chore(deps): update dependency onnxruntime-web to v1.30.0

[renovate]

Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
onnxruntime-web	1.27.0 → 1.30.0

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Release Notes

Microsoft/onnxruntime (onnxruntime-web)

v1.30.0: ONNX Runtime v1.30.0

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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

• Added LoRA adapter support with plugin EP allocators, preserved custom allocators during EP registration, and reset plugin stream chunks before release (#​32221, #​32272, #​31983).
• Fixed CUDA plugin device discovery on WSL and improved Windows ARM64 packaging (#​32517, #​32355).

Execution Provider Updates

CUDA EP

Attention and Decoding

• Added INT4 paged KV caches with per-channel scales and an is_causal attribute to PagedAttention (#​32515, #​32225).
• Extended paged XQA with speculative decoding, query-to-KV head group size 6, head size 256 including FP16 caches, and native block tables for 128-token pages (#​32340, #​32108, #​32229, #​32263, #​32127).
• Enabled split-KV for paged FlashAttention decode and improved PagedAttention dispatch diagnostics (#​32102, #​32099).
• Added VarlenCausalConvWithState for continuous batching and compact variable-length causal-convolution state updates (#​32168, #​32290).
• Added a compact GatedDeltaNet operator and BFloat16 support for CUDA GatedDeltaNet (#​32282, #​32307).

MoE and Quantized Matrix Multiplication

• Added an opt-in FP8 DeepGEMM MoE decode path for supported fixed-shape QMoE workloads on Hopper GPUs (ORT_QMOE_FP4_DEEPGEMM=1, default off). This path is disabled on Windows (#​32122, #​32485).
• Bounded QMoE workspace with configurable row tiling and FP8 weight-dequantization scratch memory by tiling over output columns (#​32097, #​32129).
• Vectorized NVFP4 weight dequantization for prefill, tuned NVFP4 GEMV tiling for Qwen multi-token prediction, and extended speculative-decode GEMVs to 64 rows (#​32128, #​32140, #​32289).
• Tuned FP4 and FP8 GEMV scheduling for 48-SM SM121 GPUs, including FP8 KSplit32 scheduling, and improved FP8 GEMV residency for grids just beyond two blocks per SM (#​32408, #​32409, #​32433).
• Added an opt-in split-K GEMV path for small-N FP16 MatMul shapes and refined fpA-intB GEMV support checks (#​31478, #​32338).

Operators

• Improved TopK, ArgMax, and ArgMin performance for wide last axes, and accelerated low-lane INT64 CumSum (#​32404, #​32092, #​32238).
• Added a single-memcpy Slice fast path for contiguous subregions and removed pinned-buffer use from Split and Concat fast paths (#​28902, #​32410).
• Registered BF16 ReduceMean kernels and fixed ScatterElements reduction dispatch by element type and signed-zero handling in Abs (#​32326, #​29879, #​31477).

WebGPU EP

• Improved PagedAttention and added PagedAttention metadata, GPT-OSS support, and INT8 KV-cache block quantization (#​31727, #​32277, #​32284).
• Added INT64 Gather support and optimized MatMulNBits wide tiles with subgroup shuffle (#​31714, #​31703).
• Extended convolution fusion with eight additional activations, fused activation handling in the im2col path, and uniform-based activation parameters for Conv/MatMul (#​32117, #​32185, #​32116).
• Reused subgroup-matrix MatMul for pointwise convolution and added convolution-weight prepacking for the im2col-matmul path (#​32304, #​32420).
• Enabled GELU and BiasGELU fusion, added transpose-optimizer handling for Elu and contrib GELU variants, and enabled LayerNorm fusion to fix FP16 inference correctness (#​32053, #​32118, #​32294).
• Added subgroup-size control, selected subgroup size 32 for subgroup-matrix MatMul/Gemm, and enabled the subgroup-matrix path in WASM builds (#​32056, #​32306, #​32269).
• Scaled Dawn pipeline-compilation workers with CPU count, vectorized Split when all output segments are vec4-aligned, and selected pooling paths based on occupancy (#​29820, #​32251, #​32313).
• Exposed safe graph-capture I/O in Python, added a robustness provider option, and enabled forwarding of onnxruntime_perf_test -i options to WebGPU (#​32074, #​31971, #​32316).
• Fixed synchronization for user-provided GPUDevice instances, corrected MatMul pipeline-cache keys and the 1D-dispatch shader fast path, and changed copy_tensors misuse to report errors instead of terminating the process (#​32259, #​32048, #​32343, #​32315).

