refactor: Update for new api

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

core/conversion/conversion.cpp

@@ -215,15 +215,15 @@ void MarkOutputs(ConversionCtx* ctx, at::ArrayRef<const torch::jit::Value*> outp
               ctx->logger, "Marking Output " << out->debugName() << " named " << name << " in engine (ctx.MarkOutput)");
           ctx->num_outputs += 1;
         } else if (out_ivalue.isTuple()) {
-          TRTORCH_THROW_ERROR("Tuple type. Only a single tensor or a TensorList type is supported.");
+          TORCHTRT_THROW_ERROR("Tuple type. Only a single tensor or a TensorList type is supported.");
         } else if (out_ivalue.isList()) {
-          TRTORCH_THROW_ERROR("List type. Only a single tensor or a TensorList type is supported.");
+          TORCHTRT_THROW_ERROR("List type. Only a single tensor or a TensorList type is supported.");
         } else if (out_ivalue.isScalar()) {
-          TRTORCH_THROW_ERROR("Scalar type. Only a single tensor or a TensorList type is supported.");
+          TORCHTRT_THROW_ERROR("Scalar type. Only a single tensor or a TensorList type is supported.");
         } else if (out_ivalue.isTensor()) {
           // prim::NumToTensor will go to here
           std::string name = std::string("output_") + std::to_string(ctx->num_outputs);
-          auto out_tensor = trtorch::core::conversion::converters::tensor_to_const(ctx, out_ivalue.toTensor(), "");
+          auto out_tensor = converters::tensor_to_const(ctx, out_ivalue.toTensor(), "");
           out_tensor->setName(name.c_str());
           ctx->net->markOutput(*out_tensor);
           LOG_INFO(

core/partitioning/shape_analysis.cpp

@@ -112,7 +112,7 @@ void getSegmentsOutputByRunning(
       // set the input_shape and data_type
       at::ScalarType t = ivalues_maps[i].toTensor().scalar_type();
       if (!partition_info.truncate_long_and_double && (t == at::kLong || t == at::kDouble)) {
-        TRTORCH_THROW_ERROR(
+        TORCHTRT_THROW_ERROR(
             "Unable to process subgraph input type of at::kLong/at::kDouble, try to compile model with truncate_long_and_double enabled");
       } else if (partition_info.truncate_long_and_double && t == at::kLong) {
         ivalues_maps[i] = ivalues_maps[i].toTensor().to(at::kInt);
@@ -123,7 +123,7 @@ void getSegmentsOutputByRunning(
       }
       c10::optional<nvinfer1::DataType> dtype = util::optTypeMetaToTRTDataType(ivalues_maps[i].toTensor().dtype());
       if (dtype == c10::nullopt) {
-        TRTORCH_THROW_ERROR("Unsupported input data type " << ivalues_maps[i].toTensor().dtype());
+        TORCHTRT_THROW_ERROR("Unsupported input data type " << ivalues_maps[i].toTensor().dtype());
       }
       if (ivalues_maps[i].toTensor().sizes().size() == 0) {
         // handle Scalar types, which has sizes of []