Very rough implementation of bcast for CuSparseVector

lib/cusparse/broadcast.jl

@@ -84,7 +84,8 @@ end
         end
     end
 end
-@inline function _capturescalars(arg) # this definition is just an optimization (to bottom out the recursion slightly sooner)
+@inline function _capturescalars(arg)
+    # this definition is just an optimization (to bottom out the recursion slightly sooner)
     if scalararg(arg)
         return (), () -> (arg,) # add scalararg
     elseif scalarwrappedarg(arg)
@@ -103,7 +104,6 @@ end
 
 ## COV_EXCL_START
 ## iteration helpers
-
 """
     CSRIterator{Ti}(row, args...)
 
@@ -288,15 +288,20 @@ end
 end
 
 # helpers to index a sparse or dense array
-function _getindex(arg::Union{CuSparseDeviceMatrixCSR,CuSparseDeviceMatrixCSC}, I, ptr)
+@inline function _getindex(arg::Union{CuSparseDeviceMatrixCSR{Tv},
+                                      CuSparseDeviceMatrixCSC{Tv},
+                                      CuSparseDeviceVector{Tv}}, I, ptr)::Tv where {Tv}
     if ptr == 0
-        zero(eltype(arg))
+        return zero(Tv)
     else
-        @inbounds arg.nzVal[ptr]
+        return @inbounds arg.nzVal[ptr]::Tv
     end
 end
-_getindex(arg, I, ptr) = Broadcast._broadcast_getindex(arg, I)
 
+@inline function _getindex(arg::CuDeviceArray{Tv}, I, ptr)::Tv where {Tv}
+    return @inbounds arg[I]::Tv
+end
+@inline _getindex(arg, I, ptr) = Broadcast._broadcast_getindex(arg, I)
 
 ## sparse broadcast implementation
 
@@ -305,8 +310,46 @@ iter_type(::Type{<:CuSparseMatrixCSR}, ::Type{Ti}) where {Ti} = CSRIterator{Ti}
 iter_type(::Type{<:CuSparseDeviceMatrixCSC}, ::Type{Ti}) where {Ti} = CSCIterator{Ti}
 iter_type(::Type{<:CuSparseDeviceMatrixCSR}, ::Type{Ti}) where {Ti} = CSRIterator{Ti}
 
+_has_row(A, offsets, row::Int32, fpreszeros::Bool) = fpreszeros ? 0i32 : row
+_has_row(A::CuDeviceArray, offsets, row::Int32, ::Bool) = row
+function _has_row(A::CuSparseDeviceVector, offsets, row::Int32, ::Bool)::Int32
+    for row_ix in 1i32:length(A.iPtr)
+        arg_row = @inbounds A.iPtr[row_ix]
+        arg_row == row && return row_ix
+        arg_row > row && break
+    end
+    return 0i32
+end
+
+function _get_my_row(first_row)::Int32
+    row_ix = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
+    return row_ix + first_row - 1i32
+end
+
+function compute_offsets_kernel(::Type{<:CuSparseVector}, first_row::Ti, last_row::Ti,
+                                fpreszeros::Bool, offsets::AbstractVector{Pair{Ti, NTuple{N, Ti}}},
+                                args...) where {Ti, N}
+    row = _get_my_row(first_row)
+    row > last_row && return
+
+    # TODO load arg.iPtr slices into shared memory
+    row_is_nnz = 0i32
+    arg_row_is_nnz = ntuple(Val(N)) do i
+        arg = @inbounds args[i]
+        _has_row(arg, offsets, row, fpreszeros)::Int32
+    end
+    row_is_nnz = 0i32
+    for i in 1:N
+        row_is_nnz |= @inbounds arg_row_is_nnz[i]
+    end
+    key = (row_is_nnz == 0i32) ? typemax(Ti) : row
+    @inbounds offsets[row - first_row + 1i32] = key => arg_row_is_nnz
+    return
+end
+
 # kernel to count the number of non-zeros in a row, to determine the row offsets
-function compute_offsets_kernel(T::Type{<:Union{CuSparseMatrixCSR, CuSparseMatrixCSC}}, offsets::AbstractVector{Ti},
+function compute_offsets_kernel(T::Type{<:Union{CuSparseMatrixCSR, CuSparseMatrixCSC}},
+                                offsets::AbstractVector{Ti},
                                 args...) where Ti
     # every thread processes an entire row
     leading_dim = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
@@ -331,8 +374,30 @@ function compute_offsets_kernel(T::Type{<:Union{CuSparseMatrixCSR, CuSparseMatri
     return
 end
 
