[dependabot]

Updated Microsoft.ML.OnnxRuntime from 1.29.0 to 1.32.0.

Release notes

Sourced from Microsoft.ML.OnnxRuntime's releases.

1.32.0

ONNX Runtime 1.32.0 reintegrates ONNX 1.23.2 with opset 28 support, introduces CPU/GPU hybrid MoE offloading and shared-layout CPU QMoE kernels, expands device-memory, weightless-compilation, and Python model-package APIs, dramatically accelerates session initialization on large graphs, and strengthens runtime and optimizer reliability. These notes cover changes since ONNX Runtime 1.31.1. CUDA and WebGPU kernel updates are summarized here; detailed provider notes are covered by their separate plugin EP releases.

Highlights

• Reintegrated ONNX 1.23.2 with opset 28 operator support (SpaceToDepth, Mod, signed BitShift, and BFloat16 extensions) and updated DLPack to v1.3 across all implemented ORT numeric and boolean tensor types (#​33134, #​32362, #​33848).
• Added a session-scoped CPU/GPU hybrid MoE offload planner and execution backend for quantized QMoE (NVFP4, INT4, INT3, and INT2) and block-packed MoEPacked models, backed by shared-layout AVX-512/AVX2 CPU QMoE kernels and memory-mapped expert storage (#​33450, #​33451, #​33452, #​33973).
• Expanded core APIs with OrtApi::EpDevice_GetMemoryCapacity, explicit multi-mode weightless compilation (ModelCompilationOptions_SetWeightlessMode and ep.enable_weightless_mode), Python bindings for the Model Package APIs, and zero-copy CPU buffer borrowing in AddExternalInitializers (#​33447, #​32899, #​33640, #​33877).
• Eliminated superlinear bottlenecks in allocation planning, constant sharing, graph I/O mapping, and topological sorting—reducing session initialization on 100k-node constant-heavy graphs from minutes to seconds—and added pipelined pinned-memory staging for external GPU weight loading (#​33407, #​33939, #​34009, #​33839).
• Expanded shared generative AI and quantized contrib operators, including Kimi Delta Attention (GatedDeltaNet per-key-dimension decay), right-padded chunked prefill in GroupQueryAttention, native GGUF/block-packed operators (GatherPacked, MatMulPacked, MoEPacked), and sparse-attention index sharing (#​33817, #​33503, #​33953, #​33224).

Announcements & Compatibility

• CUDA Plugin EP v0.4.0 and WebGPU Plugin EP v0.6.0 are matched with ONNX Runtime 1.32.0 (#​33214). For contrib-op compatibility, core and the plugin must use the same contributed-operator schemas; building both from the same ONNX Runtime revision is recommended. Schema mismatches can cause incorrect execution or crashes and may not be detected during plugin registration.
• ACL EP removed: Following its deprecation in 1.31.0, the Arm Compute Library (ACLExecutionProvider) execution provider, its build options, and its standalone C registration API have been removed. Python's get_all_providers() no longer lists ACLExecutionProvider; applications should remove it from explicit provider lists (#​33851).
• Legacy provider-bridge NvTensorRTRTXExecutionProvider removed: In-tree provider-bridge NvTensorRTRTXExecutionProvider sources have been removed in favor of the standalone NVIDIA TensorRT RTX plugin EP registered via OrtApi::RegisterExecutionProviderLibrary. NvTensorRtRtxProviderOptions is retained as a deprecated declaration for source compatibility, and legacy AppendExecutionProvider calls now report a deprecation error directing callers to the plugin EP (#​32276).
• ONNX 1.23.2 integration: ONNX has been upgraded to 1.23.2, raising supported opset ceilings to 28 and restoring ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT6E2M3 and ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT6E3M2 public element-type mappings and Cast conversions (#​33134).
• DLPack v1.3 boolean contract change: DLPack has been upgraded to v1.3 with native kDLBool metadata and bidirectional support for all implemented ORT numeric and boolean types, including all five float8 formats, packed float4, int4/uint4, and int2/uint2 (float6 remains unsupported in DLPack). The low-level is_bool_tensor override was removed, and uint8 DLPack metadata now always imports as uint8 (#​33848).
• Weightless compilation API migration: OrtCompileApi::ModelCompilationOptions_SetWeightlessEnabled and the ep.enable_weightless / ep.enable_weightless_ep_context_nodes session options are deprecated in favor of ModelCompilationOptions_SetWeightlessMode and ep.enable_weightless_mode ("0" = NONE, "1" = EXTERNAL_ONLY, "2" = ALL). EPs can now advertise multi-mode capability via OrtWeightlessSupport_ALL_OR_EXTERNAL_ONLY and weightless_supported_modes (#​32899).
• Training CI retirement: Training build and test lanes were retired from active GitHub Actions CI workflows while retaining inference build coverage (#​33836).

