CHANGELOG.md

Changelog - NVFP4 Reference Implementation

[1.0.0] - December 3, 2025

Added

Core Implementation

• Complete NVFP4 CPU inference for DeepSeek V3.2 (671B parameters)
• NVFP4 dequantization kernels with dual-level scaling
• Multi-Head Latent Attention (MLA) with NVFP4 support
• Mixture of Experts (MoE) with 256 experts
• FP8 and BF16 fallback support
• Sharded weight loading (73 shards, 391GB)

Test Suite

• test_nvfp4_kernel.py - NVFP4 math unit tests (5 tests, all passing)
• test_model_loading.py - Weight loading integration tests
• test_forward_pass.py - Forward pass validation tests
• test_minimal_generation.py - Token generation tests

Documentation

• README.md - User guide with quick start and examples
• IMPLEMENTATION_SUMMARY.md - Technical implementation details
• COMPLETION_REPORT.md - Project completion summary
• CODE_REVIEW_FIXES.md - Code review documentation
• FINAL_SUMMARY.md - Final project status

Tools

• tools/fp8_to_nvfp4_streaming.py - FP8 to NVFP4 conversion script
• tools/README.md - Quantization tools documentation
• encoding/README.md - Message encoding documentation

Fixed

Critical Bugs

• FP8 scale linking after load_state_dict() (generate.py:213-234)
• NVFP4 attribute safety checks for mixed quantization (model.py:685)
• NVFP4 decode path value projection computation (model.py:674-706)
  • Was computing projections from current token only
  • Now correctly projects attended context from KV cache
  • Verified correct by Claude Opus 4.5 code review

Performance Issues

• Added LUT device caching to eliminate repeated transfers (nvfp4_kernel.py:29-39)
• Added wkv_b weight caching for decode optimization (~2GB cache, ~10% CPU improvement)
• Improved code clarity and comment accuracy

Code Quality

• Added type hints to critical functions
• Removed all emojis (60+ instances) for professional tone
• Removed environment-specific references
• Cleaned up documentation for open-source release

Validated

Testing

• All unit tests passing
• Model loading: 73 shards, 391GB, no errors
• Forward pass: Valid outputs, no NaN/Inf
• Token generation: Natural, coherent output ('你好！👋 ')

Code Review

• Comprehensive review by Claude Opus 4.5 (2 rounds)
• All critical issues identified and fixed
• Mathematical correctness verified
• Performance optimizations validated

Performance

Measured Metrics

• Model loading: 8-10 minutes (73 shards)
• Forward pass: 2-5 minutes (single pass)
• Tokens/second: 0.003-0.01 (CPU reference)
• Memory usage: ~260GB (model + overhead + 2GB cache)
• CPU utilization: ~10% improvement after optimizations

Quantization Quality

• Compression: 16x vs FP32
• Conversion: 30,769 weights, 0 errors
• Mean quantization error: 0.14-1.8
• Relative error: 18-42%
• Output quality: Coherent and semantically appropriate

Technical Details

NVFP4 Format

• E2M1 floating point (4 bits per value)
• 16 representable values: {0, ±0.5, ±1, ±1.5, ±2, ±3, ±4, ±6}
• Dual-level scaling: per-block FP8 E4M3 + global FP32
• Packed storage: 2 values per uint8 byte

Architecture Support

• DeepSeek V3.2 transformer (61 layers)
• MLA with sparse attention indexing
• MoE with 256 routed + 1 shared expert
• FP8 KV cache
• Message encoding for DeepSeek V3.2 format

Known Limitations

• CPU-only (GPU Triton kernels incomplete)
• Slow performance: 2-5 minutes per token (expected for CPU)
• Memory intensive: Requires ~400GB RAM minimum
• Single-sample inference only (no batching)

Future Work

• Complete Triton NVFP4 kernels for GPU acceleration (100-1000x speedup)
• Quality validation against FP8/FP16 baselines
• Perplexity benchmarking
• Batch inference support
• Mixed-precision modes

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Acknowledgments

• Original DeepSeek V3.2 model by DeepSeek AI
• NVFP4 format specification by NVIDIA
• Code review and validation by Claude Opus 4.5
• Quantization tools based on NVIDIA TensorRT Model Optimizer

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License

MIT License (inherited from DeepSeek V3.2)