/**
 * Copyright 2019 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 "backend/kernel_compiler/common_utils.h"
#include <unordered_map>
#include <map>
#include <iostream>
#include <utility>
#include <fstream>
#include <algorithm>
#include <thread>
#include "nlohmann/json.hpp"
#include "backend/session/anf_runtime_algorithm.h"
#include "utils/ms_utils.h"
#include "ir/manager.h"
#include "ir/meta_tensor.h"
#include "base/core_ops.h"
#include "ir/graph_utils.h"
#include "utils/ms_context.h"
#include "mindspore/ccsrc/debug/common.h"

namespace mindspore {
namespace kernel {
constexpr char kAxis[] = "axis";
constexpr char kTypeInt32[] = "Int32";
const std::unordered_map<std::string, TypeId> type_id_maps = {
  {"float", TypeId::kNumberTypeFloat32},   {"float16", TypeId::kNumberTypeFloat16},
  {"float32", TypeId::kNumberTypeFloat32}, {"float64", TypeId::kNumberTypeFloat64},
  {"int", TypeId::kNumberTypeInt},         {"int8", TypeId::kNumberTypeInt8},
  {"int16", TypeId::kNumberTypeInt16},     {"int32", TypeId::kNumberTypeInt32},
  {"int64", TypeId::kNumberTypeInt64},     {"uint", TypeId::kNumberTypeUInt},
  {"uint8", TypeId::kNumberTypeUInt8},     {"uint16", TypeId::kNumberTypeUInt16},
  {"uint32", TypeId::kNumberTypeUInt32},   {"uint64", TypeId::kNumberTypeUInt64},
  {"bool", TypeId::kNumberTypeBool},       {"complex64", TypeId::kNumberTypeComplex64}};

const std::map<TypeId, std::string> type_id_str_map = {
  {TypeId::kNumberTypeFloat32, "float32"}, {TypeId::kNumberTypeFloat16, "float16"},
  {TypeId::kNumberTypeFloat, "float"},     {TypeId::kNumberTypeFloat64, "float64"},
  {TypeId::kNumberTypeInt, "int"},         {TypeId::kNumberTypeInt8, "int8"},
  {TypeId::kNumberTypeInt16, "int16"},     {TypeId::kNumberTypeInt32, "int32"},
  {TypeId::kNumberTypeInt64, "int64"},     {TypeId::kNumberTypeUInt, "uint"},
  {TypeId::kNumberTypeUInt8, "uint8"},     {TypeId::kNumberTypeUInt16, "uint16"},
  {TypeId::kNumberTypeUInt32, "uint32"},   {TypeId::kNumberTypeUInt64, "uint64"},
  {TypeId::kNumberTypeBool, "bool"},       {TypeId::kNumberTypeComplex64, "complex64"}};

const std::unordered_map<std::string, std::string> dtype_shortdtype_map_ = {
  {"float16", "f16"}, {"float32", "f32"}, {"float64", "f64"}, {"int8", "i8"},    {"int16", "i16"},  {"int32", "i32"},
  {"int64", "i64"},   {"uint8", "u8"},    {"uint16", "u16"},  {"uint32", "u32"}, {"uint64", "u64"}, {"bool", "bool"},
};

const std::unordered_map<std::string, size_t> dtype_nbyte_map = {
  {"float16", sizeof(float) / 2},  {"float32", sizeof(float)},  {"float64", sizeof(float) * 2},
  {"int8", sizeof(int) / 4},       {"int16", sizeof(int) / 2},  {"int32", sizeof(int)},
  {"int64", sizeof(int) * 2},      {"uint8", sizeof(int) / 4},  {"uint16", sizeof(int) / 2},
  {"uint32", sizeof(int)},         {"uint64", sizeof(int) * 2}, {"bool", sizeof(char)},
  {"complex64", sizeof(float) * 2}};

