new_exec.h

#include <iostream>
#include <string>

#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>

#include "paddle/fluid/framework/executor_gc_helper.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/platform/init.h"

#include <chrono>
#include <gperftools/profiler.h>


//USE_OP(fill_constant);
//USE_OP(elementwise_add);

using namespace std;

namespace paddle {
namespace framework {

class RuntimeInferShapeContext : public InferShapeContext {
 public:
  RuntimeInferShapeContext(const OperatorBase& op, const RuntimeContext& ctx)
      : op_(op), ctx_(ctx) {}

  bool HasInput(const std::string& name) const override {
    // has only one input
    const auto& ins = ctx_.inputs;
    auto it = ins.find(name);
    if (it == ins.end()) {
      return false;
    }
    const auto& in = it->second;
    if (in.size() == 0) return false;
    PADDLE_ENFORCE_EQ(
        in.size(), 1UL,
        platform::errors::InvalidArgument(
            "Input %s should not contain more than one inputs.", name));
    return in[0] != nullptr;
  }

  bool HasOutput(const std::string& name) const override {
    // has only one output
    const auto& outs = ctx_.outputs;
    auto it = outs.find(name);
    if (it == outs.end()) {
      return false;
    }
    const auto& out = it->second;
    if (out.size() == 0) {
      return false;
    }
    PADDLE_ENFORCE_EQ(
        out.size(), 1UL,
        platform::errors::InvalidArgument(
            "Output %s should not contain more than one outputs.", name));
    return out[0] != nullptr;
  }

  bool HasInputs(const std::string& name) const override {
    const auto& ins = ctx_.inputs;
    auto it = ins.find(name);
    if (it == ins.end() || it->second.empty()) {
      return false;
    }
    for (auto& input : it->second) {
      if (input == nullptr) {
        return false;
      }
    }
    return true;
  }

  bool HasOutputs(const std::string& name) const override {
    const auto& outs = ctx_.outputs;
    auto it = outs.find(name);
    if (it == outs.end() || it->second.empty()) {
      return false;
    }
    for (auto& output : it->second) {
      if (output == nullptr) {
        return false;
      }
    }
    return true;
  }

  AttrReader Attrs() const override { return AttrReader(op_.Attrs()); }

  std::vector<std::string> Inputs(const std::string& name) const override {
    return op_.Inputs(name);
  }

  std::vector<std::string> Outputs(const std::string& name) const override {
    return op_.Outputs(name);
  }

  std::string GetInputNameByIdx(size_t idx) const override {
    auto& op_proto =
        paddle::framework::OpInfoMap::Instance().Get(op_.Type()).proto_;
    PADDLE_ENFORCE_LT(idx, op_proto->inputs().size(),
                      platform::errors::OutOfRange(
                          "The index should be less than the size of inputs of "
                          "operator %s, but got index is %d and size is %d",
                          op_.Type(), idx, op_proto->inputs().size()));
    return op_proto->inputs()[idx].name();
  }

  std::string GetOutputNameByIdx(size_t idx) const override {
    auto& op_proto =
        paddle::framework::OpInfoMap::Instance().Get(op_.Type()).proto_;
    PADDLE_ENFORCE_LT(
        idx, op_proto->outputs().size(),
        platform::errors::OutOfRange(
            "The index should be less than the size of outputs of "
            "operator %s, but got index is %d and size is %d",
            op_.Type(), idx, op_proto->outputs().size()));
    return op_proto->outputs()[idx].name();
  }

  void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
                size_t j = 0) override {
    auto in_it = ctx_.inputs.find(in);
    auto out_it = ctx_.outputs.find(out);
    PADDLE_ENFORCE_NE(
        in_it, ctx_.inputs.end(),
        platform::errors::NotFound("Input %s does not exist.", in));
    PADDLE_ENFORCE_NE(
        out_it, ctx_.outputs.end(),
        platform::errors::NotFound("Output %s does not exist.", out));
    PADDLE_ENFORCE_LT(i, in_it->second.size(),
                      platform::errors::InvalidArgument(
                          "The index of input dimension is out of range, "
                          "excepted index less than %zu, but received %zu.",
                          in_it->second.size(), i));
    PADDLE_ENFORCE_LT(j, out_it->second.size(),
                      platform::errors::InvalidArgument(
                          "The index of output dimension is out of range, "
                          "excepted index less than %zu, but received %zu.",
                          out_it->second.size(), j));

