Insert trailing 1s instead of leading before broadcasting

examples/layernorm.jl

@@ -45,7 +45,7 @@ function layer_norm_fwd(X::ct.TileArray{Float32, 2}, W::ct.TileArray{Float32, 1}
     while j <= num_tiles
         tx = ct.load(X, (bid_m, j), (1, TILE_N); padding_mode=ct.PaddingMode.Zero)
         # Mask for valid elements
-        mask = ct.broadcast_to(((j - Int32(1)) * Int32(TILE_N) .+ ct.arange((TILE_N,), Int32)) .<= N, (1, TILE_N))
+        mask = reshape(((j - Int32(1)) * Int32(TILE_N) .+ ct.arange((TILE_N,), Int32)) .<= N, (1, TILE_N))
         centered_tx = ifelse.(mask, tx .- mean, 0.0f0)
         var = var .+ (centered_tx .^ 2.0f0)
         j += Int32(1)
@@ -96,7 +96,7 @@ bid_m and j are 1-indexed (block ID and tile index).
     indices = ct.arange((TILE_N,), Int32)
     offset = (j - Int32(1)) * Int32(TILE_N)
     global_indices = offset .+ indices
-    mask = ct.broadcast_to(global_indices .<= N, (1, TILE_N))
+    mask = reshape(global_indices .<= N, (1, TILE_N))
 
     xhat_masked = ifelse.(mask, xhat, 0.0f0)
     wdy_masked = ifelse.(mask, wdy, 0.0f0)

src/compiler/intrinsics/core.jl

@@ -63,7 +63,7 @@ end
 """
     broadcast_tile_to_shape!(cb, tt, tv::CGVal, target_shape::Vector{Int}, dtype::TypeId) -> Value
 
-Broadcast a tile to a target shape by inserting ReshapeOp (for leading 1s) and BroadcastOp.
+Broadcast a tile to a target shape by inserting ReshapeOp (for trailing 1s) and BroadcastOp.
 Returns the value after broadcasting, or the original value if shapes already match.
 """
 function broadcast_tile_to_shape!(cb::CodeBuilder, tt::TypeTable, tv::CGVal,
@@ -78,11 +78,11 @@ function broadcast_tile_to_shape!(cb::CodeBuilder, tt::TypeTable, tv::CGVal,
     current_val = tv.v
     current_shape = src_shape
 
-    # Step 1: Add leading 1s via ReshapeOp if needed (dimension mismatch)
+    # Step 1: Add trailing 1s via ReshapeOp if needed (dimension mismatch)
+    # Follows Julia convention: (n,) pads to (n, 1) — first dimension aligns.
     if length(current_shape) < length(target_shape)
-        # Prepend 1s to match target ndim
         n_extra = length(target_shape) - length(current_shape)
-        new_shape = vcat(fill(1, n_extra), current_shape)
+        new_shape = vcat(current_shape, fill(1, n_extra))
         reshaped_type = tile_type!(tt, dtype, new_shape)
         current_val = encode_ReshapeOp!(cb, reshaped_type, current_val)
         current_shape = new_shape

test/execution/broadcast.jl

@@ -191,6 +191,27 @@ end
     end
 end
 
+@testset "1D-to-2D broadcast: (64,) .+ (64, 128)" begin
+    function broadcast_1d_2d_kernel(a::ct.TileArray{Float32,1}, b::ct.TileArray{Float32,2},
+                                     c::ct.TileArray{Float32,2})
+        col = ct.load(a, 1, (64,))           # 1D: (64,)
+        tile = ct.load(b, (1, 1), (64, 128))  # 2D: (64, 128)
+        result = col .+ tile                   # broadcast (64,) → (64, 1) → (64, 128)
+        ct.store(c, (1, 1), result)
+        return
+    end
+
+    m, n = 64, 128
+    a = CUDA.rand(Float32, m)
+    b = CUDA.rand(Float32, m, n)
+    c = CUDA.zeros(Float32, m, n)
+
+    ct.launch(broadcast_1d_2d_kernel, 1, a, b, c)
+
+    expected = Array(a) .+ Array(b)  # Julia: (64,) broadcasts along dim 1
+    @test Array(c) ≈ expected
+end
+
 end
 
 @testset "comparison operations" begin