// Define all patterns here for different schedule
const std::unordered_map<FusionType, std::string> fusion_type_name_maps = {
  {FusionType::BN_UPDATE_GRAD, "bn_update_grad"},
  {FusionType::BN_GRAD_REDUCE, "bn_grad_reduce"},
  {FusionType::LAYER_NORM_GRAD, "layer_norm_grad"},
  {FusionType::L2LOSS_MUL_ADDN, "l2loss_mul_addn"},
  {FusionType::ELEMWISE, "ElemWise"},
  {FusionType::PURE_BROADCAST, "PureBroadcast"},
  {FusionType::COMMREDUCE, "CommReduce"},
  {FusionType::SEGMENT, "Segment"},
  {FusionType::INPLACE, "Inplace"},
  {FusionType::MATMUL, "Matmul"},
  {FusionType::MATMUL_V2, "Matmul_v2"},
  {FusionType::GEMM, "GEMM"},
  {FusionType::CONV, "Convolution"},
  {FusionType::CONV2D_BACKPROP_INPUT, "Conv2d_backprop_input"},
  {FusionType::CONV2D_BACKPROP_FILTER, "Conv2d_backprop_filter"},
  {FusionType::CONV3D_BACKPROP_INPUT, "Conv3d_backprop_input"},
  {FusionType::CONV3D_BACKPROP_FILTER, "Conv3d_backprop_filter"},
  {FusionType::CUBE_LAYER_NORM, "cube_layer_norm"},
  {FusionType::OPAQUE, "Opaque"},
  {FusionType::BN_REDUCE, "bn_reduce"},
  {FusionType::BN_UPDATE, "bn_update"},
  {FusionType::SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, "softmax_cross_entropy_with_logits"},
  {FusionType::L2_NORMALIZE, "l2_normalize"},
  {FusionType::SOFTMAX, "softmax_pattern"},
  {FusionType::L2_LOSS, "l2_loss"},
  {FusionType::ASCEND_QUANT, "quant"},
  {FusionType::ASCEND_DEQUANT, "dequant"},
  {FusionType::ASCEND_ANTI_QUANT, "anti_quant"},
  {FusionType::STRIDED_READ, "strided_read"},
  {FusionType::STRIDED_WRITE, "strided_write"},
  {FusionType::ASCEND_DEQUANT_S16, "dequant_s16"},
  {FusionType::ASCEND_REQUANT, "requant"},
  {FusionType::ASCEND_REQUANT_S16, "requant_s16"},
  {FusionType::MAX_POOL, "MaxPool"},
  {FusionType::DEPTHWISECONV, "DepthwiseConvolution"},
  {FusionType::CONV3D, "Conv3d"},
  {FusionType::POOL2D, "Pool2d"},
  {FusionType::POOL3D, "Pool3d"},
  {FusionType::READ_SELECT, "read_select"},
  {FusionType::WRITE_SELECT, "write_select"},
  {FusionType::COSINE_EMBEDDING_LOSS, "cosine_embedding_loss"},
  {FusionType::DILATION_PATTERN, "dilation"},
  {FusionType::BROAD_CAST, "Broadcast"},
  {FusionType::BATCH_MATMUL, "BatchMatmul"},
  {FusionType::CONFUSION_TRANSPOSE, "confusiontranspose"},
  {FusionType::UNKNOWN_FUSION_TYPE, ""}};

std::string GetFusionNameByType(const kernel::FusionType &type) {
  auto iter = fusion_type_name_maps.find(type);
  if (iter == fusion_type_name_maps.end()) {
    MS_LOG(EXCEPTION) << "Illegal fusion type: " << type;
  }
  return iter->second;
}

FusionType GetFusionTypeByName(const std::string &name) {
  std::string fusion_name_upper = name;
  transform(fusion_name_upper.begin(), fusion_name_upper.end(), fusion_name_upper.begin(), ::toupper);
  auto iter =
    std::find_if(fusion_type_name_maps.begin(), fusion_type_name_maps.end(), [&fusion_name_upper](const auto &it) {
      std::string name_upper = it.second;
      transform(name_upper.begin(), name_upper.end(), name_upper.begin(), ::toupper);
      return fusion_name_upper == name_upper;
    });

  if (iter == fusion_type_name_maps.end()) {
    MS_LOG(EXCEPTION) << "Illegal fusion name: " << name;
  }
  return iter->first;
}

void KernelMeta::Initialize() {
  kernel_meta_path_ = std::string(kGpuKernelMeta) + "/";

#if defined(_WIN32) || defined(_WIN64)
  auto ret = mkdir(kernel_meta_path_.c_str());
#else
  auto ret = mkdir(kernel_meta_path_.c_str(), S_IRWXG | S_IRWXU);
#endif
  if (ret != 0) {
    MS_LOG(INFO) << "kernel dir [" << kernel_meta_path_ << "], will be created later";
  }
  initialized_ = true;
}

std::string KernelMeta::Search(const std::string &kernel_name) const {
  if (!initialized_) {
    return "";
  }

  auto iter = kernel_meta_map_.find(kernel_name);
  if (iter == kernel_meta_map_.end()) {
    return "";
  } else {
    return iter->second;
  }
}

bool KernelMeta::Insert(const std::string &kernel_name, const std::string &kernel_json) {
  if (!initialized_) {
    return false;
  }
  kernel_meta_map_[kernel_name] = kernel_json;
  return true;
}

bool CheckCache(const std::string &kernel_name) {
  // check cache.
  KernelMeta *bin_map = KernelMeta::GetInstance();
  if (bin_map == nullptr) {
    MS_LOG(DEBUG) << "kernel cache is invalid.";
    return false;
  }
  std::string kernel_json = bin_map->Search(kernel_name);
  bool ret = (!kernel_json.empty());
  if (ret) {
    MS_LOG(INFO) << "Kernel name:" << kernel_name << " has registered.";
  } else {
    MS_LOG(INFO) << "Kernel name:" << kernel_name << " will been registered.";
  }
  return ret;
}

KernelPackPtr SearchCache(const std::string &kernel_name, const std::string &processor) {
  // search cache.
  KernelMeta *bin_map = KernelMeta::GetInstance();
  if (bin_map == nullptr) {
    MS_LOG(DEBUG) << "kernel cache is invalid.";
    return nullptr;
  }

  std::string kernel_json = bin_map->Search(kernel_name);
  if (!kernel_json.empty()) {
    KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
    // just a tmp solution.
    if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
      MS_LOG(ERROR) << "Read cache json and bin file failed[" << kernel_json << "].";
      return nullptr;
    } else {
      return kernel_pack;
    }
  } else {
    MS_LOG(INFO) << "cache kernel not found[" << kernel_name << "].";
    return nullptr;
  }
}