WebNN EP

• Added SkipLayerNormalization support and corrected output-rank validation and fallback data-type support checks (#​32377, #​31708, #​32067, #​32293).

TensorRT & DirectML

• Fixed TensorRT shape-value handling (#​32415).
• Validated kernel_shape and output_padding lengths during DirectML kernel setup (#​31999).

CPU & Core Optimizations

MLAS and CPU Kernels

• Added fused LinearAttention kernels for AVX-512, Arm64 NEON, and SVE (#​31674, #​32178, #​32356).
• Added AVX2 LayerNorm/RMSNorm kernels and registered BFloat16 LayerNorm/RMSNorm kernels on the CPU EP (#​31973, #​31974).
• Added Arm SVE i8mm INT8 QGEMM kernels and enabled the SBGemm fast-math path on Darwin Arm64 (#​31146, #​32152).
• Improved NCHWc convolution thread utilization, added HardSwish fusion for MobileNetV3 models, and introduced an AVX-512 16-wide Erf kernel and NCHWc reorder improvements for MobileCLIP-S0 (#​31660, #​31957, #​31958).
• Optimized INT4 weight prepacking on CPU, rejected KleidiAI Q4 prepacking with dynamic scales, and fixed Arm64 SymmQgemm INT16 overflow (#​31690, #​32068, #​32057).
• Fixed FP16 QuantizeLinear rounding, prevented CPU TensorScatter index overflow, serialized ScatterND string updates, and widened Compress loop counters (#​32452, #​32012, #​32033, #​32008).
• Improved edge-case handling for empty LpNormalization inputs, zero-element BiasGelu/FastGelu, extreme Trilu diagonals, and empty reduction axes (#​32020, #​31698, #​32013, #​32156).

Graph, Optimizer, and Runtime

• Hardened graph optimizers against model-supplied indices and invalid fusion inputs, including MatMulNBits dequantization block sizes, GQA projection shapes, and Slice starts rank (#​31670, #​31678, #​32018, #​32044).
• Prevented overlapping MatMulIntegerToFloat fusions, skipped overridable-initializer fusion, fixed FuseInitializersTransformer consumer-input lookup, and avoided fusing identity transposes into Gemm (#​32038, #​32143, #​32426, #​32435).
• Fixed optional zero-point input handling in QDQ, empty-initializer axis scaling, and NodeAttrHelper string-default lifetimes (#​32051, #​32138, #​32019).
• Released external-data loaders after graph initialization, added portable random-access file reads to Env, and clarified how external-initializer paths interact with EP context paths (#​32502, #​32503, #​32442).
• Avoided redundant ONNX schema registration when static registration is enabled, updated cpuinfo with thread-safe deinitialization, and prevented telemetry crashes in shell-less containers (#​32353, #​32300, #​32226).
• Reduced the active-session mutex scope around ETW callback registration and filtered out display adapters using the Microsoft Basic Render Driver (#​32000, #​32006).

Language Bindings & Web

• Retained Python asynchronous-run resources and pinned C# RunAsync arguments until completion (#​32041, #​32015).
• Fixed Java provider-option value cleanup, validated Rust tensor element types, and made Rust string-tensor output extraction safe (#​31828, #​32035, #​32045).
• Avoided duplicate N-API cleanup-hook registration in Node.js and added FP16 support to the JavaScript WebGPU Conv3DNaive shader (#​32469, #​32357).
• Fixed WinML image-dimension overflow (#​32046).

