diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index cf7ffc5af19..e349aefd4cb 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -94,6 +94,12 @@ class BinaryOpsTest(XLATestCase):
           np.array([5, 6, 7, 8], dtype=dtype),
           expected=np.array([-160, -81, -28, -4], dtype=dtype))
 
+      self._testBinary(
+          gen_math_ops._sqrt_grad,
+          np.array([4, 3, 2, 1], dtype=dtype),
+          np.array([5, 6, 7, 8], dtype=dtype),
+          expected=np.array([0.625, 1, 1.75, 4], dtype=dtype))
+
       self._testBinary(
           gen_nn_ops._softplus_grad,
           np.array([4, 3, 2, 1], dtype=dtype),
diff --git a/tensorflow/compiler/tests/randomized_tests.cc b/tensorflow/compiler/tests/randomized_tests.cc
index 6a0bdf1ed15..e380fdd7f4e 100644
--- a/tensorflow/compiler/tests/randomized_tests.cc
+++ b/tensorflow/compiler/tests/randomized_tests.cc
@@ -2496,6 +2496,16 @@ TEST_F(OpTest, Sqrt) {
   });
 }
 
+TEST_F(OpTest, SqrtGrad) {
+  Repeatedly([this]() {
+    auto dims = RandomDims();
+    return ExpectTfAndXlaOutputsAreClose(OpTestBuilder("SqrtGrad")
+                                             .RandomInput(DT_FLOAT, dims)
+                                             .RandomInput(DT_FLOAT, dims)
+                                             .Attr("T", DT_FLOAT));
+  });
+}
+
 TEST_F(OpTest, SquaredDifference) {
   Repeatedly([this]() {
     auto dims = BroadcastableDims();
diff --git a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc
index ded20a9a3ce..f9bb1e2fb1d 100644
--- a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc
+++ b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc
@@ -107,6 +107,10 @@ XLA_MAKE_BINARY(
     b->Mul(b->Pow(lhs, XlaHelpers::IntegerLiteral(b, input_type(0), 3)),
            b->Div(rhs, XlaHelpers::IntegerLiteral(b, input_type(0), -2)),
            extend_dimensions));
+XLA_MAKE_BINARY(SqrtGrad,
+                b->Div(b->Mul(rhs,
+                              XlaHelpers::FloatLiteral(b, input_type(0), 0.5)),
+                       lhs, extend_dimensions));
 
 static xla::ComputationDataHandle Square(xla::ComputationBuilder* builder,
                                          const xla::ComputationDataHandle& x) {