OpenHarmony-v5.1.0-Release/s

/**
 * Copyright 2023 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 <cstdint>
#include <functional>
#include <map>
#include <utility>
#include <vector>
#include <unordered_map>
#include "base/base.h"
#include "backend/common/graph_kernel/convert_input_and_attr.h"
#include "backend/common/graph_kernel/core/graph_kernel_callback.h"
#include "backend/common/graph_kernel/core/graph_kernel_utils.h"
#include "include/backend/anf_runtime_algorithm.h"
#include "include/backend/optimizer/helper.h"
#include "include/api/format.h"
#include "ops/auto_generate/gen_ops_primitive.h"
#include "ops/array_ops.h"
#include "ops/op_def.h"
#include "ops/op_utils.h"
#include "ops/sequence_ops.h"
#include "utils/anf_utils.h"
#include "utils/check_convert_utils.h"

namespace mindspore::graphkernel {
namespace {
const std::set<std::string> &GetConvertInputAttrOps() {
  static const std::set<std::string> convert_input_attr_ops = {
    prim::kPrimSoftmax->name(),       prim::kPrimReduceSum->name(),       prim::kPrimReduceMax->name(),
    prim::kPrimReduceMin->name(),     prim::kPrimReduceMean->name(),      prim::kPrimOneHot->name(),
    prim::kPrimMinimumGrad->name(),   prim::kPrimMaximumGrad->name(),     prim::kPrimGather->name(),
    prim::kPrimCumSum->name(),        prim::kPrimArgmin->name(),          prim::kPrimArgmax->name(),
    prim::kPrimBiasAdd->name(),       prim::kPrimBiasAddGrad->name(),     prim::kPrimLayerNorm->name(),
    prim::kPrimLayerNormGrad->name(), prim::kPrimLogSoftmax->name(),      prim::kPrimLogSoftmaxGrad->name(),
    prim::kPrimStridedSlice->name(),  prim::kPrimAdamWeightDecay->name(), prim::kPrimMatMul->name(),
    prim::kPrimBatchMatMul->name(),
  };
  return convert_input_attr_ops;
}

const std::map<std::string, std::vector<size_t>> &GetConvertKernelObjOps() {
  static const std::map<std::string, std::vector<size_t>> convert_kernel_obj_ops = {
    {prim::kPrimReshape->name(), {2}},
    {prim::kPrimReduceSum->name(), {2}},           // axis is tuple(int)
    {prim::kPrimReduceMax->name(), {2}},           // axis is tuple(int)
    {prim::kPrimReduceMin->name(), {2}},           // axis is tuple(int)
    {prim::kPrimReduceMean->name(), {2}},          // axis is tuple(int)
    {prim::kPrimStridedSlice->name(), {2, 3, 4}},  // begin, end, strides
    {prim::kPrimTile->name(), {2}},
    {prim::kPrimTranspose->name(), {2}},
  };
  return convert_kernel_obj_ops;
}

ValuePtr EnumToFormat(const ValuePtr &value) {
  if (!value->isa<Int64Imm>()) {
    MS_LOG(EXCEPTION) << value->ToString() << " is not Int64Imm.";
  }
  auto val = GetValue<int64_t>(value);
  if (val == Format::NCHW) {
    return MakeValue("NCHW");
  } else if (val == Format::NHWC) {
    return MakeValue("NHWC");
  } else if (val == Format::NCDHW) {
    return MakeValue("NCDHW");
  } else {
    MS_LOG(EXCEPTION) << value->ToString() << " is unexpected.";
  }
}

ValuePtr FormatToEnum(const ValuePtr &value) {
  auto format = GetValue<std::string>(value);
  if (format == "NCHW") {
    return MakeValue<int64_t>(Format::NCHW);
  } else if (format == "NHWC") {
    return MakeValue<int64_t>(Format::NHWC);
  } else if (format == "NCDHW") {
    return MakeValue<int64_t>(Format::NCDHW);
  } else {
    MS_LOG(EXCEPTION) << value->ToString() << " value:" << format << " is unexpected.";
  }
}

ValuePtr EnumToDtype(const ValuePtr &value) {
  if (!value->isa<Int64Imm>()) {
    MS_LOG(EXCEPTION) << value->ToString() << " is not Int64Imm.";
  }
  auto val = GetValue<int64_t>(value);
  return TypeIdToType(static_cast<TypeId>(val));
}