KernelPackPtr InsertCache(const std::string &kernel_name, const std::string &processor) {
  MS_LOG(INFO) << "kernel name:" << kernel_name << ", processr:" << processor;
  KernelMeta *bin_map = KernelMeta::GetInstance();
  std::string kernel_json;
  if (processor == kProcessorAiCore || processor == kProcessorAiCpu) {
    kernel_json = kCceKernelMeta;
  } else {
    kernel_json = bin_map->kernel_meta_path();
  }
  (void)kernel_json.append(kernel_name).append(kJsonSuffix);
  KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
  if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
    MS_LOG(ERROR) << "Read json and bin file failed[" << kernel_json << "].";
    return nullptr;
  }

  if (bin_map == nullptr) {
    MS_LOG(DEBUG) << "kernel cache is invalid.";
    return nullptr;
  }
  if (bin_map->Insert(kernel_name, kernel_json)) {
    MS_LOG(INFO) << "Insert to cache success[" << kernel_json << "], kernelname[" << kernel_name << "].";
  }
  return kernel_pack;
}

TypeId DtypeToTypeId(const std::string &dtypes) {
  auto iter = type_id_maps.find(dtypes);
  if (iter != type_id_maps.end()) {
    return iter->second;
  } else {
    MS_EXCEPTION(ArgumentError) << "Illegal input device dtype:" << dtypes;
  }
}

std::string TypeId2String(TypeId type_id, bool unknown_as_default) {
  auto iter = type_id_str_map.find(type_id);
  if (iter == type_id_str_map.end()) {
    if (!unknown_as_default) {
      MS_EXCEPTION(ArgumentError) << "Illegal input dtype." << TypeIdLabel(type_id);
    }
    return "float32";
  }
  return iter->second;
}

std::string Dtype2ShortType(const std::string &dtypes) {
  auto iter = dtype_shortdtype_map_.find(dtypes);
  if (iter != dtype_shortdtype_map_.end()) {
    return iter->second;
  } else {
    MS_EXCEPTION(ArgumentError) << "Illegal input dtype:" << dtypes;
  }
}

size_t GetDtypeNbyte(const std::string &dtypes) {
  auto iter = dtype_nbyte_map.find(dtypes);
  if (iter != dtype_nbyte_map.end()) {
    return iter->second;
  } else {
    MS_EXCEPTION(ArgumentError) << "Illegal input dtype:" << dtypes;
  }
}

bool SetInputKernelBuilderInfo(const std::vector<std::shared_ptr<OpIOInfo>> &inputs, size_t real_input_num,
                               size_t builder_idex, const std::vector<int64_t> &dyn_input_sizes,
                               const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder) {
  MS_EXCEPTION_IF_NULL(builder);

  std::vector<TypeId> inputs_device_type;
  std::vector<std::string> inputs_format;
  size_t dyn_input_idx = 0;
  size_t kernel_info_index = 0;
  MS_EXCEPTION_IF_NULL(inputs[0]);
  size_t kernel_info_cnt = inputs[0]->dtypes().size();

  for (const auto &input : inputs) {
    MS_EXCEPTION_IF_NULL(input);
    std::string param_type = input->param_type();
    std::vector<std::string> dtypes = input->dtypes();
    std::vector<std::string> formats = input->formats();
    if (dtypes.size() != kernel_info_cnt || formats.size() != kernel_info_cnt) {
      MS_LOG(DEBUG) << "Set input kernel builder info, dtyps size != formats size.";
      return false;
    }

    if (param_type == "dynamic") {
      if (dyn_input_sizes.empty()) {
        MS_LOG(DEBUG) << "Set input kernel builder info, dyn_input_sizes's size is 0 when param_type is dynamic";
        return false;
      }

      for (int64_t t = 0; t < dyn_input_sizes[dyn_input_idx]; t++) {
        kernel_info_index++;
        auto type_id = DtypeToTypeId(dtypes[builder_idex]);
        inputs_device_type.push_back(type_id);
        inputs_format.push_back(formats[builder_idex]);
      }
      dyn_input_idx++;
    } else if (param_type == "required") {
      kernel_info_index++;
      auto type_id = DtypeToTypeId(dtypes[builder_idex]);
      inputs_device_type.push_back(type_id);
      inputs_format.push_back(formats[builder_idex]);
    } else {
      if (kernel_info_index < real_input_num) {
        MS_LOG(INFO) << "Set input kernel builder info, input type is optional, input index is :" << kernel_info_index;
        kernel_info_index++;
        auto type_id = DtypeToTypeId(dtypes[builder_idex]);
        inputs_device_type.push_back(type_id);
        inputs_format.push_back(formats[builder_idex]);
      }
    }
  }

  builder->SetInputsDeviceType(inputs_device_type);
  builder->SetInputsFormat(inputs_format);
  return true;
}

bool SetOutputKernelBuilderInfo(const std::vector<std::shared_ptr<OpIOInfo>> &outputs, size_t builder_idex,
                                const size_t &real_output_num,
                                const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder) {
  // not now but in the next we need to support dynamic output case
  MS_EXCEPTION_IF_NULL(builder);

  size_t output_idx = 0;
  std::vector<TypeId> outputs_device_type;
  std::vector<std::string> outputs_format;
  MS_EXCEPTION_IF_NULL(outputs[0]);
  size_t kernel_info_cnt = outputs[0]->dtypes().size();

