[EE/BE] add float variant to unary_ufunc_realhbbf16_to_floathbf16 #12277
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Differential Revision: [D77759060](https://our.internmc.facebook.com/intern/diff/D77759060/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/12277
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit c18c05e with merge base ba19c75 ( BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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| double rsqrt(double x) { | ||
| template <typename T> | ||
| T rsqrt(T x) { | ||
| return 1.0 / std::sqrt(x); |
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think this should be T(1.0) otherwise we're forcing computation in double, right?
| double rsqrt(double x) { | ||
| template <typename T> | ||
| T rsqrt(T x) { | ||
| return 1.0 / std::sqrt(x); |
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| #define DEFINE_UNARY_UFUNC_REALHBBF16_TO_FLOATHBF16(op_name, fn) \ | ||
| Tensor& op_name(KernelRuntimeContext& ctx, const Tensor& in, Tensor& out) { \ | ||
| return internal::unary_ufunc_realhbbf16_to_floathbf16( \ | ||
| fn, fn, ctx, in, out); \ | ||
| } | ||
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why not define this macro to use the full namespace (prefixed with :: so it works from anywhere) so that backends/cadence can use it too?
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…2289) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #12277 by @manuelcandales ^ 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/manuelcandales/130/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/manuelcandales/130/head Merge bot PR base: https://github.com/pytorch/executorch/tree/main Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/manuelcandales/130/orig @diff-train-skip-merge Co-authored-by: Manuel Candales <[email protected]>
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Differential Revision: D77759060