diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/pack-scalable-inner-tile.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/pack-scalable-inner-tile.mlir new file mode 100644 index 0000000000000..a0fd3f7d87083 --- /dev/null +++ b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/pack-scalable-inner-tile.mlir @@ -0,0 +1,181 @@ +// REQUIRES: arm-emulator + +// This test is a clone of pack-dynamic-inner-tile.mlir, but the inner tile is +// vector.vscale * %c8 rather than %c8. In order to demonstrate the impact of +// using scalable vectors, vscale is set to 2 so that that the run-time tile +// size is [16, 1] rather than [8, 1]. +// +// Note that you can also tweak the size of vscale by passing this flag to +// QEMU: +// * -cpu max,sve-max-vq=[1-16] +// (select the value between 1 and 16). + +// DEFINE: %{compile} = mlir-opt %s \ +// DEFINE: --transform-interpreter --test-transform-dialect-erase-schedule \ +// DEFINE: --lower-vector-mask \ +// DEFINE: -canonicalize -cse --convert-vector-to-scf \ +// DEFINE: -arm-sve-legalize-vector-storage -convert-vector-to-llvm="enable-arm-sve" -test-lower-to-llvm -o %t + +// DEFINE: %{entry_point} = main +// DEFINE: %{run} = %mcr_aarch64_cmd %t -e %{entry_point} -entry-point-result=void --march=aarch64 --mattr="+sve"\ +// DEFINE: -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%native_mlir_arm_runner_utils + +// RUN: rm -f %t && %{compile} && %{run} | FileCheck %s + +/// End-to-end test for tensor.pack where one of the inner tile sizes is +/// scalable. + +func.func @main() { + // Allocate and initialise the inputs + %A_alloc = tensor.empty() : tensor<7x16xi32> + + %A = arith.constant dense<[ + [ 1, 8, 15, 22, 29, 36, 43, 50, 57, 64, 71, 78, 85, 92, 99 , 106], + [ 2, 9, 16, 23, 30, 37, 44, 51, 58, 65, 72, 79, 86, 93, 100, 107], + [ 3, 10, 17, 24, 31, 38, 45, 52, 59, 66, 73, 80, 87, 94, 101, 108], + [ 4, 11, 18, 25, 32, 39, 46, 53, 60, 67, 74, 81, 88, 95, 102, 109], + [ 5, 12, 19, 26, 33, 40, 47, 54, 61, 68, 75, 82, 89, 96, 103, 110], + [ 6, 13, 20, 27, 34, 41, 48, 55, 62, 69, 76, 83, 90, 97, 104, 111], + [ 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, 91, 98, 105, 112] + ]> : tensor<7x16xi32> + + func.call @pack(%A) : (tensor<7x16xi32>) -> () + + return +} + +func.func private @pack(%A: tensor<7x16xi32>) { + %c1 = arith.constant 1 : index + %pad_val = arith.constant 123 : i32 + + // Set vscale to 2 (vector width = 256). This will have identical effect to: + // * qemu-aarch64 -cpu max,sve-max-vq=2 (...) + %c256 = arith.constant 256 : i32 + func.call @setArmVLBits(%c256) : (i32) -> () + + // Scalable tile size + %vs = vector.vscale + %c8 = arith.constant 8 : index + %tile_size = arith.muli %c8, %vs : index + + %A_pack_empty = tensor.empty(%c1, %tile_size) : tensor + + %A_pack = tensor.pack %A + padding_value(%pad_val : i32) + inner_dims_pos = [0, 1] + inner_tiles = [%tile_size, 1] + into %A_pack_empty : tensor<7x16xi32> -> tensor + + %A_cast = tensor.cast %A_pack : tensor to tensor<*xi32> + + // Print the results + // CHECK: Unranked Memref base@ = 0{{.