fix: updates for breaking changes

.github/workflows/CI.yml

@@ -39,7 +39,7 @@ jobs:
         os:
           - ubuntu-latest
         version:
-          - 'nightly' # coverage fast on nightly
+          - '1'
         threads:
           - '3'
           - '4'
@@ -82,7 +82,6 @@ jobs:
           - ubuntu-latest
         version:
           - '1.6'
-          - '1'
     steps:
       - uses: actions/checkout@c85c95e3d7251135ab7dc9ce3241c5835cc595a9
       - uses: julia-actions/setup-julia@f40c4b69330df1d22e7590c12e76dc2f9c66e0bc

Project.toml

@@ -1,7 +1,7 @@
 name = "SimpleChains"
 uuid = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5"
 authors = ["Chris Elrod <[email protected]> and contributors"]
-version = "0.4.6"
+version = "0.4.7"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
@@ -38,22 +38,14 @@ LayoutPointers = "0.1.3"
 LoopVectorization = "0.12.104"
 ManualMemory = "0.1.8"
 Polyester = "0.4, 0.5, 0.6, 0.7"
+Random = "<0.0.1, 1"
 SIMDTypes = "0.1"
 SLEEFPirates = "0.6"
-Static = "0.8.4"
+Static = "0.8.4, 1"
 StaticArrayInterface = "1"
 StaticArrays = "1"
-StrideArraysCore = "0.4.7"
+StrideArraysCore = "0.4.7, 0.5"
 UnPack = "1"
 VectorizationBase = "0.21.40"
 VectorizedRNG = "0.2.13"
 julia = "1.6"
-
-[extras]
-Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
-ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
-
-[targets]
-test = ["Aqua", "ForwardDiff", "Zygote", "Test"]

docs/src/examples/mnist.md

@@ -43,7 +43,7 @@ We define the inputs as being statically sized `(28,28,1)` images.
 Specifying the input sizes allows these to be checked.
 Making them static, which we can do either in our simple chain, or by adding
 static sizing to the images themselves using a package like [StrideArrays.jl](https://github.com/JuliaSIMD/StrideArrays.jl)
-or [HybridArrays.jl](git@github.com:JuliaArrays/HybridArrays.jl.git). These packages are recommended
+or [HybridArrays.jl](https://github.com/JuliaArrays/HybridArrays.jl). These packages are recommended
 for allowing you to mix dynamic and static sizes; the batch size should probably
 be left dynamic, as you're unlikely to want to specialize code generation on this,
 given that it is likely to vary, increasing compile times while being unlikely to

src/SimpleChains.jl

@@ -42,6 +42,7 @@ import ChainRulesCore
 import ForwardDiff
 import LoopVectorization
 import StaticArrays
+using StaticArrays: SVector, SMatrix
 using Random: AbstractRNG
 
 using LoopVectorization: matmul_params, @turbo

src/chain_rules.jl

@@ -162,9 +162,9 @@ function valgrad_noloss(
     @inbounds @simd ivdep for i in eachindex(parg)
       parg2[i] = parg[i]
     end
-    pm += aoff
+    pm = __add(pm, aoff)
     g = PtrArray(Ptr{T}(pm), (glen,))
-    pm += goff
+    pm = __add(pm, goff)
     l, pbl =
       chain_valgrad_pullback!(pointer(g), parg2, layers, pointer(params), pm)
   end

