Fix matrix factorizations of block sparse arrays with abstract block type (#154)

Author: mtfishman

Project.toml

@@ -1,7 +1,7 @@
 name = "BlockSparseArrays"
 uuid = "2c9a651f-6452-4ace-a6ac-809f4280fbb4"
 authors = ["ITensor developers <[email protected]> and contributors"]
-version = "0.7.17"
+version = "0.7.18"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

src/factorizations/eig.jl

@@ -18,9 +18,10 @@ using MatrixAlgebraKit:
 
 for f in [:default_eig_algorithm, :default_eigh_algorithm]
   @eval begin
-    function MatrixAlgebraKit.$f(arrayt::Type{<:AbstractBlockSparseMatrix}; kwargs...)
-      alg = $f(blocktype(arrayt); kwargs...)
-      return BlockPermutedDiagonalAlgorithm(alg)
+    function MatrixAlgebraKit.$f(::Type{<:AbstractBlockSparseMatrix}; kwargs...)
+      return BlockPermutedDiagonalAlgorithm() do block
+        return $f(block; kwargs...)
+      end
     end
   end
 end
@@ -45,12 +46,23 @@ function MatrixAlgebraKit.check_input(
   return nothing
 end
 
+function output_type(f::typeof(eig_full!), A::Type{<:AbstractMatrix{T}}) where {T}
+  DV = Base.promote_op(f, A)
+  !isconcretetype(DV) && return Tuple{AbstractMatrix{complex(T)},AbstractMatrix{complex(T)}}
+  return DV
+end
+function output_type(f::typeof(eigh_full!), A::Type{<:AbstractMatrix{T}}) where {T}
+  DV = Base.promote_op(f, A)
+  !isconcretetype(DV) && return Tuple{AbstractMatrix{real(T)},AbstractMatrix{T}}
+  return DV
+end
+
 for f in [:eig_full!, :eigh_full!]
   @eval begin
     function MatrixAlgebraKit.initialize_output(
       ::typeof($f), A::AbstractBlockSparseMatrix, alg::BlockPermutedDiagonalAlgorithm
     )
-      Td, Tv = fieldtypes(Base.promote_op($f, blocktype(A), typeof(alg.alg)))
+      Td, Tv = fieldtypes(output_type($f, blocktype(A)))
       D = similar(A, BlockType(Td))
       V = similar(A, BlockType(Tv))
       return (D, V)
@@ -60,7 +72,9 @@ for f in [:eig_full!, :eigh_full!]
     )
       check_input($f, A, (D, V))
       for I in eachstoredblockdiagindex(A)
-        D[I], V[I] = $f(@view(A[I]), alg.alg)
+        block = @view!(A[I])
+        block_alg = block_algorithm(alg, block)
+        D[I], V[I] = $f(block, block_alg)
       end
       for I in eachunstoredblockdiagindex(A)
         # TODO: Support setting `LinearAlgebra.I` directly, and/or
@@ -72,19 +86,31 @@ for f in [:eig_full!, :eigh_full!]
   end
 end
 
+function output_type(f::typeof(eig_vals!), A::Type{<:AbstractMatrix{T}}) where {T}
+  D = Base.promote_op(f, A)
+  !isconcretetype(D) && return AbstractVector{complex(T)}
+  return D
+end
+function output_type(f::typeof(eigh_vals!), A::Type{<:AbstractMatrix{T}}) where {T}
+  D = Base.promote_op(f, A)
+  !isconcretetype(D) && return AbstractVector{real(T)}
+  return D
+end
+
 for f in [:eig_vals!, :eigh_vals!]
   @eval begin
     function MatrixAlgebraKit.initialize_output(
       ::typeof($f), A::AbstractBlockSparseMatrix, alg::BlockPermutedDiagonalAlgorithm
     )
-      T = Base.promote_op($f, blocktype(A), typeof(alg.alg))
+      T = output_type($f, blocktype(A))
       return similar(A, BlockType(T), axes(A, 1))
     end
     function MatrixAlgebraKit.$f(
       A::AbstractBlockSparseMatrix, D, alg::BlockPermutedDiagonalAlgorithm
     )
       for I in eachblockstoredindex(A)
-        D[I] = $f(@view!(A[I]), alg.alg)
+        block = @view!(A[I])
+        D[I] = $f(block, block_algorithm(alg, block))
       end
       return D
     end

