Add QNN EP option context_node_name_prefix to set EPContext node name prefix

onnxruntime/core/providers/qnn/qnn_execution_provider.cc

@@ -378,6 +378,17 @@ QNNExecutionProvider::QNNExecutionProvider(const ProviderOptions& provider_optio
     LOGS_DEFAULT(VERBOSE) << "User specified enable_htp_fp16_precision: " << enable_HTP_FP16_precision_;
   }
 
+  // For the case that workaround QNN context PD memory limit, user need split the model into pieces and
+  // generate the QNN context model separately.
+  // It could happen that the generated EPContext node in separate graph has same node name.
+  // User can set this context_node_name_prefix for each split pieces to avoid that happens.
+  static const std::string QNN_CONTEXT_NODE_NAME_PREFIX = "context_node_name_prefix";
+  auto context_node_name_prefix_pos = provider_options_map.find(QNN_CONTEXT_NODE_NAME_PREFIX);
+  if (context_node_name_prefix_pos != provider_options_map.end()) {
+    context_node_name_prefix_ = context_node_name_prefix_pos->second;
+    LOGS_DEFAULT(VERBOSE) << "User specified QNN context node name prefix: " << context_node_name_prefix_;
+  }
+
   qnn_backend_manager_ = std::make_unique<qnn::QnnBackendManager>(
       std::move(backend_path),
       profiling_level_etw,
@@ -612,7 +623,7 @@ QNNExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph_viewer
   const auto gen_metadef_name = [&]() {
     uint64_t model_hash;
     int metadef_id = metadef_id_generator_.GenerateId(graph_viewer, model_hash);
-    return MakeString(QNN, "_", model_hash, "_", metadef_id);
+    return MakeString(QNN, context_node_name_prefix_, "_", model_hash, "_", metadef_id);
   };
 
   // For model with EPContext, make sure each partition only has one single EPContext node

onnxruntime/core/providers/qnn/qnn_execution_provider.h

@@ -80,6 +80,7 @@ class QNNExecutionProvider : public IExecutionProvider {
   std::unordered_map<std::string, std::unique_ptr<qnn::QnnModel>> qnn_models_;
   bool context_cache_enabled_ = false;
   std::string context_cache_path_cfg_ = "";
+  std::string context_node_name_prefix_ = "";
   bool disable_cpu_ep_fallback_ = false;  // True if CPU EP fallback has been disabled for this session.
   bool qnn_context_embed_mode_ = true;
   int32_t vtcm_size_in_mb_ = 0;

onnxruntime/test/perftest/command_args_parser.cc

@@ -97,6 +97,7 @@
       "\t    [QNN only] [device_id]: The ID of the device to use when setting 'htp_arch'. Defaults to '0' (for single device). \n"
       "\t    [QNN only] [enable_htp_fp16_precision]: Enable the HTP_FP16 precision so that the float32 model will be inferenced with fp16 precision. \n"
       "\t    Otherwise, it will be fp32 precision. Only works for float32 model. Defaults to '0' (with FP32 precision.). \n"
+      "\t    [QNN only] [context_node_name_prefix]: QNN EPContext node name prefix to make sure the generated node name unique for split models. e.g 'phi3_part1'.\n"
       "\t    [Example] [For QNN EP] -e qnn -i \"backend_path|/folderpath/libQnnCpu.so\" \n"
       "\n"
       "\t    [TensorRT only] [trt_max_partition_iterations]: Maximum iterations for TensorRT parser to get capability.\n"

onnxruntime/test/providers/qnn/qnn_ep_context_test.cc

@@ -279,6 +279,45 @@
   ASSERT_EQ(std::remove(context_binary_file.c_str()), 0);
 }
 
+TEST_F(QnnHTPBackendTests, QnnContextGenerationNodeNamePrefix) {
+  ProviderOptions provider_options;
+#if defined(_WIN32)
+  provider_options["backend_path"] = "QnnHtp.dll";
+#else
+  provider_options["backend_path"] = "libQnnHtp.so";
+#endif
+  std::string node_name_prefix = "node_name_prefix_test";
+  provider_options["context_node_name_prefix"] = node_name_prefix;
+
+  // Add kMSDomain to cover contrib op like Gelu
+  const std::unordered_map<std::string, int> domain_to_version = {{"", 13}, {kMSDomain, 1}};
+
+  auto& logging_manager = DefaultLoggingManager();
+  logging_manager.SetDefaultLoggerSeverity(logging::Severity::kERROR);
+
+  const std::string context_binary_file = "./qnn_ctx_2_inputs_order_test_gen.onnx";
+  Ort::SessionOptions so;
+  so.AddConfigEntry(kOrtSessionOptionEpContextEnable, "1");
+  so.AddConfigEntry(kOrtSessionOptionEpContextFilePath, context_binary_file.c_str());
+  so.AppendExecutionProvider("QNN", provider_options);
+
+  Ort::Session session(*ort_env, ORT_TSTR("testdata/qnn_ctx_2_inputs_order_test.onnx"), so);
+
+  // Make sure the Qnn context cache binary file is generated
+  EXPECT_TRUE(std::filesystem::exists(context_binary_file.c_str()));
+
+  std::shared_ptr<Model> model;
+  ASSERT_STATUS_OK(Model::Load(ToPathString(context_binary_file), model, nullptr, DefaultLoggingManager().DefaultLogger()));
+  for (auto& node : model->MainGraph().Nodes()) {
+    if (node.OpType() == "EPContext") {
+      EXPECT_TRUE(node.Name().find(node_name_prefix) != std::string::npos);
+    }
+  }
+
+  // clean up
+  ASSERT_EQ(std::remove(context_binary_file.c_str()), 0);
+}
+
 // Run QDQ model on HTP 3 times
 // 1st run will generate the Qnn context cache onnx file
 // 2nd run directly loads and run from Qnn context cache model