!323 Remove reduplicated useless proto

Merge pull request !323 from 张晓昆/master
4 years ago · db5ce472de
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -195,8 +195,40 @@ target_compile_options(parser_caffe_proto_obj PRIVATE
    $<$<AND:$<STREQUAL:${TARGET_SYSTEM_NAME},Windows>,$<STREQUAL:${CMAKE_CONFIGURATION_TYPES},Release>>:/MT>
 )

 ############ lib_caffe_parser.so ############
 add_library(_caffe_parser SHARED
    $<TARGET_OBJECTS:parser_caffe_proto_obj>
 )

 target_compile_definitions(_caffe_parser PRIVATE
    google=ascend_private
 )

 target_include_directories(_caffe_parser PRIVATE
    ${CMAKE_CURRENT_LIST_DIR}
 )

 target_link_options(_caffe_parser PRIVATE
    -Wl,-Bsymbolic
 )

 target_link_libraries(_caffe_parser PRIVATE
    $<BUILD_INTERFACE:intf_pub>
    -Wl,--no-as-needed
    ascend_protobuf
    -Wl,--as-needed
 )

 ############ install ############
 set(INSTALL_BASE_DIR "")
 set(INSTALL_LIBRARY_DIR lib)

 install(TARGETS _caffe_parser OPTIONAL
    LIBRARY DESTINATION ${INSTALL_LIBRARY_DIR}
 )


 add_subdirectory(parser)
 add_subdirectory(parser/common)
 add_subdirectory(parser/func_to_graph)
 add_subdirectory(parser/onnx)
 add_subdirectory(parser/proto/caffe)
--- a/parser/caffe/proto/caffe/caffe.proto
+++ b/parser/caffe/proto/caffe/caffe.proto
--- a/parser/caffe/proto/ge_ir.proto
+++ b/parser/caffe/proto/ge_ir.proto
@@ -1,193 +0,0 @@
 syntax = "proto3";

 package ge.proto;

 enum DataType
 {
    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
    DT_FLOAT     = 1;  // float type
    DT_FLOAT16   = 2;  // fp16 type
    DT_INT8      = 3;  // int8 type
    DT_UINT8     = 4;  // uint8 type
    DT_INT16     = 5;  // int16 type
    DT_UINT16    = 6;  // uint16 type
    DT_INT32     = 7;  //
    DT_INT64     = 8;  // int64 type
    DT_UINT32    = 9;  // unsigned int32
    DT_UINT64    = 10;  // unsigned int64
    DT_BOOL      = 11;  // bool type
    DT_DOUBLE    = 12; // double type
    DT_STRING = 13;            // string type
    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
    DT_COMPLEX64 = 16;         // complex64 type
    DT_COMPLEX128 = 17;        // complex128 type
    DT_QINT8 = 18;             // qint8 type
    DT_QINT16 = 19;            // qint16 type
    DT_QINT32 = 20;            // qint32 type
    DT_QUINT8 = 21;            // quint8 type
    DT_QUINT16 = 22;           // quint16 type
    DT_RESOURCE  = 23;         // resource type
    DT_STRING_REF = 24;        // string_ref type
    DT_DUAL      = 25;              /**< dual output type */
    DT_VARIANT = 26;           // variant type
    DT_BF16 = 27;              // bf16 type
    DT_INT4 = 28;              // int4 type
 }

 message AttrDef
 {
    message ListValue
    {
        enum ListValueType{
          VT_LIST_NONE = 0;
          VT_LIST_STRING = 1;
          VT_LIST_INT = 2;
          VT_LIST_FLOAT = 3;
          VT_LIST_BOOL = 4;
          VT_LIST_BYTES = 5;
          VT_LIST_TENSOR_DESC = 6;
          VT_LIST_TENSOR = 7;
          VT_LIST_GRAPH = 8;
          VT_LIST_NAMED_ATTRS = 9;
          VT_LIST_DATA_TYPE = 10;
        }
        repeated bytes s             = 2;                    // "list(string)"
        repeated int64 i             = 3;  // "list(int)"
        repeated float f             = 4;   // "list(float)"
        repeated bool  b             = 5;  // "list(bool)"
        repeated bytes bt            = 7;
        repeated TensorDescriptor td = 8;
        repeated TensorDef t         = 9;
        repeated GraphDef g          = 10;
 	    repeated NamedAttrs na       = 11;
 	    repeated int64 dt            = 12; // list ge::DataType

 	    ListValueType val_type       = 20;
    }

    message ListListInt{
        message ListInt{
            repeated int64 list_i             = 1; // list int
        }
        repeated ListInt list_list_i             = 1; // list list int
    }

    oneof value
    {
        bytes            s    = 2;  // "string"
        int64            i    = 3;  // "int"
        float            f    = 4;  // "float"
        bool             b    = 5;  // "bool"
        bytes            bt   = 7;
        ListValue        list = 1;   // any "list(...)"
        NamedAttrs       func = 10;  // Used to support attr nesting
        TensorDescriptor td   = 11;  // GeTensorDesc type
        TensorDef        t    = 12;  // GeTensor type
        GraphDef         g    = 13;  // Graph type
        ListListInt      list_list_int  = 14;  // List List Int type
        int64            dt   = 15; // ge::DataType
    }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs
 {
    string               name = 1;
    map<string, AttrDef> attr = 2;
 }

 // Shape / dimension description, using row-major order
 message ShapeDef
 {
    repeated int64 dim = 1;  // Size of each dimension
 }

 // Multidimensional data description
 message TensorDescriptor
 {
    string   name   = 1;  // Optional parameter, tensor name

    DataType dtype  = 2;  // tensor datatype
    ShapeDef shape  = 3;  // Shape / dimension
    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"

    bool has_out_attr = 9;
    int64 size = 10;
    int64 weight_size = 11;
    bool reuse_input = 12;
    bool output_tensor = 13;
    string device_type = 14;
    bool input_tensor =15;
    int64 real_dim_cnt = 16;
    int64 reuse_input_index = 17;
    int64 data_offset = 18;
    int64 cmps_size = 19;
    string cmps_tab = 20;
    int64 cmps_tab_offset = 21;

 	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
 }

 // GeTensor definition
 message TensorDef
 {
    TensorDescriptor desc = 1;  // Tensor description
    bytes            data = 2;  // Tensor data
 }


 // Operator description
 message OpDef
 {
    string name = 1;  // name
    string type = 2;  // type

    repeated string input = 5;  // input original op name + outgoing index. op_name：index

    map<string, AttrDef> attr = 10;  // Set of operator parameter fields

    bool has_out_attr = 20;
    int64 id = 21;
    int64 stream_id =22;
    repeated string input_name = 23;
    repeated string src_name = 24;
    repeated int64 src_index = 25;
    repeated string dst_name = 26;
    repeated int64 dst_index = 27;
    repeated int64 input_i = 28;
    repeated int64 output_i = 29;
    repeated int64 workspace = 30;
    repeated int64 workspace_bytes = 31;
    repeated bool is_input_const = 32;
    repeated TensorDescriptor input_desc = 33;
    repeated TensorDescriptor output_desc = 34;
    repeated string subgraph_name = 35;
 }

 // Graph definition
 message GraphDef
 {
    string name   = 1;   //  name

    repeated string input  = 4;  // Graph input
    repeated string output = 5;  // Graph output

    repeated OpDef op      = 6;  // List of operators

 	map<string, AttrDef> attr = 11;  // Extended field
 }

 // model definition
 message ModelDef
 {
 	string name         = 1;  // name
 	uint32 version      = 2;  // IR Proto verion
 	string custom_version = 3;  // User model version number, passed in by user

    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef

    map<string, AttrDef> attr = 11;  // Extended field
 }

--- a/parser/caffe/proto/om.proto
+++ b/parser/caffe/proto/om.proto
@@ -1,396 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 enum TargetType
 {
    MINI = 0;
    TINY = 1;
    LITE = 2;
 }

 // offline model
 message ModelDef {
    string name = 1;
    uint32 version = 2;

    uint64 memory_size = 10;
    uint32 stream_num = 11;
    uint32 event_num = 12;
    uint64 weight_size = 13;
    uint32 label_num = 15;
    repeated OpDef op = 20;
    TargetType target_type = 23;

    map<string, AttrDef> attr = 30;
 };

 // operator define
 message OpDef {
    string name = 1;
    string type = 2;

    uint32 id = 3;
    uint32 stream_id = 4;

    repeated string input_name = 5;

    repeated string src_name = 8;
    repeated int32 src_index = 9;
    repeated int64 input = 10;
    repeated int64 output = 11;
    repeated TensorDescriptor input_desc = 12;
    repeated TensorDescriptor output_desc = 13;
    repeated WeightDef weights = 14;
    repeated string dst_name = 15;
    repeated int32 dst_index = 16;

    repeated int64 workspace = 20;
    repeated uint32 workspace_bytes = 21;

    repeated string weight_name = 22;
    repeated bool is_input_const = 23;

    map<string, AttrDef> attr = 30;

    QuantizeFactorParams quantize_factor = 31;

    oneof op_params {
        // start at 100 here
        SendOpParams sender_param = 100;
        RecvOpParams receiver_param = 200;
        ConvolutionOpParams convolution_param = 300;
        PoolingOpParams pooling_param = 400;
        EltwiseOpParams eltwise_param = 500;
        BatchNormOpParams batchnorm_param = 600;
        ScaleOpParams scale_param = 700;
        FullConnectionOpParams full_connection_param = 800;
        SoftmaxOpParams softmax_param = 900;
        ActivationOpParams activation_param = 1000;
        ReshapeOpParams reshape_param = 1100;
    }
 };

 message SendOpParams {
    uint32 event_id = 1;
 };

 message RecvOpParams {
    uint32 event_id = 1;
 };

 enum QuantizeScaleType
 {
    VECTOR_SCALE = 0;
    SCALAR_SCALE = 1;
 }

 enum QuantizeScaleMode
 {
    NORMAL_MODE = 0;
    SQRT_MODE = 1;
 }

 enum QuantizeAlgorithm
 {
    NON_OFFSET_ALGO = 0;
    HALF_OFFSET_ALGO = 1;
    ALL_OFFSET_ALGO = 2;
 }
 message QuantizeFactor
 {
    QuantizeScaleMode scale_mode = 1;
    bytes scale_value = 2;
    int64 scale_offset = 3;
    bytes offset_data_value = 4;
    int64 offset_data_offset = 5;
    bytes offset_weight_value = 6;
    int64 offset_weight_offset = 7;
    bytes offset_pad_value = 8;
    int64 offset_pad_offset = 9;
 };

 message QuantizeCalcFactor
 {
    bytes offsetw = 1;
    int64 offsetw_offset = 2;
    bytes offsetd = 3;
    int64 offsetd_offset = 4;
    bytes scalereq = 5;
    int64 scaledreq_offset = 6;
    bytes offsetdnext = 7;
    int64 offsetdnext_offset = 8;
 }

 message QuantizeFactorParams
 {
    QuantizeAlgorithm quantize_algo = 1;
    QuantizeScaleType scale_type = 2;
    QuantizeFactor quantize_param = 3;
    QuantizeFactor dequantize_param = 4;
    QuantizeFactor requantize_param = 5;
    QuantizeCalcFactor quantizecalc_param = 6;
 };

 message ConvolutionOpParams {
    int32 mode = 1;
    int32 algo = 2;
    int32 pad_mode = 3;
    uint32 group = 4;
    uint32 num_output = 5;

    repeated uint32 pad = 10;
    repeated uint32 stride = 11;
    repeated uint32 dilation = 12;
    repeated uint32 kernel = 13;

    float alpha = 20;
    float beta = 21;

    WeightDef filter = 40;
    WeightDef bias = 41;

    bool relu_flag = 62;
    repeated uint32 adj = 70;
    repeated uint32 target_shape = 71;
    repeated uint32 before_pad = 72;
 };

 message PoolingOpParams {
    int32 mode = 1;
    int32 nan_opt = 2;
    int32 pad_mode = 3;
    bool global_pooling = 4;

    repeated uint32 window = 10;
    repeated uint32 pad = 11;
    repeated uint32 stride = 12;
    bool ceil_mode = 13;
    int32 data_mode  = 14;

    float alpha = 20;
    float beta = 21;
    repeated uint32 before_pad = 22;
 };

 message EltwiseOpParams {
    int32 mode = 1;
    repeated float coeff = 2;
    float alpha = 3;
    float beta = 4;
    repeated WeightDef weight = 5;
    bool relu_flag = 6;
 };

 message ActivationOpParams {
    int32 mode = 1;
    float coef = 2;
    float alpha = 3;
    float beta = 4;
 };

 message BatchNormOpParams {
    int32 mode = 1;

    float alpha = 2;
    float beta = 3;
    double epsilon = 4;//optinal,[default = 1e-5]
    bool use_global_stats = 5; //optinal,by default true,testing mode
    float  moving_average_fraction = 6; //optinal,[default = .999];

    WeightDef estimated_mean = 7;
    WeightDef estimated_variance = 8;

    WeightDef scale = 9;
    WeightDef bias = 10;
 };

 message ScaleOpParams {
    WeightDef scale = 1;
    WeightDef bias = 2;
 };

 message ReshapeOpParams {
    float alpha = 1;
    float beta = 2;
    ShapeDef shape = 3;
    int32 axis = 4;
    int32 num_axes = 5;
    int32 format = 6;
 };

 message SoftmaxOpParams {
    int32 algo = 1;
    int32 mode = 2;
    float alpha = 3;
    float beta = 4;
 };

 message FullConnectionOpParams {
    WeightDef filter = 1;
    WeightDef bias = 2;
    uint32 num_output = 3;
    bool relu_flag = 12;
 };

 message FlattenOpParams {
    float alpha = 1;
    float beta = 2;
    int32 start_axis = 3;
    int32 end_axis = 4;
 }

 message AddLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message MulLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message AddOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message MulOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message SubOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message BiasAddOpParams {
    float alpha = 1;
    float beta = 2;

