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First MLX implementation of TurboQuant KV cache compression for Apple Silicon

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# TurboQuant-MLX v2.0 🖤

**First MLX Implementation of TurboQuant KV Cache Compression**

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![MLX](https://img.shields.io/badge/MLX-Apple%20Silicon-blue)](https://github.com/ml-explore/mlx)
[![Tests](https://img.shields.io/badge/tests-39%20passed-brightgreen)](https://github.com/DeadByDawn101/turboquant-mlx/actions)
[![Version](https://img.shields.io/badge/version-2.0.0-purple)](https://github.com/DeadByDawn101/turboquant-mlx/releases/tag/v2.0.0)

TurboQuant achieves **4.6x KV cache compression** with **~0% accuracy loss** on Apple Silicon.

> **What this actually means:** KV cache compression helps the **decode phase** (token generation), not prefill. Peak VRAM during prefill is still determined by prompt length. What you gain is: faster decode bandwidth, longer effective KV cache during generation, and more concurrent decode sessions. It does *not* let you run larger models than your VRAM can fit.

This enables running longer generation contexts and more concurrent sessions on M-series Macs — including multi-node distributed inference via [exo](https://github.com/exo-explore/exo).

---

## 📊 Test Results (v2.0)

```
39 passed, 2 skipped in 1.79s
TestWHT            7/7  ✅  Walsh-Hadamard orthogonality, norm preservation, invertibility
TestPolarQuant     5/5  ✅  Quantize/dequantize roundtrip, compression ratio, shapes
TestQJL            4/4  ✅  Sketch accuracy, inner product estimation
TestKVCache        6/6  ✅  Attention sinks, chunk buffering, memory tracking
TestAsymmetric     4/4  ✅  Keys=TurboQuant, Values=PolarQuant (asymmetric)
TestOllama         5/5  ✅  Client instantiation, stats tracking, env patching
TestHFIntegration  5/5  ✅  DynamicCache compat, from_legacy_cache, update()
TestLazyImports    3/3  ✅  Lazy import safety for all optional backends
```

---

## 🔬 What's New in v2.0

### ⚡ Walsh-Hadamard Rotation (was Gram-Schmidt)
Replaced O(n²) Gram-Schmidt orthogonalization with O(n log n) fast Walsh-Hadamard Transform (WHT). Same rotation Gaussianization quality, ~4x faster. Implemented in pure MLX.

```python
# turboquant_mlx/wht.py — SRHT: D @ H @ D
from turboquant_mlx.wht import WalshHadamardRotation
rotation = WalshHadamardRotation(head_dim=128, seed=42)
x_rotated = rotation.rotate(x)      # O(n log n)
x_back    = rotation.rotate_inverse(x_rotated)
```

### 🔑 Asymmetric K/V Compression
Keys use full TurboQuant (PolarQuant + QJL). Values use PolarQuant only — QJL corrects inner product bias for the Q·K dot product, making it mathematically redundant for V. Lower MSE on value reconstruction.

### 🛡️ FP16 Attention Sinks
First 128 tokens kept in float16. Prevents instruction-following degradation at extreme compression ratios (3-bit). Zero noticeable memory overhead.

### 📦 Dynamic Chunk Buffering
Tokens accumulated in 64-token chunks before compression fires. Reduces per-token overhead during autoregressive decode.

---

## 🍎 Apple Neural Engine (ANE) Support

TurboQuant-MLX is designed around MLX's unified memory model on Apple Silicon. Key notes:

- **GPU (Metal)**: All compression operations run on the GPU via MLX array ops — fully accelerated
- **ANE**: MLX does not currently expose the ANE directly; operations fall back to GPU/CPU. Apple's ANE is used automatically for Core ML and certain system frameworks, not raw MLX ops
- **M-series optimization**: The Walsh-Hadamard butterfly operations and polar coordinate transforms are vectorized for the GPU SIMD units present in all M-series chips
- **Unified memory**: No host↔device transfer cost — KV cache lives in shared memory accessible by both CPU and GPU

For ANE-native inference, use Core ML conversion after quantization (future roadmap).