*}} rank = 4 offset = 0 sizes = [1, 16, 16, 1] strides = [256, 16, 1, 1] data = + // Tile 1: ((vscale x 8) x 1) + // CHECK-NEXT: 1 + // CHECK-NEXT: 2 + // CHECK-NEXT: 3 + // CHECK-NEXT: 4 + // CHECK-NEXT: 5 + // CHECK-NEXT: 6 + // CHECK-NEXT: 7 + // Expect pad value after 7 elements + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // Tile 2: ((vscale x 8) x 1) + // CHECK-NEXT: 8 + // CHECK-NEXT: 9 + // CHECK-NEXT: 10 + // CHECK-NEXT: 11 + // CHECK-NEXT: 12 + // CHECK-NEXT: 13 + // CHECK-NEXT: 14 + // Expect pad value after further 7 elements + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // CHECK-NEXT: 123 + // Tile 3: ((vscale x 8) x 1) + // CHECK-NEXT: 15 + // CHECK-NEXT: 16 + // ... + call @printMemrefI32(%A_cast) : (tensor<*xi32>) -> () + + return +} + +module @transforms attributes { transform.with_named_sequence } { + transform.named_sequence @__transform_main(%module: !transform.any_op {transform.consume}) { + %pack = transform.structured.match ops{["tensor.pack"]} in %module : (!transform.any_op) -> !transform.any_op + + // 1. Tile so that we can decompose tensor.pack into tensor.pad and other + // Ops (see step 2) + %tiled_pack_op_p, %loops:2 = transform.structured.tile_using_for %pack tile_sizes [1, 1] + : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) + + // 2. Decompose the tiled pack Op into (trimmed for brevity): + // + // %padded = tensor.pad %slice_of_A (..) : + // tensor to tensor<8x1xi32> + // %inserted_slice = tensor.insert_slice %padded into %slice_of_A_pack (...) : + // tensor<8x1xi32> into tensor<1x1x?x1xi32> + // + // (NOTE: no tile is transposed, hence no linalg.transpose) + // + // This is followed by this decomposition of the pad Op: + // + // %c123_i32 = arith.constant 123 : i32 + // %slice_of_A = tensor.extract_slice %A[%3, %arg3] [%4, %5] [1, 1] : + // tensor<7x16xi32> to tensor + // %empty = tensor.empty() : tensor<8x1xi32> + // %fill = linalg.fill ins(%c123_i32 : i32) outs(%empty : + // tensor<8x1xi32>) -> tensor<8x1xi32> + // %inserted_slice = tensor.insert_slice %slice_of_A into %fill[0, 0] [%4, %5] [1, 1] : + // tensor into tensor<8x1xi32> + // + %func_op = transform.get_parent_op %tiled_pack_op_p {isolated_from_above} : (!transform.any_op) -> !transform.op<"func.func"> + transform.apply_patterns to %func_op { + transform.apply_patterns.linalg.decompose_pack_unpack + transform.apply_patterns.linalg.decompose_pad + } : !transform.op<"func.func"> + + // 3. Vectorize linalg.fill. + // Vector sizes match the inner tiles in the payload IR. + %fill = transform.structured.match ops{["linalg.fill"]} in %func_op : (!transform.op<"func.func">) -> !transform.any_op + transform.structured.vectorize %fill vector_sizes [[8], 1] : !transform.any_op + + transform.apply_patterns to %func_op { + transform.apply_patterns.tensor.fold_tensor_subset_ops + transform.apply_patterns.canonicalization + } : !transform.op<"func.func"> + + // 3. Bufferize before lowering to LLVM + %bufferize = transform.bufferization.one_shot_bufferize %module + {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op + + // 4. Canonicalize + rank-reducing patters (to get rid of the trailing unit + // dim). + %func_op_bufferized = transform.structured.match ops{["func.func"]} in %bufferize : (!transform.any_op) -> !transform.op<"func.func"> + transform.apply_patterns to %func_op_bufferized { + transform.apply_patterns.vector.rank_reducing_subview_patterns + transform.apply_patterns.vector.drop_unit_dims_with_shape_cast + transform.apply_patterns.canonicalization + } : !transform.op<"func.func"> + + transform.yield + } +} + +func.func private @printMemrefI32(%ptr : tensor<*xi32>) +func.func private @setArmVLBits(%bits : i32)