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Changelog

[Unreleased]

[0.19.1] - 2026-04-21

Fixed

  • Broken POM for skainet-backend-cpu: The 0.19.0 POM for sk.ainet.core:skainet-backend-cpu-* declared a runtime dependency on sk.ainet:skainet-backend-api-jvm:unspecified — wrong group coordinate and no valid version, because skainet-backend-api was not configured to publish and the root allprojects { group = "sk.ainet" } disagreed with the GROUP=sk.ainet.core used by vanniktech's maven publish plugin. Consumers pulling 0.19.0 hit unresolved-dependency errors. Fixed by:
    • Applying vanniktech.mavenPublish and setting POM_ARTIFACT_ID=skainet-backend-api on skainet-backend-api so it is actually published alongside the BOM entry that already referenced it.
    • Aligning allprojects { group = "sk.ainet.core" } with the GROUP property and pinning version from VERSION_NAME so project(...) coordinates in generated POMs are consistent.
  • CI guard: New verify-published-poms job publishes to the local Maven repository and fails the build if any generated .pom contains <version>unspecified</version> or references a project-local group outside sk.ainet.core, preventing a regression of this class of coordinate bug.

[0.19.0] - 2026-04-20

Added

Tokenizers

  • Qwen / GPT-2 Byte-Level BPE Tokenizer: QwenByteLevelBpeTokenizer implements the full GPT-2-style pipeline — byte-to-unicode mapping, GPT-2 pretokenization regex, merge-rank BPE, and atomic special-token splitting. Builds from either GGUF metadata (fromGgufFields) or a HuggingFace tokenizer.json (fromTokenizerJson). Verified against Qwen2.5-0.5B reference token IDs from HuggingFace transformers. (#463)
  • LLaMA / SentencePiece Tokenizer: SentencePieceTokenizer implements the llama.cpp SPM pipeline — whitespace escape (▁), code-point symbol split, score-priority BPE (the SPM rule, opposite of the merge-rank rule used for GPT-2 BPE), and <0xNN> byte fallback for unknown characters. Builds from GGUF (tokenizer.ggml.model == "llama") and HuggingFace tokenizer.json (model.type == "Unigram"). Verified against TinyLlama-1.1B reference token IDs from HuggingFace transformers. (#464)
  • TokenizerFactory with Per-Architecture Dispatch: Tokenizer selection is now per-architecture, not per file format. TokenizerFactory.fromGguf(fields) and .fromTokenizerJson(json) inspect tokenizer.ggml.model / model.type and dispatch to the right implementation — Qwen/GPT-2 → byte-level BPE, LLaMA/Gemma/TinyLlama → SentencePiece — regardless of whether weights come from GGUF or SafeTensors. (#463)
  • Tokenizer Interface: Common surface implemented by TekkenTokenizer, QwenByteLevelBpeTokenizer, and SentencePieceTokenizer (encode, decode, vocabSize, bosTokenId, eosTokenId).
  • GGUF Tokenizer Metadata: GgufModelMetadata now exposes tokenizerModel, tokenizerTokens, tokenizerMerges, tokenizerTokenTypes, bosTokenId, and eosTokenId so callers can build a tokenizer without re-parsing the raw field map.

StableHLO → IREE compilation

  • Whisper Encoder E2E: Whisper encoder now compiles end-to-end via SKaiNET → StableHLO → IREE.
  • Real StableHLO Lowerings: softmax, layerNorm, and rmsnorm now lower to real StableHLO ops (reductions, broadcast_in_dim, standard ops) instead of custom_call stubs. (#467, #479, #480)
  • New Op Converters: gather / embedding, and concat / slice / cast StableHLO converters. (#483, #489)
  • Activation Alias: silu / SiLU registered as an alias for swish in ActivationOperationsConverter. (#484)
  • ConstantMaterializationPolicy: Seam for externalizing large weight tensors out of the StableHLO module (enables .irpa externalization). (#524)
  • Splat Constant Folding: Uniform-value tensor constants collapsed to dense<v> splat instead of fully materialized arrays. (#522)
  • SSA Value Type Tracking: Tracks SSA value types so reshape emits the operand's declared type, producing valid MLIR. (#521)
  • Tensor Encoding in Output: tensor_encoding comments in StableHLO output and a top-level skainet.tensor_encodings module attribute. (#473, #477)

IREE .irpa weight files

  • skainet-io-iree-params Module: New module with IrpaWriter for writing IREE Parameter Archive (.irpa) files. Accepts FileBacked handles via mmap on JVM / Android for zero-copy weight export. (#523, #525, #528, #529)

