Suggested by zygoloid while looking at #5678
```
CHECK failure at toolchain/lower/context.cpp:62: !llvm::verifyModule(*llvm_module_, &errs): Verifier errors: Instruction does not dominate all uses!
%.loc17_46.1.temp = alloca { i1, i32, i32 }, align 8, !dbg !13
%tuple.elem0.loc17_46.2.tuple.elem = getelementptr inbounds nuw { i1, i32, i32 }, ptr %.loc17_46.1.temp, i32 0, i32 0, !dbg !13
Instruction does not dominate all uses!
%.loc17_46.1.temp = alloca { i1, i32, i32 }, align 8, !dbg !13
%tuple.elem1.loc17_46.2.tuple.elem = getelementptr inbounds nuw { i1, i32, i32 }, ptr %.loc17_46.1.temp, i32 0, i32 1, !dbg !13
Instruction does not dominate all uses!
%.loc17_46.1.temp = alloca { i1, i32, i32 }, align 8, !dbg !13
%tuple.elem2.loc17_46.2.tuple.elem = getelementptr inbounds nuw { i1, i32, i32 }, ptr %.loc17_46.1.temp, i32 0, i32 2, !dbg !13
```
Adds a `--llvm-verifier` flag to be able to turn this off easily,
particularly for debugging the LLVM IR.
The call workaround is due to a verifier requirement `inlinable function
call in a function with debug info must have a !dbg location`. It
specifically comes up for the `++x` case, with `%1 = call i32
@"_CConvert.8b3d5d6a6c17be04:ImplicitAs.Core.b88d1103f417c6d4"(i32
%other)`. I think #5397 is in the direction of a fix for that, but #5397
was set aside because it puts the debug info in too many places.
Instead, address this by adding a stub location for calls that don't
have a good location. I'm deliberately putting this next to the TODO so
that it's easier to understand the association.
---------
Co-authored-by: Dana Jansens <danakj@orodu.net>
Add check support for `for` loops following #1885. This also adds a
basic `Optional` type to the prelude, as that's necessary to support the
new `Iterate` interface.
Depends on #5688, #5697. Those PRs aren't stacked here, but this change
will crash until they land.
---------
Co-authored-by: Jon Ross-Perkins <jperkins@google.com>
Previously we created allocas for temporaries at whatever point in the
output LLVM function we'd reached. This would result in these being
dynamic allocas (performing a dynamic stack allocation), which is
inefficent and can lead to a stack overflow if it happens in a loop.
Switch to putting the allocas in the entry block instead, and instead
generate a lifetime start marker when we reach the point where the
temporary is introduced. We already did this for local variables; this
is just factoring out and reusing that code.
In a facet type constraint, you can write `where .Self impls T` for any
facet type `T`, or the constant `type`. It is possible to write `type`
in different ways though, with a `NameRef` instruction appearing on the
RHS instead of `TypeType`. In this case, the canonical constant value's
instruction will still be `TypeType`, so make eval look at the canonical
instruction to see this.
Add a test with an `alias Type = type` which hits this case.
After this change, we only will accept and find one of the following on
the RHS of `impls`:
- `TypeType`
- A facet type
- An error, if the source code had something else there, which will
already be diagnosed. Tested by `fail_right_of_impls_non_type.carbon`
and `fail_right_of_impls_non_facet_type.carbon`.
So we handle these three cases, and drop the implicit handling of other
things which will never appear there.
For some of these, it's just replacing with min_prelude/none.carbon.
Some had min_preludes specified, and I'm generally switching those to
none.carbon as well. The one exception is the destroy.carbon test, which
I noticed because of #5678
`AddImportedInstruction` was turning errors in an instruction into a
Runtime constant value instead of an Error, which led to crashes when
importing an instruction that had an error inside it somewhere.
Fixes#5726
The `IsPeriodSelf` function is problematic, as it's possible for a
FacetType to contain multiple `.Self` bindings which refer to different
selves, when one FacetType is nested within another: `I where .Self.J =
(K where .Self impls type)`.
The `WhereExpr` instruction contains the instruction of the `.Self` of
that `where` clause, which is what `IsPeriodSelf` is looking for, so we
can compare with its constant value instead.
- Update all the llvm::Function pointers after function replacement.
Some were previously left in an inconsistent state.
- Only do function replacement once, after converging on the canonical
specific to use.
The `full.carbon` prelude just sets a flag indicating an explicit intent
to include the full prelude. Once all tests include some prelude file,
an error can be enabled (currently it's commented out) that requires an
`INCLUDE-FILE` of some min-prelude to be present in all `check/` and
`lower/` file tests.
Update remaining parts of lowering, in particular the lowering of
aggregates, to handle lowering within a specific from a different file
than its generic. Look up information about a type in the current
specific and in its file rather than performing lookups for the type in
the generic and its file.
