deps(python): update onnxruntime requirement from <2,>=1.28.0 to >=1.30.0,<2

[dependabot]

Updates the requirements on onnxruntime to permit the latest version.

Release notes

Sourced from onnxruntime's releases.

ONNX Runtime v1.30.0

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).

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Commits

• f2c39fe [CUDA] Add INT4 paged KV cache with per-channel scales (#32515)
• 5894ba8 Add portable random-access file reads to Env (#32503)
• a2ee3eb Fix CUDA plugin device discovery on WSL (#32517)
• b652e59 [WebGPU] Prepack Conv weights for the im2col-matmul path (#32420)
• 0f0f29f Get rid of spurious warning about not being able to find spectre mitigation (...
• 23dd651 Register ONNX schemas only when static registration is disabled (#32353)
• 33af5d3 Release external data loaders after graph initialization (#32502)
• 2e3c24d Clarify external initializer and EP context path interaction (#32442)
• e76036b [CUDA] Pin FP8 GEMV residency for grids just past two blocks per SM (#32433)
• 82583c5 Add session option for a BNHS GroupQueryAttention Value cache layout (#32139)
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