Backend API

  • skainet-backend-api Module: New module cleanly separating backend contracts; CPU backend now depends on it. (#468)
  • TensorEncoding Metadata: Accessor for TensorSpec.metadata and propagation through TraceToGraphBuilder.finalize, keeping quantization encoding visible end-to-end. (#469)

Java API (0.19.0 surface polish)

  • Annotated StableHloConverterFactory and TokenizerFactory for idiomatic Java call sites. (#400)
  • Renamed TensorSpecEncoding.kt class for Java callers. (#400)
  • Added skainet-backend-api to the BOM. (#400)
  • New ReleaseApiJavaTest covering the 0.19.0 Java surface. (#400)

Docs (Antora migration)

  • Antora + Diátaxis: Migrated docs to Antora with Divio / Diátaxis layout (tutorials, how-tos, reference, explanation). (#494)
  • skainet-docs-ui v1.1.1: Adopted the new theme with Diátaxis card-grid landing page. (#501)
  • Operator Coverage Matrix: Emit cross-backend Operator Coverage Matrix generated from TensorOps surface scan. (#494, #511)
  • Ops Docs: KDoc @param extraction, real version stamps, LaTeX rendering, fixed partials, and dropped void backend. (#511, #513)
  • Dokka API Bundle: Wired into the Antora site build. (#494)
  • Local Mermaid: Drop kroki, render Mermaid locally via mmdc. (#496)

Platform targets

  • androidNativeArm32: Added across core modules. (#503)

Fixed

  • Byte-Level BPE Broken for Qwen/GPT-2 Models: Previously there was no GPT-2-style byte-level BPE tokenizer in the repo, and GgufModelMetadata ignored tokenizer.ggml.merges entirely — so any Qwen / GPT-2 / Mistral-Nemo model encoded text into garbage tokens (byte-level chars instead of merged vocab IDs), blocking chat mode and tool calling. The new QwenByteLevelBpeTokenizer + TokenizerFactory dispatch fix the issue for both GGUF and SafeTensors sources. (#463)
  • No SentencePiece Path for LLaMA-Family GGUF Models: TokenizerFactory previously threw UnsupportedTokenizerException for tokenizer.ggml.model == "llama", leaving LLaMA / TinyLlama / Gemma / Mistral-v0.1 GGUFs untokenizable. The new SentencePieceTokenizer closes that gap. (#464)
  • GGUF UInt Fields Silently Dropped: GGUF UINT32 fields (e.g. tokenizer.ggml.bos_token_id) arrive from StreamingGGUFReader as kotlin.UInt, which is a value class — not a subclass of kotlin.Number — so a plain as? Number cast was returning null. The new toIntFlexible helper handles every signed and unsigned numeric type GGUF can produce, restoring the BOS/EOS/UNK ids on the tokenizer builders.
  • Graph Conv Output Shape Inference: conv1d / conv2d / conv3d operations in graph inference previously produced placeholder output shapes, breaking downstream shape-dependent passes. Graph ops now compute real output shapes. (#536, #537)
  • Conv1d/Conv3d Not Recorded: conv1d and conv3d were not routed through the recording decorator, so they disappeared from traced computation graphs. (#532, #533)
  • Static Conv1d HLO Shape Crash: Conv1d StableHLO lowering crashed when trace attributes were missing; now falls back to TensorRef shape / dtype. (#530, #531)
  • Flatten Hardcoded to MNIST Shape: NetworkBuilder.flatten() returned a hardcoded lastDimension = 1568 (the MNIST CNN value); any other architecture — e.g. a 64-channel CNN over 32×32 inputs — crashed with ArrayIndexOutOfBoundsException in the following dense() layer. The DSL now tracks per-sample shape through a new input(IntArray) overload, conv1d / conv2d / conv3d, maxPool2d, avgPool2d, and upsample2d, reusing the ConvShapeUtils arithmetic introduced in #537; flatten() reads the tracked shape and honors startDim / endDim, and Conv* layers can auto-infer inChannels from the declared input. (#535, #538)
  • StableHLO transpose / dot_general MLIR Emission: Fixed malformed MLIR produced by stablehlo.transpose and stablehlo.dot_general that blocked IREE compilation. (#520)
  • WasmJS / JS / Native Compile: Replaced JVM-only putIfAbsent with a common-stdlib idiom. (#485)
  • Antora Container: HOME=/tmp so Chromium crashpad can launch during Mermaid rendering in CI. (#534)
  • bundleDokkaIntoSite CI Permission Failure: Fixed docs pipeline permission error. (#496)
  • Pandoc Artifacts in Docs: Stripped pandoc anchors and demoted heading levels in migrated pages. (#496)

