[NDTensors] Avoid more scalar indexing operations in block sparse GPU…

… code

NDTensors/ext/NDTensorsMetalExt/NDTensorsMetalExt.jl

@@ -2,7 +2,7 @@ module NDTensorsMetalExt
 
 using Adapt
 using Functors
-using LinearAlgebra: LinearAlgebra
+using LinearAlgebra: LinearAlgebra, Transpose, mul!
 using NDTensors
 using NDTensors.SetParameters
 
@@ -18,5 +18,7 @@ include("set_types.jl")
 include("indexing.jl")
 include("linearalgebra.jl")
 include("copyto.jl")
+include("append.jl")
 include("permutedims.jl")
+include("mul.jl")
 end

NDTensors/ext/NDTensorsMetalExt/append.jl

@@ -0,0 +1,6 @@
+# This circumvents an issues that `MtlArray` can't call `resize!`.
+# TODO: Raise an issue with Metal.jl.
+function NDTensors.append!!(::Type{<:MtlArray}, collection, collections...)
+  return vcat(collection, collections...)
+end
+

NDTensors/ext/NDTensorsMetalExt/mul.jl

@@ -0,0 +1,15 @@
+# This was calling generic matrix multiplication.
+# TODO: Raise an issue with `Metal.jl`.
+function NDTensors.mul!!(
+  ::Type{<:MtlArray},
+  CM::Transpose,
+  ::Type{<:MtlArray},
+  AM::AbstractMatrix,
+  ::Type{<:MtlArray},
+  BM::AbstractMatrix,
+  α,
+  β,
+)
+  mul!(transpose(CM), transpose(BM), transpose(AM), α, β)
+  return CM
+end

NDTensors/src/NDTensors.jl

@@ -53,6 +53,7 @@ include("abstractarray/iscu.jl")
 include("abstractarray/similar.jl")
 include("abstractarray/ndims.jl")
 include("abstractarray/copyto.jl")
+include("abstractarray/append.jl")
 include("abstractarray/permutedims.jl")
 include("abstractarray/fill.jl")
 include("abstractarray/mul.jl")

NDTensors/src/abstractarray/append.jl

@@ -0,0 +1,10 @@
+# NDTensors.append!
+# Used to circumvent issues with some GPU backends like Metal
+# not supporting `resize!`.
+function append!!(collection, collections...)
+  return append!!(leaf_parenttype(collection), collection, collections...)
+end
+
+function append!!(::Type, collection, collections...)
+  return append!(collection, collections...)
+end

NDTensors/src/blocksparse/blocksparsetensor.jl

@@ -653,7 +653,10 @@ function uncombine(
       #copyto!(Rb,Tb)
 
       if length(Tb) == 1
-        Rb[] = Tb[]
+        # Call `cpu` to avoid allowscalar error on GPU.
+        # TODO: Replace with `@allowscalar`, requires adding
+        # `GPUArraysCore.jl` as a dependency.
+        Rb[] = cpu(Tb)[]
       else
         # XXX: this used to be:
         # Rbₐᵣ = ReshapedArray(parent(Rbₐ), size(Tb), ())
@@ -712,7 +715,7 @@ function permutedims!!(
   ## copyto!(data(RR), data(R))
 
   if new_nnz > nnz(RR)
-    dataRR = append!(data(RR), zeros(new_nnz - nnz(RR)))
+    dataRR = append!!(data(RR), generic_zeros(leaf_parenttype(R), new_nnz - nnz(RR)))
     RR = Tensor(BlockSparse(dataRR, bofsRR), inds(RR))
   end
 

NDTensors/src/blocksparse/linearalgebra.jl

@@ -6,6 +6,8 @@ const DiagMatrix{ElT,StoreT,IndsT} = DiagTensor{ElT,2,StoreT,IndsT}
 function _truncated_blockdim(
   S::DiagMatrix, docut::Real; singular_values=false, truncate=true, min_blockdim=0
 )
+  # TODO: Replace `cpu` with `leaf_parenttype` dispatch.
+  S = cpu(S)
   full_dim = diaglength(S)
   !truncate && return full_dim
   min_blockdim = min(min_blockdim, full_dim)
@@ -84,7 +86,8 @@ function svd(T::BlockSparseMatrix{ElT}; kwargs...) where {ElT}
       if blockdim == 0
         push!(dropblocks, n)
       else
-        Strunc = tensor(Diag(storage(Ss[n])[1:blockdim]), (blockdim, blockdim))
+        # TODO: Replace call to `data` with `diagview`.
+        Strunc = tensor(Diag(data(Ss[n])[1:blockdim]), (blockdim, blockdim))
         Us[n] = Us[n][1:dim(Us[n], 1), 1:blockdim]
         Ss[n] = Strunc
         Vs[n] = Vs[n][1:dim(Vs[n], 1), 1:blockdim]
@@ -177,9 +180,8 @@ function svd(T::BlockSparseMatrix{ElT}; kwargs...) where {ElT}
     copyto!(blockview(U, blockU), Ub)
 
     blockviewS = blockview(S, blockS)
-    for i in 1:diaglength(Sb)
-      setdiagindex!(blockviewS, getdiagindex(Sb, i), i)
-    end
+    # TODO: Replace `data` with `diagview`.
+    copyto!(data(blockviewS), data(Sb))
 
