// Copyright 2018 The MACE Authors. All Rights Reserved.
//
// 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.

/**
 * Usage:
 * mace_run --model=mobi_mace.pb \
 *          --input=input_node  \
 *          --output=output_node  \
 *          --input_shape=1,224,224,3   \
 *          --output_shape=1,224,224,2   \
 *          --input_file=input_data \
 *          --output_file=mace.out  \
 *          --model_data_file=model_data.data \
 *          --device=GPU
 */
#include <stdint.h>
#include <cstdio>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <numeric>

#include "gflags/gflags.h"
#include "mace/public/mace.h"
#include "mace/port/env.h"
#include "mace/port/file_system.h"
#include "mace/utils/logging.h"
#include "mace/utils/memory.h"
#include "mace/utils/string_util.h"

#ifdef MODEL_GRAPH_FORMAT_CODE
#include "mace/codegen/engine/mace_engine_factory.h"
#endif

namespace mace {
namespace tools {
namespace validation {

void ParseShape(const std::string &str, std::vector<int64_t> *shape) {
  std::string tmp = str;
  while (!tmp.empty()) {
    int dim = atoi(tmp.data());
    shape->push_back(dim);
    size_t next_offset = tmp.find(",");
    if (next_offset == std::string::npos) {
      break;
    } else {
      tmp = tmp.substr(next_offset + 1);
    }
  }
}

std::string FormatName(const std::string input) {
  std::string res = input;
  for (size_t i = 0; i < input.size(); ++i) {
    if (!isalnum(res[i])) res[i] = '_';
  }
  return res;
}

DeviceType ParseDeviceType(const std::string &device_str) {
  if (device_str.compare("CPU") == 0) {
    return DeviceType::CPU;
  } else if (device_str.compare("GPU") == 0) {
    return DeviceType::GPU;
  } else if (device_str.compare("HEXAGON") == 0) {
    return DeviceType::HEXAGON;
  } else if (device_str.compare("HTA") == 0) {
    return DeviceType::HTA;
  } else {
    return DeviceType::CPU;
  }
}

DataFormat ParseDataFormat(const std::string &data_format_str) {
  if (data_format_str == "NHWC") {
    return DataFormat::NHWC;
  } else if (data_format_str == "NCHW") {
    return DataFormat::NCHW;
  } else if (data_format_str == "OIHW") {
    return DataFormat::OIHW;
  } else {
    return DataFormat::NONE;
  }
}

DEFINE_string(model_name,
              "",
              "model name in yaml");
DEFINE_string(input_node,
              "",
              "input nodes, separated by comma");
DEFINE_string(input_shape,
              "",
              "input shapes, separated by colon and comma");
DEFINE_string(output_node,
              "",
              "output nodes, separated by comma");
DEFINE_string(output_shape,
              "",
              "output shapes, separated by colon and comma");
DEFINE_string(input_data_format,
              "NHWC",
              "input data formats, NONE|NHWC|NCHW");
DEFINE_string(output_data_format,
              "NHWC",
              "output data formats, NONE|NHWC|NCHW");
DEFINE_string(input_file,
              "",
              "input file name | input file prefix for multiple inputs.");
DEFINE_string(output_file,
              "",
              "output file name | output file prefix for multiple outputs");
// TODO(liyin): support batch validation
DEFINE_string(input_dir,
              "",
              "input directory name");
DEFINE_string(output_dir,
              "output",
              "output directory name");
DEFINE_string(opencl_binary_file,
              "",
              "compiled opencl binary file path");
DEFINE_string(opencl_parameter_file,
              "",
              "tuned OpenCL parameter file path");
DEFINE_string(model_data_file,
              "",
              "model data file name, used when EMBED_MODEL_DATA set to 0 or 2");
DEFINE_string(model_file,
              "",
              "model file name, used when load mace model in pb");
DEFINE_string(device, "GPU", "CPU/GPU/HEXAGON");
DEFINE_int32(round, 1, "round");
DEFINE_int32(restart_round, 1, "restart round");
DEFINE_int32(malloc_check_cycle, -1, "malloc debug check cycle, -1 to disable");
DEFINE_int32(gpu_perf_hint, 3, "0:DEFAULT/1:LOW/2:NORMAL/3:HIGH");
DEFINE_int32(gpu_priority_hint, 3, "0:DEFAULT/1:LOW/2:NORMAL/3:HIGH");
DEFINE_int32(omp_num_threads, -1, "num of openmp threads");
DEFINE_int32(cpu_affinity_policy, 1,
             "0:AFFINITY_NONE/1:AFFINITY_BIG_ONLY/2:AFFINITY_LITTLE_ONLY");

