Use julia 1.10 in CI

Project.toml

@@ -1,10 +1,9 @@
+authors = ["Anton Reinhard <anton.reinhard@proton.me>"]
 name = "MetagraphOptimization"
 uuid = "3e869610-d48d-4942-ba70-c1b702a33ca4"
-authors = ["Anton Reinhard <anton.reinhard@proton.me>"]
 version = "0.1.0"
 
 [deps]
-AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 AccurateArithmetic = "22286c92-06ac-501d-9306-4abd417d9753"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
@@ -19,7 +18,6 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Roots = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-oneAPI = "8f75cd03-7ff8-4ecb-9b8f-daf728133b1b"
 
 [extras]
 CUDA_Runtime_jll = "76a88914-d11a-5bdc-97e0-2f5a05c973a2"

src/MetagraphOptimization.jl

@@ -196,8 +196,10 @@ include("devices/impl.jl")
 
 include("devices/numa/impl.jl")
 include("devices/cuda/impl.jl")
-include("devices/rocm/impl.jl")
-include("devices/oneapi/impl.jl")
+# can currently not use AMDGPU because of incompatability with the newest rocm drivers
+# include("devices/rocm/impl.jl")
+# oneapi seems also broken for now
+# include("devices/oneapi/impl.jl")
 
 include("scheduler/interface.jl")
 include("scheduler/greedy.jl")

src/models/abc/compute.jl

@@ -84,13 +84,11 @@ Compute a sum over the vector. Use an algorithm that accounts for accumulated er
 Linearly many FLOP with growing data.
 """
 function compute(::ComputeTaskABC_Sum, data...)::Float64
-    return sum_kbn([data...])
-
-    #=s = 0.0im
+    s = 0.0im
     for d in data
         s += d
     end
-    return s=#
+    return s
 end
 
 function compute(::ComputeTaskABC_Sum, data::AbstractArray)::Float64

src/models/abc/create.jl

@@ -27,6 +27,9 @@ Return a ProcessInput of randomly generated [`ABCParticle`](@ref)s from a [`ABCP
 Note: This uses RAMBO to create a valid process with conservation of momentum and energy.
 """
 function gen_process_input(processDescription::ABCProcessDescription)
+    inParticleTypes = keys(processDescription.inParticles)
+    outParticleTypes = keys(processDescription.outParticles)
+
     massSum = 0
     inputMasses = Vector{Float64}()
     for (particle, n) in processDescription.inParticles
@@ -63,7 +66,8 @@ function gen_process_input(processDescription::ABCProcessDescription)
     index = 1
     for (particle, n) in processDescription.outParticles
         for _ in 1:n
-            push!(outputParticles, particle(final_momenta[index]))
+            mom = final_momenta[index]
+            push!(outputParticles, particle(SFourMomentum(-mom.E, mom.px, mom.py, mom.pz)))
             index += 1
         end
     end

src/models/qed/particle.jl

@@ -313,7 +313,7 @@ Return the factor of a vertex in a QED feynman diagram.
     return -1im * e * gamma()
 end
 
-@inline function QED_inner_edge(p::QEDParticle)
+@inline function QED_inner_edge(p::QEDParticle)::DiracMatrix
     return propagator(particle(p), p.momentum)
 end
 

src/optimization/random_walk.jl

@@ -27,8 +27,7 @@ function optimize_step!(optimizer::RandomWalkOptimizer, graph::DAG)
             # push
 
             # choose one of fuse/split/reduce
-            # TODO refactor fusions so they actually work
-            option = rand(r, 2:3)
+            option = rand(r, 1:3)
             if option == 1 && !isempty(operations.nodeFusions)
                 push_operation!(graph, rand(r, collect(operations.nodeFusions)))
                 return true

test/known_graphs.jl

@@ -1,8 +1,6 @@
 using MetagraphOptimization
 using Random
 
-RNG = Random.MersenneTwister(321)
-
 function test_known_graph(name::String, n, fusion_test = true)
     @testset "Test $name Graph ($n)" begin
         graph = parse_dag(joinpath(@__DIR__, "..", "input", "$name.txt"), ABCModel())
@@ -11,7 +9,7 @@ function test_known_graph(name::String, n, fusion_test = true)
         if (fusion_test)
             test_node_fusion(graph)
         end
-        test_random_walk(RNG, graph, n)
+        test_random_walk(graph, n)
     end
 end
 