ValuePtr DtypeToEnum(const ValuePtr &value) {
  if (!value->isa<Type>()) {
    MS_LOG(EXCEPTION) << value->ToString() << " is not Type.";
  }
  auto type_id = value->cast<TypePtr>()->type_id();
  return MakeValue<int64_t>(type_id);
}

using ArgHandlerFunc = std::function<ValuePtr(const ValuePtr &)>;

ArgHandlerFunc GetArgHandlerFunc(const std::string &arg_handler) {
  static const std::unordered_map<std::string, ArgHandlerFunc> arg_handler_funcs = {
    {"str_to_enum", EnumToFormat},
    {"dtype_to_type_id", EnumToDtype},
  };
  if (arg_handler_funcs.find(arg_handler) != arg_handler_funcs.end()) {
    return arg_handler_funcs.at(arg_handler);
  } else {
    return nullptr;
  }
}

ArgHandlerFunc GetOppArgHandlerFunc(const std::string &arg_handler) {
  static const std::unordered_map<std::string, ArgHandlerFunc> opp_arg_handler_funcs = {
    {"str_to_enum", FormatToEnum},
    {"dtype_to_type_id", DtypeToEnum},
  };
  if (opp_arg_handler_funcs.find(arg_handler) != opp_arg_handler_funcs.end()) {
    return opp_arg_handler_funcs.at(arg_handler);
  } else {
    return nullptr;
  }
}
}  // namespace

void ConvertFrontEndToGraphKernel::AddConstInputToAttr(const CNodePtr &cnode, const size_t input_index,
                                                       const std::string &arg_name, const std::string &arg_handler,
                                                       const PrimitivePtr &primitive) {
  if (input_index >= cnode->size() - 1) {
    MS_LOG(EXCEPTION) << "The index of args in op_def `" << input_index
                      << "` should less than the inputs size minus one `" << cnode->size() - 1 << "`.";
  }
  auto input_node = cnode->inputs()[input_index + 1];

  ValuePtr value = nullptr;
  if (input_node->isa<ValueNode>()) {
    auto value_node = input_node->cast<ValueNodePtr>();
    value = value_node->value();
  } else if (input_node->isa<Parameter>()) {
    auto parameter_node = input_node->cast<ParameterPtr>();
    value = parameter_node->abstract()->BuildValue();
  }
  if (value == nullptr) {
    MS_LOG(EXCEPTION) << cnode->ToString() << " is not Value.";
  }
  if (value->isa<ValueAny>()) {
    MS_LOG(EXCEPTION) << cnode->ToString() << " is ValueAny.";
  }
  if (!arg_handler.empty() && !value->isa<None>()) {
    auto arg_handler_func = GetArgHandlerFunc(arg_handler);
    MS_EXCEPTION_IF_NULL(arg_handler_func);
    value = arg_handler_func(value);
    primitive->AddAttr(arg_name, value);
    return;
  }

  if (!value->isa<tensor::Tensor>()) {
    primitive->AddAttr(arg_name, value);
    return;
  }
  auto value_vector = CheckAndConvertUtils::CheckTensorIntValue(arg_name, value, primitive->name());
  auto tensor = value->cast<tensor::TensorPtr>();
  auto tensor_shape = tensor->shape_c();
  MS_LOG(DEBUG) << cnode->ToString() << " 's input[" << input_index << "] is tensor.";
  if (tensor_shape.empty()) {
    primitive->AddAttr(arg_name, MakeValue(value_vector[0]));
  } else {
    primitive->AddAttr(arg_name, MakeValue(value_vector));
  }
}

