Merge pull request #79 from mcabbott/avxci3

Debug a CI crash?

Author: mcabbott

.github/workflows/ci.yml

@@ -26,7 +26,7 @@ jobs:
           - '1'
           - '6' # t>2 might be ignored on Julia <= 1.5
         version:
-          - '1.4'
+          - '1.5'
           - '1' # automatically expands to the latest stable 1.x release of Julia
     steps:
       - uses: actions/checkout@v2

Project.toml

@@ -1,31 +1,33 @@
 name = "Tullio"
 uuid = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"
 authors = ["Michael Abbott"]
-version = "0.2.14"
+version = "0.3.0"
 
 [deps]
 DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 
 [compat]
-CUDA = "1, 2"
+CUDA = "2"
+CUDAKernels = "0.1"
 DiffRules = "1"
-FillArrays = "0.10"
+FillArrays = "0.10, 0.11"
 ForwardDiff = "0.10"
-KernelAbstractions = "0.5.2"
-LoopVectorization = "0.8.26, 0.9.20"
+KernelAbstractions = "0.6"
+LoopVectorization = "0.12.12"
 NamedDims = "0.2"
 OffsetArrays = "1"
 Requires = "1"
 TensorOperations = "3"
 Tracker = "0.2"
-VectorizationBase = "0.12.33, 0.15.7"
-Zygote = "0.6"
-julia = "1.3"
+VectorizationBase = "0.19.30"
+Zygote = "0.6.9"
+julia = "1.5"
 
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+CUDAKernels = "72cfdca4-0801-4ab0-bf6a-d52aa10adc57"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
@@ -42,4 +44,4 @@ VectorizationBase = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "CUDA", "FillArrays", "ForwardDiff", "KernelAbstractions", "LinearAlgebra", "LoopVectorization", "NamedDims", "OffsetArrays", "Printf", "Random", "TensorOperations", "Tracker", "VectorizationBase", "Zygote"]
+test = ["Test", "CUDA", "CUDAKernels", "FillArrays", "ForwardDiff", "KernelAbstractions", "LinearAlgebra", "LoopVectorization", "NamedDims", "OffsetArrays", "Printf", "Random", "TensorOperations", "Tracker", "VectorizationBase", "Zygote"]

src/eval.jl

@@ -51,19 +51,13 @@ using Requires
 
 @inline anyone(cond::Bool) = cond
 
+#=
+
 @init @require LoopVectorization = "bdcacae8-1622-11e9-2a5c-532679323890" begin
-    using .LoopVectorization
-    if isdefined(LoopVectorization, :SVec) # version 0.8, for Julia ⩽1.5
-        using .LoopVectorization.VectorizationBase: SVec, Mask, prevpow2
-        @require ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" begin
-            # Dual numbers + svec, not needed on version 0.9
-            include("grad/avxdual.jl")
-        end
-    else # version 0.9, supports Julia 1.6
-        using .LoopVectorization.VectorizationBase: Vec, Mask, prevpow2
-        SVec{N,T} = Vec{N,T}
+    using .LoopVectorization # version 0.9+ only now
+    using .LoopVectorization.VectorizationBase: Vec, Mask, prevpow2
+    SVec{N,T} = Vec{N,T}
     end
-#=
     # Functions needed for safe vectorised max gradient
     @inline Tullio.onlyone(cond::Bool, seen::SVec) = cond && allzero(seen)
 
@@ -75,9 +69,10 @@ using Requires
     @inline allzero(seen::SVec) = iszero((!iszero(seen)).u)
 
     @inline Tullio.anyone(cond::Mask) = !iszero(cond.u)
-=#
 end
 
+=#
+
 #========== CuArrays ==========#
 
 using Requires

test/cuda.jl

@@ -1,6 +1,6 @@
 
 using Tullio, Test
-using CUDA, KernelAbstractions
+using CUDA, CUDAKernels, KernelAbstractions
 CUDA.allowscalar(false)
 using Tracker, ForwardDiff
 @tullio grad=Base

test/gradients.jl

@@ -6,7 +6,7 @@ This file is run several times
 =#
 
 using Tullio, Test, ForwardDiff, Random
-# using Tracker; _gradient(x...) = Tracker.gradient(x...); GRAD = :Tracker
+# using Tracker; _gradient(x...) = Tracker.gradient(x...); GRAD = :Tracker; macro printline() end
 
 function gradtest(f, dims)
     x = randn(dims...)
@@ -16,8 +16,6 @@ end
 
 @testset "simple" begin
 
-if Tullio._GRAD[] != :Dual || VERSION >= v"1.5" # These 3 give errors on Julia 1.4, LV 0.8, I have no idea why.
-
     @test _gradient(x -> sum(@tullio y[i] := 2*x[i]), rand(3))[1] == [2,2,2]
     @test _gradient(x -> sum(@tullio y[i] := 2*x[i] + i), rand(3))[1] == [2,2,2]
 
@@ -32,7 +30,6 @@ if Tullio._GRAD[] != :Dual || VERSION >= v"1.5" # These 3 give errors on Julia 1
     g_fd = ForwardDiff.gradient(x -> sum(sin, g2(x)), r100)
     @test g_fd ≈ _gradient(x -> sum(sin, g2(x)), r100)[1]
 