Build, Packaging & CI

• Upgraded CUTLASS to 4.7 and cuDNN Frontend to 1.27, and enabled compact fpA-intB kernel builds by default (#​32111, #​32324).
• Fixed Windows CUDA 12.9 SM120 compilation, CUDA 13 CCCL include paths in plugin builds, Windows DeepGEMM build errors, and PagedAttention builds without FlashAttention (#​32114, #​32392, #​32485, #​32327).
• Added D3D12 Agility SDK support for Dawn WebGPU builds while excluding WebGPU plugin EP builds from Agility SDK use (#​32253, #​32380).
• Added BTI support to MLAS AArch64 assembly, restored runtime vector-extension checks on RISC-V, and disabled POWER MLAS kernels when VSX is unavailable (#​32070, #​32406, #​32389).
• Improved CUDA plugin ARM64 build parallelism and packaging tests, and based plugin development versions on commit timestamps (#​32165, #​32399, #​32072, #​32095).
• Separated provider compilation from archive creation, corrected Python-binding delay-load linkage, and fixed Linux minimal-build dependencies and spurious Windows Spectre-mitigation warnings (#​32162, #​31637, #​32491, #​32516).
• Expanded PagedAttention CUDA test coverage and head-size-256 paged XQA coverage, corrected GatedDeltaNet test verification, and removed a Hugging Face download dependency from a dynamic-quantization test (#​31687, #​32262, #​32329, #​32443).
• Added WGSL template tests to CI and enabled WebGPU CI on WebGPU plugin EP release branches (#​32214, #​32090).

Contributors

Thanks to our 57 human contributors for this release!

@​4n4ny4, @​apsonawane, @​arnej27959, @​baijumeswani, @​bmehta001, @​chilo-ms, @​crvineeth97, @​daijh, @​danfiedler-msft, @​danielsongmicrosoft, @​dannyota, @​DKAIN-py, @​edgchen1, @​ericcraw, @​eserscor, @​fanchenkong1, @​hanbitmyths, @​hariharans29, @​hdharpure9922, @​Honry, @​jambayk, @​javier-intel, @​jchen10, @​jiafatom, @​jnagi-intel, @​justinchuby, @​kadyrbekovhamit-cyber, @​kunal-vaishnavi, @​Lapis0x0, @​LOGO127, @​Manogna-Sree, @​martin-klacer-arm, @​mei1127, @​miaobin, @​mirounga, @​miyanyan, @​MohamedElashri, @​mustjab, @​Nikhi00718, @​Noperi0r, @​Novestars, @​pkubaj, @​preetha-intel, @​qjia7, @​rvandermeulen, @​sanaa-hamel-microsoft, @​skottmckay, @​sushraja-msft, @​swetha097, @​sylvesterkaczmarek, @​tianleiwu, @​titaiwangms, @​toothache, @​xadupre, @​xhcao, @​xiaofeihan1, @​Zestion

Full Changelog: rel-1.29.1...rel-1.30.0

Release highlights were prepared with AI assistance.

v1.29.0: ONNX Runtime v1.29.0

Compare Source

Announcements & Breaking Changes

• onnxruntime-web has announced the deprecation of WebGL and JSEP. The native WebGPU EP is the recommended path going forward. See the deprecation and migration plans for details (#​29716, #​31683).
• POSIX telemetry is now available on Linux, macOS, Android, and iOS when ONNX Runtime is built with telemetry enabled. It does not change the public ABI, WebAssembly remains telemetry-free, and setting ORT_DISABLE_TELEMETRY=1 before initialization disables non-Windows telemetry for the process (#​27379, #​29872).
• The unused internal onnxruntime/python/tools/tensorrt dashboard tooling was removed. This does not affect the TensorRT Execution Provider APIs (#​29395).

Security Fixes

Path, bounds, and input validation

• Fixed a path traversal vulnerability in TensorRT and NvTensorRTRTX engine refitting by making external-data path validation unconditional (#​29396).
• Validated the CPU MoE k attribute against the number of experts and fixed a CPU TensorScatter security issue (#​29907, #​29916).
• Added missing rank, shape, and parameter validation for pooling, LSTM and DynamicQuantizeLSTM, Sampling, FeatureVectorizer, SkipLayerNorm, QLinearConv, Whisper decoding, RNN activations, GridSample, contrib Range, and CropAndResize (#​29254, #​29255, #​29265, #​29579, #​29595, #​29605, #​29871, #​31636, #​31671, #​31675, #​31676, #​31684).
• Hardened CUDA indexing and buffer handling in GridSample, transpose, GatherBlockQuantized, InstanceNormalization, LayerNorm/RMSNorm, BeamSearch, DeformConv, AveragePool, and MaxPool (#​29581, #​29631, #​29638, #​31640, #​31642, #​31644, #​31645, #​31647, #​31650).
• Fixed packed sub-byte tensor over-copying in OrtApi::GetValue and validated DML constant tensor byte sizes (#​29157, #​31665).

