Functions with clauses with differing number of patterns type-check but are not correctly compiled #1706
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lukaszcz
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NB: Lambda clauses with differing number of patterns will have the same issue. |
janmasrovira
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Nov 28, 2023
This pr applies a number of fixes to the new typechecker. The fixes implemented are: 1. When guessing the arity of the body, we properly use the type information of the variables in the patterns. 2. When generating wildcards, we name them properly so that they align with the name in the type signature. 3. When compiling named applications, we inline all clauses of the form `fun : _ := body`. This is a workaround to #2247 and #2517 4. I've had to ignore test027 (Church numerals). While the typechecker passes and one can see that the types are correct, there is a lambda where its clauses have different number of patterns. Our goal is to support that in the near future (#1706). This is the conflicting lambda: ``` mutual num : Nat → Num := λ : Nat → Num {| (zero : Nat) := czero | ((suc n : Nat)) {A} := csuc (num n) {A}} ``` 5. I've added non-trivial a compilation test involving monad transformers.
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paulcadman
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May 31, 2024
This pr introduces parallelism in the pipeline to gain performance. I've included benchmarks at the end. - Closes #2750. # Flags: There are two new global flags: 1. `-N / --threads`. It is used to set the number of capabilities. According to [GHC documentation](https://hackage.haskell.org/package/base-4.20.0.0/docs/GHC-Conc.html#v:setNumCapabilities): _Set the number of Haskell threads that can run truly simultaneously (on separate physical processors) at any given time_. When compiling in parallel, we create this many worker threads. The default value is `-N auto`, which sets `-N` to half the number of logical cores, capped at 8. 2. `--dev-show-thread-ids`. When given, the thread id is printed in the compilation progress log. E.g.  # Parallel compilation 1. I've added `src/Parallel/ParallelTemplate.hs` which contains all the concurrency related code. I think it is good to keep this code separated from the actual compiler code. 2. I've added a progress log (only for the parallel driver) that outputs a log of the compilation progress, similar to what stack/cabal do. # Code changes: 1. I've removed the `setup` stage where we were registering dependencies. Instead, the dependencies are registered when the `pathResolver` is run for the first time. This way it is safer. 1. Now the `ImportTree` is needed to run the pipeline. Cycles are detected during the construction of this tree, so I've removed `Reader ImportParents` from the pipeline. 3. For the package pathresolver, we do not support parallelism yet (we could add support for it in the future, but the gains will be small). 4. When `-N1`, the pipeline remains unchanged, so performance should be the same as in the main branch (except there is a small performance degradation due to adding the `-threaded` flag). 5. I've introduced `PipelineOptions`, which are options that are used to pass options to the effects in the pipeline. 6. `PathResolver` constraint has been removed from the `upTo*` functions in the pipeline due to being redundant. 7. I've added a lot of `NFData` instances. They are needed to force the full evaluation of `Stored.ModuleInfo` in each of the threads. 2. The `Cache` effect uses [`SharedState`](https://hackage.haskell.org/package/effectful-core-2.3.0.1/docs/Effectful-State-Static-Shared.html) as opposed to [`LocalState`](https://hackage.haskell.org/package/effectful-core-2.3.0.1/docs/Effectful-Writer-Static-Local.html). Perhaps we should provide different versions. 3. I've added a `Cache` handler that accepts a setup function. The setup is triggered when a miss is detected. It is used to lazily compile the modules in parallel. # Tests 1. I've adapted the smoke test suite to ignore the progress log in the stderr. 5. I've had to adapt `tests/positive/Internal/Lambda.juvix`. Due to laziness, a crash happening in this file was not being caught. The problem is that in this file we have a lambda function with different number of patterns in their clauses, which we currently do not support (#1706). 6. I've had to comment out the definition ``` x : Box ((A : Type) → A → A) := box λ {A a := a}; ``` From the test as it was causing a crash (#2247). # Future Work 1. It should be investigated how much performance we lose by fully evaluating the `Stored.ModuleInfo`, since some information in it will be discarded. It may be possible to be more fine-grained when forcing evaluation. 8. The scanning of imports to build the import tree is sequential. Now, we build the import tree from the entry point module and only the modules that are imported from it are in the tree. However, we have discussed that at some point we should make a distinction between `juvix` _the compiler_ and `juvix` _the build tool_. When using `juvix` as a build tool it makes sense to typecheck/compile (to stored core) all modules in the project. When/if we do this, scanning imports in all modules in parallel becomes trivial. 