add Metal extension for batched_mul

.buildkite/pipeline.yml

@@ -55,6 +55,27 @@ steps:
       NNLIB_TEST_CPU: "false"
       JULIA_NUM_THREADS: 4
 
+  - label: ":julia: Julia 1 + Metal GPU"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1"
+      - JuliaCI/julia-test#v1:
+          test_args: "--quickfail"
+      - JuliaCI/julia-coverage#v1:
+          codecov: true
+          dirs:
+            - src
+            - ext
+    agents:
+      queue: "juliaecosystem"
+      os: "macos"
+      arch: "aarch64"
+    timeout_in_minutes: 180
+    env:
+      NNLIB_TEST_METAL: "true"
+      NNLIB_TEST_CPU: "false"
+      JULIA_NUM_THREADS: 4
+
   - label: "Benchmarks"
     plugins:
       - JuliaCI/julia#v1:

Project.toml

@@ -19,6 +19,7 @@ EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
+Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
 
 [extensions]
 NNlibAMDGPUExt = "AMDGPU"
@@ -27,6 +28,7 @@ NNlibCUDAExt = "CUDA"
 NNlibEnzymeCoreExt = "EnzymeCore"
 NNlibFFTWExt = "FFTW"
 NNlibForwardDiffExt = "ForwardDiff"
+NNlibMetalExt = "Metal"
 
 [compat]
 AMDGPU = "0.9.4, 1"
@@ -40,6 +42,7 @@ ForwardDiff = "0.10.36"
 GPUArraysCore = "0.1"
 KernelAbstractions = "0.9.2"
 LinearAlgebra = "<0.0.1, 1"
+Metal = "1.4.2"
 Random = "<0.0.1, 1"
 Statistics = "1"
 cuDNN = "1"

ext/NNlibMetalExt/NNlibMetalExt.jl

@@ -0,0 +1,50 @@
+module NNlibMetalExt
+
+using Metal, NNlib
+using NNlib: AbstractRNG  # === Random.AbstractRNG
+
+# Random
+NNlib._rng_from_array(::MtlArray) = Metal.MPS.default_rng()
+
+NNlib._rng_compat_array(rng::Metal.MPS.RNG, A::MtlArray) = nothing
+NNlib._rng_compat_array(rng::AbstractRNG, A::MtlArray) = throw(ArgumentError(
+    "cannot use rng::$(typeof(rng)) with array::MtlArray, only Metal's own RNG type works"))
+
+# Batched matrix multiplication
+function NNlib._batched_gemm!(::Type{<:MtlArray}, transA::Char, transB::Char, α::Number, A, B, β::Number, C)
+    eltype(C) <: Complex && @warn "don't trust this on complex arrays!" transA transB
+    Metal.MPS.matmul!(C, A, B, α, β, transA != 'N', transB != 'N') # transA, transB, α, A, B, β, C)
+end
+
+#=
+
+help?> Metal.MPS.matmul!
+  matMulMPS(a::MtlMatrix, b::MtlMatrix, c::MtlMatrix, alpha=1, beta=1,
+            transpose_left=false, transpose_right=false)
+
+  A MPSMatrixMultiplication kernel thay computes: c = alpha * op(a) * beta * op(b) + beta * C
+
+  This function should not typically be used. Rather, use the normal LinearAlgebra interface with
+  any MtlArray and it should be accelerated using Metal Performance Shaders.
+
+=#
+
+using NNlib: BatchedAdjoint, BatchedTranspose, BatchedAdjOrTrans
+using Adapt
+using Adapt: WrappedArray
+
+const MetalBatchedAdjoint{T} = BatchedAdjoint{T, <: MtlArray{T}}
+const MetalBatchedTranspose{T} = BatchedTranspose{T, <: MtlArray{T}}
+const MetalBatchedAdjOrTrans{T} = Union{MetalBatchedAdjoint{T}, MetalBatchedTranspose{T}}
+const WrappedMetalBatchedAdjOrTrans{T, N} = WrappedArray{T, N, MetalBatchedAdjOrTrans{T}, MetalBatchedAdjOrTrans{T}}
+
+Base.print_array(io::IO, b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}) = Base.print_array(io, adapt(Array, b))
+Base._show_nonempty(io::IO, b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}, prefix::String) = Base._show_nonempty(io, adapt(Array, b), prefix)
+Base.show_vector(io::IO, b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}, opn, cls) = Base.show_vector(io, adapt(Array, b), opn, cls)
+
+Base.convert(::Type{T}, b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}) where {T<:Array} = Base.convert(T, adapt(Array, b))
+Base.Array{T, N}(b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}) where {T, N} = Array{T, N}(adapt(Array, b))
+Base.collect(b::Union{MetalBatchedAdjOrTrans, WrappedMetalBatchedAdjOrTrans}) = collect(adapt(Array, b))
+
+
+end  # module NNlibMetalExt

