deps: Bump Microsoft.ML.OnnxRuntime from 1.28.0 to 1.30.0

[dependabot]

Updated Microsoft.ML.OnnxRuntime from 1.28.0 to 1.30.0.

Release notes

Sourced from Microsoft.ML.OnnxRuntime's releases.

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

1.29.0

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).
  ... (truncated)

1.28.2

This is a patch release on top of v1.28.1, containing a targeted fix for Compile API model serialization.

Highlights

Bug Fixes

• Fixed Compile API callback serialization to prevent duplicate graph nodes, inputs, outputs, and value information in emitted optimized models, including models with embedded or external initializers (#​32303)

Contributors

Thanks to our contributor for this release!

@​adrastogi

Full Changelog: v1.28.1...v1.28.2

These release notes were drafted with assistance from GitHub Copilot.

1.28.1

This is a patch release on top of v1.28.0, containing support for device-free WebGPU compilation, improved compatibility with sandboxed Windows processes, and targeted graph-validation fixes.

WebGPU EP

• Added support for device-free compile-only sessions, enabling offline graph transformation and optimized-model serialization without access to GPU hardware (#​29681)

Bug Fixes

• Prevented an access violation in Windows processes under Win32k lockdown by skipping DXGI device discovery (#​29755)
• Allowed zero-input EPContext nodes, aligning their schema with support for compiling zero-input models (#​29799)
• 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)

Contributors

Thanks to our 4 contributors for this release!

@​apsonawane, @​shiyi9801, @​adrastogi, @​mingmingtasd

Full Changelog: v1.28.0...v1.28.1

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