[ROCm] Set thread_work_size to 16 for vectorized elementwise kernels for MI300X #160444
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* thread_work_size of 16 is giving better perf with many workloads cherry-pick of ROCm@fb81400
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/160444
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…for MI300X (#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of ROCm@fb81400 Pull Request resolved: #160444 Approved by: https://github.com/jeffdaily
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…for MI300X (pytorch#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of fb81400 Pull Request resolved: pytorch#160444 Approved by: https://github.com/jeffdaily
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…for MI300X (#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of ROCm@fb81400 Pull Request resolved: #160444 Approved by: https://github.com/jeffdaily
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…for MI300X (pytorch#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of ROCm@fb81400 Pull Request resolved: pytorch#160444 Approved by: https://github.com/jeffdaily
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…for MI300X (pytorch#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of fb81400 Pull Request resolved: pytorch#160444 Approved by: https://github.com/jeffdaily
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…for MI300X (pytorch#160444) * thread_work_size of 16 is giving better perf with many workloads for MI300X cherry-pick of ROCm@fb81400 Pull Request resolved: pytorch#160444 Approved by: https://github.com/jeffdaily
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cherry-pick of ROCm@fb81400
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @naromero77amd