Remove or fix all remaining uses of raw `TypeId` in
lower/function_context and lower/handle*, so that the type from the
specific is consistently always used when lowering a specific function.
---------
Co-authored-by: Geoff Romer <gromer@google.com>
This script runs benchmarks written using Google Benchmark repeatedly,
and collects the results from JSON to render them nicely and provide
statistical information across the runs.
Because this runs the binaries repeatedly, this can help account for
run-to-run variations that are pervasive in many of Carbon's benchmarks,
such as ASLR and other process-specific differences.
It's most basic mode runs a benchmark multiple times and shows both
median and confidence intervals.
It also supports two comparison modes:
1) Regular expressions can be provided that describe collections of
related benchmarks where one is the "main" benchmark and the others
are comparable. For example, Carbon's data structure vs. data
structures from LLVM or Abseil. These will be rendered with the main
benchmark first, followed by a comparison relative to a "baseline" of
each comparable benchmark.
2) A baseline benchmark binary, and potentially different command line
flags, can be provided to run two benchmark binaries and compute
a comparison for each benchmark within them.
Across all of these, the script works to present the best text UI it can
in the console. I may have gotten a bit obsessed with rendering the
benchmark results in a way that is really pretty. There are lots of
fancy color coding and progress bars, etc., when run in in the terminal.
For the basic mode without any comparisons, the results look like:
```
Computing statistically significant deltas only wherethe P-value < 𝛂 of 0.05
Metric key:
BenchmarkName... <median> ± <% at 95th conf>
Benchmark ┃ CPU Time ┃ bytes_per_second
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━
BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench>. │ 3.051 ns ± 2.721% │ 327.8 M ± 2.765%
BM_LatencyHash<RandValues<uint8_t>, AbseilHashBench>. │ 3.395 ns ± 4.377% │ 294.6 M ± 4.572%
BM_LatencyHash<RandValues<uint8_t>, LLVMHashBench>... │ 6.125 ns ± 2.662% │ 163.3 M ± 2.726%
BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> │ 3.105 ns ± 3.947% │ 644.1 M ± 4.109%
BM_LatencyHash<RandValues<uint16_t>, AbseilHashBench> │ 3.433 ns ± 4.308% │ 582.6 M ± 4.502%
BM_LatencyHash<RandValues<uint16_t>, LLVMHashBench>.. │ 6.127 ns ± 2.540% │ 326.5 M ± 2.587%
BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> │ 3.082 ns ± 2.846% │ 1.298 G ± 2.923%
BM_LatencyHash<RandValues<uint32_t>, AbseilHashBench> │ 3.401 ns ± 3.611% │ 1.176 G ± 3.739%
BM_LatencyHash<RandValues<uint32_t>, LLVMHashBench>.. │ 6.209 ns ± 4.064% │ 644.3 M ± 4.236%
BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> │ 3.122 ns ± 2.871% │ 2.563 G ± 2.956%
BM_LatencyHash<RandValues<uint64_t>, AbseilHashBench> │ 3.426 ns ± 2.811% │ 2.335 G ± 2.892%
BM_LatencyHash<RandValues<uint64_t>, LLVMHashBench>.. │ 6.497 ns ± 3.081% │ 1.231 G ± 3.179%
```
For the first comparison mode on one of Carbon's benchmarks, the results
look like:
```
Computing statistically significant deltas only wherethe P-value < 𝛂 of 0.05
Metric key:
BenchmarkName... <median> ± <% at 95th conf>
vs Comparable: 👍 <delta> p=<U-test P-value>
<median> ± <% at 95th conf>
Benchmark ┃ CPU Time ┃ bytes_per_second
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━
BM_LatencyHash<RandValues<uint8_t>, CarbonHashBench>. │ 3.037 ns ± 1.781% │ 329.2 M ± 1.813%
vs Abseil: │ 👍 -8.200% p=0.000183 │ 👍 8.933% p=0.000183
│ 3.309 ns ± 2.064% │ 302.2 M ± 2.022%
vs LLVM: │ 👍 -49.401% p=0.000183 │ 👍 97.632% p=0.000183
│ 6.003 ns ± 1.502% │ 166.6 M ± 1.480%
│ │
BM_LatencyHash<RandValues<uint16_t>, CarbonHashBench> │ 3.026 ns ± 1.816% │ 661 M ± 1.784%
vs Abseil: │ 👍 -8.599% p=0.000183 │ 👍 9.408% p=0.000183
│ 3.311 ns ± 1.873% │ 604.1 M ± 1.839%
vs LLVM: │ 👍 -49.829% p=0.000183 │ 👍 99.319% p=0.000183
│ 6.031 ns ± 2.806% │ 331.6 M ± 2.730%
│ │
BM_LatencyHash<RandValues<uint32_t>, CarbonHashBench> │ 3.017 ns ± 2.696% │ 1.326 G ± 2.625%
vs Abseil: │ 👍 -9.754% p=0.000183 │ 👍 10.808% p=0.000183
