[mlir][sparse] change runners to c_runners

mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec-const.mlir

 //
 // NOTE: this test requires gpu-sm80
 //
 // RUN: mlir-opt %s \
 // RUN:   --sparse-compiler="enable-runtime-library=false parallelization-strategy=dense-outer-loop gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71" \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%mlir_cuda_runtime \
-// RUN:   --shared-libs=%mlir_runner_utils \
+// RUN:   --shared-libs=%mlir_c_runner_utils \
 // RUN:   --e main --entry-point-result=void \
 // RUN: | FileCheck %s
 
 #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
 
 module {
   // Compute matrix vector y = Ax
   func.func @matvec(%A: tensor<1024x64xf64, #CSR>, %x: tensor<64xf64>, %y_in: tensor<1024xf64>) -> tensor<1024xf64> {

mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec-lib.mlir

 //
 // NOTE: this test requires gpu-sm80
 //
 // with RT lib (SoA COO):
 //
 // RUN: mlir-opt %s \
 // RUN:   --sparse-compiler="enable-runtime-library=true enable-gpu-libgen gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71"  \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%mlir_cuda_runtime \
-// RUN:   --shared-libs=%mlir_runner_utils \
+// RUN:   --shared-libs=%mlir_c_runner_utils \
 // RUN:   --e main --entry-point-result=void \
 // RUN: | FileCheck %s
 //
 // TODO: without RT lib (AoS COO):
 
 #SortedCOO = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu", "singleton" ]
 }>

mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-matvec.mlir

 //
 // NOTE: this test requires gpu-sm80
 //
 // RUN: mlir-opt %s \
 // RUN:   --sparse-compiler="enable-runtime-library=false parallelization-strategy=dense-outer-loop gpu-triple=nvptx64-nvidia-cuda gpu-chip=sm_80 gpu-features=+ptx71" \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%mlir_cuda_runtime \
-// RUN:   --shared-libs=%mlir_runner_utils \
+// RUN:   --shared-libs=%mlir_c_runner_utils \
 // RUN:   --e main --entry-point-result=void \
 // RUN: | FileCheck %s
 
 #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
 
 module {
   // Compute matrix vector y = Ax
   func.func @matvec(%A: tensor<?x?xf64, #CSR>, %x: tensor<?xf64>, %y_in: tensor<?xf64>) -> tensor<?xf64> {

mlir/test/Integration/Dialect/SparseTensor/GPU/CUDA/sparse-mma-2-4-f16.mlir

 //
 // NOTE: this test requires gpu-sm80
 //
 // RUN: mlir-opt \
 // RUN: --pass-pipeline="builtin.module(gpu.module(strip-debuginfo,convert-gpu-to-nvvm,convert-nvgpu-to-nvvm,affine-expand-index-ops,lower-affine,convert-arith-to-llvm),convert-vector-to-llvm,canonicalize,cse,gpu.module(gpu-to-cubin{chip=sm_80 features=+ptx71}))" \
 // RUN: %s \
 // RUN: | mlir-opt --convert-scf-to-cf -convert-cf-to-llvm --convert-vector-to-llvm \
 // RUN:            --convert-arith-to-llvm --gpu-to-llvm --reconcile-unrealized-casts \
 // RUN: | mlir-cpu-runner \
 // RUN:   --shared-libs=%mlir_cuda_runtime \
-// RUN:   --shared-libs=%mlir_runner_utils \
+// RUN:   --shared-libs=%mlir_c_runner_utils \
 // RUN:   --e main --entry-point-result=void \
 // RUN: | FileCheck %s
 
 module attributes {gpu.container_module} {
 
   // Kernels that run on the device.
 
   gpu.module @kernels {