Fix several warnings

torch_xla/csrc/convolution.cpp

@@ -271,7 +271,6 @@ xla::XlaOp BuildConvolutionBias(
   CHECK_GE(node_inputs.size(), size_t(4));
   const auto window_strides = XlaHelpers::I64List(
       node->get<std::vector<int64_t>>(attr::stride).value());
-  const auto node_outputs = node->outputs();
   const auto conv = BuildConvolution(node, input, kernel, conv_precision);
   auto broadcast_sizes = XlaHelpers::SizesOfXlaOp(conv);
   CHECK_EQ(broadcast_sizes.size(), 4);

torch_xla/csrc/data_ops.cpp

@@ -173,7 +173,6 @@ xla::XlaOp BuildCat(const Node* node,
 }
 
 std::vector<xla::XlaOp> BuildChunk(const Node* node, const xla::XlaOp& input) {
-  const auto node_input = node->inputs()[0];
   int64_t chunks = node->get<int64_t>(attr::chunks).value();
   int64_t dim = node->get<int64_t>(attr::dim).value();
   XLA_CHECK_GE(dim, 0) << "Negative dimension specified for chunk operator";

torch_xla/csrc/helpers.cpp

@@ -79,6 +79,7 @@ xla::PrimitiveType XlaHelpers::MakeXlaPrimitiveType(
       return xla::PrimitiveType::S64;
     default:
       LOG(FATAL) << "Type not supported: " << scalar_type;
+      return xla::PrimitiveType::PRIMITIVE_TYPE_INVALID;
   }
 }
 

torch_xla/csrc/log_softmax.cpp

@@ -58,7 +58,6 @@ xla::XlaOp BuildLogSoftmaxGrad(const Node* node, const xla::XlaOp& grad_output,
   // Inspired from tf2xla.
   xla::int64 dim = node->get<int64_t>(attr::dim).value();
 
-  const auto node_inputs = node->inputs();
   auto input_size = XlaHelpers::SizesOfXlaOp(grad_output);
   std::vector<xla::int64> broadcast_dimensions;
   for (size_t broadcast_dim = 0; broadcast_dim < input_size.size();

torch_xla/csrc/nll_loss.cpp

@@ -79,10 +79,6 @@ xla::XlaOp BuildNllLossBackward(const Node* node, const xla::XlaOp& logits,
   const int kBatchDim = 0;
   auto builder = logits.builder();
   const auto logits_shape = XlaHelpers::ShapeOfXlaOp(logits);
-  const auto zero =
-      XlaHelpers::ScalarValue<float>(0, logits_shape.element_type(), builder);
-  const auto one =
-      XlaHelpers::ScalarValue<float>(1, logits_shape.element_type(), builder);
   xla::XlaOp one_hot_labels = LabelsToOneHot(
       /*builder=*/builder,
       /*depth=*/logits_shape.dimensions(1),

torch_xla/csrc/torch_util.cpp

@@ -27,7 +27,7 @@ py::object XlaPackTensorList(const XlaModule::TensorBatchVector& outputs) {
     }
     tuple[i] = replica_tuple;
   }
-  return tuple;
+  return std::move(tuple);
 }
 
 }  // namespace jit