Add MFU cal utils.
Longfang Zhao
parent
c1d990c81c
commit
17f67f9897
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"""
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Qualitative eval of a MiniMind pretrain (or post-pretrain) checkpoint by sampling
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text continuations from a fixed prompt set spanning multiple domains.
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For pretrain (base) models — does NOT use chat template. Use eval_llm.py for SFT models.
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Usage:
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# Default: 64M pretrain ckpt
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python eval_completion.py
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# 1B ckpt
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python eval_completion.py --hidden_size 2048 --num_hidden_layers 20 --weight pretrain
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# Custom prompt
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python eval_completion.py --prompt "今天天气不错，我打算"
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# Save outputs to markdown for design doc
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python eval_completion.py --output_md eval_results.md
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What to look for:
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✓ Grammatically coherent Chinese, no repetition loops
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✓ Stays roughly on topic of the prefix
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✓ Variety across runs (different temperature should give different outputs)
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✗ Repetitive: "的的的的" / 同一句重复 → 训练或解码有问题
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✗ 全部生成都一模一样 → 模型坍缩 / temperature 太低
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✗ 乱码 / 非中文片段（如果是纯中文 prefix）→ tokenizer 问题
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"""
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import os
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import sys
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__package__ = "scripts"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import warnings
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import time
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import torch
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from transformers import AutoTokenizer, TextStreamer
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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warnings.filterwarnings('ignore')
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DEFAULT_PROMPTS = [
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("叙事", "在很久很久以前，有一个小村庄，"),
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("日常", "今天天气不错，我打算"),
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("技术", "Python 是一种编程语言，它的特点是"),
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("事实", "中华人民共和国成立于"),
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("代码", "def fibonacci(n):\n if n < 2:"),
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("科学", "牛顿第一定律说的是"),
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("文学", "李白是唐代著名的"),
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("数学", "二的十次方等于"),
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("常识", "水的沸点在标准大气压下是"),
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("地理", "中国的首都是"),
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]
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def load_model(args):
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cfg = MiniMindConfig(
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hidden_size=args.hidden_size,
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num_hidden_layers=args.num_hidden_layers,
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use_moe=bool(args.use_moe),
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)
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model = MiniMindForCausalLM(cfg)
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moe_suffix = '_moe' if args.use_moe else ''
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ckp = f'{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
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state_dict = torch.load(ckp, map_location='cpu', weights_only=True)
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model.load_state_dict(state_dict, strict=False)
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model = model.half().eval().to(args.device)
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return model, cfg
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@torch.no_grad()
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def main():
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parser = argparse.ArgumentParser(description="MiniMind Pretrain Completion Eval")
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parser.add_argument('--hidden_size', type=int, default=768)
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parser.add_argument('--num_hidden_layers', type=int, default=8)
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parser.add_argument('--use_moe', type=int, default=0, choices=[0, 1])
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parser.add_argument('--save_dir', type=str, default='../out')
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parser.add_argument('--weight', type=str, default='pretrain')
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parser.add_argument('--tokenizer_path', type=str, default='../model')
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parser.add_argument('--prompt', type=str, default=None,
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help='单条自定义 prompt；不指定则跑 DEFAULT_PROMPTS 全集')
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parser.add_argument('--max_new_tokens', type=int, default=80)
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parser.add_argument('--temperature', type=float, default=0.7)
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parser.add_argument('--top_p', type=float, default=0.9)
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parser.add_argument('--top_k', type=int, default=50)
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parser.add_argument('--repetition_penalty', type=float, default=1.1)
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parser.add_argument('--num_samples_per_prompt', type=int, default=1,
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help='每个 prompt 采样几次（验证多样性）')
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parser.add_argument('--output_md', type=str, default=None,
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help='输出 markdown 报告路径（可选）')
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parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu')
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parser.add_argument('--seed', type=int, default=42)
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args = parser.parse_args()
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torch.manual_seed(args.seed)
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print(f'[load] ckpt={args.save_dir}/{args.weight}_{args.hidden_size}.pth')
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model, cfg = load_model(args)
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
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prompts = [('custom', args.prompt)] if args.prompt else DEFAULT_PROMPTS
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md_lines = []
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md_lines.append(f'# Completion Eval: `{args.weight}_{args.hidden_size}.pth`')
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md_lines.append(f'temperature={args.temperature}, top_p={args.top_p}, top_k={args.top_k}, '