    WeightDef bias = 10;
 };

 message MatMulOpParams {
    float alpha = 1;
    float beta = 2;
    bool transposeX = 3;
    bool transposeW = 4;

    WeightDef filter = 10;
    WeightDef bias = 12;
 };

 message RsqrtOpParams {
    float alpha = 1;
    float beta = 2;
 };


 message WeightDef {
    int32 format = 1;
    int32 data_type = 2;
    ShapeDef shape = 3;
    bytes data = 4;
    int64 data_offset = 5;
    uint32 cmps_size = 6;
    bytes cmps_tab = 7;
    int64 cmps_tab_offset = 10;
    CompressInfo cmps_info = 8;
    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
 }

 message ShapeDef {
    repeated int64 dim = 1;
 }

 enum DeviceType {
  NPU = 0;                   // In default, we will use NPU.
  CPU = 1;                   // CPU
 }

 message AllOffsetQuantizeInfo {
 	float scale  = 1;
 	int32 offset = 2;
 } 

 message TensorDescriptor {
    int32 format = 1;
    int32 data_type = 2;
    repeated int64 dim = 3;
    uint32 size = 4;
    bool reuse_input = 5;
    bool output_tensor = 7;
    DeviceType device_type = 8;
    bool input_tensor = 9;
    uint32 real_dim_cnt = 10;
    uint32 reuse_input_index = 11;
    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
 }

 message CompressInfo {
    int32 blockRow = 1;                             // block row
    int32 blockCol = 2;                             // block col
    int32 fractalK = 3;                             // fractal K
    int32 fractalN = 4;                             // fractal N
    int32 lastFractalK = 5;                         // K of last fractal
    int32 lastFractalN = 6;                         // N of last fractal
    int32 cubeSize = 7;                             // cube's length
    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
 }

 message AttrDef {
  message ListValue {
    repeated string s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated uint32 u = 6 [packed = true];         // "list(uint)"
    repeated bytes bt = 7;
  }

  oneof value {
    string s = 2;                // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    uint32 u = 6;                // "uint32"
    bytes bt = 7;
    ListValue list = 1;          // any "list(...)"
    NamedAttrs func = 10;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs {
  string name = 1;
  map<string, AttrDef> attr = 2;
 }

--- a/parser/caffe/proto/task.proto
+++ b/parser/caffe/proto/task.proto
@@ -1,179 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 message ModelTaskDef {
    string version = 1;

    map<string, string> attr = 9; // Extended field
    repeated TaskDef task = 10;

    uint64 memory_size = 11;
    uint32 stream_num = 12;
    uint32 event_num = 13;
    uint64 weight_size = 14;

    repeated bytes op = 15; // input/output opdef in bytes

    uint64 base_addr = 16;    // base addr
    uint64 weight_addr = 17;  // weight addr
    uint32 batch_num = 18;
 }


 message TaskDef {
    uint32 id = 1;
    uint32 type = 2;

    uint32 stream_id = 10;
    uint32 event_id = 11;

    KernelDef kernel = 20;
    KernelExDef kernel_ex = 21;
    KernelHcclDef kernel_hccl = 25;
    EventExDef event_ex = 26;
    LogTimeStampDef log_timestamp = 28;

    uint32 label_id = 30;

    MemcpyAsyncDef memcpy_async = 31;
    StreamSwitchDef stream_switch = 32;
    StreamActiveDef stream_active = 33;
    bytes private_def = 34;
    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
    StreamSwitchNDef stream_switch_n = 36;

    LabelSetDef label_set = 37;
    LabelGotoExDef label_goto_ex = 38;
    LabelSwitchByIndexDef label_switch_by_index = 39;
    KernelDefWithHandle kernel_with_handle = 40;
 }

 message KernelDef {
    KernelContext context = 1;

    string stub_func = 10;
    uint32 block_dim = 11;
    uint32 args_size = 12;
    bytes args = 13;
    bytes sm_desc = 14;
    bytes flowtable = 15;
    string so_name = 16;
    string kernel_name = 17;
    bytes kernel_ext_info = 18;
    uint32 kernel_ext_info_size = 19;
 }

 message KernelDefWithHandle {
    KernelContext context = 1;

    uint64 handle = 10;
    string dev_func = 11;
    uint32 block_dim = 12;
    uint32 args_size = 13;
    bytes args = 14;
    bytes sm_desc = 15;
    string original_kernel_key = 16;
    string node_info = 17;
 }

 message KernelContext {
    uint32 kernel_type = 1;
    uint32 op_id = 2;                              // OP type in CCE
    uint32 kernel_func_id = 3;
    uint32 op_index = 4;                           // TE/Custom operator
    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
    bytes args_offset = 6;                         // args offset information
    uint32 args_count = 7;                         // args count
    repeated uint32 origin_op_index = 8;
 }


 message KernelExDef {
    uint32 flags = 1;

    uint32 op_index = 4;
    uint32 args_size = 12;
    bytes args = 13;
    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
    uint32 task_info_size = 15;
    bytes kernel_ext_info = 16;
    uint32 kernel_ext_info_size = 17;
 }


 message KernelHcclDef {
    uint32 op_index = 8;
    string hccl_type = 9;
 }


 message EventExDef {
    uint32 op_index = 1;
    uint32 event_type = 2;
 }

 message LogTimeStampDef {
    uint64 logid = 1;
    bool notify = 2;
    uint32 flat = 3;
 }

 message MemcpyAsyncDef {
    uint64 dst = 1;
    uint64 dst_max = 2;
    uint64 src = 3;
    uint64 count = 4;
    uint32 kind = 5;
    uint32 op_index = 6;
 }

 message StreamSwitchDef {
    uint32 op_index = 1;
    uint32 true_stream_id = 2;
    int64 value = 3;
    uint64 value_ptr = 4;
    uint32 data_type = 5;
 }

 message StreamActiveDef {
    uint32 op_index = 1;
    uint32 active_stream_id = 2;
 }

 message StreamSwitchNDef {
    uint32 op_index = 1;
    uint32 size = 2;
    repeated int64 target_value = 3;
    repeated uint32 true_stream_id = 4;
    uint32 element_size = 5;
    uint32 data_type = 6;
 }

 message LabelSetDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelGotoExDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelSwitchByIndexDef {
    uint32 op_index = 1;
    uint32 label_max = 2;
 }
--- a/parser/common/proto/ge_ir.proto
+++ b/parser/common/proto/ge_ir.proto
@@ -1,193 +0,0 @@
 syntax = "proto3";

 package ge.proto;

 enum DataType
 {
    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
    DT_FLOAT     = 1;  // float type
    DT_FLOAT16   = 2;  // fp16 type
    DT_INT8      = 3;  // int8 type
    DT_UINT8     = 4;  // uint8 type
    DT_INT16     = 5;  // int16 type
    DT_UINT16    = 6;  // uint16 type
    DT_INT32     = 7;  //
    DT_INT64     = 8;  // int64 type
    DT_UINT32    = 9;  // unsigned int32
    DT_UINT64    = 10;  // unsigned int64
    DT_BOOL      = 11;  // bool type
    DT_DOUBLE    = 12; // double type
    DT_STRING = 13;            // string type
    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
    DT_COMPLEX64 = 16;         // complex64 type
    DT_COMPLEX128 = 17;        // complex128 type
    DT_QINT8 = 18;             // qint8 type
    DT_QINT16 = 19;            // qint16 type
    DT_QINT32 = 20;            // qint32 type
    DT_QUINT8 = 21;            // quint8 type
    DT_QUINT16 = 22;           // quint16 type
    DT_RESOURCE  = 23;         // resource type
    DT_STRING_REF = 24;        // string_ref type
    DT_DUAL      = 25;              /**< dual output type */
    DT_VARIANT = 26;           // variant type
    DT_BF16 = 27;              // bf16 type
    DT_INT4 = 28;              // int4 type
 }

 message AttrDef
 {
    message ListValue
    {
        enum ListValueType{
          VT_LIST_NONE = 0;
          VT_LIST_STRING = 1;
          VT_LIST_INT = 2;
          VT_LIST_FLOAT = 3;
          VT_LIST_BOOL = 4;
          VT_LIST_BYTES = 5;
          VT_LIST_TENSOR_DESC = 6;
          VT_LIST_TENSOR = 7;
          VT_LIST_GRAPH = 8;
          VT_LIST_NAMED_ATTRS = 9;
          VT_LIST_DATA_TYPE = 10;
        }
        repeated bytes s             = 2;                    // "list(string)"
        repeated int64 i             = 3;  // "list(int)"
        repeated float f             = 4;   // "list(float)"
        repeated bool  b             = 5;  // "list(bool)"
        repeated bytes bt            = 7;
        repeated TensorDescriptor td = 8;
        repeated TensorDef t         = 9;
        repeated GraphDef g          = 10;
 	    repeated NamedAttrs na       = 11;
 	    repeated int64 dt            = 12; // list ge::DataType

 	    ListValueType val_type       = 20;
    }

    message ListListInt{
        message ListInt{
            repeated int64 list_i             = 1; // list int
        }
        repeated ListInt list_list_i             = 1; // list list int
    }

    oneof value
    {
        bytes            s    = 2;  // "string"
        int64            i    = 3;  // "int"
        float            f    = 4;  // "float"
        bool             b    = 5;  // "bool"
        bytes            bt   = 7;
        ListValue        list = 1;   // any "list(...)"
        NamedAttrs       func = 10;  // Used to support attr nesting
        TensorDescriptor td   = 11;  // GeTensorDesc type
        TensorDef        t    = 12;  // GeTensor type
        GraphDef         g    = 13;  // Graph type
        ListListInt      list_list_int  = 14;  // List List Int type
        int64            dt   = 15; // ge::DataType
    }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs
 {
    string               name = 1;
    map<string, AttrDef> attr = 2;
 }

 // Shape / dimension description, using row-major order
 message ShapeDef
 {
    repeated int64 dim = 1;  // Size of each dimension
 }

 // Multidimensional data description
 message TensorDescriptor
 {
    string   name   = 1;  // Optional parameter, tensor name

    DataType dtype  = 2;  // tensor datatype
    ShapeDef shape  = 3;  // Shape / dimension
    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"

    bool has_out_attr = 9;
    int64 size = 10;
    int64 weight_size = 11;
    bool reuse_input = 12;
    bool output_tensor = 13;
    string device_type = 14;
    bool input_tensor =15;
    int64 real_dim_cnt = 16;
    int64 reuse_input_index = 17;
    int64 data_offset = 18;
    int64 cmps_size = 19;
    string cmps_tab = 20;
    int64 cmps_tab_offset = 21;

 	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
 }

 // GeTensor definition
 message TensorDef
 {
    TensorDescriptor desc = 1;  // Tensor description
    bytes            data = 2;  // Tensor data
 }


 // Operator description
 message OpDef
 {
    string name = 1;  // name
    string type = 2;  // type

    repeated string input = 5;  // input original op name + outgoing index. op_name：index

    map<string, AttrDef> attr = 10;  // Set of operator parameter fields

    bool has_out_attr = 20;
    int64 id = 21;
    int64 stream_id =22;
    repeated string input_name = 23;
    repeated string src_name = 24;
    repeated int64 src_index = 25;
    repeated string dst_name = 26;
    repeated int64 dst_index = 27;
    repeated int64 input_i = 28;
    repeated int64 output_i = 29;
    repeated int64 workspace = 30;
    repeated int64 workspace_bytes = 31;
    repeated bool is_input_const = 32;
    repeated TensorDescriptor input_desc = 33;
    repeated TensorDescriptor output_desc = 34;
    repeated string subgraph_name = 35;
 }

 // Graph definition
 message GraphDef
 {
    string name   = 1;   //  name

    repeated string input  = 4;  // Graph input
    repeated string output = 5;  // Graph output

    repeated OpDef op      = 6;  // List of operators

 	map<string, AttrDef> attr = 11;  // Extended field
 }

 // model definition
 message ModelDef
 {
 	string name         = 1;  // name
 	uint32 version      = 2;  // IR Proto verion
 	string custom_version = 3;  // User model version number, passed in by user

    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef

    map<string, AttrDef> attr = 11;  // Extended field
 }

--- a/parser/common/proto/insert_op.proto
+++ b/parser/common/proto/insert_op.proto
@@ -1,140 +0,0 @@
 syntax = "proto3";

 package domi;

 message InsertNewOps {
 	repeated AippOpParams aipp_op = 1;
 	repeated MultiShapeOpParams multi_shape_op = 2;
 }

 message AippOpParams {
 	enum InputFormat {
 		UNDEFINED = 0;
 		YUV420SP_U8 = 1;
 		XRGB8888_U8 = 2;
 		RGB888_U8   = 3;
 		YUV400_U8   = 4;
 		NC1HWC0DI_FP16 = 5;
 		NC1HWC0DI_S8 = 6;
 		ARGB8888_U8 = 7;
 		YUYV_U8 = 8;
 		YUV422SP_U8 = 9;
 		AYUV444_U8 = 10;
 		RAW10 = 11;
 		RAW12 = 12;
 		RAW16 = 13;
 		RAW24 = 14;
 		RGB16 = 15;
 		RGB20 = 16;
 		RGB24 = 17;
 		RGB8_IR = 18;
 		RGB16_IR = 19;
 		RGB24_IR = 20;
 	}

 	enum AippMode {
 		undefined = 0;
 		static = 1;
 		dynamic = 2;
 	}

 	// AIPP模式，区分静态AIPP和动态AIPP
 	AippMode aipp_mode = 1;

 	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
 	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
 	uint32 related_input_rank = 2;

        // related_input_name is optional and the top name of data node which inserts aipp
        string related_input_name = 6;

 	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
 	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
 	// 配置值 <= Data算子输出边的个数。
 	repeated uint32 input_edge_idx = 3;

 	// [Begin] 动态AIPP参数，配置静态AIPP时无效
 	uint32 max_src_image_size = 4;