    Variable* in_var = in_it->second[i];
    Variable* out_var = out_it->second[j];

    PADDLE_ENFORCE_EQ(
        in_var->Type(), out_var->Type(),
        platform::errors::InvalidArgument(
            "The type of input (%s) and output (%s) are inconsistent.", in,
            out));

    if (in_var->IsType<framework::SelectedRows>()) {
      auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
      auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
      out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
      out_sele_rows->set_rows(in_sele_rows.rows());
      out_sele_rows->set_height(in_sele_rows.height());
    } else if (in_var->IsType<framework::LoDTensor>()) {
      auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
      auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
      out_lod_tensor->Resize(in_lod_tensor.dims());
    } else {
      PADDLE_THROW(platform::errors::Unimplemented(
          "Currently, the input type of ShareDim only can be LoDTensor "
          "or SelectedRows."));
    }
  }

  void ShareAllLoD(const std::string& in,
                   const std::string& out) const override {
    auto in_it = ctx_.inputs.find(in);
    auto out_it = ctx_.outputs.find(out);
    PADDLE_ENFORCE_NE(in_it, ctx_.inputs.end(),
                      platform::errors::NotFound(
                          "Input [%s] found error in Op [%s]", in, op_.Type()));
    PADDLE_ENFORCE_NE(
        out_it, ctx_.outputs.end(),
        platform::errors::NotFound("Output [%s] found error in Op [%s]", out,
                                   op_.Type()));

    auto& in_var_list = in_it->second;
    auto& out_var_list = out_it->second;

    PADDLE_ENFORCE_EQ(
        in_var_list.size(), out_var_list.size(),
        platform::errors::PreconditionNotMet(
            "Op [%s]: Input var size should be equal with output var size",
            op_.Type()));

    auto& out_var_names = op_.Outputs(out);

    for (size_t i = 0; i < in_var_list.size(); ++i) {
      if (out_var_names[i] == framework::kEmptyVarName) {
        continue;
      }

      Variable* in_var = in_var_list[i];
      if (!in_var->IsType<LoDTensor>()) return;
      Variable* out_var = out_var_list[i];
      PADDLE_ENFORCE_EQ(out_var->IsType<LoDTensor>(), true,
                        platform::errors::PreconditionNotMet(
                            "The %d-th output of Output(%s) must be LoDTensor.",
                            i, out_var_names[i]));
      auto& in_tensor = in_var->Get<LoDTensor>();
      auto* out_tensor = out_var->GetMutable<LoDTensor>();
      out_tensor->set_lod(in_tensor.lod());
#ifdef PADDLE_WITH_MKLDNN
      if (in_tensor.layout() != DataLayout::kMKLDNN)
#endif
        out_tensor->set_layout(in_tensor.layout());
    }
  }

  void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
                size_t j = 0) const override {
    auto in_it = ctx_.inputs.find(in);
    auto out_it = ctx_.outputs.find(out);
    PADDLE_ENFORCE_NE(
        in_it, ctx_.inputs.end(),
        platform::errors::NotFound("Input %s does not exist.", in));
    PADDLE_ENFORCE_NE(
        out_it, ctx_.outputs.end(),
        platform::errors::NotFound("Output %s does not exist.", out));
    PADDLE_ENFORCE_LT(i, in_it->second.size(),
                      platform::errors::InvalidArgument(
                          "The index of input dimension is out of range, "
                          "excepted index less than %zu, but received %zu.",
                          in_it->second.size(), i));
    PADDLE_ENFORCE_LT(j, out_it->second.size(),
                      platform::errors::InvalidArgument(
                          "The index of output dimension is out of range, "
                          "excepted index less than %zu, but received %zu.",
                          out_it->second.size(), j));

