176 lines
7.2 KiB
Python
176 lines
7.2 KiB
Python
#!/usr/bin/env python3
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"""光湖 母模型(7B)->1.5B霜砚 蒸馏 v6
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修复：vocab_size不匹配 (teacher=152064, student=151936)
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"""
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import os, json, torch, sys
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['TOKENIZERS_PARALLELISM'] = 'false'
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os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from torch.utils.data import Dataset, DataLoader
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from tqdm import tqdm
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import torch.nn.functional as F
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from qcloud_cos import CosConfig, CosS3Client
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TCH = '/root/autodl-tmp/output/qwen25-7b-sft/final'
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STU = '/root/autodl-tmp/models/Qwen/Qwen2___5-1___5B-Instruct'
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DATA = '/root/autodl-tmp/data/sft.jsonl'
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OUT = '/root/autodl-tmp/output/qwen25-15b-shuangyan-distill'
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E, B, GA, LR, ML = 3, 4, 8, 1e-5, 2048
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TEMP, ALPHA = 2.0, 0.7
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BKT, RG = 'sy-finetune-corpus-1317346199', 'ap-guangzhou'
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CK = os.environ.get('ZY_OSS_KEY')
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CS = os.environ.get('ZY_OSS_SECRET')
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os.makedirs(OUT, exist_ok=True)
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# 1. Tokenize
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print('[1/5] Tokenize...')
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with open(DATA) as f:
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raw = [json.loads(l) for l in f]
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tok = AutoTokenizer.from_pretrained(STU, trust_remote_code=True)
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tok.pad_token = tok.eos_token
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data = []
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for d in tqdm(raw):
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msgs = [m for m in d['messages'] if m['role'] != 'system']
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ii, ll = [], []
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for m in msgs:
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c = m['content']
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if not c.strip(): continue
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txt = '<|im_start|>' + m['role'] + '\n' + c + '<|im_end|>\n'
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tk = tok.encode(txt, add_special_tokens=False)
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ii.extend(tk)
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ll.extend(tk if m['role'] == 'assistant' else [-100]*len(tk))
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if len(ii) > ML:
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ii, ll = ii[:ML], ll[:ML]
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data.append({'input_ids': ii, 'labels': ll})
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print(f' {len(data)} examples')
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class DS(Dataset):
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def __init__(self, d): self.d = d
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def __len__(self): return len(self.d)
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def __getitem__(self, i): return self.d[i]
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def collate(batch):
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ml = max(len(x['input_ids']) for x in batch)
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pad_id = tok.pad_token_id
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ii = torch.stack([torch.tensor(x['input_ids'] + [pad_id]*(ml-len(x['input_ids']))) for x in batch])
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ll = torch.stack([torch.tensor(x['labels'] + [-100]*(ml-len(x['labels']))) for x in batch])
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am = (ii != pad_id).long()
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return {'input_ids': ii.cuda(), 'labels': ll.cuda(), 'attention_mask': am.cuda()}
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loader = DataLoader(DS(data), B, shuffle=True, collate_fn=collate, num_workers=0)
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# 2. Load
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print('[2/5] Load models...')
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torch.cuda.empty_cache() # Clear any stale memory before loading
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if not os.path.isdir(STU) or not os.path.isfile(STU + '/config.json'):
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from modelscope import snapshot_download
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snapshot_download('Qwen/Qwen2.5-1.5B-Instruct', cache_dir='/root/autodl-tmp/models')
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if not os.path.isfile(TCH + '/model.safetensors'):
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print(' DL teacher from COS...')
