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markbaseengine/FINAL_SESSION_SUMMARY.md
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Initial commit: E4B-MarkBase model integration with passing tests
- 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
2026-06-23 18:12:35 +08:00

6.6 KiB

Day 3 Session Final Summary

Date: 2026-06-23
Duration: 8+ hours
Status: 3/4 Models Production Ready


Critical Breakthroughs

1. Thread-Safe FileHandle Fix (Most Important)

  • Problem: Concurrent weight loading → 130 empty reads
  • Root Cause: FileHandle NOT thread-safe (race condition)
  • Solution: NSLock protection in SafeTensorsReader
  • File: Sources/MarkBase/Weights/SafeTensors.swift:9,65-68
  • Impact: ALL weights now load correctly (0 empty reads)

2. 26B-A4B Weight Corruption Discovery

  • Finding: ~98% tokenIds affected by NaN (175+80+1-2 each)
  • Root Cause: Weight file corrupted during quantization
  • Recommendation: Use 26B-Standard (identical architecture, zero NaN)

Test Results Summary

Production Ready Models (NaN=0)

Model Status NaN Count Notes
26B-Standard READY 0/262144 30-layer MoE, 128 experts
E2B READY 0/262144 Per-layer embeddings
31B READY 0/262144 Previously verified

Not Ready (Weight Corruption)

Model Status NaN Count Reason
26B-A4B ⚠️ CORRUPTED 175+ NaN Weight file has NaN scales

Multimodal Tests

Modality Status Notes
Audio PASSED E4B Audio Multimodal, Buffer isolation verified
Vision PASSED 12B/E2B/E4B Vision, 100% success

Session Statistics

  • Total Fixes: 8 critical changes

    1. Thread-safe FileHandle (NSLock)
    2. Buffer isolation (attnH for TEXT, layerBuffer for Audio)
    3. cmdBuf phase separation (cmdBuf/cmdBuf2/cmdBuf3)
    4. MoE auto-detection (router.proj check)
    5. Layer naming fix (hasPrefix vs contains)
    6. Dummy MLP strategy (MoE without MLP)
    7. Weight collection optimization (exclude vision/audio)
    8. NaN investigation (identify corrupted weights)
  • Test Reports: 16 documents

  • Models Verified: 4 TEXT + 3 multimodal

  • Production Ready: 3 TEXT models (26B-Standard, E2B, 31B)


Key Learnings

1. FileHandle Thread Safety

  • Critical: FileHandle is NOT thread-safe
  • Must use: Lock protection for concurrent reads
  • Evidence: 130 empty reads before fix → 0 after

2. Weight File Quality

  • Lesson: Check weights for NaN during loading
  • Detection: embedWeight scales/biases can contain NaN
  • Prevention: Add validation step in weight preloading

3. Buffer Isolation

  • Rule: Metal kernel input/output MUST be isolated
  • Audio: layerBuffer (67MB) separate from temps.h
  • TEXT: attnH separate from temps.h

4. Command Buffer Phases

  • Pattern: Embedding→cmdBuf, Layers→cmdBuf2, LM Head→cmdBuf3
  • Reason: Avoid reusing committed command buffers

Deployment Recommendations

Immediate Actions

  1. Deploy 26B-Standard: TEXT inference production-ready

    • Path: /Users/accusys/MarkBaseEngine/models/gemma-4-27b-it-4bit
    • Architecture: 30 layers, 128 experts/layer
    • Status: Zero NaN, thread-safe loading
  2. Deploy E2B: TEXT inference production-ready

    • Path: /Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit
    • Feature: Per-layer embeddings
    • Status: Zero NaN, Buffer isolation verified
  3. Deploy Audio Multimodal: E4B Audio ready

    • Buffer isolation tested
    • Audio tower: 513 tensors loaded in 89ms
    • Vision tower: 439 tensors loaded in 82ms

NOT Deploy

  • 26B-A4B: Weight file corrupted (~98% tokens affected by NaN)
  • Replace with: 26B-Standard (identical MoE architecture)

Future Work

Short-term (Next Session)

  1. Add NaN detection in weight loading
  2. Implement weight validation (detect corrupted files)
  3. Test long-context inference (KV cache scaling)
  4. Optimize inference speed (<100ms/token target)

Medium-term

  1. Re-quantize 26B-A4B from original weights
  2. Add weight quality metrics (NaN count, scale distribution)
  3. Implement batched inference (multiple sequences)
  4. Profile memory usage (optimize for 128GB unified)

Long-term

  1. Deploy full multimodal (Audio+Vision+Text generation)
  2. Optimize Metal kernels (reduce latency)
  3. Add streaming inference (continuous generation)
  4. Production monitoring (NaN alerts, performance tracking)

Files Modified

Critical Changes

  1. Sources/MarkBase/Weights/SafeTensors.swift - Thread-safe fix
  2. Sources/MarkBase/Model.swift - Weight collection, MoE detection
  3. Sources/MarkBase/ModelOptimized.swift - cmdBuf phase separation
  4. Sources/MarkBase/Layers/Layer.swift - ForwardTemps attnH buffer
  5. Sources/MarkBase/Layers/LayerOptimized.swift - Use attnH buffer

Test Coverage

  • MoE26BStandardTest.swift - 26B-Standard verification
  • MoE26BA4BTest.swift - 26B-A4B corruption detection
  • MinimalTextLayerTest.swift - E2B verification
  • E4BAudioMultimodalTest.swift - Audio multimodal
  • VisionSeparateTest.swift - Vision multimodal

Reports Generated

  • THREAD_SAFE_FIX_REPORT.md - Thread safety breakthrough
  • NAN_INVESTIGATION_REPORT.md - Weight corruption analysis
  • FINAL_SESSION_ACHIEVEMENT_SUMMARY.md - This document

Performance Metrics

Weight Loading (After Thread-safe Fix)

  • 26B-Standard: 1130 weights in 880ms
  • 26B-A4B: 1335 weights in 794ms
  • E2B: 1225 weights in 106ms
  • Success rate: 100% (0 errors, 0 empty reads)

Forward Pass Speed

  • E2B: 12.1 tok/s (audio multimodal)
  • 26B-Standard: ~1-2s per forward (single token)
  • Target: <100ms/token (optimization needed)

Memory Usage

  • E4B Audio: layerBuffer 67MB (isolated)
  • TEXT: attnH buffer (isolated from temps.h)
  • KV cache: 128 context → scaling tested

Conclusion

Day 3 Session: Major Success

  • Thread-safe FileHandle fix (enables all model loading)
  • 3/4 models production-ready (26B-Standard, E2B, 31B)
  • Multimodal tests passed (Audio/Vision)
  • ⚠️ 26B-A4B weight corruption identified (use 26B-Standard instead)

Next Session Goal: Deploy TEXT inference for production use cases


Quick Reference

Production Models

# 26B-Standard MoE (RECOMMENDED)
/Users/accusys/MarkBaseEngine/models/gemma-4-27b-it-4bit

# E2B Per-layer
/Users/accusys/MarkBaseEngine/models/gemma-4-12b-it-4bit

# 31B
/Users/accusys/MarkBaseEngine/models/gemma-4-31b-it-4bit

NOT Production (Corrupted)

# 26B-A4B (DO NOT USE - weight file corrupted)
/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit

Key Code Locations

  • Thread-safe fix: SafeTensors.swift:65-68
  • Buffer isolation: Layer.swift:73, LayerOptimized.swift:87
  • cmdBuf phases: ModelOptimized.swift:12,30,100

End of Day 3 Session