bool RunModel(const std::string &model_name,
              const std::vector<std::string> &input_names,
              const std::vector<std::vector<int64_t>> &input_shapes,
              const std::vector<DataFormat> &input_data_formats,
              const std::vector<std::string> &output_names,
              const std::vector<std::vector<int64_t>> &output_shapes,
              const std::vector<DataFormat> &output_data_formats,
              float cpu_capability) {
  DeviceType device_type = ParseDeviceType(FLAGS_device);

  int64_t t0 = NowMicros();
  // config runtime
  MaceStatus status;
  MaceEngineConfig config(device_type);
  status = config.SetCPUThreadPolicy(
          FLAGS_omp_num_threads,
          static_cast<CPUAffinityPolicy >(FLAGS_cpu_affinity_policy));
  if (status != MaceStatus::MACE_SUCCESS) {
    LOG(WARNING) << "Set openmp or cpu affinity failed.";
  }
#ifdef MACE_ENABLE_OPENCL
  std::shared_ptr<GPUContext> gpu_context;
  if (device_type == DeviceType::GPU) {
    const char *storage_path_ptr = getenv("MACE_INTERNAL_STORAGE_PATH");
    const std::string storage_path =
        std::string(storage_path_ptr == nullptr ?
                    "/data/local/tmp/mace_run/interior" : storage_path_ptr);
    std::vector<std::string> opencl_binary_paths = {FLAGS_opencl_binary_file};

    gpu_context = GPUContextBuilder()
        .SetStoragePath(storage_path)
        .SetOpenCLBinaryPaths(opencl_binary_paths)
        .SetOpenCLParameterPath(FLAGS_opencl_parameter_file)
        .Finalize();

    config.SetGPUContext(gpu_context);
    config.SetGPUHints(
        static_cast<GPUPerfHint>(FLAGS_gpu_perf_hint),
        static_cast<GPUPriorityHint>(FLAGS_gpu_priority_hint));
  }
#endif  // MACE_ENABLE_OPENCL

  std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_graph_data =
    make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
  if (FLAGS_model_file != "") {
    auto fs = GetFileSystem();
    status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_file.c_str(),
        &model_graph_data);
    if (status != MaceStatus::MACE_SUCCESS) {
      LOG(FATAL) << "Failed to read file: " << FLAGS_model_file;
    }
  }

  std::unique_ptr<mace::port::ReadOnlyMemoryRegion> model_weights_data =
    make_unique<mace::port::ReadOnlyBufferMemoryRegion>();
  if (FLAGS_model_data_file != "") {
    auto fs = GetFileSystem();
    status = fs->NewReadOnlyMemoryRegionFromFile(FLAGS_model_data_file.c_str(),
        &model_weights_data);
    if (status != MaceStatus::MACE_SUCCESS) {
      LOG(FATAL) << "Failed to read file: " << FLAGS_model_data_file;
    }
  }

  std::shared_ptr<mace::MaceEngine> engine;
  MaceStatus create_engine_status;

