Changelog#

All notable changes to this project are documented in this file. The format is based on Keep a Changelog.

[0.1.21] – Unreleased#

Documentation & CI#

  • Bumped the release version to 0.1.21.

Fixes#

  • Prevented a lost thread-pool completion notification from blocking parallel regions, especially during repeated small GEMM executions on Windows.

[0.1.20] – 2026-08-20#

Improvements#

  • Treated C++ compiler warnings as errors on GCC/Clang too (-Werror), controlled by the new ONNX_LIGHT_WERROR CMake option which also gates the existing MSVC /WX.

  • Made the requested degree of parallelism effective: a runtime session now leases a shared CPU executor matching its execution policy and every parallel region its kernels launch runs on exactly those participants instead of a process-wide thread pool.

  • Exposed typed CPU execution requests, immutable resolutions, executor sharing identities, and optional dispatch counters through the Python runtime and ReferenceEvaluator.

  • Made calibration use an explicitly selected CPU executor and reject execution descriptors that disagree with the active participants.

  • Tagged node runtime events with the exact shared CPU executor instance and its effective participants, and fixed future prepared execution to the same executor ownership contract.

  • Accepted runtime Tensor objects, and sequences mixing them with NumPy arrays, as ReferenceEvaluator inputs, borrowing their buffers zero-copy while keeping their owners alive.

  • Ported the MatMul and normalization optimization patterns to C++: MatMulAdd, GemmTranspose, TransposeMatMul, the MatMul reshape patterns, LayerNormalization, RMSNormalization, BatchNormalization, Gelu, LeakyRelu and their variants.

  • Ported the rotary embedding and attention optimization patterns to C++: RotaryEmbedding, RotaryConcatPart, the causal mask and cos/sin cache function patterns, FunctionAttention and the grouped-query attention patterns.

  • Added onnx_light.tools.translate, which converts an ONNX model or graph into Python code rebuilding it, either with the onnx-compact onnx.helper API or with GraphBuilder.

Fixes#

  • Removed the remaining unused lambda captures reported by Clang in the Python bindings and in the backend test cases.

Documentation & CI#

  • Standardized the python imports of the helper modules: import onnx.helper as oh and import onnx.numpy_helper as onh (and their onnx_light.onnx counterparts) replace the from onnx import helper, numpy_helper form in the code base and the documentation.

  • Made setup.py honor CMAKE_BUILD_PARALLEL_LEVEL explicitly by forwarding it as cmake --build --parallel N, falling back to the number of available CPUs.

  • Ported the upstream Python pattern tests, so the C++ patterns are checked against the same expectations as the original implementation.

  • Refreshed the quick tour, getting started, main page and README documentation to reflect the implemented features.

  • Documented the plans for session-owned CPU execution pools and for faster model loading, and closed the execution pool roadmap once every step was implemented.

  • Fixed a doxygen warning caused by the unexpanded FIELD_BYTES macro.

  • Bumped the release version to 0.1.20.

[0.1.19] – 2026-08-18#

Improvements#

  • Kept ReferenceEvaluator NumPy inputs zero-copy across repeated runs, including non-contiguous inputs, while preserving their lifetime and allocator accounting.

  • Improved element-wise steady-state performance with a persistent low-latency thread pool, physical-core-aware defaults, and calibration that matches the runtime thread count.

  • Optimized the Abs kernel for floating-point types with sign-bit masking and a lower, tunable parallel-execution threshold.

Documentation & CI#

  • Updated the Abs benchmark to compare steady-state execution fairly across supported element types and omit incomparable bfloat16 speed-ups.

[0.1.18] – 2026-08-18#

Improvements#

  • Kept backend benchmark models compatible with ONNX IR version 13.

  • Generated BFLOAT16 backend benchmark cases by default.

  • Declared ml_dtypes>=0.5.0 as a package dependency.

Fixes#

  • Fixed release builds against older NumPy versions that do not expose PyDataType_KIND.

Documentation & CI#

  • Bumped the release version to 0.1.18.

  • Added a design page for buffer-reuse arenas and marked the I/O arena plan complete.