-# broadcast kernels that iterate the elements of sparse arrays
-function sparse_to_sparse_broadcast_kernel(f, output::T, offsets::Union{AbstractVector,Nothing}, args...) where {Ti, T<:Union{CuSparseDeviceMatrixCSR{<:Any,Ti},CuSparseDeviceMatrixCSC{<:Any,Ti}}}
+function sparse_to_sparse_broadcast_kernel(f::F, output::CuSparseDeviceVector{Tv,Ti},
+                                           offsets::AbstractVector{Pair{Ti, NTuple{N, Ti}}},
+                                           args...) where {Tv, Ti, N, F}
+    row_ix = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
+    row_ix > output.nnz && return
+    row_and_ptrs = @inbounds offsets[row_ix]
+    row          = @inbounds row_and_ptrs[1]
+    arg_ptrs     = @inbounds row_and_ptrs[2]
+    vals = ntuple(Val(N)) do i
+        arg = @inbounds args[i]
+        # ptr is 0 if the sparse vector doesn't have an element at this row
+        # ptr is 0 if the arg is a scalar AND f preserves zeros
+        ptr = @inbounds arg_ptrs[i]
+        _getindex(arg, row, ptr)::Tv
+    end
+    output_val = f(vals...)
+    @inbounds output.iPtr[row_ix]  = row
+    @inbounds output.nzVal[row_ix] = output_val
+    return
+end
+
+function sparse_to_sparse_broadcast_kernel(f, output::T, offsets::Union{AbstractVector,Nothing},
+                                           args...) where {Ti, T<:Union{CuSparseDeviceMatrixCSR{<:Any,Ti},
+                                                                        CuSparseDeviceMatrixCSC{<:Any,Ti}}}
     # every thread processes an entire row
     leading_dim = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
     leading_dim_size = output isa CuSparseDeviceMatrixCSR ? size(output, 1) : size(output, 2)
@@ -345,7 +410,7 @@ function sparse_to_sparse_broadcast_kernel(f, output::T, offsets::Union{Abstract
     # fetch the row offset, and write it to the output
     @inbounds begin
         output_ptr = output_ptrs[leading_dim] = offsets[leading_dim]
-        if leading_dim == leading_dim_size 
+        if leading_dim == leading_dim_size
             output_ptrs[leading_dim+1i32] = offsets[leading_dim+1i32]
         end
     end
@@ -368,7 +433,8 @@ function sparse_to_sparse_broadcast_kernel(f, output::T, offsets::Union{Abstract
 
     return
 end
-function sparse_to_dense_broadcast_kernel(T::Type{<:Union{CuSparseMatrixCSR{Tv, Ti}, CuSparseMatrixCSC{Tv, Ti}}}, f,
+function sparse_to_dense_broadcast_kernel(T::Type{<:Union{CuSparseMatrixCSR{Tv, Ti},
+                                                          CuSparseMatrixCSC{Tv, Ti}}}, f,
                                           output::CuDeviceArray, args...) where {Tv, Ti}
     # every thread processes an entire row
     leading_dim = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
@@ -392,6 +458,28 @@ function sparse_to_dense_broadcast_kernel(T::Type{<:Union{CuSparseMatrixCSR{Tv,
 
