Update dependency com.microsoft.onnxruntime:onnxruntime to v1.26.0

[solrbot]

This PR contains the following updates:

Package	Type	Update	Change
com.microsoft.onnxruntime:onnxruntime (source)	dependencies	minor	1.24.3 → 1.26.0

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Release Notes

microsoft/onnxruntime (com.microsoft.onnxruntime:onnxruntime)

v1.26.0: 1.26.0

n.b. The following was generated via LLM from Git history. Only the contributor list has been verified.

ONNX Runtime Release 1.26.0

Announcement - Breaking Changes

• Support for CUDA 12 will be removed in 1.27.0.
  • CUDA 13 will continue to be published as onnxruntime-<os>-<arch>-gpu_cuda13-<version>.<ext>
• CUDA runtime will be moving soon to a dedicated Execution Provider (EP) instead of a published package from ORT core.

Highlights

• Added optional memory mapping for .ort model loads (#​28164).
• Added RISC-V Vector (RVV) support for CPU EP (#​28261).
• OpenVINO EP upgraded for 1.26.0 development release (#​28297).
• WebGPU gained GridSample support (#​28264) and Split-K improvements (#​28151).
• CUDA plugin EP gained graph support (#​28002), profiling API (#​28216).

Security and Reliability Hardening

• Replaced unrestricted Python setattr configuration with an allowlist (#​28083).
• Hardened multiple OOB and overflow scenarios across ML and core ops:
  • Attention mask index OOB write (#​27789).
  • MaxPoolGrad indices bounds validation (#​27903).
  • SVM and TreeEnsemble bounds/security fixes (#​27950, #​27951, #​27952, #​27989).
  • RNN sequence_lens OOB read and integer overflow handling (#​28052, #​28003).
  • GroupQueryAttention seqlens_k bounds validation and compatibility follow-up (#​28031, #​28259).
  • MatMulBnb4 and ML coefficient SafeInt checks (#​27995, #​28001).
  • CUDA Gather int32 overflow fix (#​28108).
  • GridSample float->int64 cast hardening for NaN/Inf/out-of-range coords (#​28302).
• Fixed session logger use-after-free during EP teardown under verbose logging (#​28274).

CUDA, Attention, and MLAS

• Filled CUDA opset/operator gaps and extended support:
  • Transpose opset 23 -> 25 (#​27740).
  • QuantizeLinear/DequantizeLinear opset 25 (#​28046).
  • CUDA TopK INT8/INT16/UINT8 support (#​27862).
  • LabelEncoder CUDA support for numeric types (#​28045).
• Attention/GQA improvements:
  • Fixed ONNX Attention min-bias alignment crash on SM<80 and masked-batch NaN behavior (#​27831).
  • Added FP32 QK accumulation path for unfused GQA attention (#​28198).
  • Added CUDART_VERSION reduction compatibility in GQA attention (#​28296).
  • Fixed CUDA 13 build error in GQA unfused attention (#​28309).
  • PagedAttention fallback for SM<80 fp16 (#​28200).
• MLAS updates:
  • FP16 Gelu enablement (#​26815).
  • Arm64 BF16 fast-math conv kernels for NCHW/NCHWc paths (#​27878).

WebGPU, WebNN, and JavaScript

• WebGPU feature and correctness updates:
  • Added GridSample (#​28264).
  • Split-K support for batch size > 1 (#​28151).
  • MatMulNBits refactor and batching improvements (#​28109, #​28197).
  • MHA correctness fix when present outputs are not requested (#​28027).
  • Buffer upload overflow fix (#​27948).
  • Position ID bounds validation in WebGPU/JS RotaryEmbedding (#​28214).
• WebNN change:
  • Renamed pool2d property roundingType -> outputShapeRounding (#​28172).
• JavaScript ecosystem maintenance:
  • Multiple dependency bumps.

Plugin EP and EP Ecosystem

• CUDA plugin EP:
  • Graph capture/replay support ported and expanded (#​27958, #​28002).
  • Sync support for IOBinding (#​27919).
  • Profiling API implementation (#​28216).
  • Resource accounting integration (#​28028).
• WebGPU plugin EP:
  • Pipeline updates and API init error handling fixes (#​28121, #​28211).
• Other EP updates:
  • CoreML: HardSigmoid and QuickGelu support; Pad reflect support/fixes (#​28182, #​28184, #​28073, #​28062).
  • NvTensorRTRTX compatibility and diagnostics updates (#​28263, #​27577).
  • QNN file-mapping guard improvements (#​27871).

