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[executorch][native-ggml] Compute cuBLAS GEMMs in f32 in the CUDA sample tests - #24538
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…ple tests Pull Request resolved: #24363 By default ggml-CUDA's cuBLAS GEMMs compute in f16 for f16 and dequantized weights, and in TF32 for f32 ones, so the linear samples measured those defaults rather than ggml's kernels. The CUDA sample-test targets set `GGML_CUDA_CUBLAS_COMPUTE_TYPE=f32` and `NVIDIA_TF32_OVERRIDE=0`; llama.cpp's defaults, `test_ggml_ops` and the bundle are unchanged. Authored with Claude Code. Differential Revision: [D128254889](https://our.internmc.facebook.com/intern/diff/D128254889/) **NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D128254889/)! ghstack-source-id: 463657744
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24538
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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: #24363 by @digantdesai
^ 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/digantdesai/190/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/190/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/189/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/190/orig
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