Iteration 88/100: MLX-native ternary quantization implementation

Author: cloudcover95

src/quantization/mlx_quantizer.py

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+# path: BitNet-mlx/src/quantization/mlx_quantizer.py
+#!/usr/bin/env python3
+"""
+MLX Quantizer
+
+Production-grade MLX-native implementation of 1.58-bit ternary quantization.
+Supports post-training quantization and direct inference on Apple Silicon.
+"""
+
+import logging
+from typing import Any, Dict, Optional
+
+import mlx.core as mx
+
+import numpy as np
+
+from .ternary_pipeline import TernaryPipeline
+
+logging.basicConfig(level=logging.INFO, format="[*] %(asctime)s - %(message)s")
+
+
+class MLXQuantizer:
+    """
+    MLX-native 1.58-bit ternary quantizer.
+    """
+
+    def __init__(self, output_dim: int = 128):
+        self.pipeline = TernaryPipeline(output_dim=output_dim)
+        logging.info("MLXQuantizer initialized")
+
+    def quantize_weights(self, weights: mx.array) -> Dict[str, Any]:
+        """
+        Quantize a weight matrix to ternary using the TDA pipeline.
+        Returns ternary weights + metadata.
+        """
+        # Convert to numpy for the analysis pipeline
+        weights_np = np.array(weights)
+
+        result = self.pipeline.run(weights_np)
+
+        # Create ternary weights in MLX
+        ternary_weights = mx.array(result["projection"]["ternary_weights"])
+
+        return {
+            "ternary_weights": ternary_weights,
+            "original_shape": weights.shape,
+            "ternary_shape": ternary_weights.shape,
+            "persistence_score": result["signature"]["persistence_score"],
+            "sparsity": result["projection"].get("sparsity", 0.0),
+        }
+
+    def quantize_linear_layer(self, weight: mx.array, bias: Optional[mx.array] = None) -> Dict[str, Any]:
+        """
+        Quantize a full linear layer (weight + optional bias).
+        """
+        q_weight = self.quantize_weights(weight)
+
+        result = {
+            "weight": q_weight,
+            "bias": bias,
+            "is_quantized": True,
+        }
+
+        if bias is not None:
+            result["bias"] = bias  # Bias usually kept in higher precision
+
+        return result
+
+    def create_quantized_linear(self, in_features: int, out_features: int) -> Dict[str, Any]:
+        """
+        Create a new quantized linear layer with random init.
+        """
+        weight = mx.random.normal((out_features, in_features))
+        return self.quantize_linear_layer(weight)
+
+    def matmul_ternary(self, a: mx.array, ternary_weight: mx.array) -> mx.array:
+        """
+        Efficient ternary matrix multiplication.
+        In production this can be further optimized with custom Metal kernels.
+        """
+        # Simple implementation - can be replaced with optimized kernel
+        return mx.matmul(a, ternary_weight.T)