New Features

Core APIs & Runtime

• Added OrtApi::EpDevice_GetMemoryCapacity (C++ EpDevice::GetMemoryCapacity()) and OrtEpFactory::GetDeviceMemoryCapacity to query device-global total memory and estimated currently available memory (DEFAULT or HOST_ACCESSIBLE), with built-in and plugin CUDA EP support (#​33447).
• Expanded the app–EP weightless contract so EPs advertise all supported weightless modes (weightless_supported_modes), applications request an explicit mode (ModelCompilationOptions_SetWeightlessMode / ep.enable_weightless_mode), and compiled EPContext models record weightless_mode and external_dependencies (source_model, external_weights, ep_context_binary) in model metadata (#​32899).
• Extended session.use_external_initializer_file_buffers_directly="1" to AddExternalInitializers for zero-copy borrowing of naturally aligned CPU buffers larger than 127 bytes on little-endian hosts, and documented the read-only guarantee for external initializers added via OrtModelEditorApi::AddInitializerToGraph with data_is_external=true (#​33877, #​33717).
• Added lock-free multi-worker pinned-memory staging (session.external_data_loader.*) to accelerate external initializer loading into GPU memory across built-in and plugin CUDA EPs (#​33839).
• Added a session-scoped MoE/QMoE CPU/GPU offload planner and memory-budget ledger (session.moe.offload.*), generic session.kernel_dispatch_policy (auto, latency, memory, safe) configuration plumbing, Level-1 workspace/prepack memory accounting fallback for nodes absent from memory-profile CSVs, and internal capability-planning protocol types (#​33451, #​33042, #​33311, #​33841).
• Enabled cross-thread reuse of idle device streams when graph capture is disabled and providers register device-selection callbacks, and enabled compile-session profiling (<profile_file>_compile) in onnxruntime_perf_test when -p is used with --compile_ep_context (#​33053, #​33694).
• Made onnxruntime_DISABLE_EXCEPTIONS (--disable_exceptions) usable without onnxruntime_MINIMAL_BUILD and added the onnxruntime_DISABLE_STREAM=ON CMake option to disable device-stream support in non-minimal builds (#​33120, #​33482).

ONNX Opset 28 & Shared Contrib Operators

These updates affect core ONNX and contributed operator schemas and cross-provider behavior, not only the separately released GPU plugins.

• Added ONNX opset 28 operator support: native SpaceToDepth (DCR and CRD) on CPU and CUDA with version-split registrations across DML, JS, WebGPU, and QNN; opset-28 floating floor-modulus Mod on CPU, CUDA, and DML; opset-28 BFloat16 support for Compress, OneHot, ReverseSequence, Unique, Celu, and Einsum on CPU (plus Compress, OneHot, and ReverseSequence on CUDA); and BFloat16 Pad (opsets 21–25) on CPU and CUDA (#​32362, #​32369, #​32378, #​34062).
• Extended GatedDeltaNet with Kimi Delta Attention (KDA) per-key-dimension decay across CPU, CUDA, and WebGPU, and extended GatedDeltaNet and CausalConvWithState state/gate handling for multi-token speculative decoding (#​33817, #​33471).
• Added an optional query_seqlens input to GroupQueryAttention to skip right-padded prefill tokens and idle batch entries (query_seqlens[b] = 0) on CPU and CUDA, and added q_norm_epsilon / k_norm_epsilon attributes to preserve distinct Q and K normalization epsilons during GQA pre-norm fusion (#​33503, #​33505, #​33832, #​33363).
• Defined the INT3 QMoE v1 packed-code contract, added opt-in post-SiLU clipping (swiglu_limit_after_activation) and tanh-GEGLU (activation_type="geglu") support to MoE/QMoE schemas, and added GatherPacked (CPU/CUDA), MatMulPacked (CUDA, including native Bolt global_scale), and MoEPacked (CUDA) to consume native GGUF/block-packed quantized weights directly (#​33394, #​33954, #​33633, #​33953, #​33962).
• Added PackedSparseAttentionIndexerMerge for multi-token prediction index sharing and optional state_status / mid-state outputs on PackedSparseAttentionIndexer across CUDA and WebGPU; extended GatedAdd with an optional projected gate_weight across CPU, CUDA, and WebGPU; and supported empty unpacked weights in CPU and WebGPU MatMulNBits (#​33224, #​33383, #​33472, #​33470, #​33559).
• Registered core contrib schemas for provider-targeted LLM and speculative-decoding operators, including SwiGLU, RMSNormRotary, HyperConnectionLowRankMix, RouterGateUpProj, SpeculativePrefixCommit, GatedDeltaNetStateReplay, ConvStateReplay, and MatMulNBitsQLoRA (#​32126, #​33469, #​33384, #​33650, #​33474, #​33213, #​33493, #​33370).

