install make target #6

StefanKarpinski · 2011-04-27T05:37:32Z

You should be able to do make install and have a compiled copy of all of julia installed on a system. The default location should be under /usr/local but should be configurable. It would be nice even for us developers to have both an installed (unbroken) copy of julia and have the development copy still live in the repo directory. That way when the development version is broken, you can still use julia for other stuff.

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ViralBShah · 2011-07-09T18:14:39Z

I feel comfortable that in the 1.0 release, people just build and run out of the source directory as we do now.

To install in /usr/local and have a stable + dev copy may require us to do a fair amount of testing with JULIA_HOME, paths etc.

In my opinion, this can be 2.0.

StefanKarpinski · 2011-07-11T19:56:34Z

It just occurred to me that this could be as simple as automatically making a symlink in ~/bin/ otherwise the getting started section may not work since julia directory may not be in the path.

ViralBShah · 2012-01-08T06:34:35Z

We do have a make install target now, and even ability to create debian packages. Closing this one.

JeffBezanson · 2012-01-08T08:15:12Z

make install needs to be documented. I don't understand how to use it, since it copies files to $(DESTDIR)/usr/share/julia. So what do I set DESTDIR to? Not /, since the first thing it does is rm -fr $(DESTDIR). Looks dangerous...

StefanKarpinski · 2012-01-08T17:59:53Z

I reopened this because no one besides Viral knows how this works and I'm not clear that it fully does. The intention of an install target is that you can build, make install, and then delete the build directory and have a standalone installation that continues to work without the source code, containing just the necessary executables and libraries.

ViralBShah · 2012-01-08T18:05:07Z

It installs a working copy of julia in DESTDIR. DESTDIR is a standard variable used by autoconf and such, and also by the debian build system.

So yes, you can do make install DESTDIR=$HOME/julia, and then delete the sources and get a working julia. This is how the debian package is built also. The issue that Jeff raised is that we need to have a DESTDIR by default so that nothing silly is done.

Can you try it out, and once satisfied, close it?

StefanKarpinski · 2012-01-08T18:10:11Z

Sure. We should probably default to /usr/local/julia since that's a standard destination for self-compiled installations (c.f. packaged installs). On Debian it can use /usr/share/julia since you said it didn't like /usr/local/julia for some reason.

ViralBShah · 2012-01-08T20:21:39Z

Using /usr/share on debian and /usr/local elsewhere is a bit of a kludge, since you have to look for /etc/debian_version and do something different on debian. Also, people may not always have root access. In such cases, we could take the view that DESTDIR has to be explicitly passed.

-viral

On Jan 8, 2012, at 11:40 PM, Stefan Karpinski wrote:

Sure. We should probably default to /usr/local/julia since that's a standard destination for self-compiled installations (c.f. packaged installs). On Debian it can use /usr/share/julia since you said it didn't like /usr/local/julia for some reason.

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#6 (comment)

StefanKarpinski · 2012-01-08T20:28:39Z

The default install location when you use ./configure to install software is /usr/local. Defaulting to anything else is surprising and bad. Debian is different because we're distributing a package, which would be expected not to go under /usr/local since it's precompiled.

ViralBShah · 2012-01-08T21:10:20Z

The Debian build process needs to invoke the install target, the way it is now.

-viral

On 09-Jan-2012, at 1:58 AM, Stefan Karpinskireply@reply.github.com wrote:

The default install location when you use ./configure to install software is /usr/local. Defaulting to anything else is surprising and bad. Debian is different because we're distributing a package, which would be expected not to go under /usr/local since it's precompiled.

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#6 (comment)

ViralBShah · 2012-01-08T21:27:19Z

Maybe we are getting to the point where we should start using autotools.

-viral

On 09-Jan-2012, at 1:58 AM, Stefan Karpinskireply@reply.github.com wrote:

The default install location when you use ./configure to install software is /usr/local. Defaulting to anything else is surprising and bad. Debian is different because we're distributing a package, which would be expected not to go under /usr/local since it's precompiled.

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#6 (comment)

StefanKarpinski · 2012-01-08T22:05:33Z

Ugh. Autotools is the worst. What would it provide us with?

On Jan 8, 2012, at 4:27 PM, "Viral B. Shah"reply@reply.github.com wrote:

Maybe we are getting to the point where we should start using autotools.

-viral

On 09-Jan-2012, at 1:58 AM, Stefan Karpinskireply@reply.github.com wrote:

The default install location when you use ./configure to install software is /usr/local. Defaulting to anything else is surprising and bad. Debian is different because we're distributing a package, which would be expected not to go under /usr/local since it's precompiled.

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#6 (comment)

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#6 (comment)

ViralBShah · 2012-01-09T02:44:34Z

A lot of other build tools understand autotools, and everyone understands how to use it, interfaces etc. That would be the only reason to use it.

-viral

On Jan 9, 2012, at 3:35 AM, Stefan Karpinski wrote:

Ugh. Autotools is the worst. What would it provide us with?

On Jan 8, 2012, at 4:27 PM, "Viral B. Shah"reply@reply.github.com wrote:

Maybe we are getting to the point where we should start using autotools.

-viral

On 09-Jan-2012, at 1:58 AM, Stefan Karpinskireply@reply.github.com wrote:

The default install location when you use ./configure to install software is /usr/local. Defaulting to anything else is surprising and bad. Debian is different because we're distributing a package, which would be expected not to go under /usr/local since it's precompiled.

Reply to this email directly or view it on GitHub:
#6 (comment)

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#6 (comment)

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#6 (comment)

StefanKarpinski · 2012-01-09T15:17:41Z

I guess the only thing it would really give us would be ./configure --prefix=WHATEVER that Debian and people in general would know how to interact with. Ok, well, what's the absolute bare minimum we can get away with using it? I really don't want to have to run my makefiles through automake and turn them into incomprehensible nonsense. Maybe we can have a Make.ac.inc generated from Make.ac.inc.ac that keeps the nonsense sequestered and sets up the variables that allow control of the install process?

Update the Makefile's install target to not use rm, cp, etc. and instead use the install command. The default install path is $(DESTDIR)/usr/share/julia. In case DESTDIR is not specified during make, the installation happens in /usr/share/julia. I would have liked this to be /usr/local/julia instead, but the debian package creation scripts do not like this, and it calls the install target in the Makefile to create the package. I think /usr/share/julia is acceptable as a default for the time being. I believe this sufficiently addresses issue #6 even though the solution is not completely satisfactory.

ViralBShah · 2012-01-26T06:31:34Z

As of commit 7dca4d7, we now have an install target that can be used by calling make install DESTDIR=..., and a dist target that creates a tarball.

The install target installs in $(DESTDIR)/usr/share/julia/, whereas the tarball creates all files in ./julia.

I think this just about takes care of everything discussed in this issue.

Currently the `@nospecialize`-d `push!(::Vector{Any}, ...)` can only take a single item and we will end up with runtime dispatch when we try to call it with multiple items: ```julia julia> code_typed(push!, (Vector{Any}, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing │ %2 = Base.arraylen(a)::Int64 │ Base.arrayset(true, a, item, %2)::Vector{Any} └── return a ) => Vector{Any} julia> code_typed(push!, (Vector{Any}, Any, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ %1 = Base.append!(a, iter)::Vector{Any} └── return %1 ) => Vector{Any} ``` This commit adds a new specialization that it can take arbitrary-length items. Our compiler should still be able to optimize the single-input case as before via the dispatch mechanism. ```julia julia> code_typed(push!, (Vector{Any}, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing │ %2 = Base.arraylen(a)::Int64 │ Base.arrayset(true, a, item, %2)::Vector{Any} └── return a ) => Vector{Any} julia> code_typed(push!, (Vector{Any}, Any, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ %1 = Base.arraylen(a)::Int64 │ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000002, 0x0000000000000002))::Nothing └── goto #7 if not true 2 ┄ %4 = φ (#1 => 1, #6 => %14)::Int64 │ %5 = φ (#1 => 1, #6 => %15)::Int64 │ %6 = Base.getfield(x, %4, true)::Any │ %7 = Base.add_int(%1, %4)::Int64 │ Base.arrayset(true, a, %6, %7)::Vector{Any} │ %9 = (%5 === 2)::Bool └── goto #4 if not %9 3 ─ goto #5 4 ─ %12 = Base.add_int(%5, 1)::Int64 └── goto #5 5 ┄ %14 = φ (#4 => %12)::Int64 │ %15 = φ (#4 => %12)::Int64 │ %16 = φ (#3 => true, #4 => false)::Bool │ %17 = Base.not_int(%16)::Bool └── goto #7 if not %17 6 ─ goto #2 7 ┄ return a ) => Vector{Any} ``` This commit also adds the equivalent implementations for `pushfirst!`.