Changed

  • compile-hlo Dependencies: Dropped vestigial skainet-backend-cpu dependency from compile-hlo jvmMain. (#472)
  • Moved-LLM Docs: Replaced relocated LLM pages with redirect stubs pointing at the standalone repo. (#499)
  • Maven Group / Version Refs: Bumped stale version references and fixed Maven group coordinates. (#499)

Removed

  • Stale TURBOQUANT_ISSUES.md tracker at the repo root. (#490)

Dependencies

  • agp: 9.1.0 → 9.1.1.
  • com.networknt:json-schema-validator: 3.0.1 → 3.0.2.
  • org.jetbrains.kotlinx:kotlinx-serialization-json: bumped to 1.11.0.
  • actions/checkout: 4 → 6.
  • actions/upload-pages-artifact: 3 → 5.
  • actions/cache: 4 → 5.
  • actions/setup-java: 4 → 5.
  • actions/deploy-pages: 4 → 5.
  • actions/github-script: 8 → 9.
  • docker/build-push-action: 5 → 7.
  • docker/setup-buildx-action: 3 → 4.

[0.18.0] - 2026-04-08

Added

  • TurboQuant KV-Cache Compression: Runtime KV-cache compression for LLM inference using rotation-based quantization (Google Research TurboQuant paper). Supports PolarOnly and PolarPlusQjl variants with 2/3/4/8-bit encoding.
    • TurboQuantCodec: End-to-end encode/decode pipeline (random rotation, scalar quantization, QJL residual, bit-packing).
    • TurboQuantKvCacheStore: Compressed KV cache with per-head TurboQuant blocks and asymmetric K/V policies.
    • TurboQuantPresets: Named presets — safe-lowbit (Q8_0-K + TQ4-V), balanced (TQ4/TQ4), experimental-max (TQ3/TQ3).
    • KvCacheStore.turboQuant("balanced", ...): One-line factory for skainet-transformers integration.
    • CompressedKvAttention: SDPA bridge with FULL_TILE and RAW_STORAGE dequant strategies.
    • @KvCache and @KvCacheBypass DSL annotations for declarative KV cache configuration.
    • KvCacheAnnotationResolver: Resolve annotations to cache instances.
    • TurboQuantUsage: Documented integration guide with compilable examples.
  • Memory Architecture Hardening: First-class storage and placement abstractions for zero-copy, quantization-preserving tensor management.
    • TensorStorage: Runtime descriptor replacing ad-hoc array passing (logical type, physical encoding, buffer ownership, placement).
    • TensorEncoding: Sealed hierarchy — Dense, Q4_K, Q8_0, TernaryPacked, TurboQuantPolar, TurboQuantPolarQjl, Opaque.
    • BufferHandle: Five ownership modes — Owned, Borrowed, Aliased, FileBacked, DeviceResident.
    • Placement: Device/memory-domain intent with fallback policies (CPU_HEAP, MMAP_WEIGHTS, GPU_PREFERRED).
    • LogicalDType: Semantic numeric types separate from physical encoding.
    • PackedBlockStorage: Unified contract for all packed quantized formats.
    • MemoryPlanner, MemoryTracker, ActiveMemoryTracker: Placement resolution and copy diagnostics.
  • KV-Cache Subsystem: KvCacheStore interface with append-by-token writes, layer/head addressing, eviction, and DefaultKvCacheStore (dense FP32 baseline).
  • Quantization-Preserving Loaders: StreamingGGUFReader and StreamingSafeTensorsReader produce TensorStorage with FileBacked or Borrowed handles (no forced densification).
    • StorageAwareSafeTensorsLoader: Zero-copy file-backed SafeTensors loading.
    • Completed Quants.kt port: byteShapeToQuantShape, quantByteSize, isBlockQuantized, validateQuantizedBytes.
  • Tekken Tokenizer: Mistral Tekken (tiktoken-based BPE) tokenizer support.
  • CPU SIMD TurboQuant Kernels: JvmTurboQuantKernels with Java Vector API acceleration for abs-max, quantize, dequantize, and Walsh-Hadamard butterfly.
  • JMH Benchmarks: TurboQuant encode/decode throughput, bit-packing, rotation, and KV cache append/read benchmarks (TurboQuantBenchmarks.kt).
  • Storage Benchmarks: Dequantization throughput (Q4_K, Q8_0, Ternary), buffer accessor, and TensorData bridge benchmarks (StorageBenchmarks.kt).
  • New Ops: sin, cos, tanh, convTranspose1d.
  • New Layers: TransposedConv1d, Snake activation, LayerScale.

