Add Base.repeat methods for Tile

README.md

@@ -184,12 +184,13 @@ Tile IR operations.
 ### Shape
 | Operation | Description |
 |-----------|-------------|
-| `ct.broadcast_to(tile, shape)` | Broadcast to target shape |
-| `transpose(tile)` | Transpose 2D tile |
 | `reshape(tile, shape)` | Reshape (same element count) |
+| `transpose(tile)` | Transpose 2D tile |
 | `permutedims(tile, perm)` | Permute dimensions |
+| `repeat(tile, counts...)` `repeat(tile; inner, outer)` | Repeat values along dimensions |
 | `ct.extract(tile, index, shape)` | Extract sub-tile |
 | `ct.cat((a, b), axis)` | Concatenate tiles |
+| `ct.broadcast_to(tile, shape)` | Broadcast to target shape |
 | `dropdims(tile; dims)` | Remove singleton dimensions |
 
 ### Matrix

src/language/operations.jl

@@ -786,6 +786,81 @@ reshaped = reshape(tile, (2, 16))  # Shape (2, 16), still 32 elements
 end
 @inline Base.reshape(tile::Tile{T}, dims::Int...) where {T} = reshape(tile, dims)
 
+"""
+    repeat(tile::Tile, counts::Integer...) -> Tile
+
+Repeat a tile along each dimension (outer repetition), matching `Base.repeat`.
+`counts[i]` is the number of times to tile the whole tile along dimension `i`;
+dimensions beyond `length(counts)` are repeated once, and counts beyond
+`ndims(tile)` introduce new trailing dimensions.
+
+`counts` must be compile-time constants, and every resulting dimension
+`size(tile, i) * counts[i]` (as well as the new repeat dimensions) must be a
+power of two.
+
+# Example
+```julia
+tile = ct.load(arr, (1, 1), (4, 8))  # Shape (4, 8)
+tiled = repeat(tile, 2, 2)           # Shape (8, 16)
+```
+"""
+Base.repeat(tile::Tile, counts::Integer...) = repeat(tile; outer = counts)
+
+"""
+    repeat(tile::Tile; inner=nothing, outer=nothing) -> Tile
+
+Keyword form of `repeat`, matching `Base.repeat`. `inner[i]` repeats each element
+`inner[i]` times along dimension `i` (inner repetition), while `outer[i]` tiles the
+whole tile `outer[i]` times along dimension `i` (outer repetition). When both are
+given, the inner repetition is applied first. Each of `inner`/`outer` may be an
+`Integer` or a tuple, and must be a compile-time constant.
+
+# Example
+```julia
+tile = ct.load(arr, (1, 1), (2, 4))     # Shape (2, 4)
+repeat(tile; inner=(2, 1), outer=(1, 2)) # Shape (4, 8)
+```
+"""
+function Base.repeat(tile::Tile; inner = nothing, outer = nothing)
+    t = inner === nothing ? tile :
+        _repeat(tile, :inner, inner isa Integer ? (inner,) : Tuple(inner))
+    return outer === nothing ? t :
+           _repeat(t, :outer, outer isa Integer ? (outer,) : Tuple(outer))
+end
+
+# Implements both inner and outer repetition by reshaping the tile to interleave
+# a singleton dimension next to each data dimension, broadcasting that singleton
+# up to the repeat count, then collapsing each pair back together. For outer
+# repetition the data dimension is the fast (inner) one within each pair; for
+# inner repetition the repeat count is. Shapes derive from the tile type and the
+# (constant) counts, so const-prop folds them away.
+function _repeat(tile::Tile, mode::Symbol, counts::Tuple{Vararg{Integer}})
+    sz = size(tile)
+    N = ndims(tile)
+    M = length(counts)
+    P = max(N, M)
+    szp = ntuple(i -> i <= N ? sz[i] : 1, P)
+    cntp = ntuple(i -> i <= M ? Int(counts[i]) : 1, P)
+
+    # Nothing to repeat and no new dimensions: identity.
+    (all(==(1), cntp) && P == N) && return tile
+
+    data_first = mode === :outer
+    interleaved = ntuple(2P) do j
+        pair, lo = divrem(j - 1, 2)
+        (lo == 0) == data_first ? szp[pair + 1] : 1
+    end
+    target = ntuple(2P) do j
+        pair, lo = divrem(j - 1, 2)
+        (lo == 0) == data_first ? szp[pair + 1] : cntp[pair + 1]
+    end
+    final = ntuple(i -> szp[i] * cntp[i], P)
+
+    t = reshape(tile, interleaved)
+    t = broadcast_to(t, target)
+    return reshape(t, final)
+end
+
 """
     permutedims(tile::Tile{T, S}, perm) -> Tile{T, permuted_shape}
 

