1 files changed, 87 insertions, 0 deletions
diff --git a/llama.cpp/examples/model-conversion/scripts/causal/compare-logits.py b/llama.cpp/examples/model-conversion/scripts/causal/compare-logits.py
new file mode 100755
index 0000000..83bd14c
--- /dev/null
+++ b/llama.cpp/examples/model-conversion/scripts/causal/compare-logits.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+
+import sys
+import numpy as np
+from pathlib import Path
+import os
+
+# Add utils directory to path for direct script execution
+sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
+from common import get_model_name_from_env_path, compare_tokens, exit_with_warning  # type: ignore[import-not-found]
+
+def quick_logits_check(pytorch_file, llamacpp_file):
+    """Lightweight sanity check before NMSE"""
+
+    try:
+        pytorch_logits = np.fromfile(pytorch_file, dtype=np.float32)
+        llamacpp_logits = np.fromfile(llamacpp_file, dtype=np.float32)
+    except Exception as e:
+        print(f"❌ NOK: Failed to load files - {e}")
+        return False
+
+    # Check shapes match
+    if pytorch_logits.shape != llamacpp_logits.shape:
+        print(f"❌ NOK: Shape mismatch - PyTorch: {pytorch_logits.shape}, llama.cpp: {llamacpp_logits.shape}")
+        return False
+
+    # Calculate key metrics
+    diff = pytorch_logits - llamacpp_logits
+    abs_diff = np.abs(diff)
+    max_diff = np.max(abs_diff)
+
+    # Get top 10 predictions from both models
+    pytorch_top10 = np.argsort(pytorch_logits)[-10:][::-1]
+    llamacpp_top10 = np.argsort(llamacpp_logits)[-10:][::-1]
+    print(f"Top 10 PyTorch logits: {pytorch_logits[pytorch_top10]}")
+    print(f"Top 10 llama.cpp logits: {llamacpp_logits[llamacpp_top10]}")
+    print(f"Max absolute difference: {max_diff:.4f}")
+
+    return True
+
+def main():
+    model_path = os.environ.get('MODEL_PATH')
+    model_name = get_model_name_from_env_path('MODEL_PATH')
+    data_dir = Path("data")
+    pytorch_file = data_dir / f"pytorch-{model_name}.bin"
+
+    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
+    print(f"Using converted model: {llamacpp_model_name}")
+    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"
+
+    if not pytorch_file.exists():
+        print(f"Error: PyTorch logits file not found: {pytorch_file}")
+        print("Please run scripts/run-org-model.sh first to generate this file.")
+        sys.exit(1)
+
+    if not llamacpp_file.exists():
+        print(f"Error: llama.cpp logits file not found: {llamacpp_file}")
+        print("Please run scripts/run-converted-model.sh first to generate this file.")
+        sys.exit(1)
+
+    print("Checked all required files were found. Proceeding...\n")
+
+    # Verify tokens as they are a prerequisite for logits comparison.
+    print("🔍 Token Comparison Check")
+    print("=" * 40)
+    if not compare_tokens(f"pytorch-{model_name}", f"llamacpp-{llamacpp_model_name}"):
+        exit_with_warning("\n❌ Token mismatch detected", model_path)
+    print()
+
+    print("🔍 GGML Model Validation for model ", model_name)
+    print("=" * 40)
+    print(f"PyTorch logits  : {pytorch_file}")
+    print(f"llama.cpp logits: {llamacpp_file}")
+    print()
+
+    success = quick_logits_check(pytorch_file, llamacpp_file)
+
+    # Exit with appropriate code
+    if success:
+        print("✅ OK: Lightweight model check successful!")
+        print("       Ok to proceed with NMSE check...")
+        sys.exit(0)
+    else:
+        exit_with_warning(f"❌ NOK: Top 10 predictions don't match - generation will differ", model_path)
+
+if __name__ == "__main__":
+    main()