ZacharyZcR
31 lines
1.6 KiB
Markdown
31 lines
1.6 KiB
Markdown
# Metal backend (Apple Silicon, experimental)
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On Apple Silicon the decode profile is matmul-bound, and unified memory removes
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the PCIe copy tax that keeps CUDA's streaming experts on the CPU — so colibrì
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has an opt-in Metal backend that runs the **routed-expert SwiGLU (batched,
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zero-copy from the RAM slabs)**, the **fused decode attention** (full MLA layer
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in one command buffer, S≤4), and **prefill's large GEMMs** on the GPU.
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Token-exact vs the CPU path.
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```bash
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cd c
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make glm METAL=1 # macOS only; no Xcode needed (shader compiles at runtime)
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make metal-test # standalone kernel/attention correctness vs CPU reference
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COLI_METAL=1 COLI_MODEL=/path/glm52_i4 ./coli chat --ram 96
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```
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Measured on an M4 Max (128 GB, warm cache, MTP on): CPU 0.30 → Metal
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**0.42 tok/s (~1.4×)** (best config adds `DIRECT=1`; ~3× vs this machine's
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first cold run). An M5 Max with a 46.9 GB learned pin reached **2.06 tok/s**
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([#103](https://github.com/JustVugg/colibri/issues/103); see also the
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[M5 Max performance report](METAL-M5MAX-PERF-REPORT.md)).
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Key design points: Metal's ~5 ms submit latency makes per-matmul dispatch a
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loss — everything is batched into few command buffers per layer, and the
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resident experts' GPU work is submitted *before* the missed experts' disk reads
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so I/O and compute overlap. `COLI_METAL_GEMM_MIN` tunes the prefill GEMM row
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threshold (default 16). Streaming, cache, MTP, DSA and the persistence formats
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are unchanged; every GPU path falls back to the CPU per-block on any fault.
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Numerics are dequant→f32-MAC (same as the CUDA tier); greedy outputs are
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byte-identical to the CPU engine.
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