When calling `jl_error()` or `jl_errorf()`, we must check to see if we are so early in the bringup process that it is dangerous to attempt to construct a backtrace because the data structures used to provide line information are not properly setup. This can be easily triggered by running: ``` julia -C invalid ``` On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name" branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part of the backtrace printing process, which fails as the object maps are not fully initialized. See the below `gdb` stacktrace for details: ``` $ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid ... fatal: error thrown and no exception handler available. ErrorException("Invalid CPU name "invalid".") Thread 1 "julia" received signal SIGSEGV, Segmentation fault. 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory. #0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 #1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258 #2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181 #3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210 #4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636 #5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657 #6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090 #7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605 #8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638 #9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654 #10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77 #11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823 #12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044 #13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585 #14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648 #15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131 #16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184 #17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424 #18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157 #19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741 #20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728 #21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705 #22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59 ``` This solution adds a check against `jl_error_sym` as a data structure that gets initialized relatively late in the bringup process.

When calling `jl_error()` or `jl_errorf()`, we must check to see if we are so early in the bringup process that it is dangerous to attempt to construct a backtrace because the data structures used to provide line information are not properly setup. This can be easily triggered by running: ``` julia -C invalid ``` On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name" branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part of the backtrace printing process, which fails as the object maps are not fully initialized. See the below `gdb` stacktrace for details: ``` $ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid ... fatal: error thrown and no exception handler available. ErrorException("Invalid CPU name "invalid".") Thread 1 "julia" received signal SIGSEGV, Segmentation fault. 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory. #0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 #1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258 #2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181 #3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210 #4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636 #5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657 #6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090 #7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605 #8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638 #9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654 #10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77 #11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823 #12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044 #13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585 #14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648 #15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131 #16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184 #17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424 #18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157 #19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741 #20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728 #21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705 #22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59 ``` To prevent this, we simply avoid calling `jl_errorf` this early in the process, punting the problem to a later PR that can update guard conditions within `jl_error*`.

When calling `jl_error()` or `jl_errorf()`, we must check to see if we are so early in the bringup process that it is dangerous to attempt to construct a backtrace because the data structures used to provide line information are not properly setup. This can be easily triggered by running: ``` julia -C invalid ``` On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name" branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part of the backtrace printing process, which fails as the object maps are not fully initialized. See the below `gdb` stacktrace for details: ``` $ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid ... fatal: error thrown and no exception handler available. ErrorException("Invalid CPU name "invalid".") Thread 1 "julia" received signal SIGSEGV, Segmentation fault. 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory. #0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 #1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258 #2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181 #3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210 #4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636 #5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657 #6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090 #7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605 #8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638 #9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654 #10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77 #11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823 #12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044 #13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585 #14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648 #15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131 #16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184 #17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424 #18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157 #19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741 #20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728 #21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705 #22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59 ``` To prevent this, we simply avoid calling `jl_errorf` this early in the process, punting the problem to a later PR that can update guard conditions within `jl_error*`. (cherry picked from commit 21ab24e)

…Lang#45790) Currently the `@nospecialize`-d `push!(::Vector{Any}, ...)` can only take a single item and we will end up with runtime dispatch when we try to call it with multiple items: ```julia julia> code_typed(push!, (Vector{Any}, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing │ %2 = Base.arraylen(a)::Int64 │ Base.arrayset(true, a, item, %2)::Vector{Any} └── return a ) => Vector{Any} julia> code_typed(push!, (Vector{Any}, Any, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ %1 = Base.append!(a, iter)::Vector{Any} └── return %1 ) => Vector{Any} ``` This commit adds a new specialization that it can take arbitrary-length items. Our compiler should still be able to optimize the single-input case as before via the dispatch mechanism. ```julia julia> code_typed(push!, (Vector{Any}, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000001, 0x0000000000000001))::Nothing │ %2 = Base.arraylen(a)::Int64 │ Base.arrayset(true, a, item, %2)::Vector{Any} └── return a ) => Vector{Any} julia> code_typed(push!, (Vector{Any}, Any, Any)) 1-element Vector{Any}: CodeInfo( 1 ─ %1 = Base.arraylen(a)::Int64 │ $(Expr(:foreigncall, :(:jl_array_grow_end), Nothing, svec(Any, UInt64), 0, :(:ccall), Core.Argument(2), 0x0000000000000002, 0x0000000000000002))::Nothing └── goto JuliaLang#7 if not true 2 ┄ %4 = φ (JuliaLang#1 => 1, JuliaLang#6 => %14)::Int64 │ %5 = φ (JuliaLang#1 => 1, JuliaLang#6 => %15)::Int64 │ %6 = Base.getfield(x, %4, true)::Any │ %7 = Base.add_int(%1, %4)::Int64 │ Base.arrayset(true, a, %6, %7)::Vector{Any} │ %9 = (%5 === 2)::Bool └── goto JuliaLang#4 if not %9 3 ─ goto JuliaLang#5 4 ─ %12 = Base.add_int(%5, 1)::Int64 └── goto JuliaLang#5 5 ┄ %14 = φ (JuliaLang#4 => %12)::Int64 │ %15 = φ (JuliaLang#4 => %12)::Int64 │ %16 = φ (JuliaLang#3 => true, JuliaLang#4 => false)::Bool │ %17 = Base.not_int(%16)::Bool └── goto JuliaLang#7 if not %17 6 ─ goto JuliaLang#2 7 ┄ return a ) => Vector{Any} ``` This commit also adds the equivalent implementations for `pushfirst!`.