test/ext_metal/batched_mul.jl

@@ -0,0 +1,82 @@
+@testset "batched_mul" begin
+    using NNlib: batched_mul, batched_mul!, batched_vec,
+                 batched_adjoint, batched_transpose
+
+    A = randn(Float32, 3,3,2);
+    B = randn(Float32, 3,3,2);
+
+    C = batched_mul(A, B)
+    @test MtlArray(C) ≈ batched_mul(MtlArray(A), MtlArray(B))
+
+    Ct = batched_mul(batched_transpose(A), B)
+    @test MtlArray(Ct) ≈ batched_mul(batched_transpose(MtlArray(A)), MtlArray(B))
+
+    Ca = batched_mul(A, batched_adjoint(B))
+    @test MtlArray(Ca) ≈ batched_mul(MtlArray(A), batched_adjoint(MtlArray(B)))
+
+    # 5-arg batched_mul!
+    C .= pi
+    batched_mul!(C, A, B, 2f0, 3f0)
+    gpuCpi = MtlArray(similar(C)) .= pi
+    @test MtlArray(C) ≈ batched_mul!(gpuCpi, MtlArray(A), MtlArray(B), 2f0, 3f0)
+
+    # PermutedDimsArray
+    @test MtlArray(Ct) ≈ batched_mul(PermutedDimsArray(MtlArray(A), (2,1,3)), MtlArray(B))
+
+    D = permutedims(B, (1,3,2))
+    Cp = batched_mul(batched_adjoint(A), B)
+    @test_broken MtlArray(Cp) ≈ batched_mul(batched_adjoint(MtlArray(A)), PermutedDimsArray(MtlArray(D), (1,3,2)))
+
+    # Methods which reshape
+    M = randn(Float32, 3,3)
+
+    Cm = batched_mul(A, M)
+    @test MtlArray(Cm) ≈ batched_mul(MtlArray(A), MtlArray(M))
+
+    Cv = batched_vec(permutedims(A,(3,1,2)), M)
+    @test_broken MtlArray(Cv) ≈ batched_vec(PermutedDimsArray(MtlArray(A),(3,1,2)), MtlArray(M))
+end
+
+function print_array_strs(x)
+    str = sprint((io, x)->show(io, MIME"text/plain"(), x), x)
+    return @view split(str, '\n')[2:end]
+end
+
+@testset "BatchedAdjOrTrans" begin
+    x = rand(Float32, 3, 4, 2)
+    y = MtlArray(x)
+
+    bax = batched_adjoint(x)
+    btx = batched_transpose(x)
+    bay = batched_adjoint(y)
+    bty = batched_transpose(y)
+
+    @test sprint(show, bax) == sprint(show, bay)
+    @test sprint(show, btx) == sprint(show, bty)
+
+    @test print_array_strs(bax) == print_array_strs(bay)
+    @test print_array_strs(btx) == print_array_strs(bty)
+
+    @test Array(bax) == Array(bay)
+    @test collect(bax) == collect(bay)
+    @test Array(btx) == Array(bty)
+    @test collect(btx) == collect(bty)
+
+    for shape in (:, (12, 2))
+        rbax = reshape(bax, shape)
+        rbtx = reshape(btx, shape)
+        rbay = reshape(bay, shape)
+        rbty = reshape(bty, shape)
+
+        @test sprint(show, rbax) == sprint(show, rbay)
+        @test sprint(show, rbtx) == sprint(show, rbty)
+
+        @test print_array_strs(rbax) == print_array_strs(rbay)
+        @test print_array_strs(rbtx) == print_array_strs(rbty)
+
+        @test Array(rbax) == Array(rbay)
+        @test collect(rbax) == collect(rbay)
+        @test Array(rbtx) == Array(rbty)
+        @test collect(rbtx) == collect(rbty)
+    end
+end

test/ext_metal/runtests.jl

@@ -0,0 +1,6 @@
+
+Metal.allowscalar(false)
+
+@testset "Batched multiplication" begin
+    include("batched_mul.jl")
+end

test/runtests.jl

@@ -23,6 +23,7 @@ DocMeta.setdocmeta!(NNlib, :DocTestSetup, :(using NNlib, UnicodePlots); recursiv
 
 # ENV["NNLIB_TEST_CUDA"] = "true" # uncomment to run CUDA tests
 # ENV["NNLIB_TEST_AMDGPU"] = "true" # uncomment to run AMDGPU tests
+# ENV["NNLIB_TEST_METAL"] = "true" # uncomment to run Metal tests
 # ENV["NNLIB_TEST_CPU"] = "false" # uncomment to skip CPU tests
 
 const rng = StableRNG(123)
@@ -174,4 +175,21 @@ end
     else
         @info "Skipping AMDGPU tests, set NNLIB_TEST_AMDGPU=true to run them."
     end
+
+    if get(ENV, "NNLIB_TEST_METAL", "false") == "true"
+        Pkg.add(["Metal"])
+
+        using Metal
+        if Metal.functional()
+            @testset "Metal" begin
+                # nnlib_testsuite(CUDABackend; skip_tests=Set(("Scatter", "Gather")))
+
+                include("ext_metal/runtests.jl")
+            end
+        else
+            @info "Metal.jl package is not functional. Skipping Metal tests."
+        end
+    else
+        @info "Skipping Metal tests, set NNLIB_TEST_METAL=true to run them"
+    end
 end