[mlir][sparse] enable a few vectorized runs in integration tests

[aartbik]

Summary

Recent changes outside sparse compiler exposed the requirement of running a
new pass (lower-affine) but this only became apparent with private testing.
By adding some vectorized runs to integration test, we will detect the need
for such changes earlier and also widen codegen coverage of course.

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

 // RUN: mlir-opt %s \
 // RUN:   --sparsification --sparse-tensor-conversion \
 // RUN:   --convert-vector-to-scf --convert-scf-to-std \
 // RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
 // RUN:   --std-bufferize --finalizing-bufferize  \
 // RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
 // RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \
 // RUN: mlir-cpu-runner \
 // RUN:  -e entry -entry-point-result=void  \
 // RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
 // RUN: FileCheck %s
+//
+// Do the same run, but now with SIMDization as well. This should not change the outcome.
+//
+// RUN: mlir-opt %s \
+// RUN:   --sparsification="vectorization-strategy=2 vl=16 enable-simd-index32" --sparse-tensor-conversion \
+// RUN:   --convert-vector-to-scf --convert-scf-to-std \
+// RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
+// RUN:   --std-bufferize --finalizing-bufferize --lower-affine \
+// RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
+// RUN: TENSOR0="%mlir_integration_test_dir/data/wide.mtx" \
+// RUN: mlir-cpu-runner \
+// RUN:  -e entry -entry-point-result=void  \
+// RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
+// RUN: FileCheck %s
 
 !Filename = type !llvm.ptr<i8>
 
 #SparseMatrix = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
   pointerBitWidth = 8,
   indexBitWidth = 8
 }>

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

 // RUN: mlir-opt %s \
 // RUN:   --sparsification --sparse-tensor-conversion \
 // RUN:   --convert-vector-to-scf --convert-scf-to-std \
 // RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
 // RUN:   --std-bufferize --finalizing-bufferize  \
 // RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
 // RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \
 // RUN: mlir-cpu-runner \
 // RUN:  -e entry -entry-point-result=void  \
 // RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
 // RUN: FileCheck %s
+//
+// Do the same run, but now with SIMDization as well. This should not change the outcome.
+//
+// RUN: mlir-opt %s \
+// RUN:   --sparsification="vectorization-strategy=2 vl=4 enable-simd-index32" --sparse-tensor-conversion \
+// RUN:   --convert-vector-to-scf --convert-scf-to-std \
+// RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
+// RUN:   --std-bufferize --finalizing-bufferize --lower-affine \
+// RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
+// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \
+// RUN: mlir-cpu-runner \
+// RUN:  -e entry -entry-point-result=void  \
+// RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
+// RUN: FileCheck %s
+//
 
 !Filename = type !llvm.ptr<i8>
 
 #SparseMatrix = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
   pointerBitWidth = 32,
   indexBitWidth = 32
 }>