When calling `jl_error()` or `jl_errorf()`, we must check to see if we are so early in the bringup process that it is dangerous to attempt to construct a backtrace because the data structures used to provide line information are not properly setup. This can be easily triggered by running: ``` julia -C invalid ``` On an `i686-linux-gnu` build, this will hit the "Invalid CPU Name" branch in `jitlayers.cpp`, which calls `jl_errorf()`. This in turn calls `jl_throw()`, which will eventually call `jl_DI_for_fptr` as part of the backtrace printing process, which fails as the object maps are not fully initialized. See the below `gdb` stacktrace for details: ``` $ gdb -batch -ex 'r' -ex 'bt' --args ./julia -C invalid ... fatal: error thrown and no exception handler available. ErrorException("Invalid CPU name "invalid".") Thread 1 "julia" received signal SIGSEGV, Segmentation fault. 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 1277 /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h: No such file or directory. #0 0xf75bd665 in std::_Rb_tree<unsigned int, std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo>, std::_Select1st<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> >, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__k=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_tree.h:1277 JuliaLang#1 std::map<unsigned int, JITDebugInfoRegistry::ObjectInfo, std::greater<unsigned int>, std::allocator<std::pair<unsigned int const, JITDebugInfoRegistry::ObjectInfo> > >::lower_bound (__x=<optimized out>, this=0x248) at /usr/local/i686-linux-gnu/include/c++/9.1.0/bits/stl_map.h:1258 JuliaLang#2 jl_DI_for_fptr (fptr=4155049385, symsize=symsize@entry=0xffffcfa8, slide=slide@entry=0xffffcfa0, Section=Section@entry=0xffffcfb8, context=context@entry=0xffffcf94) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1181 JuliaLang#3 0xf75c056a in jl_getFunctionInfo_impl (frames_out=0xffffd03c, pointer=4155049385, skipC=0, noInline=0) at /cache/build/default-amdci5-4/julialang/julia-master/src/debuginfo.cpp:1210 JuliaLang#4 0xf7a6ca98 in jl_print_native_codeloc (ip=4155049385) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:636 JuliaLang#5 0xf7a6cd54 in jl_print_bt_entry_codeloc (bt_entry=0xf0798018) at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:657 JuliaLang#6 jlbacktrace () at /cache/build/default-amdci5-4/julialang/julia-master/src/stackwalk.c:1090 JuliaLang#7 0xf7a3cd2b in ijl_no_exc_handler (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:605 JuliaLang#8 0xf7a3d10a in throw_internal (ct=ct@entry=0xf070c010, exception=<optimized out>, exception@entry=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:638 JuliaLang#9 0xf7a3d330 in ijl_throw (e=0xf0794010) at /cache/build/default-amdci5-4/julialang/julia-master/src/task.c:654 JuliaLang#10 0xf7a905aa in ijl_errorf (fmt=fmt@entry=0xf7647cd4 "Invalid CPU name \"%s\".") at /cache/build/default-amdci5-4/julialang/julia-master/src/rtutils.c:77 JuliaLang#11 0xf75a4b22 in (anonymous namespace)::createTargetMachine () at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:823 JuliaLang#12 JuliaOJIT::JuliaOJIT (this=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/jitlayers.cpp:1044 JuliaLang#13 0xf7531793 in jl_init_llvm () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8585 JuliaLang#14 0xf75318a8 in jl_init_codegen_impl () at /cache/build/default-amdci5-4/julialang/julia-master/src/codegen.cpp:8648 JuliaLang#15 0xf7a51a52 in jl_restore_system_image_from_stream (f=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2131 JuliaLang#16 0xf7a55c03 in ijl_restore_system_image_data (buf=0xe859c1c0 <jl_system_image_data> "8'\031\003", len=125161105) at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2184 JuliaLang#17 0xf7a55cf9 in jl_load_sysimg_so () at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:424 JuliaLang#18 ijl_restore_system_image (fname=0x80a0900 "/build/bk_download/julia-d78fdad601/lib/julia/sys.so") at /cache/build/default-amdci5-4/julialang/julia-master/src/staticdata.c:2157 JuliaLang#19 0xf7a3bdfc in _finish_julia_init (rel=rel@entry=JL_IMAGE_JULIA_HOME, ct=<optimized out>, ptls=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:741 JuliaLang#20 0xf7a3c8ac in julia_init (rel=<optimized out>) at /cache/build/default-amdci5-4/julialang/julia-master/src/init.c:728 JuliaLang#21 0xf7a7f61d in jl_repl_entrypoint (argc=<optimized out>, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/src/jlapi.c:705 JuliaLang#22 0x080490a7 in main (argc=3, argv=0xffffddf4) at /cache/build/default-amdci5-4/julialang/julia-master/cli/loader_exe.c:59 ``` To prevent this, we simply avoid calling `jl_errorf` this early in the process, punting the problem to a later PR that can update guard conditions within `jl_error*`.