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f'rep_penalty={args.repetition_penalty}, max_new={args.max_new_tokens}\n')
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for label, prompt in prompts:
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print(f'\n{"=" * 60}')
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print(f'[{label}] Prompt: {prompt!r}')
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md_lines.append(f'## [{label}] `{prompt}`\n')
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for sample_i in range(args.num_samples_per_prompt):
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print(f'\n--- Sample {sample_i + 1} ---')
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# 必须加 BOS：训练时所有样本以 [BOS] 开头，eval 时不加 BOS 模型会产生 degenerate
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# 输出（"的的的"/"是是是" 重复），因为它从没见过"无 BOS 开头"的序列
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body = tokenizer(prompt, add_special_tokens=False).input_ids
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ids = torch.tensor([[tokenizer.bos_token_id] + body], device=args.device)
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t0 = time.time()
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output_ids = model.generate(
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ids,
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max_new_tokens=args.max_new_tokens,
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temperature=args.temperature,
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top_p=args.top_p,
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top_k=args.top_k,
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repetition_penalty=args.repetition_penalty,
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do_sample=True,
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eos_token_id=tokenizer.eos_token_id,
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)
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elapsed = time.time() - t0
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text = tokenizer.decode(output_ids[0].tolist(), skip_special_tokens=True)
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continuation = text[len(prompt):]
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print(continuation)
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new_tokens = output_ids.shape[1] - ids.shape[1]
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print(f' [{new_tokens} new tokens in {elapsed:.2f}s = {new_tokens/elapsed:.1f} tok/s]')
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md_lines.append(f'**Sample {sample_i + 1}**: {continuation.strip()}\n')
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md_lines.append('')
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if args.output_md:
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with open(args.output_md, 'w') as f:
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f.write('\n'.join(md_lines))
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print(f'\n[saved] {args.output_md}')
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if __name__ == "__main__":
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main()
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"""
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Compute perplexity of a MiniMind checkpoint on a holdout text dataset.
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Usage:
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# Default: 64M pretrain ckpt, hash-sampled 1% of pretrain_t2t.jsonl
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python eval_perplexity.py
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# 1B ckpt with explicit holdout
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python eval_perplexity.py --hidden_size 2048 --num_hidden_layers 20 \\
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--weight pretrain --holdout_file ../dataset/holdout.jsonl
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# Compare two ckpts (e.g. before/after QAT)
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python eval_perplexity.py --weight pretrain
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python eval_perplexity.py --weight qat
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Reference PPL ranges (rough):
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64M from-scratch on Chinese pretrain holdout: 8-15
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200M: 6-10
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1B: 5-8
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Anything > 30 → training is broken (data leak, lr too high, etc.)
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Anything < 5 → suspect data leak (test set seen during training)
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"""
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import os
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import sys
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__package__ = "scripts"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import argparse
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import json
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import math
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import warnings
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import time
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import torch
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from transformers import AutoTokenizer
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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warnings.filterwarnings('ignore')
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def load_model(args):
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cfg = MiniMindConfig(
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hidden_size=args.hidden_size,
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num_hidden_layers=args.num_hidden_layers,
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use_moe=bool(args.use_moe),
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)
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model = MiniMindForCausalLM(cfg)
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moe_suffix = '_moe' if args.use_moe else ''
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ckp = f'{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
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state_dict = torch.load(ckp, map_location='cpu', weights_only=True)
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# strict=False — tied embeddings have duplicate keys; load handles via shared storage
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model.load_state_dict(state_dict, strict=False)
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model = model.half().eval().to(args.device)
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return model, cfg
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def iter_holdout(args):
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"""Yield text strings from holdout. Either a separate file or hash-sampled from pretrain."""
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if args.holdout_file:
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with open(args.holdout_file) as f:
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for line in f:
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d = json.loads(line)
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yield d.get('text') or d.get('content') or ''
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return
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# Hash-based holdout: take 1/sampling_mod of training file (deterministic, repeatable)
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# NOTE: only meaningful as proxy — model HAS seen these during training.
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# True holdout requires reserving samples BEFORE training (Week 1 trainer fix).