  for (const auto &output : outputs) {
    MS_EXCEPTION_IF_NULL(output);
    if (output_idx >= real_output_num) {
      MS_LOG(DEBUG) << "real_output_num:" << real_output_num << ", output_idx:" << output_idx << " is out of limit!";
      continue;
    }
    size_t output_num = 0;
    if (output->param_type() == "dynamic") {
      if (outputs.size() > 1) {
        MS_EXCEPTION(ArgumentError) << "Dynamic output is unsupported multi output!";
      }
      output_num = real_output_num;
    } else if (output->param_type() == "required") {
      output_num = 1;
    } else {
      if (output_idx < real_output_num) {
        MS_LOG(DEBUG) << "Set output kernel builder info, output type is optional, output index is :" << output_idx;
        output_num = 1;
      }
    }

    for (size_t i = 0; i < output_num; i++) {
      std::vector<std::string> dtypes = output->dtypes();
      std::vector<std::string> formats = output->formats();
      if (dtypes.size() != kernel_info_cnt || formats.size() != kernel_info_cnt) {
        MS_LOG(DEBUG) << "Set output kernel builder info, dtyps size != formats size.";
        return false;
      }
      auto type_id = DtypeToTypeId(dtypes[builder_idex]);
      outputs_device_type.push_back(type_id);
      outputs_format.push_back(formats[builder_idex]);
      output_idx++;
    }
  }

  builder->SetOutputsFormat(outputs_format);
  builder->SetOutputsDeviceType(outputs_device_type);
  return true;
}

void SetKernelBuildInfo(const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder, Processor processor,
                        const std::shared_ptr<const OpInfo> &op_info_ptr) {
  MS_EXCEPTION_IF_NULL(builder);
  MS_EXCEPTION_IF_NULL(op_info_ptr);

  auto imply_type = op_info_ptr->imply_type();
  builder->SetProcessor(processor);
  std::string fusion_name = op_info_ptr->fusion_type();
  auto fusion_type = GetFusionTypeByName(fusion_name);
  builder->SetFusionType(fusion_type);

  if (imply_type == kAKG) {
    builder->SetKernelType(AKG_KERNEL);
  } else if (imply_type == kAICPU) {
    builder->SetKernelType(AICPU_KERNEL);
  } else {
    builder->SetKernelType(TBE_KERNEL);
  }
}

bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr<const OpInfo> &op_info_ptr, Processor processor,
                   std::vector<std::shared_ptr<KernelBuildInfo>> *const kernel_info_list) {
  MS_EXCEPTION_IF_NULL(kernel_node);
  MS_EXCEPTION_IF_NULL(kernel_info_list);
  size_t real_input_num = AnfAlgo::GetInputTensorNum(kernel_node);
  size_t real_output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
  std::vector<std::shared_ptr<OpIOInfo>> inputs = op_info_ptr->inputs_ptr();
  std::vector<std::shared_ptr<OpIOInfo>> outputs = op_info_ptr->outputs_ptr();
  std::vector<int64_t> dyn_input_sizes;
  auto primitive = AnfAlgo::GetCNodePrimitive(kernel_node);
  MS_EXCEPTION_IF_NULL(primitive);
  if (primitive->GetAttr("dyn_input_sizes") != nullptr) {
    dyn_input_sizes = GetValue<std::vector<int64_t>>(primitive->GetAttr("dyn_input_sizes"));
  }
  if (inputs.size() > 0) {
    MS_EXCEPTION_IF_NULL(inputs[0]);
    size_t kernel_info_cnt = inputs[0]->dtypes().size();
    for (size_t j = 0; j < kernel_info_cnt; j++) {
      auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
      MS_EXCEPTION_IF_NULL(builder);
      SetKernelBuildInfo(builder, processor, op_info_ptr);

      if (!SetInputKernelBuilderInfo(inputs, real_input_num, j, dyn_input_sizes, builder)) {
        MS_LOG(DEBUG) << "Parse kernel metadata, set inputs kernel builder info failed.";
        return false;
      }

      if (outputs.size() > 0) {
        if (!SetOutputKernelBuilderInfo(outputs, j, real_output_num, builder)) {
          MS_LOG(DEBUG) << "Parse kernel metadata, set outputs kernel builder info failed.";
          return false;
        }
      }

      kernel_info_list->push_back(builder->Build());
    }
  } else if (outputs.size() > 0) {
    MS_EXCEPTION_IF_NULL(outputs[0]);
    size_t kernel_info_cnt = outputs[0]->dtypes().size();
    for (size_t j = 0; j < kernel_info_cnt; j++) {
      auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
      MS_EXCEPTION_IF_NULL(builder);
      SetKernelBuildInfo(builder, processor, op_info_ptr);

      if (!SetOutputKernelBuilderInfo(outputs, j, real_output_num, builder)) {
        MS_LOG(DEBUG) << "Parse kernel metadata, set outputs kernel builder info failed.";
        return false;
      }

      kernel_info_list->push_back(builder->Build());
    }
  } else {
    if (processor == AICPU) {
      auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
      MS_EXCEPTION_IF_NULL(builder);
      SetKernelBuildInfo(builder, processor, op_info_ptr);
      kernel_info_list->push_back(builder->Build());
    }
  }
  return true;
}

void SaveJsonInfo(const std::string &json_name, const std::string &info, const std::string &base_path) {
  std::string path = base_path + json_name + kInfoSuffix;
  auto realpath = Common::GetRealPath(path);
  if (!realpath.has_value()) {
    MS_LOG(ERROR) << "Get real path failed, path=" << path;
    return;
  }
  ChangeFileMode(realpath.value(), S_IWUSR);
  std::ofstream filewrite(realpath.value());
  if (!filewrite.is_open()) {
    MS_LOG(ERROR) << "Open file '" << realpath.value() << "' failed!";
    return;
  }
  filewrite << info << std::endl;
  filewrite.close();
  ChangeFileMode(realpath.value(), S_IRUSR);
}

