Merge pull request #19724 from Sacha0/mixedbc

broadcast[!] over combinations of scalars and sparse vectors/matrices

base/broadcast.jl

@@ -202,7 +202,9 @@ Note that `dest` is only used to store the result, and does not supply
 arguments to `f` unless it is also listed in the `As`,
 as in `broadcast!(f, A, A, B)` to perform `A[:] = broadcast(f, A, B)`.
 """
-@inline function broadcast!{N}(f, C::AbstractArray, A, Bs::Vararg{Any,N})
+@inline broadcast!{N}(f, C::AbstractArray, A, Bs::Vararg{Any,N}) =
+    broadcast_c!(f, containertype(C, A, Bs...), C, A, Bs...)
+@inline function broadcast_c!{N}(f, ::Type, C::AbstractArray, A, Bs::Vararg{Any,N})
     shape = indices(C)
     @boundscheck check_broadcast_indices(shape, A, Bs...)
     keeps, Idefaults = map_newindexer(shape, A, Bs)

base/sparse/higherorderfns.jl

@@ -5,9 +5,11 @@ module HigherOrderFns
 # This module provides higher order functions specialized for sparse arrays,
 # particularly map[!]/broadcast[!] for SparseVectors and SparseMatrixCSCs at present.
 import Base: map, map!, broadcast, broadcast!
+import Base.Broadcast: containertype, promote_containertype,
+    broadcast_indices, broadcast_c, broadcast_c!
 
-using Base: tail, to_shape
-using ..SparseArrays: SparseVector, SparseMatrixCSC, indtype
+using Base: front, tail, to_shape
+using ..SparseArrays: SparseVector, SparseMatrixCSC, AbstractSparseArray, indtype
 
 # This module is organized as follows:
 # (1) Define a common interface to SparseVectors and SparseMatrixCSCs sufficient for
@@ -837,15 +839,52 @@ end
 
 
 # (9) broadcast[!] over combinations of broadcast scalars and sparse vectors/matrices
-#
-# TODO: The minimal snippet below is not satisfying: A better solution would achieve
-# the same for (1) all broadcast scalar types (Base.Broadcast.containertype(x) == Any?) and
-# (2) any combination (number, order, type mixture) of broadcast scalars.
-#
-broadcast{Tf}(f::Tf, x::Union{Number,Bool}, A::SparseMatrixCSC) = broadcast(y -> f(x, y), A)
-broadcast{Tf}(f::Tf, A::SparseMatrixCSC, y::Union{Number,Bool}) = broadcast(x -> f(x, y), A)
-# NOTE: The following two method definitions work around #19096. These definitions should
-# be folded into the two preceding definitions on resolution of #19096.
+
+# broadcast shape promotion for combinations of sparse arrays and other types
+broadcast_indices(::Type{AbstractSparseArray}, A) = indices(A)
+# broadcast container type promotion for combinations of sparse arrays and other types
+containertype{T<:SparseVecOrMat}(::Type{T}) = AbstractSparseArray
+# combinations of sparse arrays with broadcast scalars should yield sparse arrays
+promote_containertype(::Type{Any}, ::Type{AbstractSparseArray}) = AbstractSparseArray
+promote_containertype(::Type{AbstractSparseArray}, ::Type{Any}) = AbstractSparseArray
+# combinations of sparse arrays with anything else should fall back to generic dense broadcast
+promote_containertype(::Type{Array}, ::Type{AbstractSparseArray}) = Array
+promote_containertype(::Type{Tuple}, ::Type{AbstractSparseArray}) = Array
+promote_containertype(::Type{AbstractSparseArray}, ::Type{Array}) = Array
+promote_containertype(::Type{AbstractSparseArray}, ::Type{Tuple}) = Array
+
+# broadcast[!] entry points for combinations of sparse arrays and other types
+@inline function broadcast_c{N}(f, ::Type{AbstractSparseArray}, mixedargs::Vararg{Any,N})
+    parevalf, passedargstup = capturescalars(f, mixedargs)
+    return broadcast(parevalf, passedargstup...)
+end
+@inline function broadcast_c!{N}(f, ::Type{AbstractSparseArray}, dest::SparseVecOrMat, mixedsrcargs::Vararg{Any,N})
+    parevalf, passedsrcargstup = capturescalars(f, mixedsrcargs)
+    return broadcast!(parevalf, dest, passedsrcargstup...)
+end
+# capturescalars takes a function (f) and a tuple of mixed sparse vectors/matrices and
+# broadcast scalar arguments (mixedargs), and returns a function (parevalf, i.e. partially
+# evaluated f) and a reduced argument tuple (passedargstup) containing only the sparse
+# vectors/matrices in mixedargs in their orginal order, and such that the result of
+# broadcast(parevalf, passedargstup...) is broadcast(f, mixedargs...)
+@inline capturescalars(f, mixedargs) =
+    capturescalars((passed, tofill) -> f(tofill...), (), mixedargs...)
+# Recursion cases for capturescalars
+@inline capturescalars(f, passedargstup, scalararg, mixedargs...) =
+    capturescalars(capturescalar(f, scalararg), passedargstup, mixedargs...)
+@inline capturescalars(f, passedargstup, nonscalararg::SparseVecOrMat, mixedargs...) =
+    capturescalars(passnonscalar(f), (passedargstup..., nonscalararg), mixedargs...)
+@inline passnonscalar(f) = (passed, tofill) -> f(Base.front(passed), (last(passed), tofill...))
+@inline capturescalar(f, scalararg) = (passed, tofill) -> f(passed, (scalararg, tofill...))
+# Base cases for capturescalars
+@inline capturescalars(f, passedargstup, scalararg) =
+    (capturelastscalar(f, scalararg), passedargstup)
+@inline capturescalars(f, passedargstup, nonscalararg::SparseVecOrMat) =
+    (passlastnonscalar(f), (passedargstup..., nonscalararg))
+@inline passlastnonscalar(f) = (passed...) -> f(Base.front(passed), (last(passed),))
+@inline capturelastscalar(f, scalararg) = (passed...) -> f(passed, (scalararg,))
+
+# NOTE: The following two method definitions work around #19096.
 broadcast{Tf,T}(f::Tf, ::Type{T}, A::SparseMatrixCSC) = broadcast(y -> f(T, y), A)
 broadcast{Tf,T}(f::Tf, A::SparseMatrixCSC, ::Type{T}) = broadcast(x -> f(x, T), A)
 