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with open(args.train_file) as f:
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for line in f:
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if hash(line) % args.sampling_mod == 0:
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d = json.loads(line)
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yield d.get('text') or d.get('content') or ''
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@torch.no_grad()
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def main():
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parser = argparse.ArgumentParser(description="MiniMind Perplexity Eval")
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parser.add_argument('--hidden_size', type=int, default=768)
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parser.add_argument('--num_hidden_layers', type=int, default=8)
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parser.add_argument('--use_moe', type=int, default=0, choices=[0, 1])
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parser.add_argument('--save_dir', type=str, default='../out')
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parser.add_argument('--weight', type=str, default='pretrain',
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help='ckpt 前缀，如 pretrain / full_sft / qat')
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parser.add_argument('--tokenizer_path', type=str, default='../model')
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parser.add_argument('--holdout_file', type=str, default=None,
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help='独立 holdout jsonl 文件；若为 None 则从 train_file 按 hash 抽样')
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parser.add_argument('--train_file', type=str, default='../dataset/pretrain_t2t.jsonl',
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help='没 holdout 时从这里 hash 抽样作 proxy holdout')
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parser.add_argument('--sampling_mod', type=int, default=1000,
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help='hash 抽样模数：1000=取 0.1%, 100=取 1%')
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parser.add_argument('--max_samples', type=int, default=500)
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parser.add_argument('--max_seq_len', type=int, default=512)
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parser.add_argument('--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu')
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args = parser.parse_args()
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print(f'[load] ckpt={args.save_dir}/{args.weight}_{args.hidden_size}.pth')
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model, cfg = load_model(args)
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
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pad_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
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total_loss, total_tokens, n_samples = 0.0, 0, 0
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t0 = time.time()
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# ⚠️ 必须加 BOS/EOS：minimind PretrainDataset 训练时格式是 [BOS] + tokens + [EOS] + pad。
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# eval 时不加 BOS，第一个 token 的 loss 会异常高（模型从来没见过"无 BOS 开头"），
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# 整体 PPL 会虚高 ~4x（实测从 4.7 → 20.2）。
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bos_id = tokenizer.bos_token_id
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eos_id = tokenizer.eos_token_id
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for text in iter_holdout(args):
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if not text or len(text) < 10:
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continue
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body = tokenizer(text, max_length=args.max_seq_len - 2,
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truncation=True, add_special_tokens=False).input_ids
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tokens = [bos_id] + body + [eos_id]
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if len(tokens) < 2:
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continue
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ids = torch.tensor([tokens], device=args.device)
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labels = ids.clone()
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labels[labels == pad_id] = -100
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out = model(input_ids=ids, labels=labels)
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n_tokens = (labels != -100).sum().item() - 1 # 减 1：last token 不算 loss
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if n_tokens <= 0:
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continue
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total_loss += out.loss.item() * n_tokens
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total_tokens += n_tokens
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n_samples += 1
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if n_samples >= args.max_samples:
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break
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avg_loss = total_loss / max(total_tokens, 1)
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ppl = math.exp(avg_loss)
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elapsed = time.time() - t0
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print(f'\n{"=" * 60}')
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print(f'Samples evaluated: {n_samples}')
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print(f'Total tokens: {total_tokens:,}')
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print(f'Avg loss: {avg_loss:.4f}')
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print(f'Perplexity (PPL): {ppl:.2f}')
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print(f'Elapsed: {elapsed:.1f}s ({total_tokens/elapsed:.0f} tokens/s)')
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print(f'{"=" * 60}')
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if args.holdout_file is None:
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print(f'⚠️ Used hash-sampled "holdout" from train_file (model HAS seen these).')
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print(f' For real eval, reserve a holdout BEFORE training.')