  while (true) {
    // Create Engine
    int64_t t0 = NowMicros();
#ifdef MODEL_GRAPH_FORMAT_CODE
    if (model_name.empty()) {
      LOG(INFO) << "Please specify model name you want to run";
      return false;
    }
    create_engine_status =
          CreateMaceEngineFromCode(model_name,
                                   reinterpret_cast<const unsigned char *>(
                                     model_weights_data->data()),
                                   model_weights_data->length(),
                                   input_names,
                                   output_names,
                                   config,
                                   &engine);
#else
    (void)(model_name);
    if (model_graph_data == nullptr || model_weights_data == nullptr) {
      LOG(INFO) << "Please specify model graph file and model data file";
      return false;
    }
    create_engine_status =
        CreateMaceEngineFromProto(reinterpret_cast<const unsigned char *>(
                                    model_graph_data->data()),
                                  model_graph_data->length(),
                                  reinterpret_cast<const unsigned char *>(
                                    model_weights_data->data()),
                                  model_weights_data->length(),
                                  input_names,
                                  output_names,
                                  config,
                                  &engine);
#endif
    int64_t t1 = NowMicros();

    if (create_engine_status != MaceStatus::MACE_SUCCESS) {
      LOG(ERROR) << "Create engine runtime error, retry ... errcode: "
                 << create_engine_status.information();
    } else {
      double create_engine_millis = (t1 - t0) / 1000.0;
      LOG(INFO) << "Create Mace Engine latency: " << create_engine_millis
                << " ms";
      break;
    }
  }
  int64_t t1 = NowMicros();
  double init_millis = (t1 - t0) / 1000.0;
  LOG(INFO) << "Total init latency: " << init_millis << " ms";

  const size_t input_count = input_names.size();
  const size_t output_count = output_names.size();

  std::map<std::string, mace::MaceTensor> inputs;
  std::map<std::string, mace::MaceTensor> outputs;
  for (size_t i = 0; i < input_count; ++i) {
    // Allocate input and output
    // only support float and int32, use char for generalization
    // sizeof(int) == 4, sizeof(float) == 4
    int64_t input_size =
        std::accumulate(input_shapes[i].begin(), input_shapes[i].end(), 4,
                        std::multiplies<int64_t>());
    auto buffer_in = std::shared_ptr<char>(new char[input_size],
                                           std::default_delete<char[]>());
    // load input
    std::ifstream in_file(FLAGS_input_file + "_" + FormatName(input_names[i]),
                          std::ios::in | std::ios::binary);
    if (in_file.is_open()) {
      in_file.read(buffer_in.get(), input_size);
      in_file.close();
    } else {
      LOG(INFO) << "Open input file failed";
      return -1;
    }
    inputs[input_names[i]] = mace::MaceTensor(input_shapes[i], buffer_in,
        input_data_formats[i]);
  }

  for (size_t i = 0; i < output_count; ++i) {
    // only support float and int32, use char for generalization
    int64_t output_size =
        std::accumulate(output_shapes[i].begin(), output_shapes[i].end(), 4,
                        std::multiplies<int64_t>());
    auto buffer_out = std::shared_ptr<char>(new char[output_size],
                                            std::default_delete<char[]>());
    outputs[output_names[i]] = mace::MaceTensor(output_shapes[i], buffer_out,
        output_data_formats[i]);
  }

  LOG(INFO) << "Warm up run";
  double warmup_millis;
  while (true) {
    int64_t t3 = NowMicros();
    MaceStatus warmup_status = engine->Run(inputs, &outputs);
    if (warmup_status != MaceStatus::MACE_SUCCESS) {
      LOG(ERROR) << "Warmup runtime error, retry ... errcode: "
                 << warmup_status.information();
      do {
#ifdef MODEL_GRAPH_FORMAT_CODE
        create_engine_status =
          CreateMaceEngineFromCode(model_name,
                                   reinterpret_cast<const unsigned char *>(
                                     model_weights_data->data()),
                                   model_weights_data->length(),
                                   input_names,
                                   output_names,
                                   config,
                                   &engine);
#else
        create_engine_status =
            CreateMaceEngineFromProto(reinterpret_cast<const unsigned char *>(
                                        model_graph_data->data()),
                                      model_graph_data->length(),
                                      reinterpret_cast<const unsigned char *>(
                                        model_weights_data->data()),
                                      model_weights_data->length(),
                                      input_names,
                                      output_names,
                                      config,
                                      &engine);
#endif
      } while (create_engine_status != MaceStatus::MACE_SUCCESS);
    } else {
      int64_t t4 = NowMicros();
      warmup_millis = (t4 - t3) / 1000.0;
      LOG(INFO) << "1st warm up run latency: " << warmup_millis << " ms";
      break;
    }
  }

