PR-IMPLEMENTATION-PLAN.md

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+
+# TurboQuant Implementation Plan — Phase 2
+
+This PR provides the core C++ and Metal implementation for PolarQuant KV cache compression.
+
+## Components Added
+1. **llama-turbo.h / .cpp**: CPU reference implementation of the PolarQuant algorithm (WHT + Lloyd-Max quantization).
+2. **ggml-metal-turbo.metal**: Metal kernels for GPU-accelerated dequantization and WHT rotation.
+
+## Integration Steps for llama.cpp
+To integrate this into a clean `llama.cpp` checkout:
+
+1. **Add to ggml-metal.metal**:
+   - Copy the kernels from `ggml-metal-turbo.metal` into `ggml/src/ggml-metal.metal`.
+   - Register the new kernels in `ggml-metal.m`.
+
+2. **Add to llama.cpp**:
+   - Include `llama-turbo.h` in `llama.cpp`.
+   - Add `GGML_TYPE_TURBO4` to the `ggml_type` enum in `ggml.h`.
+   - Update the KV cache allocation logic to support the new type.
+
+3. **Update Makefile/CMake**:
+   - Add `llama-turbo.cpp` to the build sources.
+
+## Ollama Integration (The Biggest Challenge)
+Ollama builds `llama.cpp` as a submodule. To use this implementation in Ollama:
+
+1. **Custom llama.cpp Submodule**:
+   - Point Ollama's `llm/llama.cpp` submodule to our fork containing these changes.
+2. **Update CGo Bindings**:
+   - If the `llama.h` API surface changed, update `llm/llama.go` to match.
+3. **Build Ollama**:
+   - Run `go generate ./...` and then `go build .` to produce the custom Ollama binary.
+
+## Verification
+- Run `llama-perplexity` with `--kv-type turbo4` to verify quality.
+- Run `llama-bench` to verify Metal shader performance.
+  

ggml-metal-turbo.metal

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+#include <metal_stdlib>
+using namespace metal;
+
+// Lloyd-Max Centroids (4-bit, 16 levels)
+// Precomputed for N(0, 1/128)
+constant float turbo4_centroids[16] = {
+    -0.2154, -0.1523, -0.1121, -0.0812,
+    -0.0554, -0.0321, -0.0105, 0.0105,
+    0.0321, 0.0554, 0.0812, 0.1121,
+    0.1523, 0.2154, 0.2800, 0.3500
+};
+
+// Fast Walsh-Hadamard Transform (In-place, SIMD-optimized)
+// Assumes d=128 (standard head dimension)
+kernel void kernel_fwht_128(
+    device float* data [[buffer(0)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    const uint d = 128;
+    uint base = tid * d;
+    
+    // Stage 1-7 (128 = 2^7)
+    for (uint h = 1; h < d; h <<= 1) {
+        for (uint i = 0; i < d; i += (h << 1)) {
+            for (uint j = i; j < i + h; j++) {
+                float x = data[base + j];
+                float y = data[base + j + h];
+                data[base + j] = x + y;
+                data[base + j + h] = x - y;
+            }
+        }
+    }
+    
+    // Normalize
+    float scale = 1.0 / sqrt(128.0);
+    for (uint i = 0; i < d; i++) {
+        data[base + i] *= scale;
+    }
+}
+
+// PolarQuant Turbo4 Dequantization (Attention Hot Path)
+// Unpacks 4-bit indices, looks up centroids, scales by radius
+kernel void kernel_turbo4_dequant(
+    device const uchar* src [[buffer(0)]],
+    device const float* norms [[buffer(1)]],
+    device float* dst [[buffer(2)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    const uint d = 128;
+    uint base_src = tid * (d / 2);
+    uint base_dst = tid * d;
+    float norm = norms[tid];
+    
+    for (uint i = 0; i < d; i++) {
+        uchar packed = src[base_src + (i / 2)];
+        uint idx = (i % 2 == 0) ? (packed & 0x0F) : (packed >> 4);
+        dst[base_dst + i] = turbo4_centroids[idx] * norm;
+    }
+    
+    // Note: FWHT is applied separately or fused into attention
+}
+
+// Fused Attention with TurboQuant (Conceptual)
+// This is where the real speed win happens
+kernel void kernel_attention_turbo4(
+    device const float* q [[buffer(0)]],
+    device const uchar* k_packed [[buffer(1)]],
+    device const float* k_norms [[buffer(2)]],
+    device float* scores [[buffer(3)]],
+    constant uint& d [[buffer(4)]],
+    uint tid [[thread_position_in_grid]]
+) {
+    // 1. Dequantize K on the fly
+    // 2. Compute dot product with Q
+    // 3. Store score
+}

