graphengine/tests/ut/ge/graph/load/davinci_model_unittest.cc

/**
 * Copyright 2019-2020 Huawei Technologies Co., Ltd
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include <gtest/gtest.h>

#define private public
#define protected public
#include "graph/utils/graph_utils.h"
#include "common/profiling/profiling_manager.h"
#include "graph/load/new_model_manager/davinci_model.h"

using namespace std;

namespace ge {
extern OpDescPtr CreateOpDesc(string name, string type);

class UtestDavinciModel : public testing::Test {
 protected:
  void SetUp() {}

  void TearDown() {}
};

TEST_F(UtestDavinciModel, init_success) {
  DavinciModel model(0, nullptr);
  ComputeGraphPtr graph = make_shared<ComputeGraph>("default");
  ProfilingManager::Instance().is_load_profiling_ = true;

  GeModelPtr ge_model = make_shared<GeModel>();
  ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  AttrUtils::SetInt(ge_model, ATTR_MODEL_MEMORY_SIZE, 5120000);
  AttrUtils::SetInt(ge_model, ATTR_MODEL_STREAM_NUM, 1);

  shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  ge_model->SetModelTaskDef(model_task_def);

  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  TensorUtils::SetSize(tensor, 512);

  OpDescPtr op_input = CreateOpDesc("data", DATA);
  op_input->AddInputDesc(tensor);
  op_input->AddOutputDesc(tensor);
  op_input->SetInputOffset({1024});
  op_input->SetOutputOffset({1024});
  NodePtr node_input = graph->AddNode(op_input);    // op_index = 0

  OpDescPtr op_kernel = CreateOpDesc("square", "Square");
  op_kernel->AddInputDesc(tensor);
  op_kernel->AddOutputDesc(tensor);
  op_kernel->SetInputOffset({1024});
  op_kernel->SetOutputOffset({1024});
  NodePtr node_kernel = graph->AddNode(op_kernel);  // op_index = 1

  OpDescPtr op_memcpy = CreateOpDesc("memcpy", MEMCPYASYNC);
  op_memcpy->AddInputDesc(tensor);
  op_memcpy->AddOutputDesc(tensor);
  op_memcpy->SetInputOffset({1024});
  op_memcpy->SetOutputOffset({5120});
  NodePtr node_memcpy = graph->AddNode(op_memcpy);  // op_index = 2

  OpDescPtr op_output = CreateOpDesc("output", NETOUTPUT);
  op_output->AddInputDesc(tensor);
  op_output->SetInputOffset({5120});
  op_output->SetSrcName( { "memcpy" } );
  op_output->SetSrcIndex( { 0 } );
  NodePtr node_output = graph->AddNode(op_output);  // op_index = 3


  domi::TaskDef *task_def1 = model_task_def->add_task();
  task_def1->set_stream_id(0);
  task_def1->set_type(RT_MODEL_TASK_KERNEL);
  domi::KernelDef *kernel_def = task_def1->mutable_kernel();
  kernel_def->set_stub_func("stub_func");
  kernel_def->set_args_size(64);
  string args(64, '1');
  kernel_def->set_args(args.data(), 64);
  domi::KernelContext *context = kernel_def->mutable_context();
  context->set_op_index(1);
  context->set_kernel_type(2);    // ccKernelType::TE
  uint16_t args_offset[9] = {0};
  context->set_args_offset(args_offset, 9 * sizeof(uint16_t));

  domi::TaskDef *task_def2 = model_task_def->add_task();
  task_def2->set_stream_id(0);
  task_def2->set_type(RT_MODEL_TASK_MEMCPY_ASYNC);
  domi::MemcpyAsyncDef *memcpy_async = task_def2->mutable_memcpy_async();
  memcpy_async->set_src(1024);
  memcpy_async->set_dst(5120);
  memcpy_async->set_dst_max(512);
  memcpy_async->set_count(1);
  memcpy_async->set_kind(RT_MEMCPY_DEVICE_TO_DEVICE);
  memcpy_async->set_op_index(2);

  EXPECT_EQ(model.Assign(ge_model), SUCCESS);
  EXPECT_EQ(model.Init(), SUCCESS);

  EXPECT_EQ(model.input_addrs_list_.size(), 1);
  EXPECT_EQ(model.output_addrs_list_.size(), 1);
  EXPECT_EQ(model.task_list_.size(), 2);

  OutputData output_data;
  vector<OutputTensorInfo> outputs;
  EXPECT_EQ(model.GenOutputTensorInfo(&output_data, outputs), SUCCESS);
  EXPECT_EQ(output_data.blobs.size(), 1);
  EXPECT_EQ(outputs.size(), 1);

