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[Vulkan] Match ATen special values in pow.Tensor_Scalar - #23212
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power_of only special-cased zero bases and used a 1e-5 tolerance to detect odd integer exponents, so NaN, infinities, signed zeros and negative bases with fractional exponents gave wrong results. It now follows ATen case by case, including the FP32 sqrt and rsqrt path for exponents of plus or minus 0.5. The requested tensor dtype survives device storage emulation, so FP16 keeps its distinct ATen special-value semantics on SwiftShader. Explicit nearest-even output conversion preserves the FP16 conformance cases on hardware GPUs. Authored with OpenAI Codex; split planned with Claude Code.
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/23212
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power_of only special-cased zero bases and used a 1e-5 tolerance to detect odd integer exponents, so NaN, infinities, signed zeros and negative bases with fractional exponents gave wrong results. It now follows ATen case by case: FP32 uses sqrt/rsqrt for exponents of plus or minus 0.5, while FP16 rounds the scalar exponent to half and uses generic pow. Tests cover exponents 2.0001 and 2049, whose half conversion changes the result category or sign. Devices without full FP16 support store half tensors as float. vTensor now records the requested dtype, and the binary scalar shaders receive it as a specialization constant so pow rounds the exponent and result to half with round-to-nearest-even. Explicit conversion also handles native FP16 stores whose rounding mode differs. These rules follow the graph's requested dtype, including force_fp16. Other operators retain their existing FP32 intermediate behavior on emulating devices. Part 12/15 of the Vulkan transformer and operator-conformance stack. The dependency #23250 has landed. Rebased onto `origin/main` at `265ed38b340180af63b1feafed2671e623da19ef`; review against main. Integration PR: #23254. Validation after rebase: Rebuilt ExecuTorch 1.6.0+87402fb from source with Vulkan enabled and PyTorch 2.14.0. Ran `TestVulkanDynamic.test_power_special_values` through the public Buck Python adapter on both drivers. SwiftShader passed all 40 combinations in 98.598 seconds; MoltenVK 1.4.1 on Apple M1 Pro passed all 40 in 96.458 seconds. Both runs had zero failures, errors, or skips. Each run covers FP32 and FP16, TEXTURE_3D and BUFFER, and all ten exponents, with NaN and signed-zero checks against PyTorch. Native macOS arm64 SwiftShader at `1e80438d2b93ef36a7c05f8d2b81233bac0e3d16` reports `shaderFloat16 = false` and `storageBuffer16BitAccess = false`, so that run exercises FP16 storage emulation. Lintrunner passed for all eight changed files; `git diff --check` and `pip check` passed. `git range-diff` confirms the rebase preserved the original patch. Recreates #23212 through ghstack. Prior review discussion remains on that PR. Authored with OpenAI Codex; split planned with Claude Code. cc @SS-JIA @manuelcandales @digantdesai @cbilgin
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Superseded by #23251, part 12/15 of the ghstack replacement series. The current integration PR is #23254. This PR is closed in favor of the replacement; its previous description and review history are retained.
power_of only special-cased zero bases and used a 1e-5 tolerance to detect odd integer exponents, so NaN, infinities, signed zeros and negative bases with fractional exponents gave wrong results. It now follows ATen case by case, including the FP32 sqrt and rsqrt path for exponents of plus or minus 0.5. The requested tensor dtype survives device storage emulation, so FP16 keeps its distinct ATen special-value semantics on SwiftShader. Explicit nearest-even output conversion preserves the FP16 conformance cases on hardware GPUs.
Part 12/15 of the Vulkan transformer and operator-conformance stack. Depends on #23211; review against the selected base branch. Integration PR: #23162.
Validation: 1 passed, 2 warnings in 72.00s (0:01:12). Lintrunner and git diff --check pass. Native tests use MoltenVK with portable CPU kernels; hardware and SwiftShader CI are pending.
Authored with OpenAI Codex; split planned with Claude Code.
cc @SS-JIA @manuelcandales @digantdesai @cbilgin