src/factorizations/lq.jl

@@ -3,8 +3,9 @@ using MatrixAlgebraKit: MatrixAlgebraKit, default_lq_algorithm, lq_compact!, lq_
 function MatrixAlgebraKit.default_lq_algorithm(
   A::Type{<:AbstractBlockSparseMatrix}; kwargs...
 )
-  alg = default_lq_algorithm(blocktype(A); kwargs...)
-  return BlockPermutedDiagonalAlgorithm(alg)
+  return BlockPermutedDiagonalAlgorithm() do block
+    return default_lq_algorithm(block; kwargs...)
+  end
 end
 
 function similar_output(
@@ -58,8 +59,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     L[brow, brow], Q[brow, bcol] = MatrixAlgebraKit.initialize_output(
-      lq_compact!, @view!(A[bI]), alg.alg
+      lq_compact!, block, block_alg
     )
   end
 
@@ -105,8 +108,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     L[brow, brow], Q[brow, bcol] = MatrixAlgebraKit.initialize_output(
-      lq_full!, @view!(A[bI]), alg.alg
+      lq_full!, block, block_alg
     )
   end
 
@@ -154,7 +159,9 @@ function MatrixAlgebraKit.lq_compact!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     lq = (@view!(L[brow, brow]), @view!(Q[brow, bcol]))
-    lq′ = lq_compact!(@view!(A[bI]), lq, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    lq′ = lq_compact!(block, lq, block_alg)
     @assert lq === lq′ "lq_compact! might not be in-place"
   end
 
@@ -183,7 +190,9 @@ function MatrixAlgebraKit.lq_full!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     lq = (@view!(L[brow, brow]), @view!(Q[brow, bcol]))
-    lq′ = lq_full!(@view!(A[bI]), lq, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    lq′ = lq_full!(block, lq, block_alg)
     @assert lq === lq′ "lq_full! might not be in-place"
   end
 

src/factorizations/qr.jl

@@ -2,10 +2,11 @@ using MatrixAlgebraKit:
   MatrixAlgebraKit, default_qr_algorithm, lq_compact!, lq_full!, qr_compact!, qr_full!
 
 function MatrixAlgebraKit.default_qr_algorithm(
-  A::Type{<:AbstractBlockSparseMatrix}; kwargs...
+  ::Type{<:AbstractBlockSparseMatrix}; kwargs...
 )
-  alg = default_qr_algorithm(blocktype(A); kwargs...)
-  return BlockPermutedDiagonalAlgorithm(alg)
+  return BlockPermutedDiagonalAlgorithm() do block
+    return default_qr_algorithm(block; kwargs...)
+  end
 end
 
 function similar_output(
@@ -59,8 +60,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     Q[brow, bcol], R[bcol, bcol] = MatrixAlgebraKit.initialize_output(
-      qr_compact!, @view!(A[bI]), alg.alg
+      qr_compact!, block, block_alg
     )
   end
 
@@ -106,8 +109,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     Q[brow, bcol], R[bcol, bcol] = MatrixAlgebraKit.initialize_output(
-      qr_full!, @view!(A[bI]), alg.alg
+      qr_full!, block, block_alg
     )
   end
 
@@ -155,7 +160,9 @@ function MatrixAlgebraKit.qr_compact!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     qr = (@view!(Q[brow, bcol]), @view!(R[bcol, bcol]))
-    qr′ = qr_compact!(@view!(A[bI]), qr, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    qr′ = qr_compact!(block, qr, block_alg)
     @assert qr === qr′ "qr_compact! might not be in-place"
   end
 
@@ -184,7 +191,9 @@ function MatrixAlgebraKit.qr_full!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     qr = (@view!(Q[brow, bcol]), @view!(R[bcol, bcol]))
-    qr′ = qr_full!(@view!(A[bI]), qr, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    qr′ = qr_full!(block, qr, block_alg)
     @assert qr === qr′ "qr_full! might not be in-place"
   end
 

src/factorizations/svd.jl

@@ -10,24 +10,26 @@ A wrapper for `MatrixAlgebraKit.AbstractAlgorithm` that implements the wrapped a
 a block-by-block basis, which is possible if the input matrix is a block-diagonal matrix or
 a block permuted block-diagonal matrix.
 """
-struct BlockPermutedDiagonalAlgorithm{A<:MatrixAlgebraKit.AbstractAlgorithm} <:
-       MatrixAlgebraKit.AbstractAlgorithm
-  alg::A
+struct BlockPermutedDiagonalAlgorithm{F} <: MatrixAlgebraKit.AbstractAlgorithm
+  falg::F
+end
+function block_algorithm(alg::BlockPermutedDiagonalAlgorithm, a::AbstractMatrix)
+  return block_algorithm(alg, typeof(a))
+end
+function block_algorithm(alg::BlockPermutedDiagonalAlgorithm, A::Type{<:AbstractMatrix})
+  return alg.falg(A)
 end
 
