[mlir][tensor] Add e2e test for tensor.unpack with dynamic tile sizes

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

* llvm#119379, llvm#121393, llvm#121400.

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

Note, this PR complements llvm#115698 in which similar test for
`tensor.pack` was added.

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

@@ -9,7 +9,8 @@
 // RUN: rm -f %t && %{compile} && %{run} | FileCheck %s
 
 /// End-to-end test for tensor.pack where one of the inner tile sizes is
-/// dynamic.
+/// dynamic. See unpack-dynamic-inner-tile.mlir for a similar test for
+/// tensor.unpack.
 
 func.func @main() {
   // Allocate and initialise the inputs
@@ -46,7 +47,7 @@ func.func private @pack(%A: tensor<7x16xi32>) {
   %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 =
+  // CHECK: Unranked Memref base@ = 0x{{.*}} 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

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

@@ -0,0 +1,110 @@
+// DEFINE: %{compile} =  mlir-opt %s \
+// DEFINE:  -transform-interpreter -test-transform-dialect-erase-schedule |\
+// DEFINE: mlir-opt \
+// DEFINE:  -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.unpack where one of the inner tile sizes is
+/// dynamic. See pack-dynamic-inner-tile.mlir for a similar test for tensor.pack.
+
+func.func @main() {
+  // Allocate and initialise the inputs
+  %A_alloc = tensor.empty() : tensor<7x3xi32>
+
+  %A = arith.constant dense<[
+  [[[1],
+   [2],
+   [3],
+   [4],
+   [5],
+   [6],
+   [7],
+   [123]],
+  [[8],
+   [9],
+   [10],
+   [11],
+   [12],
+   [13],
+   [14],
+   [123]],
+  [[15],
+   [16],
+   [17],
+   [18],
+   [19],
+   [20],
+   [21],
+   [123]]]
+  ]> : tensor<1x3x8x1xi32>
+
+  %A_cast = tensor.cast %A : tensor<1x3x8x1xi32> to tensor<?x3x?x1xi32>
+  func.call @unpack(%A_cast) : (tensor<?x3x?x1xi32>) -> ()
+
+  return
+}
+
+func.func private @unpack(%A: tensor<?x3x?x1xi32>) {
+  %c1 = arith.constant 1 : index
+  %pad_val = arith.constant 123 : i32
+
+  // Dynamic tile size
+  %tile_size = arith.constant 8 : index
+  %A_unpack_empty = tensor.empty() : tensor<7x3xi32>
+
+  %A_unpack = tensor.unpack %A
+    inner_dims_pos = [0, 1]
+    inner_tiles = [%tile_size, 1]
+    into %A_unpack_empty : tensor<?x3x?x1xi32> -> tensor<7x3xi32>
+  %A_cast = tensor.cast %A_unpack : tensor<7x3xi32> to tensor<*xi32>
+
+  // Print the results
+  // CHECK: Unranked Memref base@ = 0x{{.*}} rank = 2 offset = 0 sizes = [7, 3] strides = [3, 1] data =
+  // CHECK-NEXT: [1,   8,   15],
+  // CHECK-NEXT:  [2,   9,   16],
+  // CHECK-NEXT:  [3,   10,   17],
+  // CHECK-NEXT:  [4,   11,   18],
+  // CHECK-NEXT:  [5,   12,   19],
+  // CHECK-NEXT:  [6,   13,   20],
+  // CHECK-NEXT:  [7,   14,   21]
+  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.unpack"]} in %module : (!transform.any_op) -> !transform.any_op
+
+    // 1. Tile so that we can decompose tensor.pack
+    // Ops (see step 2)
+    %c8 = transform.param.constant 8 : i64 -> !transform.param<i64>
+    %tiled_pack_op_p, %loops:2 = transform.structured.tile_using_for %pack tile_sizes [%c8, 1]
+       : (!transform.any_op, !transform.param<i64>) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+
+    // 2. Decompose the tiled unpack Op into tensor.extract_slice + tensor.insert_slice:
+    %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. Bufferize before lowering to LLVM
+    %bufferize = transform.bufferization.one_shot_bufferize %module
+      {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op
+
+   // 4. Canonicalize
+    %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.canonicalization
+    } : !transform.op<"func.func">
+
+    transform.yield
+  }
+}
+
+func.func private @printMemrefI32(%ptr : tensor<*xi32>)