Merge pull request #19105 from facaiy:ENH/unsafe_div

PiperOrigin-RevId: 208352779

3 files changed, 51 insertions, 0 deletions

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index 588a45ea43..f4be60a183 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -379,9 +379,11 @@ tf_cc_test(
     srcs = ["gradients/math_grad_test.cc"],
     deps = [
         ":cc_ops",
+        ":client_session",
         ":grad_op_registry",
         ":grad_testutil",
         ":gradient_checker",
+        ":gradients",
         ":math_grad",
         ":testutil",
         "//tensorflow/core:lib_internal",
diff --git a/tensorflow/cc/gradients/math_grad.cc b/tensorflow/cc/gradients/math_grad.cc
index d95dd879b4..5dcf00857d 100644
--- a/tensorflow/cc/gradients/math_grad.cc
+++ b/tensorflow/cc/gradients/math_grad.cc
@@ -441,6 +441,22 @@ Status RealDivGrad(const Scope& scope, const Operation& op,
 }
 REGISTER_GRADIENT_OP("RealDiv", RealDivGrad);
 
+Status UnsafeDivGrad(const Scope& scope, const Operation& op,
+                     const std::vector<Output>& grad_inputs,
+                     std::vector<Output>* grad_outputs) {
+  auto x_1 = ConjugateHelper(scope, op.input(0));
+  auto x_2 = ConjugateHelper(scope, op.input(1));
+  // y = x_1 / x_2
+  // dy/dx_1 = 1/x_2
+  // dy/dx_2 = -x_1/x_2^2
+  auto gx_1 = UnsafeDiv(scope, grad_inputs[0], x_2);
+  auto gx_2 =
+      Mul(scope, grad_inputs[0],
+          UnsafeDiv(scope, UnsafeDiv(scope, Neg(scope, x_1), x_2), x_2));
+  return BinaryGradCommon(scope, op, grad_outputs, gx_1, gx_2);
+}
+REGISTER_GRADIENT_OP("UnsafeDiv", UnsafeDivGrad);
+
 Status SquaredDifferenceGrad(const Scope& scope, const Operation& op,
                              const std::vector<Output>& grad_inputs,
                              std::vector<Output>* grad_outputs) {
diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc
index c6c9262786..88aef1fab4 100644
--- a/tensorflow/cc/gradients/math_grad_test.cc
+++ b/tensorflow/cc/gradients/math_grad_test.cc
@@ -13,8 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/cc/client/client_session.h"
 #include "tensorflow/cc/framework/grad_op_registry.h"
 #include "tensorflow/cc/framework/gradient_checker.h"
+#include "tensorflow/cc/framework/gradients.h"
 #include "tensorflow/cc/framework/testutil.h"
 #include "tensorflow/cc/gradients/grad_testutil.h"
 #include "tensorflow/cc/ops/standard_ops.h"
@@ -46,6 +48,7 @@ using ops::SegmentSum;
 using ops::SquaredDifference;
 using ops::Sub;
 using ops::Sum;
+using ops::UnsafeDiv;
 
 // TODO(andydavis) Test gradient function against numeric gradients output.
 // TODO(andydavis) As more gradients are added move common test functions
@@ -851,6 +854,36 @@ TEST_F(NaryGradTest, RealDiv) {
   RunTest({x}, {x_shape}, {y}, {x_shape});
 }
 
+TEST_F(NaryGradTest, UnsafeDiv) {
+  {
+    TensorShape x_shape({3, 2, 5});
+    const auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
+    // Test x / (1 + |x|) rather than x_1 / x_2 to avoid triggering large
+    // division errors in the numeric estimator used by the gradient checker.
+    const auto y = UnsafeDiv(
+        scope_, x, Add(scope_, Const<float>(scope_, 1), Abs(scope_, x)));
+    RunTest({x}, {x_shape}, {y}, {x_shape});
+  }
+  {
+    // Return 0 gradient (rather than NaN) for division by zero.
+    const auto x = Placeholder(scope_, DT_FLOAT);
+    const auto zero = Const<float>(scope_, 0.0);
+    const auto y = UnsafeDiv(scope_, x, zero);
+
+    std::vector<Output> grad_outputs;
+    TF_EXPECT_OK(AddSymbolicGradients(scope_, {y}, {x}, &grad_outputs));
+    ClientSession session(scope_);
+    std::vector<Tensor> grad_result;
+    TF_EXPECT_OK(
+        session.Run({{x, {-3.0f, 0.0f, 3.0f}}}, grad_outputs, &grad_result));
+    EXPECT_EQ(grad_result.size(), 1);
+    EXPECT_EQ(grad_result[0].NumElements(), 3);
+    EXPECT_EQ(grad_result[0].flat<float>()(0), 0.0f);
+    EXPECT_EQ(grad_result[0].flat<float>()(1), 0.0f);
+    EXPECT_EQ(grad_result[0].flat<float>()(2), 0.0f);
+  }
+}
+
 TEST_F(NaryGradTest, SquaredDifference) {
   TensorShape x1_shape({3, 2, 5});
   TensorShape x2_shape({2, 5});