 	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
 	bool support_rotation = 5;

 	// [End] 动态AIPP参数


 	// [Begin] 静态AIPP参数，配置动态AIPP时无效
 	InputFormat input_format = 51;
 	bool csc_switch = 52;
 	float cpadding_value = 53;
 	bool rbuv_swap_switch = 54;
 	bool ax_swap_switch = 55;
 	bool single_line_mode = 56;

 	int32 src_image_size_w = 57;
 	int32 src_image_size_h = 58;

 	bool crop = 59;
 	int32 load_start_pos_w = 60;
 	int32 load_start_pos_h = 61;
 	int32 crop_size_w = 62;
 	int32 crop_size_h = 63;

 	bool resize = 64;
 	int32 resize_output_w = 65;
 	int32 resize_output_h = 66;

 	bool padding = 67;
 	int32 left_padding_size = 68;
 	int32 right_padding_size = 69;
 	int32 top_padding_size = 70;
 	int32 bottom_padding_size = 71;
 	float padding_value = 72;

 	int32 mean_chn_0 = 10;
 	int32 mean_chn_1 = 11;
 	int32 mean_chn_2 = 12;
 	int32 mean_chn_3 = 19;
 	float min_chn_0 = 13;
 	float min_chn_1 = 14;
 	float min_chn_2 = 15;
 	float min_chn_3 = 20;
 	repeated float var_reci_chn_0 = 16;
 	repeated float var_reci_chn_1 = 17;
 	repeated float var_reci_chn_2 = 18;
 	repeated float var_reci_chn_3 = 21;

 	repeated int32 matrix_r0c0 = 30;
 	repeated int32 matrix_r0c1 = 31;
 	repeated int32 matrix_r0c2 = 32;
 	repeated int32 matrix_r1c0 = 33;
 	repeated int32 matrix_r1c1 = 34;
 	repeated int32 matrix_r1c2 = 35;
 	repeated int32 matrix_r2c0 = 36;
 	repeated int32 matrix_r2c1 = 37;
 	repeated int32 matrix_r2c2 = 38;
 	repeated int32 output_bias_0 = 39;
 	repeated int32 output_bias_1 = 40;
 	repeated int32 output_bias_2 = 41;
 	repeated int32 input_bias_0 = 42;
 	repeated int32 input_bias_1 = 43;
 	repeated int32 input_bias_2 = 44;

 	// [End] 静态AIPP参数

       // The n number that is used for raw/rgbir data into f16 transformation.
       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
       uint32 raw_rgbir_to_f16_n = 45;
 }

 message MultiShapeOpParams {
 	enum MultiShapeMode {
 		batch      = 0;             //动态batch
 		resolution = 1;             //动态分辨率，扩展用
 	}

 	MultiShapeMode    mode                      = 1;        //算子模式
 	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入


 	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
 }
--- a/parser/common/proto/om.proto
+++ b/parser/common/proto/om.proto
@@ -1,396 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 enum TargetType
 {
    MINI = 0;
    TINY = 1;
    LITE = 2;
 }

 // offline model
 message ModelDef {
    string name = 1;
    uint32 version = 2;

    uint64 memory_size = 10;
    uint32 stream_num = 11;
    uint32 event_num = 12;
    uint64 weight_size = 13;
    uint32 label_num = 15;
    repeated OpDef op = 20;
    TargetType target_type = 23;

    map<string, AttrDef> attr = 30;
 };

 // operator define
 message OpDef {
    string name = 1;
    string type = 2;

    uint32 id = 3;
    uint32 stream_id = 4;

    repeated string input_name = 5;

    repeated string src_name = 8;
    repeated int32 src_index = 9;
    repeated int64 input = 10;
    repeated int64 output = 11;
    repeated TensorDescriptor input_desc = 12;
    repeated TensorDescriptor output_desc = 13;
    repeated WeightDef weights = 14;
    repeated string dst_name = 15;
    repeated int32 dst_index = 16;

    repeated int64 workspace = 20;
    repeated uint32 workspace_bytes = 21;

    repeated string weight_name = 22;
    repeated bool is_input_const = 23;

    map<string, AttrDef> attr = 30;

    QuantizeFactorParams quantize_factor = 31;

    oneof op_params {
        // start at 100 here
        SendOpParams sender_param = 100;
        RecvOpParams receiver_param = 200;
        ConvolutionOpParams convolution_param = 300;
        PoolingOpParams pooling_param = 400;
        EltwiseOpParams eltwise_param = 500;
        BatchNormOpParams batchnorm_param = 600;
        ScaleOpParams scale_param = 700;
        FullConnectionOpParams full_connection_param = 800;
        SoftmaxOpParams softmax_param = 900;
        ActivationOpParams activation_param = 1000;
        ReshapeOpParams reshape_param = 1100;
    }
 };

 message SendOpParams {
    uint32 event_id = 1;
 };

 message RecvOpParams {
    uint32 event_id = 1;
 };

 enum QuantizeScaleType
 {
    VECTOR_SCALE = 0;
    SCALAR_SCALE = 1;
 }

 enum QuantizeScaleMode
 {
    NORMAL_MODE = 0;
    SQRT_MODE = 1;
 }

 enum QuantizeAlgorithm
 {
    NON_OFFSET_ALGO = 0;
    HALF_OFFSET_ALGO = 1;
    ALL_OFFSET_ALGO = 2;
 }
 message QuantizeFactor
 {
    QuantizeScaleMode scale_mode = 1;
    bytes scale_value = 2;
    int64 scale_offset = 3;
    bytes offset_data_value = 4;
    int64 offset_data_offset = 5;
    bytes offset_weight_value = 6;
    int64 offset_weight_offset = 7;
    bytes offset_pad_value = 8;
    int64 offset_pad_offset = 9;
 };

 message QuantizeCalcFactor
 {
    bytes offsetw = 1;
    int64 offsetw_offset = 2;
    bytes offsetd = 3;
    int64 offsetd_offset = 4;
    bytes scalereq = 5;
    int64 scaledreq_offset = 6;
    bytes offsetdnext = 7;
    int64 offsetdnext_offset = 8;
 }

 message QuantizeFactorParams
 {
    QuantizeAlgorithm quantize_algo = 1;
    QuantizeScaleType scale_type = 2;
    QuantizeFactor quantize_param = 3;
    QuantizeFactor dequantize_param = 4;
    QuantizeFactor requantize_param = 5;
    QuantizeCalcFactor quantizecalc_param = 6;
 };

 message ConvolutionOpParams {
    int32 mode = 1;
    int32 algo = 2;
    int32 pad_mode = 3;
    uint32 group = 4;
    uint32 num_output = 5;

    repeated uint32 pad = 10;
    repeated uint32 stride = 11;
    repeated uint32 dilation = 12;
    repeated uint32 kernel = 13;

    float alpha = 20;
    float beta = 21;

    WeightDef filter = 40;
    WeightDef bias = 41;

    bool relu_flag = 62;
    repeated uint32 adj = 70;
    repeated uint32 target_shape = 71;
    repeated uint32 before_pad = 72;
 };

 message PoolingOpParams {
    int32 mode = 1;
    int32 nan_opt = 2;
    int32 pad_mode = 3;
    bool global_pooling = 4;

    repeated uint32 window = 10;
    repeated uint32 pad = 11;
    repeated uint32 stride = 12;
    bool ceil_mode = 13;
    int32 data_mode  = 14;

    float alpha = 20;
    float beta = 21;
    repeated uint32 before_pad = 22;
 };

 message EltwiseOpParams {
    int32 mode = 1;
    repeated float coeff = 2;
    float alpha = 3;
    float beta = 4;
    repeated WeightDef weight = 5;
    bool relu_flag = 6;
 };

 message ActivationOpParams {
    int32 mode = 1;
    float coef = 2;
    float alpha = 3;
    float beta = 4;
 };

 message BatchNormOpParams {
    int32 mode = 1;

    float alpha = 2;
    float beta = 3;
    double epsilon = 4;//optinal,[default = 1e-5]
    bool use_global_stats = 5; //optinal,by default true,testing mode
    float  moving_average_fraction = 6; //optinal,[default = .999];

    WeightDef estimated_mean = 7;
    WeightDef estimated_variance = 8;

    WeightDef scale = 9;
    WeightDef bias = 10;
 };

 message ScaleOpParams {
    WeightDef scale = 1;
    WeightDef bias = 2;
 };

 message ReshapeOpParams {
    float alpha = 1;
    float beta = 2;
    ShapeDef shape = 3;
    int32 axis = 4;
    int32 num_axes = 5;
    int32 format = 6;
 };

 message SoftmaxOpParams {
    int32 algo = 1;
    int32 mode = 2;
    float alpha = 3;
    float beta = 4;
 };

 message FullConnectionOpParams {
    WeightDef filter = 1;
    WeightDef bias = 2;
    uint32 num_output = 3;
    bool relu_flag = 12;
 };

 message FlattenOpParams {
    float alpha = 1;
    float beta = 2;
    int32 start_axis = 3;
    int32 end_axis = 4;
 }

 message AddLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message MulLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message AddOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message MulOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message SubOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message BiasAddOpParams {
    float alpha = 1;
    float beta = 2;

    WeightDef bias = 10;
 };

 message MatMulOpParams {
    float alpha = 1;
    float beta = 2;
    bool transposeX = 3;
    bool transposeW = 4;

    WeightDef filter = 10;
    WeightDef bias = 12;
 };

 message RsqrtOpParams {
    float alpha = 1;
    float beta = 2;
 };


 message WeightDef {
    int32 format = 1;
    int32 data_type = 2;
    ShapeDef shape = 3;
    bytes data = 4;
    int64 data_offset = 5;
    uint32 cmps_size = 6;
    bytes cmps_tab = 7;
    int64 cmps_tab_offset = 10;
    CompressInfo cmps_info = 8;
    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
 }

 message ShapeDef {
    repeated int64 dim = 1;
 }

 enum DeviceType {
  NPU = 0;                   // In default, we will use NPU.
  CPU = 1;                   // CPU
 }

 message AllOffsetQuantizeInfo {
 	float scale  = 1;
 	int32 offset = 2;
 } 

 message TensorDescriptor {
    int32 format = 1;
    int32 data_type = 2;
    repeated int64 dim = 3;
    uint32 size = 4;
    bool reuse_input = 5;
    bool output_tensor = 7;
    DeviceType device_type = 8;
    bool input_tensor = 9;
    uint32 real_dim_cnt = 10;
    uint32 reuse_input_index = 11;
    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
 }

 message CompressInfo {
    int32 blockRow = 1;                             // block row
    int32 blockCol = 2;                             // block col
    int32 fractalK = 3;                             // fractal K
    int32 fractalN = 4;                             // fractal N
    int32 lastFractalK = 5;                         // K of last fractal
    int32 lastFractalN = 6;                         // N of last fractal
    int32 cubeSize = 7;                             // cube's length
    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
 }

 message AttrDef {
  message ListValue {
    repeated string s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated uint32 u = 6 [packed = true];         // "list(uint)"
    repeated bytes bt = 7;
  }

  oneof value {
    string s = 2;                // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    uint32 u = 6;                // "uint32"
    bytes bt = 7;
    ListValue list = 1;          // any "list(...)"
    NamedAttrs func = 10;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs {
  string name = 1;
  map<string, AttrDef> attr = 2;
 }

--- a/parser/common/proto/tensorflow/attr_value.proto
+++ b/parser/common/proto/tensorflow/attr_value.proto
@@ -1,62 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "AttrValueProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "tensor.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing the value for an attr used to configure an Op.
 // Comment indicates the corresponding attr type.  Only the field matching the
 // attr type may be filled.
 message AttrValue {
  // LINT.IfChange
  message ListValue {
    repeated bytes s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated DataType type = 6 [packed = true];  // "list(type)"
    repeated TensorShapeProto shape = 7;         // "list(shape)"
    repeated TensorProto tensor = 8;             // "list(tensor)"
    repeated NameAttrList func = 9;              // "list(attr)"
  }
  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)

  oneof value {
    bytes s = 2;                 // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    DataType type = 6;           // "type"
    TensorShapeProto shape = 7;  // "shape"
    TensorProto tensor = 8;      // "tensor"
    ListValue list = 1;          // any "list(...)"

    // "func" represents a function. func.name is a function's name or
    // a primitive op's name. func.attr.first is the name of an attr
    // defined for that function. func.attr.second is the value for
    // that attr in the instantiation.
    NameAttrList func = 10;

    // This is a placeholder only used in nodes defined inside a
    // function.  It indicates the attr value will be supplied when
    // the function is instantiated.  For example, let us suppose a
    // node "N" in function "FN". "N" has an attr "A" with value
    // placeholder = "foo". When FN is instantiated with attr "foo"
    // set to "bar", the instantiated node N's attr A will have been
    // given the value "bar".
    string placeholder = 9;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NameAttrList {
  string name = 1;
  map<string, AttrValue> attr = 2;
 }
--- a/parser/common/proto/tensorflow/function.proto
+++ b/parser/common/proto/tensorflow/function.proto
@@ -1,100 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "FunctionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "node_def.proto";
 import "op_def.proto";

 // A library is a set of named functions.
 message FunctionDefLibrary {
  repeated FunctionDef function = 1;
  repeated GradientDef gradient = 2;
 }

 // A function can be instantiated when the runtime can bind every attr
 // with a value. When a GraphDef has a call to a function, it must
 // have binding for every attr defined in the signature.
 //   * device spec, etc.
 message FunctionDef {
  // The definition of the function's name, arguments, return values,
  // attrs etc.
  OpDef signature = 1;

  // Attributes specific to this function definition.
  map<string, AttrValue> attr = 5;

  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
  reserved 2;

  // In both of the following fields, there is the need to specify an
  // output that is used as either the input to another node (in
  // `node_def`) or as a return value of the function (in `ret`).
  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
  // list in some cases (instead of just single outputs).  Also, we
  // need to be able to deal with lists of unknown length (so the
  // output index may not be known at function definition time).  So
  // we use the following format instead:
  // * "fun_in" where "fun_in" is the name of a function input arg in
  //   the `signature` field above.  This represents that input, whether
  //   it is a single tensor or a list.
  // * "fun_in:0" gives the first element of a function input arg (a
  //   non-list input is considered a list of length 1 for these
  //   purposes).
  // * "node:out" where "node" is the name of a node in `node_def` and
  //   "out" is the name one of its op's output arguments (the name
  //   comes from the OpDef of the node's op). This represents that
  //   node's output, whether it is a single tensor or a list.
  //   Note: We enforce that an op's output arguments are never
  //   renamed in the backwards-compatibility test.
  // * "node:out:0" gives the first element of a node output arg (a
  //   non-list output is considered a list of length 1 for these
  //   purposes).
  //
  // NOT CURRENTLY SUPPORTED (but may be in the future):
  // * "node:out:-1" gives last element in a node output list
  // * "node:out:1:" gives a list with all but the first element in a
  //   node output list
  // * "node:out::-1" gives a list with all but the last element in a
  //   node output list

  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
  // may have values of type `placeholder` and the `input` field uses
  // the "output" format above.

