feat: add single shard test script

test_quantize_single_shard.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""Test quantization on single shard only."""
+
+import gc
+import torch
+from pathlib import Path
+from safetensors.torch import load_file, save_file
+from bitsandbytes.functional import quantize_nf4
+from transformers import AutoConfig
+
+
+def test_single_shard(model_path, output_dir):
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+
+    import glob
+    shards = sorted(glob.glob(f"{model_path}/*.safetensors"))
+    
+    if len(shards) == 0:
+        print("No shards found!")
+        return
+    
+    # Use only first shard
+    shard_file = shards[0]
+    print(f"Testing with single shard: {shard_file}")
+    
+    state_dict = load_file(shard_file, device="cuda:0")
+    
+    weight_keys = [
+        k for k, v in state_dict.items()
+        if "weight" in k and isinstance(v, torch.Tensor) and v.dim() == 2
+    ]
+    
+    print(f"Found {len(weight_keys)} weight tensors\n")
+    
+    quantized = 0
+    failed = 0
+    
+    for key in weight_keys[:5]:  # Test first 5 layers
+        try:
+            weight = state_dict[key]
+            qweight, qstate = quantize_nf4(weight, blocksize=64, compress_statistics=True)
+            state_dict[key] = qweight
+            quantized += 1
+            print(f"✓ {key}")
+        except Exception as e:
+            failed += 1
+            print(f"✗ {key}: {e}")
+    
+    print(f"\nResults: {quantized} quantized, {failed} failed")
+    
+    # Save test output
+    test_output = output_dir / "test_shard.safetensors"
+    state_dict_cpu = {k: v.cpu() if isinstance(v, torch.Tensor) else v for k, v in state_dict.items()}
+    save_file(state_dict_cpu, test_output)
+    print(f"Saved test output to: {test_output}")
+
+
+if __name__ == "__main__":
+    test_single_shard("/data/models/Ornith-1.0-35B", "/data/models/test_quantize")