[mlir][sparse] Make three integration tests run with the codegen path.

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex32.mlir

-// RUN: mlir-opt %s --sparse-compiler | \
-// RUN: mlir-cpu-runner \
-// RUN:  -e entry -entry-point-result=void  \
-// RUN:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
-// RUN: FileCheck %s
+// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
+// DEFINE: mlir-cpu-runner \
+// DEFINE:  -e entry -entry-point-result=void  \
+// DEFINE:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
+// DEFINE: FileCheck %s
+//
+// RUN: %{command}
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{option} = enable-runtime-library=false
+// RUN: %{command}
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 #trait_op = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a (in)
     affine_map<(i) -> (i)>,  // b (in)
     affine_map<(i) -> (i)>   // x (out)

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex64.mlir

-// RUN: mlir-opt %s --sparse-compiler | \
-// RUN: mlir-cpu-runner \
-// RUN:  -e entry -entry-point-result=void  \
-// RUN:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
-// RUN: FileCheck %s
+// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
+// DEFINE: mlir-cpu-runner \
+// DEFINE:  -e entry -entry-point-result=void  \
+// DEFINE:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
+// DEFINE: FileCheck %s
+//
+// RUN: %{command}
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{option} = enable-runtime-library=false
+// RUN: %{command}
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 #trait_op = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a (in)
     affine_map<(i) -> (i)>,  // b (in)
     affine_map<(i) -> (i)>   // x (out)

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_re_im.mlir

-// RUN: mlir-opt %s --sparse-compiler | \
-// RUN: mlir-cpu-runner \
-// RUN:  -e entry -entry-point-result=void  \
-// RUN:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
-// RUN: FileCheck %s
+// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
+// DEFINE: mlir-cpu-runner \
+// DEFINE:  -e entry -entry-point-result=void  \
+// DEFINE:  -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
+// DEFINE: FileCheck %s
+//
+// RUN: %{command}
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{option} = enable-runtime-library=false
+// RUN: %{command}
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 #trait_op = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a (in)
     affine_map<(i) -> (i)>   // x (out)
   ],
%0 = linalg.generic #trait_op
           linalg.yield %1 : f32
     } -> tensor<?xf32, #SparseVector>
     return %0 : tensor<?xf32, #SparseVector>
   }
 
   func.func @dump(%arg0: tensor<?xf32, #SparseVector>) {
     %c0 = arith.constant 0 : index
     %d0 = arith.constant -1.0 : f32
+    %n = sparse_tensor.number_of_entries %arg0 : tensor<?xf32, #SparseVector>
+    vector.print %n : index
     %values = sparse_tensor.values %arg0 : tensor<?xf32, #SparseVector> to memref<?xf32>
-    %0 = vector.transfer_read %values[%c0], %d0: memref<?xf32>, vector<4xf32>
-    vector.print %0 : vector<4xf32>
+    %0 = vector.transfer_read %values[%c0], %d0: memref<?xf32>, vector<3xf32>
+    vector.print %0 : vector<3xf32>
     %indices = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<?xf32, #SparseVector> to memref<?xindex>
-    %1 = vector.transfer_read %indices[%c0], %c0: memref<?xindex>, vector<4xindex>
-    vector.print %1 : vector<4xindex>
+    %1 = vector.transfer_read %indices[%c0], %c0: memref<?xindex>, vector<3xindex>
+    vector.print %1 : vector<3xindex>
     return
   }
 
   // Driver method to call and verify functions cim and cre.
   func.func @entry() {
     // Setup sparse vectors.
     %v1 = arith.constant sparse<
        [ [0], [20], [31] ],
          [ (5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>
     %sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
 
     // Call sparse vector kernels.
     %0 = call @cre(%sv1)
        : (tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xf32, #SparseVector>
 
     %1 = call @cim(%sv1)
        : (tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xf32, #SparseVector>
 
     //
     // Verify the results.
     //
-    // CHECK: ( 5.13, 3, 5, -1 )
-    // CHECK-NEXT: ( 0, 20, 31, 0 )
-    // CHECK-NEXT: ( 2, 4, 6, -1 )
-    // CHECK-NEXT: ( 0, 20, 31, 0 )
+    // CHECK:      3
+    // CHECK-NEXT: ( 5.13, 3, 5 )
+    // CHECK-NEXT: ( 0, 20, 31 )
+    // CHECK-NEXT: 3
+    // CHECK-NEXT: ( 2, 4, 6 )
+    // CHECK-NEXT: ( 0, 20, 31 )
     //
     call @dump(%0) : (tensor<?xf32, #SparseVector>) -> ()
     call @dump(%1) : (tensor<?xf32, #SparseVector>) -> ()
 
     // Release the resources.
     bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f32>, #SparseVector>
     bufferization.dealloc_tensor %0 : tensor<?xf32, #SparseVector>
     bufferization.dealloc_tensor %1 : tensor<?xf32, #SparseVector>
     return
   }
 }