diff --git a/tensorflow/compiler/xla/service/gpu/gpu_conv_rewriter.cc b/tensorflow/compiler/xla/service/gpu/gpu_conv_rewriter.cc
index 9d34bb39ba8..fb8c05798d8 100644
--- a/tensorflow/compiler/xla/service/gpu/gpu_conv_rewriter.cc
+++ b/tensorflow/compiler/xla/service/gpu/gpu_conv_rewriter.cc
@@ -321,38 +321,11 @@ MatchBackwardInput(HloInstruction* conv) {
   const auto no_match_result =
       std::make_tuple(false, Window(), ConvolutionDimensionNumbers(), nullptr);
 
-  // TODO: Theoretically cuDNN supports grouped convolutions also
-  // for the backward input convolution, but based on the cudnn's current state
-  // there is not much performance improvement when using the
-  // cudnn backward input API for grouped conv.
-  // This needs to be re-evaluated for future cuDNN versions.
-  // Note that we already have the necessary code down below, the only thing to
-  // enable it is to remove the following early return.
-  if (conv->feature_group_count() > 1) {
-    return no_match_result;
-  }
-
   // Match instruction pattern.
   CHECK_EQ(HloOpcode::kConvolution, conv->opcode());
   HloInstruction* reverse_filter = conv->mutable_operand(1);
   ConvolutionDimensionNumbers dnums = conv->convolution_dimension_numbers();
 
-  // Match BackwardInput for a depthwise convolution and thunk it to forward
-  // convolution Output feature dimension and input feature dimension has been
-  // swapped in the bridge. Hence to get the actual input features we need to
-  // query the output feature dimension
-  auto kernel_out_feature_dim = dnums.kernel_output_feature_dimension();
-  auto kernel_out_features =
-      reverse_filter->shape().dimensions(kernel_out_feature_dim);
-
-  // For a depthwise convolution, the input features must be equal to the
-  // feature_group_count. We can leverage this property to match a depthwise
-  // convolution and thunk it to forward conv
-  if (conv->feature_group_count() > 1 &&
-      kernel_out_features == conv->feature_group_count()) {
-    return no_match_result;
-  }
-
   // We pattern-match to a backwards input conv if:
   //
   //  - all spatial dims of the filter are reversed