update

onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc

@@ -365,46 +365,6 @@ std::unique_lock<OrtMutex> TensorrtExecutionProvider::GetApiLock() const {
   return std::unique_lock<OrtMutex>(singleton);
 }
 
-Status GetShapeOfShapeTensor(Ort::ConstValue& input_tensor,
-                             std::vector<int32_t>& shape_values,
-                             nvinfer1::ICudaEngine* trt_engine,
-                             int binding_index,
-                             cudaStream_t stream) {
-  auto tensor_info = input_tensor.GetTensorTypeAndShapeInfo();
-  const auto tensor_shapes = tensor_info.GetShape();
-  const auto tensor_type = tensor_info.GetElementType();
-  nvinfer1::Dims dims = trt_engine->getBindingDimensions(static_cast<int>(binding_index));
-  int nb_dims = dims.nbDims;
-  int shape_size = nb_dims == 0 ? 1 : static_cast<int>(tensor_shapes[0]);  // The shape of the "shape tensor" is either zero dimension (scalar) or 1-dimension
-  shape_values.resize(shape_size, 1);
-
-  switch (tensor_type) {
-    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32: {
-      auto input = std::make_unique<int32_t[]>(shape_size);
-      CUDA_RETURN_IF_ERROR(cudaMemcpyAsync(input.get(), input_tensor.GetTensorData<int32_t>(), shape_size * sizeof(int32_t), cudaMemcpyDeviceToHost, stream));
-      CUDA_RETURN_IF_ERROR(cudaStreamSynchronize(stream));
-      for (int j = 0; j < shape_size; ++j) {
-        shape_values[j] = input[j];
-      }
-      break;
-    }
-    case ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64: {
-      auto input = std::make_unique<int64_t[]>(shape_size);
-      CUDA_RETURN_IF_ERROR(cudaMemcpyAsync(input.get(), input_tensor.GetTensorData<int64_t>(), shape_size * sizeof(int64_t), cudaMemcpyDeviceToHost, stream));
-      CUDA_RETURN_IF_ERROR(cudaStreamSynchronize(stream));
-      for (int j = 0; j < shape_size; ++j) {
-        shape_values[j] = static_cast<int32_t>(input[j]);
-      }
-      break;
-    }
-    default: {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, EP_FAIL,
-                             "TensorRT shape tensor data type: " + std::to_string(tensor_type) + " not supported.");
-    }
-  }
-  return Status::OK();
-}
-
 /*
  * Get the shape of "shape tensor" input 
  */
@@ -2239,7 +2199,7 @@ TensorrtExecutionProvider::GetCapability(const GraphViewer& graph,
   // If the model consists of only a single "EPContext" contrib op, it means TRT EP can fetch the precompiled engine info from the node and 
   // load the engine directly without having to go through the processes of graph proto reconstruction, calling TRT parser and engine compilation.
   // So, simply return the ComputeCapability here.
-  if (IsFusedGraphHasCtxNode(graph)) {
+  if (GraphHasCtxNode(graph)) {
     if (IsValidCtxNode(graph)) {
       SubGraph_t supported_node_vector = {{0}, false};
       std::unique_ptr<IndexedSubGraph> sub_graph = GetSubGraph(supported_node_vector, graph, TRTGenerateId(graph), 0);
@@ -3715,7 +3675,7 @@ common::Status TensorrtExecutionProvider::Compile(const std::vector<FusedNodeAnd
     }
 
     Status status;
-    if (IsFusedGraphHasCtxNode(graph_body_viewer)) {
+    if (GraphHasCtxNode(graph_body_viewer)) {
       status = CreateNodeComputeFromPrecompiledEngine(graph_body_viewer, fused_node, input_map, output_map, node_compute_funcs);
     } else {
       status = CreateNodeComputeFromOrtGraph(graph_body_viewer, fused_node, input_map, output_map, node_compute_funcs);

onnxruntime/core/providers/tensorrt/tensorrt_execution_provider_utils.h

@@ -706,7 +706,7 @@ bool ParseProfileShapes(std::string profile_shapes_string, std::unordered_map<st
  *
  *  Note: Please see more details about "EPContext" contrib op in contrib_defs.cc
  */
-bool IsFusedGraphHasCtxNode(const GraphViewer& graph) {
+bool GraphHasCtxNode(const GraphViewer& graph) {
   if (graph.NumberOfNodes() == 1) {
     for (int i = 0; i < graph.MaxNodeIndex(); ++i) {
       auto node = graph.GetNode(i);