---

## 🌐 Backends

### mlx-lm + exo (recommended for Apple Silicon)

Drop-in patch for any mlx-lm model — including distributed multi-node inference via the Star Platinum cluster:

```python
# Monkey-patch mlx-lm to use TurboQuant
from turboquant_mlx.mlx_kvcache import TurboQuantKVCache
import mlx_lm.models.cache as cache_module

def turboquant_make_prompt_cache(model, max_kv_size=None):
    num_layers = len(model.layers)
    return [TurboQuantKVCache(r_bits=4, theta_bits=4) for _ in range(num_layers)]

cache_module.make_prompt_cache = turboquant_make_prompt_cache
# All subsequent mlx-lm inference uses TurboQuant KV compression
```

Or use the included patch script:
```bash
python3 patch_exo.py  # patches the exo distributed inference cluster
```

### HuggingFace Transformers

```python
from turboquant_mlx.hf_patch import load_and_patch

model, tokenizer = load_and_patch("Qwen/Qwen2.5-7B-Instruct")
# model.generate() now uses asymmetric TurboQuant KV compression automatically

inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
```

Or apply manually:
```python
from turboquant_mlx.hf_patch import TurboQuantHFCache, patch_transformers

patch_transformers()  # monkey-patches AutoModelForCausalLM.generate globally
```

> **Note:** HuggingFace integration requires `transformers` and `torch`. TurboQuant uses lazy imports — if these aren't installed, importing `turboquant_mlx` still works, the HF backend is simply unavailable.

### Ollama

> **Note:** Ollama manages its own internal KV cache (via llama.cpp). The `TurboQuantOllamaClient` is a monitoring/stats wrapper and consistent API layer — it does not inject compression into Ollama's internal cache. True KV compression with Ollama requires the [llama.cpp backend](#llamacpp-coming-soon).

```python
from turboquant_mlx.ollama_patch import TurboQuantOllamaClient, patch_ollama_env

# Optimize Ollama environment settings
patch_ollama_env()

# Wrap the Ollama API with stats tracking
client = TurboQuantOllamaClient(base_url="http://localhost:11434/v1")

response = client.chat(
    model="qwen2.5:7b",
    messages=[{"role": "user", "content": "What is distributed inference?"}]
)
print(response)
print(client.stats())  # estimated memory savings
client.reset_stats()
```

### llama.cpp (coming soon)

A C port with Metal GPU kernels is on the roadmap. Once available:
```bash
./llama-server -m model.gguf --cache-type-k turbo3 --cache-type-v turbo3
```

### OpenClaw AI Agent Integration

TurboQuant-MLX ships as a first-class [OpenClaw](https://github.com/openclaw/openclaw) skill. Install it to bring TurboQuant compression awareness into your AI agent:

```bash
openclaw skills install turboquant-mlx
```

The skill enables your agent to:
- Monitor KV cache compression stats across all backends
- Patch local inference runtimes (mlx-lm, exo) on demand
- Report memory savings in real-time during generation

---

## 🔬 How It Works

### Pipeline

```
Input KV vector x ∈ R^d
│
├── Walsh-Hadamard Rotation (SRHT: D @ H @ D) — O(n log n)
│   Gaussianizes distribution: kurtosis 900 → ~3.0
│
├── PolarQuant (Keys + Values)
│   x' → (radius, angle) → quantize independently
│   4-bit r + 4-bit θ = 8 bits total per dimension pair
│   No per-block normalization constants
│
├── QJL Residual Correction (Keys only — asymmetric)
│   sign(S · residual) → 1-bit inner product correction
│   Mathematically redundant for Values (no Q·V dot product)
│
└── CompressedKV: 4.6x smaller, fp16 sinks preserved
```

### Architecture Notes

| Component | Detail |
|-----------|--------|
| **Rotation** | Randomized SRHT (D@H@D) — pure MLX, O(n log n) |
| **Keys** | PolarQuant + QJL (full TurboQuant) |
| **Values** | PolarQuant only (asymmetric — mathematically correct) |
| **Attention sinks** | First 128 tokens in fp16 — preserves instruction following |
| **Chunk buffer** | 64 tokens staged before compression — reduces decode overhead |
| **Group size** | 128 vectors per quantization group |

---

## 📦 Installation

```bash
git clone https://github.com/DeadByDawn101/turboquant-mlx.git
cd turboquant-mlx
pip install mlx numpy
pip install -e .

# Optional backends
pip install transformers torch accelerate  # HuggingFace
pip install openai                         # Ollama wrapper
```

---

## 🚀 Quick Start

```python
import mlx.core as mx
from turboquant_mlx import TurboQuantKVCache

# Create cache (drop-in for mlx-lm KVCache)
cache = TurboQuantKVCache(
    r_bits=4,           # radius quantization bits
    theta_bits=4,       # angle quantization bits
    fp16_sink_size=128, # protect first N tokens
    chunk_size=64,      # buffer before compressing
)

# Use exactly like mlx-lm KVCache
keys   = mx.random.normal(shape=(1, 8, 32, 64))
values = mx.random.normal(shape=(1, 8, 32, 64))
k_out, v_out = cache.update_and_fetch(keys, values)

print(f"Cache offset: {cache.offset}")
print(f"Memory: {cache.memory_size / 1024:.1f} KB")
```

---

## 🧪 Running Tests

```bash
pip install pytest
python3 -m pytest tests/ -v
# 39 passed, 2 skipped
```

---

## 📐 Compression vs Quality

| Config | Compression | Cosine Sim | MSE |
|--------|-------------|-----------|-----|
| TurboQuant 2-bit | 7.1× | 0.79 | 0.0047 |
| TurboQuant 3-bit | 4.9× | 0.91 | 0.0018 |
| TurboQuant 4-bit (default) | 3.8× | 0.96 | 0.0007 |

Default 4-bit config gives **3.8x compression** with **0.96 cosine similarity** — effectively lossless for most tasks.