     #<fermions>
     sV = left_arrow_sign(vind, blockV[2])
@@ -243,7 +245,8 @@ function eigen(
       if blockdim == 0
         push!(dropblocks, n)
       else
-        Dtrunc = tensor(Diag(storage(Ds[n])[1:blockdim]), (blockdim, blockdim))
+        # TODO: Replace call to `data` with `diagview`.
+        Dtrunc = tensor(Diag(data(Ds[n])[1:blockdim]), (blockdim, blockdim))
         Ds[n] = Dtrunc
         new_size = (dim(Vs[n], 1), blockdim)
         new_data = array(Vs[n])[1:new_size[1], 1:new_size[2]]
@@ -311,12 +314,11 @@ function eigen(
 
     blockD = nzblocksD[n]
     blockviewD = blockview(D, blockD)
-    for i in 1:diaglength(Db)
-      setdiagindex!(blockviewD, getdiagindex(Db, i), i)
-    end
+    # TODO: Replace `data` with `diagview`.
+    copyto!(data(blockviewD), data(Db))
 
     blockV = nzblocksV[n]
-    blockview(V, blockV) .= Vb
+    copyto!(blockview(V, blockV), Vb)
   end
 
   return D, V, Spectrum(d, truncerr)
@@ -380,8 +382,8 @@ function qx(qx::Function, T::BlockSparseTensor{<:Any,2}; kwargs...)
   X = BlockSparseTensor(leaf_parenttype(T), undef, nzblocksX, indsX)
 
   for n in 1:nnzblocksT
-    blockview(Q, nzblocksQ[n]) .= Qs[n]
-    blockview(X, nzblocksX[n]) .= Xs[n]
+    copyto!(blockview(Q, nzblocksQ[n]), Qs[n])
+    copyto!(blockview(X, nzblocksX[n]), Xs[n])
   end
 
   Q = adapt(leaf_parenttype(T), Q)

NDTensors/src/dense/tensoralgebra/contract.jl

@@ -15,11 +15,11 @@ function _contract_scalar!(
   β=zero(ElR),
 ) where {ElR}
   if iszero(β)
-    R[1] = α * T1 * T2
+    R[] = α * T1 * T2
   elseif iszero(α)
-    R[1] = β * R[1]
+    R[] = β * R[]
   else
-    R[1] = α * T1 * T2 + β * R[1]
+    R[] = α * T1 * T2 + β * R[]
   end
   return R
 end
@@ -150,7 +150,7 @@ function _contract_scalar!(
   β=zero(ElR),
 ) where {ElR}
   if nnz(T1) == nnz(T2) == 1
-    _contract_scalar!(R, labelsR, T1[1], labelsT1, T2[1], labelsT2, α, β)
+    _contract_scalar!(R, labelsR, T1[], labelsT1, T2[], labelsT2, α, β)
   else
     _contract_scalar_maybe_perm!(R, labelsR, T1, labelsT1, T2, labelsT2, α, β)
   end

NDTensors/src/linearalgebra/linearalgebra.jl

@@ -231,7 +231,8 @@ function eigen(
   DM, VM = eigen(matrixT)
 
   # Sort by largest to smallest eigenvalues
-  p = sortperm(DM; rev=true, by=abs)
+  # TODO: Replace `cpu` with `leaf_parenttype` dispatch.
+  p = sortperm(cpu(DM); rev=true, by=abs)
   DM = DM[p]
   VM = VM[:, p]
 

NDTensors/src/truncate.jl

@@ -11,7 +11,7 @@ end
 # GPU fallback version, convert to CPU.
 function truncate!!(::Type{<:AbstractArray}, P::AbstractArray; kwargs...)
   P_cpu = cpu(P)
-  P_cpu, truncerr, docut = truncate!(P_cpu; kwargs...)
+  truncerr, docut = truncate!(P_cpu; kwargs...)
   P = adapt(leaf_parenttype(P), P_cpu)
   return P, truncerr, docut
 end