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - add special SROA handling for `NamedTuple` generated by keyword sorter: With the change on `structdiff`, IR for a call with type-unstable keyword arguments after inlining would look like: ``` %1 = tuple(a, b, c)::Tuple{Any, Any, Any} %2 = NamedTuple{(:a, :b, :c)(%1)::NamedTuple{(:a, :b, :c), _A} where _A<:Tuple{Any, Any, Any} %3 = Core.getfield(%2, :a)::Any %4 = Core.getfield(%2, :b)::Any %5 = Core.getfield(%2, :c)::Any [... other body of the keyword func ...] ``` We can implement a bit hacky special handling within our SROA pass that checks if this definition (%2) is partly well-known `NamedTuple` construction, where its names are fully known, and also checks if its call argument (%1) is fully-known `tuple` call. In a case when the length of the `NamedTuple` names and the length of the arguments for the `tuple` call, we can safely replace those `getfield` calls with the corresponding `tuple` call argument, while letting the later DCE pass to delete the constructions of tuple and named-tuple altogether. With these changes, the IR for the example `NewInstruction` constructor is fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - add special SROA handling for `NamedTuple` generated by keyword sorter: With the change on `structdiff`, IR for a call with type-unstable keyword arguments after inlining would look like: ``` %1 = tuple(a, b, c)::Tuple{Any, Any, Any} %2 = NamedTuple{(:a, :b, :c)(%1)::NamedTuple{(:a, :b, :c), _A} where _A<:Tuple{Any, Any, Any} %3 = Core.getfield(%2, :a)::Any %4 = Core.getfield(%2, :b)::Any %5 = Core.getfield(%2, :c)::Any [... other body of the keyword func ...] ``` We can implement a bit hacky special handling within our SROA pass that checks if this definition (%2) is partly well-known `NamedTuple` construction, where its names are fully known, and also checks if its call argument (%1) is fully-known `tuple` call. In a case when the length of the `NamedTuple` names and the length of the arguments for the `tuple` call, we can safely replace those `getfield` calls with the corresponding `tuple` call argument, while letting the later DCE pass to delete the constructions of tuple and named-tuple altogether. With these changes, the IR for the example `NewInstruction` constructor is fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - add special SROA handling for `NamedTuple` generated by keyword sorter: With the change on `structdiff`, IR for a call with type-unstable keyword arguments after inlining would look like: ``` %1 = tuple(a, b, c)::Tuple{Any, Any, Any} %2 = NamedTuple{(:a, :b, :c)(%1)::NamedTuple{(:a, :b, :c), _A} where _A<:Tuple{Any, Any, Any} %3 = Core.getfield(%2, :a)::Any %4 = Core.getfield(%2, :b)::Any %5 = Core.getfield(%2, :c)::Any [... other body of the keyword func ...] ``` We can implement a bit hacky special handling within our SROA pass that checks if this definition (%2) is partly well-known `NamedTuple` construction, where its names are fully known, and also checks if its call argument (%1) is fully-known `tuple` call. In a case when the length of the `NamedTuple` names and the length of the arguments for the `tuple` call, we can safely replace those `getfield` calls with the corresponding `tuple` call argument, while letting the later DCE pass to delete the constructions of tuple and named-tuple altogether. With these changes, the IR for the example `NewInstruction` constructor is fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - Add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - Tweak the core `NamedTuple{names}(args::Tuple)` constructor so that it directly forms `:splatnew` allocation rather than redirects to the general `NamedTuple` constructor, that could be confused for abstract input tuple type. - Improve `nfields_tfunc` accuracy as for abstract `NamedTuple` types. This improvement lets `inline_splatnew` to handle more abstract `NamedTuple`s, especially whose names are fully known but its fields tuple type is abstract. Those improvements are combined to allow our SROA pass to optimize away `NamedTuple` and `tuple` calls generated for keyword argument handling. E.g. the IR for the example `NewInstruction` constructor is now fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - Add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - Tweak the core `NamedTuple{names}(args::Tuple)` constructor so that it directly forms `:splatnew` allocation rather than redirects to the general `NamedTuple` constructor, that could be confused for abstract input tuple type. - Improve `nfields_tfunc` accuracy as for abstract `NamedTuple` types. This improvement lets `inline_splatnew` to handle more abstract `NamedTuple`s, especially whose names are fully known but its fields tuple type is abstract. Those improvements are combined to allow our SROA pass to optimize away `NamedTuple` and `tuple` calls generated for keyword argument handling. E.g. the IR for the example `NewInstruction` constructor is now fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - Add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - Tweak the core `NamedTuple{names}(args::Tuple)` constructor so that it directly forms `:splatnew` allocation rather than redirects to the general `NamedTuple` constructor, that could be confused for abstract input tuple type. - Improve `nfields_tfunc` accuracy as for abstract `NamedTuple` types. This improvement lets `inline_splatnew` to handle more abstract `NamedTuple`s, especially whose names are fully known but its fields tuple type is abstract. Those improvements are combined to allow our SROA pass to optimize away `NamedTuple` and `tuple` calls generated for keyword argument handling. E.g. the IR for the example `NewInstruction` constructor is now fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - Add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - Tweak the core `NamedTuple{names}(args::Tuple)` constructor so that it directly forms `:splatnew` allocation rather than redirects to the general `NamedTuple` constructor, that could be confused for abstract input tuple type. - Improve `nfields_tfunc` accuracy as for abstract `NamedTuple` types. This improvement lets `inline_splatnew` to handle more abstract `NamedTuple`s, especially whose names are fully known but its fields tuple type is abstract. Those improvements are combined to allow our SROA pass to optimize away `NamedTuple` and `tuple` calls generated for keyword argument handling. E.g. the IR for the example `NewInstruction` constructor is now fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This commit tries to fix and improve performance for calling keyword funcs whose arguments types are not fully known but `@nospecialize`-d. The final result would look like (this particular example is taken from our Julia-level compiler implementation): ```julia abstract type CallInfo end struct NoCallInfo <: CallInfo end struct NewInstruction stmt::Any type::Any info::CallInfo line::Union{Int32,Nothing} # if nothing, copy the line from previous statement in the insertion location flag::Union{UInt8,Nothing} # if nothing, IR flags will be recomputed on insertion function NewInstruction(@nospecialize(stmt), @nospecialize(type), @nospecialize(info::CallInfo), line::Union{Int32,Nothing}, flag::Union{UInt8,Nothing}) return new(stmt, type, info, line, flag) end end @nospecialize function NewInstruction(newinst::NewInstruction; stmt=newinst.stmt, type=newinst.type, info::CallInfo=newinst.info, line::Union{Int32,Nothing}=newinst.line, flag::Union{UInt8,Nothing}=newinst.flag) return NewInstruction(stmt, type, info, line, flag) end @Specialize using BenchmarkTools struct VirtualKwargs stmt::Any type::Any info::CallInfo end vkws = VirtualKwargs(nothing, Any, NoCallInfo()) newinst = NewInstruction(nothing, Any, NoCallInfo(), nothing, nothing) runner(newinst, vkws) = NewInstruction(newinst; vkws.stmt, vkws.type, vkws.info) @benchmark runner($newinst, $vkws) ``` > on master ``` BenchmarkTools.Trial: 10000 samples with 186 evaluations. Range (min … max): 559.898 ns … 4.173 μs ┊ GC (min … max): 0.00% … 85.29% Time (median): 605.608 ns ┊ GC (median): 0.00% Time (mean ± σ): 638.170 ns ± 125.080 ns ┊ GC (mean ± σ): 0.06% ± 0.85% █▇▂▆▄ ▁█▇▄▂ ▂ ██████▅██████▇▇▇██████▇▇▇▆▆▅▄▅▄▂▄▄▅▇▆▆▆▆▆▅▆▆▄▄▅▅▄▃▄▄▄▅▃▅▅▆▅▆▆ █ 560 ns Histogram: log(frequency) by time 1.23 μs < Memory estimate: 32 bytes, allocs estimate: 2. ``` > on this commit ```julia BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 3.080 ns … 83.177 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 3.098 ns ┊ GC (median): 0.00% Time (mean ± σ): 3.118 ns ± 0.885 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇█▆▅▄▂ ▂▄▆▆▇████████▆▃▃▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▂▂▂▁▂▂▂▂▂▂▁▁▂▁▂▂▂▂▂▂▂▂▂ ▃ 3.08 ns Histogram: frequency by time 3.19 ns < Memory estimate: 0 bytes, allocs estimate: 0. ``` So for this particular case it achieves roughly 200x speed up. This is because this commit allows inlining of a call to keyword sorter as well as removal of `NamedTuple` call. Especially this commit is composed of the following improvements: - Add early return case for `structdiff`: This change improves the return type inference for a case when compared `NamedTuple`s are type unstable but there is no difference in their names, e.g. given two `NamedTuple{(:a,:b),T} where T<:Tuple{Any,Any}`s. And in such case the optimizer will remove `structdiff` and succeeding `pairs` calls, letting the keyword sorter to be inlined. - Tweak the core `NamedTuple{names}(args::Tuple)` constructor so that it directly forms `:splatnew` allocation rather than redirects to the general `NamedTuple` constructor, that could be confused for abstract input tuple type. - Improve `nfields_tfunc` accuracy as for abstract `NamedTuple` types. This improvement lets `inline_splatnew` to handle more abstract `NamedTuple`s, especially whose names are fully known but its fields tuple type is abstract. Those improvements are combined to allow our SROA pass to optimize away `NamedTuple` and `tuple` calls generated for keyword argument handling. E.g. the IR for the example `NewInstruction` constructor is now fairly optimized, like: ```julia julia> Base.code_ircode((NewInstruction,Any,Any,CallInfo)) do newinst, stmt, type, info NewInstruction(newinst; stmt, type, info) end |> only 2 1 ── %1 = Base.getfield(_2, :line)::Union{Nothing, Int32} │╻╷ Type##kw │ %2 = Base.getfield(_2, :flag)::Union{Nothing, UInt8} ││┃ getproperty │ %3 = (isa)(%1, Nothing)::Bool ││ │ %4 = (isa)(%2, Nothing)::Bool ││ │ %5 = (Core.Intrinsics.and_int)(%3, %4)::Bool ││ └─── goto #3 if not %5 ││ 2 ── %7 = %new(Main.NewInstruction, _3, _4, _5, nothing, nothing)::NewInstruction NewInstruction └─── goto #10 ││ 3 ── %9 = (isa)(%1, Int32)::Bool ││ │ %10 = (isa)(%2, Nothing)::Bool ││ │ %11 = (Core.Intrinsics.and_int)(%9, %10)::Bool ││ └─── goto #5 if not %11 ││ 4 ── %13 = π (%1, Int32) ││ │ %14 = %new(Main.NewInstruction, _3, _4, _5, %13, nothing)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 5 ── %16 = (isa)(%1, Nothing)::Bool ││ │ %17 = (isa)(%2, UInt8)::Bool ││ │ %18 = (Core.Intrinsics.and_int)(%16, %17)::Bool ││ └─── goto #7 if not %18 ││ 6 ── %20 = π (%2, UInt8) ││ │ %21 = %new(Main.NewInstruction, _3, _4, _5, nothing, %20)::NewInstruction│││╻ NewInstruction └─── goto #10 ││ 7 ── %23 = (isa)(%1, Int32)::Bool ││ │ %24 = (isa)(%2, UInt8)::Bool ││ │ %25 = (Core.Intrinsics.and_int)(%23, %24)::Bool ││ └─── goto #9 if not %25 ││ 8 ── %27 = π (%1, Int32) ││ │ %28 = π (%2, UInt8) ││ │ %29 = %new(Main.NewInstruction, _3, _4, _5, %27, %28)::NewInstruction │││╻ NewInstruction └─── goto #10 ││ 9 ── Core.throw(ErrorException("fatal error in type inference (type bound)"))::Union{} └─── unreachable ││ 10 ┄ %33 = φ (#2 => %7, #4 => %14, #6 => %21, #8 => %29)::NewInstruction ││ └─── goto #11 ││ 11 ─ return %33 │ => NewInstruction ```

@benchmark

This is part of the work to address #51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. ## Current Status This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. ## Obligatory benchmark Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto #5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto #4 if not %13 3 ─ goto #5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto #6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto #7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto #8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@benchmark

This is part of the work to address #51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto #5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto #4 if not %13 3 ─ goto #5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto #6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto #7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto #8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@benchmark

This is part of the work to address #51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto #5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto #4 if not %13 3 ─ goto #5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto #6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto #7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto #8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@benchmark

This is part of the work to address #51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto #5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto #4 if not %13 3 ─ goto #5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto #6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto #7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto #8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@benchmark

This is part of the work to address #51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. ## Current Status This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. ## Obligatory benchmark Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto #5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto #4 if not %13 3 ─ goto #5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto #6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto #7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto #8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@benchmark