    Variable* in_var = in_it->second.at(i);
    if (!in_var->IsType<LoDTensor>()) return;
    Variable* out_var = out_it->second.at(j);
    PADDLE_ENFORCE_EQ(
        out_var->IsType<LoDTensor>(), true,
        platform::errors::InvalidArgument(
            "The %zu-th output of Output(%s) must be LoDTensor.", j, out));
    auto& in_tensor = in_var->Get<LoDTensor>();
    auto* out_tensor = out_var->GetMutable<LoDTensor>();
    out_tensor->set_lod(in_tensor.lod());

// TODO(dzhwinter) : reuse ShareLoD in most operators.
// Need to call ShareLayout explicitly in sequence related ops.
// Shall we have a better method to shared info between in/out Tensor?
#ifdef PADDLE_WITH_MKLDNN
    // Fix me: ugly workaround below
    // Correct solution:
    //    set_layout() should NOT be called here (i.e. ShareLoD). Instead,
    //    layout of output tensor should be set "manually" in Compute()
    //    of each OPKernel. The reason layout should NOT be shared between
    //    input and output "automatically" (now by InferShape()->ShareLoD())
    //    is that layout transform may occur after InferShape().
    // Workaround:
    //    Skip set_layout() when input layout is kMKLDNN
    //    This is to avoid kMKLDNN is populated wrongly into a non-MKLDNN
    //    OPKernel. In all MKLDNN OPkernel, set_layout(kMKLDNN) should be called
    //    in Compute()
    if (in_tensor.layout() != DataLayout::kMKLDNN)
#endif
      out_tensor->set_layout(in_tensor.layout());
  }

  int32_t GetLoDLevel(const std::string& in, size_t i = 0) const override {
    PADDLE_THROW(platform::errors::PreconditionNotMet(
        "GetLoDLevel is only used in compile time. The calculation of "
        "output's actual lod is different among operators so that should be "
        "set in the runtime kernel."));
  }

  void SetLoDLevel(const std::string& out, int32_t lod_level,
                   size_t j = 0) const override {
    PADDLE_THROW(platform::errors::PreconditionNotMet(
        "SetLoDLevel is only used in compile time. The calculation of "
        "output's actual lod is different among operators so that should be "
        "set in the runtime kernel."));
  }

  bool IsRuntime() const override { return true; }

  // TODO(paddle-dev): Can this be template?
  std::vector<InferShapeVarPtr> GetInputVarPtrs(
      const std::string& name) override {
    const std::vector<Variable*>& vars = InputVars(name);
    std::vector<InferShapeVarPtr> res;
    res.reserve(vars.size());
    res.insert(res.begin(), vars.begin(), vars.end());
    return res;
  }

  std::vector<InferShapeVarPtr> GetOutputVarPtrs(
      const std::string& name) override {
    const std::vector<Variable*>& vars = OutputVars(name);
    std::vector<InferShapeVarPtr> res;
    res.reserve(vars.size());
    res.insert(res.begin(), vars.begin(), vars.end());
    return res;
  }

  DDim GetInputDim(const std::string& name) const override {
    const std::vector<Variable*>& vars = InputVars(name);
    PADDLE_ENFORCE_EQ(
        vars.size(), 1UL,
        platform::errors::InvalidArgument(
            "Input(%s) should hold one element, but now it holds %zu elements.",
            name, vars.size()));
    return this->GetDim(vars[0]);
  }

  std::vector<DDim> GetInputsDim(const std::string& name) const override {
    const std::vector<Variable*>& vars = InputVars(name);
    return GetDims(vars);
  }

  std::vector<proto::VarType::Type> GetInputsVarType(
      const std::string& name) const override {
    return GetVarTypes(InputVars(name));
  }

  std::vector<proto::VarType::Type> GetOutputsVarType(
      const std::string& name) const override {
    return GetVarTypes(OutputVars(name));
  }

  void SetOutputDim(const std::string& name, const DDim& dim) override {
    //cerr << "set out dim" << endl;
    auto& vars = OutputVars(name);
    PADDLE_ENFORCE_EQ(
        vars.size(), 1UL,
        platform::errors::InvalidArgument("Output(%s) should hold one element, "
                                          "but now it holds %zu elements.",
                                          name, vars.size()));
    SetDim(vars[0], dim);
  }

  void SetOutputsDim(const std::string& name,
                     const std::vector<DDim>& dims) override {
    auto& vars = OutputVars(name);
    SetDims(vars, dims);
  }

