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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
2.6 KiB
2.6 KiB
性能優化指南
基準測試
運行完整基準測試：
swift run G12BServer ./model markbase --benchmark
輸出示例：
╔══════════════════════════════════════╗
║ Performance Benchmark ║
║ Model: markbase ║
╚══════════════════════════════════════╝
📊 Model Loading Benchmark
────────────────────────────────────────
Engine initialization: 0.123s
Model loading: 2.456s
Total: 2.579s
Layers: 48
Vocab: 262144
Hidden: 3840
📊 Token Generation Benchmark
────────────────────────────────────────
Run 1: 20 tokens in 1.052s (19.0 tok/s)
Run 2: 20 tokens in 1.048s (19.1 tok/s)
Run 3: 20 tokens in 1.050s (19.0 tok/s)
Average: 20 tokens in 1.050s
Speed: 19.0 tok/s
📊 Tokenizer Benchmark
────────────────────────────────────────
Encode: "Hello, world!..." -> 5 tokens in 0.012ms
Decode: 5 tokens -> "Hello, world!..." in 0.005ms
📊 Buffer Pool Benchmark
────────────────────────────────────────
Without pool: 0.123s (8130 allocs/s)
With pool: 0.045s (22222 allocs/s)
Speedup: 2.7x
優化技術
1. SIMD 優化
- Attention: 17.22x 提升 (threadgroup cache + float4)
- Matmul: 2.93x 提升 (SIMD batch processing)
- RMS Norm: 4.53x 提升 (parallel reduction)
2. Kernel 融合
融合 kernels 減少 dispatch 次數：
rms_norm_matmul_fused- 融合 RMS Norm + Matmulgelu_eltwise_mul_fused- 融合 GELU + Elementwise Mulresidual_rms_norm_fused- 融合 Residual + RMS Normattention_o_proj_fused- 融合 Attention + O Projection
3. Buffer Pool
緩衝區池減少內存分配：
- 重用 MTLBuffer
- 256 字節對齊
- 2.7x 加速比
4. 異步推理
異步生成減少阻塞：
- AsyncTokenGenerator
- Prefetcher
- 非阻塞 forward pass
性能對比
| 優化項 | 基準 | 優化後 | 提升 |
|---|---|---|---|
| Attention | 3.75ms | 0.22ms | 17.22x |
| Matmul | 1.38ms | 0.42ms | 2.93x |
| RMS Norm | 0.748ms | 0.165ms | 4.53x |
| 總體 | 0.100s/token | 0.051s/token | 2x |
未來優化
Phase 4+
- Float16 支持
- Batch inference
- Paged attention
- Speculative decoding