Supply chain and tooling

• Updated npm lockfiles, refreshed the Next.js end-to-end fixture lockfile for security advisories, and upgraded adm-zip for onnxruntime-node (#​29827, #​29926, #​31192).

New Features

Core APIs & Runtime

• Default intra-op and inter-op thread-pool sizes can now be set with ORT_INTRA_OP_NUM_THREADS and ORT_INTER_OP_NUM_THREADS. Explicit thread settings still take precedence, and 0 preserves machine-sized defaults (#​29688).
• Added weightless-model support for all initializer types, allowed zero-input EpContext nodes, and wired maximum-shape inference into workspace estimation (#​29607, #​29799, #​31613).
• Added ONNX-domain support for rotary embedding and a fused MRotaryEmbedding contrib operator for Qwen mRoPE variants (#​29261, #​31728).
• Added multi-shape profiling to onnxruntime_perf_test through --data_shape, plus verbose graph-transformer tracing and broader inference-session error-path coverage (#​29555, #​29558, #​29569, #​29571).

Execution Provider ABI & Plugin EPs

• WebGPU now supports device-free compile-only sessions for offline graph transformation (#​29681).
• Expanded CUDA plugin EP packaging and testing, including Windows ARM64 package and size options, updated package outputs, and aligned architecture selections across Python, C API, TensorRT, Node.js, and plugin packages (#​31635, #​31722, #​31992).
• Improved plugin lifecycle handling by unloading failed EP library loads and fixing allocator-deleter lifetime (#​29634, #​29770).

Execution Provider Updates

NVIDIA CUDA EP

Attention and decoding

• Added PagedAttention with quantized KV cache, XQA decode, MLA, QK-Norm, and head-sink support (#​29912).
• Extended quantized KV-cache support with attention sinks, independent and per-channel scales, sliding-window cache support, and a fused K/V dequantization launch (#​29900, #​29904, #​31480).
• Added a cuDNN SDPA decode tier to the standard ONNX Attention CUDA kernel and enabled cuDNN SDPA for contrib Attention (#​29715, #​29717).
• Added attention_bias support to the GroupQueryAttention unfused path and state_window support to LinearAttention and CausalConvWithState for MTP (#​29525, #​31157).
• Fixed LinearAttention on GPUs with limited shared memory (#​31982).

MoE and quantized GEMM

• Added NVFP4 QMoE, including native FP4xFP4 prefill on SM120, faster decode GEMV, fused routing/finalization paths, and reduced activation and weight-dequantization overhead (#​29697, #​29824, #​29887, #​29919, #​31156, #​31159, #​31349, #​31479).
• Added MatMulBlockQuantizedFp4Weight and MatMulBlockQuantizedFp8Weight, plus block-scaled tensor-core/GEMV decode paths, packed FP4 decode, M-tiling, and folded W8A8 activation QDQ (#​29818, #​29850, #​29896, #​31155, #​31481).
• Improved MatMulNBits and QMoE robustness and efficiency by optimizing 8-bit dequantization, releasing raw MXFP4 initializers after prepack, and fixing subgraph prepacking and mixed FP8/FP4 build failures (#​29852, #​31141, #​31154, #​31350).

Operators and collectives

• Added LinearAttentionGate, GatedRMSNorm, and GatedAdd contrib operators (#​31158, #​31835).
• Added bfloat16 support to AllReduce, AllGather, and AllToAll (#​31571).
• Fixed the default zero point in CUDA GatherBlockQuantized (#​31693).

WebGPU EP

• Added DFT, HardSwish, Max/Min, Trilu, GRU, PRelu, MatMulBnb4, and MRotaryEmbedding support (#​29454, #​29587, #​29828, #​29833, #​29840, #​29845, #​30512, #​31976).
• Expanded integer support across Clip, Reshape, Cast, Add, Tile, Concat, Expand, Gather, CumSum, Max, and Min (#​29830, #​29834, #​29835, #​29839, #​29844, #​29847, #​29854, #​29861, #​29897, #​29918, #​31049, #​31702, #​31709).
• Added the initial WebGPU PagedAttention implementation and moved Softmax and non-flash Attention to online algorithms (#​29694, #​29724, #​31611).
• Improved MatMulNBits wide-tile accumulation precision (#​29611).
• Added and extended Intel subgroup-matrix MatMul/Gemm kernels, including f16, batched-B, and odd-N support (#​29592, #​29749, #​29813, #​29893).
• Reduced cold-start and upload overhead with deferred dispatch and staging-buffer improvements; tuned FlashAttention, subgroup Gemm/MatMul, Split-K on Panther Lake, and Xe im2col-matmul (#​29271, #​29505, #​29557, #​29586, #​29846, #​30514).
• Upgraded Dawn and improved reliability by avoiding exceptions in Dawn callbacks, fixing a Linux adapter-failure self-deadlock, correcting Windows x86 transfer callbacks, and fixing TurboQuant batched sequence lengths (#​29389, #​29591, #​29625, #​29752, #​31568).