  // By convention, "op" in node_def is resolved by consulting with a
  // user-defined library first. If not resolved, "func" is assumed to
  // be a builtin op.
  repeated NodeDef node_def = 3;

  // A mapping from the output arg names from `signature` to the
  // outputs from `node_def` that should be returned by the function.
  map<string, string> ret = 4;
 }

 // GradientDef defines the gradient function of a function defined in
 // a function library.
 //
 // A gradient function g (specified by gradient_func) for a function f
 // (specified by function_name) must follow the following:
 //
 // The function 'f' must be a numerical function which takes N inputs
 // and produces M outputs. Its gradient function 'g', which is a
 // function taking N + M inputs and produces N outputs.
 //
 // I.e. if we have
 //    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
 // then, g is
 //    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
 //                                      dL/dy1, dL/dy2, ..., dL/dy_M),
 // where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
 // loss function). dL/dx_i is the partial derivative of L with respect
 // to x_i.
 message GradientDef {
  string function_name = 1;  // The function name.
  string gradient_func = 2;  // The gradient function's name.
 }
--- a/parser/common/proto/tensorflow/graph.proto
+++ b/parser/common/proto/tensorflow/graph.proto
@@ -1,56 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "node_def.proto";
 import "function.proto";
 import "versions.proto";

 // Represents the graph of operations
 message GraphDef {
  repeated NodeDef node = 1;

  // Compatibility versions of the graph.  See core/public/version.h for version
  // history.  The GraphDef version is distinct from the TensorFlow version, and
  // each release of TensorFlow will support a range of GraphDef versions.
  VersionDef versions = 4;

  // Deprecated single version field; use versions above instead.  Since all
  // GraphDef changes before "versions" was introduced were forward
  // compatible, this field is entirely ignored.
  int32 version = 3 [deprecated = true];

  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
  //
  // "library" provides user-defined functions.
  //
  // Naming:
  //   * library.function.name are in a flat namespace.
  //     NOTE: We may need to change it to be hierarchical to support
  //     different orgs. E.g.,
  //     { "/google/nn", { ... }},
  //     { "/google/vision", { ... }}
  //     { "/org_foo/module_bar", { ... }}
  //     map<string, FunctionDefLib> named_lib;
  //   * If node[i].op is the name of one function in "library",
  //     node[i] is deemed as a function call. Otherwise, node[i].op
  //     must be a primitive operation supported by the runtime.
  //
  //
  // Function call semantics:
  //
  //   * The callee may start execution as soon as some of its inputs
  //     are ready. The caller may want to use Tuple() mechanism to
  //     ensure all inputs are ready in the same time.
  //
  //   * The consumer of return values may start executing as soon as
  //     the return values the consumer depends on are ready.  The
  //     consumer may want to use Tuple() mechanism to ensure the
  //     consumer does not start until all return values of the callee
  //     function are ready.
  FunctionDefLibrary library = 2;
 };
--- a/parser/common/proto/tensorflow/graph_library.proto
+++ b/parser/common/proto/tensorflow/graph_library.proto
@@ -1,14 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;

 import "graph.proto";

 message GeGraphDef {
  string name = 1;
  GraphDef graph = 2;
 }

 message GraphDefLibrary {
  repeated GeGraphDef graph_def = 1;
 };
--- a/parser/common/proto/tensorflow/node_def.proto
+++ b/parser/common/proto/tensorflow/node_def.proto
@@ -1,63 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "NodeProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";

 message NodeDef {
  // The name given to this operator. Used for naming inputs,
  // logging, visualization, etc.  Unique within a single GraphDef.
  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
  string name = 1;

  // The operation name.  There may be custom parameters in attrs.
  // Op names starting with an underscore are reserved for internal use.
  string op = 2;

  // Each input is "node:src_output" with "node" being a string name and
  // "src_output" indicating which output tensor to use from "node". If
  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
  // may optionally be followed by control inputs that have the format
  // "^node".
  repeated string input = 3;

  // A (possibly partial) specification for the device on which this
  // node should be placed.
  // The expected syntax for this string is as follows:
  //
  // DEVICE_SPEC ::= PARTIAL_SPEC
  //
  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
  // CONSTRAINT ::= ("job:" JOB_NAME)
  //              | ("replica:" [1-9][0-9]*)
  //              | ("task:" [1-9][0-9]*)
  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
  //
  // Valid values for this string include:
  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
  // * "/job:worker/device:GPU:3"                   (partial specification)
  // * ""                                    (no specification)
  //
  // If the constraints do not resolve to a single device (or if this
  // field is empty or not present), the runtime will attempt to
  // choose a device automatically.
  string device = 4;

  // Operation-specific graph-construction-time configuration.
  // Note that this should include all attrs defined in the
  // corresponding OpDef, including those with a value matching
  // the default -- this allows the default to change and makes
  // NodeDefs easier to interpret on their own.  However, if
  // an attr with a default is not specified in this list, the
  // default will be used.
  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
  // one of the names from the corresponding OpDef's attr field).
  // The values must have a type matching the corresponding OpDef
  // attr's type field.
  // Add some examples here showing best practices.
  map<string, AttrValue> attr = 5;
 };
--- a/parser/common/proto/tensorflow/op_def.proto
+++ b/parser/common/proto/tensorflow/op_def.proto
@@ -1,164 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "OpDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "types.proto";

 // Defines an operation. A NodeDef in a GraphDef specifies an Op by
 // using the "op" field which should match the name of a OpDef.
 // LINT.IfChange
 message OpDef {
  // Op names starting with an underscore are reserved for internal use.
  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
  string name = 1;

  // For describing inputs and outputs.
  message ArgDef {
    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
    string name = 1;

    // Human readable description.
    string description = 2;

    // Describes the type of one or more tensors that are accepted/produced
    // by this input/output arg.  The only legal combinations are:
    // * For a single tensor: either the "type" field is set or the
    //   "type_attr" field is set to the name of an attr with type "type".
    // * For a sequence of tensors with the same type: the "number_attr"
    //   field will be set to the name of an attr with type "int", and
    //   either the "type" or "type_attr" field will be set as for
    //   single tensors.
    // * For a sequence of tensors, the "type_list_attr" field will be set
    //   to the name of an attr with type "list(type)".
    DataType type = 3;
    string type_attr = 4;    // if specified, attr must have type "type"
    string number_attr = 5;  // if specified, attr must have type "int"
    // If specified, attr must have type "list(type)", and none of
    // type, type_attr, and number_attr may be specified.
    string type_list_attr = 6;

    // For inputs: if true, the inputs are required to be refs.
    //   By default, inputs can be either refs or non-refs.
    // For outputs: if true, outputs are refs, otherwise they are not.
    bool is_ref = 16;
  };

  // Description of the input(s).
  repeated ArgDef input_arg = 2;

  // Description of the output(s).
  repeated ArgDef output_arg = 3;

  // Description of the graph-construction-time configuration of this
  // Op.  That is to say, this describes the attr fields that will
  // be specified in the NodeDef.
  message AttrDef {
    // A descriptive name for the argument.  May be used, e.g. by the
    // Python client, as a keyword argument name, and so should match
    // the regexp "[a-z][a-z0-9_]+".
    string name = 1;

    // One of the type names from attr_value.proto ("string", "list(string)",
    // "int", etc.).
    string type = 2;

    // A reasonable default for this attribute if the user does not supply
    // a value.  If not specified, the user must supply a value.
    AttrValue default_value = 3;

    // Human-readable description.
    string description = 4;


    // --- Constraints ---
    // These constraints are only in effect if specified.  Default is no
    // constraints.

    // For type == "int", this is a minimum value.  For "list(___)"
    // types, this is the minimum length.
    bool has_minimum = 5;
    int64 minimum = 6;

    // The set of allowed values.  Has type that is the "list" version
    // of the "type" field above (uses the "list" field of AttrValue).
    // If type == "type" or "list(type)" above, then the "type" field
    // of "allowed_values.list" has the set of allowed DataTypes.
    // If type == "string" or "list(string)", then the "s" field of
    // "allowed_values.list" has the set of allowed strings.
    AttrValue allowed_values = 7;
  }
  repeated AttrDef attr = 4;

  // Optional deprecation based on GraphDef versions.
  OpDeprecation deprecation = 8;

  // One-line human-readable description of what the Op does.
  string summary = 5;

  // Additional, longer human-readable description of what the Op does.
  string description = 6;

  // -------------------------------------------------------------------------
  // Which optimizations this operation can participate in.

  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
  bool is_commutative = 18;

  // If is_aggregate is true, then this operation accepts N >= 2
  // inputs and produces 1 output all of the same type.  Should be
  // associative and commutative, and produce output with the same
  // shape as the input.  The optimizer may replace an aggregate op
  // taking input from multiple devices with a tree of aggregate ops
  // that aggregate locally within each device (and possibly within
  // groups of nearby devices) before communicating.
  bool is_aggregate = 16;  // for things like add

  // Other optimizations go here, like
  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.

  // -------------------------------------------------------------------------
  // Optimization constraints.

  // Ops are marked as stateful if their behavior depends on some state beyond
  // their input tensors (e.g. variable reading op) or if they have
  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
  // must always produce the same output for the same input and have
  // no side-effects.
  //
  // By default Ops may be moved between devices.  Stateful ops should
  // either not be moved, or should only be moved if that state can also
  // be moved (e.g. via some sort of save / restore).
  // Stateful ops are guaranteed to never be optimized away by Common
  // Subexpression Elimination (CSE).
  bool is_stateful = 17;  // for things like variables, queue

  // -------------------------------------------------------------------------
  // Non-standard options.

  // By default, all inputs to an Op must be initialized Tensors.  Ops
  // that may initialize tensors for the first time should set this
  // field to true, to allow the Op to take an uninitialized Tensor as
  // input.
  bool allows_uninitialized_input = 19;  // for Assign, etc.
 };
 // LINT.ThenChange(
 //     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)

 // Information about version-dependent deprecation of an op
 message OpDeprecation {
  // First GraphDef version at which the op is disallowed.
  int32 version = 1;

  // Explanation of why it was deprecated and what to use instead.
  string explanation = 2;
 };

 // A collection of OpDefs
 message OpList {
  repeated OpDef op = 1;
 };
--- a/parser/common/proto/tensorflow/resource_handle.proto
+++ b/parser/common/proto/tensorflow/resource_handle.proto
@@ -1,29 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
 message ResourceHandleProto {
  // Unique name for the device containing the resource.
  string device = 1;

  // Container in which this resource is placed.
  string container = 2;

  // Unique name of this resource.
  string name = 3;

  // Hash code for the type of the resource. Is only valid in the same device
  // and in the same execution.
  uint64 hash_code = 4;

  // For debug-only, the name of the type pointed to by this handle, if
  // available.
  string maybe_type_name = 5;
 };
--- a/parser/common/proto/tensorflow/tensor.proto
+++ b/parser/common/proto/tensorflow/tensor.proto
@@ -1,94 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "resource_handle.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing a tensor.
 message TensorProto {
  DataType dtype = 1;

  // Shape of the tensor.
  TensorShapeProto tensor_shape = 2;

  // Only one of the representations below is set, one of "tensor_contents" and
  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
  // contain repeated fields it would require another extra set of messages.

  // Version number.
  //
  // In version 0, if the "repeated xxx" representations contain only one
  // element, that element is repeated to fill the shape.  This makes it easy
  // to represent a constant Tensor with a single value.
  int32 version_number = 3;

  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
  // can be used for all tensor types. The purpose of this representation is to
  // reduce serialization overhead during RPC call by avoiding serialization of
  // many repeated small items.
  bytes tensor_content = 4;

  // Type specific representations that make it easy to create tensor protos in
  // all languages.  Only the representation corresponding to "dtype" can
  // be set.  The values hold the flattened representation of the tensor in
  // row major order.