Processor GetProcessor(const string &processor) {
  if (processor == kProcessorAiCore) return Processor::AICORE;
  if (processor == kProcessorAiCpu) return Processor::AICPU;
  if (processor == kProcessorCuda) return Processor::CUDA;
  MS_LOG(DEBUG) << "Unknown processor type.";
  return Processor::UNKNOWN;
}

std::string GetProcessor(const AnfNodePtr &anf_node) {
  MS_EXCEPTION_IF_NULL(anf_node);
  std::string device;
  switch (AnfAlgo::GetProcessor(anf_node)) {
    case Processor::AICORE:
      device = kProcessorAiCore;
      break;

    case Processor::AICPU:
      device = kProcessorAiCpu;
      break;

    case Processor::CUDA:
      device = kProcessorCuda;
      break;

    default:
      MS_LOG(DEBUG) << "Unknown processor type.";
      break;
  }
  return device;
}

bool IsSameShape(const std::vector<size_t> &shape_a, const std::vector<size_t> &shape_b) {
  if (shape_a.size() != shape_b.size()) {
    return false;
  }
  for (size_t i = 0; i < shape_a.size(); ++i) {
    if (shape_a[i] != shape_b[i]) {
      return false;
    }
  }
  return true;
}

int Sign(float x) {
  if (x > 0) {
    return 1;
  }
  if (x < 0) {
    return -1;
  }
  return 0;
}

std::pair<AnfNodePtr, size_t> GetKernelInput(const AnfNodePtr &anf_node, size_t index) {
  MS_EXCEPTION_IF_NULL(anf_node);

  if (index >= AnfAlgo::GetInputTensorNum(anf_node)) {
    MS_EXCEPTION(ArgumentError) << "Index is out of the size of anf_node inputs.";
  }

  auto cnode = anf_node->cast<CNodePtr>();
  if (cnode == nullptr) {
    return AnfAlgo::VisitKernel(anf_node, 0);
  } else {
    return AnfAlgo::VisitKernel(anf_node->cast<CNodePtr>()->input(index + 1), 0);
  }
}

std::vector<std::pair<AnfNodePtr, std::pair<size_t, size_t>>> GetInputIndex(const std::vector<AnfNodePtr> &node_list,
                                                                            const std::vector<AnfNodePtr> &input_list) {
  std::vector<std::pair<AnfNodePtr, std::pair<size_t, size_t>>> input_index;
  for (size_t i = 0; i < input_list.size(); ++i) {
    auto const &input = input_list[i];
    MS_EXCEPTION_IF_NULL(input);
    bool found = false;
    auto mng = input->func_graph()->manager();
    MS_EXCEPTION_IF_NULL(mng);
    const NodeUsersMap &users = mng->node_users();
    auto input_users = users.find(input);
    if (input_users == users.end() || input_users->second.empty()) {
      MS_EXCEPTION(ArgumentError) << "Input [" << i << "][" << input->DebugString(2) << "] of ["
                                  << input->func_graph()->ToString() << "] has no users.";
    }

    for (auto const &input_user : input_users->second) {
      for (auto const &anf_node : node_list) {
        if (anf_node != input_user.first) {
          continue;
        }

        std::vector<int64_t> dyn_input_sizes;
        auto prim = AnfAlgo::GetCNodePrimitive(anf_node);
        MS_EXCEPTION_IF_NULL(prim);
        if (prim->GetAttr(kAttrDynInputSizes) != nullptr) {
          dyn_input_sizes = GetValue<const std::vector<int64_t>>(prim->GetAttr(kAttrDynInputSizes));
        }

        if (dyn_input_sizes.empty()) {
          input_index.push_back(std::make_pair(anf_node, std::make_pair(IntToSize(input_user.second - 1), 0)));
          found = true;
          break;
        } else {
          int used_as_idx = input_user.second - 1;
          int accum_idx = 0;
          size_t dyn_i = 0;
          for (; dyn_i < dyn_input_sizes.size(); ++dyn_i) {
            accum_idx += LongToInt(dyn_input_sizes[dyn_i]);
            if (used_as_idx < accum_idx) {
              input_index.push_back(std::make_pair(
                anf_node, std::make_pair(dyn_i, IntToSize(used_as_idx - (accum_idx - dyn_input_sizes[dyn_i])))));
              break;
            }
          }
          if (dyn_i != dyn_input_sizes.size()) {
            found = true;
            break;
          }
        }
      }
      if (found) {
        break;
      }
    }

