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maleadtmaleadt
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Remove BLAS methods.
Doing so isn't portable, not every back-end might have a BLAS, and we should be implementing LinearAlgebra instead.
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‎docs/src/interface.md

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@@ -60,6 +60,4 @@ should provide implementations of the following interfaces:
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GPUArrays.backend
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GPUArrays.device
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GPUArrays.unsafe_reinterpret
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GPUArrays.blas_module
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GPUArrays.blasbuffer
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```

‎src/host/linalg.jl

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@@ -1,119 +1,5 @@
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# integration with LinearAlgebra stdlib
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## low-level BLAS calls
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function blas_module(A)
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error("$(typeof(A)) doesn't support BLAS operations")
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end
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function blasbuffer(A)
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error("$(typeof(A)) doesn't support BLAS operations")
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end
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for elty in (Float32, Float64, ComplexF32, ComplexF64)
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T = VERSION >= v"1.3.0-alpha.115" ? :(Union{($elty), Bool}) : elty
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@eval begin
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function BLAS.gemm!(
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transA::AbstractChar, transB::AbstractChar, alpha::$T,
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A::AbstractGPUVecOrMat{$elty}, B::AbstractGPUVecOrMat{$elty},
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beta::$T, C::AbstractGPUVecOrMat{$elty}
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)
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blasmod = blas_module(A)
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result = blasmod.gemm!(
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transA, transB, alpha,
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blasbuffer(A), blasbuffer(B), beta, blasbuffer(C)
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)
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C
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end
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end
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end
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for elty in (Float64, Float32)
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@eval begin
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function BLAS.scal!(
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n::Integer, DA::$elty,
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DX::AbstractGPUArray{$elty, N}, incx::Integer
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) where N
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blasmod = blas_module(DX)
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blasmod.scal!(n, DA, blasbuffer(DX), incx)
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DX
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end
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end
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end
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LinearAlgebra.rmul!(s::Number, X::AbstractGPUArray) = rmul!(X, s)
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function LinearAlgebra.rmul!(X::AbstractGPUArray{T}, s::Number) where T <: BLAS.BlasComplex
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R = typeof(real(zero(T)))
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N = 2*length(X)
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buff = unsafe_reinterpret(R, X, (N,))
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BLAS.scal!(N, R(s), buff, 1)
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X
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end
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function LinearAlgebra.rmul!(X::AbstractGPUArray{T}, s::Number) where T <: Union{Float32, Float64}
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BLAS.scal!(length(X), T(s), X, 1)
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X
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end
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for elty in (Float32, Float64, ComplexF32, ComplexF64)
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T = VERSION >= v"1.3.0-alpha.115" ? :(Union{($elty), Bool}) : elty
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@eval begin
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function BLAS.gemv!(trans::AbstractChar, alpha::$T, A::AbstractGPUVecOrMat{$elty}, X::AbstractGPUVector{$elty}, beta::$T, Y::AbstractGPUVector{$elty})
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m, n = size(A, 1), size(A, 2)
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if trans == 'N' && (length(X) != n || length(Y) != m)
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throw(DimensionMismatch("A has dimensions $(size(A)), X has length $(length(X)) and Y has length $(length(Y))"))
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elseif trans == 'C' && (length(X) != m || length(Y) != n)
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throw(DimensionMismatch("A' has dimensions $n, $m, X has length $(length(X)) and Y has length $(length(Y))"))
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elseif trans == 'T' && (length(X) != m || length(Y) != n)
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throw(DimensionMismatch("A.' has dimensions $n, $m, X has length $(length(X)) and Y has length $(length(Y))"))
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end
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blasmod = blas_module(A)
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blasmod.gemv!(
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trans, alpha,
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blasbuffer(A), blasbuffer(X), beta, blasbuffer(Y)
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)
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Y
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end
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end
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end
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for elty in (Float32, Float64, ComplexF32, ComplexF64)
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@eval begin
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function BLAS.axpy!(
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alpha::Number, x::AbstractGPUArray{$elty}, y::AbstractGPUArray{$elty}
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)
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if length(x) != length(y)
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throw(DimensionMismatch("x has length $(length(x)), but y has length $(length(y))"))
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end
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blasmod = blas_module(x)
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blasmod.axpy!($elty(alpha), blasbuffer(vec(x)), blasbuffer(vec(y)))
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y
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end
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end
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end
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for elty in (Float32, Float64, ComplexF32, ComplexF64)
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@eval begin
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function BLAS.gbmv!(trans::AbstractChar, m::Integer, kl::Integer, ku::Integer, alpha::($elty), A::AbstractGPUMatrix{$elty}, X::AbstractGPUVector{$elty}, beta::($elty), Y::AbstractGPUVector{$elty})
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n = size(A, 2)
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if trans == 'N' && (length(X) != n || length(Y) != m)
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throw(DimensionMismatch("A has dimensions $n, $m, X has length $(length(X)) and Y has length $(length(Y))"))
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elseif trans == 'C' && (length(X) != m || length(Y) != n)
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throw(DimensionMismatch("A' has dimensions $n, $m, X has length $(length(X)) and Y has length $(length(Y))"))
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elseif trans == 'T' && (length(X) != m || length(Y) != n)
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throw(DimensionMismatch("A.' has dimensions $n, $m, X has length $(length(X)) and Y has length $(length(Y))"))
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end
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blasmod = blas_module(A)
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blasmod.gbmv!(
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trans, m, kl, ku, alpha,
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blasbuffer(A), blasbuffer(X), beta, blasbuffer(Y)
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)
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Y
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end
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end
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end
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## high-level functionality
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function LinearAlgebra.transpose!(At::AbstractGPUArray{T, 2}, A::AbstractGPUArray{T, 2}) where T
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gpu_call(At, A) do ctx, At, A
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idx = @cartesianidx A ctx

‎src/reference.jl

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@@ -269,11 +269,4 @@ to_device(ctx, x::Base.RefValue{<: JLArray}) = Base.RefValue(to_device(ctx, x[])
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GPUArrays.unsafe_reinterpret(::Type{T}, A::JLArray, size::Tuple) where T =
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reshape(reinterpret(T, A.data), size)
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# linear algebra
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using LinearAlgebra
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GPUArrays.blas_module(::JLArray) = LinearAlgebra.BLAS
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GPUArrays.blasbuffer(A::JLArray) = A.data
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end

‎test/testsuite.jl

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@@ -38,7 +38,6 @@ include("testsuite/mapreduce.jl")
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include("testsuite/broadcasting.jl")
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include("testsuite/linalg.jl")
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include("testsuite/fft.jl")
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include("testsuite/blas.jl")
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include("testsuite/random.jl")
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@@ -55,7 +54,6 @@ function test(AT::Type{<:AbstractGPUArray})
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TestSuite.test_broadcasting(AT)
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TestSuite.test_linalg(AT)
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TestSuite.test_fft(AT)
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TestSuite.test_blas(AT)
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TestSuite.test_random(AT)
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end
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‎test/testsuite/blas.jl

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