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@ddwkim ddwkim commented Aug 17, 2025

Make sure to read the contributing guidelines before submitting a PR

This is sqrt implementation in F32 for Vulkan.
I have found the implementation was missing while porting a diffusion model scheduler in GGML.
The following is the result of test-backend-ops.

./build/bin/test-backend-ops -o SQRT
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA RTX A6000, compute capability 8.6, VMM: yes
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = NVIDIA RTX A6000 (NVIDIA) | uma: 0 | fp16: 1 | bf16: 0 | warp size: 32 | shared memory: 49152 | int dot: 1 | matrix cores: KHR_coopmat
Testing 3 devices

Backend 1/3: CUDA0
  Device description: NVIDIA RTX A6000
  Device memory: 48636 MB (46852 MB free)

  SQRT(type=f16,ne=[10,3,3,2]): OK
  SQRT(type=f32,ne=[10,3,3,2]): OK
  10842/10842 tests passed
  Backend CUDA0: OK
Backend 2/3: Vulkan0
  Device description: NVIDIA RTX A6000
  Device memory: 49140 MB (49140 MB free)

  SQRT(type=f16,ne=[10,3,3,2]): not supported [Vulkan0] 
  SQRT(type=f32,ne=[10,3,3,2]): OK
  10842/10842 tests passed
  Backend Vulkan0: OK
Backend 3/3: CPU
  Skipping CPU backend
3/3 backends passed
OK

@ddwkim ddwkim requested a review from 0cc4m as a code owner August 17, 2025 11:33
@github-actions github-actions bot added Vulkan Issues specific to the Vulkan backend ggml changes relating to the ggml tensor library for machine learning labels Aug 17, 2025
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Thank you, nice work.

@0cc4m 0cc4m merged commit 19f4dec into ggml-org:master Aug 17, 2025
46 of 47 checks passed
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