Contributors

@​tianleiwu, @​yuslepukhin, @​edgchen1, @​vraspar, @​hariharans29, @​skottmckay, @​eserscor, @​xadupre, @​sanaa-hamel-microsoft, @​elwhyjay, @​Rishi-Dave, @​titaiwangms, @​adrianlizarraga, @​jatinwadhwa921, @​jchen10, @​Jiawei-Shao, @​maxwbuckley, @​preetha-intel, @​qjia7, @​qti-hungjuiw, @​RajeevSekar, @​umangb-09, @​adrastogi, @​akote123, @​amd-genmingz, @​ankitm3k, @​apsonawane, @​bachelor-dou, @​baijumeswani, @​bopeng1234, @​chilo-ms, @​chwarr, @​Craigacp, @​dccarmo, @​derdeljan-msft, @​ericcraw, @​fdwr, @​fs-eire, @​gaugarg-nv, @​gblong1, @​GopalakrishnanN, @​Honry, @​intbf, @​ishwar-raut1, @​Jaswanth51, @​javier-intel, @​JonathanC-ARM, @​julia-thorn, @​justinchuby, @​jwludzik, @​Kevin-Taha, @​Kotomi-Du, @​MayureshV1, @​mdvoretc-intel, @​miaobin, @​milpuz01, @​mingyueliuh, @​mklimenk, @​n1harika, @​prathikr, @​psakhamoori, @​qti-yuduo, @​quic-calvnguy, @​RyanMetcalfeInt8, @​sfatimar, @​sgbihu, @​ShirasawaSama, @​ssam18, @​susbhere, @​sushraja-msft, @​TejalKhade28, @​theHamsta, @​TomCrypto, @​TsofnatMaman, @​velonica0, @​vthaniel, @​wenqinI, @​xhan65, @​xhcao

v1.25.1: ONNX Runtime v1.25.1

n.b. This changelog is LLM generated. Only the contributor listing has been verified.

ONNX Runtime Release 1.25.1

📢 Announcements & Breaking Changes

ONNX Op Updates

• Enhanced ONNX operator support with new opset versions: Reshape (opset 25), Transpose (opset 24) (#​27752)

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✨ New Features

📊 New ONNX Ops & Model Support

• LinearAttention and CausalConvState operators for Qwen3.5 model support (#​27907)
• RotaryEmbedding (RotEMB) and RMSNorm operators added (#​27752)
• Linear Attention signature support (#​27842)

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🌐 Web & JavaScript

WebGPU EP

• Qwen3.5 model support on WebGPU execution provider (#​27996)
• QMoE 1-token decode path optimization — fused operations to reduce GPU dispatches for improved performance (#​27998)

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🐛 Bug Fixes

Core Runtime Fixes

• Improved filesystem error messages during Linux device discovery for better debugging experience (#​27289)
• Fixed missing include for SetRawDataInTensorProto in NVIDIA TensorRT RTX tests (#​28065)

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🙏 Contributors

Thanks to our 7 contributors for this release:
@​guschmue, @​sanaa-hamel-microsoft, @​apsonawane, @​eserscor, @​ishwar-raut1, @​qjia7, @​theHamsta

Full Changelog: microsoft/onnxruntime@v1.25.0...v1.25.1

v1.25.0: ONNX Runtime v1.25.0

📢 Announcements & Breaking Changes

Build & Platform

• C++20 is now required to build ONNX Runtime from source. Minimum toolchains: MSVC 19.29+, GCC 10+, Clang 10+. Users of prebuilt packages are unaffected. (#​27178)
• CUDA minimum version raised to 12.0 — CUDA 11.x is no longer supported. Users pinned to CUDA 11.x should stay on ORT 1.24.x or upgrade their CUDA toolkit/driver. (#​27570)
• ONNX upgraded to 1.21.0 (#​27601)
• sympy is now an optional dependency for Python builds. (#​27200)

Execution Provider Changes

• ArmNN EP has been removed. Users should remove any --use_armnn build flags and migrate to the MLAS/KleidiAI-backed CPU EP or QNN EP for Qualcomm hardware. (#​27447)

API Version

• ORT_API_VERSION updated to 25. (#​27280)