Plugin Execution Provider Infrastructure

• Fixed an OrtMemoryInfo ABI mismatch across the ORT/plugin EP boundary by converting OrtMemoryInfo instances exclusively through the C API (#​33571).
• Zero-initialized OrtEpFactory structs in provider-bridge EP factories so unimplemented optional callbacks are null, and prevented double DLL initialization and handle leaks when probing and registering plugin EP libraries (#​33117, #​33701).
• Hardened portable model-package EP recreation, custom-domain preservation, UTF-8 path handling, and shared_assets path confinement and symlink validation (#​32790, #​33826).

Execution Provider Updates

... (truncated)

1.31.1

ONNX Runtime 1.31.1 is a patch release that fixes an OpenVINO-enabled Python build regression and corrects WASM Debug external-data tests. These notes cover changes since ONNX Runtime 1.31.0.

Highlights

• Fixed a provider-info namespace mismatch that caused compilation failures when building with both OpenVINO and Python enabled (#​33327).
• Fixed WASM Debug test failures by skipping file-backed external-data cases unsupported by the WASM test module, while retaining compile-output checks, buffer-destination coverage, and native test coverage. This is a test-only change and does not change runtime behavior (#​33226).

Contributors

Thanks to our 3 human contributors for this release!

@​eserscor, @​mustjab, @​tianleiwu

Full Changelog: rel-1.31.0...rel-1.31.1

Release highlights were prepared with AI assistance.

1.31.0

ONNX Runtime 1.31.0 stabilizes model-package and EPContext data APIs, improves CPU model loading and quantized MoE inference, expands device-based execution-provider selection, and strengthens model-loading and runtime reliability. These notes cover changes since ONNX Runtime 1.30.0. CUDA and WebGPU kernel updates are summarized here; detailed provider notes are covered by their separate plugin EP releases.

Highlights

• Promoted the model-package API and EPContext data callbacks to stable C and C++ APIs, with EPContext callback support added across language bindings (#​33166, #​32265).
• Reduced CPU session-initialization overhead by parallelizing eligible weight prepacking, and accelerated block-wise INT4/INT8 QMoE experts with MLAS QNBit kernels and grouped expert dispatch (#​31691, #​32644, #​32668).
• Added x86 FP16 LayerNorm/RMSNorm acceleration, Arm KleidiAI SVE2.1 FP16 GEMM support, and additional RISC-V vector kernels (#​32715, #​32670, #​32710).
• Enabled CoreML participation in device-based EP selection, including Apple Neural Engine selection through PREFER_NPU (#​31975).
• Added workspace-memory accounting and verification for constrained-memory graph partitioning, alongside broader model, graph, and tensor validation (#​31962, #​32189).

