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NppAI v0.3.0

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@deser1 deser1 released this 03 Oct 08:44
· 419 commits to main since this release
v0.3.0
cdbec50
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Learn about vigilant mode.

NppAI v0.3.0

NppAI v0.3.0 focuses on model integrity, CPU compatibility, tokenizer reliability, end-to-end testing, and CI performance regression detection.

Added

  • Versioned .nppai model format v2.
  • Explicit model magic value and format version.
  • SHA-256 payload integrity verification.
  • Explicit model payload length metadata.
  • End-to-end generation tests covering:
    • v2 model loading
    • SHA-256 validation
    • BPE tokenization
    • inference
    • generated output
  • Scalar vs SIMD parity tests for:
    • FP32 matrix multiplication
    • INT8 matrix multiplication
    • RMSNorm
  • Extended tokenizer tests covering:
    • UTF-8 round trips
    • source-code whitespace
    • long inputs
    • chained BPE merges
  • CI tensor benchmark regression gate.
  • Per-size FP32 and INT8 performance limits.
  • Benchmark regression diagnostics.

Changed

  • Added runtime AVX2/FMA capability detection on supported x64 Windows systems.
  • Added automatic scalar fallback when AVX2/FMA is unavailable.
  • Removed the global x64 AVX2 compiler requirement.
  • Strengthened model loading with exact tensor payload-size validation.
  • Truncated model payloads are rejected.
  • Model files containing unexpected trailing data are rejected.
  • New training exports use .nppai model format v2.
  • Legacy v1 model files remain supported.
  • Benchmark artifacts are preserved when the CI performance gate fails.

Security & Integrity

  • SHA-256 verification is performed before model state is committed.
  • Exact payload-length validation happens before tensor allocation.
  • Corrupted v2 model payloads are rejected.
  • Unsupported model-format versions are rejected.

SHA-256 provides integrity and corruption detection. It does not provide model authenticity or cryptographic signing.

Performance

NppAI now performs runtime AVX2/FMA detection instead of requiring AVX2 globally.

Supported CPUs can use optimized SIMD kernels, while systems without the required instruction set automatically use the scalar implementation.

CI also monitors FP32 and INT8 tensor performance to detect major performance regressions.

Compatibility

  • Windows x64
  • Runtime AVX2/FMA detection
  • Scalar CPU fallback
  • Legacy .nppai v1 model support
  • .nppai v2 with SHA-256 integrity verification

Known Limitations

  • Legacy v1 models do not contain SHA-256 integrity metadata.
  • SHA-256 does not authenticate the origin of a model.
  • Non-MSVC x86/x64 builds currently use the scalar path instead of runtime AVX2/FMA dispatch.
  • CI benchmark limits are intentionally conservative to account for GitHub-hosted runner variance.

Release

Version: v0.3.0

Release commit:

cdbec50466e149e3589adb3708dd2eb4862a4c79

This release includes the development work completed after NppAI v0.2.0 through the v0.3.0 release preparation.

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