63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
"""损失探针：用未训练的预训练模型走完整管线，逐行算 loss（层 1 疑点排查第二步）。
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判读（训练日志初始 loss ≈ 7.5）：
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- 探针也 ≈ 7：管线一致，loss 高是数据/模型现实 → 去查数据（垃圾长文、乱码占比）；
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- 探针 ≈ 2-4：管线（本脚本与训练共用）没问题但训练环节另有妖 → 查训练循环差异。
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同时打印 HF 模型内建 CE（同一数学的独立实现）交叉验证 sft_loss。
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远程运行（CPU 即可，约 1-2 分钟）：
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python -u scripts/diag_loss_probe.py
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"""
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from ars_opd.configs import SFTConfig
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from ars_opd.data import SFTCollator, load_sft_dataset
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from ars_opd.trainer import sft_loss
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MODEL = "Qwen/Qwen3-0.6B"
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cfg = SFTConfig(
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dataset_path="data/dapo-math-17k-unique.parquet",
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output_dir="/tmp/diag",
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subset_size=8,
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seed=42,
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teacher_completions_path="data/teacher_completions_dapo1k_minimax-m3.jsonl",
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)
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ds = load_sft_dataset(cfg)
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tok = AutoTokenizer.from_pretrained(MODEL)
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collator = SFTCollator(
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tok,
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max_length=cfg.max_length,
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max_prompt_length=cfg.max_prompt_length,
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enable_thinking=False,
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)
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model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.float32)
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model.eval()
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print(f"{'行':>3} {'sft_loss':>9} {'HF内建CE':>9} {'监督tok':>7} 解答开头")
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total, total_n = 0.0, 0
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for i in range(len(ds)):
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batch = collator([ds[i]])
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with torch.no_grad():
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out = model(
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input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
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)
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ours, n = sft_loss(
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out.logits, batch["input_ids"], batch["labels"], batch["attention_mask"]
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)
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# 交叉验证：HF 内建损失（labels 传入模型，内部自动移位）与 sft_loss
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# 是同一数学的两个独立实现，单行 batch 下应当几乎相等
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hf = model(
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input_ids=batch["input_ids"],
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attention_mask=batch["attention_mask"],
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labels=batch["labels"],
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).loss
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head = ds[i]["messages"][-1]["content"][:40].replace("\n", " ")
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print(f"{i:>3} {ours.item():>9.3f} {hf.item():>9.3f} {n:>7} {head}", flush=True)
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total += ours.item() * n
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total_n += n
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print(f"\n按 token 加权平均: {total / total_n:.3f}（对照训练日志初始 loss ≈ 7.5）", flush=True)
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