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Deletes a bunch of code and speeds up lowering time of local laplacian with 20 pyramid levels by ~2.5%
It was O(n) for n facts. This makes it O(log(n)) This was particularly bad for pipelines with lots of inputs or outputs, because those pipelines have lots of asserts, which make for lots of facts to substitute in. Speeds up lowering of local laplacian with 20 pyramid levels (which has only one input and one output) by 1.09x Speeds up lowering of the adams 2019 cost model training pipeline (lots of weight inputs and lots outputs due to derivatives) by 1.5x Speeds up resnet50 (tons of weight inputs) lowering by 7.3x!
…o abadams/faster_substitute_facts
…o abadams/faster_substitute_facts
…o abadams/faster_substitute_facts
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Ready for review |
steven-johnson
approved these changes
Apr 18, 2024
| std::vector<const Variable *> pop_list; | ||
| std::vector<const Variable *> bounds_pop_list; | ||
| std::vector<Expr> truths, falsehoods; | ||
| std::set<Expr, IRDeepCompare> truths, falsehoods; |
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Have you considered unordered_set?
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comparing Exprs for < is actually a lot cheaper than hashing them, because you can early out as soon as you find an IRNodeType that differs instead of having to descend to the bottom.
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Might be worth putting that in a comment then :-)
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Meh, that was a bit of a guess. Nowhere in the compiler do we use unordered sets or maps with Expr keys, because we don't have a hash function. I'm just doing what we do everywhere else.
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Failure unrelated (seems to be a performance flake). Ready for review. |
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Oh oops, you already approved it. |
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It was O(n) for n facts. This makes it O(log(n))
This was particularly bad for pipelines with lots of inputs or outputs,
because those pipelines have lots of asserts, which make for lots of
facts to substitute in.
Speeds up lowering of local laplacian with 20 pyramid levels (which has
only one input and one output) by 1.09x
Speeds up lowering of the adams 2019 cost model training pipeline (lots
of weight inputs and lots outputs due to derivatives) by 1.5x
Speeds up resnet50 (tons of weight inputs) lowering by 7.3x!