[Hexagon] Launcher modifications to make use of the new device API (apache#9356)

* Use custom lookup_linked_params function in
app/hexagon_launcher.

* Use new device api in hexagon launcher.

* Ensure app/hexagon_launcher uses kDLCPU for external allocations
so that CopyDataFromTo knows how to handle the provided memory.

* Use loadfile_hexagon in app/hexagon_launcher.

* Update hexagon launcher's get_output method to
utilize NDArray's for copying in order to exercise
the DeviceAPI.

* Apply clang formatting

Author: csullivan

apps/hexagon_launcher/launcher_core.cc

@@ -148,12 +148,13 @@ const tvm::runtime::PackedFunc get_module_func(tvm::runtime::Module module,
 }
 
 void reset_device_api() {
-  const tvm::runtime::PackedFunc api = get_runtime_func("device_api.cpu");
+  const tvm::runtime::PackedFunc api = get_runtime_func("device_api.hexagon.v2");
   tvm::runtime::Registry::Register("device_api.hexagon", true).set_body(api);
 }
 
 tvm::runtime::Module load_module(const std::string& file_name) {
-  static const tvm::runtime::PackedFunc loader = get_runtime_func("runtime.module.loadfile_so");
+  static const tvm::runtime::PackedFunc loader =
+      get_runtime_func("runtime.module.loadfile_hexagon");
   tvm::runtime::TVMRetValue rv = loader(file_name);
   if (rv.type_code() == kTVMModuleHandle) {
     return rv.operator tvm::runtime::Module();
@@ -169,7 +170,10 @@ tvm::runtime::Module create_graph_executor(const std::string& graph_json,
   uint64_t device_type = device.device_type;
   uint64_t device_id = device.device_id;
 
+  std::string linked_params = "tvm.runtime.hexagon.lookup_linked_params";
+  const tvm::runtime::PackedFunc lookup_linked_params = get_runtime_func(linked_params);
   // Use default param lookup function (linked into the module).
-  tvm::runtime::TVMRetValue rv = create_executor(graph_json, graph_module, device_type, device_id);
+  tvm::runtime::TVMRetValue rv =
+      create_executor(graph_json, graph_module, lookup_linked_params, device_type, device_id);
   return rv.operator tvm::runtime::Module();
 }

apps/hexagon_launcher/launcher_core.h

@@ -89,6 +89,8 @@ struct Model {
 
   static tvm::Device device() { return tvm::Device{static_cast<DLDeviceType>(kDLHexagon), 0}; }
 
+  static tvm::Device external() { return tvm::Device{static_cast<DLDeviceType>(kDLCPU), 0}; }
+
   tvm::runtime::PackedFunc run;
 };
 

apps/hexagon_launcher/launcher_hexagon.cc

@@ -26,6 +26,8 @@ extern "C" {
 #include <qurt_hvx.h>
 }
 
+#include <tvm/runtime/object.h>
+
 #include <algorithm>
 #include <memory>
 #include <string>
@@ -106,7 +108,7 @@ AEEResult __QAIC_HEADER(launcher_rpc_set_input)(remote_handle64 handle, int inpu
 
   DLTensor tensor{
       const_cast<unsigned char*>(input_value),
-      Model::device(),
+      Model::external(),
       meta->ndim,
       meta->dtype,
       const_cast<int64_t*>(meta->shape),
@@ -153,6 +155,16 @@ AEEResult __QAIC_HEADER(launcher_rpc_get_output)(remote_handle64 handle, int out
   tvm::runtime::PackedFunc get_output = get_module_func(TheModel->graph_executor, "get_output");
   tvm::runtime::NDArray output = get_output(output_idx);
 
+  std::vector<int64_t> shape_vec{output->shape, output->shape + output->ndim};
+
+  auto* container = new tvm::runtime::NDArray::Container(
+      static_cast<void*>(output_value), shape_vec, output->dtype, Model::external());
+  container->SetDeleter([](tvm::Object* container) {
+    delete static_cast<tvm::runtime::NDArray::Container*>(container);
+  });
+
+  tvm::runtime::NDArray host_output(GetObjectPtr<tvm::Object>(container));
+
   if (meta_size != 0) {
     auto* meta = reinterpret_cast<tensor_meta*>(output_meta);
     if (meta_size < meta->meta_size(output->ndim)) {
@@ -170,8 +182,7 @@ AEEResult __QAIC_HEADER(launcher_rpc_get_output)(remote_handle64 handle, int out
       return error_too_small(__func__, "value_size", value_size, data_size);
     }
 
-    auto data = reinterpret_cast<decltype(output_value)>(output->data);
-    std::copy(data, data + data_size, output_value);
+    host_output.CopyFrom(output);
   }
 
   return AEE_SUCCESS;