github microsoft/onnxruntime v1.25.0
ONNX Runtime v1.25.0
on GitHub

10 hours ago

📢 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)

🔒 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)

✨ 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)

🖥️ 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)

🌐 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

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)

🧠 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)

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)

🔌 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)

🐛 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)

🙏 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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