Announcements & Compatibility

• CUDA Plugin EP v0.3.0 and WebGPU Plugin EP v0.5.0 are matched with ONNX Runtime 1.31.0. For contrib-op compatibility, core and the plugin must use the same contributed-operator schemas; building both from the same ONNX Runtime revision is recommended. Schema mismatches can cause incorrect execution or crashes and may not be detected during plugin registration.
• GatherBlockQuantized behavior change: out-of-range indices now produce zero output slices across CPU, CUDA, and WebGPU for both integer and floating-point quantized formats. This deliberately differs from ONNX Gather error semantics (#​32480).
• Experimental API migration: model-package consumers should use OrtApi::GetModelPackageApi and the stable OrtModelPackageApi table. EPContext callback registration now uses stable APIs; the corresponding experimental lookup entries were removed. The stable native callback contract carries callback/state pointers, with payload limits enforced by application callbacks, bindings, or EPs rather than a native read-options policy (#​33166, #​32265).
• ACL EP is deprecated and will be removed in a future release. Source builds using --use_acl now receive a deprecation warning (#​32564).
• The ONNX dependency remains at 1.22.0. The initial ONNX 1.23 integration was reverted pending upstream fixes; this release does not ship that integration's new opset or FLOAT6 support (#​33073).
• Supported native builds now default to 1DS telemetry. Windows TraceLogging remains selectable with --use_windows_telemetry or onnxruntime_USE_WINDOWS_TELEMETRY=ON; telemetry can be disabled with --no_telemetry (#​32384).

New Features

Core APIs & Runtime

• Stabilized the model-package C and C++ APIs for loading packages, selecting compatible model variants, and creating sessions (#​33166).
• Stabilized named EPContext data read/write callbacks for application-managed storage. Added EP support negotiation so registered callbacks do not silently fall back to filesystem I/O (#​32265).
• Extended the Compile API to write external initializers and EP context data to buffers, enabling in-memory compilation workflows for models larger than 2 GB (#​31347).
• Added workspace-memory accounting, source reporting, and post-partition reservation verification. Set session.strict_workspace_verification=1 to fail initialization on workspace declaration overruns or nonzero orphaned reservations; strict verification is not supported for ORT-format model loads (#​31962, #​32189).
• Added the opt-in source-build option onnxruntime_DISABLE_DEVICE_DISCOVERY=ON to skip platform accelerator probing and report only the CPU device (#​32592).

Shared Contrib Operators

These updates affect core operator schemas and cross-provider behavior, not only the separately released GPU plugins.

• Extended EngramGate and NGramHashMapping for Qwen4-Exp, and added VarlenNGramHashMapping for packed DeepSeek Engram batching with request-local history. Packed hashing also supports Qwen4-Exp head offsets, EOS resets, and segment boundaries (#​32285, #​32358, #​32467).
• Added the activation attribute to GatedRMSNorm for SiLU, its Swish alias, and Sigmoid gating across CPU, CUDA, and WebGPU. SiLU remains the default (#​32512).
• Added weightless hyper-connection operators BranchwiseRMSNorm, ScaledSiLU, HyperConnectionPreMix, and HyperConnectionPostMix, with CPU, CUDA, and WebGPU implementations (#​32687).
• Extended GatherBlockQuantized to FP8/FP4 data, full-axis blocks, and scale broadcasting, and improved CPU UINT8 dequantization. See the out-of-range-index behavior change above (#​32480, #​32621).

Plugin Execution Provider Infrastructure

• Added support for statically linked plugin EPs, with a static WebGPU plugin build configuration (#​32395).
• Preserved caller streams in single-tensor plugin data transfers and handled null plugin allocator returns (#​32666, #​32683).
• Deduplicated AutoEP custom-op domains and corrected EP assignment reporting after NHWC layout transformation (#​32711, #​33184).
• Added reference guidance for compiled-model compatibility validation, including multi-device scenarios (#​29168).

Execution Provider Updates

CUDA EP

CUDA updates include sparse attention and packed continuous batching, expanded paged speculative decoding, INT2 and mixed-width QMoE execution, opt-in MatMul auto-tuning, improved MXFP4 support on Blackwell consumer GPUs, and reliability fixes.
... (truncated)

1.30.1

This is a patch release on top of v1.30.0, containing targeted model-validation and memory-safety fixes, improved Plugin EP data transfers, and build and packaging fixes.