bool ConvertFrontEndToGraphKernel::Process(const CNodePtr &cnode, const ops::OpDefPtr &op_def,
                                           const PrimitivePtr &primitive) {
  const auto &op_def_args = op_def->args_;
  const auto &op_def_indexes = op_def->indexes_;
  bool changed = false;
  auto ori_input_size = AnfUtils::GetInputTensorNum(cnode);
  if (op_def_args.size() != ori_input_size) {
    MS_LOG(EXCEPTION) << "The size of args in op_def `" << op_def->args_.size()
                      << "` should be equal to the inputs size minus one `" << ori_input_size << "`.";
  }
  auto iter = op_def_args.crbegin();
  auto new_input_size = op_def_args.size();
  for (; iter != op_def_args.crend(); ++iter, --new_input_size) {
    // as_init_arg_ == 1 indicate the arg need convert, the arg need convert is at the tail of the list
    if (iter->as_init_arg_ != 1) {
      break;
    }
    const auto &arg_name = iter->arg_name_;
    const auto &arg_handler = iter->arg_handler_;
    MS_LOG(DEBUG) << cnode->ToString() << " convert input to attr: " << arg_name;
    if (auto index_iter = op_def_indexes.find(arg_name); index_iter != op_def_indexes.end()) {
      AddConstInputToAttr(cnode, index_iter->second, arg_name, arg_handler, primitive);
      changed = true;
    } else {
      MS_LOG(EXCEPTION) << primitive->name() << " not found index of attr[" << arg_name << "] in op def indexes.";
    }
  }
  auto inputs = cnode->inputs();
  if (changed) {
    // remainder args in op_def_arg is the size of new input args
    AnfNodePtrList new_inputs(inputs.begin(), inputs.begin() + new_input_size + 1);
    for (size_t i = ori_input_size; i < inputs.size() - 1; ++i) {
      new_inputs.emplace_back(inputs[i + 1]);
    }
    cnode->set_inputs(new_inputs);
    auto cb = Callback::Instance();
    MS_EXCEPTION_IF_NULL(cb);
    cb->ResetKernelInfoInputs(cnode, {});
  }
  return changed;
}

bool ConvertFrontEndToGraphKernel::Run(const FuncGraphPtr &func_graph) {
  bool changed = false;
  MS_EXCEPTION_IF_NULL(func_graph);
  MS_EXCEPTION_IF_NULL(func_graph->get_return());
  auto todos = TopoSort(func_graph->get_return());
  for (auto &node : todos) {
    if (!OpDefAdapter::NeedConvertInputAndAttr(node)) {
      continue;
    }
    auto primitive = GetCNodePrimitive(node);
    if (primitive == nullptr) {
      continue;
    }
    const auto &op_name = primitive->name();
    auto op_def = mindspore::ops::GetOpDef(op_name);
    if (op_def == nullptr) {
      MS_LOG(WARNING) << op_name << " not found in op def.";
      continue;
    }
    auto cnode = dyn_cast<CNode>(node);
    changed = Process(cnode, op_def, primitive) || changed;
  }
  if (changed) {
    auto mng = GkUtils::GetFuncGraphManager(func_graph);
    GkUtils::UpdateFuncGraphManager(mng, func_graph);
  }
  return changed;
}

void ConvertGraphKernelToFrontEnd::AddAttrToInput(const CNodePtr &cnode, const std::string &arg_name,
                                                  const std::string &arg_handler, const PrimitivePtr &primitive,
                                                  size_t pos) {
  auto value = primitive->GetAttr(arg_name);
  if (!arg_handler.empty()) {
    auto opp_arg_handler_func = GetOppArgHandlerFunc(arg_handler);
    MS_EXCEPTION_IF_NULL(opp_arg_handler_func);
    value = opp_arg_handler_func(value);
  }
  auto value_node = opt::CreateValueNodeWithKernelInfo(cnode->func_graph(), value);
  auto inputs = cnode->inputs();
  inputs.insert(inputs.begin() + pos, value_node);
  cnode->set_inputs(inputs);
  primitive->DelAttr(arg_name);
}

bool ConvertGraphKernelToFrontEnd::ConvertInputsType(const CNodePtr &cnode, size_t idx, ops::OP_DTYPE fe_arg_type) {
  // Only convert ValueNode(tensor with dtype int64_t) to ValueNode(Tuple of int64_t) now.
  MS_EXCEPTION_IF_NULL(cnode);
  auto input = cnode->input(idx);
  MS_EXCEPTION_IF_NULL(input);
  if (!input->isa<ValueNode>()) {
    return false;
  }

  auto origin_type = AnfAlgo::GetAbstractObjectType(input->abstract());
  if (origin_type != kObjectTypeTensorType || fe_arg_type != ops::DT_TUPLE_INT) {
    return false;
  }

  auto value_opt = ops::GetArrayValue<int64_t>(input->cast<ValueNodePtr>()->value());
  if (!value_opt.has_value()) {
    return false;
  }

  auto value_vec = value_opt.value().ToVector();
  auto func_graph = cnode->func_graph();
  auto new_input = opt::CreateValueNodeWithKernelInfo(func_graph, MakeValue<std::vector<int64_t>>(value_vec));
  MS_LOG(DEBUG) << "Change [" << idx << "] input from " << input->DebugString() << " to " << new_input->DebugString()
                << " for " << cnode->fullname_with_scope();
  cnode->set_input(idx, new_input);
  return true;
}

bool ConvertGraphKernelToFrontEnd::Process(const AnfNodePtr &node) {
  auto primitive = GetCNodePrimitive(node);
  MS_EXCEPTION_IF_NULL(primitive);
  const auto &op_name = primitive->name();
  auto op_def = mindspore::ops::GetOpDef(op_name);
  if (op_def == nullptr) {
    MS_LOG(WARNING) << op_name << " not found in op def.";
    return false;
  }
  const auto &op_def_args = op_def->args_;

