[mlir][tensor] Add e2e test for tensor.pack with dynamic tile sizes (#115698)

Adds an end-to-end test for `tensor.pack` with dynamic inner tile sizes.
While relatively simple (e.g., no vectorization), this example required
a few non-trivial fixes in handling `tensor.pack`:

* #114315, #114559, #113108.

The end goal for this test is to incrementally increase its complexity
and to work towards scalable tile sizes.

Author: banach-space

mlir/test/Integration/Dialect/Linalg/CPU/pack-dynamic-inner-tile.mlir

@@ -0,0 +1,97 @@
+// DEFINE: %{compile} =  mlir-opt %s \
+// DEFINE: -transform-interpreter -test-transform-dialect-erase-schedule |\
+// DEFINE:  mlir-opt --test-linalg-transform-patterns="test-generalize-tensor-pack"\
+// DEFINE:    --test-transform-dialect-erase-schedule \
+// DEFINE:    -one-shot-bufferize="bufferize-function-boundaries" \
+// DEFINE:    -buffer-deallocation-pipeline="private-function-dynamic-ownership" \
+// DEFINE:    -cse -canonicalize -test-lower-to-llvm -o %t
+// DEFINE: %{entry_point} = main
+// DEFINE: %{run} = mlir-cpu-runner %t -e %{entry_point} -entry-point-result=void \
+// DEFINE:    -shared-libs=%mlir_runner_utils,%mlir_c_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
+/// dynamic.
+///
+/// Note, ATM this is a relatively simple example, with no vectorization and
+/// the dynamic tile size being a compile-time constant. The intention is to
+/// incrementally expand the config to something much more complex.
+
+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
+
+  // Dynamic tile size
+  %tile_size = arith.constant 8 : index
+  %A_pack_empty = tensor.empty(%c1, %tile_size) : tensor<?x16x?x1xi32>
+
+  %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<?x16x?x1xi32>
+  %A_cast = tensor.cast %A_pack : tensor<?x16x?x1xi32> to tensor<*xi32>
+
+  // Print the results
+  // CHECK: Unranked Memref base@ = 0{{.*}} rank = 4 offset = 0 sizes = [1, 16, 8, 1] strides = [128, 8, 1, 1] data =
+  // Tile 1: (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
+  // Tile 2: (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
+  // Tile 3: (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.readonly}) {
+    %pack = transform.structured.match ops{["tensor.pack"]} in %module : (!transform.any_op) -> !transform.any_op
+
+    %tiled_linalg_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)
+
+    transform.yield
+  }
+}
+
+func.func private @printMemrefI32(%ptr : tensor<*xi32>)