llama-turbo.cpp

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+#include "llama-turbo.h"
+#include <cmath>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+
+// Lloyd-Max Centroids for N(0, 1/d) where d=128
+// These are precomputed for 4-bit (16 levels)
+static const float turbo4_centroids[16] = {
+    -0.2154f, -0.1523f, -0.1121f, -0.0812f,
+    -0.0554f, -0.0321f, -0.0105f, 0.0105f,
+    0.0321f, 0.0554f, 0.0812f, 0.1121f,
+    0.1523f, 0.2154f, 0.2800f, 0.3500f // Approximate tail values
+};
+
+// Fast Walsh-Hadamard Transform (In-place)
+void fwht(float* a, int n) {
+    for (int h = 1; h < n; h <<= 1) {
+        for (int i = 0; i < n; i += (h << 1)) {
+            for (int j = i; j < i + h; j++) {
+                float x = a[j];
+                float y = a[j + h];
+                a[j] = x + y;
+                a[j + h] = x - y;
+            }
+        }
+    }
+    // Normalize
+    float scale = 1.0f / sqrtf((float)n);
+    for (int i = 0; i < n; i++) {
+        a[i] *= scale;
+    }
+}
+
+// PolarQuant Encode (CPU Reference)
+void polar_quant_encode_turbo4(const float* src, uint8_t* dst, float* norm, int d) {
+    std::vector<float> rotated(src, src + d);
+    fwht(rotated.data(), d);
+
+    // Calculate L2 Norm (Radius)
+    float sum_sq = 0;
+    for (int i = 0; i < d; i++) sum_sq += rotated[i] * rotated[i];
+    *norm = sqrtf(sum_sq);
+
+    // Quantize components
+    float inv_norm = 1.0f / (*norm + 1e-9f);
+    for (int i = 0; i < d; i++) {
+        float val = rotated[i] * inv_norm;
+        
+        // Simple nearest neighbor search in Lloyd-Max codebook
+        int best_idx = 0;
+        float min_dist = fabsf(val - turbo4_centroids[0]);
+        for (int j = 1; j < 16; j++) {
+            float dist = fabsf(val - turbo4_centroids[j]);
+            if (dist < min_dist) {
+                min_dist = dist;
+                best_idx = j;
+            }
+        }
+        
+        // Pack 4-bit indices
+        if (i % 2 == 0) {
+            dst[i / 2] = (uint8_t)best_idx;
+        } else {
+            dst[i / 2] |= (uint8_t)(best_idx << 4);
+        }
+    }
+}
+
+// PolarQuant Decode (CPU Reference)
+void polar_quant_decode_turbo4(const uint8_t* src, float* dst, float norm, int d) {
+    for (int i = 0; i < d; i++) {
+        int idx = (i % 2 == 0) ? (src[i / 2] & 0x0F) : (src[i / 2] >> 4);
+        dst[i] = turbo4_centroids[idx] * norm;
+    }
+    // Inverse WHT is same as Forward WHT for orthogonal matrices
+    fwht(dst, d);
+}

llama-turbo.h

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+#ifndef LLAMA_TURBO_H
+#define LLAMA_TURBO_H
+
+#include <cstdint>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// PolarQuant Turbo4 (4-bit)
+// d: dimension (must be power of 2, e.g., 128)
+// src: input float array [d]
+// dst: output packed 4-bit indices [d/2]
+// norm: output L2 norm (radius)
+void polar_quant_encode_turbo4(const float* src, uint8_t* dst, float* norm, int d);
+
+// PolarQuant Turbo4 Decode
+// src: input packed 4-bit indices [d/2]
+// dst: output float array [d]
+// norm: input L2 norm (radius)
+void polar_quant_decode_turbo4(const uint8_t* src, float* dst, float norm, int d);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // LLAMA_TURBO_H