 function MatrixAlgebraKit.default_svd_algorithm(
-  A::Type{<:AbstractBlockSparseMatrix}; kwargs...
+  ::Type{<:AbstractBlockSparseMatrix}; kwargs...
 )
-  alg = default_svd_algorithm(blocktype(A); kwargs...)
-  return BlockPermutedDiagonalAlgorithm(alg)
+  return BlockPermutedDiagonalAlgorithm() do block
+    return default_svd_algorithm(block; kwargs...)
+  end
 end
 
-function output_type(
-  ::typeof(svd_compact!),
-  A::Type{<:AbstractMatrix{T}},
-  Alg::Type{<:MatrixAlgebraKit.AbstractAlgorithm},
-) where {T}
-  USVᴴ = Base.promote_op(svd_compact!, A, Alg)
+function output_type(::typeof(svd_compact!), A::Type{<:AbstractMatrix{T}}) where {T}
+  USVᴴ = Base.promote_op(svd_compact!, A)
   !isconcretetype(USVᴴ) &&
     return Tuple{AbstractMatrix{T},AbstractMatrix{realtype(T)},AbstractMatrix{T}}
   return USVᴴ
@@ -36,7 +38,7 @@ end
 function similar_output(
   ::typeof(svd_compact!), A, S_axes, alg::MatrixAlgebraKit.AbstractAlgorithm
 )
-  BU, BS, BVᴴ = fieldtypes(output_type(svd_compact!, blocktype(A), typeof(alg.alg)))
+  BU, BS, BVᴴ = fieldtypes(output_type(svd_compact!, blocktype(A)))
   U = similar(A, BlockType(BU), (axes(A, 1), S_axes[1]))
   S = similar(A, BlockType(BS), S_axes)
   Vᴴ = similar(A, BlockType(BVᴴ), (S_axes[2], axes(A, 2)))
@@ -81,8 +83,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     U[brow, bcol], S[bcol, bcol], Vt[bcol, bcol] = MatrixAlgebraKit.initialize_output(
-      svd_compact!, @view!(A[bI]), alg.alg
+      svd_compact!, block, block_alg
     )
   end
 
@@ -140,8 +144,10 @@ function MatrixAlgebraKit.initialize_output(
   # allocate output
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
     U[brow, bcol], S[bcol, bcol], Vt[bcol, bcol] = MatrixAlgebraKit.initialize_output(
-      svd_full!, @view!(A[bI]), alg.alg
+      svd_full!, block, block_alg
     )
   end
 
@@ -196,7 +202,9 @@ function MatrixAlgebraKit.svd_compact!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     usvᴴ = (@view!(U[brow, bcol]), @view!(S[bcol, bcol]), @view!(Vᴴ[bcol, bcol]))
-    usvᴴ′ = svd_compact!(@view!(A[bI]), usvᴴ, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    usvᴴ′ = svd_compact!(block, usvᴴ, block_alg)
     @assert usvᴴ === usvᴴ′ "svd_compact! might not be in-place"
   end
 
@@ -226,7 +234,9 @@ function MatrixAlgebraKit.svd_full!(
   for bI in eachblockstoredindex(A)
     brow, bcol = Tuple(bI)
     usvᴴ = (@view!(U[brow, bcol]), @view!(S[bcol, bcol]), @view!(Vᴴ[bcol, bcol]))
-    usvᴴ′ = svd_full!(@view!(A[bI]), usvᴴ, alg.alg)
+    block = @view!(A[bI])
+    block_alg = block_algorithm(alg, block)
+    usvᴴ′ = svd_full!(block, usvᴴ, block_alg)
     @assert usvᴴ === usvᴴ′ "svd_full! might not be in-place"
   end
 

test/Project.toml

@@ -31,7 +31,7 @@ DiagonalArrays = "0.3"
 GPUArraysCore = "0.2"
 JLArrays = "0.2"
 LinearAlgebra = "1"
-MatrixAlgebraKit = "0.2"
+MatrixAlgebraKit = "0.2.5"
 Random = "1"
 SafeTestsets = "0.1"
 SparseArraysBase = "0.5.11"