Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
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Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
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Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
Distinguish the various forms of mutation, and do basic replaying of these effects when the function is created (imperfect, temporary).
[ghstack-poisoned]
This PR gets a first fixture working end-to-end with the new mutability and aliasing model. Key changes:
* Add a feature flag to enable the model. When enabled we no longer call InferReferenceEffects or InferMutableRanges, and instead use the new equivalents.
* Adds a pass that infers Place-specific effects based on mutable ranges and instruction effects. This is necessary to satisfy existing code that requires operand effects to be populated.
* Adds a pass that infers the outwardly-visible capturing/aliasing behavior of a function expression. The idea is that this can bubble up and be used in conjunction with the `Apply` effect to get precise inference of things like `array.map(() => { ... })`.
[ghstack-poisoned]
This PR gets a first fixture working end-to-end with the new mutability and aliasing model. Key changes:
* Add a feature flag to enable the model. When enabled we no longer call InferReferenceEffects or InferMutableRanges, and instead use the new equivalents.
* Adds a pass that infers Place-specific effects based on mutable ranges and instruction effects. This is necessary to satisfy existing code that requires operand effects to be populated.
* Adds a pass that infers the outwardly-visible capturing/aliasing behavior of a function expression. The idea is that this can bubble up and be used in conjunction with the `Apply` effect to get precise inference of things like `array.map(() => { ... })`.
[ghstack-poisoned]
This PR gets a first fixture working end-to-end with the new mutability and aliasing model. Key changes:
* Add a feature flag to enable the model. When enabled we no longer call InferReferenceEffects or InferMutableRanges, and instead use the new equivalents.
* Adds a pass that infers Place-specific effects based on mutable ranges and instruction effects. This is necessary to satisfy existing code that requires operand effects to be populated.
* Adds a pass that infers the outwardly-visible capturing/aliasing behavior of a function expression. The idea is that this can bubble up and be used in conjunction with the `Apply` effect to get precise inference of things like `array.map(() => { ... })`.
[ghstack-poisoned]
Alternate take at a new mutability and alising model, aiming to replace `InferReferenceEffects` and `InferMutableRanges`. My initial passes at this were more complicated than necessary, and I've iterated to refine and distill this down to the core concepts. There are two effects that track information flow: `capture` and `alias`:
* Given distinct values A and B. After capture A -> B, mutate(B) does *not* modify A. This more precisely captures the semantic of the previous `Store` effect. As an example, `array.push(item)` has the effect `capture item -> array` and `mutate(array)`. The array is modified, not the item.
* Given distinct values A and B. After alias A -> B, mutate(B) *does* modify A. This is because B now refers to the same value as A.
* Given distinct values A and B, after *either* capture A -> B *or* alias A -> B, transitiveMutate(B) counts as a mutation of A. Transitive mutation is the default, and places that previously used `Store` will use non-transitive mutate effects instead.
Conceptually "capture A -> B" means that a reference to A was "captured" (or stored) within A, but there is not a directly aliasing. Whereas "alias A -> B" means literal value aliasing.
The idea is that our previous sequential fixpoint loops in InferMutableRanges can instead work by first looking at transitive mutations, then look at non-transitive mutations. And aliasing groups can be built purely based on the `alias` effect.
Lots more to do here but the structure is coming together.
[ghstack-poisoned]
Alternate take at a new mutability and alising model, aiming to replace `InferReferenceEffects` and `InferMutableRanges`. My initial passes at this were more complicated than necessary, and I've iterated to refine and distill this down to the core concepts. There are two effects that track information flow: `capture` and `alias`:
* Given distinct values A and B. After capture A -> B, mutate(B) does *not* modify A. This more precisely captures the semantic of the previous `Store` effect. As an example, `array.push(item)` has the effect `capture item -> array` and `mutate(array)`. The array is modified, not the item.
* Given distinct values A and B. After alias A -> B, mutate(B) *does* modify A. This is because B now refers to the same value as A.
* Given distinct values A and B, after *either* capture A -> B *or* alias A -> B, transitiveMutate(B) counts as a mutation of A. Transitive mutation is the default, and places that previously used `Store` will use non-transitive mutate effects instead.
Conceptually "capture A -> B" means that a reference to A was "captured" (or stored) within A, but there is not a directly aliasing. Whereas "alias A -> B" means literal value aliasing.
The idea is that our previous sequential fixpoint loops in InferMutableRanges can instead work by first looking at transitive mutations, then look at non-transitive mutations. And aliasing groups can be built purely based on the `alias` effect.
Lots more to do here but the structure is coming together.
[ghstack-poisoned]
Alternate take at a new mutability and alising model, aiming to replace `InferReferenceEffects` and `InferMutableRanges`. My initial passes at this were more complicated than necessary, and I've iterated to refine and distill this down to the core concepts. There are two effects that track information flow: `capture` and `alias`:
* Given distinct values A and B. After capture A -> B, mutate(B) does *not* modify A. This more precisely captures the semantic of the previous `Store` effect. As an example, `array.push(item)` has the effect `capture item -> array` and `mutate(array)`. The array is modified, not the item.
* Given distinct values A and B. After alias A -> B, mutate(B) *does* modify A. This is because B now refers to the same value as A.