This is part of the work to address JuliaLang#51352 by attempting to allow the compiler to perform SRAO on persistent data structures like `PersistentDict` as if they were regular immutable data structures. These sorts of data structures have very complicated internals (with lots of mutation, memory sharing, etc.), but a relatively simple interface. As such, it is unlikely that our compiler will have sufficient power to optimize this interface by analyzing the implementation. We thus need to come up with some other mechanism that gives the compiler license to perform the requisite optimization. One way would be to just hardcode `PersistentDict` into the compiler, optimizing it like any of the other builtin datatypes. However, this is of course very unsatisfying. At the other end of the spectrum would be something like a generic rewrite rule system (e-graphs anyone?) that would let the PersistentDict implementation declare its interface to the compiler and the compiler would use this for optimization (in a perfect world, the actual rewrite would then be checked using some sort of formal methods). I think that would be interesting, but we're very far from even being able to design something like that (at least in Base - experiments with external AbstractInterpreters in this direction are encouraged). This PR tries to come up with a reasonable middle ground, where the compiler gets some knowledge of the protocol hardcoded without having to know about the implementation details of the data structure. The basic ideas is that `Core` provides some magic generic functions that implementations can extend. Semantically, they are not special. They dispatch as usual, and implementations are expected to work properly even in the absence of any compiler optimizations. However, the compiler is semantically permitted to perform structural optimization using these magic generic functions. In the concrete case, this PR introduces the `KeyValue` interface which consists of two generic functions, `get` and `set`. The core optimization is that the compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)` into `v` without additional legality checks. In particular, the compiler performs no type checks, conversions, etc. The higher level implementation code is expected to do all that. This approach closely matches the general direction we've been taking in external AbstractInterpreters for embedding additional semantics and optimization opportunities into Julia code (although we generally use methods there, rather than full generic functions), so I think we have some evidence that this sort of approach works reasonably well. Nevertheless, this is certainly an experiment and the interface is explicitly declared unstable. ## Current Status This is fully working and implemented, but the optimization currently bails on anything but the simplest cases. Filling all those cases in is not particularly hard, but should be done along with a more invasive refactoring of SROA, so we should figure out the general direction here first and then we can finish all that up in a follow-up cleanup. ## Obligatory benchmark Before: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 993 evaluations. Range (min … max): 32.940 ns … 28.754 μs ┊ GC (min … max): 0.00% … 99.76% Time (median): 49.647 ns ┊ GC (median): 0.00% Time (mean ± σ): 57.519 ns ± 333.275 ns ┊ GC (mean ± σ): 10.81% ± 2.22% ▃█▅ ▁▃▅▅▃▁ ▁▃▂ ▂ ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃ 32.9 ns Histogram: frequency by time 68.6 ns < Memory estimate: 128 bytes, allocs estimate: 4. julia> @code_typed foo() CodeInfo( 1 ─ %1 = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %2 = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64} │ %3 = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64} │ %4 = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %5 = $(Expr(:boundscheck, true))::Bool └── goto JuliaLang#5 if not %5 2 ─ %7 = Base.sub_int(1, 1)::Int64 │ %8 = Base.bitcast(UInt64, %7)::UInt64 │ %9 = Base.getfield(%4, :size)::Tuple{Int64} │ %10 = $(Expr(:boundscheck, true))::Bool │ %11 = Base.getfield(%9, 1, %10)::Int64 │ %12 = Base.bitcast(UInt64, %11)::UInt64 │ %13 = Base.ult_int(%8, %12)::Bool └── goto JuliaLang#4 if not %13 3 ─ goto JuliaLang#5 4 ─ %16 = Core.tuple(1)::Tuple{Int64} │ invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{} └── unreachable 5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} │ Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}} └── goto JuliaLang#6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto JuliaLang#7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto JuliaLang#8 8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64 └── return %29 ``` After: ``` julia> using BenchmarkTools julia> function foo() a = Base.PersistentDict(:a => 1) return a[:a] end foo (generic function with 1 method) julia> @benchmark foo() BenchmarkTools.Trial: 10000 samples with 1000 evaluations. Range (min … max): 2.459 ns … 11.320 ns ┊ GC (min … max): 0.00% … 0.00% Time (median): 2.460 ns ┊ GC (median): 0.00% Time (mean ± σ): 2.469 ns ± 0.183 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂ █ ▁ █ ▂ █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █ 2.46 ns Histogram: log(frequency) by time 2.47 ns < Memory estimate: 0 bytes, allocs estimate: 0. julia> @code_typed foo() CodeInfo( 1 ─ return 1 ```