Changed

  • Streaming GGUF as Default: StreamingGGUFReader is now the recommended GGUF loading path (memory-efficient, supports quantized types).
  • DSL Annotations: Extended PlacementAnnotations.kt with @KvCache(preset=...) and @KvCacheBypass for TurboQuant configuration.

Fixed

  • Int Overflow for Large Tensors: Fixed StreamingTensorInfo.nBytes and StreamingSafeTensorInfo.sizeInBytes from Int to Long, preventing silent overflow for tensors > 2 GB. Fixes loading of Gemma 4 E4B and future large models. (#452)
  • Legacy GGUFReader Overflow Guard: Added explicit overflow check with actionable error message for tensors > 2 GB in the legacy eager loader.

Dependencies

  • io.github.kotest:kotest: 6.1.9 → 6.1.11.
  • com.squareup:kotlinpoet: 2.2.0 → 2.3.0.

[0.17.0] - 2026-03-25

Added

  • Core Engine Focus: Refactored the repository to focus on the core ComputeGraph framework, compiler, and backends.
  • Standalone Ecosystem: Extracted high-level LLM and transformer implementations to dedicated repositories (SKaiNET-LLM and SKaiNET-transformers).
  • LLM-as-DSL: High-level DSL for defining and running LLM architectures within the core ComputeGraph framework.
  • ComputeGraphExecutor: New optimized executor with support for fusion passes and trace-to-DAG bridging.
  • SDPA & Gather: Implementation of Scaled Dot-Product Attention (SDPA) and gather/indexSelect ops across backends.
  • EmbeddingAdapter: Streamlined embedding layer integration for transformer models.

Changed

  • Optimized LLM execution: Integrated fusion passes for faster inference on supported backends.
  • Improved Tensor API: Refined Tensor interface and updated ComputeGraphExecutor for better type safety and performance.
  • Dependency Cleanups: Removed stale references to LLM and transformer code already moved to the standalone skainet-transformers repository.

Fixed

  • Embedding Padding: Fixed paddingIdx handling in embedding layers.
  • Concatenation: Resolved rank-specific issues in tensor concatenation (rank > 1).
  • Compilation: Fixed various build and compilation errors after module migrations.

[0.16.0] - 2026-03-08

Added

  • Deduplicated LLM infrastructure: unified KvCache, softmax, RoPE, and sampling logic across modules for improved maintainability.
  • Updated skainet-bom: Refactored the Bill of Materials (BOM) to use local project() references for better build consistency.

Changed

  • LLM Module Extraction: Extracted and moved core LLM modules to the standalone SKaiNET-LLM repository to reduce core codebase footprint.
  • Transformer Code Cleanup: Removed redundant code that has been moved to the SKaiNET-transformers repository.

Fixed

  • Dependency Graph: Resolved inverted dependency issues in the LLM infrastructure.

[0.15.3] - 2026-03-07

Added

  • System Prompt Support (Java): Added systemPrompt support to KLlamaJava and KLlamaSession for prepending system instructions to conversations.
  • Model Module Extraction: Extracted model-specific code into dedicated skainet-models modules for better separation of concerns and maintainability.
  • Enhanced Smoke Tests: Refactored smoke-test.sh to support multiple runners via JSON configuration and improved LLM loading verification.

Fixed

  • Whisper HLO Generation: Fixed StableHLO MLIR generation for Whisper models.
  • Compilation: Fixed various Kotlin/JVM compilation errors.

[0.14.0] - 2026-03-03

Added

  • First-Class Java 21+ Support: Complete Java API surface with SKaiNET entry point, TensorJavaOps, builder-pattern model definition (SequentialModelBuilder), KLlamaJava/KBertJava facades, JavaAgentLoop for tool-calling agents, and TrainingLoop builder.
  • Maven BOM: New sk.ainet:skainet-bom artifact for one-line version management across all modules.
  • Java Documentation: Added Getting Started, LLM Inference, and Model Training guides.
  • Java 25 Performance Documentation: Added documentation for JVM CPU backend performance advantages.
  • WasmWasi Target: Added wasmWasi target support across all KMP modules.
  • StableHLO MLIR Streaming API: New HloGenerator public API with generic Model + Tensor interface and streaming MLIR output.
  • ReductionOperationsConverter: Added support for reduction operations in StableHLO export.
  • JVM Performance (Jlama Techniques): MemorySegment-based tensors, SIMD GEMM kernels, paged KV cache, batch attention for prompt prefill, fused QKV projections, and cached quantized weights.
  • Native RandomAccessSource: POSIX pread()-based source for memory-efficient GGUF parsing.
  • MemorySegment Weight Conversion: New NATIVE_OPTIMIZED quant policy and MemSegWeightConverter pipeline with Arena lifecycle management.
  • Lazy Transpose: Added lazy transpose for Q4/Q8 MemorySegment tensors and MemSeg FP32 transpose.
  • Java CLI App: New Java-based KLlama CLI application.