     return
 end
+
+function sparse_to_dense_broadcast_kernel(::Type{<:CuSparseVector}, f::F,
+                                          output::CuDeviceArray{Tv},
+                                          offsets::AbstractVector{Pair{Ti, NTuple{N, Ti}}},
+                                          args...) where {Tv, F, N, Ti}
+    # every thread processes an entire row
+    row_ix = threadIdx().x + (blockIdx().x - 1i32) * blockDim().x
+    row_ix > length(output) && return
+    row_and_ptrs = @inbounds offsets[row_ix]
+    row          = @inbounds row_and_ptrs[1]
+    arg_ptrs     = @inbounds row_and_ptrs[2]
+    vals = ntuple(Val(length(args))) do i
+        arg = @inbounds args[i]
+        # ptr is 0 if the sparse vector doesn't have an element at this row
+        # ptr is row if the arg is dense OR a scalar with non-zero-preserving f
+        # ptr is 0 if the arg is a scalar AND f preserves zeros
+        ptr = @inbounds arg_ptrs[i]
+        _getindex(arg, row, ptr)::Tv
+    end
+    @inbounds output[row] = f(vals...)
+    return
+end
 ## COV_EXCL_STOP
 
 function Broadcast.copy(bc::Broadcasted{<:Union{CuSparseVecStyle,CuSparseMatStyle}})
@@ -405,12 +493,14 @@ function Broadcast.copy(bc::Broadcasted{<:Union{CuSparseVecStyle,CuSparseMatStyl
         error("broadcast with multiple types of sparse arrays ($(join(sparse_types, ", "))) is not supported")
     end
     sparse_typ = typeof(bc.args[first(sparse_args)])
-    sparse_typ <: Union{CuSparseMatrixCSR,CuSparseMatrixCSC} ||
-        error("broadcast with sparse arrays is currently only implemented for CSR and CSC matrices")
+    sparse_typ <: Union{CuSparseMatrixCSR,CuSparseMatrixCSC,CuSparseVector} ||
+        error("broadcast with sparse arrays is currently only implemented for vectors and CSR and CSC matrices")
     Ti = if sparse_typ <: CuSparseMatrixCSR
         reduce(promote_type, map(i->eltype(bc.args[i].rowPtr), sparse_args))
     elseif sparse_typ <: CuSparseMatrixCSC
         reduce(promote_type, map(i->eltype(bc.args[i].colPtr), sparse_args))
+    elseif sparse_typ <: CuSparseVector
+        reduce(promote_type, map(i->eltype(bc.args[i].iPtr), sparse_args))
     end
 
     # determine the output type
@@ -433,23 +523,32 @@ function Broadcast.copy(bc::Broadcasted{<:Union{CuSparseVecStyle,CuSparseMatStyl
 