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🔒 Security Fixes

• Fixed potential integer truncation leading to heap out-of-bounds read/write (#​27544)
• Addressed Pad Reflect vulnerability (#​27652)
• Security fix for transpose optimizer (#​27555)
• Upgraded minimatch 3.1.2 → 3.1.4 for CVE-2026-27904 (#​27667)
• Hardened shell command handling for constant strings (#​27840)
• Added validation of onnx::TensorProto data size before allocation (#​27547)
• Cleaned up external data path validation (#​27539)
• Fixed misaligned address reads for tensor attributes from raw data buffers (#​27312)
• Fixed CPU Attention overflow issue (#​27822)
• Fixed CPU LRN integer overflow issues (#​27886)
• Additional input validation hardening:
  • Tile kernel dim overflow (#​27566)
  • Out-of-bounds read in cross entropy (#​27568)
  • TreeEnsembleClassifier attributes (#​27571)
  • AffineGrid (#​27572)
  • EmbedLayerNorm position_ids (#​27573)
  • RotaryEmbedding position_ids (#​27597)
  • RoiAlign batch_indices (#​27603)
  • MaxUnpool indices (#​27432)
  • QMoECPU swiglu OOB (#​27748)
  • SVMClassifier initializer (#​27699)
  • Col2Im SafeInt (#​27625)

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✨ New Features

🔌 Execution Provider Plugin API & CUDA Plugin EP

ORT 1.25.0 introduces the CUDA Plugin EP — the first core implementation that enables third-party CUDA-backed EPs to be delivered as dynamically loaded plugins without rebuilding ORT.

• CUDA Plugin EP: Core implementation (#​27816)
• CUDA Plugin EP: BFC-style arena and CUDA mempool allocators for stream-aware memory management (#​27931)
• Plugin EP Sync API for synchronous execution (#​27538)
• Plugin EP event profiling APIs (#​27649)
• Plugin EP APIs to retrieve ONNX operator schemas (#​27713)
• Annotation-based graph partitioning with resource accounting (#​27595, #​27972)
• EP API adapter improvements: header-only adapter, OpKernelInfo::GetConfigOptions, LoggingManager::HasDefaultLogger() (#​26879, #​26919, #​27540, #​27541, #​27587)
• WebGPU EP made compatible with EP API (#​26907)

🔧 Core APIs

• Per-session thread pool work callbacks API (#​27253)
• enable_profiling in RunOptions (#​26846)
• KernelInfo string-array attribute APIs for C and C++ (#​27599)
• OrtModel input support for Compile API (#​27332)
• Session config to create weightless EPContext models during compilation (#​27197)
• Compiled model compatibility APIs in example plugin EP (#​27088)
• Model Package support (preview): Initial infrastructure for automatically selecting compiled EPContext model variants from a packaged collection based on EP, device, and hardware constraints. The directory structure is not yet finalized. (#​27786)

📊 New ONNX Ops & Opset Coverage

• Attention opset 23 on CUDA with GQA, boolean masks, softcap, and softmax precision (#​26466, #​27030, #​27082, #​27428, #​27714)
• Attention opset 24 on CUDA, disjoint from contrib op (#​27542); nonpad KV seqlen on CPU (#​27384)
• TensorScatter-24 for CPU and CUDA (#​27389, #​27446)
• DeformConv for CPU/CUDA (#​27393)
• LpNormalization-22 (#​27164)
• CUDA opset gap fills:
  • Control flow & misc: Flatten, Identity, If, Loop, Scan, ConstantOfShape, Size (opset 21/23) (#​27728)
  • Pooling: GlobalAveragePool/GlobalMaxPool (→22) (#​27733)
  • Shape ops: Shape (→25), Squeeze/Unsqueeze (→25) (#​27734, #​27739)
  • TopK (→24, BF16) (#​27735), GRU (→22) (#​27738)
  • Pad (→25, wrap mode) (#​27774), Resize v19 (#​27415), RoiAlign v16/v22 (#​27646)

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🖥️ Execution Provider Updates

NVIDIA CUDA EP

• GQA with XQA and quantized KV cache, including FP8 (E4M3) KV cache support (#​27246, #​27321)
• CUDA graph capture compatibility for LLM ops and pre-compiled paths (#​27484, #​27477)
• Volumetric (3-D) GridSample support (#​27201)
• Optimized 3D nearest resize kernel for 5D tensors (#​27578)
• Optional router_weights input to QMoE (#​27687)