  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
  // have some pointless zero padding for each value here.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];
 };

 // Protocol buffer representing the serialization format of DT_VARIANT tensors.
 message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
 }
--- a/parser/common/proto/tensorflow/tensor_shape.proto
+++ b/parser/common/proto/tensorflow/tensor_shape.proto
@@ -1,45 +0,0 @@
 // Protocol buffer representing the shape of tensors.

 syntax = "proto3";
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorShapeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 package domi.tensorflow;

 // Dimensions of a tensor.
 message TensorShapeProto {
  // One dimension of the tensor.
  message Dim {
    // Size of the tensor in that dimension.
    // This value must be >= -1, but values of -1 are reserved for "unknown"
    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
    // that work with TensorShapeProto may fail at runtime when deserializing
    // a TensorShapeProto containing a dim value of -1.
    int64 size = 1;

    // Optional name of the tensor dimension.
    string name = 2;
  };

  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
  // for a 30 x 40 2D tensor.  If an entry has size -1, this
  // corresponds to a dimension of unknown size. The names are
  // optional.
  //
  // The order of entries in "dim" matters: It indicates the layout of the
  // values in the tensor in-memory representation.
  //
  // The first entry in "dim" is the outermost dimension used to layout the
  // values, the last entry is the innermost dimension.  This matches the
  // in-memory layout of RowMajor Eigen tensors.
  //
  // If "dim.size()" > 0, "unknown_rank" must be false.
  repeated Dim dim = 2;

  // If true, the number of dimensions in the shape is unknown.
  //
  // If true, "dim.size()" must be 0.
  bool unknown_rank = 3;
 };
--- a/parser/common/proto/tensorflow/types.proto
+++ b/parser/common/proto/tensorflow/types.proto
@@ -1,74 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // LINT.IfChange
 enum DataType {
  // Not a legal value for DataType.  Used to indicate a DataType field
  // has not been set.
  DT_INVALID = 0;

  // Data types that all computation devices are expected to be
  // capable to support.
  DT_FLOAT = 1;
  DT_DOUBLE = 2;
  DT_INT32 = 3;
  DT_UINT8 = 4;
  DT_INT16 = 5;
  DT_INT8 = 6;
  DT_STRING = 7;
  DT_COMPLEX64 = 8;  // Single-precision complex
  DT_INT64 = 9;
  DT_BOOL = 10;
  DT_QINT8 = 11;     // Quantized int8
  DT_QUINT8 = 12;    // Quantized uint8
  DT_QINT32 = 13;    // Quantized int32
  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
  DT_QINT16 = 15;    // Quantized int16
  DT_QUINT16 = 16;   // Quantized uint16
  DT_UINT16 = 17;
  DT_COMPLEX128 = 18;  // Double-precision complex
  DT_HALF = 19;
  DT_RESOURCE = 20;
  DT_VARIANT = 21;  // Arbitrary C++ data types
  DT_UINT32 = 22;
  DT_UINT64 = 23;

  // Do not use!  These are only for parameters.  Every enum above
  // should have a corresponding value below (verified by types_test).
  DT_FLOAT_REF = 101;
  DT_DOUBLE_REF = 102;
  DT_INT32_REF = 103;
  DT_UINT8_REF = 104;
  DT_INT16_REF = 105;
  DT_INT8_REF = 106;
  DT_STRING_REF = 107;
  DT_COMPLEX64_REF = 108;
  DT_INT64_REF = 109;
  DT_BOOL_REF = 110;
  DT_QINT8_REF = 111;
  DT_QUINT8_REF = 112;
  DT_QINT32_REF = 113;
  DT_BFLOAT16_REF = 114;
  DT_QINT16_REF = 115;
  DT_QUINT16_REF = 116;
  DT_UINT16_REF = 117;
  DT_COMPLEX128_REF = 118;
  DT_HALF_REF = 119;
  DT_RESOURCE_REF = 120;
  DT_VARIANT_REF = 121;
  DT_UINT32_REF = 122;
  DT_UINT64_REF = 123;
 }
 // LINT.ThenChange(
 //    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
 //    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)
--- a/parser/common/proto/tensorflow/versions.proto
+++ b/parser/common/proto/tensorflow/versions.proto
@@ -1,31 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "VersionsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Version information for a piece of serialized data
 //
 // There are different types of versions for each type of data
 // (GraphDef, etc.), but they all have the same common shape
 // described here.
 //
 // Each consumer has "consumer" and "min_producer" versions (specified
 // elsewhere).  A consumer is allowed to consume this data if
 //
 //   producer >= min_producer
 //   consumer >= min_consumer
 //   consumer not in bad_consumers
 //
 message VersionDef {
  // The version of the code that produced this data.
  int32 producer = 1;

  // Any consumer below this version is not allowed to consume this data.
  int32 min_consumer = 2;

  // Specific consumer versions which are disallowed (e.g. due to bugs).
  repeated int32 bad_consumers = 3;
 };
--- a/parser/func_to_graph/proto/attr_value.proto
+++ b/parser/func_to_graph/proto/attr_value.proto
@@ -1,62 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "AttrValueProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "tensor.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing the value for an attr used to configure an Op.
 // Comment indicates the corresponding attr type.  Only the field matching the
 // attr type may be filled.
 message AttrValue {
  // LINT.IfChange
  message ListValue {
    repeated bytes s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated DataType type = 6 [packed = true];  // "list(type)"
    repeated TensorShapeProto shape = 7;         // "list(shape)"
    repeated TensorProto tensor = 8;             // "list(tensor)"
    repeated NameAttrList func = 9;              // "list(attr)"
  }
  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)

  oneof value {
    bytes s = 2;                 // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    DataType type = 6;           // "type"
    TensorShapeProto shape = 7;  // "shape"
    TensorProto tensor = 8;      // "tensor"
    ListValue list = 1;          // any "list(...)"

    // "func" represents a function. func.name is a function's name or
    // a primitive op's name. func.attr.first is the name of an attr
    // defined for that function. func.attr.second is the value for
    // that attr in the instantiation.
    NameAttrList func = 10;

    // This is a placeholder only used in nodes defined inside a
    // function.  It indicates the attr value will be supplied when
    // the function is instantiated.  For example, let us suppose a
    // node "N" in function "FN". "N" has an attr "A" with value
    // placeholder = "foo". When FN is instantiated with attr "foo"
    // set to "bar", the instantiated node N's attr A will have been
    // given the value "bar".
    string placeholder = 9;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NameAttrList {
  string name = 1;
  map<string, AttrValue> attr = 2;
 }
--- a/parser/func_to_graph/proto/function.proto
+++ b/parser/func_to_graph/proto/function.proto
@@ -1,100 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "FunctionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "node_def.proto";
 import "op_def.proto";

 // A library is a set of named functions.
 message FunctionDefLibrary {
  repeated FunctionDef function = 1;
  repeated GradientDef gradient = 2;
 }

 // A function can be instantiated when the runtime can bind every attr
 // with a value. When a GraphDef has a call to a function, it must
 // have binding for every attr defined in the signature.
 //   * device spec, etc.
 message FunctionDef {
  // The definition of the function's name, arguments, return values,
  // attrs etc.
  OpDef signature = 1;

  // Attributes specific to this function definition.
  map<string, AttrValue> attr = 5;

  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
  reserved 2;

  // In both of the following fields, there is the need to specify an
  // output that is used as either the input to another node (in
  // `node_def`) or as a return value of the function (in `ret`).
  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
  // list in some cases (instead of just single outputs).  Also, we
  // need to be able to deal with lists of unknown length (so the
  // output index may not be known at function definition time).  So
  // we use the following format instead:
  // * "fun_in" where "fun_in" is the name of a function input arg in
  //   the `signature` field above.  This represents that input, whether
  //   it is a single tensor or a list.
  // * "fun_in:0" gives the first element of a function input arg (a
  //   non-list input is considered a list of length 1 for these
  //   purposes).
  // * "node:out" where "node" is the name of a node in `node_def` and
  //   "out" is the name one of its op's output arguments (the name
  //   comes from the OpDef of the node's op). This represents that
  //   node's output, whether it is a single tensor or a list.
  //   Note: We enforce that an op's output arguments are never
  //   renamed in the backwards-compatibility test.
  // * "node:out:0" gives the first element of a node output arg (a
  //   non-list output is considered a list of length 1 for these
  //   purposes).
  //
  // NOT CURRENTLY SUPPORTED (but may be in the future):
  // * "node:out:-1" gives last element in a node output list
  // * "node:out:1:" gives a list with all but the first element in a
  //   node output list
  // * "node:out::-1" gives a list with all but the last element in a
  //   node output list

  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
  // may have values of type `placeholder` and the `input` field uses
  // the "output" format above.

  // By convention, "op" in node_def is resolved by consulting with a
  // user-defined library first. If not resolved, "func" is assumed to
  // be a builtin op.
  repeated NodeDef node_def = 3;

  // A mapping from the output arg names from `signature` to the
  // outputs from `node_def` that should be returned by the function.
  map<string, string> ret = 4;
 }

 // GradientDef defines the gradient function of a function defined in
 // a function library.
 //
 // A gradient function g (specified by gradient_func) for a function f
 // (specified by function_name) must follow the following:
 //
 // The function 'f' must be a numerical function which takes N inputs
 // and produces M outputs. Its gradient function 'g', which is a
 // function taking N + M inputs and produces N outputs.
 //
 // I.e. if we have
 //    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
 // then, g is
 //    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
 //                                      dL/dy1, dL/dy2, ..., dL/dy_M),
 // where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
 // loss function). dL/dx_i is the partial derivative of L with respect
 // to x_i.
 message GradientDef {
  string function_name = 1;  // The function name.
  string gradient_func = 2;  // The gradient function's name.
 }
--- a/parser/func_to_graph/proto/graph.proto
+++ b/parser/func_to_graph/proto/graph.proto
@@ -1,56 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "node_def.proto";
 import "function.proto";
 import "versions.proto";

 // Represents the graph of operations
 message GraphDef {
  repeated NodeDef node = 1;

  // Compatibility versions of the graph.  See core/public/version.h for version
  // history.  The GraphDef version is distinct from the TensorFlow version, and
  // each release of TensorFlow will support a range of GraphDef versions.
  VersionDef versions = 4;

  // Deprecated single version field; use versions above instead.  Since all
  // GraphDef changes before "versions" was introduced were forward
  // compatible, this field is entirely ignored.
  int32 version = 3 [deprecated = true];

  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
  //
  // "library" provides user-defined functions.
  //
  // Naming:
  //   * library.function.name are in a flat namespace.
  //     NOTE: We may need to change it to be hierarchical to support
  //     different orgs. E.g.,
  //     { "/google/nn", { ... }},
  //     { "/google/vision", { ... }}
  //     { "/org_foo/module_bar", { ... }}
  //     map<string, FunctionDefLib> named_lib;
  //   * If node[i].op is the name of one function in "library",
  //     node[i] is deemed as a function call. Otherwise, node[i].op
  //     must be a primitive operation supported by the runtime.
  //
  //
  // Function call semantics:
  //
  //   * The callee may start execution as soon as some of its inputs
  //     are ready. The caller may want to use Tuple() mechanism to
  //     ensure all inputs are ready in the same time.
  //
  //   * The consumer of return values may start executing as soon as
  //     the return values the consumer depends on are ready.  The
  //     consumer may want to use Tuple() mechanism to ensure the
  //     consumer does not start until all return values of the callee
  //     function are ready.
  FunctionDefLibrary library = 2;
 };
--- a/parser/func_to_graph/proto/graph_library.proto
+++ b/parser/func_to_graph/proto/graph_library.proto
@@ -1,14 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;

 import "graph.proto";

 message GeGraphDef {
  string name = 1;
  GraphDef graph = 2;
 }

 message GraphDefLibrary {
  repeated GeGraphDef graph_def = 1;
 };
--- a/parser/func_to_graph/proto/node_def.proto
+++ b/parser/func_to_graph/proto/node_def.proto
@@ -1,63 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "NodeProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";

 message NodeDef {
  // The name given to this operator. Used for naming inputs,
  // logging, visualization, etc.  Unique within a single GraphDef.
  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
  string name = 1;

  // The operation name.  There may be custom parameters in attrs.
  // Op names starting with an underscore are reserved for internal use.
  string op = 2;

  // Each input is "node:src_output" with "node" being a string name and
  // "src_output" indicating which output tensor to use from "node". If
  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
  // may optionally be followed by control inputs that have the format
  // "^node".
  repeated string input = 3;

  // A (possibly partial) specification for the device on which this
  // node should be placed.
  // The expected syntax for this string is as follows:
  //
  // DEVICE_SPEC ::= PARTIAL_SPEC
  //
  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
  // CONSTRAINT ::= ("job:" JOB_NAME)
  //              | ("replica:" [1-9][0-9]*)
  //              | ("task:" [1-9][0-9]*)
  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
  //
  // Valid values for this string include:
  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
  // * "/job:worker/device:GPU:3"                   (partial specification)
  // * ""                                    (no specification)
  //
  // If the constraints do not resolve to a single device (or if this
  // field is empty or not present), the runtime will attempt to
  // choose a device automatically.
  string device = 4;

  // Operation-specific graph-construction-time configuration.
  // Note that this should include all attrs defined in the
  // corresponding OpDef, including those with a value matching
  // the default -- this allows the default to change and makes
  // NodeDefs easier to interpret on their own.  However, if
  // an attr with a default is not specified in this list, the
  // default will be used.
  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
  // one of the names from the corresponding OpDef's attr field).
  // The values must have a type matching the corresponding OpDef
  // attr's type field.
  // Add some examples here showing best practices.
  map<string, AttrValue> attr = 5;
 };
--- a/parser/func_to_graph/proto/op_def.proto
+++ b/parser/func_to_graph/proto/op_def.proto
@@ -1,164 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "OpDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "types.proto";

 // Defines an operation. A NodeDef in a GraphDef specifies an Op by
 // using the "op" field which should match the name of a OpDef.
 // LINT.IfChange
 message OpDef {
  // Op names starting with an underscore are reserved for internal use.
  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
  string name = 1;

  // For describing inputs and outputs.
  message ArgDef {
    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
    string name = 1;

    // Human readable description.
    string description = 2;

    // Describes the type of one or more tensors that are accepted/produced
    // by this input/output arg.  The only legal combinations are:
    // * For a single tensor: either the "type" field is set or the
    //   "type_attr" field is set to the name of an attr with type "type".
    // * For a sequence of tensors with the same type: the "number_attr"
    //   field will be set to the name of an attr with type "int", and
    //   either the "type" or "type_attr" field will be set as for
    //   single tensors.
    // * For a sequence of tensors, the "type_list_attr" field will be set
    //   to the name of an attr with type "list(type)".
    DataType type = 3;
    string type_attr = 4;    // if specified, attr must have type "type"
    string number_attr = 5;  // if specified, attr must have type "int"
    // If specified, attr must have type "list(type)", and none of
    // type, type_attr, and number_attr may be specified.
    string type_list_attr = 6;

    // For inputs: if true, the inputs are required to be refs.
    //   By default, inputs can be either refs or non-refs.
    // For outputs: if true, outputs are refs, otherwise they are not.
    bool is_ref = 16;
  };

  // Description of the input(s).
  repeated ArgDef input_arg = 2;

  // Description of the output(s).
  repeated ArgDef output_arg = 3;

  // Description of the graph-construction-time configuration of this
  // Op.  That is to say, this describes the attr fields that will
  // be specified in the NodeDef.
  message AttrDef {
    // A descriptive name for the argument.  May be used, e.g. by the
    // Python client, as a keyword argument name, and so should match
    // the regexp "[a-z][a-z0-9_]+".
    string name = 1;

    // One of the type names from attr_value.proto ("string", "list(string)",
    // "int", etc.).
    string type = 2;

    // A reasonable default for this attribute if the user does not supply
    // a value.  If not specified, the user must supply a value.
    AttrValue default_value = 3;

    // Human-readable description.
    string description = 4;


    // --- Constraints ---
    // These constraints are only in effect if specified.  Default is no
    // constraints.