Highlights

Bug Fixes

• Hardened contrib-op shape inference with additional rank, attribute, initializer, and overflow validation for CausalConvWithState, GatedDeltaNet, GroupQueryAttention, and SparseAttention (#​32607).
• Prevented out-of-bounds reads in EmbedLayerNormalization shape inference with scalar beta, ConvTransposeWithDynamicPads, and shape-inference input-data lookup (#​32610, #​32679, #​32681).
• Rejected malformed graphs containing inputs to zero-input operator schemas or subgraphs on non-control-flow nodes (#​32633, #​32641).
• Prevented an out-of-bounds read during session initialization when a Loop or Scan body input is also an initializer, rejecting the invalid graph instead of crashing (#​32609).
• Fixed a heap buffer overflow caused by reusing packed sub-byte tensor storage for a larger full-byte tensor (#​32611).
• Preserved the caller's execution stream in Plugin EP single-tensor data transfers, allowing asynchronous copies to receive the supplied stream while retaining synchronous-copy behavior (#​32666).

Build and Packaging

• Fixed Windows build-tools setup to use system-trusted certificates and improved proxy handling (#​33142).
• Selected Python 3.12 explicitly before package validation in the combined packaging stage (#​33561).
• Restricted FP8 XQA test cases to supported GPU compute capabilities (#​32559).

Contributors

Thanks to our 8 contributors for this release!

@​adrastogi, @​bbernhar, @​eserscor, @​miaobin, @​shiyi9801, @​tianleiwu, @​wangw-1991, @​xiaofeihan1

Full Changelog: v1.30.0...v1.30.1

These release notes were drafted with AI assistance.

1.30.0

ONNX Runtime 1.30.0 expands generative AI inference, improves CPU and GPU performance, adds Go bindings, and strengthens runtime reliability. These notes cover changes since ONNX Runtime 1.29.1.

Highlights

• Expanded CUDA inference support with variable-length causal convolution for continuous batching, speculative decoding in paged XQA, and INT4 paged KV caches with per-channel scales (#​32168, #​32340, #​32515).
• Improved WebGPU PagedAttention, added GPT-OSS support and INT8 KV-cache block quantization, and extended convolution optimizations (#​31727, #​32277, #​32284, #​32420).
• Added fused CPU LinearAttention kernels for AVX-512, Arm64 NEON, and SVE, plus AVX2 LayerNorm/RMSNorm acceleration (#​31674, #​31973, #​32178, #​32356).
• Added Go bindings for the ONNX Runtime C API and DeepSeek Engram contrib operators (#​29615, #​32268).

Announcements & Compatibility

• FP4 QMoE kernels are now enabled by default in CUDA builds, with Windows build support added in this release. Source builds can opt out with -Donnxruntime_USE_FP4_QMOE=OFF (#​32096, #​32163).
• CUDA fpA-intB builds now default to a compact kernel set for FP16 activations, INT4/INT8 weights, scale-only quantization, and block_size=32. Set -Donnxruntime_USE_FPA_INTB_GEMM_FULL=ON when building from source to retain the full kernel set, including BF16, zero-point, bias, larger-block-size, and native Hopper variants (#​32324).
• CPU FP16 Gemm and MatMul execution is gated on hardware acceleration. CPU-assigned FP16 nodes without a matching kernel now fall back to FP32 (#​32301, #​32197).
• WebGPU plugin EP packaging now supports Linux AArch64. Plugin versions were advanced to WebGPU 0.4.0 and CUDA 0.2 (#​32287, #​31960, #​31970).

Security & Reliability

Model Loading, Memory, and Input Validation

• Limited nested model-graph depth and canonicalized external-data locations to harden model loading (#​32344, #​32135).
• Added checked rounding for BFC arena allocations and fixed prepacked-weight reference lifetimes (#​32010, #​32040).
• Strengthened shape, rank, and parameter validation for Split, Scan, GatherND, ScatterND, SpaceToDepth/DepthToSpace, Crop, Conv, Normalizer, and pooling (#​29461, #​31668, #​32034, #​32039, #​32076, #​32157, #​32160, #​32161, #​32345, #​32349).
• Hardened generation and attention input handling, including attention-attribute narrowing, BifurcationDetector inputs, generation subgraph shapes, and QEmbed segment inputs. BeamSearch buffer expansion now uses dynamic shape storage (#​31648, #​31701, #​32009, #​32078, #​32144).
• Validated TreeEnsemble node references and bounded subtree comparison, rejected non-finite CPU RoiAlign coordinates, and required ImageScaler bias to match the channel count (#​32031, #​32043, #​32011, #​32002).
• Added an allowlist of safe LoRA adapter parameter data types, validated MatMulFpQ4 shape inputs, and checked MLAS blockwise quantization/dequantization index ranges (#​31682, #​32032, #​32007).

