#!/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
"""

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

# ========== 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

os.makedirs(OUT, exist_ok=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"

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...")
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)})

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 = {}
    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])
    return batch

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 = Trainer(model=model, args=args, train_dataset=ds, data_collator=collate)

# ========== 5. Go ==========
print("[5/5] Starting training!")
gpu = torch.cuda.get_device_name(0)
mem = torch.cuda.get_device_properties(0).total_memory / 1e9
print(f"  GPU: {gpu} ({mem:.1f}GB) | Epochs: {EPOCHS} | Eff batch: {BS*GA} | LR: {LR}")
sys.stdout.flush()

trainer.train()

# ========== 6. Save ==========
print("Saving model...")
final = os.path.join(OUT, "final")
trainer.save_model(final)
tokenizer.save_pretrained(final)

# ⚠️ 关键修复：Qwen chat template 使用 <|im_end|> (151645) 作为对话EOS
# 但 config.json 中默认 eos_token_id=151643 (<|endoftext|>)
# 不修复会导致部署时模型无限生成 → 死循环乱码
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:
    tok_cfg = json.load(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("DONE!")