Save and restore function attributes across optimization. (#438)

Works around a Julia issue.

Author: maleadt

src/optim.jl

@@ -238,7 +238,31 @@ function optimize!(@nospecialize(job::CompilerJob), mod::LLVM.Module)
         combine_mul_add!(pm)
         div_rem_pairs!(pm)
 
+        if VERSION < v"1.10.0-DEV.1144"
+            # save function attributes to work around JuliaGPU/GPUCompiler#437
+            current_attrs = Dict{String,Any}()
+            for f in functions(mod)
+                attrs = function_attributes(f)
+                length(attrs) == 0 && continue
+                current_attrs[LLVM.name(f)] = collect(attrs)
+            end
+        end
+
         run!(pm, mod)
+
+        if VERSION < v"1.10.0-DEV.1144"
+            # restore function attributes
+            for (fn, attrs) in current_attrs
+                haskey(functions(mod), fn) || continue
+                f = functions(mod)[fn]
+
+                for attr in attrs
+                    # NOTE: there's no function attributes that contain a type,
+                    #       so we can just blindly add them back
+                    push!(function_attributes(f), attr)
+                end
+            end
+        end
     end
 
     # target-specific optimizations

test/native.jl

@@ -305,6 +305,25 @@ end
     @test occursin(r"^define.*julia_f_expensive"m, ir)
 end
 
+@testset "function attributes" begin
+    @inline function convergent_barrier()
+        Base.llvmcall(("""
+            declare void @barrier() #1
+
+            define void @entry() #0 {
+                call void @barrier()
+                ret void
+            }
+
+            attributes #0 = { alwaysinline }
+            attributes #1 = { convergent }""", "entry"),
+        Nothing, Tuple{})
+    end
+
+    ir = sprint(io->native_code_llvm(io, convergent_barrier, Tuple{}; dump_module=true, raw=true))
+    @test occursin(r"attributes #. = \{ convergent \}", ir)
+end
+
 end
 
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