src/dense.jl

@@ -1004,7 +1004,7 @@ end
 function dense_param_update!(::TurboDense{true}, Ā, C̄, B)
   Kp1 = static_size(Ā, StaticInt(2))
   K = Kp1 - StaticInt(1)
-  dense!(identity, nothing, view(Ā, :, static(1):K), C̄, B', False())
+  dense!(identity, nothing, view(Ā, :, static(1):K), C̄, __adjoint(B), False())
   @turbo for m ∈ axes(Ā, 1)
     s = zero(eltype(Ā))
     for n ∈ axes(C̄, 2)
@@ -1014,12 +1014,12 @@ function dense_param_update!(::TurboDense{true}, Ā, C̄, B)
   end
 end
 function dense_param_update!(::TurboDense{false}, Ā, C̄, B)
-  dense!(identity, nothing, Ā, C̄, B', False())
+  dense!(identity, nothing, Ā, C̄, __adjoint(B), False())
 end
 function dense_param_update!(::TurboDense{true}, Ā, C̄, B, inds)
   Kp1 = static_size(Ā, StaticInt(2))
   K = Kp1 - StaticInt(1)
-  denserev!(identity, nothing, view(Ā, :, static(1):K), C̄, B', inds, False())
+  denserev!(identity, nothing, view(Ā, :, static(1):K), C̄, __adjoint(B), inds, False())
   @turbo for m ∈ axes(Ā, 1)
     s = zero(eltype(Ā))
     for n ∈ axes(C̄, 2)
@@ -1029,7 +1029,7 @@ function dense_param_update!(::TurboDense{true}, Ā, C̄, B, inds)
   end
 end
 function dense_param_update!(::TurboDense{false}, Ā, C̄, B, inds)
-  dense!(identity, nothing, Ā, C̄, B', inds, False())
+  dense!(identity, nothing, Ā, C̄, __adjoint(B), inds, False())
 end
 
 @inline function dense!(

src/dropout.jl

@@ -120,7 +120,7 @@ function valgrad_layer!(
     VectorizedRNG.storestate!(rng, state)
   end # GC preserve  
 
-  pg, x, p, align(pu + ((static(7) + N) >>> static(3)))
+  pg, x, p, align(__add(pu, ((static(7) + N) >>> static(3))))
 end
 
 function pullback_arg!(

src/simple_chain.jl

@@ -820,7 +820,7 @@ function valgrad_core(
 ) where {T}
   @unpack layers = c
   g = PtrArray(Ptr{T}(pu), (glen,))
-  l = unsafe_valgrad!(c, pu + align(glen * static_sizeof(T)), g, params, arg)
+  l = unsafe_valgrad!(c, __add(pu, align(glen * static_sizeof(T))), g, params, arg)
   Base.FastMath.add_fast(
     l,
     apply_penalty!(g, getpenalty(c), params, static_size(arg))
@@ -838,7 +838,7 @@ function valgrad_core_sarray(
   l = Base.FastMath.add_fast(
     unsafe_valgrad!(
       c,
-      pu + align(static(L) * static_sizeof(T)),
+      __add(pu, align(static(L) * static_sizeof(T))),
       g,
       params,
       arg

src/utils.jl

@@ -336,3 +336,10 @@ function _add_memory(t::Tuple, p)
   (A, B...)
 end
 _add_memory(::Nothing, p) = nothing
+
+__add(x, y) = x + y
+__add(x::Ptr, ::StaticInt{N}) where {N} = x + N
+__add(::StaticInt{N}, y::Ptr) where {N} = y + N
+
+__adjoint(x) = x'
+__adjoint(x::SVector{N, <:Real}) where {N} = SMatrix{1, N, eltype(x)}(x.data)

test/runtests.jl

@@ -1,6 +1,6 @@
 using SimpleChains
 using Test, Aqua, ForwardDiff, Zygote, ChainRules, Random
-@static if VERSION >= v"1.9"
+@static if VERSION ≥ v"1.9"
   using JET: @test_opt
 else
   macro test_opt(ex)
@@ -84,12 +84,8 @@ InteractiveUtils.versioninfo(; verbose = true)
       SquaredLoss"""
 
       @test sprint((io, t) -> show(io, t), sc) == print_str0
-      if VERSION >= v"1.6"
-        @test sprint((io, t) -> show(io, t), scflp) == print_str1
-      else
-        # typename doesn't work on 1.5
-        @test_broken sprint((io, t) -> show(io, t), scflp) == print_str1
-      end
+      @test sprint((io, t) -> show(io, t), scflp) == print_str1
+
       p = SimpleChains.init_params(scflp, T; rng = Random.default_rng())
       g = similar(p)
       let sc = SimpleChains.remove_loss(sc)
@@ -574,5 +570,4 @@ end
 Aqua.test_all(
   SimpleChains;
   ambiguities = false,
-  project_toml_formatting = false
 )