add test exec;

paddle/fluid/framework/new_exec.cc

+#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/pybind/pybind.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_;
+};
+
+
+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>();
+      var_scope->var_list.push_back(std::unique_ptr<Variable>(v));
+  }
+}
+
+void build_op_func_list( const framework::ProgramDesc& pdesc, std::vector<std::unique_ptr<OperatorBase> >& op_list, std::vector<OpFuncNode>& vec_func_list, const VariableScope& var_scope )
+{
+    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 = false;
+        op_list.push_back( OpRegistry::CreateOp(*op) );
+        //cerr << "create op" << endl;
+        auto* op_base = op_list.back().get();
+
+        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;
+        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<std::unique_ptr<OperatorBase>>& op_list, const VariableScope& var_scope)
+{
+    for( size_t i = 0; i < vec_func_list.size(); ++i )    
+    {
+        auto& func_node = vec_func_list[i];
+        auto op_base = op_list[i].get();
+        // 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) {                
+                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) {                
+                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_list[i].get()), runtime_context);
+        dynamic_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 );
+    }
+}
+
+
+
+}
+}
+
+
+int main()
+{   
+    paddle::framework::InitDevices();
+    paddle::framework::VariableScope global_scope;
+
+    
+    {
+        auto test_prog = paddle::framework::load_from_file( "lm_startup_program");
+        paddle::framework::build_variable_scope( test_prog, &global_scope );
+
+
+        std::vector<paddle::framework::OpFuncNode> vec_func_list;
+        std::vector<std::unique_ptr<paddle::framework::OperatorBase>> op_list;
+        paddle::framework::build_op_func_list( test_prog, op_list, vec_func_list, global_scope);
+
+        paddle::framework::exec_op_func_list( vec_func_list, op_list, global_scope ); 
+    }
+
+    cerr << "run main" << endl;
+    auto main_prog = paddle::framework::load_from_file( "lm_main_program");
+
+    paddle::framework::build_variable_scope( main_prog, &global_scope );
+
+
+    std::vector<paddle::framework::OpFuncNode> vec_main_func_list;
+    std::vector<std::unique_ptr<paddle::framework::OperatorBase>> op_main_list;
+    paddle::framework::build_op_func_list( main_prog, op_main_list, vec_main_func_list, global_scope);
+
+    auto start = std::chrono::steady_clock::now();
+    ProfilerStart("new_executor.prof");
+    for ( size_t i = 0; i < 2320; ++i )
+    {
+        if( i % 200 == 0)
+        {   
+            cerr << i << endl;
+        }
+        paddle::framework::exec_op_func_list( vec_main_func_list, op_main_list, global_scope ); 
+    }
+    ProfilerStop();
+    auto end = std::chrono::steady_clock::now();
+    std::chrono::duration<double> diff = end-start;
+
+    cerr << "time cost " << diff.count() << endl;
+
+
+    return 1;
+
+}