concretize methods

src/SciMLOperators.jl

@@ -98,6 +98,8 @@ export update_coefficients!,
 
        issquare,
        islinear,
+       concretize,
+       isconvertible,
 
        has_adjoint,
        has_expmv,

src/basic.jl

@@ -761,6 +761,7 @@
 
 getops(L::InvertedOperator) = (L.L,)
 islinear(L::InvertedOperator) = islinear(L.L)
+isconvertible(::InvertedOperator) = false
 
 has_mul(L::InvertedOperator) = has_ldiv(L.L)
 has_mul!(L::InvertedOperator) = has_ldiv!(L.L)

src/batch.jl

@@ -67,6 +67,12 @@
                     )
 end
 
+function Base.convert(::Type{AbstractMatrix}, L::BatchedDiagonalOperator)
+    m, n = size(L)
+    msg = """$L cannot be represented by an $m × $n AbstractMatrix"""
+    throw(ArgumentError(msg))
+end
+
 LinearAlgebra.issymmetric(L::BatchedDiagonalOperator) = true
 function LinearAlgebra.ishermitian(L::BatchedDiagonalOperator)
     if isreal(L)
@@ -91,6 +97,7 @@
     update_func_isconstant(L.update_func) & update_func_isconstant(L.update_func!)
 end
 islinear(::BatchedDiagonalOperator) = true
+isconvertible(::BatchedDiagonalOperator) = false
 has_adjoint(L::BatchedDiagonalOperator) = true
 has_ldiv(L::BatchedDiagonalOperator) = all(x -> !iszero(x), L.diag)
 has_ldiv!(L::BatchedDiagonalOperator) = has_ldiv(L)

src/func.jl

@@ -111,6 +111,7 @@
 * `has_mul5` - `true` if the operator provides a five-argument `mul!` via the signature `op(v, u, p, t, α, β; <accepted_kwargs>)`. This trait is inferred if no value is provided.
 * `isconstant` - `true` if the operator is constant, and doesn't need to be updated via `update_coefficients[!]` during operator evaluation.
 * `islinear` - `true` if the operator is linear. Defaults to `false`.
+* `isconvertible` - `true` a cheap `convert(AbstractMatrix, L.op)` method is available. Defaults to `false`.
 * `batch` - Boolean indicating if the input/output arrays comprise of batched column-vectors stacked in a matrix. If `true`, the input/output arrays must be `AbstractVecOrMat`s, and the length of the  second dimension (the batch dimension) must be the same. The batch dimension is not involved in size computation. For example, with `batch = true`, and `size(output), size(input) = (M, K), (N, K)`, the `FunctionOperator` size is set to `(M, N)`. If `batch = false`, which is the default, the `input`/`output` arrays may of any size so long as `ndims(input) == ndims(output)`, and the `size` of `FunctionOperator` is set to `(length(input), length(output))`.
 * `ifcache` - Allocate cache arrays in constructor. Defaults to `true`. Cache can be generated afterwards by calling `cache_operator(L, input, output)`
 * `cache` - Pregenerated cache arrays for in-place evaluations. Expected to be of type and shape `(similar(input), similar(output),)`. The constructor generates cache if no values are provided. Cache generation by the constructor can be disabled by setting the kwarg `ifcache = false`.
@@ -138,6 +139,7 @@
                           has_mul5::Union{Nothing,Bool}=nothing,
                           isconstant::Bool = false,
                           islinear::Bool = false,
+                          isconvertible::Bool = false,
 
                           batch::Bool = false,
                           ifcache::Bool = true,
@@ -248,6 +250,7 @@
 
     traits = (;
               islinear = islinear,
+              isconvertible = isconvertible,
               isconstant = isconstant,
 
               opnorm = opnorm,
@@ -480,6 +483,8 @@
                     )
 end
 
+Base.convert(::Type{AbstractMatrix}, L::FunctionOperator) = convert(AbstractMatrix, L.op)
+
 function Base.resize!(L::FunctionOperator, n::Integer)
 