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if __name__ == "__main__":
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main()
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@ -16,7 +16,11 @@ from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from model.model_minimind import MiniMindConfig
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from dataset.lm_dataset import PretrainDataset
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from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler
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from trainer.trainer_utils import (
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get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode,
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setup_seed, init_model, SkipBatchSampler,
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detect_gpu_peak_tflops_bf16, compute_model_flops_per_token,
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)
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warnings.filterwarnings('ignore')
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@ -26,8 +30,9 @@ def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
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last_log_time, last_log_step = start_time, start_step
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last_step = start_step
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for step, (input_ids, labels) in enumerate(loader, start=start_step + 1):
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input_ids = input_ids.to(args.device)
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labels = labels.to(args.device)
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# non_blocking=True overlaps host->device copy with compute (pin_memory=True 已在 DataLoader)
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input_ids = input_ids.to(args.device, non_blocking=True)
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labels = labels.to(args.device, non_blocking=True)
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last_step = step
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lr = get_lr(epoch * iters + step, args.epochs * iters, args.learning_rate)
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for param_group in optimizer.param_groups:
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@ -52,14 +57,20 @@ def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
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if step % args.log_interval == 0 or step == iters:
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now = time.time()
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spend_time = now - start_time
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window_its = (step - last_log_step) / max(now - last_log_time, 1e-6)
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window_dt = max(now - last_log_time, 1e-6)
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window_steps = step - last_log_step
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window_its = window_steps / window_dt
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# MFU = 实际 FLOPs/s / GPU 峰值；按 window 内的 token 吞吐算（避开 warmup / ckpt save 污染）
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# tokens 用 per-rank batch × seq_len × world_size × window_steps（DDP 每卡同时处理 batch）
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tokens_window = window_steps * args.batch_size * args.max_seq_len * world_size
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mfu = (flops_per_token * tokens_window / window_dt) / (gpu_peak_tflops * 1e12) * 100
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last_log_time, last_log_step = now, step
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current_loss = loss.item() * args.accumulation_steps
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current_aux_loss = res.aux_loss.item() if res.aux_loss is not None else 0.0
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current_logits_loss = current_loss - current_aux_loss
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current_lr = optimizer.param_groups[-1]['lr']
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eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60
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Logger(f'[{time.strftime("%H:%M:%S")}] Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), it/s: {window_its:.2f}, loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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Logger(f'[{time.strftime("%H:%M:%S")}] Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), it/s: {window_its:.2f}, MFU: {mfu:.1f}%, loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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if wandb: wandb.log({"loss": current_loss, "logits_loss": current_logits_loss, "aux_loss": current_aux_loss, "learning_rate": current_lr, "epoch_time": eta_min})
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if (step % args.save_interval == 0 or step == iters) and is_main_process():
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@ -108,6 +119,8 @@ if __name__ == "__main__":
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parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain", help="wandb项目名")