test/device/tile.jl

@@ -473,6 +473,75 @@ end
     end
 end
 
+@testset "repeat" begin
+    @testset "1D outer repeat matches Base.repeat" begin
+        function repeat_1d_kernel(x::ct.TileArray{Float32,1}, y::ct.TileArray{Float32,1})
+            bid = ct.bid(1)
+            tile = ct.load(x, bid, (8,))
+            ct.store(y, bid, repeat(tile, 4))  # (8,) -> (32,)
+            return
+        end
+
+        x = CuArray(Float32.(1:8))
+        y = CUDA.zeros(Float32, 32)
+
+        @cuda backend=cuTile repeat_1d_kernel(x, y)
+
+        @test Array(y) ≈ repeat(Float32.(1:8), 4)
+    end
+
+    @testset "2D outer repeat matches Base.repeat" begin
+        function repeat_2d_kernel(x::ct.TileArray{Float32,2}, y::ct.TileArray{Float32,2})
+            bid = ct.bid(1)
+            tile = ct.load(x, (bid, 1), (2, 4))
+            ct.store(y, (bid, 1), repeat(tile, 2, 2))  # (2, 4) -> (4, 8)
+            return
+        end
+
+        src = Float32.(reshape(1:8, 2, 4))
+        x = CuArray(src)
+        y = CUDA.zeros(Float32, 4, 8)
+
+        @cuda backend=cuTile repeat_2d_kernel(x, y)
+
+        @test Array(y) ≈ repeat(src, 2, 2)
+    end
+
+    @testset "2D inner repeat matches Base.repeat" begin
+        function repeat_inner_kernel(x::ct.TileArray{Float32,2}, y::ct.TileArray{Float32,2})
+            bid = ct.bid(1)
+            tile = ct.load(x, (bid, 1), (2, 4))
+            ct.store(y, (bid, 1), repeat(tile; inner=(2, 1)))  # (2, 4) -> (4, 4)
+            return
+        end
+
+        src = Float32.(reshape(1:8, 2, 4))
+        x = CuArray(src)
+        y = CUDA.zeros(Float32, 4, 4)
+
+        @cuda backend=cuTile repeat_inner_kernel(x, y)
+
+        @test Array(y) ≈ repeat(src; inner=(2, 1))
+    end
+
+    @testset "2D combined inner+outer repeat matches Base.repeat" begin
+        function repeat_inner_outer_kernel(x::ct.TileArray{Float32,2}, y::ct.TileArray{Float32,2})
+            bid = ct.bid(1)
+            tile = ct.load(x, (bid, 1), (2, 4))
+            ct.store(y, (bid, 1), repeat(tile; inner=(2, 1), outer=(1, 2)))  # (2, 4) -> (4, 8)
+            return
+        end
+
+        src = Float32.(reshape(1:8, 2, 4))
+        x = CuArray(src)
+        y = CUDA.zeros(Float32, 4, 8)
+
+        @cuda backend=cuTile repeat_inner_outer_kernel(x, y)
+
+        @test Array(y) ≈ repeat(src; inner=(2, 1), outer=(1, 2))
+    end
+end
+
 @testset "permutedims" begin
     @testset "2D permutedims (transpose-like)" begin
         function permute_2d_kernel(x::ct.TileArray{Float32,2}, y::ct.TileArray{Float32,2})