[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723

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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723

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@meta-clameta-claBot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Jul 4, 2026
JCNTH added 3 commits July 4, 2026 10:32
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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ghost merged commit d71f607 into gh/JulianCloudNTH/75/origJul 4, 2026
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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Merged
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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gh/JulianCloudNTH/76/orig
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/orig
@diff-train-skip-merge
@JCNTH
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
Merged
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Jul 4, 2026
Merged
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

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@diff-train-skip-merge
@JCNTH
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/orig
@diff-train-skip-merge
@JCNTH
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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Merged
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/orig
@diff-train-skip-merge
@JCNTH
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
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This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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Merged
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/orig
@diff-train-skip-merge
@JCNTH
[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test - #20723
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fold unit test#20723
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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: #20584 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/76/base

ghstack PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/head

Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/75/orig

Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/JulianCloudNTH/76/orig
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[ExecuTorch][WebGPU] 2D compute dispatch tests — prefill golden + fol…
db65e54
…d unit test
Pull Request resolved: #20584
**Test coverage for the 2D dispatch fold, stacked above the cap-lift op.**
**Problem**: The 2D fold is load-bearing index math — a wrong `{x, y}` means out-of-bounds writes or dropped threads — and the prefill shapes that exercise it previously threw at the 1D cap, so they were untested.
**Solution**: A device-free unit test for the fold arithmetic, plus two single-shot prefill SDPA golden configs that fold each kernel family.
- **Before**: no coverage for >65535-workgroup dispatch; `llama1b_prefill_512`/`_2048` shapes threw at the cap
- **After**: `fold_workgroup_count_2d` unit-tested at the cap boundaries, and the two prefill shapes run as goldens
**Implementation**:
- `test/native/test_dispatch_2d.cpp` — device-free unit test for `utils::fold_workgroup_count_2d`: the 1D fast path, the 2D fold, the real Llama-1B QK counts at S=512 (`{65535, 3}`) and S=2048 (`{65535, 33}`), and the needs-3rd-dimension throw; asserts each `{x, y}` covers `[0, count)`
- `llama1b_prefill_512` + `llama1b_prefill_2048` configs appended to the byte-mirrored `CONFIGS` (`test_sdpa.py`) and `kSdpaConfigs` (`test_webgpu_native.cpp`)
- Registers `webgpu_dispatch_2d_test` in CMake + the native CI script
**Constraints**:
- The Python/C++ config entries byte-mirror each other (kept in sync)
- `add` shares the element-form path with QK, so it is covered structurally; a dedicated >16M-element `add` fold case is omitted as disproportionate
Co-authored-with: Claude Code.
ghstack-source-id: 399812923
@exported-using-ghexport
Differential Revision: [D109517683](https://our.internmc.facebook.com/intern/diff/D109517683/)
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🔗 Helpful Links
🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20723
Note: Links to docs will display an error until the docs builds have been completed.
This comment was automatically generated by Dr. CI and updates every 15 minutes.
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[ExecuTorch][WebGPU] 2D-fold mul + permute dispatch (lift 65535 1D cap)