GPU Bounds and Resource Lifetimes

• Hardened CUDA indexing and buffer-size arithmetic in MatMulNBits, RemovePadding, RotaryEmbedding, SparseAttention, Whisper beam search, NMS, QDQ, and GatherElements (#​31643, #​31994, #​31995, #​31996, #​31998, #​32014, #​32029, #​32030).
• Fixed overflow in CUDA reduction scans and Softmax offset arithmetic, and handled zero-sized outputs in CUDA random-generator kernels (#​32137, #​32330, #​31997).
• Fixed CUDA MultiHeadAttention shared-cache scratch lifetimes and kept CudaAsyncBuffer staging storage alive across CUDA graph replay (#​31968, #​32121).
• Fixed WebGPU out-of-bounds subgroup-matrix loads for partial tiles, zero-initialized writable device-allocator buffers, and rejected foreign GPU handles in built-in data transfers (#​32364, #​32063, #​32317).

Dependencies and Tooling

• Upgraded Protobuf to 33.6 and refreshed Python documentation dependencies, including an ONNX security-related update (#​29906, #​32190, #​32424).
• Updated JavaScript dependencies including js-yaml, joi, fast-uri, and the Next.js end-to-end fixture (#​32397, #​32486, #​32488, #​32505, #​32508).
• Pinned GitHub Actions to full-length commit SHAs and strengthened packaging infrastructure with authenticated package feeds and NPM network isolation (#​32176, #​32005, #​32440).

New Features

Core APIs & Runtime

• Added Go bindings for the ONNX Runtime C API (#​29615).
• Extended memory importing with host-pointer support and added access to preallocated outputs through KernelContext::GetPreallocatedOutput (#​29726, #​32089).
• Added packed-attention workspace recipes and estimates, and made workspace input-shape handling aware of optional inputs (#​32283, #​32321, #​32312).
• Added DeepSeek Engram contrib operators, EngramGate and NGramHashMapping, and expanded kernel coverage for Qwen-3.5 operators (#​32268, #​32106).

Plugin Execution Providers

... (truncated)

1.29.1

This is a patch release on top of v1.29.0, containing GroupQueryAttention capability and KV-cache layout improvements, plugin Execution Provider performance tooling updates, and targeted graph and optimizer fixes.

GroupQueryAttention

• Added bidirectional GroupQueryAttention support on CPU and CUDA through a backward-compatible causal attribute, with explicit handling for unsupported execution paths (#​31704)
• Added a session option and Execution Provider metadata contract for using the BNHS Value KV-cache layout, with graph transformations that preserve compatibility with the existing BNSH operator schema (#​32139)
• Added CPU support for attention_bias with a sliding-window KV cache, including explicit position IDs and post-eviction bias indexing (#​32302)

Runtime and Performance Tools

• Fixed Compile API model serialization when output-model and custom initializer-location callbacks are used together, preventing duplicate graph fields in emitted models (#​32303)
• Updated onnxruntime_perf_test to use plugin Execution Provider device allocators for generated inputs, loaded test data, and pre-allocated outputs, avoiding unnecessary per-run host/device copies (#​32244)

Bug Fixes and Documentation

• Hardened FastGelu fusion to skip malformed Mul and Pow patterns (#​32016)
• Added validation for in-memory external initializer references, rejecting unregistered or mismatched data before graph transformation (#​32042)
• Restored the C API documentation workflow by switching the pinned Doxygen download to the official GitHub release asset (#​32210)

Contributors

Thanks to our 7 contributors for this release!

@​adrastogi, @​apsonawane, @​edgchen1, @​javier-intel, @​jnagi-intel, @​tianleiwu, @​Wayne-Ch

_{Release highlights were drafted with AI assistance and are subject to release-team review.}

Full Changelog: v1.29.0...v1.29.1

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