GLM-4.7-REAP-50-W4A16/scripts/run_reap.py

#!/usr/bin/env python3
"""
REAP (Router-Experts Activation Pruning) for GLM-4.7 MoE

This script prunes MoE experts from GLM-4.7 using the REAP methodology from Cerebras.
Requires: https://github.com/Cerebras/reap (or fork with GLM support)

Usage:
    python run_reap.py --compression-ratio 0.50 --model-path /path/to/GLM-4.7

For observation reuse (instant pruning at different ratios):
    python run_reap.py --compression-ratio 0.35 --reuse-observations observations_1360_angular-seed_42.pt
"""

import argparse
import subprocess
import sys
from pathlib import Path


def main():
    parser = argparse.ArgumentParser(description="REAP pruning for GLM-4.7")
    parser.add_argument("--model-path", type=str, required=True,
                        help="Path to GLM-4.7 model")
    parser.add_argument("--compression-ratio", type=float, required=True,
                        help="Compression ratio (0.30 = keep 70%, 0.50 = keep 50%)")
    parser.add_argument("--output-dir", type=str, default=None,
                        help="Output directory (default: auto-generated)")
    parser.add_argument("--dataset", type=str,
                        default="0xSero/glm47-reap-calibration-v2",
                        help="Calibration dataset")
    parser.add_argument("--samples", type=int, default=1360,
                        help="Number of calibration samples")
    parser.add_argument("--seed", type=int, default=42,
                        help="Random seed")
    parser.add_argument("--distance", type=str, default="angular",
                        choices=["angular", "cosine", "euclidean"],
                        help="Distance measure for expert clustering")
    parser.add_argument("--reuse-observations", type=str, default=None,
                        help="Path to pre-computed observations file for instant pruning")
    parser.add_argument("--reap-repo", type=str, default="./reap",
                        help="Path to REAP repository")

    args = parser.parse_args()

    # Validate
    if not Path(args.model_path).exists():
        print(f"ERROR: Model path not found: {args.model_path}")
        sys.exit(1)

    reap_script = Path(args.reap_repo) / "src" / "reap" / "prune.py"
    if not reap_script.exists():
        print(f"ERROR: REAP prune.py not found at: {reap_script}")
        print("Clone the REAP repo: git clone https://github.com/Cerebras/reap")
        sys.exit(1)

    # Build output directory name
    if args.output_dir is None:
        ratio_pct = int(args.compression_ratio * 100)
        args.output_dir = f"./GLM-4.7-REAP-{ratio_pct}"

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)

    # Build command
    cmd = [
        sys.executable, str(reap_script),
        "--model-name", args.model_path,
        "--dataset-name", args.dataset,
        "--compression-ratio", str(args.compression_ratio),
        "--prune-method", "reap",
        "--seed", str(args.seed),
        "--do-eval", "false",
        "--profile", "false",
        "--samples_per_category", str(args.samples),
        "--model_max_length", "2048",
        "--distance_measure", args.distance,
        "--record_pruning_metrics_only", "true",
        "--output_file_name", f"observations_{args.samples}_{args.distance}-seed_{args.seed}.pt",
    ]

    if args.reuse_observations:
        cmd.extend(["--load_observations", args.reuse_observations])
        print(f"Reusing observations from: {args.reuse_observations}")
        print("This enables instant pruning without re-running calibration!")

    print("=" * 60)
    print(f"REAP Pruning: GLM-4.7 @ {args.compression_ratio*100:.0f}% compression")
    print("=" * 60)
    print(f"Model: {args.model_path}")
    print(f"Output: {args.output_dir}")
    print(f"Dataset: {args.dataset} ({args.samples} samples)")
    print(f"Distance: {args.distance}")
    print("=" * 60)

    # Run REAP
    env = {
        **dict(__import__('os').environ),
        "CUDA_VISIBLE_DEVICES": "0,1,2,3,4,5,6,7",
        "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
    }

    result = subprocess.run(cmd, env=env)

    if result.returncode == 0:
        print("\n" + "=" * 60)
        print("REAP pruning complete!")
        print(f"Pruned model saved to: {args.output_dir}")
        print("=" * 60)
    else:
        print(f"\nERROR: REAP failed with code {result.returncode}")
        sys.exit(1)


if __name__ == "__main__":
    main()