     # input/output to `L` must be `AbstractVector`s
@@ -526,6 +531,7 @@
 end
 
 islinear(L::FunctionOperator) = L.traits.islinear
+isconvertible(L::FunctionOperator) = L.traits.isconvertible
 isconstant(L::FunctionOperator) = L.traits.isconstant
 has_adjoint(L::FunctionOperator) = !(L.op_adjoint isa Nothing)
 has_mul(::FunctionOperator{iip}) where{iip} = true

src/interface.jl

@@ -187,37 +187,53 @@
 Base.iszero(::AbstractSciMLOperator) = false # TODO
 
 """
+$SIGNATURES
+
 Check if `adjoint(L)` is lazily defined.
 """
 has_adjoint(L::AbstractSciMLOperator) = false # L', adjoint(L)
 """
+$SIGNATURES
+
 Check if `expmv!(v, L, u, t)`, equivalent to `mul!(v, exp(t * A), u)`, is
 defined for `Number` `t`, and `AbstractArray`s `u, v` of appropriate sizes.
 """
 has_expmv!(L::AbstractSciMLOperator) = false # expmv!(v, L, t, u)
 """
+$SIGNATURES
+
 Check if `expmv(L, u, t)`, equivalent to `exp(t * A) * u`, is defined for
 `Number` `t`, and `AbstractArray` `u` of appropriate size.
 """
 has_expmv(L::AbstractSciMLOperator) = false # v = exp(L, t, u)
 """
+$SIGNATURES
+
 Check if `exp(L)` is defined lazily defined.
 """
 has_exp(L::AbstractSciMLOperator) = islinear(L)
 """
+$SIGNATURES
+
 Check if `L * u` is defined for `AbstractArray` `u` of appropriate size.
 """
 has_mul(L::AbstractSciMLOperator) = true # du = L*u
 """
+$SIGNATURES
+
 Check if `mul!(v, L, u)` is defined for `AbstractArray`s `u, v` of
 appropriate sizes.
 """
 has_mul!(L::AbstractSciMLOperator) = true # mul!(du, L, u)
 """
+$SIGNATURES
+
 Check if `L \\ u` is defined for `AbstractArray` `u` of appropriate size.
 """
 has_ldiv(L::AbstractSciMLOperator) = false # du = L\u
 """
+$SIGNATURES
+
 Check if `ldiv!(v, L, u)` is defined for `AbstractArray`s `u, v` of
 appropriate sizes.
 """
@@ -244,7 +260,57 @@
           ) = true
 isconstant(L::AbstractSciMLOperator) = all(isconstant, getops(L))
 
-#islinear(L) = false
+"""
+    isconvertible(L) -> Bool
+
+Checks if `L` can be cheaply converted to an `AbstractMatrix` via eager fusion.
+"""
+isconvertible(L::AbstractSciMLOperator) = all(isconvertible, getops(L))
+
+isconvertible(::Union{
+                      # LinearAlgebra
+                      AbstractMatrix,
+                      UniformScaling,
+                      Factorization,
+
+                      # Base
+                      Number,
+
+                      # SciMLOperators
+                      AbstractSciMLScalarOperator,
+                     }
+             ) = true
+
+"""
+    concretize(L) -> AbstractMatrix
+
+    concretize(L) -> Number
+
+Convert `SciMLOperator` to a concrete type via eager fusion. This method is a
+no-op for types that are already concrete.
+"""
+concretize(L::Union{
+                    # LinearAlgebra
+                    AbstractMatrix,
+                    Factorization,
+
+                    # SciMLOperators
+                    AbstractSciMLOperator,
+                   }
+          ) =  convert(AbstractMatrix, L)
+
+concretize(L::Union{
+                    # LinearAlgebra
+                    UniformScaling,
+
+                    # Base
+                    Number,
+
+                    # SciMLOperators
+                    AbstractSciMLScalarOperator,
+                   }
+          ) = convert(Number, L)
+
 """
 $SIGNATURES
 
@@ -349,22 +415,22 @@
 function Base.conj(L::AbstractSciMLOperator)
     isreal(L) && return L
     @warn """using convert-based fallback for Base.conj"""
-    convert(AbstractMatrix, L) |> conj
+    concretize(L) |> conj
 end
 
 function Base.:(==)(L1::AbstractSciMLOperator, L2::AbstractSciMLOperator)
     @warn """using convert-based fallback for Base.=="""
     size(L1) != size(L2) && return false
-    convert(AbstractMatrix, L1) == convert(AbstractMatrix, L1)
+    concretize(L1) == concretize(L2)
 end
 