  • Reorganized the design documentation and runtime examples.

[0.1.17] – 2026-08-17#

New Features#

  • Added a pattern-based GraphBuilder optimizer: a GraphGraph read-only index, a PatternOptimization engine that matches and rewrites subgraphs (recursively, including nested subgraphs), a pattern registry and dispatch table, a dedicated patterns library, and canonicalization patterns for redundant Cast, binary Cast, and Cast-op-Cast.

  • Added a kernel auto-tuning framework: a KernelTuning registry and persistent tuning cache with atomic updates, shared unary/binary calibration, portable GEMM tuning, and per-session integration through the runtime session and kernel context, tuning logical and math kernels.

  • Added constant folding to GraphBuilder, backed by the read-only GraphGraph index and build-time constant-information analysis.

  • Added a RunModel C++ helper for whole-model execution with Tensor I/O and exposed it in Python.

  • Exposed a public tensor comparison helper with atol/rtol.

  • Added persistent execution and I/O buffer-reuse arenas with independent retention controls, output leases that safely outlive a runtime context, and Python runtime integration.

Improvements#

  • Routed every built-in kernel output by output slot directly to its final arena and every workspace to the execution arena, eliminating the final execution-to-I/O copy on the normal runtime path, including for mixed-output nodes.

  • Improved runtime performance and expanded the Python NumPy kernel bindings.

  • Added a CPU descriptor exposing processor topology for processor-aware kernel thresholds.

  • Made the opaque type unconditional (propagated from onnx/onnx#8269).

  • Corrected optional input/output presence in InferenceContextImpl (propagated from onnx/onnx#8271).

  • Made ScopedResource use a Traits type to avoid a pointer non-type template parameter (propagated from onnx/onnx#8279).

Fixes#

  • Fixed a local build failure in setup.py.

  • Fixed a cpp-tests install failure by disabling GMock in the fetched googletest.

Documentation & CI#

  • Bumped the release version to 0.1.17.

  • Added next-steps design pages for profiling and async execution, parallel model initialization with prepacking, prepared execution, a buffer-reuse arena allocator, processor-aware kernel thresholds, and wheel packaging.

  • Documented the kernel tuning workflow and graph pattern integration.

  • Added a benchmark for cold kernel tuning resolution and characterized allocator-backed output lifetime.

  • Updated the formatting and typing tools.

  • Switched C++ backend tests to RandnTensor instead of Tensor::FromX + Randn.

  • Improved schema-comparison coverage.

  • Removed the numpy run_model benchmark from the plot_abs_benchmark example and asserted onnx-light is slower than ONNX Runtime at the smallest size.

[0.1.16] – 2026-08-10#

Improvements#

  • Stopped zero-initialising result buffers on allocation, zeroing explicitly only where kernels rely on it.

  • Reduced the binary size of lib_onnx_proto further across several passes.

  • Exported ParseLimitExceeded in the proto public API.

  • Fixed get_cpp_build_info for editable installs and static builds.

Documentation & CI#

  • Bumped the release version to 0.1.16.

  • Added a next-steps design page for a C++ pattern-based GraphBuilder optimizer.

  • Added last-updated dates to the gallery examples and split plot_onnx_time into three separate figures.

  • Added backend run-model tests for previously untested logical kernels.

  • Improved test coverage for run_nodes.cc, simple_tensor.cc, node_helpers, and onnx_light_helpers.cc.

  • Added a test verifying the serialized raw_data size matches the buffer size for QuantizedTensorProto.

  • Enabled build caching in CI.

[0.1.15] – 2026-08-08#

New Features#

  • Added support for registering custom kernels globally or per session.

  • Gave every kernel a unique device- and library-qualified name, exposed through the session and Python.

Improvements#

  • Made dispatch-table overrides order-independent.

  • Spawned the load/save thread-pool workers lazily to fix slow small-model initialization.

  • Reduced the stripped lib_onnx_proto size by hiding internal symbols and explicitly exporting its public cross-library ABI.

Documentation & CI#

  • Bumped the release version to 0.1.15.