     # the kernels below parallelize across rows or cols, not elements, so it's unlikely
     # we'll launch many threads. to maximize utilization, parallelize across blocks first.
-    rows, cols = size(bc)
+    rows, cols = get(size(bc), 1, 1), get(size(bc), 2, 1)   # `size(bc, ::Int)` is missing
     function compute_launch_config(kernel)
         config = launch_configuration(kernel.fun)
         if sparse_typ <: CuSparseMatrixCSR
             threads = min(rows, config.threads)
-            blocks = max(cld(rows, threads), config.blocks)
+            blocks  = max(cld(rows, threads), config.blocks)
             threads = cld(rows, blocks)
         elseif sparse_typ <: CuSparseMatrixCSC
             threads = min(cols, config.threads)
-            blocks = max(cld(cols, threads), config.blocks)
+            blocks  = max(cld(cols, threads), config.blocks)
             threads = cld(cols, blocks)
+        elseif sparse_typ <: CuSparseVector
+            threads = 512
+            blocks  = max(cld(rows, threads), config.blocks)
         end
         (; threads, blocks)
     end
-
+    # for CuSparseVec, figure out the actual row range we need to address, e.g. if m = 2^20
+    # but the only rows present in any sparse vector input are between 2 and 128, no need to
+    # launch massive threads.
+    # TODO: use the difference here to set the thread count
+    overall_first_row = one(Ti)
+    overall_last_row = Ti(rows)
+    offsets = nothing
     # allocate the output container
-    if !fpreszeros
+    if !fpreszeros && sparse_typ <: Union{CuSparseMatrixCSR, CuSparseMatrixCSC}
         # either we have dense inputs, or the function isn't preserving zeros,
         # so use a dense output to broadcast into.
         output = CuArray{Tv}(undef, size(bc))
@@ -466,20 +565,20 @@ function Broadcast.copy(bc::Broadcasted{<:Union{CuSparseVecStyle,CuSparseMatStyl
             end
         end
         broadcast!(bc.f, output, nonsparse_args...)
-    elseif length(sparse_args) == 1
+    elseif length(sparse_args) == 1 && sparse_typ <: Union{CuSparseMatrixCSR, CuSparseMatrixCSC}
         # we only have a single sparse input, so we can reuse its structure for the output.
         # this avoids a kernel launch and costly synchronization.
         sparse_arg = bc.args[first(sparse_args)]
         if sparse_typ <: CuSparseMatrixCSR
             offsets = rowPtr = sparse_arg.rowPtr
-            colVal = similar(sparse_arg.colVal)
-            nzVal = similar(sparse_arg.nzVal, Tv)
-            output = CuSparseMatrixCSR(rowPtr, colVal, nzVal, size(bc))
+            colVal  = similar(sparse_arg.colVal)
+            nzVal   = similar(sparse_arg.nzVal, Tv)
+            output  = CuSparseMatrixCSR(rowPtr, colVal, nzVal, size(bc))
         elseif sparse_typ <: CuSparseMatrixCSC
             offsets = colPtr = sparse_arg.colPtr
-            rowVal = similar(sparse_arg.rowVal)
-            nzVal = similar(sparse_arg.nzVal, Tv)
-            output = CuSparseMatrixCSC(colPtr, rowVal, nzVal, size(bc))
+            rowVal  = similar(sparse_arg.rowVal)
+            nzVal   = similar(sparse_arg.nzVal, Tv)
+            output  = CuSparseMatrixCSC(colPtr, rowVal, nzVal, size(bc))
         end
         # NOTE: we don't use CUSPARSE's similar, because that copies the structure arrays,
         #       while we do that in our kernel (for consistency with other code paths)
@@ -490,43 +589,79 @@ function Broadcast.copy(bc::Broadcasted{<:Union{CuSparseVecStyle,CuSparseMatStyl
             CuArray{Ti}(undef, rows+1)
         elseif sparse_typ <: CuSparseMatrixCSC
             CuArray{Ti}(undef, cols+1)
+        elseif sparse_typ <: CuSparseVector
+            CUDA.@allowscalar begin
+                arg_first_rows = ntuple(Val(length(bc.args))) do i
+                    bc.args[i] isa CuSparseVector && return bc.args[i].iPtr[1]
+                    return one(Ti)
+                end
+                arg_last_rows = ntuple(Val(length(bc.args))) do i
+                    bc.args[i] isa CuSparseVector && return bc.args[i].iPtr[end]
