`CHECK` fail in `Conv2DBackpropInput` in TensorFlow

@@ -37,6 +37,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/conv_2d.h"
 #include "tensorflow/core/kernels/conv_grad_ops.h"
 #include "tensorflow/core/kernels/conv_grad_shape_utils.h"
+#include "tensorflow/core/kernels/fill_functor.h"
 #ifdef TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS
 #include "tensorflow/core/kernels/xsmm_conv2d.h"
 #endif
@@ -436,6 +437,15 @@ class Conv2DBackpropInputOp : public OpKernel {
       return;
     }
 
+    // If shapes are valid but `out_backprop` is empty, in_backprop should be
+    // set to all zeros.  Otherwise, cudnn/dnnl fail with an empty input.
+    if (out_backprop.NumElements() == 0) {
+      functor::SetZeroFunctor<Device, T> set_zero;
+      set_zero(context->eigen_device<Device>(),
+               in_backprop->template flat<T>());
+      return;
+    }
+
     // For now we take the stride from the second and third dimensions only (we
     // do not support striding on the batch or depth dimension).
     const int stride_rows = GetTensorDim(strides_, data_format_, 'H');
@@ -554,6 +564,15 @@ class Conv2DCustomBackpropInputOp : public OpKernel {
       return;
     }
 
+    // If shapes are valid but `out_backprop` is empty, in_backprop should be
+    // set to all zeros.  Otherwise, cudnn/dnnl fail with an empty input.
+    if (out_backprop.NumElements() == 0) {
+      functor::SetZeroFunctor<Device, T> set_zero;
+      set_zero(context->eigen_device<Device>(),
+               in_backprop->template flat<T>());
+      return;
+    }
+
 // TODO(ezhulenev): Remove custom kernel and move XSMM support to
 // LaunchConv2DBackpropInputOp functor.
 #if defined TENSORFLOW_USE_LIBXSMM_CONVOLUTIONS && \

@@ -1103,6 +1103,23 @@ def testConv2DInputSizesContainsOnlySpatialDimensionsBackpropInput(self):
           use_gpu=use_gpu,
           err=1e-5)
 
+  @test_util.run_in_graph_and_eager_modes
+  @test_util.disable_xla("b/239598470")
+  def testConv2DBackpropInputDegenerateBackpropInput(self):
+    input_sizes = [3, 1, 1, 2]
+    expected_output = np.zeros(input_sizes).flatten()
+    for (data_format, use_gpu) in GetTestConfigs():
+      self._RunAndVerifyBackpropInput(
+          input_sizes=input_sizes,
+          filter_sizes=[1, 3, 2, 3],
+          output_sizes=[3, 1, 0, 3],
+          strides=[1, 2],
+          padding="VALID",
+          expected=expected_output,
+          data_format=data_format,
+          use_gpu=use_gpu,
+          err=1e-5)
+
   # Testing for backprops
   def _RunAndVerifyBackpropFilter(self,
                                   input_sizes,