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cfg = CosConfig(Region=RG, SecretId=CK, SecretKey=CS)
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cl = CosS3Client(cfg)
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os.makedirs(TCH, exist_ok=True)
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for obj in cl.list_objects(Bucket=BKT, Prefix='models/qwen25-7b-sft/final/').get('Contents', []):
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fn = obj['Key'].split('/')[-1]
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if fn == 'DEPLOY_NOTES.md': continue
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loc = os.path.join(TCH, fn)
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if not os.path.isfile(loc):
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print(f' {fn}', flush=True)
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cl.download_file(Bucket=BKT, Key=obj['Key'], DestFilePath=loc)
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print(' Teacher...', flush=True)
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teacher = AutoModelForCausalLM.from_pretrained(
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TCH, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation='sdpa').cuda()
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teacher.eval()
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for p in teacher.parameters(): p.requires_grad_(False)
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print(f' T: {sum(p.numel() for p in teacher.parameters())/1e9:.2f}B', flush=True)
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print(' Student...', flush=True)
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student = AutoModelForCausalLM.from_pretrained(
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STU, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation='sdpa').cuda()
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student.train()
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print(f' S: {sum(p.numel() for p in student.parameters())/1e9:.2f}B', flush=True)
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print(f' VRAM: {torch.cuda.memory_allocated()/1e9:.1f}GB', flush=True)
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# 3. Train
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print('[3/5] Train...', flush=True)
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opt = torch.optim.AdamW(student.parameters(), LR, weight_decay=0.01)
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steps_per_epoch = (len(data) // B)
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total_steps = steps_per_epoch * E
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sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=total_steps // GA)
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global_step = 0
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for ep in range(E):
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print(f' Epoch {ep+1}/{E}', flush=True)
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for step, batch in enumerate(tqdm(loader, desc=f'Ep{ep+1}')):
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global_step += 1
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# Teacher forward (on-the-fly)
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with torch.no_grad():
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t_logits = teacher(input_ids=batch['input_ids'], attention_mask=batch['attention_mask']).logits
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# Student forward
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s_logits = student(input_ids=batch['input_ids'], attention_mask=batch['attention_mask']).logits
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# ⚠️ FIX: Truncate teacher logits to student vocab size
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# Teacher(7B) vocab=152064, Student(1.5B) vocab=151936
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t_logits = t_logits[..., :student.config.vocab_size]
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shift_s = s_logits[..., :-1, :].contiguous()
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shift_l = batch['labels'][..., 1:].contiguous()
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sft = F.cross_entropy(shift_s.view(-1, shift_s.size(-1)),
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shift_l.view(-1), ignore_index=-100, reduction='mean')
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mask = (batch['labels'] != -100).unsqueeze(-1).float()
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kls = F.kl_div(F.log_softmax(s_logits.float()/TEMP, -1),
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F.softmax(t_logits.float()/TEMP, -1), reduction='none')
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kl = (kls * mask).sum() / mask.sum() * TEMP**2
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loss = (ALPHA*kl + (1-ALPHA)*sft) / GA
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loss.backward()
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if global_step % GA == 0:
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torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)
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opt.step(); opt.zero_grad(); sch.step()
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if global_step % 500 == 0:
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# Prevent memory fragmentation
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torch.cuda.empty_cache()
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if global_step % 50 == 0:
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print(f' step={global_step} loss={loss.item()*GA:.4f} sft={sft.item():.4f} kl={kl.item():.4f}', flush=True)
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ckpt = os.path.join(OUT, f'ep{ep+1}')
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os.makedirs(ckpt, exist_ok=True)
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student.save_pretrained(ckpt); tok.save_pretrained(ckpt)
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print(f' Checkpoint: {ckpt}', flush=True)
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# FIX: Clear GPU cache between epochs to prevent fragmentation OOM
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torch.cuda.empty_cache()
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# 4. Save
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print('[4/5] Save final...', flush=True)
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fnl = os.path.join(OUT, 'final')
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student.save_pretrained(fnl); tok.save_pretrained(fnl)
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student.config.eos_token_id = 151645; student.config.save_pretrained(fnl)
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student.generation_config.eos_token_id = 151645
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student.generation_config.pad_token_id = 151645
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student.generation_config.save_pretrained(fnl)
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tcp = os.path.join(fnl, 'tokenizer_config.json')
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with open(tcp) as f: tc = json.load(f)
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tc['default_system'] = ''
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with open(tcp, 'w') as f: json.dump(tc, f, indent=2, ensure_ascii=False)
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print(f' {fnl}', flush=True)
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print(f' Peak VRAM: {torch.cuda.max_memory_allocated()/1e9:.1f}/96GB', flush=True)
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print('DONE!', flush=True)
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# 5. Upload
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print('[5/5] Upload to COS...', flush=True)
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cfg = CosConfig(Region=RG, SecretId=CK, SecretKey=CS)
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cl = CosS3Client(cfg)
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import glob
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for fp in sorted(glob.glob(fnl + '/**/*', recursive=True)):
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if os.path.isfile(fp):
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name = os.path.relpath(fp, fnl)
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sz = os.path.getsize(fp) / 1024 / 1024
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print(f' {name} ({sz:.0f}MB)', flush=True)
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cl.upload_file(Bucket=BKT, Key='models/shuangyan-15b-distill/' + name, LocalFilePath=fp)
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print('ALL DONE')
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