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Model: 0xSero/GLM-4.7-REAP-50-W4A16 Source: Original Platform
2026-07-20 16:27:07 +08:00
commit 2a5085337d
33 changed files with 1192 additions and 0 deletions
--- a/scripts/run_autoround.py
+++ b/scripts/run_autoround.py
@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""
+AutoRound W4A16 Quantization for GLM-4.7 REAP models
+
+This script quantizes a REAP-pruned GLM-4.7 model to INT4 weights using Intel's AutoRound.
+Reduces model size by ~4x while maintaining quality.
+
+Requirements:
+    pip install auto-round
+
+Usage:
+    python run_autoround.py --model-path ./GLM-4.7-REAP-50 --output-dir ./GLM-4.7-REAP-50-W4A16
+"""
+
+import argparse
+import subprocess
+import sys
+from pathlib import Path
+
+
+def main():
+    parser = argparse.ArgumentParser(description="AutoRound W4A16 quantization")
+    parser.add_argument("--model-path", type=str, required=True,
+                        help="Path to REAP-pruned model")
+    parser.add_argument("--output-dir", type=str, default=None,
+                        help="Output directory (default: {model-path}-W4A16)")
+    parser.add_argument("--bits", type=int, default=4,
+                        help="Weight bit width (default: 4)")
+    parser.add_argument("--group-size", type=int, default=128,
+                        help="Quantization group size (default: 128)")
+    parser.add_argument("--format", type=str, default="auto_gptq",
+                        choices=["auto_gptq", "auto_awq", "auto_round"],
+                        help="Output format (default: auto_gptq)")
+    parser.add_argument("--iters", type=int, default=200,
+                        help="Optimization iterations (default: 200)")
+
+    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)
+
+    # Build output directory
+    if args.output_dir is None:
+        args.output_dir = f"{args.model_path}-W{args.bits}A16"
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+
+    # Get model size info
+    model_size_gb = sum(f.stat().st_size for f in Path(args.model_path).rglob("*.safetensors")) / (1024**3)
+    expected_output_gb = model_size_gb / 4  # ~4x compression for W4
+
+    print("=" * 60)
+    print(f"AutoRound W{args.bits}A16 Quantization")
+    print("=" * 60)
+    print(f"Input Model: {args.model_path}")
+    print(f"Input Size: {model_size_gb:.1f} GB")
+    print(f"Output: {args.output_dir}")
+    print(f"Expected Output Size: ~{expected_output_gb:.1f} GB")
+    print(f"Config: {args.bits}-bit, group_size={args.group_size}, format={args.format}")
+    print("=" * 60)
+    print("\nThis will take ~2-3 hours for a 92-layer MoE model...")
+    print()
+
+    # Build command
+    cmd = [
+        "auto-round",
+        "--model", args.model_path,
+        "--bits", str(args.bits),
+        "--group_size", str(args.group_size),
+        "--format", args.format,
+        "--output_dir", args.output_dir,
+        "--iters", str(args.iters),
+    ]
+
+    result = subprocess.run(cmd)
+
+    if result.returncode == 0:
+        # Calculate actual output size
+        output_size_gb = sum(f.stat().st_size for f in Path(args.output_dir).rglob("*.safetensors")) / (1024**3)
+        compression = model_size_gb / output_size_gb if output_size_gb > 0 else 0
+
+        print("\n" + "=" * 60)
+        print("AutoRound quantization complete!")
+        print(f"Output: {args.output_dir}")
+        print(f"Output Size: {output_size_gb:.1f} GB ({compression:.1f}x compression)")
+        print("=" * 60)
+    else:
+        print(f"\nERROR: AutoRound failed with code {result.returncode}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()