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split_pile_data.py

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+#!/usr/bin/env python3
+"""
+划分 Pile 数据集为 train/val/test
+与 BabyLM 保持相同的命名格式
+"""
+
+import numpy as np
+from pathlib import Path
+import json
+
+def split_pile_data(
+    input_file="batch_0_to_1000.npy",
+    output_dir="tokenized",
+    train_ratio=0.8,
+    val_ratio=0.1,
+    seed=42
+):
+    """划分 Pile 数据"""
+    print("="*70)
+    print("📊 Splitting Pile Dataset")
+    print("="*70)
+    print(f"Input: {input_file}")
+    print(f"Output: {output_dir}/")
+    print(f"Split: train={train_ratio:.0%}, val={val_ratio:.0%}, test={1-train_ratio-val_ratio:.0%}")
+    print(f"Seed: {seed}")
+    print("="*70)
+    print()
+    
+    # 加载数据
+    print("📥 Loading data...")
+    data = np.load(input_file, allow_pickle=False)
+    print(f"  Shape: {data.shape}")
+    print(f"  Dtype: {data.dtype}")
+    print(f"  Total samples: {len(data):,}")
+    print(f"  Sequence length: {data.shape[1]}")
+    print()
+    
+    # 设置随机种子
+    np.random.seed(seed)
+    
+    # 打乱索引
+    print("🔀 Shuffling indices...")
+    indices = np.arange(len(data))
+    np.random.shuffle(indices)
+    
+    # 计算划分点
+    n_total = len(data)
+    n_train = int(n_total * train_ratio)
+    n_val = int(n_total * val_ratio)
+    
+    # 划分索引
+    train_indices = indices[:n_train]
+    val_indices = indices[n_train:n_train + n_val]
+    test_indices = indices[n_train + n_val:]
+    
+    print("✂️  Splitting...")
+    print(f"  Train: {len(train_indices):,} samples ({len(train_indices)/n_total*100:.1f}%)")
+    print(f"  Val:   {len(val_indices):,} samples ({len(val_indices)/n_total*100:.1f}%)")
+    print(f"  Test:  {len(test_indices):,} samples ({len(test_indices)/n_total*100:.1f}%)")
+    print()
+    
+    # 创建输出目录
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    print("💾 Saving splits...")
+    
+    # Train
+    print(f"  → train.npy...")
+    np.save(output_dir / "train.npy", data[train_indices])
+    size_mb = (output_dir / "train.npy").stat().st_size / (1024**2)
+    print(f"    ✅ {size_mb:.1f} MB")
+    
+    # Val (eval)
+    print(f"  → eval.npy...")
+    np.save(output_dir / "eval.npy", data[val_indices])
+    size_mb = (output_dir / "eval.npy").stat().st_size / (1024**2)
+    print(f"    ✅ {size_mb:.1f} MB")
+    
+    # Test
+    print(f"  → test.npy...")
+    np.save(output_dir / "test.npy", data[test_indices])
+    size_mb = (output_dir / "test.npy").stat().st_size / (1024**2)
+    print(f"    ✅ {size_mb:.1f} MB")
+    
+    # 创建 metadata.json（与 BabyLM 格式一致）
+    print(f"  → metadata.json...")
+    metadata = {
+        "vocab_size": int(data.max()) + 1,  # 最大 token ID + 1
+        "sequence_length": int(data.shape[1]),
+        "num_train": int(len(train_indices)),
+        "num_eval": int(len(val_indices)),
+        "num_test": int(len(test_indices)),
+        "total_samples": int(n_total),
+        "dtype": str(data.dtype),
+        "seed": seed
+    }
+    
+    with open(output_dir / "metadata.json", 'w') as f:
+        json.dump(metadata, f, indent=2)
+    print(f"    ✅ Saved")
+    
+    print()
+    print("="*70)
+    print("✅ Split Complete!")
+    print("="*70)
+    print()
+    print("📁 Output structure:")
+    print(f"  {output_dir}/")
+    print(f"    ├── train.npy     ({len(train_indices):,} samples)")
+    print(f"    ├── eval.npy      ({len(val_indices):,} samples)")
+    print(f"    ├── test.npy      ({len(test_indices):,} samples)")
+    print(f"    └── metadata.json")
+    print()
+    print("📊 Metadata:")
+    for key, value in metadata.items():
+        print(f"    {key}: {value:,}" if isinstance(value, int) else f"    {key}: {value}")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", default="batch_0_to_1000.npy")
+    parser.add_argument("--output", default="tokenized")
+    parser.add_argument("--train_ratio", type=float, default=0.8)
+    parser.add_argument("--val_ratio", type=float, default=0.1)
+    parser.add_argument("--seed", type=int, default=42)
+    
+    args = parser.parse_args()
+    
+    split_pile_data(
+        args.input,
+        args.output,
+        args.train_ratio,
+        args.val_ratio,
+        args.seed
+    )

tokenized/eval.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f23909dbc1792a6dccd6df07edddc14ce73057052d818c2480f0c6efb7e44d2e
+size 419635328

tokenized/metadata.json

@@ -0,0 +1,10 @@
+{
+  "vocab_size": 50277,
+  "sequence_length": 2049,
+  "num_train": 819200,
+  "num_eval": 102400,
+  "num_test": 102400,
+  "total_samples": 1024000,
+  "dtype": "uint16",
+  "seed": 42
+}

tokenized/test.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:268ddabefd38e964d30accfc3c8bef0e4b8529fa14f2f79eee49f08d355124a9
+size 419635328

tokenized/train.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42c93dfeb23d67941bc8c837892f49431f01182188961ed932a558c6995d2db1
+size 3357081728