Changed

  • Android KMP Plugin Migration: Migrated Android subprojects to androidMultiplatformLibrary plugin for AGP 9 compatibility.
  • Refactored Model Loading: Extracted shared dequantization, registry, tensor naming, and decoder runtime into reusable components.
  • JDK Requirement Relaxed: Allow JDK >= 21 instead of requiring exactly JDK 21.
  • Gradle Upgrade: Updated to Gradle 9.3.1.
  • Kotlin Upgrade: Bumped Kotlin from 2.2.21 to 2.3.10.
  • Kotlin Compile Testing: Replaced abandoned kotlin-compile-testing with kctfork for Kotlin 2.3.0 compatibility.

Fixed

  • StableHLO MLIR Export: Fixed MLIR export to produce valid IREE-compilable output.
  • OOM in Dequantization Benchmark: Fixed out-of-memory in DEQUANTIZE_TO_FP32 E2E benchmark test.
  • Quantized MatMul: Fixed block offset calculation in quantized matrix multiplication.
  • CI Stability: Fixed AAPT2 daemon crashes and improved Android build stability.
  • Documentation CI: Fixed workflow permissions for PR comments.
  • Deprecated API Usage: Fixed createTempDir() deprecation in data-simple integration tests.

Dependencies

  • com.gradleup.shadow: 9.3.1 → 9.3.2.
  • com.fasterxml.jackson.core:jackson-databind: 2.21.0 → 2.21.1.
  • ch.qos.logback:logback-classic: 1.5.27 → 1.5.32.
  • io.github.kotest:kotest: 6.1.3 → 6.1.4.
  • org.jetbrains.kotlinx:kotlinx-io-core: 0.8.2 → 0.9.0.
  • com.vanniktech.maven.publish: → 0.36.0.
  • org.jetbrains.kotlinx.kover: → 0.9.7.
  • actions/setup-node: 4 → 6.
  • actions/upload-artifact: 6 → 7.
  • actions/download-artifact: 7 → 8.
  • junit-platform-launcher added for CI test execution.

Contributors

Thank you to the following contributors for their work on this release:

  • Dhia Chemingui (@dhiaspaner) — Android KMP plugin migration (#385, #386)

[0.13.0] - 2026-02-12

Added

  • Tool Calling: Added support for tool calling in KLlama, including a new skainet-kllama-agent module.
  • Gemma 3n Support: New skainet-kgemma module for Google's Gemma 3n E2B multimodal models.
  • Extended SafeTensors Support: Added SafeTensors weight loading support for both KLlama CLI and Gemma models.
  • HuggingFace Tokenizer: Initial support for HuggingFace-style tokenizers in Gemma models.

Changed

  • Named Arguments: Refactored various internal APIs to use named arguments for better optional parameter support.
  • System Prompt Handling: Improved system prompt formatting and handling in agentic workflows.

[0.12.0] - 2026-02-10

Added

  • BERT Support: Full support for BERT-based models with SafeTensors weight loading.
  • kbert-cli: New CLI tool for running BERT inference, supporting text encoding and cosine similarity computation.
  • WordPiece Tokenizer: Implementation of WordPiece tokenizer for BERT models.

[0.11.0] - 2026-02-08

Added

  • TinyFoA Support: Implemented missing operators (abs, sign, clamp, lt, ge, narrow, pad2d, unfold) to support TinyFoA (AAAI 2025) training pipeline for memory-efficient on-device learning.
  • Multi-platform KLlama: Added macOS target support for the KLlama runtime.
  • Custom Backends Documentation: Added detailed guide and examples for injecting custom backends into KLlama.

Fixed

  • Improved robustness of TinyFoA operations with comprehensive unit tests.