 protected:
  DDim GetDim(Variable* var) const {
    PADDLE_ENFORCE_NOT_NULL(
        var, platform::errors::InvalidArgument("Input variable is nullptr."));
    if (var->IsType<LoDTensor>()) {
      return var->Get<LoDTensor>().dims();
    } else if (var->IsType<SelectedRows>()) {
      return var->Get<SelectedRows>().GetCompleteDims();
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "Only LoDTensor or SelectedRows support 'GetDim', but input "
          "Variable's type is %s.",
          ToTypeName(var->Type())));
    }
  }

  std::vector<DDim> GetDims(const std::vector<Variable*>& vars) const {
    std::vector<DDim> ret;
    ret.reserve(vars.size());
    std::transform(vars.begin(), vars.end(), std::back_inserter(ret),
                   [this](Variable* var) { return this->GetDim(var); });
    return ret;
  }

  std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
    PADDLE_THROW(platform::errors::PreconditionNotMet(
        "GetRepeatedDims method only ban be used in compile time."));
  }

  void SetDim(Variable* var, const DDim& dim) {
   
    if (var->IsType<LoDTensor>()) {
   
      var->GetMutable<LoDTensor>()->Resize(dim);
    } else if (var->IsType<SelectedRows>()) {
      var->GetMutable<SelectedRows>()->set_height(dim[0]);
    } else {
      PADDLE_THROW(platform::errors::Unimplemented(
          "Variable type error, expect LoDTensor or SelectedRows, but received "
          "(%s).",
          ToTypeName(var->Type())));
    }
  }

  void SetDims(const std::vector<Variable*>& vars,
               const std::vector<DDim>& dims) {
    size_t length = vars.size();
    PADDLE_ENFORCE_EQ(length, dims.size(),
                      platform::errors::InvalidArgument(
                          "The number of input variables do not match the "
                          "number of input dimensions, the number of variables "
                          "is %zu, the number of dimensions is %zu.",
                          length, dims.size()));
    for (size_t i = 0; i < length; ++i) {
      if (vars[i] == nullptr) {
        continue;
      }
      SetDim(vars[i], dims[i]);
    }
  }

  void SetRepeatedDims(const std::string& name,
                       const std::vector<DDim>& dims) override {
    PADDLE_THROW(platform::errors::PreconditionNotMet(
        "SetRepeatedDims method only can be used in compile time."));
  }

  std::vector<proto::VarType::Type> GetVarTypes(
      const std::vector<Variable*>& vars) const {
    std::vector<proto::VarType::Type> retv;
    retv.resize(vars.size());
    std::transform(vars.begin(), vars.end(), retv.begin(),
                   std::bind(std::mem_fn(&RuntimeInferShapeContext::GetVarType),
                             this, std::placeholders::_1));
    return retv;
  }

  proto::VarType::Type GetVarType(Variable* var) const {
    return ToVarType(var->Type());
  }

 private:
  const std::vector<Variable*>& InputVars(const std::string& name) const {
    auto it = ctx_.inputs.find(name);
    PADDLE_ENFORCE_NE(
        it, ctx_.inputs.end(),
        platform::errors::NotFound(
            "Operator (%s) does not have the input (%s).", op_.Type(), name));
    return it->second;
  }

  const std::vector<Variable*>& OutputVars(const std::string& name) const {
    auto it = ctx_.outputs.find(name);
    PADDLE_ENFORCE_NE(
        it, ctx_.outputs.end(),
        platform::errors::NotFound(
            "Operator (%s) does not have the outputs (%s).", op_.Type(), name));
    return it->second;
  }

  const OperatorBase& op_;
  const RuntimeContext& ctx_;
};



framework::ProgramDesc load_from_file( const std::string& file_name )
{
  std::ifstream fin(file_name, std::ios::in | std::ios::binary);
  fin.seekg(0, std::ios::end);
  std::string buffer(fin.tellg(), ' ');
  fin.seekg(0, std::ios::beg);
  fin.read(&buffer[0], buffer.size());
  fin.close();

