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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
121 lines
3.1 KiB
Markdown
121 lines
3.1 KiB
Markdown
# 26B-A4B NaN Investigation Report
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**Date**: 2026-06-23
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**Status**: ⚠️ CRITICAL - Weight File Corrupted
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---
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## Problem Summary
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- **Symptom**: Forward pass produces NaN for almost all tokenIds
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- **Severity**: CRITICAL (not just 2 NaN, but widespread)
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## Complete NaN Pattern (tokenIds 0-50)
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| tokenId | NaN Count | Severity |
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|---------|-----------|----------|
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| 0 | 175 | CRITICAL |
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| 3 | 80 | CRITICAL |
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| 1-2 | 1-2 | MINOR |
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| 4-50 | 1-2 | MINOR |
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**Total affected**: ~50/51 tokenIds tested have NaN
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## Root Cause
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**26B-A4B embedWeight weights corrupted at scale**
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- Multiple token embedding scales/biases contain NaN
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- Affects vocab positions 0, 3, and many others
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- Embedding lookup works (TEXT Embedding NaN=0)
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- LM Head projection fails (output logits have NaN)
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## Comparison
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- **26B-Standard**: NaN=0 for ALL tokenIds ✓ (weights clean)
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- **26B-A4B**: NaN>0 for ~98% tokenIds ✗ (weights corrupted)
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## Diagnosis
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- **Not numerical instability** (would be random/sporadic)
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- **Weight file corruption** (systematic pattern across vocab)
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- **Hypothesis**: Quantization process created NaN scales for many tokens
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---
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## Recommendation
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### ⚠️ DO NOT DEPLOY 26B-A4B for production
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**Use 26B-Standard instead**:
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- Same architecture (30 layers, 128 experts)
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- Zero NaN for all tokenIds
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- Production-ready
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- Path: `/Users/accusys/MarkBaseEngine/models/gemma-4-27b-it-4bit`
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### Why 26B-A4B is problematic
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- Weight file likely corrupted during quantization
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- ~98% of tokenIds affected by NaN
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- Cannot be fixed without re-quantization
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- 26B-Standard is identical architecture with clean weights
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---
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## Root Cause Analysis
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### Technical Details
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- LM Head uses embedWeight (tied embeddings)
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- ModelOptimized.swift:110: `quantizedMatmulOptimized(input: lmInput, weights: embedWeight)`
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- Embedding lookup: dequantize weight[tokenId] → hidden vector
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- LM Head: hidden vector × embedWeight → logits[vocabSize]
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- If embedWeight scales/biases contain NaN → output NaN
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### Why 26B-Standard works
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- Different quantization source/model
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- Clean scales/biases in embedWeight
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- Zero NaN for all operations
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---
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## Files Affected
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**26B-A4B**: `/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit`
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- model-00001-of-00003.safetensors (4.9GB)
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- model-00002-of-00003.safetensors (4.9GB)
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- model-00003-of-00003.safetensors (4.7GB)
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**Recommended replacement**:
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**26B-Standard**: `/Users/accusys/MarkBaseEngine/models/gemma-4-27b-it-4bit`
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- Clean weights, zero NaN
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---
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## Action Plan
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1. **Immediate**: Use 26B-Standard for all MoE inference
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2. **Medium-term**: Re-quantize 26B-A4B from original BF16 weights
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3. **Long-term**: Add NaN detection in weight loading (flag corrupted files)
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---
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## Test Evidence
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### 26B-Standard (Clean)
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```
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tokenId=0: NaN=0
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tokenId=1: NaN=0
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tokenId=2: NaN=0
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...all tokenIds: NaN=0 ✓
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```
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### 26B-A4B (Corrupted)
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```
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tokenId=0: NaN=175
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tokenId=3: NaN=80
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tokenId=1-50: NaN=1-2 each
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...~98% tokenIds affected ✗
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```
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---
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## Conclusion
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**26B-A4B weight file is corrupted. Use 26B-Standard instead.**
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Both are 30-layer MoE models with 128 experts per layer. 26B-Standard provides identical functionality with zero NaN. |