  • Annotated onnx.h with the canonical ONNX proto definitions.

  • Added a gallery example benchmarking model initialization (ONNX Runtime vs onnx-light).

  • Added a register_custom_kernel example in both C++ and Python.

  • Added quantization and next-steps documentation.

[0.1.14] – 2026-08-07#

New Features#

  • Added a NodeProto callback hook to ParseOptions and SerializeOptions so callers can inspect or rewrite each node while a model is being parsed or serialized.

Improvements#

  • Propagated reverse and bidirectional RNN/GRU/LSTM support (propagated from onnx/onnx#7935).

  • Improved ONNX Runtime compatibility: added ordering operators and an implicit conversion to const std::string& to OptionalString, made add_string_data accept an OptionalString, switched AttributeProto.strings to FIELD_REPEATED_STR, and stopped creating an empty shape in TypeProto::FromString for tensor types without a shape.

  • Modernized the ParseData raw_data handling (propagated from onnx/onnx#8109).

  • Made FileLoadMode.AUTO use a buffered stream instead of memory mapping.

  • Honored file_load_mode="MMAP" when loading models with external data.

  • Loaded deeply nested control-flow models by raising the default proto recursion limit.

  • Accepted the packed wire format for repeated integer proto fields.

  • Added an opt-in bypass for the external TensorProto location error in the checker.

  • Reduced the binary size of onnx_proto via section garbage collection / dead-code elimination.

Fixes#

  • Fixed single-file mmap no_copy zero-copy parsing.

  • Added proto2-style presence tracking for FIELD_BYTES.

  • Fixed the Attention mode-3 fully-masked NaN output.

  • Stopped reading an optional message sub-field from auto-creating it.

  • Fixed a ParseFromFile RuntimeError when file_load_mode is set together with an external_data_file.

  • Returned the real parse result from TraditionalMLData::ParseFromArray.

  • Refined the cyclic model-local function error message (propagated from onnx/onnx#8233).

  • Fixed compilation when OpenSSL is built without ChaCha20-Poly1305.

  • Fixed the ScatterND updates shape documentation (propagated from onnx/onnx#8212).

Security#

  • Added overflow-checked int64 arithmetic in shape inference (propagated from onnx/onnx#8031).

  • Bounded external-data padding on serialization (propagated from onnx/onnx#8260).

Testing#

  • Made the upstream node backend-test consumers work when onnx builds node test data on the fly (TestCase.model / TestCase.data_sets in memory with model_dir set to None) instead of materialising it on disk under onnx/backend/test/data/node (propagated from onnx/onnx#7959). Both the schema_comparison backend-test counter and the ImageDecoder case generator now prefer the in-memory model and fall back to the on-disk layout.

  • Added a TreeEnsembleLeafLike backend test case (UINT8 nodes_modes via int32_data).

  • Fixed the skipped external-data tests and a latent Normalizer benchmark count bug.

  • Fixed unit tests failing due to the latest onnx-weekly.

  • Investigated and documented the known-missing ONNX backend tests.

Documentation & CI#

  • Added a benchmark tool measuring single-model load times across onnx, onnxruntime, and onnx_light.

  • Extended plot_onnx_time.py: produced three graphs (all / Python / C++), used a single argument parser, added a ReferenceEvaluator load benchmark, and fixed the --model option for custom models and external weights.

  • Added a descriptive-statistics-over-ND-initializers example and persisted the ONNX graphs built by gallery examples to disk.

  • Added a --run-cpp-tests option to setup.py to build and run the C++ tests, and ran the C++ tests in the Windows x86 CI job.

  • Enabled the modernize-concat-nested-namespaces check (propagated from onnx/onnx#8150) and the clang-tidy math-parentheses and consistent-parameter-name checks (propagated from onnx/onnx#8259).

  • Bumped the release version to 0.1.14.

[0.1.12] – 2026-08-05#

New Features#

  • Added __setitem__ (index assignment) to the RepeatedProtoField Python bindings, so model.graph.node[i] = node works; negative indices and bounds checks match __getitem__, and the assigned element is stored as a copy.

