tokenize=False)
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enc = tokenizer(texts, truncation=True, max_length=8192, add_special_tokens=False)
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return {"input_ids": enc["input_ids"], "labels": enc["input_ids"].copy()}
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dataset = dataset.map(tokenize, remove_columns=["messages"], num_proc=8)
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total_tokens = sum(len(x["input_ids"]) for x in dataset)
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post_pad = sum(x["input_ids"].count(tokenizer.pad_token_id) for x in dataset) if hasattr(tokenizer, "pad_token_id") else 0
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print(f" Total tokens: {total_tokens:,}")
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def collate(features):
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max_len = max(len(f["input_ids"]) for f in features)
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batch = {}
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for k in ["input_ids", "labels", "attention_mask"]:
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pad = tokenizer.pad_token_id if k != "labels" else -100
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batch[k] = torch.tensor([f[k] + [pad]*(max_len-len(f[k])) for f in features])
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return batch
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model.config.use_cache = False
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# ========== 4. Training args ==========
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print("[4/5] Training config...")
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args = TrainingArguments(
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output_dir=OUT, num_train_epochs=EPOCHS,
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per_device_train_batch_size=BS, gradient_accumulation_steps=GA,
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learning_rate=LR, warmup_ratio=0.05, lr_scheduler_type="cosine",
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bf16=True, tf32=True, logging_steps=10,
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save_strategy="epoch", save_total_limit=3,
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remove_unused_columns=False, dataloader_num_workers=4,
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gradient_checkpointing=True, optim="adamw_torch",
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report_to="none", ddp_find_unused_parameters=False,
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)
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trainer = Trainer(model=model, args=args, train_dataset=ds, data_collator=collate)
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# ========== 5. Go ==========
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print("[5/5] Starting training!")
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gpu = torch.cuda.get_device_name(0)
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mem = torch.cuda.get_device_properties(0).total_memory / 1e9
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print(f" GPU: {gpu} ({mem:.1f}GB) | Epochs: {EPOCHS} | Eff batch: {BS*GA} | LR: {LR}")
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sys.stdout.flush()
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trainer.train()
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# ========== 6. Save ==========
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print("Saving model...")
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final = os.path.join(OUT, "final")
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trainer.save_model(final)
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tokenizer.save_pretrained(final)
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# ⚠️ 关键修复：Qwen chat template 使用 <|im_end|> (151645) 作为对话EOS
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# 但 config.json 中默认 eos_token_id=151643 (<|endoftext|>)
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# 不修复会导致部署时模型无限生成 → 死循环乱码
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model.config.eos_token_id = 151645
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model.config.save_pretrained(final)
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# 修复 tokenizer 默认system prompt
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tok_cfg_path = os.path.join(final, "tokenizer_config.json")
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with open(tok_cfg_path) as f:
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tok_cfg = json.load(f)
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tok_cfg["default_system"] = ""
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with open(tok_cfg_path, "w") as f:
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json.dump(tok_cfg, f, indent=2, ensure_ascii=False)
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peak = torch.cuda.max_memory_allocated() / 1e9
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print(f" Model: {final}")
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print(f" Peak VRAM: {peak:.2f}GB / {mem:.1f}GB")
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print(f" DONE!")
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