  // 1. Convert attr to input.
  auto cnode = node->cast<CNodePtr>();
  MS_EXCEPTION_IF_NULL(cnode);
  auto ori_input_size = AnfUtils::GetInputTensorNum(cnode);
  if (ori_input_size > op_def_args.size()) {
    MS_LOG(INFO) << node->fullname_with_scope() << " ori_input_size:" << ori_input_size << " > "
                 << "op_def_args.size():" << op_def_args.size();
  }

  std::vector<size_t> update_indices;
  for (auto i = ori_input_size; i < op_def_args.size(); i++) {
    // as_init_arg_ == 1 indicate the arg need convert
    if (op_def_args[i].as_init_arg_ != 1) {
      MS_LOG(EXCEPTION) << primitive->name() << "'s input:" << op_def_args[i].arg_name_
                        << " must have as_init_arg_ when convert attr to input.";
    }
    MS_LOG(DEBUG) << cnode->DebugString() << " convert attr [" << op_def_args[i].arg_name_ << "] to input: " << i;
    ConvertGraphKernelToFrontEnd::AddAttrToInput(cnode, op_def_args[i].arg_name_, op_def_args[i].arg_handler_,
                                                 primitive, i + 1);
    (void)update_indices.emplace_back(i + 1);
  }

  // 2. Convert inputs type.
  auto obj_map_iter = GetConvertKernelObjOps().find(op_name);
  if (obj_map_iter != GetConvertKernelObjOps().end()) {
    auto indices = obj_map_iter->second;
    for (auto idx : indices) {
      if (ConvertGraphKernelToFrontEnd::ConvertInputsType(cnode, idx, op_def_args[idx - 1].arg_dtype_)) {
        (void)update_indices.emplace_back(idx);
      }
    }
  }
  bool changed = !update_indices.empty();
  if (changed) {
    auto cb = Callback::Instance();
    MS_EXCEPTION_IF_NULL(cb);
    cb->ResetKernelInfoInputs(cnode, update_indices);
  }
  return changed;
}

bool ConvertGraphKernelToFrontEnd::Run(const FuncGraphPtr &func_graph) {
  bool changed = false;
  MS_EXCEPTION_IF_NULL(func_graph);
  MS_EXCEPTION_IF_NULL(func_graph->get_return());
  auto todos = TopoSort(func_graph->get_return());
  for (auto &node : todos) {
    if (OpDefAdapter::NeedConvertGK2FE(node)) {
      changed = ConvertGraphKernelToFrontEnd::Process(node) || changed;
    }
  }
  if (changed) {
    auto mng = GkUtils::GetFuncGraphManager(func_graph);
    GkUtils::UpdateFuncGraphManager(mng, func_graph);
  }
  return changed;
}

bool OpDefAdapter::NeedConvertInputAndAttr(const AnfNodePtr &node) {
  return node->isa<CNode>() && GetConvertInputAttrOps().count(AnfUtils::GetCNodeName(node)) != 0;
}

bool OpDefAdapter::NeedConvertGK2FE(const AnfNodePtr &node) {
  auto cnode = node->cast<CNodePtr>();
  if (cnode == nullptr) {
    return false;
  }
  auto op_name = AnfUtils::GetCNodeName(node);
  if (GetConvertInputAttrOps().count(op_name) > 0) {
    return true;
  }
  auto obj_map_iter = GetConvertKernelObjOps().find(op_name);
  if (obj_map_iter == GetConvertKernelObjOps().end()) {
    return false;
  }
  auto &index = obj_map_iter->second;
  // if the input type is tensor, it need to convert to the type (like tuple) that match OpDef.
  for (auto idx : index) {
    if (idx < cnode->size() && cnode->input(idx)->abstract()->GetShape()->isa<abstract::TensorShape>()) {
      return true;
    }
  }
  return false;
}
}  // namespace mindspore::graphkernel