diff --git a/mlir/lib/Dialect/SparseTensor/Pipelines/SparseTensorPipelines.cpp b/mlir/lib/Dialect/SparseTensor/Pipelines/SparseTensorPipelines.cpp
--- a/mlir/lib/Dialect/SparseTensor/Pipelines/SparseTensorPipelines.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Pipelines/SparseTensorPipelines.cpp
@@ -49,6 +49,7 @@
   pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
   pm.addPass(createMemRefToLLVMPass());
   pm.addNestedPass<func::FuncOp>(createConvertMathToLLVMPass());
+  pm.addPass(createConvertMathToLibmPass());
   pm.addPass(createConvertFuncToLLVMPass());
   pm.addPass(createReconcileUnrealizedCastsPass());
 }
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tanh.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tanh.mlir
new file mode 100644
--- /dev/null
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_tanh.mlir
@@ -0,0 +1,76 @@
+// RUN: mlir-opt %s --sparse-compiler | \
+// 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
+
+#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
+
+#trait_op = {
+  indexing_maps = [
+    affine_map<(i) -> (i)>   // X (out)
+  ],
+  iterator_types = ["parallel"],
+  doc = "X(i) = OP X(i)"
+}
+
+module {
+  // Performs zero-preserving math to sparse vector.
+  func @sparse_tanh(%vec: tensor<?xf64, #SparseVector>
+                          {linalg.inplaceable = true})
+                       -> tensor<?xf64, #SparseVector> {
+    %0 = linalg.generic #trait_op
+      outs(%vec: tensor<?xf64, #SparseVector>) {
+        ^bb(%x: f64):
+          %1 = math.tanh %x : f64
+          linalg.yield %1 : f64
+    } -> tensor<?xf64, #SparseVector>
+    return %0 : tensor<?xf64, #SparseVector>
+  }
+
+  // Dumps a sparse vector of type f64.
+  func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
+    // Dump the values array to verify only sparse contents are stored.
+    %c0 = arith.constant 0 : index
+    %d0 = arith.constant -1.0 : f64
+    %0 = sparse_tensor.values %arg0
+      : tensor<?xf64, #SparseVector> to memref<?xf64>
+    %1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<32xf64>
+    vector.print %1 : vector<32xf64>
+    // Dump the dense vector to verify structure is correct.
+    %dv = sparse_tensor.convert %arg0
+        : tensor<?xf64, #SparseVector> to tensor<?xf64>
+    %2 = bufferization.to_memref %dv : memref<?xf64>
+    %3 = vector.transfer_read %2[%c0], %d0: memref<?xf64>, vector<32xf64>
+    vector.print %3 : vector<32xf64>
+    memref.dealloc %2 : memref<?xf64>
+    return
+  }
+
+  // Driver method to call and verify vector kernels.
+  func @entry() {
+    // Setup sparse vector.
+    %v1 = arith.constant sparse<
+       [ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
+         [ -1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0 ]
+    > : tensor<32xf64>
+    %sv1 = sparse_tensor.convert %v1
+         : tensor<32xf64> to tensor<?xf64, #SparseVector>
+
+    // Call sparse vector kernel.
+    %0 = call @sparse_tanh(%sv1) : (tensor<?xf64, #SparseVector>)
+                                 -> tensor<?xf64, #SparseVector>
+
+    //
+    // Verify the results (within some precision).
+    //
+    // CHECK: {{( -0.761[0-9]*, 0.761[0-9]*, 0.96[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 )}}
+    // CHECK-NEXT {{( -0.761[0-9]*, 0, 0, 0.761[0-9]*, 0, 0, 0, 0, 0, 0, 0, 0.96[0-9]*, 0, 0, 0, 0, 0, 0.99[0-9]*, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 0, 0, 0, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 1 )}}
+    //
+    call @dump_vec_f64(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
+
+    // Release the resources.
+    sparse_tensor.release %sv1 : tensor<?xf64, #SparseVector>
+    return
+  }
+}