    if (!found) {
      MS_EXCEPTION(ArgumentError) << "Input [" << i << "][" << input->DebugString(2) << "] of ["
                                  << input->func_graph()->ToString() << "] found no related kernel info.";
    }
  }
  return input_index;
}

std::vector<std::pair<AnfNodePtr, size_t>> GetOutputIndex(const std::vector<AnfNodePtr> &node_list,
                                                          const std::vector<AnfNodePtr> &input_list,
                                                          const std::vector<AnfNodePtr> &output_list) {
  std::vector<std::pair<AnfNodePtr, size_t>> output_index;
  for (size_t i = 0; i < output_list.size(); ++i) {
    auto const &output = output_list[i];
    MS_EXCEPTION_IF_NULL(output);
    bool found = false;
    auto pree_node = AnfAlgo::VisitKernel(output, 0);
    auto pos = std::find(std::begin(node_list), std::end(node_list), pree_node.first);
    if (pos != std::end(node_list)) {
      output_index.push_back(pree_node);
      continue;
    }
    auto ret = std::find(std::begin(input_list), std::end(input_list), pree_node.first);
    if (ret != std::end(input_list)) {
      output_index.push_back(std::make_pair(pree_node.first, 0));
      found = true;
    }
    if (!found) {
      MS_EXCEPTION(ArgumentError) << "Output [" << i << "][" << output->DebugString(2) << "] of ["
                                  << output->func_graph()->ToString() << "] found no related kernel info.";
    }
  }
  return output_index;
}

void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *node_list) {
  MS_EXCEPTION_IF_NULL(node_list);
  MS_EXCEPTION_IF_NULL(func_graph);
  std::vector<AnfNodePtr> node_lists = TopoSort(func_graph->get_return());
  for (auto const &node : node_lists) {
    if (!AnfAlgo::IsRealKernel(node) || !node->isa<CNode>()) {
      continue;
    }
    auto cnode = node->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(cnode);
    if (IsValueNode<Primitive>(cnode->input(kAnfPrimitiveIndex))) {
      node_list->push_back(node);
    }
  }
}

void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *node_list,
                         std::vector<AnfNodePtr> *input_list, std::vector<AnfNodePtr> *output_list) {
  MS_EXCEPTION_IF_NULL(func_graph);
  MS_EXCEPTION_IF_NULL(node_list);
  MS_EXCEPTION_IF_NULL(input_list);

  GetValidKernelNodes(func_graph, node_list);

  auto parameters = func_graph->parameters();
  input_list->insert(input_list->begin(), parameters.begin(), parameters.end());

  GetFuncGraphOutputNodes(func_graph, output_list);
}

void GetFuncGraphOutputNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *output_list) {
  MS_EXCEPTION_IF_NULL(func_graph);
  MS_EXCEPTION_IF_NULL(output_list);
  auto func_output = func_graph->output();
  MS_EXCEPTION_IF_NULL(func_output);
  if (func_output->isa<CNode>()) {
    // multi output.
    auto cnode = func_output->cast<CNodePtr>();
    MS_EXCEPTION_IF_NULL(cnode);
    auto input0 = cnode->input(kAnfPrimitiveIndex);
    MS_EXCEPTION_IF_NULL(input0);
    if (IsPrimitive(input0, prim::kPrimMakeTuple)) {
      for (size_t input_idx = 1; input_idx < cnode->inputs().size(); ++input_idx) {
        auto input_node = cnode->input(input_idx);
        MS_EXCEPTION_IF_NULL(input_node);
        if (input_node->isa<CNode>() && AnfAlgo::GetInputTensorNum(input_node) == 0) {
          continue;
        }
        output_list->push_back(AnfAlgo::VisitKernel(input_node, 0).first);
      }
    } else {
      // single output.
      output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first);
    }
  } else {
    // single output.
    output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first);
  }
}

bool GetInputTensorValue(const AnfNodePtr &anf_node, size_t input_idx, nlohmann::json *const node_json) {
  MS_EXCEPTION_IF_NULL(anf_node);
  MS_EXCEPTION_IF_NULL(node_json);
  auto cnode = anf_node->cast<CNodePtr>();
  MS_EXCEPTION_IF_NULL(cnode);
  if (input_idx + 1 >= cnode->size()) {
    MS_EXCEPTION(ArgumentError) << "input_idx [" << input_idx << "] is out of index of inputs of ["
                                << cnode->inputs().size() << "][" << cnode->DebugString() << "]";
  }

  auto input_node = cnode->input(input_idx + 1);
  if (!IsValueNode<tensor::Tensor>(input_node)) {
    return false;
  }

  auto tensor = GetValueNode<tensor::TensorPtr>(input_node);
  if (tensor == nullptr) {
    return false;
  }

  auto type_id = tensor->data_type();
  auto *data = tensor->data_c();
  MS_EXCEPTION_IF_NULL(data);
  if (tensor->DataSize() > 1) {
    // not const tensor.
    MS_LOG(WARNING) << "Not take value of tensor whose datasize greater than 1, [" << input_node->DebugString(2) << "]";
    return false;
  }