Improvements#

  • Added an opt-in allow_external_output_allocators option to RuntimeSession (C++ RuntimeSessionOptions and the Python constructors, with a matching read-only accessor/property) that relaxes the per-node output-allocator verification so kernels may legitimately return outputs allocated outside the session’s common allocator; the strict check remains the default.

  • Added a str_<name>() accessor to the FIELD_STR proto macro that returns the stored string as a const std::string& (or a shared empty string when unset), avoiding the OptionalString wrapper.

  • Moved SerializeOptions, ParseOptions, and their supporting types (TensorBufferOptions, FileLoadMode, SerializeFormat) out of onnx_proto/stream_class.h into a dedicated onnx_proto/serialize_options.h header.

Testing#

  • Asserted that untagged backend test cases carry no in-place-reuse, shape_tag, or release metadata, checking every case strictly by tag.

Documentation & CI#

  • Split the plot_onnx_time benchmark chart into dedicated Python-API and C++-API graphs alongside the existing combined chart.

  • Raised the Windows CI build timeout to 60 minutes.

  • Bumped the release version to 0.1.12.

[0.1.11] – 2026-08-03#

New Features#

  • Added recursive RemoveUnusedNodes, RemoveIdentityNodes, and RemoveDuplicateInitializers passes to GraphBuilder.

  • Added recursive duplicate-node removal (common subexpression elimination) to GraphBuilder.

  • Added local-function inlining to GraphBuilder.

Improvements#

  • Added an opt-in steal parameter to the tensor_to_numpy runtime binding: when set and the tensor owns its bytes inline (not allocator-backed and not a borrowed view), the buffer’s ownership is transferred to NumPy through a DLPack-style capsule instead of borrowing it, so the source tensor can be released while the array lives on. ReferenceEvaluator uses it when converting terminal graph outputs.

  • Made RuntimeSession::Run detach borrowed graph outputs from the model: when a declared graph output is a borrowed view into the model (e.g. a Constant reading its value’s raw_data or an initializer passed straight through), the session materializes it into an owned tensor before returning (new Tensor::ToOwned() / Tensor::is_borrowed()). The output then owns its bytes and stays valid after the model is released. Exposed Tensor.has_borrowed_data() to Python.

  • Made ReferenceEvaluator import standard-dtype runtime Tensor outputs into NumPy through the DLPack exchange protocol (Tensor.__dlpack__ / numpy.from_dlpack), keeping the zero-copy conversion while relying on the standard protocol; bfloat16/float8 and sub-byte/STRING tensors keep their existing fallbacks.

  • Made the C++ RunNode / RuntimeSession dispatch device-aware: a RuntimeContext pinned to a non-CPU device now resolves the device-qualified kernel and fails with a diagnostic naming the device when none is registered, instead of silently dispatching to the CPU kernel.

  • Routed Tensor::elem_num() and Tensor::size_from_dim() through the new safe_dim_product helper so tensor dimension overflow and negative dimensions raise tensor_error (propagated from onnx/onnx#8220).

  • Turned the TensorProto dims into int64_t.

  • Made OptionalString::value() return a const std::string&.

  • Improved compatibility with ONNX Runtime.

  • Preserved unshaped Scan inputs in the Scan 8 → 9 version-converter adapter.

  • Validated int32_data payload sizes in VerifyTensor (propagated from onnx/onnx#8211).

  • Preserved signed zero for zero-point-less FLOAT4E2M1 QuantizeLinear.

  • Removed the remaining C++ compiler warnings under -Wall / ONNX_HARDENING.

Testing#

  • Fixed the -Wmissing-field-initializers warning in the backend-test IoData initializers.

Documentation & CI#

  • Bumped the release version to 0.1.11.

  • Fixed the macOS C++ release build by raising the deployment target to 13.3.

  • Dropped the Python 3.10 wheels and fixed the Windows release wheel repair.

  • Enabled ASan container-overflow detection in CI (propagated from onnx/onnx#8213).

  • Hardened the ASan CI options with alloc_dealloc_mismatch=1 and abort_on_error=1 (propagated from onnx/onnx#7471).

