Comments (1)
Here's what it's doing:
julia> @pretty @cast P[i,j,k] := [R[:,:,k] X₁[:,k]][i,j]
begin
@boundscheck ndims(R) == 3 || throw(ArgumentError("expected a 3-tensor R[:, :, k]"))
@boundscheck axes(R, 3) == axes(X₁, 2) || throw(DimensionMismatch("range of index k must agree"))
@boundscheck ndims(X₁) == 2 || throw(ArgumentError("expected a 2-tensor X₁[:, k]"))
local vicuña = sliceview(R, (:, :, *))
local alligator = sliceview(X₁, (:, *))
local dolphin = @__dot__([vicuña alligator]) # <-- this broadcast does nothing
local redpanda = lazystack(dolphin)
P = redpanda
end
julia> :([vicuña alligator]) |> dump
Expr
head: Symbol hcat
args: Array{Any}((2,))
1: Symbol vicuña
2: Symbol alligator
julia> @. hcat(1:2, 3:4)
2-element Vector{Matrix{Int64}}:
[1 3]
[2 4]
julia> @. [1:2 3:4]
2×2 Matrix{Int64}:
1 3
2 4
It wouldn't be impossible to make it recognise Expr(:hcat, ...)
at some early stage, and convert it to a function call. Likewise vcat, vect... from :([1 2 3; 4 5]) |> dump
also row
not hvcat
? And Float32[1:2 3:4]
is typed_hcat
. But I haven't done so. Probably writing out hcat
is best for now.
What will never work is Vector[1:2, 3:4]
, as the macro cannot distinguish this from indexing.
from tensorcast.jl.
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from tensorcast.jl.