@something

`@something` eagerly unwraps any `Some` given to it, while keeping the variable between its arguments the same. This can be an issue if a previously unpacked value is used as input to `@something`, leading to a type instability on more than two arguments (e.g. because of a fallback to `Some(nothing)`). By using different variables for each argument, type inference has an easier time handling these cases that are isolated to single branches anyway. This also adds some comments to the macro, since it's non-obvious what it does. Benchmarking the specific case I encountered this in led to a ~2x performance improvement on multiple machines. 1.10-beta3/master: ``` [sukera@tower 01]$ jl1100 -q --project=. -L 01.jl -e 'bench()' v"1.10.0-beta3" BenchmarkTools.Trial: 10000 samples with 1 evaluation. Range (min … max): 38.670 μs … 70.350 μs ┊ GC (min … max): 0.00% … 0.00% Time (median): 43.340 μs ┊ GC (median): 0.00% Time (mean ± σ): 43.395 μs ± 1.518 μs ┊ GC (mean ± σ): 0.00% ± 0.00% ▆█▂ ▁▁ ▂▂▂▂▂▂▂▂▂▁▂▂▂▃▃▃▂▂▃▃▃▂▂▂▂▂▄▇███▆██▄▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃ 38.7 μs Histogram: frequency by time 48 μs < Memory estimate: 0 bytes, allocs estimate: 0. ``` This PR: ``` [sukera@tower 01]$ julia -q --project=. -L 01.jl -e 'bench()' v"1.11.0-DEV.970" BenchmarkTools.Trial: 10000 samples with 1 evaluation. Range (min … max): 22.820 μs … 44.980 μs ┊ GC (min … max): 0.00% … 0.00% Time (median): 24.300 μs ┊ GC (median): 0.00% Time (mean ± σ): 24.370 μs ± 832.239 ns ┊ GC (mean ± σ): 0.00% ± 0.00% ▂▅▇██▇▆▅▁ ▂▂▂▂▂▂▂▂▃▃▄▅▇███████████▅▄▃▃▂▂▂▂▂▂▂▂▂▂▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▂ ▃ 22.8 μs Histogram: frequency by time 27.7 μs < Memory estimate: 0 bytes, allocs estimate: 0. ``` <details> <summary>Benchmarking code (spoilers for Advent Of Code 2023 Day 01, Part 01). Running this requires the input of that Advent Of Code day.</summary> ```julia using BenchmarkTools using InteractiveUtils isdigit(d::UInt8) = UInt8('0') <= d <= UInt8('9') someDigit(c::UInt8) = isdigit(c) ? Some(c - UInt8('0')) : nothing function part1(data) total = 0 may_a = nothing may_b = nothing for c in data digitRes = someDigit(c) may_a = @something may_a digitRes Some(nothing) may_b = @something digitRes may_b Some(nothing) if c == UInt8('\n') digit_a = may_a::UInt8 digit_b = may_b::UInt8 total += digit_a*0xa + digit_b may_a = nothing may_b = nothing end end return total end function bench() data = read("input.txt") display(VERSION) println() display(@benchmark part1($data)) nothing end ``` </details> <details> <summary>`@code_warntype` before</summary> ```julia julia> @code_warntype part1(data) MethodInstance for part1(::Vector{UInt8}) from part1(data) @ Main ~/Documents/projects/AOC/2023/01/01.jl:7 Arguments #self#::Core.Const(part1) data::Vector{UInt8} Locals @_3::Union{Nothing, Tuple{UInt8, Int64}} may_b::Union{Nothing, UInt8} may_a::Union{Nothing, UInt8} total::Int64 c::UInt8 digit_b::UInt8 digit_a::UInt8 val@_10::Any val@_11::Any digitRes::Union{Nothing, Some{UInt8}} @_13::Union{Some{Nothing}, Some{UInt8}, UInt8} @_14::Union{Some{Nothing}, Some{UInt8}} @_15::Some{Nothing} @_16::Union{Some{Nothing}, Some{UInt8}, UInt8} @_17::Union{Some{Nothing}, UInt8} @_18::Some{Nothing} Body::Int64 1 ── (total = 0) │ (may_a = Main.nothing) │ (may_b = Main.nothing) │ %4 = data::Vector{UInt8} │ (@_3 = Base.iterate(%4)) │ %6 = (@_3 === nothing)::Bool │ %7 = Base.not_int(%6)::Bool └─── goto #24 if not %7 2 ┄─ Core.NewvarNode(:(digit_b)) │ Core.NewvarNode(:(digit_a)) │ Core.NewvarNode(:(val@_10)) │ %12 = @_3::Tuple{UInt8, Int64} │ (c = Core.getfield(%12, 1)) │ %14 = Core.getfield(%12, 2)::Int64 │ (digitRes = Main.someDigit(c)) │ (val@_11 = may_a) │ %17 = (val@_11::Union{Nothing, UInt8} !== Base.nothing)::Bool └─── goto #4 if not %17 3 ── (@_13 = val@_11::UInt8) └─── goto #11 4 ── (val@_11 = digitRes) │ %22 = (val@_11::Union{Nothing, Some{UInt8}} !== Base.nothing)::Bool └─── goto #6 if not %22 5 ── (@_14 = val@_11::Some{UInt8}) └─── goto #10 6 ── (val@_11 = Main.Some(Main.nothing)) │ %27 = (val@_11::Core.Const(Some(nothing)) !== Base.nothing)::Core.Const(true) └─── goto #8 if not %27 7 ── (@_15 = val@_11::Core.Const(Some(nothing))) └─── goto #9 8 ── Core.Const(:(@_15 = Base.nothing)) 9 ┄─ (@_14 = @_15) 10 ┄ (@_13 = @_14) 11 ┄ %34 = @_13::Union{Some{Nothing}, Some{UInt8}, UInt8} │ (may_a = Base.something(%34)) │ (val@_10 = digitRes) │ %37 = (val@_10::Union{Nothing, Some{UInt8}} !== Base.nothing)::Bool └─── goto #13 if not %37 12 ─ (@_16 = val@_10::Some{UInt8}) └─── goto #20 13 ─ (val@_10 = may_b) │ %42 = (val@_10::Union{Nothing, UInt8} !== Base.nothing)::Bool └─── goto #15 if not %42 14 ─ (@_17 = val@_10::UInt8) └─── goto #19 15 ─ (val@_10 = Main.Some(Main.nothing)) │ %47 = (val@_10::Core.Const(Some(nothing)) !== Base.nothing)::Core.Const(true) └─── goto #17 if not %47 16 ─ (@_18 = val@_10::Core.Const(Some(nothing))) └─── goto #18 17 ─ Core.Const(:(@_18 = Base.nothing)) 18 ┄ (@_17 = @_18) 19 ┄ (@_16 = @_17) 20 ┄ %54 = @_16::Union{Some{Nothing}, Some{UInt8}, UInt8} │ (may_b = Base.something(%54)) │ %56 = c::UInt8 │ %57 = Main.UInt8('\n')::Core.Const(0x0a) │ %58 = (%56 == %57)::Bool └─── goto #22 if not %58 21 ─ (digit_a = Core.typeassert(may_a, Main.UInt8)) │ (digit_b = Core.typeassert(may_b, Main.UInt8)) │ %62 = total::Int64 │ %63 = (digit_a * 0x0a)::UInt8 │ %64 = (%63 + digit_b)::UInt8 │ (total = %62 + %64) │ (may_a = Main.nothing) └─── (may_b = Main.nothing) 22 ┄ (@_3 = Base.iterate(%4, %14)) │ %69 = (@_3 === nothing)::Bool │ %70 = Base.not_int(%69)::Bool └─── goto #24 if not %70 23 ─ goto #2 24 ┄ return total ``` </details> <details> <summary>`@code_native debuginfo=:none` Before </summary> ```julia julia> @code_native debuginfo=:none part1(data) .text .file "part1" .globl julia_part1_418 # -- Begin function julia_part1_418 .p2align 4, 0x90 .type julia_part1_418,@function julia_part1_418: # @julia_part1_418 # %bb.0: # %top push rbp mov rbp, rsp push r15 push r14 push r13 push r12 push rbx sub rsp, 40 mov rax, qword ptr [rdi + 8] test rax, rax je .LBB0_1 # %bb.2: # %L17 mov rcx, qword ptr [rdi] dec rax mov r10b, 1 xor r14d, r14d # implicit-def: $r12b # implicit-def: $r13b # implicit-def: $r9b # implicit-def: $sil mov qword ptr [rbp - 64], rax # 8-byte Spill mov al, 1 mov dword ptr [rbp - 48], eax # 4-byte Spill # implicit-def: $al # kill: killed $al xor eax, eax mov qword ptr [rbp - 56], rax # 8-byte Spill mov qword ptr [rbp - 72], rcx # 8-byte Spill # implicit-def: $cl jmp .LBB0_3 .p2align 4, 0x90 .LBB0_8: # in Loop: Header=BB0_3 Depth=1 mov dword ptr [rbp - 48], 0 # 4-byte Folded Spill .LBB0_24: # %post_union_move # in Loop: Header=BB0_3 Depth=1 movzx r13d, byte ptr [rbp - 41] # 1-byte Folded Reload mov r12d, r8d cmp qword ptr [rbp - 64], r14 # 8-byte Folded Reload je .LBB0_13 .LBB0_25: # %guard_exit113 # in Loop: Header=BB0_3 Depth=1 inc r14 mov r10d, ebx .LBB0_3: # %L19 # =>This Inner Loop Header: Depth=1 mov rax, qword ptr [rbp - 72] # 8-byte Reload xor ebx, ebx xor edi, edi movzx r15d, r9b movzx ecx, cl movzx esi, sil mov r11b, 1 # implicit-def: $r9b movzx edx, byte ptr [rax + r14] lea eax, [rdx - 58] lea r8d, [rdx - 48] cmp al, -10 setae bl setb dil test r10b, 1 cmovne r15d, edi mov edi, 0 cmovne ecx, ebx mov bl, 1 cmovne esi, edi test r15b, 1 jne .LBB0_7 # %bb.4: # %L76 # in Loop: Header=BB0_3 Depth=1 mov r11b, 2 test cl, 1 jne .LBB0_5 # %bb.6: # %L78 # in Loop: Header=BB0_3 Depth=1 mov ebx, r10d mov r9d, r15d mov byte ptr [rbp - 41], r13b # 1-byte Spill test sil, 1 je .LBB0_26 .LBB0_7: # %L82 # in Loop: Header=BB0_3 Depth=1 cmp al, -11 jbe .LBB0_9 jmp .LBB0_8 .p2align 4, 0x90 .LBB0_5: # in Loop: Header=BB0_3 Depth=1 mov ecx, r8d mov sil, 1 xor ebx, ebx mov byte ptr [rbp - 41], r8b # 1-byte Spill xor r9d, r9d xor ecx, ecx cmp al, -11 ja .LBB0_8 .LBB0_9: # %L90 # in Loop: Header=BB0_3 Depth=1 test byte ptr [rbp - 48], 1 # 1-byte Folded Reload jne .LBB0_23 # %bb.10: # %L115 # in Loop: Header=BB0_3 Depth=1 cmp dl, 10 jne .LBB0_11 # %bb.14: # %L122 # in Loop: Header=BB0_3 Depth=1 test r15b, 1 jne .LBB0_15 # %bb.12: # %L130.thread # in Loop: Header=BB0_3 Depth=1 movzx eax, byte ptr [rbp - 41] # 1-byte Folded Reload mov bl, 1 add eax, eax lea eax, [rax + 4*rax] add al, r12b movzx eax, al add qword ptr [rbp - 56], rax # 8-byte Folded Spill mov al, 1 mov dword ptr [rbp - 48], eax # 4-byte Spill cmp qword ptr [rbp - 64], r14 # 8-byte Folded Reload jne .LBB0_25 jmp .LBB0_13 .p2align 4, 0x90 .LBB0_23: # %L115.thread # in Loop: Header=BB0_3 Depth=1 mov al, 1 # implicit-def: $r8b mov dword ptr [rbp - 48], eax # 4-byte Spill cmp dl, 10 jne .LBB0_24 jmp .LBB0_21 .LBB0_11: # in Loop: Header=BB0_3 Depth=1 mov r8d, r12d jmp .LBB0_24 .LBB0_1: xor eax, eax mov qword ptr [rbp - 56], rax # 8-byte Spill .LBB0_13: # %L159 mov rax, qword ptr [rbp - 56] # 8-byte Reload add rsp, 40 pop rbx pop r12 pop r13 pop r14 pop r15 pop rbp ret .LBB0_21: # %L122.thread test r15b, 1 jne .LBB0_15 # %bb.22: # %post_box_union58 movabs rdi, offset .L_j_str1 movabs rax, offset ijl_type_error movabs rsi, 140008511215408 movabs rdx, 140008667209736 call rax .LBB0_15: # %fail cmp r11b, 1 je .LBB0_19 # %bb.16: # %fail movzx eax, r11b