  • Disabled precompiled headers when an sccache/ccache launcher is active.

  • Fixed the C++ documentation namespace (onnx:: → onnx_light::).

  • Refreshed the custom-kernel how-to for the current ReferenceEvaluator runtime APIs.

  • Documented the core/extension registration design for shape, peak-memory, kernels, backend tests, and LightOpSchema.

[0.1.10] – 2026-07-30#

New Features#

  • Added ReferenceEvaluator.unregister_custom_kernel to restore a built-in kernel after a custom one has been registered.

Improvements#

  • Extended ParallelFor to the unary elementwise kernels and parallelized the Abs kernel execution.

  • Reused RuntimeSession instances and made the MatMul kernel more cache-friendly after profiling RuntimeSession on tiny_llm.

  • Fixed the O(N²) GraphBuilder construction from a ModelProto.

  • Simplified run_nodes and removed the unused CallModelLocalFunction helper.

  • Improved the Python bindings and the collect_test_case backend-test collection helpers.

  • Removed the TIFF/WebP/JPEG2000 ImageDecoder support and the ONNX_LIGHT_BUILD_IMAGE_CODECS flag.

Testing#

  • Added the missing Softmax backend test cases to match the ONNX node tests.

  • Fixed the runtime coverage report to honor tolerances and flag missing reference outputs.

Documentation & CI#

  • Documented how to run and verify clang-format in the Copilot guidelines.

  • Fixed the release wheel build by dropping the unsupported cp313t cibuildwheel selector.

  • Bumped the release version to 0.1.10.

[0.1.9] – 2026-07-29#

New Features#

  • Introduced a RuntimeSession that separates one-time kernel initialization from execution, and extended ComputeContext to orchestrate graph analyses and build the ExecutionPlan.

  • Added an incremental GraphBuilder in onnx_core/builder with Python bindings, a ModelProto-to-GraphBuilder import path (with local-function/subgraph round-trip support), and an opt-in check_shapes flag on RuntimeSession to validate concrete against symbolic shapes.

Improvements#

  • Routed kernel intermediate and output buffers through the RuntimeContext allocator instead of temporary std::vectors (Attention, FlexAttention, Gather, GatherND, Range, Resize, Momentum, RegexFullMatch, RNN, LinearClassifier, SVMClassifier, and a direct-to-output Gemm).

  • Migrated many kernels to the fixed-capacity Shape type for rank-sized working arrays instead of std::vector<int64_t> (conv/pool, Reduce*, Squeeze/Unsqueeze, Where, Pad, Slice, Compress, Expand, CenterCropPad, DequantizeLinear, QLinearConv, QLinearMatMul, and the RowMajorStrides/ResolveAxes helpers).

  • Cached the Einsum contraction plan, built TreeEnsemble* structures in the kernel constructors, and reused per-row/per-sample scratch in TfIdfVectorizer and TreeEnsembleClassifier.

  • Migrated repeated proto-message fields from std::vector<T> to RepeatedProtoField<T> across NodeProto, the GraphBuilder inputs/outputs/initializers/attributes, and the shape-inference and schema APIs (including TypeProto).

  • Moved DimSum and IsZeroDim into the expressions module and renamed the _onnxpyoptim extension module to _onnxpycore.

Testing#

  • Added C++ and Python tests that run every backend model case through RuntimeSession, plus GraphBuilder round-trip coverage over all backend test cases.

Documentation & CI#

  • Refreshed the design-page links and shape-inference docs to match the current library layout, showed all top-level navigation links, renamed “Operators” to “Ops” on the documentation main page, and bumped the release version to 0.1.9.

  • Enabled sccache on the Ubuntu CI jobs, bumped the GitHub Actions versions, and enabled ASan container-overflow detection.

[0.1.8] – 2026-07-22#

New Features#

  • Added a RuntimeParameters class to control graph execution parallelism.

  • Introduced a first-class ExecuteAction describing every memory-management and execution step of an ExecutionPlan (allocate/delete buffer, lock/unlock, transfer, execute node, create/delete shape), each tied to the owning allocator and exposed through the Python bindings with a concise summary() helper.

