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Updated GitHub.Copilot.SDK from 1.0.31 to 1.0.34.

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Updated Microsoft.ML.OnnxRuntime from 1.31.1 to 1.32.0.

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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).
• This is the last release for CocoaPods, and we will switch to Swift Package Manager.
• We will use combo onnxruntime-gpu python package to reduce storage size from next release. You can test it here

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).
  ... (truncated)

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