@@ -45,7 +43,7 @@ function test_node_fusion(g::DAG)
     end
 end
 
-function test_random_walk(RNG, g::DAG, n::Int64)
+function test_random_walk(g::DAG, n::Int64)
     @testset "Test Random Walk ($n)" begin
         # the purpose here is to do "random" operations and reverse them again and validate that the graph stays the same and doesn't diverge
         reset_graph!(g)
@@ -56,18 +54,18 @@ function test_random_walk(RNG, g::DAG, n::Int64)
 
         for i in 1:n
             # choose push or pop
-            if rand(RNG, Bool)
+            if rand(Bool)
                 # push
                 opt = get_operations(g)
 
                 # choose one of fuse/split/reduce
-                option = rand(RNG, 1:3)
+                option = rand(1:3)
                 if option == 1 && !isempty(opt.nodeFusions)
-                    push_operation!(g, rand(RNG, collect(opt.nodeFusions)))
+                    push_operation!(g, rand(collect(opt.nodeFusions)))
                 elseif option == 2 && !isempty(opt.nodeReductions)
-                    push_operation!(g, rand(RNG, collect(opt.nodeReductions)))
+                    push_operation!(g, rand(collect(opt.nodeReductions)))
                 elseif option == 3 && !isempty(opt.nodeSplits)
-                    push_operation!(g, rand(RNG, collect(opt.nodeSplits)))
+                    push_operation!(g, rand(collect(opt.nodeSplits)))
                 else
                     i = i - 1
                 end
@@ -89,6 +87,8 @@ function test_random_walk(RNG, g::DAG, n::Int64)
     end
 end
 
+Random.seed!(0)
+
 test_known_graph("AB->AB", 10000)
 test_known_graph("AB->ABBB", 10000)
 test_known_graph("AB->ABBBBB", 1000, false)

test/unit_tests_execution.jl

@@ -9,7 +9,7 @@ import MetagraphOptimization.ABCParticle
 import MetagraphOptimization.interaction_result
 
 const RTOL = sqrt(eps(Float64))
-RNG = Random.MersenneTwister(0)
+RNG = Random.default_rng()
 
 function check_particle_reverse_moment(p1::SFourMomentum, p2::SFourMomentum)
     @test isapprox(abs(p1.E), abs(p2.E))
@@ -123,8 +123,6 @@ expected_result = execute(graph, process_2_4, machine, particles_2_4)
     end
 end
 
-#=
-TODO: fix precision(?) issues
 @testset "AB->ABBB after random walk" begin
     for i in 1:50
         graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
@@ -134,7 +132,6 @@ TODO: fix precision(?) issues
         @test isapprox(execute(graph, process_2_4, machine, particles_2_4), expected_result; rtol = RTOL)
     end
 end
-=#
 
 @testset "AB->AB large sum fusion" begin
     for _ in 1:20
@@ -234,19 +231,3 @@ end
         @test isapprox(execute(graph, process_2_2, machine, particles_2_2), expected_result; rtol = RTOL)
     end
 end
-
-@testset "$(process) after random walk" for process in ["ke->ke", "ke->kke", "ke->kkke"]
-    process = parse_process("ke->kkke", QEDModel())
-    inputs = [gen_process_input(process) for _ in 1:100]
-    graph = gen_graph(process)
-    gt = execute.(Ref(graph), Ref(process), Ref(machine), inputs)
-    for i in 1:50
-        graph = gen_graph(process)
-
-        optimize!(RandomWalkOptimizer(RNG), graph, 100)
-        @test is_valid(graph)
-
-        func = get_compute_function(graph, process, machine)
-        @test isapprox(func.(inputs), gt; rtol = RTOL)
-    end
-end

test/unit_tests_optimization.jl

@@ -1,7 +1,7 @@
 using MetagraphOptimization
 using Random
 
-RNG = Random.MersenneTwister(0)
+RNG = Random.default_rng()
 
 graph = parse_dag(joinpath(@__DIR__, "..", "input", "AB->ABBB.txt"), ABCModel())
 

test/unit_tests_qedmodel.jl

@@ -15,7 +15,7 @@ import MetagraphOptimization.QED_vertex
 
 def_momentum = SFourMomentum(1.0, 0.0, 0.0, 0.0)
 
-RNG = Random.MersenneTwister(0)
+RNG = Random.default_rng()
 
 testparticleTypes = [
     PhotonStateful{Incoming, PolX},