mirror of https://github.com/maderix/ANE.git
120 lines
5.1 KiB
Markdown
120 lines
5.1 KiB
Markdown
# ANE Inference — Full LLM on Apple Neural Engine
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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.
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Built on top of the [maderix/ANE](https://github.com/maderix/ANE) training runtime.
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## What This Does
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Runs **Qwen2.5-0.5B-Instruct** (24 transformer layers, 494M parameters) entirely on the ANE:
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- **169 ANE kernels** compiled at startup via `_ANEInMemoryModel`
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- **82 tokens/sec** decode on M4 Pro
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- **Zero GPU usage** — runs on 16 dedicated neural cores
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- **Correct output** — matches PyTorch reference token-for-token
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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.
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## Architecture
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```
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Token → Embedding (CPU) → 24× Transformer Layer → LM Head (CPU) → Next Token
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│
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├── RMSNorm (CPU)
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├── Q/K/V Projection (ANE conv kernel)
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├── RoPE (CPU, rotate_half)
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├── GQA Attention (CPU, 14 heads / 2 KV heads)
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├── O Projection (ANE conv kernel)
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├── Residual (CPU)
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├── RMSNorm (CPU)
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├── Gate/Up Projection (ANE conv kernel)
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├── SiLU + elementwise mul (CPU)
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├── Down Projection (ANE conv kernel)
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└── Residual (CPU)
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```
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## Quick Start
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```bash
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# 1. Convert weights from HuggingFace safetensors to flat binary
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pip install safetensors torch transformers
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python3 convert_weights.py /path/to/Qwen2.5-0.5B-Instruct qwen05b.bin
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# 2. Build
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xcrun clang -O2 -framework Foundation -framework IOSurface \
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-framework CoreML -framework Accelerate -ldl -lobjc \
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-o qwen_ane main.m
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# 3. Run (pass space-separated token IDs)
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./qwen_ane qwen05b.bin "151644 8948 198 2610 525 264 10950 17847 13" 20
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# 4. With tokenizer (requires transformers)
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python3 run.py "Say hello in one word."
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```
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## Output
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```
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=== Qwen2.5-0.5B ANE Inference ===
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Loading weights...
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Config: dim=896 hidden=4864 layers=24 heads=14 kv_heads=2 vocab=151936
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Compiling ANE kernels (169 total)...
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Compile time: 5.1s
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Prompt: 28 tokens, generating up to 10
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Prefill: 64.2 t/s (28 tokens)
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OUT: 9707 13 151645
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Decode: 82.4 t/s (2 tokens)
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→ "Hello." (matches PyTorch exactly)
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```
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## Files
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| File | What |
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|------|------|
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| `qwen_ane_infer.h` | Full 24-layer transformer forward pass, ANE kernel compilation, KV cache |
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| `main.m` | Weight loader, token I/O, main generation loop |
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| `convert_weights.py` | HuggingFace safetensors → flat f32 binary (includes Q/K/V biases) |
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| `run.py` | Python wrapper with HuggingFace tokenizer |
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## Model Support
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Currently implements **Qwen2.5** architecture:
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- GQA attention (grouped-query, `n_heads` ≠ `n_kv_heads`)
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- `rotate_half` RoPE (not interleaved pairs)
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- SwiGLU FFN (gate + up + silu + down)
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- Q/K/V bias (Qwen-specific)
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- Tied word embeddings (lm_head = embed)
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- Chunked LM head (vocab > 65536 exceeds ANE max dim)
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Adapting to other architectures (LLaMA, Gemma, Mistral) requires:
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1. Adjusting the config constants in `qwen_ane_infer.h`
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2. Updating `convert_weights.py` for the weight naming scheme
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3. Removing Q/K/V bias handling if the model doesn't have them
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4. Switching RoPE to interleaved pairs if needed
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## Requirements
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- macOS 15+ on Apple Silicon (M1/M2/M3/M4)
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- Xcode Command Line Tools (for `xcrun clang`)
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- Python 3.9+ with `safetensors`, `torch`, `transformers` (for weight conversion)
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## Known Limitations
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- **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.
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- **No persistent server** — each invocation recompiles 169 kernels (~5s). A server mode that compiles once and serves via HTTP would eliminate this overhead.
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- **Single model** — hardcoded for Qwen2.5-0.5B. Needs parameterization for other sizes.
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- **f32 weights** — 1.9GB on disk. FP16 or quantized weight support would halve this.
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## How It Works
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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.
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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).
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## License
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Same as maderix/ANE — research and educational use.
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