│ 3.344 ns ± 1.537% │ 1.196 G ± 1.514%
vs LLVM: │ 👍 -49.857% p=0.000183 │ 👍 99.427% p=0.000183
│ 6.018 ns ± 3.269% │ 664.7 M ± 3.167%
│ │
BM_LatencyHash<RandValues<uint64_t>, CarbonHashBench> │ 3.025 ns ± 3.395% │ 2.644 G ± 3.284%
vs Abseil: │ 👍 -9.812% p=0.000183 │ 👍 10.879% p=0.000183
│ 3.354 ns ± 2.640% │ 2.385 G ± 2.572%
vs LLVM: │ 👍 0.476x p=0.000183 │ 👍 2.101x p=0.000183
│ 6.357 ns ± 2.477% │ 1.258 G ± 2.418%
│ │
```
For the second mode, in this case comparing a baseline build with `-Oz`
vs an experiment with `-Os`, the results look like:
```
Computing statistically significant deltas only wherethe P-value < 𝛂 of 0.05
Metric key:
BenchmarkName... 👍 <delta> p=<U-test P-value>
baseline: <median> ± <% at 95th conf>
experiment: <median> ± <% at 95th conf>
Benchmark ┃ CPU Time ┃ bytes_per_second
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━
BM_LatencyHash<RandValues<std::pair<uint32_t, uint32_t>>, CarbonHashBench> │ 👍 -35.870% p=0.000557 │ 👍 55.930% p=0.000557
baseline: │ 5.704 ns ± 1.877% │ 1.403 G ± 1.911%
experiment: │ 3.658 ns ± 4.209% │ 2.187 G ± 4.039%
│ │
BM_LatencyHash<RandValues<std::pair<uint32_t, uint64_t>>, CarbonHashBench> │ 👍 -19.475% p=0.00119 │ 👍 24.186% p=0.00119
baseline: │ 4.974 ns ± 3.029% │ 3.217 G ± 3.124%
experiment: │ 4.005 ns ± 4.297% │ 3.995 G ± 4.120%
│ │
BM_LatencyHash<RandValues<std::pair<uint32_t, int*>>, CarbonHashBench>.... │ 👍 -11.740% p=0.00153 │ 👍 13.302% p=0.00153
baseline: │ 4.634 ns ± 3.433% │ 3.453 G ± 3.555%
experiment: │ 4.09 ns ± 2.999% │ 3.912 G ± 2.911%
│ │
```
The script itself uses a new tool for managing dependencies called `uv`:
https://docs.astral.sh/uv/ This tool allows for the script to contain an
inline set of dependencies that will be installed and cached for
subsequent runs. This seemed particularly important as dependencies like
SciPy and NumPy can be particularly difficult to manager or keep
installed in other ways, but are essential to this scripts statistical
analysis. So far, the `uv` system has been working remarkably well for
me and been a relatively pleasant experience on the whole.
I have included as much of the Python dependencies as have good type
information into the MyPy configuration to get good type checking in
pre-commit however.
Last but not least, this has been a pet project of mine for a quite a
while and so may be a bit rough around the edges as I added and tweaked
functionality based on specific benchmarks I was looking at. It feels
like its gotten useful enough to contribute somewhere, but totally open
to any refactoring or improvements needed. I tried to take a few passes
over it to organize and document the code before sending it, but I'm
sure there are still some things that could use improvement.
---------
Co-authored-by: Dana Jansens <danakj@orodu.net>
This drops file_test runtime from about 3s to 2.5s on my machine, which
is now ~30% faster than before #5653 slowed things down by adding a lot
of stuff to the production prelude.
---------
Co-authored-by: Jon Ross-Perkins <jperkins@google.com>
If convert fails after applying a substitution in deduce, fail deduction
rather than succeeding deduction with an ErrorInst in the deduced
argument.
For example, in
`toolchain/check/testdata/facet/fail_convert_class_type_to_generic_facet_value.carbon`
the `WrongGenericParam` is deduced for the first argument, then
substituted into the second parameter, but the argument can not convert
to the parameter after substitution. In this case, deduction fails
instead of producig a call with an error in the second argument. The
resulting semir drops the call with an error argument:
```
-// CHECK:STDOUT: %CallGenericMethod.specific_fn: <specific function> = specific_function %CallGenericMethod.ref, @CallGenericMethod(constants.%WrongGenericParam, <error>) [concrete = <error>]
-// CHECK:STDOUT: %CallGenericMethod.call: init %empty_tuple.type = call %CallGenericMethod.specific_fn() [concrete = <error>]
```
This drops file_test runtime from about 2.5s to 2s on my machine, which
is now ~50% faster than before
https://github.com/carbon-language/carbon-lang/pull/5653 slowed things
down by adding a lot of stuff to the production prelude.