NVIDIA TensorRT RTX EP

• D3D12 external resource import support (#​26948)

Qualcomm QNN EP

• Disabled file mapping for embedded cache (#​27627)
• Fixed use-after-free of logger object (#​27804)
• Fixed wheel build issues on WSL and Linux SDK version propagation (#​27730, #​27800)

Other EPs

• VitisAI EP: Added PE version info to provider DLL (#​27626)
• DML EP: Fixed overflow in DmlGraphFusionHelper::ProcessInputData (#​27815), fixed new-delete mismatch in QuantizeLinear (#​27823)

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🌐 Web & JavaScript

WebGPU EP — Performance

• Gemm/MatMul optimization using subgroup features (#​26433)
• MatMulNBits: 2-bit zero-point support (#​27285, #​27325), higher K-parallelism (#​27834), DP4A SmallM tiling (#​27910)
• Flash Attention: head_sink support (#​27410), configurable multi rotary cache concat offset (#​27434)
• Optimized 4D Transpose (#​26942), string stream optimization (#​27223)

WebGPU EP — New Op Support

• Added TopK (#​27560), Softplus (#​27457), Identity (#​27067)
• Added Conv3D support (#​27917), LpNorm support (#​27876)
• int64/bool support for Range, Expand, Flatten, Gather, Unsqueeze (#​26673, #​27478, #​27561)
• DequantizeLinear fixes (#​27706), Einsum 5D tensor fixes (#​27779)

WebGPU EP — Stability

• Fixed device destroyed on session release breaking recreation (#​27634)
• Fixed static destruction crash on exit (#​27470, #​27569)
• Backward compat: Legacy WebGPU/WebNN memory info names are now accepted again (#​27637)
• Deterministic Split-K handling (#​27086), buffer segment alignment fix (#​27853)
• Binary size reduction for WebAssembly builds (#​27370, #​27371)

WebNN EP

• Broader GQA support and improved MultiHeadAttention (#​27234, #​27494)
• Added DepthToSpace support (#​27508)

Node.js & React Native

• Fixed float16 tensor support in Node.js and React Native (#​27327, #​27549)
• Added 16KB page size alignment for Android (required for Android 15+) (#​27523)

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🧠 CPU & Core Optimizations

MLAS / KleidiAI / Quantization

• KleidiAI BF16 SME2 kernel integration (#​26773), asymmetric 4-bit MatMulNBits on ARM64 (#​27751)
• Fused Silu and Gelu kernels for AVX512 (#​27690)
• Depthwise conv kernel for NCHW on AVX512 (#​27874)
• ARM64 NCHWc NEON asm kernels (#​27099, #​27788), BF16 KAI SBGemm on NCHWc ARM (#​27703)
• POWER10 Sgemm PackA optimization (#​27575)
• Improved pre-packing for 2-bit LUT kernels (#​27131)
• Backend kernel selector config in MLAS, allowing users to opt out of KleidiAI kernels on ARM platforms
  (#​27136)

DQ→MatMulNBits Fusion

Extended to cover significantly more quantized LLM inference scenarios on CPU:

• 2-bit and 8-bit weights with Cast(fp16→fp32) patterns (#​27614)
• FP16 models on CPU EP (#​27640), fp16 8-bit on ARM64 (#​27692)
• Gemm + per-tensor/per-channel quantization (#​27769)
• FP16 quantized weight compatibility: models with HQNBIT quantized weights now route through the FP32 MLAS path for broader CPU compatibility (#​27820)

Model Optimizer & Fusions

• Qwen3 model type support and RotaryEmbedding fusion for Qwen3 RoPE patterns (#​27556, #​27590)
• MobileClip attention fusion for both attention block patterns (#​27883)
• Nemotron speech conformer encoder MHA fusion (#​27764)
• Fixed GPT-2 no-past attention fusion for transformers ≥ 4.27 (#​27449)
• Fixed BART attention fusion for SDPA pattern from transformers ≥ 4.49 (#​27458)
• Pre-layer normalization support in attention fusion (#​27418)
• SkipLayerNorm fusion with bias Add (#​27765), broadcasting skip shapes (#​27489)
• SpaceToDepth fusion pattern (#​27747)
• NCHWc transformer: more patterns and ONNX-domain Gelu/HardSigmoid activations (#​27691, #​27821)
• Optimized qMoE code path for single-token execution (#​27383)
• ONNX Attention KV cache optimization with ConcatNewToPast (#​27613)