  ProfilingManager::Instance().is_load_profiling_ = false;
}

TEST_F(UtestDavinciModel, init_data_op) {
  DavinciModel model(0, nullptr);
  model.ge_model_ = make_shared<GeModel>();
  model.runtime_param_.mem_base = (uint8_t *)0x08000000;
  model.runtime_param_.mem_size = 5120000;
  ComputeGraphPtr graph = make_shared<ComputeGraph>("default");

  OpDescPtr op_input = CreateOpDesc("data", DATA);
  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  TensorUtils::SetSize(tensor, 512);
  op_input->AddInputDesc(tensor);
  op_input->AddOutputDesc(tensor);
  op_input->SetInputOffset({1024});
  op_input->SetOutputOffset({5120});
  NodePtr node_input = graph->AddNode(op_input);

  OpDescPtr op_output = CreateOpDesc("output", NETOUTPUT);
  op_output->AddInputDesc(tensor);
  op_output->SetInputOffset({1024});
  op_output->SetSrcName( { "data" } );
  op_output->SetSrcIndex( { 0 } );
  NodePtr node_output = graph->AddNode(op_output);

  EXPECT_EQ(model.InitNodes(graph), SUCCESS);

  EXPECT_EQ(model.input_addrs_list_.size(), 1);
  EXPECT_EQ(model.output_addrs_list_.size(), 1);
  EXPECT_EQ(model.op_list_.size(), 2);
}

TEST_F(UtestDavinciModel, init_data_op_subgraph) {
  DavinciModel model(0, nullptr);
  model.runtime_param_.mem_base = (uint8_t *)0x08000000;
  model.runtime_param_.mem_size = 5120000;
  ComputeGraphPtr graph = make_shared<ComputeGraph>("default");

  OpDescPtr op_input = CreateOpDesc("data", DATA);
  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  op_input->AddInputDesc(tensor);
  op_input->AddOutputDesc(tensor);
  op_input->SetInputOffset({1024});
  op_input->SetOutputOffset({5120});
  NodePtr node = graph->AddNode(op_input);

  uint32_t data_op_index = 0;
  map<uint32_t, OpDescPtr> data_by_index;
  EXPECT_EQ(model.InitDataOp(nullptr, node, data_op_index, data_by_index), SUCCESS);

  EXPECT_EQ(model.input_addrs_list_.size(), 0);
  EXPECT_EQ(model.output_addrs_list_.size(), 0);
  EXPECT_EQ(data_op_index, 0);
  EXPECT_TRUE(data_by_index.empty());
}

TEST_F(UtestDavinciModel, init_netoutput_op_subgraph) {
  DavinciModel model(0, nullptr);
  model.runtime_param_.mem_base = (uint8_t *)0x08000000;
  model.runtime_param_.mem_size = 5120000;
  ComputeGraphPtr graph = make_shared<ComputeGraph>("default");

  OpDescPtr op_output = CreateOpDesc("output", NETOUTPUT);
  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  op_output->AddInputDesc(tensor);
  op_output->SetInputOffset({1024});
  op_output->SetSrcName( { "data" } );
  op_output->SetSrcIndex( { 0 } );
  NodePtr node = graph->AddNode(op_output);

  std::vector<OpDescPtr> output_op_list;
  EXPECT_EQ(model.InitNetOutput(nullptr, node, output_op_list), SUCCESS);

  EXPECT_EQ(model.input_addrs_list_.size(), 0);
  EXPECT_EQ(model.output_addrs_list_.size(), 0);
  EXPECT_TRUE(output_op_list.empty());
}

TEST_F(UtestDavinciModel, init_unknown) {
  DavinciModel model(0, nullptr);
  model.SetKnownNode(true);
  ComputeGraphPtr graph = make_shared<ComputeGraph>("default");

  GeModelPtr ge_model = make_shared<GeModel>();
  ge_model->SetGraph(GraphUtils::CreateGraphFromComputeGraph(graph));
  AttrUtils::SetInt(ge_model, ATTR_MODEL_MEMORY_SIZE, 5120000);
  AttrUtils::SetInt(ge_model, ATTR_MODEL_STREAM_NUM, 1);

  shared_ptr<domi::ModelTaskDef> model_task_def = make_shared<domi::ModelTaskDef>();
  ge_model->SetModelTaskDef(model_task_def);

  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  TensorUtils::SetSize(tensor, 512);

  OpDescPtr op_input = CreateOpDesc("data", DATA);
  op_input->AddInputDesc(tensor);
  op_input->AddOutputDesc(tensor);
  op_input->SetInputOffset({1024});
  op_input->SetOutputOffset({1024});
  NodePtr node_input = graph->AddNode(op_input);    // op_index = 0