 Base.@propagate_inbounds function Base.getindex(L::AbstractSciMLOperator, I::Vararg{Any,N}) where {N}
     @warn """using convert-based fallback for Base.getindex"""
-    convert(AbstractMatrix, L)[I...]
+    concretize(L)[I...]
 end
 function Base.getindex(L::AbstractSciMLOperator, I::Vararg{Int, N}) where {N}
     @warn """using convert-based fallback for Base.getindex"""
-    convert(AbstractMatrix, L)[I...]
+    concretize(L)[I...]
 end
 
 function Base.resize!(L::AbstractSciMLOperator, n::Integer)
@@ -375,15 +441,15 @@
 
 function LinearAlgebra.opnorm(L::AbstractSciMLOperator, p::Real=2)
     @warn """using convert-based fallback in LinearAlgebra.opnorm."""
-    opnorm(convert(AbstractMatrix, L), p)
+    opnorm(concretize(L), p)
 end
 
 for op in (
            :sum, :prod,
           )
     @eval function Base.$op(L::AbstractSciMLOperator; kwargs...)
         @warn """using convert-based fallback in $($op)."""
-        $op(convert(AbstractMatrix, L); kwargs...)
+        $op(concretize(L); kwargs...)
     end
 end
 
@@ -394,17 +460,17 @@
             )
     @eval function LinearAlgebra.$pred(L::AbstractSciMLOperator)
         @warn """using convert-based fallback in $($pred)."""
-        $pred(convert(AbstractMatrix, L))
+        $pred(concretize(L))
     end
 end
 
 function LinearAlgebra.mul!(v::AbstractArray, L::AbstractSciMLOperator, u::AbstractArray)
     @warn """using convert-based fallback in mul!."""
-    mul!(v, convert(AbstractMatrix, L), u)
+    mul!(v, concretize(L), u)
 end
 
 function LinearAlgebra.mul!(v::AbstractArray, L::AbstractSciMLOperator, u::AbstractArray, α, β)
     @warn """using convert-based fallback in mul!."""
-    mul!(v, convert(AbstractMatrix, L), u, α, β)
+    mul!(v, concretize(L), u, α, β)
 end
 #

src/matrix.jl

@@ -103,6 +103,8 @@
                            has_ldiv,
                            has_ldiv!,
                           )
+
+isconvertible(::MatrixOperator) = true
 islinear(::MatrixOperator) = true
 
 function Base.show(io::IO, L::MatrixOperator)
@@ -162,7 +164,7 @@
 
 # TODO - add tests for MatrixOperator indexing
 # propagate_inbounds here for the getindex fallback
-Base.@propagate_inbounds Base.convert(::Type{AbstractMatrix}, L::MatrixOperator) = L.A
+Base.@propagate_inbounds Base.convert(::Type{AbstractMatrix}, L::MatrixOperator) = convert(AbstractMatrix, L.A)
 Base.@propagate_inbounds Base.setindex!(L::MatrixOperator, v, i::Int) = (L.A[i] = v)
 Base.@propagate_inbounds Base.setindex!(L::MatrixOperator, v, I::Vararg{Int, N}) where{N} = (L.A[I...] = v)
 
@@ -322,6 +324,7 @@
 
 getops(L::InvertibleOperator) = (L.L, L.F,)
 islinear(L::InvertibleOperator) = islinear(L.L)
+isconvertible(L::InvertibleOperator) = isconvertible(L.L)
 
 @forward InvertibleOperator.L (
                                # LinearAlgebra
@@ -510,6 +513,7 @@
 getops(L::AffineOperator) = (L.A, L.B, L.b)
 
 islinear(::AffineOperator) = false
+isconvertible(::AffineOperator) = false
 
 function Base.show(io::IO, L::AffineOperator)
     show(io, L.A)
@@ -537,6 +541,12 @@
     L
 end
 
+function Base.convert(::Type{AbstractMatrix}, L::AffineOperator)
+    m, n = size(L)
+    msg = """$L cannot be represented by an $m × $n AbstractMatrix"""
+    throw(ArgumentError(msg))
+end
+
 has_adjoint(L::AffineOperator) = false
 has_mul(L::AffineOperator) = has_mul(L.A)
 has_mul!(L::AffineOperator) = has_mul!(L.A)

src/scalar.jl

@@ -32,6 +32,7 @@
 Base.transpose(α::AbstractSciMLScalarOperator) = α
 
 has_mul!(::AbstractSciMLScalarOperator) = true
+isconcrete(::AbstractSciMLScalarOperator) = true
 islinear(::AbstractSciMLScalarOperator) = true
 has_adjoint(::AbstractSciMLScalarOperator) = true
 

src/tensor.jl

@@ -121,6 +121,7 @@
 
 getops(L::TensorProductOperator) = L.ops
 islinear(L::TensorProductOperator) = reduce(&, islinear.(L.ops))
+isconvertible(::TensorProductOperator) = false
 Base.iszero(L::TensorProductOperator) = reduce(|, iszero.(L.ops))
 has_adjoint(L::TensorProductOperator) = reduce(&, has_adjoint.(L.ops))
 has_mul(L::TensorProductOperator) = reduce(&, has_mul.(L.ops))