9. The implementation of the `ParallelTemplate` uses low level primitives such as [forkIO](https://hackage.haskell.org/package/base-4.20.0.0/docs/Control-Concurrent.html#v:forkIO). At some point it should be refactored to use safer functions from the [`Effectful.Concurrent.Async`](https://hackage.haskell.org/package/effectful-2.3.0.0/docs/Effectful-Concurrent-Async.html) module. 10. The number of cores and worker threads that we spawn is determined by the command line. Ideally, we could use to import tree to compute an upper bound to the ideal number of cores to use. 11. We could add an animation that displays which modules are being compiled in parallel and which have finished being compiled. # Benchmarks On some benchmarks, I include the GHC runtime option [`-A`](https://downloads.haskell.org/ghc/latest/docs/users_guide/runtime_control.html#rts-flag--A%20%E2%9F%A8size%E2%9F%A9), which sometimes makes a good impact on performance. Thanks to @paulcadman for pointing this out. I've figured a good combination of `-N` and `-A` through trial and error (but this oviously depends on the cpu and juvix projects). ## Typecheck the standard library ### Clean run (88% faster than main): ``` hyperfine --warmup 1 --prepare 'juvix clean' 'juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432' 'juvix -N 4 typecheck Stdlib/Prelude.juvix' 'juvix-main typecheck Stdlib/Prelude.juvix' Benchmark 1: juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432 Time (mean ± σ): 444.1 ms ± 6.5 ms [User: 1018.0 ms, System: 77.7 ms] Range (min … max): 432.6 ms … 455.9 ms 10 runs Benchmark 2: juvix -N 4 typecheck Stdlib/Prelude.juvix Time (mean ± σ): 628.3 ms ± 23.9 ms [User: 1227.6 ms, System: 69.5 ms] Range (min … max): 584.7 ms … 670.6 ms 10 runs Benchmark 3: juvix-main typecheck Stdlib/Prelude.juvix Time (mean ± σ): 835.9 ms ± 12.3 ms [User: 788.5 ms, System: 31.9 ms] Range (min … max): 816.0 ms … 853.6 ms 10 runs Summary juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432 ran 1.41 ± 0.06 times faster than juvix -N 4 typecheck Stdlib/Prelude.juvix 1.88 ± 0.04 times faster than juvix-main typecheck Stdlib/Prelude.juvix ``` ### Cached run (43% faster than main): ``` hyperfine --warmup 1 'juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432' 'juvix -N 4 typecheck Stdlib/Prelude.juvix' 'juvix-main typecheck Stdlib/Prelude.juvix' Benchmark 1: juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432 Time (mean ± σ): 241.3 ms ± 7.3 ms [User: 538.6 ms, System: 101.3 ms] Range (min … max): 231.5 ms … 251.3 ms 11 runs Benchmark 2: juvix -N 4 typecheck Stdlib/Prelude.juvix Time (mean ± σ): 235.1 ms ± 12.0 ms [User: 405.3 ms, System: 87.7 ms] Range (min … max): 216.1 ms … 253.1 ms 12 runs Benchmark 3: juvix-main typecheck Stdlib/Prelude.juvix Time (mean ± σ): 336.7 ms ± 13.3 ms [User: 269.5 ms, System: 67.1 ms] Range (min … max): 316.9 ms … 351.8 ms 10 runs Summary juvix -N 4 typecheck Stdlib/Prelude.juvix ran 1.03 ± 0.06 times faster than juvix -N 4 typecheck Stdlib/Prelude.juvix +RTS -A33554432 1.43 ± 0.09 times faster than juvix-main typecheck Stdlib/Prelude.juvix ``` ## Typecheck the test suite of the containers library At the moment this is the biggest juvix project that we have. ### Clean run (105% faster than main) ``` hyperfine --warmup 1 --prepare 'juvix clean' 'juvix -N 6 typecheck Main.juvix +RTS -A67108864' 'juvix -N 4 typecheck Main.juvix' 'juvix-main typecheck Main.juvix' Benchmark 1: juvix -N 6 typecheck Main.juvix +RTS -A67108864 Time (mean ± σ): 1.006 s ± 0.011 s [User: 2.171 s, System: 0.162 s] Range (min … max): 0.991 s … 1.023 s 10 runs Benchmark 2: juvix -N 4 typecheck Main.juvix Time (mean ± σ): 1.584 s ± 0.046 s [User: 2.934 s, System: 0.149 s] Range (min … max): 1.535 s … 1.660 s 10 runs Benchmark 3: juvix-main typecheck Main.juvix Time (mean ± σ): 2.066 s ± 0.010 s [User: 1.939 s, System: 0.089 s] Range (min … max): 2.048 s … 2.077 s 10 runs Summary juvix -N 6 typecheck Main.juvix +RTS -A67108864 ran 1.57 ± 0.05 times faster than juvix -N 4 typecheck Main.juvix 2.05 ± 0.03 times faster than juvix-main typecheck Main.juvix ``` ### Cached run (54% faster than main) ``` hyperfine --warmup 1 'juvix -N 6 typecheck Main.juvix +RTS -A33554432' 'juvix -N 4 typecheck Main.juvix' 'juvix-main typecheck Main.juvix' Benchmark 1: juvix -N 6 typecheck Main.juvix +RTS -A33554432 Time (mean ± σ): 551.8 ms ± 13.2 ms [User: 1419.8 ms, System: 199.4 ms] Range (min … max): 535.2 ms … 570.6 ms 10 runs Benchmark 2: juvix -N 4 typecheck Main.juvix Time (mean ± σ): 636.7 ms ± 17.3 ms [User: 1006.3 ms, System: 196.3 ms] Range (min … max): 601.6 ms … 655.3 ms 10 runs Benchmark 3: juvix-main typecheck Main.juvix Time (mean ± σ): 847.2 ms ± 58.9 ms [User: 710.1 ms, System: 126.5 ms] Range (min … max): 731.1 ms … 890.0 ms 10 runs Summary juvix -N 6 typecheck Main.juvix +RTS -A33554432 ran 1.15 ± 0.04 times faster than juvix -N 4 typecheck Main.juvix 1.54 ± 0.11 times faster than juvix-main typecheck Main.juvix ```
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The following module type checks:
But trying to evaluate e.g.
f 1 2in the REPL gives an error:Compiling the file with the old backend and running it gives an incorrect result (some random value from memory).
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