cmp eax, 2 jne .LBB0_17 # %bb.20: # %box_union54 movzx eax, byte ptr [rbp - 41] # 1-byte Folded Reload movabs rcx, offset jl_boxed_uint8_cache mov rdx, qword ptr [rcx + 8*rax] jmp .LBB0_18 .LBB0_26: # %L80 movabs rax, offset ijl_throw movabs rdi, 140008495049392 call rax .LBB0_19: # %box_union movabs rdx, 140008667209736 jmp .LBB0_18 .LBB0_17: xor edx, edx .LBB0_18: # %post_box_union movabs rdi, offset .L_j_str1 movabs rax, offset ijl_type_error movabs rsi, 140008511215408 call rax .Lfunc_end0: .size julia_part1_418, .Lfunc_end0-julia_part1_418 # -- End function .type .L_j_str1,@object # @_j_str1 .section .rodata.str1.1,"aMS",@progbits,1 .L_j_str1: .asciz "typeassert" .size .L_j_str1, 11 .section ".note.GNU-stack","",@progbits ``` </details> <details> <summary>`@code_warntype` After</summary> ```julia [sukera@tower 01]$ julia -q --project=. -L 01.jl julia> data = read("input.txt"); julia> @code_warntype part1(data) MethodInstance for part1(::Vector{UInt8}) from part1(data) @ Main ~/Documents/projects/AOC/2023/01/01.jl:7 Arguments #self#::Core.Const(part1) data::Vector{UInt8} Locals @_3::Union{Nothing, Tuple{UInt8, Int64}} may_b::Union{Nothing, UInt8} may_a::Union{Nothing, UInt8} total::Int64 val@_7::Union{} val@_8::Union{} c::UInt8 digit_b::UInt8 digit_a::UInt8 ##215::Some{Nothing} ##216::Union{Nothing, UInt8} ##217::Union{Nothing, Some{UInt8}} ##212::Some{Nothing} ##213::Union{Nothing, Some{UInt8}} ##214::Union{Nothing, UInt8} digitRes::Union{Nothing, Some{UInt8}} @_19::Union{Nothing, UInt8} @_20::Union{Nothing, UInt8} @_21::Nothing @_22::Union{Nothing, UInt8} @_23::Union{Nothing, UInt8} @_24::Nothing Body::Int64 1 ── (total = 0) │ (may_a = Main.nothing) │ (may_b = Main.nothing) │ %4 = data::Vector{UInt8} │ (@_3 = Base.iterate(%4)) │ %6 = @_3::Union{Nothing, Tuple{UInt8, Int64}} │ %7 = (%6 === nothing)::Bool │ %8 = Base.not_int(%7)::Bool └─── goto #24 if not %8 2 ┄─ Core.NewvarNode(:(val@_7)) │ Core.NewvarNode(:(val@_8)) │ Core.NewvarNode(:(digit_b)) │ Core.NewvarNode(:(digit_a)) │ Core.NewvarNode(:(##215)) │ Core.NewvarNode(:(##216)) │ Core.NewvarNode(:(##217)) │ Core.NewvarNode(:(##212)) │ Core.NewvarNode(:(##213)) │ %19 = @_3::Tuple{UInt8, Int64} │ (c = Core.getfield(%19, 1)) │ %21 = Core.getfield(%19, 2)::Int64 │ %22 = c::UInt8 │ (digitRes = Main.someDigit(%22)) │ %24 = may_a::Union{Nothing, UInt8} │ (##214 = %24) │ %26 = Base.:!::Core.Const(!) │ %27 = ##214::Union{Nothing, UInt8} │ %28 = Base.isnothing(%27)::Bool │ %29 = (%26)(%28)::Bool └─── goto #4 if not %29 3 ── %31 = ##214::UInt8 │ (@_19 = Base.something(%31)) └─── goto #11 4 ── %34 = digitRes::Union{Nothing, Some{UInt8}} │ (##213 = %34) │ %36 = Base.:!::Core.Const(!) │ %37 = ##213::Union{Nothing, Some{UInt8}} │ %38 = Base.isnothing(%37)::Bool │ %39 = (%36)(%38)::Bool └─── goto #6 if not %39 5 ── %41 = ##213::Some{UInt8} │ (@_20 = Base.something(%41)) └─── goto #10 6 ── %44 = Main.Some::Core.Const(Some) │ %45 = Main.nothing::Core.Const(nothing) │ (##212 = (%44)(%45)) │ %47 = Base.:!::Core.Const(!) │ %48 = ##212::Core.Const(Some(nothing)) │ %49 = Base.isnothing(%48)::Core.Const(false) │ %50 = (%47)(%49)::Core.Const(true) └─── goto #8 if not %50 7 ── %52 = ##212::Core.Const(Some(nothing)) │ (@_21 = Base.something(%52)) └─── goto #9 8 ── Core.Const(nothing) │ Core.Const(:(val@_8 = Base.something(Base.nothing))) │ Core.Const(nothing) │ Core.Const(:(val@_8)) └─── Core.Const(:(@_21 = %58)) 9 ┄─ %60 = @_21::Core.Const(nothing) └─── (@_20 = %60) 10 ┄ %62 = @_20::Union{Nothing, UInt8} └─── (@_19 = %62) 11 ┄ %64 = @_19::Union{Nothing, UInt8} │ (may_a = %64) │ %66 = digitRes::Union{Nothing, Some{UInt8}} │ (##217 = %66) │ %68 = Base.:!::Core.Const(!) │ %69 = ##217::Union{Nothing, Some{UInt8}} │ %70 = Base.isnothing(%69)::Bool │ %71 = (%68)(%70)::Bool └─── goto #13 if not %71 12 ─ %73 = ##217::Some{UInt8} │ (@_22 = Base.something(%73)) └─── goto #20 13 ─ %76 = may_b::Union{Nothing, UInt8} │ (##216 = %76) │ %78 = Base.:!::Core.Const(!) │ %79 = ##216::Union{Nothing, UInt8} │ %80 = Base.isnothing(%79)::Bool │ %81 = (%78)(%80)::Bool └─── goto #15 if not %81 14 ─ %83 = ##216::UInt8 │ (@_23 = Base.something(%83)) └─── goto #19 15 ─ %86 = Main.Some::Core.Const(Some) │ %87 = Main.nothing::Core.Const(nothing) │ (##215 = (%86)(%87)) │ %89 = Base.:!::Core.Const(!) │ %90 = ##215::Core.Const(Some(nothing)) │ %91 = Base.isnothing(%90)::Core.Const(false) │ %92 = (%89)(%91)::Core.Const(true) └─── goto #17 if not %92 16 ─ %94 = ##215::Core.Const(Some(nothing)) │ (@_24 = Base.something(%94)) └─── goto #18 17 ─ Core.Const(nothing) │ Core.Const(:(val@_7 = Base.something(Base.nothing))) │ Core.Const(nothing) │ Core.Const(:(val@_7)) └─── Core.Const(:(@_24 = %100)) 18 ┄ %102 = @_24::Core.Const(nothing) └─── (@_23 = %102) 19 ┄ %104 = @_23::Union{Nothing, UInt8} └─── (@_22 = %104) 20 ┄ %106 = @_22::Union{Nothing, UInt8} │ (may_b = %106) │ %108 = Main.:(==)::Core.Const(==) │ %109 = c::UInt8 │ %110 = Main.UInt8('\n')::Core.Const(0x0a) │ %111 = (%108)(%109, %110)::Bool └─── goto #22 if not %111 21 ─ %113 = may_a::Union{Nothing, UInt8} │ (digit_a = Core.typeassert(%113, Main.UInt8)) │ %115 = may_b::Union{Nothing, UInt8} │ (digit_b = Core.typeassert(%115, Main.UInt8)) │ %117 = Main.:+::Core.Const(+) │ %118 = total::Int64 │ %119 = Main.:+::Core.Const(+) │ %120 = Main.:*::Core.Const(*) │ %121 = digit_a::UInt8 │ %122 = (%120)(%121, 0x0a)::UInt8 │ %123 = digit_b::UInt8 │ %124 = (%119)(%122, %123)::UInt8 │ (total = (%117)(%118, %124)) │ (may_a = Main.nothing) └─── (may_b = Main.nothing) 22 ┄ (@_3 = Base.iterate(%4, %21)) │ %129 = @_3::Union{Nothing, Tuple{UInt8, Int64}} │ %130 = (%129 === nothing)::Bool │ %131 = Base.not_int(%130)::Bool └─── goto #24 if not %131 23 ─ goto #2 24 ┄ %134 = total::Int64 └─── return %134 ``` </details> <details> <summary>`@code_native debuginfo=:none` After </summary> ```julia julia> @code_native debuginfo=:none part1(data) .text .file "part1" .globl julia_part1_1203 # -- Begin function julia_part1_1203 .p2align 4, 0x90 .type julia_part1_1203,@function julia_part1_1203: # @julia_part1_1203 ; Function Signature: part1(Array{UInt8, 1}) # %bb.0: # %top #DEBUG_VALUE: part1:data <- [DW_OP_deref] $rdi push rbp mov rbp, rsp push r15 push r14 push r13 push r12 push rbx sub rsp, 40 vxorps xmm0, xmm0, xmm0 #APP mov rax, qword ptr fs:[0] #NO_APP lea rdx, [rbp - 64] vmovaps xmmword ptr [rbp - 64], xmm0 mov qword ptr [rbp - 48], 0 mov rcx, qword ptr [rax - 8] mov qword ptr [rbp - 64], 4 mov rax, qword ptr [rcx] mov qword ptr [rbp - 72], rcx # 8-byte Spill mov qword ptr [rbp - 56], rax mov qword ptr [rcx], rdx #DEBUG_VALUE: part1:data <- [DW_OP_deref] 0 mov r15, qword ptr [rdi + 16] test r15, r15 je .LBB0_1 # %bb.2: # %L34 mov r14, qword ptr [rdi] dec r15 mov r11b, 1 mov r13b, 1 # implicit-def: $r12b # implicit-def: $r10b xor eax, eax jmp .LBB0_3 .p2align 4, 0x90 .LBB0_4: # in Loop: Header=BB0_3 Depth=1 xor r11d, r11d mov ebx, edi mov r10d, r8d .LBB0_9: # %L114 # in Loop: Header=BB0_3 Depth=1 mov r12d, esi test r15, r15 je .LBB0_12 .LBB0_10: # %guard_exit126 # in Loop: Header=BB0_3 Depth=1 inc r14 dec r15 mov r13d, ebx .LBB0_3: # %L36 # =>This Inner Loop Header: Depth=1 movzx edx, byte ptr [r14] test r13b, 1 movzx edi, r13b mov ebx, 1 mov ecx, 0 cmove ebx, edi cmovne edi, ecx movzx ecx, r10b lea esi, [rdx - 48] lea r9d, [rdx - 58] movzx r8d, sil cmove r8d, ecx cmp r9b, -11 ja .LBB0_4 # %bb.5: # %L89 # in Loop: Header=BB0_3 Depth=1 test r11b, 1 jne .LBB0_8 # %bb.6: # %L102 # in Loop: Header=BB0_3 Depth=1 cmp dl, 10 jne .LBB0_7 # %bb.13: # %L106 # in Loop: Header=BB0_3 Depth=1 test r13b, 1 jne .LBB0_14 # %bb.11: # %L114.thread # in Loop: Header=BB0_3 Depth=1 add ecx, ecx mov bl, 1 mov r11b, 1 lea ecx, [rcx + 4*rcx] add cl, r12b movzx ecx, cl add rax, rcx test r15, r15 jne .LBB0_10 jmp .LBB0_12 .p2align 4, 0x90 .LBB0_8: # %L102.thread # in Loop: Header=BB0_3 Depth=1 mov r11b, 1 # implicit-def: $sil cmp dl, 10 jne .LBB0_9 jmp .LBB0_15 .LBB0_7: # in Loop: Header=BB0_3 Depth=1 mov esi, r12d jmp .LBB0_9 .LBB0_1: xor eax, eax .LBB0_12: # %L154 mov rcx, qword ptr [rbp - 56] mov rdx, qword ptr [rbp - 72] # 8-byte Reload mov qword ptr [rdx], rcx add rsp, 40 pop rbx pop r12 pop r13 pop r14 pop r15 pop rbp ret .LBB0_15: # %L106.thread test r13b, 1 jne .LBB0_14 # %bb.16: # %post_box_union47 movabs rax, offset jl_nothing movabs rcx, offset jl_small_typeof movabs rdi, offset ".L_j_str_typeassert#1" mov rdx, qword ptr [rax] mov rsi, qword ptr [rcx + 336] movabs rax, offset ijl_type_error mov qword ptr [rbp - 48], rsi call rax .LBB0_14: # %post_box_union movabs rax, offset jl_nothing movabs rcx, offset jl_small_typeof movabs rdi, offset ".L_j_str_typeassert#1" mov rdx, qword ptr [rax] mov rsi, qword ptr [rcx + 336] movabs rax, offset ijl_type_error mov qword ptr [rbp - 48], rsi call rax .Lfunc_end0: .size julia_part1_1203, .Lfunc_end0-julia_part1_1203 # -- End function .type ".L_j_str_typeassert#1",@object # @"_j_str_typeassert#1" .section .rodata.str1.1,"aMS",@progbits,1 ".L_j_str_typeassert#1": .asciz "typeassert" .size ".L_j_str_typeassert#1", 11 .section ".note.GNU-stack","",@progbits ``` </details> Co-authored-by: Sukera <Seelengrab@users.noreply.github.com>