  if (type_id == kFloat64->type_id()) {
    (*node_json)["value"] = static_cast<double *>(data)[0];
  } else if (type_id == kFloat32->type_id()) {
    (*node_json)["value"] = static_cast<float *>(data)[0];
  } else if (type_id == kFloat16->type_id()) {
    float16 *val = static_cast<float16 *>(data);
    (*node_json)["value"] = static_cast<float>(val[0]);
  } else if (type_id == kUInt64->type_id()) {
    (*node_json)["value"] = static_cast<uint64_t *>(data)[0];
  } else if (type_id == kUInt32->type_id()) {
    (*node_json)["value"] = static_cast<uint32_t *>(data)[0];
  } else if (type_id == kUInt16->type_id()) {
    (*node_json)["value"] = static_cast<uint16_t *>(data)[0];
  } else if (type_id == kUInt8->type_id()) {
    (*node_json)["value"] = static_cast<uint8_t *>(data)[0];
  } else if (type_id == kInt64->type_id()) {
    (*node_json)["value"] = static_cast<int64_t *>(data)[0];
  } else if (type_id == kInt32->type_id()) {
    (*node_json)["value"] = static_cast<int32_t *>(data)[0];
  } else if (type_id == kInt16->type_id()) {
    (*node_json)["value"] = static_cast<int16_t *>(data)[0];
  } else if (type_id == kInt8->type_id()) {
    (*node_json)["value"] = static_cast<int8_t *>(data)[0];
  } else if (type_id == kBool->type_id()) {
    (*node_json)["value"] = static_cast<bool *>(data)[0];
  } else {
    MS_LOG(EXCEPTION) << "Unknown value type of tensor[" << cnode->DebugString() << "]";
  }
  return true;
}

bool IsWeightBoundary(const AnfNodePtr &node) {
  if (node->isa<ValueNode>()) {
    return true;
  }
  if (node->isa<Parameter>() && AnfAlgo::IsParameterWeight(node->cast<ParameterPtr>())) {
    return true;
  }
  return false;
}

std::vector<int64_t> GetReduceAttrAxis(const CNodePtr &cnode) {
  if (AnfAlgo::GetInputTensorNum(cnode) != AnfAlgo::GetOutputTensorNum(cnode) &&
      AnfAlgo::GetInputTensorNum(cnode) != 1) {
    MS_LOG(EXCEPTION) << "the kind of reduce node [" << cnode->DebugString()
                      << "] is not single input or single output ";
  }
  std::vector<int64_t> axis;
  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0);
  auto primitive = AnfAlgo::GetCNodePrimitive(cnode);
  MS_EXCEPTION_IF_NULL(primitive);
  auto axis_attr = primitive->GetAttr(kAxis);
  if (axis_attr == nullptr) {
    MS_LOG(ERROR) << "This node doesn't have axie attr.";
    return std::vector<int64_t>();
  }
  std::vector<int64_t> axis_list;
  if (axis_attr->isa<Int64Imm>()) {
    (void)axis_list.emplace_back(GetValue<int64_t>(axis_attr));
  } else {
    axis_list = GetValue<std::vector<int64_t>>(axis_attr);
  }
  for (const auto &elem : axis_list) {
    if (elem < 0) {
      (void)axis.emplace_back(input_shape.size() + elem);
    } else {
      (void)axis.emplace_back(elem);
    }
  }
  AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), cnode);
  return axis;
}

std::string GetProcessorStr(const AnfNodePtr &anf_node) {
  MS_EXCEPTION_IF_NULL(anf_node);
  std::string processor = kProcessorUnknown;
  auto kernel_info = dynamic_cast<device::KernelInfo *>(anf_node->kernel_info());
  MS_EXCEPTION_IF_NULL(kernel_info);
  auto build_info = kernel_info->select_kernel_build_info();
  // we may call this before kernel select.
  if (build_info == nullptr) {
    return processor;
  }
  switch (build_info->processor()) {
    case Processor::AICORE:
      processor = kProcessorAiCore;
      break;

    case Processor::AICPU:
      processor = kProcessorAiCpu;
      break;

    case Processor::CUDA:
      processor = kProcessorCuda;
      break;

    default:
      MS_LOG(ERROR) << "Unknown processor type.";
      break;
  }

  return processor;
}

Processor GetProcessorFromContext() {
  kernel::Processor processor = kernel::Processor::UNKNOWN;
  auto context_ptr = MsContext::GetInstance();
  MS_EXCEPTION_IF_NULL(context_ptr);
  auto device_info = context_ptr->get_param<std::string>(MS_CTX_DEVICE_TARGET);
  if (device_info == kGPUDevice) {
    processor = kernel::Processor::CUDA;
  } else if (device_info == kAscendDevice) {
    processor = kernel::Processor::AICORE;
  }
  return processor;
}

std::string GetStrProcessorFromContext() {
  auto processor = GetProcessorFromContext();
  string str_processor = kernel::kProcessorUnknown;
  if (processor == kernel::Processor::CUDA) {
    str_processor = kernel::kProcessorCuda;
  } else if (processor == kernel::Processor::AICORE) {
    str_processor = kernel::kProcessorAiCore;
  }
  return str_processor;
}

float Scaling(size_t in_size, size_t out_size, bool align_corners) {
  return (align_corners && out_size > 1) ? (in_size - 1) / static_cast<float>(out_size - 1)
                                         : in_size / static_cast<float>(out_size);
}

float ScaleGrid(const int x, const float scale) { return static_cast<float>(x) * scale; }

