[TESTS] Forward fix for CI break (#5323)

PR #5231 was authored before the `triton_gpu` -> `ttg` rename and CI is
currently broken.

Author: peterbell10

test/TritonGPU/loop-pipeline-hip.mlir

@@ -276,22 +276,22 @@ module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, ttg.targ
 // CHECK:  tt.dot
 // CHECK: scf.yield
 
-#blocked = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [8, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
-#mma = #triton_gpu.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [4, 1], instrShape = [32, 32], isTransposed = true}>
-module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, triton_gpu.target = "hip:gfx942", "triton_gpu.threads-per-warp" = 64 : i32} {
+#blocked = #ttg.blocked<{sizePerThread = [1, 4], threadsPerWarp = [8, 8], warpsPerCTA = [4, 1], order = [1, 0]}>
+#mma = #ttg.amd_mfma<{versionMajor = 3, versionMinor = 0, warpsPerCTA = [4, 1], instrShape = [32, 32], isTransposed = true}>
+module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, ttg.target = "hip:gfx942", "ttg.threads-per-warp" = 64 : i32} {
   tt.func public @_triton_gemm_kernel_atomic_rmw(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32} loc(unknown), %arg1: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32} loc(unknown), %arg2: !tt.ptr<f16> {tt.divisibility = 16 : i32, tt.pointer_range = 32 : i32} loc(unknown), %arg3: i32 {tt.divisibility = 16 : i32} loc(unknown), %arg4: i32 {tt.divisibility = 16 : i32} loc(unknown)) attributes {noinline = false} {
     %cst = arith.constant dense<32> : tensor<32x32xi32, #blocked>
     %c0_i32 = arith.constant 0 : i32
     %c1_i32 = arith.constant 1 : i32
     %c31_i32 = arith.constant 31 : i32
     %c32_i32 = arith.constant 32 : i32
     %cst_0 = arith.constant dense<0.000000e+00> : tensor<32x32xf32, #mma>
-    %0 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32, #triton_gpu.slice<{dim = 1, parent = #blocked}>>
-    %1 = tt.expand_dims %0 {axis = 1 : i32} : tensor<32xi32, #triton_gpu.slice<{dim = 1, parent = #blocked}>> -> tensor<32x1xi32, #blocked>
+    %0 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32, #ttg.slice<{dim = 1, parent = #blocked}>>
+    %1 = tt.expand_dims %0 {axis = 1 : i32} : tensor<32xi32, #ttg.slice<{dim = 1, parent = #blocked}>> -> tensor<32x1xi32, #blocked>
     %2 = tt.splat %arg4 : i32 -> tensor<32x1xi32, #blocked>
     %3 = arith.muli %1, %2 : tensor<32x1xi32, #blocked>
-    %4 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32, #triton_gpu.slice<{dim = 0, parent = #blocked}>>
-    %5 = tt.expand_dims %4 {axis = 0 : i32} : tensor<32xi32, #triton_gpu.slice<{dim = 0, parent = #blocked}>> -> tensor<1x32xi32, #blocked>
+    %4 = tt.make_range {end = 32 : i32, start = 0 : i32} : tensor<32xi32, #ttg.slice<{dim = 0, parent = #blocked}>>
+    %5 = tt.expand_dims %4 {axis = 0 : i32} : tensor<32xi32, #ttg.slice<{dim = 0, parent = #blocked}>> -> tensor<1x32xi32, #blocked>
     %6 = tt.broadcast %3 : tensor<32x1xi32, #blocked> -> tensor<32x32xi32, #blocked>
     %7 = tt.broadcast %5 : tensor<1x32xi32, #blocked> -> tensor<32x32xi32, #blocked>
     %8 = arith.addi %6, %7 : tensor<32x32xi32, #blocked>
@@ -317,19 +317,19 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
     %28:3 = scf.for %arg5 = %c0_i32 to %25 step %c1_i32 iter_args(%arg6 = %cst_0, %arg7 = %10, %arg8 = %12) -> (tensor<32x32xf32, #mma>, tensor<32x32x!tt.ptr<f16>, #blocked>, tensor<32x32x!tt.ptr<f16>, #blocked>)  : i32 {
       %32 = tt.load %arg7 : tensor<32x32x!tt.ptr<f16>, #blocked>
       %33 = tt.load %arg8 : tensor<32x32x!tt.ptr<f16>, #blocked>
-      %34 = triton_gpu.convert_layout %32 : tensor<32x32xf16, #blocked> -> tensor<32x32xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>>
-      %35 = triton_gpu.convert_layout %33 : tensor<32x32xf16, #blocked> -> tensor<32x32xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>
-      %36 = tt.dot %34, %35, %arg6 : tensor<32x32xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>> * tensor<32x32xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>> -> tensor<32x32xf32, #mma>
+      %34 = ttg.convert_layout %32 : tensor<32x32xf16, #blocked> -> tensor<32x32xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>>
+      %35 = ttg.convert_layout %33 : tensor<32x32xf16, #blocked> -> tensor<32x32xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>
+      %36 = tt.dot %34, %35, %arg6 : tensor<32x32xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>> * tensor<32x32xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>> -> tensor<32x32xf32, #mma>
       %37 = tt.addptr %arg7, %cst : tensor<32x32x!tt.ptr<f16>, #blocked>, tensor<32x32xi32, #blocked>
       %38 = tt.addptr %arg8, %27 : tensor<32x32x!tt.ptr<f16>, #blocked>, tensor<32x32xi32, #blocked>
       %39 = arith.truncf %36 : tensor<32x32xf32, #mma> to tensor<32x32xf16, #mma>
-      %40 = triton_gpu.convert_layout %39 : tensor<32x32xf16, #mma> -> tensor<32x32xf16, #blocked>
+      %40 = ttg.convert_layout %39 : tensor<32x32xf16, #mma> -> tensor<32x32xf16, #blocked>
       %41 = tt.atomic_rmw fadd, acq_rel, gpu, %16, %40, %23 : (tensor<32x32x!tt.ptr<f16>, #blocked>, tensor<32x32xf16, #blocked>, tensor<32x32xi1, #blocked>) -> tensor<32x32xf16, #blocked>
       scf.yield %36, %37, %38 : tensor<32x32xf32, #mma>, tensor<32x32x!tt.ptr<f16>, #blocked>, tensor<32x32x!tt.ptr<f16>, #blocked>
     }
     %29 = arith.truncf %28#0 : tensor<32x32xf32, #mma> to tensor<32x32xf16, #mma>
-    %30 = triton_gpu.convert_layout %16 : tensor<32x32x!tt.ptr<f16>, #blocked> -> tensor<32x32x!tt.ptr<f16>, #mma>
-    %31 = triton_gpu.convert_layout %23 : tensor<32x32xi1, #blocked> -> tensor<32x32xi1, #mma>
+    %30 = ttg.convert_layout %16 : tensor<32x32x!tt.ptr<f16>, #blocked> -> tensor<32x32x!tt.ptr<f16>, #mma>
+    %31 = ttg.convert_layout %23 : tensor<32x32xi1, #blocked> -> tensor<32x32xi1, #mma>
     tt.store %30, %29, %31 : tensor<32x32x!tt.ptr<f16>, #mma>
     tt.return
   }