Fix issue in optimized_add issue: make_optimized should be called on non args only #150955
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/150955
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laithsakka
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…n non args only " PR #149665 added a change to the optimized add this causing an issue internally. In general make_optimized should be only called with non no args. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
…n non args only " PR #149665 added a change to the optimized add this causing an issue internally. In general make_optimized should be only called with valid new_args. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
laithsakka
changed the title
… called on non args only " PR #149665 did a change to the optimized_add that is causing an issue internally. In general make_optimized should be only be called with valid new_args, new_args can become None when elements already exists also, we should break out of the loop in that case. Note that I also only maintained the optimized summation when both lhs and rhs lengths are <=2. This is ok because the optimization is based on the inductive property of adding one symbol at a time. the [2]+[2] here is serving as base case ( i feel we can also remove it ) . Note that keeping it for all sizes while correct, I am not sure if tis as efficient (we will do N log(n) insertions). there is no current justification for that. cc ezyang SherlockNoMad EikanWang jgong5 wenzhe-nrv [ghstack-poisoned]
atalman
approved these changes
Apr 10, 2025
bobrenjc93
approved these changes
Apr 10, 2025
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timocafe
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Apr 16, 2025
…non args only (pytorch#150955) PR pytorch#149665 did a change to the optimized_add that is causing an issue internally. In general make_optimized should be only be called with valid new_args, new_args can become None when elements already exists also, we should break out of the loop in that case. Note that I also only maintained the optimized summation when both lhs and rhs lengths are <=2. This is ok because the optimization is based on the inductive property of adding one symbol at a time. the [2]+[2] here is serving as base case ( i feel we can also remove it ) . Note that keeping it for all sizes while correct, I am not sure if tis as efficient (we will do N log(n) insertions). there is no current justification for that. Pull Request resolved: pytorch#150955 Approved by: https://github.com/Mingming-Ding, https://github.com/atalman, https://github.com/bobrenjc93
amathewc
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Apr 17, 2025
…non args only (pytorch#150955) PR pytorch#149665 did a change to the optimized_add that is causing an issue internally. In general make_optimized should be only be called with valid new_args, new_args can become None when elements already exists also, we should break out of the loop in that case. Note that I also only maintained the optimized summation when both lhs and rhs lengths are <=2. This is ok because the optimization is based on the inductive property of adding one symbol at a time. the [2]+[2] here is serving as base case ( i feel we can also remove it ) . Note that keeping it for all sizes while correct, I am not sure if tis as efficient (we will do N log(n) insertions). there is no current justification for that. Pull Request resolved: pytorch#150955 Approved by: https://github.com/Mingming-Ding, https://github.com/atalman, https://github.com/bobrenjc93
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Stack from ghstack (oldest at bottom):
PR #149665 did a change to the optimized_add that is causing an issue internally.
In general make_optimized should be only be called with valid new_args, new_args can become None
when elements already exists also, we should break out of the loop in that case.
Note that I also only maintained the optimized summation when both lhs and rhs lengths are <=2.
This is ok because the optimization is based on the inductive property of adding one symbol at a time.
the [2]+[2] here is serving as base case ( i feel we can also remove it ) .
Note that keeping it for all sizes while correct, I am not sure if tis as efficient (we will do N log(n) insertions).
there is no current justification for that.
cc @ezyang @SherlockNoMad @EikanWang @jgong5 @wenzhe-nrv