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parser.add_argument("--use_compile", default=1, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是，A100 上推荐默认 1）")
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parser.add_argument("--gpu_peak_tflops", type=float, default=0,
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help="GPU bf16 理论峰值 TFLOPS，用于 MFU 计算；默认 0 表示按 GPU 名自动检测（A100=312）")
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args = parser.parse_args()
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||||
|
||||
# ========== 1. 初始化环境和随机种子 ==========
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||||
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|
@ -139,8 +152,16 @@ if __name__ == "__main__":
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train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
|
||||
train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
|
||||
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
|
||||
|
||||
# fused=True 把所有 param 的 Adam 更新融成一个 CUDA kernel（A100/3090 + CUDA tensor 支持）
|
||||
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate, fused=True)
|
||||
|
||||
# ========== 5b. MFU 测量准备 ==========
|
||||
flops_per_token = compute_model_flops_per_token(model, lm_config)
|
||||
gpu_peak_tflops = detect_gpu_peak_tflops_bf16(override=args.gpu_peak_tflops)
|
||||
world_size = dist.get_world_size() if dist.is_initialized() else 1
|
||||
Logger(f'[MFU] params_flops/token={flops_per_token/1e9:.3f} GFLOPS, '
|
||||
f'GPU peak={gpu_peak_tflops:.0f} TFLOPS bf16, world_size={world_size}')
|
||||
|
||||
# ========== 6. 从ckp恢复状态 ==========
|
||||
start_epoch, start_step = 0, 0
|
||||
if ckp_data:
|
||||
|
|
@ -152,8 +173,10 @@ if __name__ == "__main__":
|
|||
|
||||
# ========== 7. 编译和分布式包装 ==========
|
||||
if args.use_compile == 1:
|
||||
model = torch.compile(model)
|
||||
Logger('torch.compile enabled')
|
||||
# max-autotune 在 Triton 层为每个 fused kernel 试多套 tile/block 配置，挑最快的。
|
||||
# 编译时间多 ~30-60s，稳态可比 default 快 5-15%。
|
||||
model = torch.compile(model, mode="max-autotune")
|
||||
Logger('torch.compile enabled (mode=max-autotune)')
|
||||
if dist.is_initialized():
|
||||
model = DistributedDataParallel(model, device_ids=[local_rank])
|
||||
|
||||
|
|
@ -163,7 +186,8 @@ if __name__ == "__main__":
|
|||
setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
|
||||
skip = start_step if (epoch == start_epoch and start_step > 0) else 0
|
||||
batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
|
||||
loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
|
||||
loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers,
|
||||
pin_memory=True, persistent_workers=args.num_workers > 0)
|
||||
if skip > 0:
|
||||
Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
|
||||
train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)
|
||||
|
|
|
|||
|
|
@ -41,6 +41,53 @@ def get_lr(current_step, total_steps, lr):
|
|||
return lr*(0.1 + 0.45*(1 + math.cos(math.pi * current_step / total_steps)))
|
||||
|
||||
|
||||
# A100/H100/3090 等常见卡的 bf16 理论峰值 TFLOPS。MoE 等价 FLOPs 是 active params 不是 total，
|
||||
# active params 算出来的 MFU 才有意义；MoE 的 active 比例由 num_experts_per_tok / num_experts 决定。
|
||||
_GPU_PEAK_BF16_TFLOPS = {
|
||||
'A100': 312, 'A100-SXM4-80GB': 312, 'A100-SXM4-40GB': 312, 'A100-PCIE-80GB': 312, 'A100-PCIE-40GB': 312,
|
||||
'H100': 989, 'H100-SXM5-80GB': 989, 'H100-PCIE-80GB': 756, 'H100 80GB HBM3': 989,
|
||||
'H200': 989,
|
||||
'RTX 3090': 71, 'GeForce RTX 3090': 71, 'RTX 3090 Ti': 80,
|
||||
'RTX 4090': 165, 'GeForce RTX 4090': 165,
|
||||
'V100': 125, 'V100-SXM2-32GB': 125,
|
||||
'L40': 181, 'L40S': 362,
|
||||
}
|
||||
|
||||
|
||||
def detect_gpu_peak_tflops_bf16(device_index: int = 0, override: float = None) -> float:
|
||||
"""Look up the bf16 TensorCore peak for the current GPU; fall back to A100 (312)."""
|
||||
if override is not None and override > 0:
|
||||
return float(override)
|
||||
if not torch.cuda.is_available():
|
||||
return 312.0
|
||||
name = torch.cuda.get_device_name(device_index)
|
||||
for key, peak in _GPU_PEAK_BF16_TFLOPS.items():
|
||||
if key in name:
|
||||
return float(peak)
|
||||
Logger(f'[MFU] Unknown GPU "{name}", falling back to A100 (312 TFLOPS). Pass --gpu_peak_tflops to override.')
|
||||
return 312.0
|
||||
|
||||
|
||||
def compute_model_flops_per_token(model, config=None) -> float:
|
||||
"""Compute the "model FLOPs" per training token (forward + backward).
|
||||
|
||||
Standard approximation for dense transformers: 6 × P, where P is the total
|
||||
trainable params count. For MoE we use active params = base + experts_per_tok × per_expert.
|
||||
"""
|
||||
raw = model.module if isinstance(model, DistributedDataParallel) else model
|
||||
raw = getattr(raw, '_orig_mod', raw)
|
||||
total = sum(p.numel() for p in raw.parameters())
|
||||
if config is not None and getattr(config, 'use_moe', False):
|
||||
n_routed = getattr(config, 'num_experts', 0)
|
||||
n_active = getattr(config, 'num_experts_per_tok', 1)
|
||||
if n_routed > 0:
|
||||
expert = sum(p.numel() for n, p in raw.named_parameters() if 'mlp.experts.0.' in n)
|
||||
base = total - expert * n_routed
|
||||
active = base + expert * n_active
|
||||
return 6.0 * active
|
||||
return 6.0 * total
|
||||
|
||||
|
||||
def init_distributed_mode():
|
||||
if int(os.environ.get("RANK", -1)) == -1:
|
||||
return 0 # 非DDP模式
|
||||
|
|
|
|||
Loading…
Reference in New Issue