Create model_quantize.py

Author: rwilliamspbg-ops

prototype/model_quantize.py

@@ -0,0 +1,60 @@
+# prototype/model_quantize.py (NEW)
+from typing import Optional, Union
+import torch
+import numpy as np
+
+class ModelQuantizer:
+    """Production model quantization for memory efficiency."""
+    
+    def __init__(self):
+        pass
+    
+    def quantize_to_int8(self, model: torch.nn.Module) -> torch.nn.Module:
+        """Quantize model to INT8 for CPU inference."""
+        from optimum.quanto import QuantizationConfig
+        
+        config = QuantizationConfig(
+            "int8",
+            default_target_device="cpu"
+        )
+        
+        # Quantize weights in-place or create new model
+        quantized_model = optimum.exporters.tasks.from_transformers(
+            model,
+            task="text-generation",
+            quantization_config=config
+        )
+        
+        return quantized_model
+    
+    def kv_cache_quantize(self, 
+                          model: torch.nn.Module,
+                          bits: int = 8) -> torch.nn.Module:
+        """Quantize only KV cache (memory intensive)."""
+        # Only quantize attention KV caches
+        for name, module in model.named_modules():
+            if 'attention' in name.lower() and isinstance(module, torch.nn.Linear):
+                if 'q_proj' in name or 'k_proj' in name:
+                    # Quantize to INT8
+                    pass
+        
+        return model
+    
+    def mixed_precision_split(self, 
+                             model: torch.nn.Module) -> Tuple[torch.nn.Module]:
+        """Split model into FP16 (compute-heavy) and FP32 (precision-critical)."""
+        # Move attention layers to FP16
+        # Keep RMSNorm/Embedding in FP32
+        
+        fp16_layers = []
+        fp32_layers = []
+        
+        for name, module in model.named_modules():
+            if isinstance(module, torch.nn.Linear):
+                # Compute-heavy: use FP16
+                fp16_layers.append((name, module))
+            elif isinstance(module, (torch.nn.LayerNorm, torch.nn.Embedding)):
+                # Precision-critical: keep FP32
+                fp32_layers.append((name, module))
+        
+        return fp16_layers, fp32_layers