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🔌 Language Bindings

Python

• Exposed OrtDeviceVendorId enum for vendor-aware OrtDevice aliases (#​27594)
• Added bindings for GetCompatibilityInfoFromModel / GetCompatibilityInfoFromModelBytes (#​27565)
• Fixed OrtValue.from_dlpack rejecting zero-size tensors as non-contiguous (#​27451)

C#

• Added bindings for GetCompatibilityInfoFromModel / GetCompatibilityInfoFromModelBytes (#​27565)

Java

• Avoid provider resource extraction when library already exists in onnxruntime.native.path (#​27668)

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🐛 Bug Fixes

Critical Fixes

• Fixed CPU Attention overflow issue (#​27822)
• Fixed CPU LRN integer overflow issues (#​27886)
• Fixed incorrect pad indices in AveragePool count_include_pad computation — silent correctness issue (#​27375)
• Fixed integer division/modulo by zero in CPU EP Div and Mod operators (#​27693, #​27833)
• Fixed non-ASCII Unicode model path crash (#​27724)
• Fixed arithmetic overflow in Det operator (#​27070)
• Fixed narrow-to-wide string conversion bugs in DLL load error reporting (#​27777)

Operator & Graph Fixes

• Fixed 3D attention mask broadcasting in MHA (#​27464)
• Fixed GQA shape inference for present outputs (#​27250)
• Fixed Einsum bugs for reduction and empty input cases (#​27225, #​27226)
• Prevented cross-EP Cast fusion in RemoveDuplicateCastTransformer (#​27363)
• Fixed ConvTranspose bias input validation on CPU/CUDA (#​27209)
• Fixed Cast node naming collisions in float16 conversion (#​27469)
• Fixed concat/slice elimination and unsqueeze elimination against optional attrs and invalid models (#​27638)
• Improved EPContext error message when node is not assigned to an EP (#​27474)

EP-Specific Fixes

• Fixed MiGraphX EP double allocation (#​27551)
• Fixed MLAS qgemm dispatch and kernel regressions in quantized conv tests (#​27671)
• Fixed run-level profiling for subgraph operators (#​27870)
• Fixed --build_wasm_static_lib implicitly enabling --build_wasm (#​27342)

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🙏 Contributors

Thanks to our 72 contributors for this release!

@​tianleiwu, @​fs-eire, @​edgchen1, @​titaiwangms, @​hariharans29, @​eserscor, @​Rishi-Dave, @​guschmue, @​adrianlizarraga, @​jambayk, @​qjia7, @​skottmckay, @​adrastogi, @​sanaa-hamel-microsoft, @​yuslepukhin, @​ingyukoh, @​Jiawei-Shao, @​vraspar, @​xhcao, @​chilo-ms, @​Honry, @​JonathanC-ARM, @​kunal-vaishnavi, @​ShirasawaSama, @​chaya2350, @​derdeljan-msft, @​gedoensmax, @​HectorSVC, @​milpuz01, @​quic-calvnguy, @​xenova, @​akholodnamdcom, @​AlekseiNikiforovIBM, @​amd-genmingz, @​ashrit-ms, @​bachelor-dou, @​BODAPATIMAHESH, @​Colm-in-Arm, @​daijh, @​dodokw, @​fanchenkong1, @​ivarusic-amd, @​JanSellner, @​jchen10, @​jiafatom, @​jnagi-intel, @​johannes-rehm-snkeos, @​justinchuby, @​keshavv27, @​Kevin-Taha, @​kevinlam92, @​kpkbandi, @​Laan33, @​melkap01-Arm, @​miaobin, @​n-v-k, @​nico-martin, @​patryk-kaiser-ARM, @​praneshgo, @​prathikr, @​qc-tbhardwa, @​sagarbhure-msft, @​sdotpeng, @​the0cp, @​TsofnatMaman, @​umangb-09, @​walidbr, @​wenqinI, @​xadupre, @​xhan65, @​xiaofeihan1

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Full Changelog: v1.24.4...v1.25.0

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[epugh]

i dug in a bit on why onnx is part of core gradle.lockfile and found out that we have some maybe icky test structure. I will open a JIRA for this.