diff --git a/tensorflow/compiler/mlir/lite/ir/tfl_ops.td b/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
index afb82d48468..4c25fe3b545 100644
--- a/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
+++ b/tensorflow/compiler/mlir/lite/ir/tfl_ops.td
@@ -1990,9 +1990,11 @@ def TFL_TileOp: TFL_Op<"tile", [NoSideEffect,
    For example, tiling [a b c d] by [2] produces [a b c d a b c d].
   }];
 
-  let arguments = (ins AnyTensor:$input, TFL_I32OrI64Tensor:$multiples);
+  let arguments = (ins
+    TensorOf<[F32, I1, I32, I64, TFL_Uint8]>:$input,
+    TFL_I32OrI64Tensor:$multiples);
 
-  let results = (outs AnyTensor:$output);
+  let results = (outs TensorOf<[F32, I1, I32, I64, TFL_Uint8]>:$output);
 
   let hasOptions = 0;
 }
diff --git a/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir b/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
index 1a6c8113202..e93f7ef5f5e 100644
--- a/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
+++ b/tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
@@ -651,6 +651,17 @@ func @pad(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> {
   // CHECK:  return %0 : tensor<?xf32>
 }
 
+func @tile(tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32> {
+^bb0(%arg0: tensor<2x3xf32>, %arg1: tensor<2xi32>):
+  %cst = constant dense<[1, 2]> : tensor<2xi32>
+  %0 = "tf.Tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32>
+  return %0 : tensor<2x6xf32>
+
+  // CHECK-LABEL: tile
+  // CHECK:  %0 = "tfl.tile"(%arg0, %cst) : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<2x6xf32>
+  // CHECK:  return %0 : tensor<2x6xf32>
+}
+
 func @padv2(tensor<2x1x3xf32>, tensor<3x2xi32>) -> tensor<? x f32> {
 ^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<3x2xi32>):
   %cst = constant dense<2.0> : tensor<f32>
diff --git a/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td b/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
index 410cac51c95..610830e5e1e 100644
--- a/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
+++ b/tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td
@@ -254,6 +254,8 @@ def : Pat<(TF_EqualOp $arg0, $arg1), (TFL_EqualOp $arg0, $arg1)>;
 
 def : Pat<(TF_PadOp $arg0, $arg1), (TFL_PadOp $arg0, $arg1)>;
 
+def : Pat<(TF_TileOp $arg0, $arg1), (TFL_TileOp $arg0, $arg1)>;
+
 def : Pat<(TF_PadV2Op $arg0, $arg1, $cst), (TFL_PadV2Op $arg0, $arg1, $cst)>;
 
 def : Pat<(TF_MeanOp $arg0, $arg1, BoolAttr:$arg2), (TFL_MeanOp $arg0, $arg1, $arg2)>;
diff --git a/tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td b/tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
index 2fd31d193a0..8ac1aaff374 100644
--- a/tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
+++ b/tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
@@ -3518,6 +3518,30 @@ num_elements: optional. If not -1, the number of elements in the list.
   TF_DerivedResultTypeAttr element_dtype = TF_DerivedResultTypeAttr<0>;
 }
 
+def TF_TileOp : TF_Op<"Tile", [NoSideEffect]> {
+  let summary = "Constructs a tensor by tiling a given tensor.";
+
+  let description = [{
+This operation creates a new tensor by replicating `input` `multiples` times.
+The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
+and the values of `input` are replicated `multiples[i]` times along the 'i'th
+dimension. For example, tiling `[a b c d]` by `[2]` produces
+`[a b c d a b c d]`.
+  }];
+
+  let arguments = (ins
+    TF_Tensor:$input,
+    TF_I32OrI64Tensor:$multiples
+  );
+
+  let results = (outs
+    TF_Tensor:$output
+  );
+
+  TF_DerivedOperandTypeAttr Tmultiples = TF_DerivedOperandTypeAttr<1>;
+  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
+}
+
 def TF_TopKV2Op : TF_Op<"TopKV2", [NoSideEffect]> {
   let summary = [{
 Finds values and indices of the `k` largest elements for the last dimension.