Add script to download skypile data

scripts/download_skypile.py

@@ -0,0 +1,163 @@
+"""
+Streaming 下载 SkyPile-150B 子采样，转成 minimind PretrainDataset 兼容格式（{"text": ...}）。
+
+特性：
+- Streaming：不全下，边读边写，磁盘只占目标量
+- Token 计数：精确控制下载量（按 budget 而非样本数停）
+- 断点续传：进程被杀也能从最后写到的行接着下
+- 进度日志：每 N 个样本打印进度
+- 简易过滤：太短 / 重复字符多的样本跳过
+
+Usage:
+    # 默认下 25B tokens（1B 模型 Chinchilla 最优）
+    python download_skypile.py
+
+    # 改下载量
+    python download_skypile.py --target_tokens 10_000_000_000
+
+    # 改输出路径
+    python download_skypile.py --output ../dataset/skypile_25b.jsonl
+
+需要 fwdproxy（HuggingFace 在 allowlist 但 OD 无直接 DNS）：
+    export https_proxy=http://fwdproxy:8080
+    export http_proxy=http://fwdproxy:8080
+"""
+import os
+import sys
+
+__package__ = "scripts"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import argparse
+import json
+import time
+import warnings
+from datasets import load_dataset
+from transformers import AutoTokenizer
+
+warnings.filterwarnings('ignore')
+
+
+def count_tokens_fast(text: str) -> int:
+    """快速估算 token 数：中文按字符 ≈ token，英文按词 × 1.3。
+    用于在线计数（精确 tokenize 太慢）。"""
+    n_chars = len(text)
+    # 中文 token ≈ 1 char；英文 1 token ≈ 4 char
+    # 简单估算：取个折中
+    return int(n_chars / 1.8)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Stream-download SkyPile-150B subset")
+    parser.add_argument('--repo', default='Skywork/SkyPile-150B', type=str)
+    parser.add_argument('--split', default='train', type=str)
+    parser.add_argument('--output', default='../dataset/skypile_25b.jsonl', type=str)
+    parser.add_argument('--target_tokens', type=int, default=25_000_000_000,
+                        help='目标 token 数（estimate），到达后停止')
+    parser.add_argument('--min_length', type=int, default=200,
+                        help='样本最小字符数，太短跳过')
+    parser.add_argument('--log_every', type=int, default=5000)
+    parser.add_argument('--tokenizer_path', type=str, default='../model',
+                        help='用 minimind tokenizer 精确计算 token 数（每 N 个样本核对一次）')
+    parser.add_argument('--exact_count_every', type=int, default=50000,
+                        help='每 N 个样本精确 tokenize 一次校准估算误差')
+    args = parser.parse_args()
+
+    output_path = os.path.abspath(args.output)
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+    # 断点续传：count 已有文件的行数
+    n_written, est_tokens_written = 0, 0
+    if os.path.exists(output_path):
+        print(f'[resume] found existing {output_path}, counting...')
+        with open(output_path) as f:
+            for line in f:
+                n_written += 1
+                try:
+                    est_tokens_written += count_tokens_fast(json.loads(line)['text'])
+                except Exception:
+                    pass
+        print(f'[resume] {n_written:,} samples already written, ~{est_tokens_written/1e9:.2f}B tokens')
+        if est_tokens_written >= args.target_tokens:
+            print('[resume] target already reached, exit')
+            return
+
+    # 校准 tokenizer（可选；下载完后跑 eval_perplexity 时会用到）
+    tokenizer = None
+    if args.tokenizer_path:
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
+            print(f'[tokenizer] loaded for calibration: vocab={tokenizer.vocab_size}')
+        except Exception as e:
+            print(f'[tokenizer] failed to load ({e}), will skip calibration')
+
+    print(f'[start] streaming {args.repo} split={args.split} → {output_path}')
+    print(f'[target] {args.target_tokens/1e9:.1f}B tokens (estimated)')
+
+    ds = load_dataset(args.repo, split=args.split, streaming=True)
+
+    skipped_short = 0
+    n_seen = 0
+    t0 = time.time()
+    calibration_ratio = 1.0  # actual_tokens / estimated_tokens，每次校准更新
+
+    with open(output_path, 'a') as fout:
+        for sample in ds:
+            n_seen += 1
+            # 跳过已写过的（断点续传）
+            if n_seen <= n_written:
+                continue
+
+            text = sample.get('text', '')
+            if len(text) < args.min_length:
+                skipped_short += 1
+                continue
+
+            est_tok = count_tokens_fast(text)
+            actual_est = int(est_tok * calibration_ratio)
+
+            # 写入 minimind 格式
+            fout.write(json.dumps({'text': text}, ensure_ascii=False) + '\n')
+            n_written += 1
+            est_tokens_written += actual_est
+
+            # 进度日志
+            if n_written % args.log_every == 0:
+                elapsed = time.time() - t0
+                rate = (n_written / elapsed) if elapsed > 0 else 0
+                progress = est_tokens_written / args.target_tokens * 100
+                eta_h = (args.target_tokens - est_tokens_written) / max(est_tokens_written / elapsed, 1) / 3600
+                print(f'[{n_written:>9,} samples] {est_tokens_written/1e9:>5.2f}B tok '
+                      f'({progress:>5.1f}%) | {rate:.0f} samples/s | ETA {eta_h:.1f}h | '
+                      f'skipped_short={skipped_short:,}')
+                fout.flush()
+
+            # 周期性精确 tokenize 几条校准估算
+            if tokenizer and n_written % args.exact_count_every == 0:
+                actual = sum(len(tokenizer(t, add_special_tokens=False).input_ids)
+                             for t in [text])
+                estimate = est_tok
+                if estimate > 0:
+                    new_ratio = actual / estimate
+                    calibration_ratio = 0.9 * calibration_ratio + 0.1 * new_ratio
+                    print(f'[calibration] est_ratio updated → {calibration_ratio:.3f} '
+                          f'(this sample: est={estimate}, actual={actual})')
+
+            if est_tokens_written >= args.target_tokens:
+                print(f'\n[done] target reached: {est_tokens_written/1e9:.2f}B est tokens')
+                break
+
+    elapsed = time.time() - t0
+    print(f'\n{"=" * 60}')
+    print(f'Total: {n_written:,} samples, ~{est_tokens_written/1e9:.2f}B est tokens')
+    print(f'Elapsed: {elapsed/60:.1f} min ({elapsed/3600:.1f}h)')
+    print(f'Skipped (too short): {skipped_short:,}')
+    print(f'Output: {output_path} ({os.path.getsize(output_path)/1e9:.2f} GB)')
+    print(f'{"=" * 60}')
+    if tokenizer:
+        print(f'⚠️  估算 token 数有 ~{abs(1-calibration_ratio)*100:.1f}% 误差。')
+        print(f'   精确数：跑 eval_perplexity.py 时会有 actual count。')
+
+
+if __name__ == "__main__":
+    main()