    // For type == "int", this is a minimum value.  For "list(___)"
    // types, this is the minimum length.
    bool has_minimum = 5;
    int64 minimum = 6;

    // The set of allowed values.  Has type that is the "list" version
    // of the "type" field above (uses the "list" field of AttrValue).
    // If type == "type" or "list(type)" above, then the "type" field
    // of "allowed_values.list" has the set of allowed DataTypes.
    // If type == "string" or "list(string)", then the "s" field of
    // "allowed_values.list" has the set of allowed strings.
    AttrValue allowed_values = 7;
  }
  repeated AttrDef attr = 4;

  // Optional deprecation based on GraphDef versions.
  OpDeprecation deprecation = 8;

  // One-line human-readable description of what the Op does.
  string summary = 5;

  // Additional, longer human-readable description of what the Op does.
  string description = 6;

  // -------------------------------------------------------------------------
  // Which optimizations this operation can participate in.

  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
  bool is_commutative = 18;

  // If is_aggregate is true, then this operation accepts N >= 2
  // inputs and produces 1 output all of the same type.  Should be
  // associative and commutative, and produce output with the same
  // shape as the input.  The optimizer may replace an aggregate op
  // taking input from multiple devices with a tree of aggregate ops
  // that aggregate locally within each device (and possibly within
  // groups of nearby devices) before communicating.
  bool is_aggregate = 16;  // for things like add

  // Other optimizations go here, like
  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.

  // -------------------------------------------------------------------------
  // Optimization constraints.

  // Ops are marked as stateful if their behavior depends on some state beyond
  // their input tensors (e.g. variable reading op) or if they have
  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
  // must always produce the same output for the same input and have
  // no side-effects.
  //
  // By default Ops may be moved between devices.  Stateful ops should
  // either not be moved, or should only be moved if that state can also
  // be moved (e.g. via some sort of save / restore).
  // Stateful ops are guaranteed to never be optimized away by Common
  // Subexpression Elimination (CSE).
  bool is_stateful = 17;  // for things like variables, queue

  // -------------------------------------------------------------------------
  // Non-standard options.

  // By default, all inputs to an Op must be initialized Tensors.  Ops
  // that may initialize tensors for the first time should set this
  // field to true, to allow the Op to take an uninitialized Tensor as
  // input.
  bool allows_uninitialized_input = 19;  // for Assign, etc.
 };
 // LINT.ThenChange(
 //     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)

 // Information about version-dependent deprecation of an op
 message OpDeprecation {
  // First GraphDef version at which the op is disallowed.
  int32 version = 1;

  // Explanation of why it was deprecated and what to use instead.
  string explanation = 2;
 };

 // A collection of OpDefs
 message OpList {
  repeated OpDef op = 1;
 };
--- a/parser/func_to_graph/proto/resource_handle.proto
+++ b/parser/func_to_graph/proto/resource_handle.proto
@@ -1,29 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
 message ResourceHandleProto {
  // Unique name for the device containing the resource.
  string device = 1;

  // Container in which this resource is placed.
  string container = 2;

  // Unique name of this resource.
  string name = 3;

  // Hash code for the type of the resource. Is only valid in the same device
  // and in the same execution.
  uint64 hash_code = 4;

  // For debug-only, the name of the type pointed to by this handle, if
  // available.
  string maybe_type_name = 5;
 };
--- a/parser/func_to_graph/proto/tensor.proto
+++ b/parser/func_to_graph/proto/tensor.proto
@@ -1,94 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "resource_handle.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing a tensor.
 message TensorProto {
  DataType dtype = 1;

  // Shape of the tensor.
  TensorShapeProto tensor_shape = 2;

  // Only one of the representations below is set, one of "tensor_contents" and
  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
  // contain repeated fields it would require another extra set of messages.

  // Version number.
  //
  // In version 0, if the "repeated xxx" representations contain only one
  // element, that element is repeated to fill the shape.  This makes it easy
  // to represent a constant Tensor with a single value.
  int32 version_number = 3;

  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
  // can be used for all tensor types. The purpose of this representation is to
  // reduce serialization overhead during RPC call by avoiding serialization of
  // many repeated small items.
  bytes tensor_content = 4;

  // Type specific representations that make it easy to create tensor protos in
  // all languages.  Only the representation corresponding to "dtype" can
  // be set.  The values hold the flattened representation of the tensor in
  // row major order.

  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
  // have some pointless zero padding for each value here.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];
 };

 // Protocol buffer representing the serialization format of DT_VARIANT tensors.
 message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
 }
--- a/parser/func_to_graph/proto/tensor_shape.proto
+++ b/parser/func_to_graph/proto/tensor_shape.proto
@@ -1,45 +0,0 @@
 // Protocol buffer representing the shape of tensors.

 syntax = "proto3";
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorShapeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 package domi.tensorflow;

 // Dimensions of a tensor.
 message TensorShapeProto {
  // One dimension of the tensor.
  message Dim {
    // Size of the tensor in that dimension.
    // This value must be >= -1, but values of -1 are reserved for "unknown"
    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
    // that work with TensorShapeProto may fail at runtime when deserializing
    // a TensorShapeProto containing a dim value of -1.
    int64 size = 1;

    // Optional name of the tensor dimension.
    string name = 2;
  };

  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
  // for a 30 x 40 2D tensor.  If an entry has size -1, this
  // corresponds to a dimension of unknown size. The names are
  // optional.
  //
  // The order of entries in "dim" matters: It indicates the layout of the
  // values in the tensor in-memory representation.
  //
  // The first entry in "dim" is the outermost dimension used to layout the
  // values, the last entry is the innermost dimension.  This matches the
  // in-memory layout of RowMajor Eigen tensors.
  //
  // If "dim.size()" > 0, "unknown_rank" must be false.
  repeated Dim dim = 2;

  // If true, the number of dimensions in the shape is unknown.
  //
  // If true, "dim.size()" must be 0.
  bool unknown_rank = 3;
 };
--- a/parser/func_to_graph/proto/types.proto
+++ b/parser/func_to_graph/proto/types.proto
@@ -1,74 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // LINT.IfChange
 enum DataType {
  // Not a legal value for DataType.  Used to indicate a DataType field
  // has not been set.
  DT_INVALID = 0;

  // Data types that all computation devices are expected to be
  // capable to support.
  DT_FLOAT = 1;
  DT_DOUBLE = 2;
  DT_INT32 = 3;
  DT_UINT8 = 4;
  DT_INT16 = 5;
  DT_INT8 = 6;
  DT_STRING = 7;
  DT_COMPLEX64 = 8;  // Single-precision complex
  DT_INT64 = 9;
  DT_BOOL = 10;
  DT_QINT8 = 11;     // Quantized int8
  DT_QUINT8 = 12;    // Quantized uint8
  DT_QINT32 = 13;    // Quantized int32
  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
  DT_QINT16 = 15;    // Quantized int16
  DT_QUINT16 = 16;   // Quantized uint16
  DT_UINT16 = 17;
  DT_COMPLEX128 = 18;  // Double-precision complex
  DT_HALF = 19;
  DT_RESOURCE = 20;
  DT_VARIANT = 21;  // Arbitrary C++ data types
  DT_UINT32 = 22;
  DT_UINT64 = 23;

  // Do not use!  These are only for parameters.  Every enum above
  // should have a corresponding value below (verified by types_test).
  DT_FLOAT_REF = 101;
  DT_DOUBLE_REF = 102;
  DT_INT32_REF = 103;
  DT_UINT8_REF = 104;
  DT_INT16_REF = 105;
  DT_INT8_REF = 106;
  DT_STRING_REF = 107;
  DT_COMPLEX64_REF = 108;
  DT_INT64_REF = 109;
  DT_BOOL_REF = 110;
  DT_QINT8_REF = 111;
  DT_QUINT8_REF = 112;
  DT_QINT32_REF = 113;
  DT_BFLOAT16_REF = 114;
  DT_QINT16_REF = 115;
  DT_QUINT16_REF = 116;
  DT_UINT16_REF = 117;
  DT_COMPLEX128_REF = 118;
  DT_HALF_REF = 119;
  DT_RESOURCE_REF = 120;
  DT_VARIANT_REF = 121;
  DT_UINT32_REF = 122;
  DT_UINT64_REF = 123;
 }
 // LINT.ThenChange(
 //    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
 //    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)
--- a/parser/func_to_graph/proto/versions.proto
+++ b/parser/func_to_graph/proto/versions.proto
@@ -1,31 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "VersionsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Version information for a piece of serialized data
 //
 // There are different types of versions for each type of data
 // (GraphDef, etc.), but they all have the same common shape
 // described here.
 //
 // Each consumer has "consumer" and "min_producer" versions (specified
 // elsewhere).  A consumer is allowed to consume this data if
 //
 //   producer >= min_producer
 //   consumer >= min_consumer
 //   consumer not in bad_consumers
 //
 message VersionDef {
  // The version of the code that produced this data.
  int32 producer = 1;

  // Any consumer below this version is not allowed to consume this data.
  int32 min_consumer = 2;

  // Specific consumer versions which are disallowed (e.g. due to bugs).
  repeated int32 bad_consumers = 3;
 };
--- a/parser/onnx/proto/om.proto
+++ b/parser/onnx/proto/om.proto
@@ -1,396 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 enum TargetType
 {
    MINI = 0;
    TINY = 1;
    LITE = 2;
 }

 // offline model
 message ModelDef {
    string name = 1;
    uint32 version = 2;

    uint64 memory_size = 10;
    uint32 stream_num = 11;
    uint32 event_num = 12;
    uint64 weight_size = 13;
    uint32 label_num = 15;
    repeated OpDef op = 20;
    TargetType target_type = 23;

    map<string, AttrDef> attr = 30;
 };

 // operator define
 message OpDef {
    string name = 1;
    string type = 2;

    uint32 id = 3;
    uint32 stream_id = 4;

    repeated string input_name = 5;

    repeated string src_name = 8;
    repeated int32 src_index = 9;
    repeated int64 input = 10;
    repeated int64 output = 11;
    repeated TensorDescriptor input_desc = 12;
    repeated TensorDescriptor output_desc = 13;
    repeated WeightDef weights = 14;
    repeated string dst_name = 15;
    repeated int32 dst_index = 16;

    repeated int64 workspace = 20;
    repeated uint32 workspace_bytes = 21;

    repeated string weight_name = 22;
    repeated bool is_input_const = 23;

    map<string, AttrDef> attr = 30;

    QuantizeFactorParams quantize_factor = 31;

    oneof op_params {
        // start at 100 here
        SendOpParams sender_param = 100;
        RecvOpParams receiver_param = 200;
        ConvolutionOpParams convolution_param = 300;
        PoolingOpParams pooling_param = 400;
        EltwiseOpParams eltwise_param = 500;
        BatchNormOpParams batchnorm_param = 600;
        ScaleOpParams scale_param = 700;
        FullConnectionOpParams full_connection_param = 800;
        SoftmaxOpParams softmax_param = 900;
        ActivationOpParams activation_param = 1000;
        ReshapeOpParams reshape_param = 1100;
    }
 };

 message SendOpParams {
    uint32 event_id = 1;
 };

 message RecvOpParams {
    uint32 event_id = 1;
 };

 enum QuantizeScaleType
 {
    VECTOR_SCALE = 0;
    SCALAR_SCALE = 1;
 }

 enum QuantizeScaleMode
 {
    NORMAL_MODE = 0;
    SQRT_MODE = 1;
 }

 enum QuantizeAlgorithm
 {
    NON_OFFSET_ALGO = 0;
    HALF_OFFSET_ALGO = 1;
    ALL_OFFSET_ALGO = 2;
 }
 message QuantizeFactor
 {
    QuantizeScaleMode scale_mode = 1;
    bytes scale_value = 2;
    int64 scale_offset = 3;
    bytes offset_data_value = 4;
    int64 offset_data_offset = 5;
    bytes offset_weight_value = 6;
    int64 offset_weight_offset = 7;
    bytes offset_pad_value = 8;
    int64 offset_pad_offset = 9;
 };

 message QuantizeCalcFactor
 {
    bytes offsetw = 1;
    int64 offsetw_offset = 2;
    bytes offsetd = 3;
    int64 offsetd_offset = 4;
    bytes scalereq = 5;
    int64 scaledreq_offset = 6;
    bytes offsetdnext = 7;
    int64 offsetdnext_offset = 8;
 }

 message QuantizeFactorParams
 {
    QuantizeAlgorithm quantize_algo = 1;
    QuantizeScaleType scale_type = 2;
    QuantizeFactor quantize_param = 3;
    QuantizeFactor dequantize_param = 4;
    QuantizeFactor requantize_param = 5;
    QuantizeCalcFactor quantizecalc_param = 6;
 };

 message ConvolutionOpParams {
    int32 mode = 1;
    int32 algo = 2;
    int32 pad_mode = 3;
    uint32 group = 4;
    uint32 num_output = 5;

    repeated uint32 pad = 10;
    repeated uint32 stride = 11;
    repeated uint32 dilation = 12;
    repeated uint32 kernel = 13;

    float alpha = 20;
    float beta = 21;

