[compiler] New inference repros/fixes #33584
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Substantially improves the last major known issue with the new inference model's implementation: inferring effects of function expressions. I knowingly used a really simple (dumb) approach in InferFunctionExpressionAliasingEffects but it worked surprisingly well on a ton of code. However, investigating during the sync I saw that we the algorithm was literally running out of memory, or crashing from arrays that exceeded the maximum capacity. We were accumluating data flow in a way that could lead to lists of data flow captures compounding on themselves and growing very large very quickly. Plus, we were incorrectly recording some data flow, leading to cases where we reported false positive "can't mutate frozen value" for example. So I went back to the drawing board. InferMutationAliasingRanges already builds up a data flow graph which it uses to figure out what values would be affected by mutations of other values, and update mutable ranges. Well, the key question that we really want to answer for inferring a function expression's aliasing effects is which values alias/capture where. Per the docs I wrote up, we only have to record such aliasing _if they are observable via mutations_. So, lightbulb: simulate mutations of the params, free variables, and return of the function expression and see which params/free-vars would be affected! That's what we do now, giving us precise information about which such values alias/capture where. When the "into" is a param/context-var we use Capture, iwhen the destination is the return we use Alias to be conservative.
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Closes #33614 |
This was referenced Jun 23, 2025
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Reviewed over VC with @mofeiZ |
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Substantially improves the last major known issue with the new inference model's implementation: inferring effects of function expressions. I knowingly used a really simple (dumb) approach in InferFunctionExpressionAliasingEffects but it worked surprisingly well on a ton of code. However, investigating during the sync I saw that we the algorithm was literally running out of memory, or crashing from arrays that exceeded the maximum capacity. We were accumluating data flow in a way that could lead to lists of data flow captures compounding on themselves and growing very large very quickly. Plus, we were incorrectly recording some data flow, leading to cases where we reported false positive "can't mutate frozen value" for example. So I went back to the drawing board. InferMutationAliasingRanges already builds up a data flow graph which it uses to figure out what values would be affected by mutations of other values, and update mutable ranges. Well, the key question that we really want to answer for inferring a function expression's aliasing effects is which values alias/capture where. Per the docs I wrote up, we only have to record such aliasing _if they are observable via mutations_. So, lightbulb: simulate mutations of the params, free variables, and return of the function expression and see which params/free-vars would be affected! That's what we do now, giving us precise information about which such values alias/capture where. When the "into" is a param/context-var we use Capture, iwhen the destination is the return we use Alias to be conservative. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/facebook/react/pull/33584). * #33626 * #33625 * #33624 * __->__ #33584 DiffTrain build for [94cf60b](94cf60b)
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Substantially improves the last major known issue with the new inference model's implementation: inferring effects of function expressions. I knowingly used a really simple (dumb) approach in InferFunctionExpressionAliasingEffects but it worked surprisingly well on a ton of code. However, investigating during the sync I saw that we the algorithm was literally running out of memory, or crashing from arrays that exceeded the maximum capacity. We were accumluating data flow in a way that could lead to lists of data flow captures compounding on themselves and growing very large very quickly. Plus, we were incorrectly recording some data flow, leading to cases where we reported false positive "can't mutate frozen value" for example. So I went back to the drawing board. InferMutationAliasingRanges already builds up a data flow graph which it uses to figure out what values would be affected by mutations of other values, and update mutable ranges. Well, the key question that we really want to answer for inferring a function expression's aliasing effects is which values alias/capture where. Per the docs I wrote up, we only have to record such aliasing _if they are observable via mutations_. So, lightbulb: simulate mutations of the params, free variables, and return of the function expression and see which params/free-vars would be affected! That's what we do now, giving us precise information about which such values alias/capture where. When the "into" is a param/context-var we use Capture, iwhen the destination is the return we use Alias to be conservative. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/facebook/react/pull/33584). * #33626 * #33625 * #33624 * __->__ #33584 DiffTrain build for [94cf60b](94cf60b)
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Substantially improves the last major known issue with the new inference model's implementation: inferring effects of function expressions. I knowingly used a really simple (dumb) approach in InferFunctionExpressionAliasingEffects but it worked surprisingly well on a ton of code. However, investigating during the sync I saw that we the algorithm was literally running out of memory, or crashing from arrays that exceeded the maximum capacity. We were accumluating data flow in a way that could lead to lists of data flow captures compounding on themselves and growing very large very quickly. Plus, we were incorrectly recording some data flow, leading to cases where we reported false positive "can't mutate frozen value" for example.
So I went back to the drawing board. InferMutationAliasingRanges already builds up a data flow graph which it uses to figure out what values would be affected by mutations of other values, and update mutable ranges. Well, the key question that we really want to answer for inferring a function expression's aliasing effects is which values alias/capture where. Per the docs I wrote up, we only have to record such aliasing if they are observable via mutations. So, lightbulb: simulate mutations of the params, free variables, and return of the function expression and see which params/free-vars would be affected! That's what we do now, giving us precise information about which such values alias/capture where. When the "into" is a param/context-var we use Capture, iwhen the destination is the return we use Alias to be conservative.
Stack created with Sapling. Best reviewed with ReviewStack.