  OpDescPtr op_kernel = CreateOpDesc("square", "Square");
  op_kernel->AddInputDesc(tensor);
  op_kernel->AddOutputDesc(tensor);
  op_kernel->SetInputOffset({1024});
  op_kernel->SetOutputOffset({1024});
  NodePtr node_kernel = graph->AddNode(op_kernel);  // op_index = 1

  OpDescPtr op_memcpy = CreateOpDesc("memcpy", MEMCPYASYNC);
  op_memcpy->AddInputDesc(tensor);
  op_memcpy->AddOutputDesc(tensor);
  op_memcpy->SetInputOffset({1024});
  op_memcpy->SetOutputOffset({5120});
  NodePtr node_memcpy = graph->AddNode(op_memcpy);  // op_index = 2

  OpDescPtr op_output = CreateOpDesc("output", NETOUTPUT);
  op_output->AddInputDesc(tensor);
  op_output->SetInputOffset({5120});
  op_output->SetSrcName( { "memcpy" } );
  op_output->SetSrcIndex( { 0 } );
  NodePtr node_output = graph->AddNode(op_output);  // op_index = 3


  domi::TaskDef *task_def1 = model_task_def->add_task();
  task_def1->set_stream_id(0);
  task_def1->set_type(RT_MODEL_TASK_KERNEL);
  domi::KernelDef *kernel_def = task_def1->mutable_kernel();
  kernel_def->set_stub_func("stub_func");
  kernel_def->set_args_size(64);
  string args(64, '1');
  kernel_def->set_args(args.data(), 64);
  domi::KernelContext *context = kernel_def->mutable_context();
  context->set_op_index(1);
  context->set_kernel_type(2);    // ccKernelType::TE
  uint16_t args_offset[9] = {0};
  context->set_args_offset(args_offset, 9 * sizeof(uint16_t));

  domi::TaskDef *task_def2 = model_task_def->add_task();
  task_def2->set_stream_id(0);
  task_def2->set_type(RT_MODEL_TASK_MEMCPY_ASYNC);
  domi::MemcpyAsyncDef *memcpy_async = task_def2->mutable_memcpy_async();
  memcpy_async->set_src(1024);
  memcpy_async->set_dst(5120);
  memcpy_async->set_dst_max(512);
  memcpy_async->set_count(1);
  memcpy_async->set_kind(RT_MEMCPY_DEVICE_TO_DEVICE);
  memcpy_async->set_op_index(2);

  EXPECT_EQ(model.Assign(ge_model), SUCCESS);
  EXPECT_EQ(model.Init(), SUCCESS);

  EXPECT_EQ(model.input_addrs_list_.size(), 1);
  EXPECT_EQ(model.output_addrs_list_.size(), 1);
  EXPECT_EQ(model.task_list_.size(), 2);

  EXPECT_EQ(model.task_list_[0]->UpdateArgs(), SUCCESS);
  EXPECT_EQ(model.task_list_[1]->UpdateArgs(), SUCCESS);

  vector<string> out_shape_info;
  model.GetModelAttr(out_shape_info);

  vector<InputOutputDescInfo> input_descs;
  vector<InputOutputDescInfo> output_descs;
  EXPECT_EQ(model.GetInputOutputDescInfo(input_descs, output_descs), SUCCESS);

  int32_t virtual_addr = 0;
  const vector<void *> inputs = { &virtual_addr };
  const vector<void *> outputs = { &virtual_addr  };
  EXPECT_EQ(model.UpdateKnownNodeArgs(inputs, outputs), SUCCESS);
}

TEST_F(UtestDavinciModel, ReturnNoOutput_test) {
  DavinciModel model(0, nullptr);

  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  TensorUtils::SetSize(tensor, 512);

  OpDescPtr var1 = CreateOpDesc("var1", VARIABLE);
  var1->AddInputDesc(tensor);
  var1->AddOutputDesc(tensor);
  var1->SetInputOffset({1024});
  var1->SetOutputOffset({1024});

  model.variable_op_list_.push_back(var1);


  EXPECT_EQ(model.ReturnNoOutput(1), PARAM_INVALID);
}

TEST_F(UtestDavinciModel, SyncVarData_test) {
  DavinciModel model(0, nullptr);

  GeTensorDesc tensor(GeShape(), FORMAT_NCHW, DT_FLOAT);
  TensorUtils::SetSize(tensor, 512);

  OpDescPtr var1 = CreateOpDesc("var1", VARIABLE);
  var1->AddInputDesc(tensor);
  var1->AddOutputDesc(tensor);
  var1->SetInputOffset({1024});
  var1->SetOutputOffset({1024});

  model.variable_op_list_.push_back(var1);

  EXPECT_NE(model.SyncVarData(), SUCCESS);
}


}  // namespace ge