+                    return Ti(rows)
+                end
+            end
+            overall_first_row = min(arg_first_rows...)
+            overall_last_row  = max(arg_last_rows...)
+            CuVector{Pair{Ti, NTuple{length(bc.args), Ti}}}(undef, overall_last_row - overall_first_row + 1)
         end
         let
-            args = (sparse_typ, offsets, bc.args...)
+            args = if sparse_typ <: CuSparseVector
+                (sparse_typ, overall_first_row, overall_last_row, fpreszeros, offsets, bc.args...)
+            else
+                (sparse_typ, offsets, bc.args...)
+            end
             kernel = @cuda launch=false compute_offsets_kernel(args...)
             threads, blocks = compute_launch_config(kernel)
             kernel(args...; threads, blocks)
         end
-
         # accumulate these values so that we can use them directly as row pointer offsets,
         # as well as to get the total nnz count to allocate the sparse output array.
         # cusparseXcsrgeam2Nnz computes this in one go, but it doesn't seem worth the effort
-        accumulate!(Base.add_sum, offsets, offsets)
-        total_nnz = @allowscalar last(offsets[end]) - 1
-
+        if !(sparse_typ <: CuSparseVector)
+            accumulate!(Base.add_sum, offsets, offsets)
+            total_nnz = @allowscalar last(offsets[end]) - 1
+        else
+            sort!(offsets; by=first)
+            total_nnz = mapreduce(x->first(x) != typemax(first(x)), +, offsets)
+        end
         output = if sparse_typ <: CuSparseMatrixCSR
             colVal = CuArray{Ti}(undef, total_nnz)
-            nzVal = CuArray{Tv}(undef, total_nnz)
+            nzVal  = CuArray{Tv}(undef, total_nnz)
             CuSparseMatrixCSR(offsets, colVal, nzVal, size(bc))
         elseif sparse_typ <: CuSparseMatrixCSC
             rowVal = CuArray{Ti}(undef, total_nnz)
-            nzVal = CuArray{Tv}(undef, total_nnz)
+            nzVal  = CuArray{Tv}(undef, total_nnz)
             CuSparseMatrixCSC(offsets, rowVal, nzVal, size(bc))
+        elseif sparse_typ <: CuSparseVector && !fpreszeros
+            CuArray{Tv}(undef, size(bc))
+        elseif sparse_typ <: CuSparseVector && fpreszeros
+            iPtr   = CUDA.zeros(Ti, total_nnz)
+            nzVal  = CUDA.zeros(Tv, total_nnz)
+            CuSparseVector(iPtr, nzVal, rows)
+        end
+        if sparse_typ <: CuSparseVector && !fpreszeros
+            nonsparse_args = map(bc.args) do arg
+                # NOTE: this assumes the broadcst is flattened, but not yet preprocessed
+                if arg isa AbstractCuSparseArray
+                    zero(eltype(arg))
+                else
+                    arg
+                end
+            end
+            broadcast!(bc.f, output, nonsparse_args...)
         end
     end
-
     # perform the actual broadcast
     if output isa AbstractCuSparseArray
-        args = (bc.f, output, offsets, bc.args...)
+        args   = (bc.f, output, offsets, bc.args...)
         kernel = @cuda launch=false sparse_to_sparse_broadcast_kernel(args...)
-        threads, blocks = compute_launch_config(kernel)
-        kernel(args...; threads, blocks)
     else
-        args = (sparse_typ, bc.f, output, bc.args...)
+        args   = sparse_typ <: CuSparseVector ? (sparse_typ, bc.f, output, offsets, bc.args...) :
+                                                (sparse_typ, bc.f, output, bc.args...)
         kernel = @cuda launch=false sparse_to_dense_broadcast_kernel(args...)
-        threads, blocks = compute_launch_config(kernel)
-        kernel(args...; threads, blocks)
     end
+    threads, blocks = compute_launch_config(kernel)
+    kernel(args...; threads, blocks)
 
     return output
 end

lib/cusparse/device.jl

@@ -19,7 +19,7 @@ struct CuSparseDeviceVector{Tv,Ti, A} <: AbstractSparseVector{Tv,Ti}
     nnz::Ti
 end
 
-Base.length(g::CuSparseDeviceVector) = prod(g.dims)
+Base.length(g::CuSparseDeviceVector) = g.len
 Base.size(g::CuSparseDeviceVector) = (g.len,)
 SparseArrays.nnz(g::CuSparseDeviceVector) = g.nnz