fix issue 136 (#137)

Project.toml

@@ -1,7 +1,7 @@
 name = "OMEinsum"
 uuid = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 authors = ["Andreas Peter <[email protected]>"]
-version = "0.6.8"
+version = "0.6.9"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

src/utils.jl

@@ -88,12 +88,12 @@ function tensorpermute(A::AbstractArray{T,N}, perm) where {T, N}
     N == 0 && return copy(A)
     # group `perm`s
     permshape = ntuple(i->size(A, @inbounds perm[i]), N)
-    newshape_slots = ones(Int, N)
+    newshape_slots = fill(-1, N)
     dk = 1  # the size of dimension-batch
     @inbounds begin
         permk = perm[1]
         newperm = [permk]
-        newshape_slots[permk] *= size(A, permk)
+        newshape_slots[permk] = size(A, permk)
     end
     @inbounds for i=2:N
         permi = perm[i]
@@ -102,12 +102,12 @@ function tensorpermute(A::AbstractArray{T,N}, perm) where {T, N}
             dk += 1
         else
             permk = permi
-            newshape_slots[permk] *= size(A, permi)
+            newshape_slots[permk] = size(A, permi)
             push!(newperm, permk)
             dk = 1
         end
     end
-    newshape = filter(!isone, newshape_slots)
+    newshape = filter(!=(-1), newshape_slots)
     newperm = sortperm(sortperm(newperm))
     A_ = reshape(A, newshape...)
     A__ = permutedims(A_, newperm)

test/einsum.jl

@@ -246,3 +246,8 @@ end
     @test OMEinsum.duplicate(x, ix, iy, size_dict) ≈ OMEinsum.loop_einsum(EinCode((ix,),iy), (x,), size_dict)
     @test OMEinsum.einsum(Duplicate(), (ix,), iy, (x,), size_dict) ≈ OMEinsum.loop_einsum(EinCode((ix,),iy), (x,), size_dict)
 end
+
+@testset "issue 136" begin
+    @test EinCode(((1,2,3),(2,)),(1,3))(ones(2,2,1), ones(2)) == reshape([2,2.0], 2, 1)
+    @test EinCode(((1,2,3),(2,)),(1,3))(ones(2,2,0), ones(2)) == reshape(zeros(0), 2, 0)
+end