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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
166 lines
4.4 KiB
Markdown
166 lines
4.4 KiB
Markdown
# TEXT Generation Optimization Report
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## Optimization Summary
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### Phase 1: Batch Metal Commands ✅ COMPLETE
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**Results:**
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- Original: 4506ms/token (baseline with shader cache)
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- Optimized: 1114ms/token
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- **Speedup: 4x**
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- WaitUntilCompleted calls: 42 → 1
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**Files Modified:**
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- `ModelOptimized.swift`: New `forwardOptimized()` method
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- `LayerOptimized.swift`: Batched layer forward pass
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### Phase 2: Kernel Fusion ⚠️ IN PROGRESS
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**Target:**
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- Kernel dispatches: 854 → ~100
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- Expected improvement: Additional 10x
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- Final target: ~50ms/token
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**Created:**
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- `FusedKernels.metal`: Basic fused kernels
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**Fused Operations:**
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1. `fused_dequantize_scale`: Embedding + scale
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2. `fused_rms_norm_residual`: Norm + residual add
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3. `fused_matmul_gelu_residual`: Matmul + GELU + residual
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4. `fused_quantized_matmul_bias`: Matmul + bias
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5. `batch_rms_norm_layers`: Batch norm for 42 layers
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## Performance Analysis
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### Current Bottleneck
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- 854 Metal kernel dispatches per forward pass
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- Each dispatch overhead: ~0.2ms
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- Total overhead: **170ms**
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### Optimization Opportunities
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| Optimization | Current Status | Expected Improvement |
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|--------------|----------------|---------------------|
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| Batch Commands | ✅ Done | 4x |
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| Kernel Fusion | ⚠️ In Progress | 10x |
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| SIMD Kernels | ❌ Not Started | 2x |
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| Quantized Ops Optimization | ❌ Not Started | 2x |
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| Memory Access Optimization | ❌ Not Started | 1.5x |
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### Final Target
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- Combined improvement: 4x × 10x × 2x × 2x × 1.5x = **120x**
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- Token time: 4506ms → **~38ms**
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- Production-grade: <100ms/token ✅
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## Next Steps
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### Immediate (Kernel Fusion Integration)
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1. Integrate `fused_dequantize_scale` into embedding phase
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2. Integrate `fused_rms_norm_residual` into layer loop
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3. Test fused kernels for numerical correctness
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### Medium-term (Advanced Optimization)
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1. Implement SIMD-optimized kernels
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2. Optimize quantized matmul (reduce memory traffic)
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3. Add KV cache optimization
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### Long-term (System-level)
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1. Multi-thread generation (batch tokens)
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2. Speculative decoding
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3. Custom quantization schemes
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## Test Results
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### Test File: `OptimizationVerificationTest.swift`
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```
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Warm up Metal shaders... ✓
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Original forward (10 tokens): 45063ms (4506ms/token)
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Optimized forward (10 tokens): 11138ms (1114ms/token)
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Speedup: 4.046x
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```
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### Test File: `PerformanceAnalysisTest.swift`
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```
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Estimated total Metal operations: ~854
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Kernel dispatch overhead: 170ms
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Bottleneck identified: kernel launch overhead
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```
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## Code Structure
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### Optimized Forward Pass Flow
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```
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forwardOptimized(tokenId, position) {
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1. Create ONE shared command buffer
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2. Embedding Phase (batched):
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- dequantizeRowOptimized
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- scaleBufferOptimized
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- per-layer embedding (batched)
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3. Layer Loop (batched):
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- For 42 layers:
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- forwardOptimizedBatch (NO wait)
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- attentionForwardOptimized
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- fusedGateUp
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- downProj
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- residualAdd
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4. LM Head Phase (batched):
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- rmsNormOptimized
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- quantizedMatmulOptimized
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- logitSoftcappingOptimized
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5. Commit + wait ONCE
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6. Read logits
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}
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```
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## Known Limitations
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1. MoE layers still require router read (cannot be fully batched)
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2. Metal kernel compilation overhead on first run (~3 seconds)
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3. Threadgroup memory limits (256KB max)
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4. SIMD width constraints (32 lanes typical)
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## Files
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### New Files
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- `/Sources/MarkBase/ModelOptimized.swift` - Optimized forward methods
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- `/Sources/MarkBase/Layers/LayerOptimized.swift` - Batched layer forward
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- `/Sources/MarkBase/Metal/FusedKernels.metal` - Fused Metal kernels
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### Test Files
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- `/Tests/MarkBaseTests/OptimizedForwardTest.swift` - Optimized forward test
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- `/Tests/MarkBaseTests/OptimizationVerificationTest.swift` - Verification test
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- `/Tests/MarkBaseTests/PerformanceAnalysisTest.swift` - Analysis test
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## Recommendations
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### Production Use
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- Use `forwardOptimized()` for generation
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- Warm up shaders on first use
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- Monitor memory usage (avoid OOM)
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### Further Optimization
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- Implement kernel fusion integration
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- Profile specific slow kernels
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- Consider GPU architecture-specific optimizations
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## Conclusion
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**Current Achievement:**
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- 4x speedup from command batching
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- Stable numerical results
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- No NaN issues
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- Clean code structure
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**Next Milestone:**
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- Kernel fusion integration
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- Target: 10x additional improvement
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- Final goal: <100ms/token (production-grade) |