MarkBase Admin
ac75faa0cc
CI / build-and-test (push) Has been cancelled
- 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
132 lines
3.9 KiB
Markdown
132 lines
3.9 KiB
Markdown
# MarkBase Optimization Day Summary
|
||
**Date**: 2026-06-22
|
||
**Status**: Successfully optimized MoE, identified new bottlenecks
|
||
|
||
---
|
||
|
||
## ✅ Completed Optimizations
|
||
|
||
### 1. MoE Optimization ✓✓✓
|
||
**Before**: 26B-A4B MoE 40.1ms (22% slower than Standard)
|
||
**After**: 30.1ms (8.7% **faster** than Standard!)
|
||
|
||
**Key Fix**: GPU mega kernel eliminates CPU dependency
|
||
- Modified: `Layer.swift` (removed router waits)
|
||
- Modified: `LayerOptimized.swift` (single command buffer)
|
||
- Result: MoE now outperforms dense models
|
||
|
||
---
|
||
|
||
### 2. Batch Processing Analysis ✓
|
||
**Discovery**: Batch processing is **slower** than single token
|
||
- Single: 29.7ms/token
|
||
- Batch(8): 76.3ms/token (2.6x slower!)
|
||
**Root Cause**: Sequential embedding lookup
|
||
- `BatchGenerationTrue.swift` still has sequential waits
|
||
- Attempted batch kernel but crashed (deferred)
|
||
|
||
---
|
||
|
||
### 3. Model Loading Analysis ✓
|
||
**Discovery**: Shard loading is fast (1ms), layer construction is slow (64s)
|
||
- 31B: 64s total, shard loading: 1.3ms ✓
|
||
- **Real bottleneck**: Layer weight reading (60 layers × ~1s)
|
||
- MoE bottleneck: 128 experts × 30 layers × ~1s = 134s
|
||
|
||
---
|
||
|
||
## ⚠ Identified New Bottlenecks
|
||
|
||
### 1. Layer Weight Loading (63s for 31B)
|
||
**Problem**: Sequential file reads during layer construction
|
||
- Each layer reads weights individually
|
||
- File IO is the bottleneck, not shard opening
|
||
|
||
**Solution**: Parallel weight pre-loading
|
||
- Pre-read all weights before layer construction
|
||
- Expected: 63s → 20s (3x speedup)
|
||
|
||
### 2. MoE Expert Loading (134s hidden cost)
|
||
**Problem**: MoE has 30 layers × 128 experts
|
||
- Each expert needs 3 weight files
|
||
- Sequential reads dominate loading time
|
||
|
||
**Solution**: Parallel expert loading
|
||
- Batch read all experts
|
||
- Expected: 134s → 30s (4.5x speedup)
|
||
|
||
### 3. Batch Embedding Kernel (deferred)
|
||
**Problem**: Current batch embedding kernel crashes
|
||
- Memory access violation
|
||
- Needs careful debugging
|
||
|
||
**Solution**: Fix batch kernel or use sequential (stable)
|
||
|
||
---
|
||
|
||
## Performance Summary
|
||
|
||
**TEXT Generation** (all models optimized):
|
||
```
|
||
E2B: 16.1ms ✓✓✓ (fastest)
|
||
E4B: 24.8ms ✓✓✓
|
||
12B: 36.2ms ✓✓✓
|
||
26B-Standard: 32.8ms ✓✓✓
|
||
26B-A4B MoE: 30.1ms ✓✓✓ (faster than Standard!)
|
||
31B: 79.4ms ✓✓✓
|
||
```
|
||
|
||
**Model Loading** (parallel shard loading implemented):
|
||
```
|
||
Shard Loading: 1.3ms ✓✓✓ (parallel)
|
||
Total Loading: 64s ⚠ (layer construction bottleneck)
|
||
```
|
||
|
||
**Batch Processing**: ⚠ slower than single (sequential embedding bottleneck)
|
||
|
||
---
|
||
|
||
## Next Steps Recommendation
|
||
|
||
**Priority 1**: Layer Weight Loading Optimization
|
||
- ROI: 3x speedup (63s → 20s)
|
||
- Complexity: Medium
|
||
- Implementation: 1-2 days
|
||
|
||
**Priority 2**: MoE Expert Loading Optimization
|
||
- ROI: 4.5x speedup (134s → 30s)
|
||
- Complexity: High
|
||
- Implementation: 2-3 days
|
||
|
||
**Priority 3**: Batch Embedding Kernel Fix
|
||
- ROI: Unknown (stability vs performance)
|
||
- Complexity: High
|
||
- Implementation: 3-5 days
|
||
|
||
---
|
||
|
||
## Files Modified
|
||
|
||
**Successful**:
|
||
- `Layer.swift`: MoE mega kernel integration (lines 969-1036, 1064-1089)
|
||
- `LayerOptimized.swift`: Single command buffer for MoE (lines 20-48)
|
||
- `Model.swift`: Parallel shard loading (lines 119-168)
|
||
|
||
- `MetalKernels.metal`: Batch embedding kernels (lines 1988-2052)
|
||
- `BatchGenerationTrue.swift`: Sequential embedding (fallback)
|
||
|
||
**Created**:
|
||
- `MOE_OPTIMIZATION_COMPLETE.md`: MoE optimization documentation
|
||
- `BATCH_PROCESSING_ANALYSIS.md`: Batch processing bottleneck analysis
|
||
- `LAYER_LOADING_ANALYSIS.md`: Layer loading bottleneck analysis
|
||
- `ModeLoadingOptimizationTest.swift`: Performance tests
|
||
|
||
---
|
||
|
||
## Conclusion
|
||
|
||
**Achievement**: Successfully optimized MoE from 40ms → 30ms (faster than Standard!)
|
||
**Discovery**: Identified 3 new bottlenecks (layer loading, MoE experts, batch embedding)
|
||
**Next**: Optimize layer weight loading for 3x speedup (highest ROI)
|
||
|
||
**Total Progress**: MoE ✓✓✓, Batch ⚠ (identified bottleneck), Loading ⚠ (identified bottleneck) |