[0.10.1] - 2026-02-01

Added

  • Benchmarking DSL: New BenchmarkDsl and BenchmarkRunner for measuring model performance and latency.
  • Execution Observers: Added ExecutionObserver API with LatencyExecutionObserver and MemorySnapshotObserver for profiling.
  • New Layers: Added RMSNormalization layer support.
  • KLlama Enhancements: Improved weight loading and initial support for GPU-accelerated attention (experimental).

Changed

  • Refactored ExecutionContext to support execution observers and better phase management.
  • Updated KLlama runtime with improved ingestion and benchmarking utilities.

[0.9.2] - 2026-01-27

Added

  • Generative AI Section: New README section with simple code for GGUF text generation.
  • Tokenizer Strategies: Automatic detection of tokenizer type (SentencePiece, BPE, WordPiece) from GGUF metadata.
  • Improved Token Decoding: Support for multi-byte UTF-8 character decoding from byte tokens.

Changed

  • Llama Runtime: Rewritten matmulNoBias for better performance and support for row-major weights.
  • GGUF Loading: Improved dequantization for Q2_K, Q4_K, Q5_K, and Q6_K formats matching llama.cpp logic.

Fixed

  • GGUF Storage Order: Fixed critical bug with column-major storage in GGUF files by implementing proper transposition during loading.
  • Llama Attention: Fixed missing attention output projection (wo) in the runtime.
  • Tokenizer: Fixed BOS token handling and multi-byte character reconstruction.

[0.9.1] - 2026-01-26

Added

  • SafeTensors Support: Initial implementation of skainet-io-safetensors for reading SafeTensors format.
  • Generalized I/O & Weight Mapping:
    • New WeightMapper and WeightLoader APIs for unified model parameter loading across formats.
    • LoadingProgress API for tracking model loading state.
    • GgufModelMetadata and OnnxModelMetadata for better inspection of model files.
  • JVM Performance: Enhanced DefaultCpuOpsJvm with JvmVectorKernels for SIMD-accelerated tensor operations using the Java Vector API.
  • Llama Enhancements:
    • Added GGUFTokenizer for better text processing.
    • Improved LlamaIngestion and ingestion pipelines.

Changed

  • Improved GGUF/ONNX Loading: Robust weight loading and metadata parsing for GGUF and ONNX models.
  • Streamlined CLI: Removed unfinished CLI samples and reorganized skainet-tensor-tools.
  • Documentation Cleanup: Removed outdated technical docs and consolidated architecture information.

Fixed

  • Improved robustness of GGUF and ONNX streaming readers.
  • Fixed various issues in WASM/JS weight parsing.

[0.8.3] - 2026-01-18

Changed

  • Updated version to 0.8.3.

[0.8.2] - 2026-01-18

Added

  • KLlama (Llama 2 port): Initial version ported from llama2-kmp, supporting GGUF models.
  • GGUF Enhancements:
    • Support for mmap for zero-copy GGUF tensor loading.
    • Embedded tokenizer support in GGUF.
    • New quantization formats: Q8_0, Q4_K, and BitNet/Ternary support (TQ1_0, TQ2_0).
    • Improved loading and bug fixes for quantization and mapping.
    • Added int64 support for GGUF.
    • Improved GGUF metadata loading.
  • Streaming Support: Added streaming support for GGUF and ONNX models.
  • Advanced Operations:
    • New activations: LeakyReLU, ELU.
    • New pooling: AvgPool2d.
    • New convolutions: Conv1d, Conv3d.
  • Optimizers & Training:
    • Added Adam and AdamW optimizers.
    • Comprehensive loss function library.
    • New Metric interface with Accuracy implementation.
    • KSP-based DSL generator for Network activations.
  • Data & Datasets:
    • Support for CIFAR-10 and Fashion-MNIST datasets.
    • New Data Transform API and Image Transform DSL.
  • Testing & Documentation:
    • skainet-test-groundtruth module for validation against PyTorch.
    • Integration tests for quantized inference and KvCache.
    • Shadow JAR support for JVM fat JAR builds.
    • New documentation for testing architecture with Mermaid diagrams.
  • WASM/JS: Initial version of a simple WASM/JS sample.

Changed

  • Simplified model support to GGUF-only (removed legacy Karpathy .bin format support).
  • Improved KLlama loading and robustness.
  • Updated roadmap with Phase 1 completion and multi-backend storage abstraction plans.
  • Improved I/O system and overall robustness.

Fixed

  • Fixed various bugs in quantization and memory mapping.
  • Resolved compilation errors and failing tests in CIFAR-10 support.
  • Fixed KSP and TracingWrapperProcessor tests to match updated log messages.
  • Fixed GGUF metadata loading issues.

