Microsoft.ML.OnnxRuntime

ONNX Runtime WebGPU Plugin EP 0.3.0 expands model and data-type coverage, improves generative-model performance, and strengthens configuration, reliability, and release tooling.

These release notes were drafted with AI assistance.

Highlights

Model and operator coverage

• Added initial PagedAttention support, MRotaryEmbedding, GRU, DFT, PRelu, HardSwish, Trilu, Max and Min, and MatMulBnb4. (#31611, #31976, #29840, #29454, #30512, #29828, #29845, #29833, #29587)
• Expanded integer support across common operators, including int64 for Add, Cast, Clip, Concat, Equal, Gather, Min, Max, ReduceSum, Reshape, Sub, Tile, and Where; uint8 for Cast, Expand, Gather, and Reshape; and int32/uint32 for CumSum and Tile. (#28804, #29392, #29830, #29834, #29839, #29844, #29847, #29854, #29861, #29897, #31049, #31702, #31709, #31714)
• Added 2-bit GatherBlockQuantized support and integrated ONNX 1.22 with opset 27. (#29054, #28754)

Generative models

• Added quantized KV cache support and expanded GQA with sliding-window cache, batched right-padded prompts, and FlashAttention graph capture for Gemma 4. (#28059, #29236, #29247, #29904)
• Fixed GQA and quantized KV cache correctness for rotary batched prefill, split-reduce decode, and per-batch sequence lengths. (#29002, #29593, #29752)

Performance

• Added deferred dispatch to parallelize shader compilation during cold start. (#29557)
• Added Intel subgroup-matrix MatMul and FP16 Gemm kernels, then extended them with batched-B and odd-N weight support and additional tiling optimizations. (#29271, #29592, #29749, #29813, #29893, #30514)
• Switched Softmax and non-flash Attention to an online softmax algorithm and tuned FlashAttention decode. (#29694, #29724, #29586)

Reliability and configuration

• Added a robustness provider option, made the pending-dispatch limit configurable, and exposed WebGPU buffer-cache options through the JavaScript API. (#31971, #28894, #29017)
• Improved validation and bounds handling in GridSample, LSTM, DynamicQuantizedLSTM, Slice, and GatherBlockQuantized, and fixed WebGPU data-transfer callbacks on Windows x86. (#28704, #29030, #29255, #29595, #31568)
• Corrected profiling timestamp alignment with the ONNX Runtime profiler. (#29021)

Build and release tooling

• Added device-free compile-only sessions for offline graph transformation and removed the dynamic Duktape/Node WGSL-generation path in favor of generated shader sources. (#29681, #28355, #29141)
• Upgraded Dawn, fixed parallel-build races, refreshed release and packaging documentation, and enabled WebGPU CI on plugin release branches. (#29389, #29040, #29777, #29059, #32090)

Contributors

Thank you to everyone who contributed to this release:

@AngelGalindo7, @daijh, @danielsongmicrosoft, @edgchen1, @fanchenkong1, @feich-ms, @guschmue, @haoxli, @hariharans29, @Honry, @huningxin, @jchen10, @Jiawei-Shao, @miaobin, @mingmingtasd, @mirounga, @mustjab, @nicholascelestin, @prathikr, @qjia7, @Reranko05, @Shivani767, @skottmckay, @ssam18, @sushraja-msft, @tairenpiao, @tianleiwu, @titaiwangms, @wuisabel-gif, @xhcao, and @xiaofeihan1.

Scope: commits affecting ONNX Runtime WebGPU Plugin EP code, tests, build integration, and packaging since plugin-ep-webgpu/v0.2.1.

Highlights

• Major performance work for attention-heavy LLMs.

  • FlashAttention decode kernels were fused and extended for any sequence length (#28389).
  • FlashAttention prefill shared-memory path was generalized (#28520).
  • Dynamic max_k_step was enabled for NVIDIA (#28511).
  • QKV bias support was added for FlashAttention in MultiHeadAttention (#28380).
  • M4 Max-specific FlashAttention optimization landed (#27780).

• Qwen3 and Gemma 4 model-path improvements.

  • QKV and MLP fusions for Qwen3-style models (#28280).
  • Q/K RMSNorm fusion into GroupQueryAttention for Qwen3-style models (#28484).
  • Opset 24 and KV-shared decoder layer support for Gemma 4 (#28501).
  • GroupQueryAttention now supports optional present-key/value outputs (#28242).

• LinearAttention and quantized-path optimizations.

  • LinearAttention subgroup optimizations and larger tile_v with subgroup support (#28412, #28519).
  • GatherBlockQuantized gained 2-bit support (#28530).

• Reliability and hardening fixes.

  • Fixes for out-of-bounds read risks in GatherBlockQuantized and Pad (#28718, #28721).
  • Fix for past_state == present_state buffer handling (#28753).
  • Fixes in QMoE numerical stability and SkipSimplifiedLayerNormalization bias behavior (#28434, #28427).
  • ConvTranspose weight shape validation improvement (#28524).

• Graph-capture and buffer-management improvements.

  • Per-graph buffer manager and session-level buffer pool updates for graph capture reuse (#28260, #28761).

Note: This section was AI-generated. It may have inaccuracies.

Contributors

Thanks to everyone who contributed to the WebGPU EP (human contributors, alphabetical):

@apsonawane, @daijh, @edgchen1, @feich-ms, @GopalakrishnanN, @guschmue, @hariharans29, @HectorSVC, @jchen10, @qjia7, @tianleiwu, @xiaofeihan1, @xenova, @yuslepukhin.

Note: This list was compiled on a best-effort basis from PRs that touched WebGPU EP-specific paths and
intentionally includes human contributors only, so it may not capture every contribution. If yours was
missed, the omission is unintentional. Your work is no less appreciated.