diff --git a/tensorflow/core/kernels/mlir_generated_cwise_op_gpu_tanh.cu.cc b/tensorflow/core/kernels/mlir_generated_cwise_op_gpu_tanh.cu.cc
index 40dd7c7e49e..70de777239f 100644
--- a/tensorflow/core/kernels/mlir_generated_cwise_op_gpu_tanh.cu.cc
+++ b/tensorflow/core/kernels/mlir_generated_cwise_op_gpu_tanh.cu.cc
@@ -14,6 +14,7 @@ limitations under the License.
 ==============================================================================*/
 
 #include <memory>
+#include <vector>
 
 #include "absl/strings/string_view.h"
 #include "absl/types/span.h"
@@ -45,9 +46,41 @@ Status CreateKernel(absl::string_view kernel_name, uint64_t num_args,
   return stream_exec->GetKernel(loader_spec, kernel_base.get());
 }
 
-class MlirGenerateTanhOp : public OpKernel {
+struct LaunchConfig {
+  se::BlockDim blockDim;
+  se::ThreadDim threadDim;
+};
+
+LaunchConfig GetLaunchConfiguration(std::vector<uint64> tile_sizes,
+                                    std::vector<uint64> unrolling_factors,
+                                    std::vector<uint64> shape) {
+  LaunchConfig result;
+  // Ensure the vectors are length 3 and pad with ones.
+  tile_sizes.resize(3, 1);
+  unrolling_factors.resize(3, 1);
+  shape.resize(3, 1);
+  // The number of threads is given by the tiling size.
+  result.threadDim = se::ThreadDim(tile_sizes[0], tile_sizes[1], tile_sizes[2]);
+  // We know that the kernel was generated by mapping the three outer-most
+  // dimensions to x,y,z dimensions. So we only need to compute those.
+  std::vector<int> block_dims(3);
+  for (int i = 0; i < 3; ++i) {
+    // Compute the number of grids. We use ceildiv here as we have to allocate
+    // an extra thread/block if the division is not even. The kernel contains
+    // code to handle the boundaries.
+    int number_of_threads =
+        (shape[i] + unrolling_factors[i] - 1) / unrolling_factors[i];
+    int number_of_grids =
+        (number_of_threads + tile_sizes[i] - 1) / tile_sizes[i];
+    block_dims[i] = number_of_grids;
+  }
+  result.blockDim = se::BlockDim(block_dims[0], block_dims[1], block_dims[2]);
+  return result;
+}
+
+class MlirGeneratedTanhOp : public OpKernel {
  public:
-  explicit MlirGenerateTanhOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
+  explicit MlirGeneratedTanhOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
 
   void Compute(OpKernelContext* ctx) override {
     auto* stream = ctx->op_device_context()->stream();
@@ -88,11 +121,13 @@ class MlirGenerateTanhOp : public OpKernel {
     args.add_argument<int64_t>(inp.NumElements());
     args.add_argument<int64_t>(1);
 
-    // TODO(b/158649746): Choose block size and thread dim according to the
-    // number of input elements. For now, this supports at most 1024 elements.
+    // This has to be aligned with the configuration that was used when building
+    // the kernels. See the corresponding build rules in `cubin_headers/BUILD`.
+    LaunchConfig config = GetLaunchConfiguration(
+        {256}, {}, {static_cast<uint64>(inp.NumElements())});
     OP_REQUIRES_OK(
-        ctx, stream->parent()->Launch(stream, se::ThreadDim(inp.NumElements()),
-                                      se::BlockDim(1), *kernel, args));
+        ctx, stream->parent()->Launch(stream, config.threadDim, config.blockDim,
+                                      *kernel, args));
   }
 
  protected:
@@ -103,26 +138,26 @@ class MlirGenerateTanhOp : public OpKernel {
   std::mutex mu_;
 };
 
-class MlirGenerateTanhF16Op : public MlirGenerateTanhOp {
+class MlirGeneratedTanhF16Op : public MlirGeneratedTanhOp {
  public:
-  explicit MlirGenerateTanhF16Op(OpKernelConstruction* ctx)
-      : MlirGenerateTanhOp(ctx) {
+  explicit MlirGeneratedTanhF16Op(OpKernelConstruction* ctx)
+      : MlirGeneratedTanhOp(ctx) {
     cubin_data_ = kTanhF16Kernel;
   }
 };
 
-class MlirGenerateTanhF32Op : public MlirGenerateTanhOp {
+class MlirGeneratedTanhF32Op : public MlirGeneratedTanhOp {
  public:
-  explicit MlirGenerateTanhF32Op(OpKernelConstruction* ctx)
-      : MlirGenerateTanhOp(ctx) {
+  explicit MlirGeneratedTanhF32Op(OpKernelConstruction* ctx)
+      : MlirGeneratedTanhOp(ctx) {
     cubin_data_ = kTanhF32Kernel;
   }
 };
 
-class MlirGenerateTanhF64Op : public MlirGenerateTanhOp {
+class MlirGeneratedTanhF64Op : public MlirGeneratedTanhOp {
  public:
-  explicit MlirGenerateTanhF64Op(OpKernelConstruction* ctx)
-      : MlirGenerateTanhOp(ctx) {
+  explicit MlirGeneratedTanhF64Op(OpKernelConstruction* ctx)
+      : MlirGeneratedTanhOp(ctx) {
     cubin_data_ = kTanhF64Kernel;
   }
 };
@@ -130,11 +165,11 @@ class MlirGenerateTanhF64Op : public MlirGenerateTanhOp {
 
 REGISTER_KERNEL_BUILDER(
     Name("Tanh").Device(DEVICE_GPU).TypeConstraint<Eigen::half>("T"),
-    MlirGenerateTanhF16Op);
+    MlirGeneratedTanhF16Op);
 REGISTER_KERNEL_BUILDER(
     Name("Tanh").Device(DEVICE_GPU).TypeConstraint<float>("T"),
-    MlirGenerateTanhF32Op);
+    MlirGeneratedTanhF32Op);
 REGISTER_KERNEL_BUILDER(
     Name("Tanh").Device(DEVICE_GPU).TypeConstraint<double>("T"),
-    MlirGenerateTanhF64Op);
+    MlirGeneratedTanhF64Op);
 }  // namespace tensorflow