Add legalization pattern for tf.SelectV2 op.

PiperOrigin-RevId: 262622407

tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir

@@ -509,6 +509,15 @@ func @select(%arg0: tensor<8xi1>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>) ->
 // CHECK:  return %0 : tensor<8xf32>
 }
 
+func @select_v2(%arg0: tensor<8xi1>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>) -> tensor<8xf32> {
+  %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<8xi1>, tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32>
+  return %0: tensor<8xf32>
+
+// CHECK-LABEL: select_v2
+// CHECK:  %0 = "tfl.select"(%arg0, %arg1, %arg2)
+// CHECK:  return %0 : tensor<8xf32>
+}
+
 func @sin(%arg0: tensor<f32>) -> tensor<f32> {
   %0 = "tf.Sin"(%arg0) : (tensor<f32>) -> tensor<f32>
   return %0 : tensor<f32>

tensorflow/compiler/mlir/lite/transforms/legalize_patterns.td

@@ -141,6 +141,7 @@ def : Pat<(TF_SquareOp $arg), (TFL_SquareOp $arg)>;
 // TODO(jpienaar): this is not true for all selects, TF's select supports rank 0
 // condition
 def : Pat<(TF_SelectOp $cond, $x, $y), (TFL_SelectOp $cond, $x, $y)>;
+def : Pat<(TF_SelectV2Op $cond, $x, $y), (TFL_SelectOp $cond, $x, $y)>;
 def : Pat<(TF_ShapeOp $arg), (TFL_ShapeOp $arg)>;
 def : Pat<(TF_SigmoidOp $arg), (TFL_LogisticOp $arg)>;
 def : Pat<(TF_SinOp F32Tensor:$arg), (TFL_SinOp $arg)>;

tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td

@@ -2823,6 +2823,63 @@ For example:
 select(condition, t, e)  # => [[1, 6], [7, 4]]
 
 
+# 'condition' tensor is [True, False]
+# 't' is [[1, 2],
+#         [3, 4]]
+# 'e' is [[5, 6],
+#         [7, 8]]
+select(condition, t, e) ==> [[1, 2],
+                             [7, 8]]
+
+```
+  }];
+
+  let arguments = (ins
+    I1Tensor:$condition,
+    TF_Tensor:$t,
+    TF_Tensor:$e
+  );
+
+  let results = (outs
+    TF_Tensor:$output
+  );
+
+  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<1>;
+}
+
+def TF_SelectV2Op : TF_Op<"SelectV2", [NoSideEffect]> {
+  let summary = "Selects elements from `x` or `y`, depending on `condition`.";
+
+  let description = [{
+The `x`, and `y` tensors must all have the same shape, and the
+output will also have that shape.
+
+The `condition` tensor must be a scalar if `x` and `y` are scalars.
+If `x` and `y` are vectors or higher rank, then `condition` must be either a
+scalar, a vector with size matching the first dimension of `x`, or must have
+the same shape as `x`.
+
+The `condition` tensor acts as a mask that chooses, based on the value at each
+element, whether the corresponding element / row in the output should be
+taken from `x` (if true) or `y` (if false).
+
+If `condition` is a vector and `x` and `y` are higher rank matrices, then
+it chooses which row (outer dimension) to copy from `x` and `y`.
+If `condition` has the same shape as `x` and `y`, then it chooses which
+element to copy from `x` and `y`.
+
+For example:
+
+```python
+# 'condition' tensor is [[True,  False]
+#                        [False, True]]
+# 't' is [[1, 2],
+#         [3, 4]]
+# 'e' is [[5, 6],
+#         [7, 8]]
+select(condition, t, e)  # => [[1, 6], [7, 4]]
+
+
 # 'condition' tensor is [True, False]
 # 't' is [[1, 2],
 #         [3, 4]]