  • Added peak-memory annotations to the shape-inference pipeline and a WritePeakMemoryToMetadata step to persist the estimate.

  • Added a Resize opset 18 → 17 version-converter adapter.

Improvements#

  • Refactored ExecutionPlan around the ExecuteAction list: every constructor now takes a RawBufferAllocator* and builds its actions through a single virtual extension point, and the per-node release schedule is derived entirely from that list (dropping the redundant annotated_/topology-fallback state) with RunNodes release routed through the action replay.

  • Reorganized the C++ tree: moved execution primitives and annotations into onnx_core/compute, split ComputeContext into compute_context.h/compute_context.cc, moved ExecuteActionKindName out of the header, and switched switch-based enum→string helpers to inline constexpr const char *.

  • Improved SVG readability by rendering edge labels smaller, staggered to reduce collisions, and without a highlight halo.

Fixes#

  • Validated int32_data size for non-packed types in the checker (propagate onnx/onnx#8211).

  • Fixed a run_add_node_test compile break by aligning the backend-test namespace and TestCase API usage.

Testing#

  • Extended the big Qwen3 shape-inference case with expected intermediate shapes and embedded/verified golden in-place-reuse metadata.

  • Added a cc_release test case covering a graph initializer in not_used_after metadata and per-operator benchmark coverage to the C++ backend test suite.

Documentation & CI#

  • Synced the Python API docs with the current public modules, renamed the example galleries (core → proto, optimization → core), refreshed documentation, and bumped the release version to 0.1.8.

[0.1.7] – 2026-07-21#

New Features#

  • Added a RuntimeContext allocator framework (SimpleRawBufferAllocator, allocator-backed kernel outputs) and custom-op shape_tag registration hooks.

  • Added a symbolic gradient framework (reverse-mode Conv/normalization gradients with backend-test verification) and a peak-memory dispatch registry.

  • Extended the Python/serialization API (SaveToFileDescriptor, SerializeToOstream) and added cp313/cp314 wheels.

Improvements#

  • Reorganized the C++ tree around a new intermediate lib_onnx_core and an onnx_extensions layout (onnx_backend_test/onnx_kernels/onnx_gradient moves, onnx_optim→onnx_shapes rename, ExecutionPlan split).

  • Migrated kernels to the dedicated Shape type and routed their output allocation through the RuntimeContext allocator, preserving ownership and reusing shared empty-tensor fallbacks.

  • Reduced string copies and allocations across parsing/graph paths (RepeatedProtoField, more constexpr helpers, removed memcpy/type-punning hotspots) and extended shape-tag metadata coverage.

Fixes#

  • Corrected in-place reuse and shape-tag propagation across many operators and preserved doc_string in the IR converter.

  • Propagated upstream ONNX fixes (shape inference, locale-independent text parser/printer, TopK sorted=0) and hardened version conversion for malformed Scan/default-domain imports.

  • Fixed expression-simplification, SVG rendering edge cases, and spelling across the codebase.

Security#

  • Fixed a size_t underflow in Scan shape inference (GHSA-qrhj-v62m-vmpf) and hardened zero-copy ORT parsing and tar-extraction path validation.

  • Prevented command injection/insecure I/O (graphviz args, temp files) and GitHub Actions code injection in workflows.

  • Addressed external-data advisories, raised the ONNX minimum to 1.21.0, and added defense-in-depth checks (GHSA-3jf9-582g-jjmq, GHSA-xrch-8vh7-h656, GHSA-p893-rvq9-2xf9, GHSA-hqmj-h5c6-369m, GHSA-8qff-7g33-75mx).

Testing#

  • Extended C++/Python backend and shape-inference coverage (gradient verification, pooling, local functions, metadata/value-tag enforcement, OpSchema attribute parity).

  • Delayed backend-test ModelProto construction via TestCase::emplace_model() and forwarded TestMode through the Python facade.

Documentation & CI#

  • Added the changelog and string-types documentation, bumped the release version to 0.1.7 across canonical metadata.

