[CPU] Improve INT8 SDPA template #3230
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3230
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 64eae18 with merge base f3fc5e7 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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| auto tmp2 = tmp1.round(); | ||
| auto tmp3 = tmp2 + vec_beta1; | ||
| auto tmp1 = at::vec::fmadd(tmp0, vec_sum_scale, vec_beta1); | ||
| auto tmp3 = tmp1.round(); |
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Could we also apply the optimization to the below masked vectorization part?
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Updated. Thanks.
| auto tmp6 = tmp5.round(); | ||
| auto tmp7 = tmp6 + vec_beta2; | ||
| auto tmp5 = at::vec::fmadd(tmp4, vec_alpha, vec_beta2); | ||
| auto tmp7 = tmp5.round(); |
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Updated. Thanks.
| auto tmp6 = tmp5.round(); | ||
| auto tmp7 = tmp6 + vec_beta2; | ||
| auto tmp5 = at::vec::fmadd(tmp4, vec_alpha, vec_beta2); | ||
| auto tmp7 = tmp5.round(); |
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Updated. Thanks.
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It brings about 1% E2E improvement when running int8 VIT on 4 cores.