  ProgramDesc program_desc( buffer );
  return program_desc;
}


struct VariableScope
{
    std::vector< std::unique_ptr<Variable> > var_list;
    std::map<std::string, int> name2id;
};




struct OpFuncNode{

    //int unsed;
    std::map< std::string, std::vector<int> > input_index;
    std::map< std::string, std::vector<int> > output_index;
    
    using OpKernelFunc = std::function<void(const ExecutionContext&)>;
    OpKernelFunc kernel_func_;
};

int convert(const platform::Place& place )
{
    if ( is_cpu_place(place )) 
    {
        return 0;
    }
    if( is_gpu_place( place ))
    {
       return 1;
    }

    return -1;
}

void build_variable_scope( const framework::ProgramDesc& pdesc, VariableScope* var_scope )
{
  auto& global_block = pdesc.Block(0);
  
  
  for (auto& var : global_block.AllVars()) {
      if (var->Name() == framework::kEmptyVarName) {
        continue;
      }
      //cerr << "var name "  << var->Name() << endl;  

      if ( var_scope->name2id.find( var->Name() ) == var_scope->name2id.end() )
      {
          var_scope->name2id[ var->Name() ] = var_scope->var_list.size();
      }
      
      auto v = new Variable();
      //v->GetMutable<LoDTensor>();
      InitializeVariable(v,  var->GetType());
      var_scope->var_list.push_back(std::unique_ptr<Variable>(v));
  }
}

void build_op_func_list( const framework::ProgramDesc& pdesc, std::vector<OperatorBase* >& op_list, 
                          std::vector<OpFuncNode>& vec_func_list,  VariableScope* var_scope,
                          const platform::Place& place )
{
    auto &global_block = pdesc.Block( 0 );

    for ( auto& op : global_block.AllOps() )
    { 
        cerr << op->Type() << endl;
        //bool debug = op->Type() == "softmax_with_cross_entropy_grad";
        bool debug = true;
        
        //cerr << "create op" << endl;
        //auto op_base_u = OpRegistry::CreateOp(*op);
        auto& info = OpInfoMap::Instance().Get( op->Type() );
        
        VariableNameMap inputs_1 = op->Inputs();
        VariableNameMap outputs_1 = op->Outputs();
        AttributeMap attrs_1 = op->GetAttrMap();
        
        if (info.Checker() != nullptr) {
          info.Checker()->Check(&attrs_1);
        }
        auto op_base = info.Creator()( op->Type(), inputs_1, outputs_1, attrs_1);
        
        auto input_names = op->Inputs();
        auto output_names = op->Outputs();
        
        OpFuncNode op_func_node;

        VariableValueMap ins_map;
        std::map< std::string, std::vector<int> > ins_name2id;
        for( auto& var_name_item : input_names)
        {
            std::vector<Variable*> input_vars;
            std::vector<int> vec_ids;
            input_vars.reserve(var_name_item.second.size());
            for (auto& var_name : var_name_item.second) {
                auto it = var_scope->name2id.find( var_name );
                assert( it != var_scope->name2id.end() );
                input_vars.push_back( var_scope->var_list[ it->second].get());
                vec_ids.push_back( it->second );
            }
            ins_map[ var_name_item.first ] = input_vars;
            ins_name2id[ var_name_item.first ] = vec_ids;

        }
        if (debug  ) cerr << "1" << endl;

        
        VariableValueMap outs_map;
        std::map<std::string, std::vector<int> > outs_name2id;
        for( auto& var_name_item : output_names )
        {
            std::vector<Variable*> output_vars;
            std::vector<int> vec_ids;
            output_vars.reserve(var_name_item.second.size());
            for (auto& var_name : var_name_item.second) {
                auto it = var_scope->name2id.find( var_name );
                assert( it != var_scope->name2id.end() );
                //cerr << it->second << "\t" << var_scope.var_list.size() << endl;
                output_vars.push_back( var_scope->var_list[ it->second].get() );
                vec_ids.push_back( it->second );
            } 
            outs_map[ var_name_item.first ] = output_vars;
            //cerr << ToTypeName(output_vars[0]->Type() ) << endl;
            outs_name2id[ var_name_item.first ] = vec_ids;            
        }


