[compiler] InferMutationAliasingRanges precisely models which values mutate when#33401
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[compiler] InferMutationAliasingRanges precisely models which values mutate when#33401josephsavona wants to merge 14 commits intogh/josephsavona/109/basefrom
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…mutate when It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
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…mutate when It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. ghstack-source-id: 6fd1662 Pull Request resolved: #33401
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| * TODO: this will incorrectly mark values as mutated in the following case: | ||
| * 1. Create value x | ||
| * 2. Create value y | ||
| * 3. Transitively mutate y | ||
| * 4. Capture x -> y |
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this todo is fixed, among other issues
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
This was referenced Jun 4, 2025
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…mutate when It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. ghstack-source-id: 6fd1662 Pull Request resolved: facebook/react#33401
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
This was referenced Jun 6, 2025
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
…ich values mutate when" It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom). This means that at each mutation, we can mark _exactly_ the set of variables/values that are affected by that specific instruction. This means we can correctly infer that `mutate(b)` can't impact `a` here: ``` a = make(); b = make(); mutate(b); // when we interpret the mutation here, a isn't captured yet b.a = a; ``` We will need to make this a fixpoint, but only if there are backedges in the CFG. [ghstack-poisoned]
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It turns out that InferMutationAliasingRanges does need a fixpoint loop, but the approach is arguably simpler overall and more precise than the previous implementation. Like InferMutationAliasingEffects (which is the new InferReferenceEffects), we build an abstract model of the heap. But here we know what the effects are, so we can do abstract interpretation of the effects. Each abstract value stores a set of values that it has captured (for transitive mutation), while each variable keeps a set of values it may directly mutate (for assign/alias/capturefrom).
This means that at each mutation, we can mark exactly the set of variables/values that are affected by that specific instruction. This means we can correctly infer that
mutate(b)can't impactahere:We will need to make this a fixpoint, but only if there are backedges in the CFG.