The former also handles vectors of pointers, which can occur after vectorization: ``` #5 0x00007f5bfe94de5e in llvm::cast<llvm::PointerType, llvm::Type> (Val=<optimized out>) at llvm/Support/Casting.h:578 578 assert(isa<To>(Val) && "cast<Ty>() argument of incompatible type!"); (rr) up #6 GCInvariantVerifier::visitAddrSpaceCastInst (this=this@entry=0x7ffd022fbf56, I=...) at julia/src/llvm-gc-invariant-verifier.cpp:66 66 unsigned ToAS = cast<PointerType>(I.getDestTy())->getAddressSpace(); (rr) call I.dump() %23 = addrspacecast <4 x ptr addrspace(10)> %wide.load to <4 x ptr addrspace(11)>, !dbg !43 ```

…ce. (#54113) The former also handles vectors of pointers, which can occur after vectorization: ``` #5 0x00007f5bfe94de5e in llvm::cast<llvm::PointerType, llvm::Type> (Val=<optimized out>) at llvm/Support/Casting.h:578 578 assert(isa<To>(Val) && "cast<Ty>() argument of incompatible type!"); (rr) up #6 GCInvariantVerifier::visitAddrSpaceCastInst (this=this@entry=0x7ffd022fbf56, I=...) at julia/src/llvm-gc-invariant-verifier.cpp:66 66 unsigned ToAS = cast<PointerType>(I.getDestTy())->getAddressSpace(); (rr) call I.dump() %23 = addrspacecast <4 x ptr addrspace(10)> %wide.load to <4 x ptr addrspace(11)>, !dbg !43 ``` Fixes aborts seen in #53070

ghost assigned StefanKarpinski Jul 9, 2011

ViralBShah closed this as completed Jan 8, 2012

StefanKarpinski reopened this Jan 8, 2012

ViralBShah closed this as completed Jan 26, 2012

anaisbetts mentioned this issue Feb 19, 2012

DESTDIR should not include /usr/ in it #391

Closed

ViralBShah mentioned this issue Mar 16, 2012

WIP: libuv rewrite and Windows port #521

Merged

vtjnash mentioned this issue Mar 17, 2012

build fails at sys.ji #600

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ViralBShah mentioned this issue Mar 18, 2012

Building julia with clang: tests fail #602

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kk49 mentioned this issue May 9, 2012

finfer causes segmentation fault #816

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forkandwait mentioned this issue May 12, 2012

Failed build on FreeBSD #829

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6e441f9c mentioned this issue May 30, 2012

Julia segfaults while loading stage0.jl #901

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spott mentioned this issue Jul 16, 2012

problem bootstrapping the sys0.ji file #1052

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dioptre mentioned this issue Jul 30, 2012

error during bootstrap on OS X #1089

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fundamental mentioned this issue Apr 11, 2013

Regression in reload() Results in REPL Hanging #2829

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Keno pushed a commit that referenced this issue Oct 9, 2023

Fix getproperty calls and handle Core.Typeof(Vararg) (fixes #6)

1938bda

vchuravy mentioned this issue Dec 2, 2023

GC error on 1.10 #52363

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Andrew-Milestone mentioned this issue Apr 28, 2024

Error adding NeuralOperators in Julia v1.10.2 #54292

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install make target #6

install make target #6

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install make target #6

install make target #6

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StefanKarpinski commented Apr 27, 2011

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