        op_func_node.input_index = ins_name2id;
        op_func_node.output_index = outs_name2id;
        RuntimeContext runtime_context( {}, {});
        runtime_context.inputs.swap( ins_map );
        runtime_context.outputs.swap(  outs_map );
        //cerr << "create runtime context" << endl;
        RuntimeInferShapeContext infer_shape_ctx(*op_base, runtime_context);
        static_cast<const framework::OperatorWithKernel*>(op_base)->InferShape( &infer_shape_ctx );
        //cerr << "fin infer shape" << endl;
        auto& all_op_kernels = OperatorWithKernel::AllOpKernels();
        auto kernels_iter = all_op_kernels.find(op->Type() );
        PADDLE_ENFORCE_NE(
            kernels_iter, all_op_kernels.end(),
            platform::errors::Unavailable(
                "There are no kernels which are registered in the %s operator.",
                op->Type() ));
        
        //cerr << "create kernel" << endl;
        using OpKernelFunc = std::function<void(const ExecutionContext&)>;
        using OpKernelMap =
             std::unordered_map<OpKernelType, OpKernelFunc, OpKernelType::Hash>;
        if (debug  ) cerr << "2" << endl;
        OpKernelMap& kernels = kernels_iter->second;
        //auto place = platform::CPUPlace();
        //auto place = platform::CUDAPlace(0);
        platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
        auto* dev_ctx = pool.Get(place);
        Scope scope;
        auto exec_ctx = ExecutionContext(*op_base, scope, *dev_ctx, runtime_context );
        if (debug  ) cerr << "21" << endl;
        auto expected_kernel_key = dynamic_cast<const framework::OperatorWithKernel*>(op_base)->GetExpectedKernelType( exec_ctx );
        if (debug  ) cerr << "22" << endl;
        //cerr << "22" << endl;
        
        // add transfer log
        //cerr << "in map size " << ins_map.size() << endl;
        VariableValueMap&  ins_map_temp = runtime_context.inputs;
        cerr << "ins map siz" <<  ins_map_temp.size() << endl;
        for( auto& var_name_item : ins_map_temp  )
        {
          cerr << "in name " << var_name_item.first << endl; 
          //auto& vec_ids = ins_name2id[ var_name_item.first ];
          for( size_t i = 0; i < var_name_item.second.size();  ++i )
          {
            auto var = var_name_item.second[i];
            auto tensor_in = static_cast<const Tensor*>(&(var->Get<LoDTensor>()));
            cerr << "i " << i << "\t" << tensor_in->IsInitialized() << endl;
            auto kernel_type_for_var = static_cast<const framework::OperatorWithKernel*>(op_base)->GetKernelTypeForVar(
                var_name_item.first, *tensor_in, expected_kernel_key);
            if( debug) 
            {
                cerr << "var name " << var_name_item.first << endl;
                cerr <<  expected_kernel_key.place_ << "\t" << kernel_type_for_var.place_ << endl;
            }
            if ( !platform::is_same_place(kernel_type_for_var.place_,
                                expected_kernel_key.place_) )
            {
              if(debug) cerr << "add data transfer" << endl;
              // need trans place
              // add var in scope
              // add copy op
              std::string new_var_name = "temp_1" + to_string( var_scope->var_list.size() + 1);
              auto v = new Variable();
              v->GetMutable<LoDTensor>();
              var_scope->name2id[ new_var_name ] = var_scope->var_list.size();
              var_scope->var_list.push_back(std::unique_ptr<Variable>(v));

              VariableNameMap copy_in_map;
              cerr << "ints name is " << input_names[var_name_item.first][i] << endl;
              copy_in_map["X"] = { input_names[var_name_item.first][i] };
              VariableNameMap copy_out_map;
              copy_out_map["Out"] = { new_var_name };
              AttributeMap attr_map;
              attr_map["dst_place_type"] = convert( place ); 

              std::map< std::string, std::vector<int> > copy_ins_name2id;
              copy_ins_name2id["X"] = ins_name2id[ var_name_item.first ];
              std::map< std::string, std::vector<int> > copy_out_name2id;
              copy_out_name2id["Out"] = { var_scope->name2id[new_var_name]};

              //vec_ids[i] = var_scope->name2id[new_var_name];
              // update out runtime_context
              op_func_node.input_index[ var_name_item.first ][i] = var_scope->name2id[new_var_name];