[0.8.1] - 2026-01-18

  • Initial release of 0.8.x series.

[0.7.1] - 2026-01-14

Added

  • Sine Approximation CLI (skainet-sine-approx-cli) as a new example application for training models.
  • TapeRecordingStrategy to handle different recording behaviors for prediction and backpropagation.
  • Comprehensive E2E tests for training sine wave approximations.
  • New documentation: autograd-basic.md explaining the autograd engine.

Changed

  • Refined Linear, Flatten, Input modules and relu activation to better support gradient tracking and context propagation.
  • Improved DefaultExecutionTape and DefaultGraphExecutionContext for more robust computation tracing.
  • Optimized internal OpSink and TraceSession handling.

Fixed

  • Infinite loop error during backpropagation tracing by implementing specialized tape recording strategies.
  • Context mismatch errors in backpropagation tracing.
  • Broken testing in the sinus sample application.

[0.7.0] - 2026-01-14

Added

  • Initial Autograd engine (DefaultGradientTape) for automatic differentiation and reverse-mode gradients.
  • Optimizer API with SgdOptimizer implementation for training neural networks.
  • Loss functions module including MSELoss and CrossEntropyLoss with configurable reductions (MEAN, SUM, NONE).
  • Training DSL and helper utilities for building training loops (trainStep, evaluateLoss).
  • Improved Graph DSL with better context propagation and support for recording computation traces.

Changed

  • Updated dependency versions and refined internal execution context APIs to support gradient tracking.
  • Refactored skainet-compile-dag to support autograd and graph inversion.

[0.6.0] - 2025-12-31

Added

  • StableHLO implementation and E2E CLI app for compiling models to CUDA via IREE.
  • ArduinoCodegen for exporting models to standalone C99 code with static memory allocation, optimized for Arduino.
  • KSP-based generation of TracingOps for automated recording pipeline updates.
  • Initial implementation of skainet-compile-hlo for high-level optimization.

Changed

  • Improved CUDA backend strategy and IREE integration.
  • Optimized long-running property tests for C code generation.
  • Refactored TracingTensorOps to use execution context for code generation.

[0.5.1] - 2025-12-26

Added

  • Common I/O abstraction with ModelReader and TensorInfo in skainet-io-core for unified model loading.
  • Efficient memory handling with non-copying slice views in MemoryChunk.
  • Unified skainet-tensor-tools CLI combining ONNX and GGUF utilities.
  • OnnxStatsCli tool for analyzing ONNX model parameters and structure.

Changed

  • Migrated project to SKaiNET-developers organization; updated repository URLs and deployment configurations.
  • Standardized artifact naming in documentation (e.g., SKaiNET-lang-core).
  • Improved GGUFReader with better alignment parsing and tensor data handling.
  • Optimized test infrastructure: increased heap size to 8GB for large model tests and added ReadmeSnippetsTest for documentation verification.

Removed

  • Legacy standalone applications and tools: skainet-KGPChat, skainet-mnist, and separate ONNX/GGUF tool modules.

[0.5.0] - 2025-12-06

Added

  • ONNX import module (skainet-io-onnx) with pbandk-generated proto surface, loader utilities, and importer that maps ONNX graphs into SKaiNET compute graphs, plus doc and tests.
  • CLI tooling: skainet-onnx-tools to export ONNX initializers to JSON and skainet-onnx-detect CLI to run YOLO detections from ONNX weights.
  • YOLOv8 model upgrades: depth/width scaling, decoupled heads with DFL projection, class-name parsing, and detection helpers to align with ONNX exports.
  • Image IO module now published with explicit API surface for bitmap <-> tensor conversions across platforms.

Changed

  • BatchNorm now reshapes stats for broadcasting and exercises JVM/native tests; CPU backend implements sqrt to support it.

Dependencies

  • Added pbandk runtime 0.16.0 for ONNX protobuf decoding.

[0.4.0] - 2025-12-03

Added

  • Recording/tracing pipeline for tensor ops (RecordingExecution/TracingTensorOps) and compute-graph DAG under sk.ainet.lang.graph, including tape-to-graph conversion and GraphViz export helpers/tests.
  • JSON export proof of concept via new skainet-compile-json module with serialization models, exportJson CLI, and tiny graph golden fixtures.
  • Multiplatform image IO module to convert platform bitmaps <-> tensors and RGB byte arrays; includes macOS implementation fixes.
  • Dedicated YOLOv8 model module (skainet-models:skainet-model-yolo) with graph assembly, config/pre/post-processing, and missing upsample/concat ops required by the model.
  • NN DSL additions: multi-input Functional wrapper, new Upsample2d/Softmax helpers, scalar DSL builder plus tensor/number operator overloads, and extra tensor view/pprint utilities.

