D101 新增：母模型→1.5B霜砚蒸馏脚本

scripts/distill_mother.py

@@ -0,0 +1,125 @@
+#!/usr/bin/env python3
+"""光湖母模型→1.5B霜砚模板 蒸馏脚本
+Teacher: Qwen2.5-7B (SFT后的母模型)
+Student: Qwen2.5-1.5B (将学会霜砚的思维方式)
+
+蒸馏方法：软蒸馏 (KL散度) + 混合SFT
+
+使用方法：
+  nohup python3 -u distill_mother.py > distill_mother.log 2>&1 &
+"""
+
+import os, json, torch, sys
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
+from datasets import Dataset
+from tqdm import tqdm
+import torch.nn.functional as F
+
+TEACHER_PATH = "/root/autodl-tmp/output/qwen25-7b-sft/final"
+STUDENT_PATH = "/root/autodl-tmp/cache/Qwen/Qwen2___5-1___5B"
+DATA = "/root/autodl-tmp/data/sft.jsonl"
+OUT = "/root/autodl-tmp/output/qwen25-15b-shuangyan-distill"
+EPOCHS = 3; BS = 4; GA = 8; LR = 1e-5; MAX_LEN = 2048; TEMP = 2.0; ALPHA = 0.7
+
+os.makedirs(OUT, exist_ok=True)
+
+print("[1/6] 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]
+print(f"  {len(raw)} examples")
+
+print("[2/6] Loading teacher (7B) and student (1.5B)...")
+tokenizer = AutoTokenizer.from_pretrained(STUDENT_PATH, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+print("  Loading teacher...")
+teacher = AutoModelForCausalLM.from_pretrained(TEACHER_PATH, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa").cuda()
+teacher.eval()
+for p in teacher.parameters():
+    p.requires_grad = False
+print(f"  Teacher: {sum(p.numel() for p in teacher.parameters())/1e9:.2f}B")
+
+print("  Loading student...")
+student = AutoModelForCausalLM.from_pretrained(STUDENT_PATH, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa").cuda()
+student.train()
+print(f"  Student: {sum(p.numel() for p in student.parameters())/1e9:.2f}B")
+
+print("[3/6] Tokenizing + generating teacher logits...")
+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]
+    with torch.no_grad():
+        inp = torch.tensor([ids]).cuda()
+        t_out = teacher(input_ids=inp)
+        t_logits = t_out.logits[0].float().cpu()
+    processed.append({"input_ids": ids, "labels": labs, "attention_mask": [1]*len(ids), "teacher_logits": t_logits})
+
+print("[4/6] Training config...")
+def distill_collate(features):
+    max_len = max(len(f["input_ids"]) for f in features)
+    batch = {}
+    for k in ["input_ids", "labels", "attention_mask"]:
+        pad = tokenizer.pad_token_id if k != "labels" else -100
+        batch[k] = torch.tensor([f[k] + [pad]*(max_len-len(f[k])) for f in features])
+    vocab_size = features[0]["teacher_logits"].size(-1)
+    tl = []
+    for f in features:
+        t = f["teacher_logits"]
+        pad_len = max_len - t.size(0)
+        tl.append(torch.cat([t, torch.zeros(pad_len, vocab_size)], dim=0) if pad_len > 0 else t[:max_len])
+    batch["teacher_logits"] = torch.stack(tl)
+    return batch
+
+class DistillTrainer(Trainer):
+    def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
+        outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], use_cache=False)
+        student_logits = outputs.logits
+        shift_logits = student_logits[..., :-1, :].contiguous()
+        shift_labels = inputs["labels"][..., 1:].contiguous()
+        sft_loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), ignore_index=-100, reduction="mean")
+        teacher_logits = inputs["teacher_logits"]
+        mask = (inputs["labels"] != -100).unsqueeze(-1).float()
+        kl_loss = F.kl_div(F.log_softmax(student_logits / TEMP, dim=-1), F.softmax(teacher_logits / TEMP, dim=-1), reduction="none")
+        kl_loss = (kl_loss * mask).sum() / mask.sum() * (TEMP ** 2)
+        return ALPHA * kl_loss + (1 - ALPHA) * sft_loss
+
+args = TrainingArguments(
+    output_dir=OUT, num_train_epochs=EPOCHS,
+    per_device_train_batch_size=BS, gradient_accumulation_steps=GA,
+    learning_rate=LR, warmup_ratio=0.05, lr_scheduler_type="cosine",
+    bf16=True, tf32=True, logging_steps=10,
+    save_strategy="epoch", save_total_limit=3,
+    remove_unused_columns=False, dataloader_num_workers=4,
+    gradient_checkpointing=True, optim="adamw_torch",
+    report_to="none", ddp_find_unused_parameters=False,
+)
+
+trainer = DistillTrainer(model=student, args=args, train_dataset=Dataset.from_list(processed), data_collator=distill_collate)
+
+print("[5/6] Starting distillation!")
+gpu = torch.cuda.get_device_name(0)
+mem = torch.cuda.get_device_properties(0).total_memory / 1e9
+print(f"  GPU: {gpu} ({mem:.1f}GB) | Temp={TEMP}, Alpha={ALPHA}")
+sys.stdout.flush()
+trainer.train()
+
+print("[6/6] Saving...")
+final = os.path.join(OUT, "final")
+trainer.save_model(final)
+tokenizer.save_pretrained(final)
+peak = torch.cuda.max_memory_allocated() / 1e9
+print(f"  Model: {final} | Peak VRAM: {peak:.2f}GB / {mem:.1f}GB")
+print("DONE!")