* Given distinct values A and B, after *either* capture A -> B *or* alias A -> B, transitiveMutate(B) counts as a mutation of A. Transitive mutation is the default, and places that previously used `Store` will use non-transitive mutate effects instead.
Conceptually "capture A -> B" means that a reference to A was "captured" (or stored) within A, but there is not a directly aliasing. Whereas "alias A -> B" means literal value aliasing.
The idea is that our previous sequential fixpoint loops in InferMutableRanges can instead work by first looking at transitive mutations, then look at non-transitive mutations. And aliasing groups can be built purely based on the `alias` effect.
Lots more to do here but the structure is coming together.
[ghstack-poisoned]
Alternate take at a new mutability and alising model, aiming to replace `InferReferenceEffects` and `InferMutableRanges`. My initial passes at this were more complicated than necessary, and I've iterated to refine and distill this down to the core concepts. There are two effects that track information flow: `capture` and `alias`:
* Given distinct values A and B. After capture A -> B, mutate(B) does *not* modify A. This more precisely captures the semantic of the previous `Store` effect. As an example, `array.push(item)` has the effect `capture item -> array` and `mutate(array)`. The array is modified, not the item.
* Given distinct values A and B. After alias A -> B, mutate(B) *does* modify A. This is because B now refers to the same value as A.
* Given distinct values A and B, after *either* capture A -> B *or* alias A -> B, transitiveMutate(B) counts as a mutation of A. Transitive mutation is the default, and places that previously used `Store` will use non-transitive mutate effects instead.
Conceptually "capture A -> B" means that a reference to A was "captured" (or stored) within A, but there is not a directly aliasing. Whereas "alias A -> B" means literal value aliasing.
The idea is that our previous sequential fixpoint loops in InferMutableRanges can instead work by first looking at transitive mutations, then look at non-transitive mutations. And aliasing groups can be built purely based on the `alias` effect.
Lots more to do here but the structure is coming together.
[ghstack-poisoned]
Alternate take at a new mutability and alising model, aiming to replace `InferReferenceEffects` and `InferMutableRanges`. My initial passes at this were more complicated than necessary, and I've iterated to refine and distill this down to the core concepts. There are two effects that track information flow: `capture` and `alias`:
* Given distinct values A and B. After capture A -> B, mutate(B) does *not* modify A. This more precisely captures the semantic of the previous `Store` effect. As an example, `array.push(item)` has the effect `capture item -> array` and `mutate(array)`. The array is modified, not the item.
* Given distinct values A and B. After alias A -> B, mutate(B) *does* modify A. This is because B now refers to the same value as A.
* Given distinct values A and B, after *either* capture A -> B *or* alias A -> B, transitiveMutate(B) counts as a mutation of A.
Conceptually "capture A -> B" means that a reference to A was "captured" (or stored) within A, but there is not a directly aliasing. Whereas "alias A -> B" means literal value aliasing.
The idea is that our previous sequential fixpoint loops in InferMutableRanges can instead work by first looking at transitive mutations, then look at non-transitive mutations. And aliasing groups can be built purely based on the `alias` effect.
Lots more to do here but the structure is coming together.
[ghstack-poisoned]
Syncing this stack internally there is a small percentage of files that lose memoization, generally for callbacks. The repro here tries to get at the core pattern, where a parameter escapes into a mutable return value. This makes the callback appear mutable, and means that calls like array.map aren't able to optimize as well — even if the array itself is transitively immutable.
The challenge is that we can't really distinguish between just capturing and true mutation right now — AnalyzeFunctions kind of has to pick one, and consider both a mutation.
[ghstack-poisoned]
Syncing this stack internally there is a small percentage of files that lose memoization, generally for callbacks. The repro here tries to get at the core pattern, where a parameter escapes into a mutable return value. This makes the callback appear mutable, and means that calls like array.map aren't able to optimize as well — even if the array itself is transitively immutable.
The challenge is that we can't really distinguish between just capturing and true mutation right now — AnalyzeFunctions kind of has to pick one, and consider both a mutation.
[ghstack-poisoned]
Basically we track a `SuspenseListRow` on the task. These keep track of
"pending tasks" that block the row. A row is blocked by:
- First itself completing rendering.
- A previous row completing.
- Any tasks inside the row and before the Suspense boundary inside the
row. This is mainly because we don't yet know if we'll discover more
SuspenseBoundaries.
- Previous row's SuspenseBoundaries completing.
If a boundary might get outlined, then we can't consider it completed
until we have written it because it determined whether other future
boundaries in the row can finish.
This is just handling basic semantics. Features not supported yet that
need follow ups later:
- CSS dependencies of previous rows should be added as dependencies of
future row's suspense boundary. Because otherwise if the client is
blocked on CSS then a previous row could be blocked but the server
doesn't know it.
- I need a second pass on nested SuspenseList semantics.
- `revealOrder="together"`
- `tail="hidden"`/`tail="collapsed"`. This needs some new runtime
semantics to the Fizz runtime and to allow the hydration to handle
missing rows in the HTML. This should also be future compatible with
AsyncIterable where we don't know how many rows upfront.
- Need to double check resuming semantics.
---------
Co-authored-by: Sebastian "Sebbie" Silbermann <silbermann.sebastian@gmail.com>
Joe Savona
Jan KassensandGitHub