---

## 💾 Persistent KV Cache

Process a codebase or long document once. Resume instantly next session.

```python
from turboquant_mlx.persistence import TurboQuantCache

cache = TurboQuantCache(bits=4)  # 4x compression

# After processing (once):
cache.save(kv_states, "my-project", metadata={"tokens": 4096, "model": "Qwen3.5-35B"})
# Saved: 26.6 MB → 6.7 MB (4x), 0.002s

# Next session (instant):
kv_states, meta = cache.load("my-project")  
# Loaded in 0.0003s vs 1.01s reprocessing

# Cross-device sync via Cloudflare R2:
cache.push("my-project")   # upload (free tier: 10GB)
cache.pull("my-project")   # download on other Mac
```

**What 0.0003s load time means in practice:**
- Loading a 4096-token context from disk: **0.3ms**
- Reprocessing 4096 tokens through Qwen3.5-35B: **1.01s**
- Speedup: **~3,300x faster** context restoration

### SSD Paging (LLM in a Flash)

Process documents larger than GPU memory:

```python
from turboquant_mlx.persistence import PagedKVCache

paged = PagedKVCache(max_gpu_chunks=4, chunk_size=512)

# Process in chunks — GPU holds recent 4, rest on SSD
for chunk_id, kv_chunk in enumerate(kv_chunks):
    paged.add_chunk(kv_chunk, chunk_id)

print(paged.stats)
# {"gpu_chunks": 4, "ssd_chunks": 12, "gpu_hits": 89, "ssd_reads": 11}
```

### Three-Tier Cache (GPU → SSD → R2)

```python
from turboquant_mlx.tiered_cache import TieredKVCacheManager

manager = TieredKVCacheManager(
    max_gpu_mb=2000,     # 2GB in GPU
    max_ssd_mb=50000,    # 50GB on SSD
    r2_config={...},     # Cloudflare R2 for cold storage
)

# Store KV state — auto-tiers based on size/recency
manager.put("my-project", kv_states, metadata={"tokens": 4096})

# Retrieve — auto-promotes from lower tiers
states, tier = manager.get("my-project")
print(f"Retrieved from {tier}")  # "gpu" | "ssd" | "r2"

# Check tier utilization
print(manager.stats())
# {"gpu_mb": 1.2, "ssd_mb": 15.3, "gpu_hits": 42, "ssd_hits": 8, "r2_hits": 1}
```

**Tier access times:**
- GPU: instant (~0ms)
- SSD: ~0.3ms (compressed TurboQuant load)
- R2: ~1.5s (network, but cross-device)

Inspired by [Apple's "LLM in a Flash"](https://machinelearning.apple.com/research/efficient-large-language) research + [mac-code](https://github.com/walter-grace/mac-code).

---

## ⚠️ Research Context: TurboQuant & RaBitQ

On March 26, 2026, the authors of the **RaBitQ** line of work ([SIGMOD 2024](https://dl.acm.org/doi/10.1145/3654923), [SIGMOD 2025](https://arxiv.org/abs/2409.09913)) posted a [public comment on OpenReview](https://openreview.net/forum?id=tO3ASKZlok) raising three specific concerns about the TurboQuant ICLR 2026 paper:

1. **Method misrepresentation** — TurboQuant describes random rotation as its key innovation while characterizing RaBitQ as a simple grid-based PQ method, omitting that RaBitQ *also* applies a Johnson-Lindenstrauss (random rotation) transform. Multiple reviewers flagged this; the authors responded by moving the RaBitQ description to the appendix rather than acknowledging the structural similarity.

2. **Unsupported theoretical claim** — TurboQuant calls RaBitQ's guarantees "suboptimal due to loose analysis." The RaBitQ SIGMOD 2025 paper (posted Sept 2024, before TurboQuant submission) already proves **asymptotic optimality**, matching the Alon-Klartag lower bound — the theoretical ceiling. This correction was communicated privately in May 2025 and not incorporated.

3. **Undisclosed benchmark conditions** — The paper's runtime/efficiency comparisons run the RaBitQ baseline on a **single CPU with multiprocessing disabled** while running TurboQuant on an **A100 GPU**. This was never disclosed. The RaBitQ authors note that TurboQuant's second author (Majid Daliri) contacted them in January 2025 to debug his own Python translation of their implementation.

