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ANE Inference — Full LLM on Apple Neural Engine

First complete LLM inference running directly on Apple's Neural Engine via reverse-engineered _ANEClient APIs. No CoreML. No Xcode compiler dependency at runtime. Token-for-token match with PyTorch.

Built on top of the maderix/ANE training runtime.

What This Does

Runs Qwen2.5-0.5B-Instruct (24 transformer layers, 494M parameters) entirely on the ANE:

169 ANE kernels compiled at startup via _ANEInMemoryModel
82 tokens/sec decode on M4 Pro
Zero GPU usage — runs on 16 dedicated neural cores
Correct output — matches PyTorch reference token-for-token

All linear projections (Q, K, V, O, gate, up, down × 24 layers + chunked LM head) compile as baked-weight 1×1 convolution kernels on ANE. Element-wise ops (RMSNorm, RoPE, softmax, SiLU, attention scores) run on CPU via Accelerate BLAS.

Architecture

Token → Embedding (CPU) → 24× Transformer Layer → LM Head (CPU) → Next Token
                              │
                              ├── RMSNorm (CPU)
                              ├── Q/K/V Projection (ANE conv kernel)
                              ├── RoPE (CPU, rotate_half)
                              ├── GQA Attention (CPU, 14 heads / 2 KV heads)
                              ├── O Projection (ANE conv kernel)
                              ├── Residual (CPU)
                              ├── RMSNorm (CPU)
                              ├── Gate/Up Projection (ANE conv kernel)
                              ├── SiLU + elementwise mul (CPU)
                              ├── Down Projection (ANE conv kernel)
                              └── Residual (CPU)

Quick Start

# 1. Convert weights from HuggingFace safetensors to flat binary
pip install safetensors torch transformers
python3 convert_weights.py /path/to/Qwen2.5-0.5B-Instruct qwen05b.bin

# 2. Build
xcrun clang -O2 -framework Foundation -framework IOSurface \
  -framework CoreML -framework Accelerate -ldl -lobjc -fobjc-arc \
  -o qwen_ane main.m

# 3. Run (single-shot, pass space-separated token IDs)
./qwen_ane qwen05b.bin "151644 8948 198 2610 525 264 10950 17847 13" 20

# 4. With tokenizer (requires transformers)
python3 run.py "Say hello in one word."

Server Mode (Recommended)

The first invocation compiles 169 ANE kernels (~5.5s). Server mode keeps them loaded so subsequent prompts respond instantly.

Socket server (best for `run.py` integration)

# Terminal 1: start the server (compiles once, stays running)
./qwen_ane qwen05b.bin --server /tmp/qwen_ane.sock

# Terminal 2: queries are instant (~0.5s instead of ~6s)
python3 run.py "What is 2+2?"
python3 run.py "Capital of France?"
python3 run.py "Count from 1 to 5"

run.py auto-detects the socket at /tmp/qwen_ane.sock and connects to it. If no server is running, it falls back to subprocess mode (slower).

You can also query the socket directly:

echo '{"tokens": [151644, 8948, 198], "max_tokens": 50}' | nc -U /tmp/qwen_ane.sock

Response format:

{"output": [9707, 0, 151645], "prefill_tps": 68.4, "decode_tps": 67.8, "prompt_tokens": 28, "gen_tokens": 3}

Stdin server (for piping/scripting)

./qwen_ane qwen05b.bin --server
# Waits for "READY", then send lines of space-separated token IDs:
# 151644 8948 198 2610 525|20
# (pipe character separates max_tokens)

Performance comparison

Mode	First prompt	Subsequent prompts
Single-shot	~6s	~6s (recompiles)
Server	~6s (startup)	~0.5s

Output

=== Qwen2.5-0.5B ANE Inference ===

Loading weights...
Config: dim=896 hidden=4864 layers=24 heads=14 kv_heads=2 vocab=151936
Compiling ANE kernels (169 total)...
Compile time: 5.1s

Prompt: 28 tokens, generating up to 10
Prefill: 64.2 t/s (28 tokens)
OUT: 9707 13 151645
Decode: 82.4 t/s (2 tokens)

→ "Hello."  (matches PyTorch exactly)

Files

File	What
`qwen_ane_infer.h`	Full 24-layer transformer forward pass, ANE kernel compilation, KV cache
`main.m`	Weight loader, token I/O, main generation loop
`convert_weights.py`	HuggingFace safetensors → flat f32 binary (includes Q/K/V biases)
`run.py`	Python wrapper with HuggingFace tokenizer

Model Support

Currently implements Qwen2.5 architecture:

GQA attention (grouped-query, n_heads ≠ n_kv_heads)
rotate_half RoPE (not interleaved pairs)
SwiGLU FFN (gate + up + silu + down)
Q/K/V bias (Qwen-specific)
Tied word embeddings (lm_head = embed)
Chunked LM head (vocab > 65536 exceeds ANE max dim)

Adapting to other architectures (LLaMA, Gemma, Mistral) requires:

Adjusting the config constants in qwen_ane_infer.h
Updating convert_weights.py for the weight naming scheme
Removing Q/K/V bias handling if the model doesn't have them
Switching RoPE to interleaved pairs if needed

Requirements

macOS 15+ on Apple Silicon (M1/M2/M3/M4)
Xcode Command Line Tools (for xcrun clang)
Python 3.9+ with safetensors, torch, transformers (for weight conversion)

Known Limitations

CPU projections only — ANE baked-weight conv kernels compile successfully but produce incorrect output (FP16 weight blob format mismatch). The USE_ANE_PROJECTIONS toggle exists but defaults to 0 (CPU via Accelerate BLAS). Fixing this would push decode speed from 82 t/s to 120+ t/s.
Single model — hardcoded for Qwen2.5-0.5B. Needs parameterization for other sizes.
f32 weights — 1.9GB on disk. FP16 or quantized weight support would halve this.

How It Works

The key insight from maderix's reverse engineering: the ANE executes compiled MIL (Machine Learning Intermediate Language) programs as atomic graph operations. Each linear projection becomes a MIL program with baked FP16 weights, compiled in-memory via _ANEInMemoryModel, and executed through IOSurface-based zero-copy I/O.

We chain 169 of these atomic operations (7 per transformer layer + 16 LM head chunks) with CPU-side element-wise ops in between. The ANE handles the compute-heavy matmuls; the CPU handles the memory-bound operations (attention scores, softmax, RoPE).

License

Same as maderix/ANE — research and educational use.

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