    WeightDef filter = 40;
    WeightDef bias = 41;

    bool relu_flag = 62;
    repeated uint32 adj = 70;
    repeated uint32 target_shape = 71;
    repeated uint32 before_pad = 72;
 };

 message PoolingOpParams {
    int32 mode = 1;
    int32 nan_opt = 2;
    int32 pad_mode = 3;
    bool global_pooling = 4;

    repeated uint32 window = 10;
    repeated uint32 pad = 11;
    repeated uint32 stride = 12;
    bool ceil_mode = 13;
    int32 data_mode  = 14;

    float alpha = 20;
    float beta = 21;
    repeated uint32 before_pad = 22;
 };

 message EltwiseOpParams {
    int32 mode = 1;
    repeated float coeff = 2;
    float alpha = 3;
    float beta = 4;
    repeated WeightDef weight = 5;
    bool relu_flag = 6;
 };

 message ActivationOpParams {
    int32 mode = 1;
    float coef = 2;
    float alpha = 3;
    float beta = 4;
 };

 message BatchNormOpParams {
    int32 mode = 1;

    float alpha = 2;
    float beta = 3;
    double epsilon = 4;//optinal,[default = 1e-5]
    bool use_global_stats = 5; //optinal,by default true,testing mode
    float  moving_average_fraction = 6; //optinal,[default = .999];

    WeightDef estimated_mean = 7;
    WeightDef estimated_variance = 8;

    WeightDef scale = 9;
    WeightDef bias = 10;
 };

 message ScaleOpParams {
    WeightDef scale = 1;
    WeightDef bias = 2;
 };

 message ReshapeOpParams {
    float alpha = 1;
    float beta = 2;
    ShapeDef shape = 3;
    int32 axis = 4;
    int32 num_axes = 5;
    int32 format = 6;
 };

 message SoftmaxOpParams {
    int32 algo = 1;
    int32 mode = 2;
    float alpha = 3;
    float beta = 4;
 };

 message FullConnectionOpParams {
    WeightDef filter = 1;
    WeightDef bias = 2;
    uint32 num_output = 3;
    bool relu_flag = 12;
 };

 message FlattenOpParams {
    float alpha = 1;
    float beta = 2;
    int32 start_axis = 3;
    int32 end_axis = 4;
 }

 message AddLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message MulLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message AddOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message MulOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message SubOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message BiasAddOpParams {
    float alpha = 1;
    float beta = 2;

    WeightDef bias = 10;
 };

 message MatMulOpParams {
    float alpha = 1;
    float beta = 2;
    bool transposeX = 3;
    bool transposeW = 4;

    WeightDef filter = 10;
    WeightDef bias = 12;
 };

 message RsqrtOpParams {
    float alpha = 1;
    float beta = 2;
 };


 message WeightDef {
    int32 format = 1;
    int32 data_type = 2;
    ShapeDef shape = 3;
    bytes data = 4;
    int64 data_offset = 5;
    uint32 cmps_size = 6;
    bytes cmps_tab = 7;
    int64 cmps_tab_offset = 10;
    CompressInfo cmps_info = 8;
    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
 }

 message ShapeDef {
    repeated int64 dim = 1;
 }

 enum DeviceType {
  NPU = 0;                   // In default, we will use NPU.
  CPU = 1;                   // CPU
 }

 message AllOffsetQuantizeInfo {
 	float scale  = 1;
 	int32 offset = 2;
 } 

 message TensorDescriptor {
    int32 format = 1;
    int32 data_type = 2;
    repeated int64 dim = 3;
    uint32 size = 4;
    bool reuse_input = 5;
    bool output_tensor = 7;
    DeviceType device_type = 8;
    bool input_tensor = 9;
    uint32 real_dim_cnt = 10;
    uint32 reuse_input_index = 11;
    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
 }

 message CompressInfo {
    int32 blockRow = 1;                             // block row
    int32 blockCol = 2;                             // block col
    int32 fractalK = 3;                             // fractal K
    int32 fractalN = 4;                             // fractal N
    int32 lastFractalK = 5;                         // K of last fractal
    int32 lastFractalN = 6;                         // N of last fractal
    int32 cubeSize = 7;                             // cube's length
    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
 }

 message AttrDef {
  message ListValue {
    repeated string s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated uint32 u = 6 [packed = true];         // "list(uint)"
    repeated bytes bt = 7;
  }

  oneof value {
    string s = 2;                // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    uint32 u = 6;                // "uint32"
    bytes bt = 7;
    ListValue list = 1;          // any "list(...)"
    NamedAttrs func = 10;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs {
  string name = 1;
  map<string, AttrDef> attr = 2;
 }

--- a/parser/onnx/proto/onnx/ge_onnx.proto
+++ b/parser/onnx/proto/onnx/ge_onnx.proto
@@ -1,563 +0,0 @@
 // Copyright (c) ONNX Project Contributors.
 // Licensed under the MIT license.

 syntax = "proto3";

 package ge.onnx;

 // Overview
 //
 // ONNX is an open specification that is comprised of the following components:
 //
 // 1)  A definition of an extensible computation graph model.
 // 2)  Definitions of standard data types.
 // 3)  Definitions of built-in operators.
 //
 // This document describes the syntax of models and their computation graphs,
 // as well as the standard data types. Together, they are referred to as the ONNX
 // Intermediate Representation, or 'IR' for short.
 //
 // The normative semantic specification of the ONNX IR is found in docs/IR.md.
 // Definitions of the built-in neural network operators may be found in docs/Operators.md.

 // Notes
 //
 // Release
 //
 // We are still in the very early stage of defining ONNX. The current
 // version of ONNX is a starting point. While we are actively working
 // towards a complete spec, we would like to get the community involved
 // by sharing our working version of ONNX.
 //
 // Protobuf compatibility
 //
 // To simplify framework compatibility, ONNX is defined using the subset of protobuf
 // that is compatible with both protobuf v2 and v3. This means that we do not use any
 // protobuf features that are only available in one of the two versions.
 //
 // Here are the most notable contortions we have to carry out to work around
 // these limitations:
 //
 //   - No 'map' (added protobuf 3.0). We instead represent mappings as lists
 //     of key-value pairs, where order does not matter and duplicates
 //     are not allowed.


 // Versioning
 //
 // ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md
 //
 // To be compatible with both proto2 and proto3, we will use a version number
 // that is not defined by the default value but an explicit enum number.
 enum Version {
  // proto3 requires the first enum value to be zero.
  // We add this just to appease the compiler.
  _START_VERSION = 0;
  // The version field is always serialized and we will use it to store the
  // version that the  graph is generated from. This helps us set up version
  // control.
  // For the IR, we are using simple numbers starting with with 0x00000001,
  // which was the version we published on Oct 10, 2017.
  IR_VERSION_2017_10_10 = 0x0000000000000001;

  // IR_VERSION 2 published on Oct 30, 2017
  // - Added type discriminator to AttributeProto to support proto3 users
  IR_VERSION_2017_10_30 = 0x0000000000000002;

  // IR VERSION 3 published on Nov 3, 2017
  // - For operator versioning:
  //    - Added new message OperatorSetIdProto
  //    - Added opset_import in ModelProto
  // - For vendor extensions, added domain in NodeProto
  IR_VERSION_2017_11_3 = 0x0000000000000003;

  // IR VERSION 4 published on Jan 22, 2019
  // - Relax constraint that initializers should be a subset of graph inputs
  // - Add type BFLOAT16
  IR_VERSION_2019_1_22 = 0x0000000000000004;

  // IR VERSION 5 published on March 18, 2019
  // - Add message TensorAnnotation.
  // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters.
  IR_VERSION_2019_3_18 = 0x0000000000000005;

  // IR VERSION 6 published on Sep 19, 2019
  // - Add support for sparse tensor constants stored in model.
  //   - Add message SparseTensorProto
  //   - Add sparse initializers
  IR_VERSION = 0x0000000000000006;
 }

 // Attributes
 //
 // A named attribute containing either singular float, integer, string, graph,
 // and tensor values, or repeated float, integer, string, graph, and tensor values.
 // An AttributeProto MUST contain the name field, and *only one* of the
 // following content fields, effectively enforcing a C/C++ union equivalent.
 message AttributeProto {

  // Note: this enum is structurally identical to the OpSchema::AttrType
  // enum defined in schema.h.  If you rev one, you likely need to rev the other.
  enum AttributeType {
    UNDEFINED = 0;
    FLOAT = 1;
    INT = 2;
    STRING = 3;
    TENSOR = 4;
    GRAPH = 5;
    SPARSE_TENSOR = 11;

    FLOATS = 6;
    INTS = 7;
    STRINGS = 8;
    TENSORS = 9;
    GRAPHS = 10;
    SPARSE_TENSORS = 12;
  }

  // The name field MUST be present for this version of the IR.
  string name = 1;           // namespace Attribute

  // if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.
  // In this case, this AttributeProto does not contain data, and it's a reference of attribute
  // in parent scope.
  // NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.
  string ref_attr_name = 21;

  // A human-readable documentation for this attribute. Markdown is allowed.
  string doc_string = 13;

  // The type field MUST be present for this version of the IR.
  // For 0.0.1 versions of the IR, this field was not defined, and
  // implementations needed to use has_field hueristics to determine
  // which value field was in use.  For IR_VERSION 0.0.2 or later, this
  // field MUST be set and match the f|i|s|t|... field in use.  This
  // change was made to accomodate proto3 implementations.
  AttributeType type = 20;   // discriminator that indicates which field below is in use

  // Exactly ONE of the following fields must be present for this version of the IR
  float f = 2;               // float
  int64 i = 3;               // int
  bytes s = 4;               // UTF-8 string
  TensorProto t = 5;         // tensor value
  GraphProto g = 6;          // graph
  SparseTensorProto sparse_tensor = 22;  // sparse tensor value
  // Do not use field below, it's deprecated.
  // optional ValueProto v = 12;         // value - subsumes everything but graph

  repeated float floats = 7;          // list of floats
  repeated int64 ints = 8;            // list of ints
  repeated bytes strings = 9;         // list of UTF-8 strings
  repeated TensorProto tensors = 10;  // list of tensors
  repeated GraphProto graphs = 11;    // list of graph
  repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors
 }

 // Defines information on value, including the name, the type, and
 // the shape of the value.
 message ValueInfoProto {
  // This field MUST be present in this version of the IR.
  string name = 1;     // namespace Value
  // This field MUST be present in this version of the IR for
  // inputs and outputs of the top-level graph.
  TypeProto type = 2;
  // A human-readable documentation for this value. Markdown is allowed.
  string doc_string = 3;
 }

 // Nodes
 //
 // Computation graphs are made up of a DAG of nodes, which represent what is
 // commonly called a "layer" or "pipeline stage" in machine learning frameworks.
 //
 // For example, it can be a node of type "Conv" that takes in an image, a filter
 // tensor and a bias tensor, and produces the convolved output.
 message NodeProto {
  repeated string input = 1;    // namespace Value
  repeated string output = 2;   // namespace Value

  // An optional identifier for this node in a graph.
  // This field MAY be absent in ths version of the IR.
  string name = 3;     // namespace Node

  // The symbolic identifier of the Operator to execute.
  string op_type = 4;  // namespace Operator
  // The domain of the OperatorSet that specifies the operator named by op_type.
  string domain = 7;   // namespace Domain

  // Additional named attributes.
  repeated AttributeProto attribute = 5;

  // A human-readable documentation for this node. Markdown is allowed.
  string doc_string = 6;
 }

 // Models
 //
 // ModelProto is a top-level file/container format for bundling a ML model and
 // associating its computation graph with metadata.
 //
 // The semantics of the model are described by the associated GraphProto.
 message ModelProto {
  // The version of the IR this model targets. See Version enum above.
  // This field MUST be present.
  int64 ir_version = 1;

  // The OperatorSets this model relies on.
  // All ModelProtos MUST have at least one entry that
  // specifies which version of the ONNX OperatorSet is
  // being imported.
  //
  // All nodes in the ModelProto's graph will bind against the operator
  // with the same-domain/same-op_type operator with the HIGHEST version
  // in the referenced operator sets.
  repeated OperatorSetIdProto opset_import = 8;

  // The name of the framework or tool used to generate this model.
  // This field SHOULD be present to indicate which implementation/tool/framework
  // emitted the model.
  string producer_name = 2;

  // The version of the framework or tool used to generate this model.
  // This field SHOULD be present to indicate which implementation/tool/framework
  // emitted the model.
  string producer_version = 3;

  // Domain name of the model.
  // We use reverse domain names as name space indicators. For example:
  // `com.facebook.fair` or `com.microsoft.cognitiveservices`
  //
  // Together with `model_version` and GraphProto.name, this forms the unique identity of
  // the graph.
  string domain = 4;

  // The version of the graph encoded. See Version enum below.
  int64 model_version = 5;

  // A human-readable documentation for this model. Markdown is allowed.
  string doc_string = 6;

  // The parameterized graph that is evaluated to execute the model.
  GraphProto graph = 7;

  // Named metadata values; keys should be distinct.
  repeated StringStringEntryProto metadata_props = 14;
 };

 // StringStringEntryProto follows the pattern for cross-proto-version maps.
 // See https://developers.google.com/protocol-buffers/docs/proto3#maps
 message StringStringEntryProto {
  string key = 1;
  string value= 2;
 };

 message TensorAnnotation {
  string tensor_name = 1;
  // <key, value> pairs to annotate tensor specified by <tensor_name> above.
  // The keys used in the mapping below must be pre-defined in ONNX spec.
  // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as
  // quantization parameter keys.
  repeated StringStringEntryProto quant_parameter_tensor_names = 2;
 }