WebNN EP

• Added uint8-packed 4-bit GatherBlockQuantized and LpNormalization, reused the shared WASM loader for Blob-backed external data, and fixed per-axis QDQ and MatMulNBits edge cases (#​29475, #​29801, #​31151, #​31152, #​31197).

OpenVINO / QNN / DML / XNNPACK / TensorRT

• OpenVINO fixed float16 constant-output corruption and output-name routing, added dot-separated KV-cache names to the stateful transform, and corrected raw-data-backed float initializer handling (#​29729, #​29882, #​29895, #​31138).
• QNN added a reshape handler for split-axis reshapes (#​29660).
• DML fixed wide-string handling and made fused graph kernels own their model paths (#​31656, #​31664).
• XNNPACK now reads dynamic Gemm M from the input tensor at compute time (#​31189).
• TensorRT deduplicated context-path handling and added a build option for fused-attention cubins (#​29640, #​31632).

CPU & Core Optimizations

MLAS

• Added Arm64 half-precision GEMM and convolution support through KleidiAI, including FP16 MatMul/Gemm/Conv paths and asymmetric Q4 and SME2 MatMulNBits kernels (#​28786, #​29654, #​29709, #​29898).
• Added a RISC-V RVV QNBitGemm backend, an Arm64 NEON fp32 RoPE kernel, portable SVE elementwise kernels with FEXPA exp, and Arm64 UDOT routing for S8U8 QGEMM (#​29537, #​29787, #​29836, #​31145).
• Added AVX2/VNNI 2-bit weight kernels and vectorized 2-bit dequantization, and improved fp16 MatMulNBits paths by avoiding fp32 temporaries and writing fp16 output directly across 2-, 4-, and 8-bit paths (#​29619, #​29766, #​29791, #​29842, #​29864, #​29901).

CPU Attention & Kernels

• Improved masked Attention performance, enabled CPU FlashAttention on Linux Arm64 through L2-cache detection, and added FP16 GQA with quantized KV cache (#​29621, #​29719, #​29825).
• Added double support to CPU Cos and int32 support to CPU Trilu, and fixed int8 QLinearSoftmax saturation and AvgPool ceil_mode/count_include_pad behavior (#​28975, #​29476, #​29629, #​29728).
• Fixed TfIdfVectorizer weight indexing and skipped MinLength logits-processor construction when eos_token_id is negative (#​29604, #​31649).
• Tightened K/V and cache-indirection shape contracts in CPU Attention and MultiHeadAttention, and fixed LinearAttention output shape inference for grouped-query attention (#​29892, #​31190, #​31634).

Graph, Optimizer, and Runtime

• Extended reshape fusion, fixed double recursion in subgraph type/shape inference, and made constant-folding output deterministic (#​29027, #​29617, #​29789).
• Fixed in-memory external initializer loading, memory-pattern allocation stream selection, and a leak in GetOverridableInitializerNames() (#​29349, #​29589, #​29616).
• Reduced small MatMul batch allocations and redundant LUT initialization (#​29085, #​29690).
• Fixed static-initialization-order crashes when importing ONNX Runtime and reduced eager runtime initialization (#​29880, #​31964).
• Negative CPU Split axes now produce an error instead of being accepted (#​31149).

Web & JavaScript

• Added on-demand loading of Blob-backed external data in JSPI builds (#​29477).
• Fixed JSEP pooling output shape for ceil_mode, allowed DFT to ignore excess input data, and fixed a webpack/Terser release-build crash (#​29627, #​29680, #​31652).

Build, Packaging & CI

• CUDA package architecture selections are now aligned across plugin EP, Python, C API, TensorRT, and Node.js pipelines. Windows arm64 is only available in CUDA plugin EP (#​31992):

  OS	CUDA	CUDA architectures (all in -real form)

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