              VariableValueMap copy_ins_value_map;              
              copy_ins_value_map["X"] = { var };
              VariableValueMap copy_outs_value_map;
              copy_outs_value_map["Out"] = { v };

              
              
              auto& copy_info = OpInfoMap::Instance().Get( "memcpy" );
              auto copy_op = copy_info.Creator()( "memcpy", copy_in_map, copy_out_map, attr_map);
              if(debug) cerr << "create memcpy" << endl;
              OpFuncNode copy_op_func_node;
              copy_op_func_node.input_index = copy_ins_name2id;
              copy_op_func_node.output_index = copy_out_name2id;

              RuntimeContext copy_runtime_context( {}, {});
              copy_runtime_context.inputs.swap( copy_ins_value_map );
              copy_runtime_context.outputs.swap(  copy_outs_value_map );
              //cerr << "create runtime context" << endl;
              RuntimeInferShapeContext copy_infer_shape_ctx(*copy_op, copy_runtime_context);
              if(debug) cerr << "before infer shape" << endl;
              static_cast<const framework::OperatorWithKernel*>(copy_op)->InferShape( &copy_infer_shape_ctx );
              if(debug) cerr << "infer shape" << endl;
              //cerr << "fin infer shape" << endl;
              auto& all_op_kernels = OperatorWithKernel::AllOpKernels();
              auto kernels_iter = all_op_kernels.find( "memcpy" );
              PADDLE_ENFORCE_NE(
                  kernels_iter, all_op_kernels.end(),
                  platform::errors::Unavailable("There are no kernels which are registered in the memcpy operator.") );
               
              
              //cerr << "create kernel" << endl;
              using OpKernelFunc = std::function<void(const ExecutionContext&)>;
              using OpKernelMap =
                  std::unordered_map<OpKernelType, OpKernelFunc, OpKernelType::Hash>;
              
              OpKernelMap& kernels = kernels_iter->second;
              //auto place = platform::CPUPlace();
              //auto place = platform::CUDAPlace(0);
              
              platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
              auto* dev_ctx = pool.Get(place);
              Scope scope;
              auto copy_exec_ctx = ExecutionContext(*copy_op, scope, *dev_ctx, copy_runtime_context );
              if (debug  ) cerr << "21" << endl;
              auto expected_kernel_key = dynamic_cast<const framework::OperatorWithKernel*>(copy_op)->GetExpectedKernelType( copy_exec_ctx );
              if (debug  ) cerr << "22" << endl;
              //cerr << "22" << endl;
              auto kernel_iter = kernels.find(expected_kernel_key);
              copy_op_func_node.kernel_func_ = OpKernelFunc( kernel_iter->second );
              copy_op_func_node.kernel_func_( copy_exec_ctx );
              if(debug) cerr << "run exe ctx" << endl;

              op_list.push_back( copy_op );
              vec_func_list.push_back( copy_op_func_node);


              var_name_item.second[i] = v;
            }
          }                    
        }
        
        op_list.push_back( op_base );
        
        auto kernel_iter = kernels.find(expected_kernel_key);

        if (debug  ) cerr << "3" << endl;
        op_func_node.kernel_func_ = OpKernelFunc(kernel_iter->second);
        if (debug  ) cerr << "3-1" << endl;
        op_func_node.kernel_func_(  exec_ctx );
        vec_func_list.push_back( op_func_node );
        if (debug  ) cerr << "5" << endl;
    }
    
}



void exec_op_func_list( const std::vector<OpFuncNode>& vec_func_list, 
                        std::vector< OperatorBase* >& op_list, 
                        const VariableScope& var_scope,
                        const platform::Place& place)
{
    for( size_t i = 0; i < vec_func_list.size(); ++i )    
    {
        auto& func_node = vec_func_list[i];
        auto op_base = op_list[i];
        // build runtime cost
        VariableValueMap ins_map;        
        for( auto& var_name_item : func_node.input_index)
        {
            std::vector<Variable*> input_vars;
            
            input_vars.reserve(var_name_item.second.size());
            for (auto& id : var_name_item.second) {    
                cerr << var_name_item.first << "\t " << id << endl;            
                input_vars.emplace_back( var_scope.var_list[ id ].get() );                
            }
            ins_map.emplace( var_name_item.first, std::move(input_vars) );            
        }