test/sparse/higherorderfns.jl

@@ -193,6 +193,7 @@ end
     end
 end
 
+
 @testset "sparse map/broadcast with result eltype not a concrete subtype of Number (#19561/#19589)" begin
     intoneorfloatzero(x) = x != 0.0 ? Int(1) : Float64(x)
     stringorfloatzero(x) = x != 0.0 ? "Hello" : Float64(x)
@@ -202,6 +203,86 @@ end
     @test broadcast(stringorfloatzero, speye(4)) == sparse(broadcast(stringorfloatzero, eye(4)))
 end
 
+@testset "broadcast[!] over combinations of scalars and sparse vectors/matrices" begin
+    N, M, p = 10, 12, 0.5
+    elT = Float64
+    s = Float32(2.0)
+    V = sprand(elT, N, p)
+    A = sprand(elT, N, M, p)
+    fV, fA = Array(V), Array(A)
+    # test combinations involving one to three scalars and one to five sparse vectors/matrices
+    spargseq, dargseq = Iterators.cycle((A, V)), Iterators.cycle((fA, fV))
+    for nargs in 1:5 # number of tensor arguments
+        nargsl = cld(nargs, 2) # number in "left half" of tensor arguments
+        nargsr = fld(nargs, 2) # number in "right half" of tensor arguments
+        spargsl = tuple(Iterators.take(spargseq, nargsl)...) # "left half" of tensor args
+        spargsr = tuple(Iterators.take(spargseq, nargsr)...) # "right half" of tensor args
+        dargsl = tuple(Iterators.take(dargseq, nargsl)...) # "left half" of tensor args, densified
+        dargsr = tuple(Iterators.take(dargseq, nargsr)...) # "right half" of tensor args, densified
+        for (sparseargs, denseargs) in ( # argument combinations including scalars
+                # a few combinations involving one scalar
+                ((s, spargsl..., spargsr...), (s, dargsl..., dargsr...)),
+                ((spargsl..., s, spargsr...), (dargsl..., s, dargsr...)),
+                ((spargsl..., spargsr..., s), (dargsl..., dargsr..., s)),
+                # a few combinations involving two scalars
+                ((s, spargsl..., s, spargsr...), (s, dargsl..., s, dargsr...)),
+                ((s, spargsl..., spargsr..., s), (s, dargsl..., dargsr..., s)),
+                ((spargsl..., s, spargsr..., s), (dargsl..., s, dargsr..., s)),
+                ((s, s, spargsl..., spargsr...), (s, s, dargsl..., dargsr...)),
+                ((spargsl..., s, s, spargsr...), (dargsl..., s, s, dargsr...)),
+                ((spargsl..., spargsr..., s, s), (dargsl..., dargsr..., s, s)),
+                # a few combinations involving three scalars
+                ((s, spargsl..., s, spargsr..., s), (s, dargsl..., s, dargsr..., s)),
+                ((s, spargsl..., s, s, spargsr...), (s, dargsl..., s, s, dargsr...)),
+                ((spargsl..., s, s, spargsr..., s), (dargsl..., s, s, dargsr..., s)),
+                ((spargsl..., s, s, s, spargsr...), (dargsl..., s, s, s, dargsr...)), )
+            # test broadcast entry point
+            @test broadcast(*, sparseargs...) == sparse(broadcast(*, denseargs...))
+            @test isa(@inferred(broadcast(*, sparseargs...)), SparseMatrixCSC{elT})
+            # test broadcast! entry point
+            fX = broadcast(*, sparseargs...); X = sparse(fX)
+            @test broadcast!(*, X, sparseargs...) == sparse(broadcast!(*, fX, denseargs...))