This doesn't support actually passing the value of the struct, which is
planned to be implemented using thunks.
`ClangDeclId` value is now `ClangDecl` which includes the mapped Carbon
instruction in addition to the Clang declaration. This allows finding
the Carbon instruction for a given Clang declaration, which is necessary
for mapping a Clang struct parameter type to the Carbon class without
doing name lookup. We don't take the instruction as part of the hash
key, as discussed in
[Discord](https://discord.com/channels/655572317891461132/768530752592805919/1380575881050718469).
To map the type, we also need to map namespaces. To avoid recursion for
inner namespaces, we use a vector.
Note that the first commit just changes the order of functions in the
file to make review easier.
C++ Interop Demo (that shows missing behavior):
```c++
// hello_world.h
struct S {
S(const S&) { x = 1; }
int x;
};
void hello_world(S s);
```
```c++
// hello_world.cpp
#include "hello_world.h"
#include <cstdio>
void hello_world2(S s) { printf("hello_world2: %d\n", s.x); }
void hello_world(S s) {
printf("hello_world: %d\n", s.x);
hello_world2(s);
}
```
```carbon
// main.carbon
library "Main";
import Cpp library "hello_world.h";
fn Run() -> i32 {
var s : Cpp.S;
Cpp.hello_world(s);
return 0;
}
```
```shell
$ clang -c hello_world.cpp
$ bazel-bin/toolchain/carbon compile main.carbon
$ bazel-bin/toolchain/carbon link hello_world.o main.o --output=demo
$ ./demo
hello_world: -1108224096
hello_world2: 1
```
Part of #5533.
`SubstInst()` replaces individual instructions, and then rebuilds them
into instructions that contain those instructions. If any instruction is
an `ErrorInst`, the final result will also be an `ErrorInst`. In
pathological cases, it's possible to generate large types, [such
as](https://github.com/carbon-language/carbon-lang/issues/5672) tuples
of tuples of tuples of tuples of `something`. If that `something` is
`ErrorInst`, we can save a lot of work by avoiding building the
surrounding types, and evaluating them all to `ErrorInst`.
The none.carbon min-prelude is not just an empty prelude, it also
prevents any prelude from being imported at all. So no import machinery
runs before the test, only the `package` statement from the prelude
would run.
Use the none.carbon min-prelude in a few tests that were specifying
`--no-prelude-import` to give it a trial run.
---------
Co-authored-by: Jon Ross-Perkins <jperkins@google.com>
Follow up of #5594.
Trying to compromise SemIR size, having enough information and
complexity of tests, I've duplicated representative tests to a separate
test file with `--dump-sem-ir-ranges=if-present`.
When building a FacetType from an existing FacetType, don't diagnose
rewrite constraints that are compatible with the existing FacetType.
To do this, we consider two RHS as identical[1] if they have the same
constant value after substituting from available rewrite constraints in
the being-constructed FacetType, since the syntactic representation of
the RHS is lost during eval.
[1]
https://docs.google.com/document/d/1Yt-i5AmF76LSvD4TrWRIAE_92kii6j5yFiW-S7ahzlg/edit?tab=t.0#heading=h.qti4vn50zwy
Adds min-preludes more tests which were seen as slow and their
surrounding neighbours. This drops the file_test runtime on my machine
from about 6s to about 4.5s.
For a few files that are clearly only testing diagnostics, we drop the
if-present semir ranges and the associated TODO.
Adds min-preludes more tests which were seen as slow and their
surrounding neighbours. This drops the file_test runtime on my machine
from about 7s to about 6s.
For a few files that are clearly only testing diagnostics, we drop the
if-present semir ranges and the associated TODO.
Adds min-preludes to a few more slowest tests, and adds them to most of
the lowering tests, with a few exceptions that make use of operators.
This take the runtime of file_test down from about 8s to about 7s on my
machine.
We add support for Negate on uints in the min-preludes.
This drops the wall clock time for running file_test from 10s to 8s on
my machine. There's many more tests to convert, as each one takes the
test from ~1s to ~100ms. Compiling the full prelude is a bit slow now
since #5653, and before that file_test was taking about 3.5s.
We introduce a few more flavours of min_prelude to support more tests.
Jon Ross-Perkins
Richard Smith
Boaz Brickner
Geoff Romer
Alina Sbirlea
Chandler CarruthandDana Jansens
dependabot[bot]