        VariableValueMap outs_map;        
        for( auto& var_name_item : func_node.output_index)
        {
            std::vector<Variable*> out_vars;
            
            out_vars.reserve(var_name_item.second.size());
            for (auto& id : var_name_item.second) {       
                cerr << var_name_item.first << "\t " << id << endl;         
                out_vars.emplace_back( var_scope.var_list[ id ].get());                 
            }            
            outs_map.emplace( var_name_item.first, std::move( out_vars ) );
        }

        RuntimeContext runtime_context( {}, {});
        runtime_context.inputs.swap( ins_map );
        runtime_context.outputs.swap(  outs_map );
        
        RuntimeInferShapeContext infer_shape_ctx( *op_base, runtime_context);
       
        //dynamic_cast<const framework::OperatorWithKernel*>(op_base)->InferShape( &infer_shape_ctx );
        //RuntimeInferShapeContext infer_shape_ctx(*op_base, runtime_context);
        static_cast<const framework::OperatorWithKernel*>(op_base)->InferShape( &infer_shape_ctx );

       
        platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
        //auto place = platform::CPUPlace();
        //auto place = platform::CUDAPlace(0);
        auto* dev_ctx = pool.Get(place);
        Scope scope;
        
      
        auto exec_context = ExecutionContext(*op_base, scope, *dev_ctx, runtime_context );
        
        func_node.kernel_func_( exec_context );
        
    }
}

class InterpreterCore
{
public:
  InterpreterCore( const platform::Place& place, const ProgramDesc& prog ) : place_(place), prog_(prog) {
    paddle::framework::InitDevices();

    is_build = false;

  } 
  void run( const std::vector<std::string> vec_name, const std::vector<framework::Tensor>& vec_tensor, const vector<std::string>& vec_fetch_name)
  {
    cerr << "run" << endl;
      // set static data
    if( is_build == false )
    {
      paddle::framework::build_variable_scope( prog_, &global_scope );
    }
    for ( size_t i = 0; i < vec_name.size(); ++i )
    {
        auto it = global_scope.name2id.find( vec_name[i] );
        cerr << "find " << ( it != global_scope.name2id.end() ) <<endl;
        assert( it != global_scope.name2id.end() );
        
        auto feed_tensor = global_scope.var_list[ it->second]->GetMutable<framework::LoDTensor>();
        cerr << " get tensor" << endl;
        feed_tensor->ShareDataWith( vec_tensor[i] );
        cerr << "share buffer with" << endl;
    }
    
    if( is_build == false )
    {
      paddle::framework::build_op_func_list( prog_, op_list, vec_func_list, &global_scope, place_);
      is_build = true;
    }
    else
    {
      paddle::framework::exec_op_func_list( vec_func_list, op_list, global_scope, place_ );
    }
    
    for( size_t i = 0; i < vec_fetch_name.size(); ++i )
    {
       auto it = global_scope.name2id.find( vec_fetch_name[i] );
        assert( it != global_scope.name2id.end() );
        
        auto fetch_tensor = global_scope.var_list[ it->second]->GetMutable<framework::LoDTensor>();


        //cerr << "out  "  << fetch_tensor->data<float>()[0] << endl;
        if ( platform::is_gpu_place(fetch_tensor->place() ) )
        {
          cerr << "fetch gpu" << endl;
            Tensor out;
            platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
            auto* dev_ctx = pool.Get(place_);
            dev_ctx->Wait();
            TensorCopySync(*fetch_tensor, platform::CPUPlace(), &out);
            dev_ctx->Wait();
            cerr << "out  " << out << endl;
        }
        else
        {

          cerr << "out  "  << *fetch_tensor << endl;
        }
    }
  }
private:
  const platform::Place& place_;
  const ProgramDesc& prog_;
  paddle::framework::VariableScope global_scope;
  std::vector<paddle::framework::OpFuncNode> vec_func_list;
  std::vector< paddle::framework::OperatorBase* > op_list;

  bool is_build;
  
};

}
}