+            @test isa(@inferred(broadcast!(*, X, sparseargs...)), SparseMatrixCSC{elT})
+            X = sparse(fX) # reset / warmup for @allocated test
+            @test_broken (@allocated broadcast!(*, X, sparseargs...)) == 0
+            # This test (and the analog below) fails for three reasons:
+            # (1) In all cases, generating the closures that capture the scalar arguments
+            #   results in allocation, not sure why.
+            # (2) In some cases, though _broadcast_eltype (which wraps _return_type)
+            #   consistently provides the correct result eltype when passed the closure
+            #   that incorporates the scalar arguments to broadcast (and, with #19667,
+            #   is inferable, so the overall return type from broadcast is inferred),
+            #   in some cases inference seems unable to determine the return type of
+            #   direct calls to that closure. This issue causes variables in both the
+            #   broadcast[!] entry points (fofzeros = f(_zeros_eltypes(args...)...)) and
+            #   the driver routines (Cx in _map_zeropres! and _broadcast_zeropres!) to have
+            #   inferred type Any, resulting in allocation and lackluster performance.
+            # (3) The sparseargs... splat in the call above allocates a bit, but of course
+            #   that issue is negligible and perhaps could be accounted for in the test.
+        end
+    end
+    # test combinations at the limit of inference (eight arguments net)
+    for (sparseargs, denseargs) in (
+            ((s, s, s, A, s, s, s, s), (s, s, s, fA, s, s, s, s)), # seven scalars, one sparse matrix
+            ((s, s, V, s, s, A, s, s), (s, s, fV, s, s, fA, s, s)), # six scalars, two sparse vectors/matrices
+            ((s, s, V, s, A, s, V, s), (s, s, fV, s, fA, s, fV, s)), # five scalars, three sparse vectors/matrices
+            ((s, V, s, A, s, V, s, A), (s, fV, s, fA, s, fV, s, fA)), # four scalars, four sparse vectors/matrices
+            ((s, V, A, s, V, A, s, A), (s, fV, fA, s, fV, fA, s, fA)), # three scalars, five sparse vectors/matrices
+            ((V, A, V, s, A, V, A, s), (fV, fA, fV, s, fA, fV, fA, s)), # two scalars, six sparse vectors/matrices
+            ((V, A, V, A, s, V, A, V), (fV, fA, fV, fA, s, fV, fA, fV)) ) # one scalar, seven sparse vectors/matrices
+        # test broadcast entry point
+        @test broadcast(*, sparseargs...) == sparse(broadcast(*, denseargs...))
+        @test isa(@inferred(broadcast(*, sparseargs...)), SparseMatrixCSC{elT})
+        # test broadcast! entry point
+        fX = broadcast(*, sparseargs...); X = sparse(fX)
+        @test broadcast!(*, X, sparseargs...) == sparse(broadcast!(*, fX, denseargs...))
+        @test isa(@inferred(broadcast!(*, X, sparseargs...)), SparseMatrixCSC{elT})
+        X = sparse(fX) # reset / warmup for @allocated test
+        @test_broken (@allocated broadcast!(*, X, sparseargs...)) == 0
+        # please see the note a few lines above re. this @test_broken
+    end
+end
+
 # Older tests of sparse broadcast, now largely covered by the tests above
 @testset "assorted tests of sparse broadcast over two input arguments" begin
     N, p = 10, 0.3