void ComputeInterpolationWeights(const size_t out_size, const size_t in_size, const float scale,
                                 CachedInterpolation *interpolation) {
  interpolation[out_size].lower = 0;
  interpolation[out_size].upper = 0;
  for (size_t i = 0; i <= out_size - 1; ++i) {
    const float in = ScaleGrid(i, scale);
    const float in_f = std::floor(in);
    interpolation[i].lower = std::max(static_cast<size_t>(in_f), static_cast<size_t>(0));
    interpolation[i].upper = std::min(static_cast<size_t>(std::ceil(in)), in_size - 1);
    interpolation[i].lerp = in - in_f;
  }
}

bool GetShapeSize(const std::vector<size_t> &shape, const TypePtr &type_ptr, int64_t *size_i) {
  MS_EXCEPTION_IF_NULL(type_ptr);
  size_t type_byte = GetTypeByte(type_ptr);
  if (type_byte == 0) {
    return false;
  }
  for (size_t j = 0; j < shape.size(); j++) {
    size_i[0] = LongMulWithOverflowCheck(size_i[0], static_cast<int>(shape[j]));
  }
  size_i[0] = LongMulWithOverflowCheck(size_i[0], SizeToInt(type_byte));
  return true;
}

void CastShapeSizeToLong(const std::vector<size_t> &shape, std::vector<int64_t> *long_shape) {
  MS_EXCEPTION_IF_NULL(long_shape);
  std::transform(shape.begin(), shape.end(), std::back_inserter(*long_shape), SizeToLong);
}

void CheckSliceValid(const std::vector<int64_t> &start, const std::vector<int64_t> &stop,
                     const std::vector<int64_t> &step, const std::vector<int64_t> &input_shape) {
  if (start.size() != stop.size() || start.size() != step.size() || start.size() > input_shape.size()) {
    MS_LOG(EXCEPTION)
      << "TensorCopySlices requires the length of begin, stride and end must be equal and less than input dimension.";
  }

  size_t size = start.size();
  for (size_t i = 0; i < size; ++i) {
    if (stop[i] <= start[i]) {
      MS_LOG(EXCEPTION) << "Invalid slice: (" << start[i] << ", " << stop[i] << " ," << step[i] << ")";
    }
    // Operator need to be generalized in the future. Only support to copy continuous memory now.
    if (step[i] != 1) {
      MS_LOG(EXCEPTION) << "The element in step only support 1, but got:" << step;
    }
  }

  size_t slice_pos = size;
  for (size_t i = 0; i < size; ++i) {
    if (stop[i] - start[i] > 1) {
      slice_pos = i;
      break;
    }
  }

  for (size_t i = slice_pos + 1; i < size; ++i) {
    if (stop[i] - start[i] != input_shape[i]) {
      MS_LOG(EXCEPTION) << "Only support copy continuous memory now. For example tensor[0, 0:100] is fine, "
                           "but tensor[0:100, 0] is not supported.";
    }
  }
}

size_t GetCopySize(const std::vector<int64_t> &dim_offset, const std::vector<int64_t> &start,
                   const std::vector<int64_t> &stop) {
  for (size_t i = 0; i < start.size(); ++i) {
    if (stop[i] - start[i] != 1) {
      return SizetMulWithOverflowCheck(LongToSize(stop[i] - start[i]), LongToSize(dim_offset[i]));
    }
  }
  return LongToSize(dim_offset[start.size() - 1]);
}

std::vector<int64_t> CalDimOffset(const std::vector<int64_t> &input_shape) {
  std::vector<int64_t> dim_offset;
  int64_t offset = 1;
  for (auto iter = input_shape.rbegin(); iter != input_shape.rend(); ++iter) {
    dim_offset.push_back(offset);
    offset = offset * (*iter);
  }
  std::reverse(dim_offset.begin(), dim_offset.end());
  return dim_offset;
}

size_t CalOffset(const std::vector<int64_t> &start, const std::vector<int64_t> &stop,
                 const std::vector<int64_t> &dim_offset) {
  size_t size = start.size();
  size_t offset = 0;
  for (size_t i = 0; i < size; ++i) {
    offset += SizetMulWithOverflowCheck(LongToSize(dim_offset[i]), start[i]);
    if (stop[i] - start[i] != 1) {
      break;
    }
  }
  return offset;
}

size_t UnitSizeInBytes(const mindspore::TypeId &t) {
  size_t bytes = 0;
  switch (t) {
    case kNumberTypeBool:
    case kNumberTypeInt8:
    case kNumberTypeUInt8:
      bytes = sizeof(int8_t);
      break;
    case kNumberTypeInt16:
    case kNumberTypeUInt16:
    case kNumberTypeFloat16:
      bytes = sizeof(int16_t);
      break;
    case kNumberTypeInt:
    case kNumberTypeUInt:
    case kNumberTypeInt32:
    case kNumberTypeUInt32:
    case kNumberTypeFloat:
    case kNumberTypeFloat32:
      bytes = sizeof(int32_t);
      break;
    case kNumberTypeUInt64:
    case kNumberTypeInt64:
    case kNumberTypeFloat64:
      bytes = sizeof(int64_t);
      break;
    default:
      MS_LOG(EXCEPTION) << "Invalid types " << t;
      break;
  }

  return bytes;
}
}  // namespace kernel
}  // namespace mindspore