Changed

  • Graph DSL relocated into the lang namespace with refreshed default execution tape/graph context wiring; removed unused integration module scaffolding.
  • Removed committed MNIST training assets; rely on download at runtime.
  • Added scalar arithmetic support across backends and void ops to match new operator overloads.

Fixed

  • Corrected unsqueeze view handling and data DSL dtype reuse; stabilized tracing/JSON/tape tests.
  • Fixed macOS image conversion path and cleaned duplicate files in the new IO/image pipeline.

Dependencies

  • io.ktor client 3.3.3 (from 3.3.2).
  • logback-classic 1.5.21 (from 1.5.20).

[0.3.0] - 2025-11-27

Added

  • Kolmogorov–Arnold Network (KAN/AKN) module and DSL support, including public factory and aliases for direct construction. Introduces Akn/AknConfig and createAkn mirroring DSL defaults.
  • Example KAN models and graphs (e.g., Sine function examples and pretrained variant) with tests and Graphviz export.
  • Additional NN DSL conveniences around initialization scopes (weights/basis/bias) and activation hooks used by KAN.

Changed

  • Minor API refinements in lang/nn DSL to better align with execution context usage for new KAN modules.

Fixed

  • Stabilized integration tests for KAN modules and examples.

Performance

  • Minor initialization performance tweaks for new modules.

Docs

  • Updated docs and samples to include KAN usage and references.

[0.2.0] - 2025-11-16

Added

  • Initial support for model code sharing API (model definition, execution, loading). Implements #196, related to #169.
  • Batch Normalization layer. Implements #193.
  • Forward hooks and simple tape recording for NN. Implements #190, related to #104.
  • Common traversal base for modules, with tests; Embedding implementation with dual value types; switched EEmbeddings to DualModule implementation.
  • Dropout (initial implementation) and phase support (training/eval) in execution context so modules can behave differently by phase. Related to #5.
  • tril op (initial version).
  • MaxPool op with DSL support; Conv2D DSL support.
  • Data API: initial version including MNIST data loader; JSON loading support (renamed loader classes from CSV to JSON) with tests. Implements #180, #181; related to #176, #179.
  • GGUF model loading implementation (initial import and working version). Implements #178, #182; related to #176, #177.
  • MatMul support in backends.
  • Nested data blocks support in DSL (data block returns a tensor); contexts for creating and collecting tensors (returning last or all created tensors).
  • JVM Ops using the Java Vector API (initial implementation) and SIMD Vector API acceleration.
  • JMH benchmarks (JVM module) and additional benchmarks.
  • Sample showing general tensor calculations (e.g., image color transformations).

Changed

  • NN DSL refactored to use ExecutionContext; added ExecutionContext parameter to forward functions.
  • Models and data APIs improved; unified tensor value creation in DSL; moved tensor creation context for safer vector/matrix/tensor creation.
  • Default CPU compute used for JS target.
  • JS and WASM Kotlin targets aligned for library packaging.
  • Gradle updated to 9.0.0; Android target namespaces fixed.

Fixed

  • Crash in schema validation task; added Kotlin compiler plugin configuration for expect/actual.
  • Activation not applied in Dense layer (fixed).
  • JVM target issues; fixed failing JVM tests; added regression tests; stabilized platform matching test (temporarily ignored) and additional general test fixes.
  • Miscellaneous build-signing validation added to avoid CI failures.

Performance

  • SIMD/Java Vector API acceleration for JVM backend operations.

Dependencies

  • com.vanniktech.maven.publish: 0.34.0 → 0.35.0.
  • io.ktor (android, cio, content-negotiation, core, darwin, js, logging): 3.3.1 → 3.3.2.
  • com.fasterxml.jackson.core:jackson-databind: 2.15.2 → 2.20.0 → 2.20.1.

Build & CI

  • GitHub Actions: use Java 22.
  • Bump actions/checkout from v4 to v5.
  • Add Gradle local caches to .gitignore.
  • Preparations for 0.2.0 release and ability to build local Maven version of the upcoming release.

Docs

  • Added hint/reference on normalization layer paper. Related to #192.

[0.1.0] - 2025-10-31

  • Initial public release of SKaiNET 0.1.0.