  double model_run_millis = -1;
  if (FLAGS_round > 0) {
    LOG(INFO) << "Run model";
    int64_t total_run_duration = 0;
    for (int i = 0; i < FLAGS_round; ++i) {
      std::unique_ptr<port::Logger> info_log;
      std::unique_ptr<port::MallocLogger> malloc_logger;
      if (FLAGS_malloc_check_cycle >= 1 && i % FLAGS_malloc_check_cycle == 0) {
        info_log = LOG_PTR(INFO);
        malloc_logger = port::Env::Default()->NewMallocLogger(
            info_log.get(), MakeString(i));
      }
      MaceStatus run_status;
      while (true) {
        int64_t t0 = NowMicros();
        run_status = engine->Run(inputs, &outputs);
        if (run_status != MaceStatus::MACE_SUCCESS) {
          LOG(ERROR) << "Mace run model runtime error, retry ... errcode: "
                     << run_status.information();
          do {
#ifdef MODEL_GRAPH_FORMAT_CODE
            create_engine_status =
              CreateMaceEngineFromCode(model_name,
                                       reinterpret_cast<const unsigned char *>(
                                         model_weights_data->data()),
                                       model_weights_data->length(),
                                       input_names,
                                       output_names,
                                       config,
                                       &engine);
#else
            create_engine_status =
                CreateMaceEngineFromProto(
                    reinterpret_cast<const unsigned char *>(
                      model_graph_data->data()),
                    model_graph_data->length(),
                    reinterpret_cast<const unsigned char *>(
                      model_weights_data->data()),
                    model_weights_data->length(),
                    input_names,
                    output_names,
                    config,
                    &engine);
#endif
          } while (create_engine_status != MaceStatus::MACE_SUCCESS);
        } else {
          int64_t t1 = NowMicros();
          total_run_duration += (t1 - t0);
          break;
        }
      }
    }
    model_run_millis = total_run_duration / 1000.0 / FLAGS_round;
    LOG(INFO) << "Average latency: " << model_run_millis << " ms";
  }

  // Metrics reporting tools depends on the format, keep in consistent
  printf("========================================================\n");
  printf("     capability(CPU)        init      warmup     run_avg\n");
  printf("========================================================\n");
  printf("time %15.3f %11.3f %11.3f %11.3f\n",
         cpu_capability, init_millis, warmup_millis, model_run_millis);


  for (size_t i = 0; i < output_count; ++i) {
    std::string output_name =
        FLAGS_output_file + "_" + FormatName(output_names[i]);
    std::ofstream out_file(output_name, std::ios::binary);
    // only support float and int32
    int64_t output_size =
        std::accumulate(output_shapes[i].begin(), output_shapes[i].end(), 4,
                        std::multiplies<int64_t>());
    out_file.write(
        outputs[output_names[i]].data<char>().get(), output_size);
    out_file.flush();
    out_file.close();
    LOG(INFO) << "Write output file " << output_name << " with size "
              << output_size << " done.";
  }

  return true;
}

int Main(int argc, char **argv) {
  std::string usage = "MACE run model tool, please specify proper arguments.\n"
                      "usage: " + std::string(argv[0])
                      + " --help";
  gflags::SetUsageMessage(usage);
  gflags::ParseCommandLineFlags(&argc, &argv, true);

  std::vector<std::string> input_names = Split(FLAGS_input_node, ',');
  std::vector<std::string> output_names = Split(FLAGS_output_node, ',');
  if (input_names.empty() || output_names.empty()) {
    LOG(INFO) << gflags::ProgramUsage();
    return 0;
  }

