Add optimized op_where #8866
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Add optimized op_where #8866
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swolchok
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/8866
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ghstack-comment-id: 2691805026 ghstack-source-id: ebe8bee4e3ca4184e91058bd7033e69e130644da ghstack-comment-id: 2691808920 Pull Request resolved: #8866
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ghstack-comment-id: 2691805026 ghstack-source-id: aaf66178b1902763ebaaaa2f0b3a312505722ea8 ghstack-comment-id: 2691808920 Pull Request resolved: #8866
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| data_out[out_index] = | ||
| data_cond[cond_index] ? data_a[a_index] : data_b[b_index]; |
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Love how clean it reads :)
Couple of high level comments,
- Not too familiar with how people use this op i.e. any common case we see often in the wild, but are there any short-circuits we can do to avoid coming here?
- Similarly, depending on the condition, if we know we are biased towards A vs. B, we can first copy A to the result and then go through data_cond and pick B.
- Lastly, I am assuming we want to do SIMD later, if we do then we can use predicates, that should make it less "branchy". It may not help much with load/stores though.
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if we know we are biased towards A vs. B
we can't possibly know this in general.
SIMD later
not currently on my agenda, but I may have to come back.
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ghstack-comment-id: 2691805026 ghstack-source-id: 7e5ac3a26e2728bc9e7fca1f37e0368efce5186e ghstack-comment-id: 2691808920 Pull Request resolved: #8866
manuelcandales
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ghstack-comment-id: 2691805026 ghstack-source-id: c88f9387a18951f40fffb1cc9971daafe7b82122 ghstack-comment-id: 2691808920 Pull Request resolved: #8866
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ghstack-comment-id: 2691805026 ghstack-source-id: 1a0f6d2c788778fcb6fcb132f7ef452cd048e3d3 ghstack-comment-id: 2691808920 Pull Request resolved: #8866
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unittest-editable failure is a known flake, so noting that we have green CI. I think this will cause -Wunused -Werror builds to fail though, so I need to fix that before merging. |
Checked, works fine, I misread my own code. |
zonglinpeng
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It materializes separate kernels for the cases where the two input data tensors have the same dtype and the third one has dtype bool.
This was referenced Mar 17, 2025
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It materializes separate kernels for the cases where the two input data tensors have the same dtype and the third one has dtype bool.