-end
     r100 = randn(100)
 
     # scalar output
@@ -68,6 +65,9 @@ end
     @test abs2_grad ≈ _gradient(v -> (@tullio s := abs2(1 + v[i]^2)), va)[1]
 
 end
+
+@printline
+
 @testset "zero-arrays" begin
 
     # Using zero-dim arrays fails on ReverseDiff & Tracker
@@ -106,6 +106,9 @@ end
     # [1.0, 4.0, 9.0, 16.0, 25.0, 36.0, 0.0, 64.0, 81.0, 100.0, 121.0] ≈ [1.0, 4.0, 9.0, 16.0, 25.0, 36.0, 49.0, 64.0, 81.0, 100.0, 121.0]
 
 end
+
+@printline
+
 @testset "shifts, etc" begin
 
     c1(N,K) = @tullio M[x,y,c] := N[x+i-1, y+j-1,c] * K[i,j]
@@ -158,6 +161,9 @@ end
     end
 
 end
+
+@printline
+
 @testset "from TensorTrace" begin
     # These can all be handled using TensorOperations
 
@@ -196,6 +202,8 @@ end
     con7(x) = @tullio C[m,n,j,i] := 44 * x[i,j,k] * r392[k,m,n]
     @test gradtest(con7, (9,2,3))
 
+    @printline
+
     ## contract! B
     con8b(x) = @tullio K[i,j] := 5 * r32[i,k] * x[k,j]
     @test gradtest(con8b, (2,3))
@@ -215,15 +223,19 @@ end
     con14(x) = @tullio K[i,j] := r3399[a,b,j,k] * x[b,c,k,i] * r33[a,c]
     @test gradtest(con14, (3,3,9,9))
 
+    @printline
+
     ## scalar -- one with :=, one without
-    sc1(x) = @tullio s = r22[b,β] * x[a,b,c] * r312[c,a,β]
-    @test gradtest(sc1, (1,2,3))
+    sc1(x) = @tullio s = r22[b,β] * x[a,b,c] * r312[c,a,β]  avx=false
+    @test gradtest(sc1, (1,2,3)) # UndefVarError: ####op#798_0 not defined
 
-    sc2(x) = @tullio s := x[γ,c] * r3399[c,γ,i,i]
+    sc2(x) = @tullio s := x[γ,c] * r3399[c,γ,i,i]  avx=false
     @test gradtest(sc2, (3,3))
 
 end
 
+@printline
+
 if Tullio._GRAD[] != :Dual
 #=
     @testset "products" begin
@@ -319,6 +331,9 @@ if Tullio._GRAD[] != :Dual
         # I suspect that @avx is re-ordering loops, which makes onlyone() incorrect.
 
     end
+
+    @printline
+
     @testset "finalisers" begin
 
         norm2(m) = @tullio n[i] := m[i,j]^2 |> sqrt
@@ -328,9 +343,11 @@ if Tullio._GRAD[] != :Dual
         @test _gradient(sum∘norm2, mat)[1] ≈ ForwardDiff.gradient(sum∘norm2, mat)
         @test gradtest(norm2, (3,4))
 
-        layer(x) = @tullio y[i,k] := mat[i,j] * x[j,k] |> tanh
+        layer(x) = @tullio y[i,k] := mat[i,j] * x[j,k] |> tanh  avx=false # this takes 15 mins +?
         @test gradtest(layer, (3,4))
 
+        @printline
+
         lse1(mat) = @tullio lse[j] := log <| exp(mat[i,j])
         @test gradtest(lse1, (3,4))
 
@@ -344,6 +361,8 @@ if Tullio._GRAD[] != :Dual
     end
 end
 
+@printline
+
 if GRAD == :Zygote
     @testset "nograd keyword" begin
 
@@ -358,3 +377,5 @@ if GRAD == :Zygote
 
     end
 end
+
+@printline

test/group-3.jl

@@ -153,20 +153,15 @@ end
 
 @testset "parsing + LoopVectorization" begin include("parsing.jl") end
 
-if test_group != "3" # Github CI fails, on some runs, "ERROR: Package Tullio errored during testing (received signal: KILL)"
-    # https://github.com/mcabbott/Tullio.jl/pull/57/checks?check_run_id=1753332805
-
-    using Tracker
-    GRAD = :Tracker
-    _gradient(x...) = Tracker.gradient(x...)
-
-    @tullio grad=Base
-    @testset "gradients: Tracker + DiffRules + LoopVectorization" begin include("gradients.jl") end
+using Tracker
+GRAD = :Tracker
+_gradient(x...) = Tracker.gradient(x...)
 
-    @tullio grad=Dual
-    @testset "gradients: Tracker + ForwardDiff + LoopVectorization" begin include("gradients.jl") end
+@tullio grad=Base
+@testset "gradients: Tracker + DiffRules + LoopVectorization" begin include("gradients.jl") end
 
-end
+@tullio grad=Dual
+@testset "gradients: Tracker + ForwardDiff + LoopVectorization" begin include("gradients.jl") end
 
 @info @sprintf("LoopVectorization tests took %.1f seconds", time()-t8)