b2dc380
Pull Request resolved: #20651
**Lift the 65535 workgroup-per-dim cap for `mul` and `permute` so they run at any numel.**
`mul.Tensor` and `permute` still used `compute_1d_workgroup_count`, which throws once `numel / wg_size > 65535` — hit by a realistic Llama-3.2-1B LoRA layer (`mul` over `[2048, 8192]` = 262k workgroups; `permute` of `[2048, 2048]` = 65536). `add`/`sub`/`div`/`fill`/`sdpa` already use the 2D fold; this brings `mul` + `permute` in line.
Key changes:
- `mul/BinaryOp.cpp`, `permute/Permute.cpp` — `compute_1d_workgroup_count` → `compute_2d_workgroup_count` (returns `utils::WgCount`); dispatch + resize hook now set both `workgroup_count_x` and `workgroup_count_y`.
- `binary_mul.wgsl`, `permute.wgsl` — `main` takes `@builtin(num_workgroups)`; flat index `gid.x + gid.y * (num_workgroups.x * wg_size)` (regenerated `*_wgsl.h`).
Mirrors the landed `add` op fold (`runtime/ops/add/{BinaryOp.cpp,binary_add.wgsl}`).
Co-authored-with: Claude Code.
ghstack-source-id: 399812930
@exported-using-ghexport
Differential Revision: [D110149677](https://our.internmc.facebook.com/intern/diff/D110149677/)
@JCNTH
[ExecuTorch][WebGPU] Use requiredFeatures instance API on native + em…
f60c388
…scripten Dawn
Pull Request resolved: #20652
**Key the `timedWaitAny` instance setup to the actual Dawn API instead of `__EMSCRIPTEN__`, so native-rig Dawn and emscripten/emdawnwebgpu use the modern `requiredFeatures` path and only the vendored Dawn uses the legacy `capabilities.*` path.**
The instance-descriptor setup was guarded by `#if defined(__EMSCRIPTEN__)`, which routed emscripten (emdawnwebgpu, emcc 4.0.19+) through the legacy `capabilities.*` API that no longer exists there. The guard now keys off the API actually present.
Key changes:
- `WebGPUDevice.cpp` — `#if defined(__EMSCRIPTEN__)` → `#if defined(WEBGPU_DAWN_INSTANCE_CAPABILITIES)`. The legacy `instance_desc.capabilities.*` path is taken only by the buck-vendored Dawn (which defines the macro); native cmake Dawn and emscripten leave it undefined and take the `requiredFeatures` / `WGPUInstanceFeatureName_TimedWaitAny` path.
Co-authored-with: Claude Code.
ghstack-source-id: 399812934
@exported-using-ghexport
Differential Revision: [D110149678](https://our.internmc.facebook.com/intern/diff/D110149678/)
@JCNTH
[ExecuTorch][WebGPU] Convert remaining native tests to GTest
5fedf64
Pull Request resolved: #20706
Convert the remaining hand-rolled `int main()` + printf/`bool ok` native tests to GTest so the whole `backends/webgpu/test/` suite is uniform, filterable via `--gtest_filter`, and self-reporting (extends the GTest conversion already applied to `test_dynamic_shape`). The five converted files are a harness-only change — every test case, tensor shape, tolerance, artifact filename, and skip condition is preserved 1:1, only the pass/fail reporting mechanism changes — and this diff additionally wires the already-GTest `webgpu_dynamic_shape_test` into the CI runner so the dynamic-shape suite actually executes.
Key changes:
- `test/test_webgpu_native.cpp`, `test/native/test_dispatch_order.cpp`, `test/native/test_index.cpp`, `test/native/test_scratch_buffer.cpp`, `test/native/test_update_cache.cpp` — `main`+`printf`/`bool ok` accumulator → `TEST()` cases using `EXPECT_*`/`ASSERT_*`; each keeps a custom `main()` that brings up the WebGPU device once then `RUN_ALL_TESTS()` (device-absent still SKIPs by returning 0). `test_index`/`test_webgpu_native` use inclusive `EXPECT_LE(err, tol)` to match the original `err > tol` fail gate exactly.
- `CMakeLists.txt` — move every native-test target into the `if(TARGET GTest::gtest)` block, linking `GTest::gtest`.
- `scripts/test_webgpu_native_ci.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` to the native-test configure so the now-gtest-gated targets are defined, and wire `webgpu_dynamic_shape_test` into the runner: export its `.pte`s + goldens via `export_dynamic_shape_cases`, add it to the built/run target list behind the same `--target help` probe, and run it guarded (mirroring the `index` test).
- `test/test_build_webgpu.sh` — add `-DEXECUTORCH_BUILD_TESTS=ON` so the local build script (which builds the now-gtest-gated targets unconditionally) still finds them.
ghstack-source-id: 399812941
@exported-using-ghexport
Differential Revision: [D110536636](https://our.internmc.facebook.com/intern/diff/D110536636/)
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