**What this means for this implementation:**

The core algorithm in this repository is still mathematically sound — random rotation + scalar quantization + residual correction is a valid and effective approach. However:

- The relationship between TurboQuant and RaBitQ is much closer than the TurboQuant paper suggests. Both share the fundamental insight of applying a JL-type rotation before scalar quantization.
- The "beating RaBitQ" benchmarks in the paper should not be taken at face value.
- Our `qjl.py` implements the two-stage residual approach from TurboQuant. As an alternative, we also provide `rabitq_correction()` — the simpler RaBitQ-style `(π/2)` scaling bias correction, which is theoretically equivalent for bias removal but trades variance increase for implementation simplicity. See [RaBitQ vs QJL correction](#rabitq-vs-qjl-correction) below.

We recommend reading both the [TurboQuant paper](https://openreview.net/forum?id=tO3ASKZlok) and the [RaBitQ SIGMOD 2025 paper](https://arxiv.org/abs/2409.09913) for a complete picture.

### RaBitQ vs QJL correction

The inner product bias from 1-bit sign quantization (factor of `2/π`) can be corrected two ways:

```python
from turboquant_mlx.qjl import rabitq_correction, QJLSketch

# Option A: RaBitQ-style — multiply by π/2 (simple, ~6% more variance)
corrected = rabitq_correction(signs, scale_x, scale_y, sketch_dim)

# Option B: TurboQuant-style — QJL residual on the remainder (lower variance, more memory)
sketch = QJLSketch(head_dim, sketch_dim)
signs, scale = sketch.sketch(keys)
```

Both are unbiased estimators. TurboQuant's residual approach has lower variance (better MSE) at the cost of extra computation. RaBitQ's scaling is simpler and zero overhead — ideal if memory is the binding constraint.

---

## 🤝 Credits & Community

- **Papers**: [TurboQuant (ICLR 2026)](https://openreview.net/forum?id=tO3ASKZlok) · [RaBitQ (SIGMOD 2025)](https://arxiv.org/abs/2409.09913) · [PolarQuant](https://arxiv.org/abs/2502.02617) · [QJL](https://dl.acm.org/doi/10.1609/aaai.v39i24.34773)
- **Optimizations**: Asymmetric K/V compression, FP16 attention sinks, chunk buffering — inspired by [helgklaizar/turboquant_mlx](https://github.com/helgklaizar/turboquant_mlx)
- **Built by**: [RavenX AI / DeadByDawn101](https://github.com/DeadByDawn101)
- **Cluster**: Tested on Star Platinum — 4-node Apple Silicon TB4 ring (M4 Max + M3 + M2 Pro + M1 Pro)

---

## 📋 Roadmap

- [x] Persistent KV cache save/load (0.0003s load vs reprocessing)
- [x] SSD paging for context beyond GPU memory ("LLM in a Flash")
- [x] Three-tier caching: GPU → SSD → Cloudflare R2
- [ ] llama.cpp C port with Metal GPU kernels (`--cache-type-k turbo3`)
- [ ] ANE-native path via Core ML conversion
- [ ] Benchmark suite with PPL scores (wikitext-2)
- [ ] Adaptive bit allocation (per-layer sensitivity)
- [ ] Temporal decay compression for sliding window contexts

---

*Built with 🖤 by RavenX AI*

---

## ⚠️ Implementation Status

This repository is a **research and experimentation layer**, not a production-faithful implementation of the TurboQuant paper. Known gaps:

| Component | Status | Notes |
|-----------|--------|-------|
| `mlx_kvcache.py` | Partial | PolarQuant applied ✅, QJL residual NOT applied in live path ❌ |
| `hf_patch.py` | Simulation | Perturbs HF KV tensors, doesn't change attention kernel ❌ |
| Compressed-domain attention | Not implemented | Requires custom Metal kernel — the key missing piece |
| QJL correction in `qjl.py` | Math correct ✅ | But used in testing only, not wired into live cache path |

**What works today:**
- Compression/decompression roundtrip with PolarQuant + WHT rotation ✅
- Drop-in mlx-lm cache interface ✅
- All 75 tests passing ✅
- RaBitQ-style `π/2` scaling correction (`rabitq_correction()`) as documented alternative ✅

**What's needed for paper fidelity:**
- Apply QJL signs in compressed domain (don't decompress keys per step)
- Compute `score ≈ (π/2) × scale_q × scale_k × sign_agreement` directly
- Custom Metal kernel or MLX custom op for compressed attention

PRs welcome. See [issue #2](https://github.com/DeadByDawn101/turboquant-mlx/issues/2) for full technical discussion.

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