[ExecuTorch][WebGPU] Add 4-bit weight-only quantized linear (et_vk.linear_q4gsw)#20262
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JulianCloudNTH merged 5 commits intoJun 13, 2026
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…ation) Pull Request resolved: #20201 Backend-agnostic GPU-timestamp infrastructure, split out so the general implementation is foundational (below SDPA) while the SDPA-specific dispatch labeling stays above the SDPA op. Composed of: `WebGPUQueryPool`, a faithful re-port of Vulkan's `vkapi::QueryPool` (`backends/vulkan/runtime/vk_api/QueryPool.{h,cpp}`) — same `ShaderDuration` data model and ticks->ns conversion; three deviations are forced by the WebGPU API (per-dispatch bracketing via a compute-pass `timestampWrites` descriptor since there is no mid-encoder `writeTimestamp`; readback via `resolveQuerySet` + buffer map rather than host-side `vkGetQueryPoolResults`; the `TimestampQuery` capability requested as an explicit device feature, fail-open if the adapter lacks it). `WebGPUDevice` gains timestamp-feature detection, and `WebGPUGraph` gains a per-dispatch `kernel_name` label plus `execute()` bracketing of each compute pass when the pool is active. Opt-in via the `WEBGPU_TIMESTAMP_QUERY` env var; off by default, so the production `execute()` path is byte-identical. The SDPA per-kernel labeling lives in the companion "for SDPA" diff above the SDPA op. Co-authored with Claude. ghstack-source-id: 392093395 @exported-using-ghexport Differential Revision: [D108188287](https://our.internmc.facebook.com/intern/diff/D108188287/)
… input_pos Pull Request resolved: #20086 Adds the fused `sdpa_with_kv_cache` op (QK attention-weights, softmax, attention-output sub-kernels over the KV cache), composing the three enablers below it: the base graph's inter-dispatch buffer passing (scratch buffers + multi-pass execute), the `update_cache` op, and the SymInt live-scalar mechanism. The QK/softmax/AV kernels mirror the Vulkan reference's flat-index/GQA/causal-mask math (NCHW, buffer-only, fp32). `input_pos` is consumed dynamically via the SymInt mechanism: the op reads `symint_buffer()` as a uniform, sizes its scratch + dispatches for the max context length, and registers a resize hook so a single delegate runs an autoregressive decode loop (feed only the new token + advancing `input_pos`) instead of a fixed baked position. Mirrors the Vulkan SymInt = live uniform-buffer design. Tests live in the stacked test-suite diff above (clean op diff here). Authored with assistance from Claude. ghstack-source-id: 392609088 @exported-using-ghexport Differential Revision: [D107595125](https://our.internmc.facebook.com/intern/diff/D107595125/)
…-graph KV cache Pull Request resolved: #20087 Adds the WebGPU SDPA test coverage as its own diff, stacked on the SDPA op (which already carries the dynamic-`input_pos` consumption) and the SymInt mechanism below it: multi-step prefill->mt->decode replay, runtime-dynamic `input_pos` (autoregressive decode), and an in-graph mutable KV cache, each compared against a torch `F.scaled_dot_product_attention` golden. - `test/ops/sdpa/test_sdpa.py`: `ReplaySeq`/`REPLAY_SEQS` + per-step replay export/golden; `DynamicSdpaModule` + `export_dynamic_decode` (one `.pte`, `input_pos` supplied at runtime as a SymInt); `DecodeCacheModule` + `export_incache_decode` (KV cache as `register_buffer` mutable buffers, so the cache persists in-graph and forward() feeds only the new token + `input_pos`). - `test/test_webgpu_native.cpp`: `test_sdpa_replay`, `test_sdpa_dynamic_decode` (+ negative control: a pinned `input_pos` diverges), `test_sdpa_incache_decode` (+ static control: a fresh Module per step diverges, proving in-graph accumulation is real), `test_symint_roundtrip`, `test_resize_hook`; shared per-element tolerance `sdpa_within_tol` (abs 1e-4 OR rel 1e-3). - `test/test_build_webgpu.sh`: export the replay / dynamic / in-graph-cache models for the native test. Authored with assistance from Claude. ghstack-source-id: 392255556 @exported-using-ghexport Differential Revision: [D107595144](https://our.internmc.facebook.com/intern/diff/D107595144/)
…near_q4gsw) Pull Request resolved: #20226 Adds the `et_vk.linear_q4gsw` operator (4-bit groupwise-symmetric weight-only linear) to the WebGPU backend: dequantize the packed int4 weight in WGSL (`(q-8)*scale`) and accumulate an fp32 matmul, consuming the serialized `[N, K/2]` uint8 weight directly (no prepack), one workgroup per output row. Mirrors the Vulkan reference (`backends/vulkan/.../impl/QuantizedLinear.cpp`). The dispatch carries a `linear_q4gsw` label for GPU-timestamp-query profiling (mirroring the SDPA kernels). The numerical test suite is in the stacked test diff. ghstack-source-id: 392908894 @exported-using-ghexport Differential Revision: [D108312283](https://our.internmc.facebook.com/intern/diff/D108312283/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20262
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #20226 by @JulianCloudNTH
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/23/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/23/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/20/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/23/orig
Differential Revision: D108312283
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