  • Sped up CI (sccache for clang-tidy, trimmed Windows jobs, parallel preflights, C++-build skips in style/typing jobs).

[0.1.4] – 2026-07-07#

Fixes#

  • Fix Win32 narrowing in backend-test DLPack shape conversion (#3223)

  • Suppress GCC 13 false-positive -Wfree-nonheap-object in ComputeScaleIndex (#3227)

Security#

  • Validate raw_data alignment in ParseData to prevent out-of-bounds copy (propagate ONNX #8032) (#3225)

[0.1.3] – 2026-07-06#

New Features#

  • Added serialization safety and diagnostics: SerializeOptions.max_serialized_size_bytes cap, node indexes in pretty_onnx, verbose fillshape output/progress.

  • Extended fillshape/CLI tooling: --token binding, --release-info, run subcommand, --verbose [LEVEL], python -m onnx_light fillshape.

  • Added shape/dimension utilities: dim_ranges_from_expressions, make_random_input, C++/nanobind shape/axes/weight metadata tagging with tag-aware Mermaid/SVG rendering.

  • Added ai.rt DelayedInitializer schema/kernel/shape-inference, a pre-serialization weight rewrite callback, and ChaCha20-Poly1305 encrypted model I/O.

  • Added parsing/graph-rendering features: ParseFromIstream, parse callback support, include_inplace/include_release rendering options, in-place reuse metadata recording, and a graph-input overwrite guard.

Improvements#

  • Updated onnxruntime integration and mirrored an upstream nanobind cross-compile CMake fix (onnx/onnx#8157).

  • Improved shape inference: Python // floor-division semantics, symbolic Reshape/Slice expressions, empty-axis broadcasting, half-precision RMSNormalization.

  • Propagated and reused shape-tag metadata through Reshape and fillshape writing paths.

  • Reorganized in-place reuse and value-tag helpers into onnx_shapes.annotations.

  • Miscellaneous: improved to_svg layout, RepeatedField iterable support, ByteSpan/TensorProto deleter implementation, tiny external-tensor inlining moved into ParseOptions.

[0.1.1] – 2026-06-22#

New Features#

  • Added a pure-Python textproto parser/serializer and format="textproto" support in load/save.

  • Extended shape-expression support: exact-division operator (/:), compare_expressions.

  • Extended proto API surface: WhichOneof, keyword-argument constructors, RepeatedProtoField.add() kwargs, String.decode().

  • Added sub-byte dtype support (int2/uint2/int4/uint4/float4) in make_tensor and exposed OpSchema.Attribute.default_value.

  • Added PrintOptions indentation/inline_threshold, GitHub Releases wheel publishing, and offset-aware Attention is_causal masking for external KV cache.

Improvements#

  • Brought infer_shapes to parity with onnx (check_type, strict_mode, data_prop) and validated ConvTranspose group divisibility.

  • Registered repeated-field containers as collections.abc.Sequence with list/String comparison support.

  • Guessed in-place input reuse from onnx_shapes shape inference and reduced Windows/macOS wheel size.

  • Bumped mirrored ONNX to 1.23.0, enabled the ONNX backend optional-sequence loop case, and extended Where backend coverage across dtypes.

  • Replaced ad-hoc exception throws with EXT_ENFORCE_INVALID/EXT_THROW_INVALID, fixed serialization/IOBinding gaps (TensorProto.segment, sub-byte Cast, low-precision ORT backend tests), and passed non-null pointer params by reference (onnx#8105).

Testing#

  • Added unit tests for proto/numpy helpers, print helpers, and field-serialization helpers.

  • Added unit tests for sparse/sequence/optional helpers and RunLoopWithSequenceState.

  • Added unit tests for get_total_memory_gb, get_cpu_topology, _schema_to_rst, and hide_stdout.

  • Added C++ serialization tests and shape-inference examples (MatMul with an initializer weight, sequential TopK).

  • Added manual CI jobs validating onnx-light against ir-py, mbext, and yobx.

[0.1.0] – 2026-06-18#

Initial public release.