  LOG(INFO) << "model name: " << FLAGS_model_name;
  LOG(INFO) << "mace version: " << MaceVersion();
  LOG(INFO) << "input node: " << FLAGS_input_node;
  LOG(INFO) << "input shape: " << FLAGS_input_shape;
  LOG(INFO) << "output node: " << FLAGS_output_node;
  LOG(INFO) << "output shape: " << FLAGS_output_shape;
  LOG(INFO) << "input_file: " << FLAGS_input_file;
  LOG(INFO) << "output_file: " << FLAGS_output_file;
  LOG(INFO) << "model_data_file: " << FLAGS_model_data_file;
  LOG(INFO) << "model_file: " << FLAGS_model_file;
  LOG(INFO) << "device: " << FLAGS_device;
  LOG(INFO) << "round: " << FLAGS_round;
  LOG(INFO) << "restart_round: " << FLAGS_restart_round;
  LOG(INFO) << "gpu_perf_hint: " << FLAGS_gpu_perf_hint;
  LOG(INFO) << "gpu_priority_hint: " << FLAGS_gpu_priority_hint;
  LOG(INFO) << "omp_num_threads: " << FLAGS_omp_num_threads;
  LOG(INFO) << "cpu_affinity_policy: " << FLAGS_cpu_affinity_policy;

  std::vector<std::string> input_shapes = Split(FLAGS_input_shape, ':');
  std::vector<std::string> output_shapes = Split(FLAGS_output_shape, ':');

  const size_t input_count = input_shapes.size();
  const size_t output_count = output_shapes.size();
  std::vector<std::vector<int64_t>> input_shape_vec(input_count);
  std::vector<std::vector<int64_t>> output_shape_vec(output_count);
  for (size_t i = 0; i < input_count; ++i) {
    ParseShape(input_shapes[i], &input_shape_vec[i]);
  }
  for (size_t i = 0; i < output_count; ++i) {
    ParseShape(output_shapes[i], &output_shape_vec[i]);
  }
  if (input_names.size() != input_shape_vec.size()
      || output_names.size() != output_shape_vec.size()) {
    LOG(INFO) << "inputs' names do not match inputs' shapes "
                 "or outputs' names do not match outputs' shapes";
    return 0;
  }
  std::vector<std::string> raw_input_data_formats =
    Split(FLAGS_input_data_format, ',');
  std::vector<std::string> raw_output_data_formats =
    Split(FLAGS_output_data_format, ',');
  std::vector<DataFormat> input_data_formats(input_count);
  std::vector<DataFormat> output_data_formats(output_count);
  for (size_t i = 0; i < input_count; ++i) {
    input_data_formats[i] = ParseDataFormat(raw_input_data_formats[i]);
  }
  for (size_t i = 0; i < output_count; ++i) {
    output_data_formats[i] = ParseDataFormat(raw_output_data_formats[i]);
  }


  // get cpu capability
  Capability cpu_capability = GetCapability(DeviceType::CPU);
  float cpu_float32_performance = cpu_capability.float32_performance.exec_time;

  bool ret = false;
  for (int i = 0; i < FLAGS_restart_round; ++i) {
    VLOG(0) << "restart round " << i;
    ret = RunModel(FLAGS_model_name,
        input_names, input_shape_vec, input_data_formats,
        output_names, output_shape_vec, output_data_formats,
        cpu_float32_performance);
  }
  if (ret) {
    return 0;
  }
  return -1;
}

}  // namespace validation
}  // namespace tools
}  // namespace mace

int main(int argc, char **argv) { mace::tools::validation::Main(argc, argv); }