 // Graphs
 //
 // A graph defines the computational logic of a model and is comprised of a parameterized
 // list of nodes that form a directed acyclic graph based on their inputs and outputs.
 // This is the equivalent of the "network" or "graph" in many deep learning
 // frameworks.
 message GraphProto {
  // The nodes in the graph, sorted topologically.
  repeated NodeProto node = 1;

  // The name of the graph.
  string name = 2;   // namespace Graph

  // A list of named tensor values, used to specify constant inputs of the graph.
  // Each TensorProto entry must have a distinct name (within the list) that
  // MAY also appear in the input list.
  repeated TensorProto initializer = 5;

  // Initializers (see above) stored in sparse format.
  repeated SparseTensorProto sparse_initializer = 15;

  // A human-readable documentation for this graph. Markdown is allowed.
  string doc_string = 10;

  // The inputs and outputs of the graph.
  repeated ValueInfoProto input = 11;
  repeated ValueInfoProto output = 12;

  // Information for the values in the graph. The ValueInfoProto.name's
  // must be distinct. It is optional for a value to appear in value_info list.
  repeated ValueInfoProto value_info = 13;

  // This field carries information to indicate the mapping among a tensor and its
  // quantization parameter tensors. For example:
  // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated,
  // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.
  repeated TensorAnnotation quantization_annotation = 14;

  // DO NOT USE the following fields, they were deprecated from earlier versions.
  // repeated string input = 3;
  // repeated string output = 4;
  // optional int64 ir_version = 6;
  // optional int64 producer_version = 7;
  // optional string producer_tag = 8;
  // optional string domain = 9;
 }

 // Tensors
 //
 // A serialized tensor value.
 message TensorProto {
  enum DataType {
    UNDEFINED = 0;
    // Basic types.
    FLOAT = 1;   // float
    UINT8 = 2;   // uint8_t
    INT8 = 3;    // int8_t
    UINT16 = 4;  // uint16_t
    INT16 = 5;   // int16_t
    INT32 = 6;   // int32_t
    INT64 = 7;   // int64_t
    STRING = 8;  // string
    BOOL = 9;    // bool

    // IEEE754 half-precision floating-point format (16 bits wide).
    // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits.
    FLOAT16 = 10;

    DOUBLE = 11;
    UINT32 = 12;
    UINT64 = 13;
    COMPLEX64 = 14;     // complex with float32 real and imaginary components
    COMPLEX128 = 15;    // complex with float64 real and imaginary components

    // Non-IEEE floating-point format based on IEEE754 single-precision
    // floating-point number truncated to 16 bits.
    // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits.
    BFLOAT16 = 16;

    // Future extensions go here.
  }

  // The shape of the tensor.
  repeated int64 dims = 1;

  // The data type of the tensor.
  // This field MUST have a valid TensorProto.DataType value
  int32 data_type = 2;

  // For very large tensors, we may want to store them in chunks, in which
  // case the following fields will specify the segment that is stored in
  // the current TensorProto.
  message Segment {
    int64 begin = 1;
    int64 end = 2;
  }
  Segment segment = 3;

  // Tensor content must be organized in row-major order.
  //
  // Depending on the data_type field, exactly one of the fields below with
  // name ending in _data is used to store the elements of the tensor.

  // For float and complex64 values
  // Complex64 tensors are encoded as a single array of floats,
  // with the real components appearing in odd numbered positions,
  // and the corresponding imaginary component apparing in the
  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
  // When this field is present, the data_type field MUST be FLOAT or COMPLEX64.
  repeated float float_data = 4 [packed = true];

  // For int32, uint8, int8, uint16, int16, bool, and float16 values
  // float16 values must be bit-wise converted to an uint16_t prior
  // to writing to the buffer.
  // When this field is present, the data_type field MUST be
  // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16
  repeated int32 int32_data = 5 [packed = true];

  // For strings.
  // Each element of string_data is a UTF-8 encoded Unicode
  // string. No trailing null, no leading BOM. The protobuf "string"
  // scalar type is not used to match ML community conventions.
  // When this field is present, the data_type field MUST be STRING
  repeated bytes string_data = 6;

  // For int64.
  // When this field is present, the data_type field MUST be INT64
  repeated int64 int64_data = 7 [packed = true];

  // Optionally, a name for the tensor.
  string name = 8; // namespace Value

  // A human-readable documentation for this tensor. Markdown is allowed.
  string doc_string = 12;

  // Serializations can either use one of the fields above, or use this
  // raw bytes field. The only exception is the string case, where one is
  // required to store the content in the repeated bytes string_data field.
  //
  // When this raw_data field is used to store tensor value, elements MUST
  // be stored in as fixed-width, little-endian order.
  // Floating-point data types MUST be stored in IEEE 754 format.
  // Complex64 elements must be written as two consecutive FLOAT values, real component first.
  // Complex128 elements must be written as two consecutive DOUBLE values, real component first.
  // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false).
  //
  // Note: the advantage of specific field rather than the raw_data field is
  // that in some cases (e.g. int data), protobuf does a better packing via
  // variable length storage, and may lead to smaller binary footprint.
  // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED
  bytes raw_data = 9;

  // Data can be stored inside the protobuf file using type-specific fields or raw_data.
  // Alternatively, raw bytes data can be stored in an external file, using the external_data field.
  // external_data stores key-value pairs describing data location. Recognized keys are:
  // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX
  //                           protobuf model was stored
  // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string.
  //                         Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
  // - "length" (optional) - number of bytes containing data. Integer stored as string.
  // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key.
  repeated StringStringEntryProto external_data = 13;

  // Location of the data for this tensor. MUST be one of:
  // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field.
  // - EXTERNAL - data stored in an external location as described by external_data field.
  enum DataLocation {
    DEFAULT = 0;
    EXTERNAL = 1;
  }

  // If value not set, data is stored in raw_data (if set) otherwise in type-specified field.
  DataLocation data_location = 14;

  // For double
  // Complex128 tensors are encoded as a single array of doubles,
  // with the real components appearing in odd numbered positions,
  // and the corresponding imaginary component apparing in the
  // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i]
  // is encoded as [1.0, 2.0 ,3.0 ,4.0]
  // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128
  repeated double double_data = 10 [packed = true];

  // For uint64 and uint32 values
  // When this field is present, the data_type field MUST be
  // UINT32 or UINT64
  repeated uint64 uint64_data = 11 [packed = true];
 }

 // A serialized sparse-tensor value
 message SparseTensorProto {
  // The sequence of non-default values are encoded as a tensor of shape [NNZ].
  // The default-value is zero for numeric tensors, and empty-string for string tensors.
  TensorProto values = 1;

  // The indices of the non-default values, which may be stored in one of two formats.
  // (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
  // corresponding to the j-th index of the i-th value (in the values tensor).
  // (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
  // must be the linearized-index of the i-th value (in the values tensor).
  // The linearized-index can be converted into an index tuple (k_1,...,k_rank)
  // using the shape provided below.
  // The indices must appear in ascending order without duplication.
  // In the first format, the ordering is lexicographic-ordering:
  // e.g., index-value [1,4] must appear before [2,1]
  TensorProto indices = 2;

  // The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
  repeated int64 dims = 3;
 }

 // Defines a tensor shape. A dimension can be either an integer value
 // or a symbolic variable. A symbolic variable represents an unknown
 // dimension.
 message TensorShapeProto {
  message Dimension {
    oneof value {
      int64 dim_value = 1;
      string dim_param = 2;   // namespace Shape
    };
    // Standard denotation can optionally be used to denote tensor
    // dimensions with standard semantic descriptions to ensure
    // that operations are applied to the correct axis of a tensor.
    // Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition
    // for pre-defined dimension denotations.
    string denotation = 3;
  };
  repeated Dimension dim = 1;
 }

 // Types
 //
 // The standard ONNX data types.
 message TypeProto {

  message Tensor {
    // This field MUST NOT have the value of UNDEFINED
    // This field MUST have a valid TensorProto.DataType value
    // This field MUST be present for this version of the IR.
    int32 elem_type = 1;
    TensorShapeProto shape = 2;
  }

  // repeated T
  message Sequence {
    // The type and optional shape of each element of the sequence.
    // This field MUST be present for this version of the IR.
    TypeProto elem_type = 1;
  };

  // map<K,V>
  message Map {
    // This field MUST have a valid TensorProto.DataType value
    // This field MUST be present for this version of the IR.
    // This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING
    int32 key_type = 1;
    // This field MUST be present for this version of the IR.
    TypeProto value_type = 2;
  };

  oneof value {
    // The type of a tensor.
    Tensor tensor_type = 1;

    // NOTE:  DNN-only implementations of ONNX MAY elect to not support non-tensor values
    //        as input and output to graphs and nodes. These types are needed to naturally
    //        support classical ML operators.  DNN operators SHOULD restrict their input
    //        and output types to tensors.

    // The type of a sequence.
    Sequence sequence_type = 4;

    // The type of a map.
    Map map_type = 5;

  }

  // An optional denotation can be used to denote the whole
  // type with a standard semantic description as to what is
  // stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition
  // for pre-defined type denotations.
  string denotation = 6;
 }

 // Operator Sets
 //
 // OperatorSets are uniquely identified by a (domain, opset_version) pair.
 message OperatorSetIdProto {
  // The domain of the operator set being identified.
  // The empty string ("") or absence of this field implies the operator
  // set that is defined as part of the ONNX specification.
  // This field MUST be present in this version of the IR when referring to any other operator set.
  string domain = 1;

  // The version of the operator set being identified.
  // This field MUST be present in this version of the IR.
  int64 version = 2;
 }
--- a/parser/proto/caffe/CMakeLists.txt
+++ b/parser/proto/caffe/CMakeLists.txt
@@ -1,31 +0,0 @@
 ############ lib_caffe_parser.so ############
 add_library(_caffe_parser SHARED
    $<TARGET_OBJECTS:parser_caffe_proto_obj>
 )

 target_compile_definitions(_caffe_parser PRIVATE
    google=ascend_private
 )

 target_include_directories(_caffe_parser PRIVATE
    ${CMAKE_CURRENT_LIST_DIR}
 )

 target_link_options(_caffe_parser PRIVATE
    -Wl,-Bsymbolic
 )

 target_link_libraries(_caffe_parser PRIVATE
    $<BUILD_INTERFACE:intf_pub>
    -Wl,--no-as-needed
    ascend_protobuf
    -Wl,--as-needed
 )

 ############ install ############
 set(INSTALL_BASE_DIR "")
 set(INSTALL_LIBRARY_DIR lib)

 install(TARGETS _caffe_parser OPTIONAL
    LIBRARY DESTINATION ${INSTALL_LIBRARY_DIR}
 )
--- a/parser/proto/caffe/caffe.proto
+++ b/parser/proto/caffe/caffe.proto
--- a/parser/proto/caffe/module.mk
+++ b/parser/proto/caffe/module.mk
@@ -1,21 +0,0 @@
 LOCAL_PATH := $(call my-dir)

 include $(CLEAR_VARS)

 LOCAL_MODULE := lib_caffe_parser

 LOCAL_CFLAGS += -Dgoogle=ascend_private
 ifeq ($(DEBUG), 1)
 LOCAL_CFLAGS += -g -O0
 endif

 LOCAL_SRC_FILES := \
    caffe.proto \

 LOCAL_C_INCLUDES := \
    third_party/protobuf/include \

 LOCAL_SHARED_LIBRARIES := \
    libascend_protobuf \

 include $(BUILD_HOST_SHARED_LIBRARY)
--- a/parser/proto/ge_ir.proto
+++ b/parser/proto/ge_ir.proto
@@ -1,193 +0,0 @@
 syntax = "proto3";

 package ge.proto;

 enum DataType
 {
    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
    DT_FLOAT     = 1;  // float type
    DT_FLOAT16   = 2;  // fp16 type
    DT_INT8      = 3;  // int8 type
    DT_UINT8     = 4;  // uint8 type
    DT_INT16     = 5;  // int16 type
    DT_UINT16    = 6;  // uint16 type
    DT_INT32     = 7;  //
    DT_INT64     = 8;  // int64 type
    DT_UINT32    = 9;  // unsigned int32
    DT_UINT64    = 10;  // unsigned int64
    DT_BOOL      = 11;  // bool type
    DT_DOUBLE    = 12; // double type
    DT_STRING = 13;            // string type
    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
    DT_COMPLEX64 = 16;         // complex64 type
    DT_COMPLEX128 = 17;        // complex128 type
    DT_QINT8 = 18;             // qint8 type
    DT_QINT16 = 19;            // qint16 type
    DT_QINT32 = 20;            // qint32 type
    DT_QUINT8 = 21;            // quint8 type
    DT_QUINT16 = 22;           // quint16 type
    DT_RESOURCE  = 23;         // resource type
    DT_STRING_REF = 24;        // string_ref type
    DT_DUAL      = 25;              /**< dual output type */
    DT_VARIANT = 26;           // variant type
    DT_BF16 = 27;              // bf16 type
    DT_INT4 = 28;              // int4 type
 }

 message AttrDef
 {
    message ListValue
    {
        enum ListValueType{
          VT_LIST_NONE = 0;
          VT_LIST_STRING = 1;
          VT_LIST_INT = 2;
          VT_LIST_FLOAT = 3;
          VT_LIST_BOOL = 4;
          VT_LIST_BYTES = 5;
          VT_LIST_TENSOR_DESC = 6;
          VT_LIST_TENSOR = 7;
          VT_LIST_GRAPH = 8;
          VT_LIST_NAMED_ATTRS = 9;
          VT_LIST_DATA_TYPE = 10;
        }
        repeated bytes s             = 2;                    // "list(string)"
        repeated int64 i             = 3;  // "list(int)"
        repeated float f             = 4;   // "list(float)"
        repeated bool  b             = 5;  // "list(bool)"
        repeated bytes bt            = 7;
        repeated TensorDescriptor td = 8;
        repeated TensorDef t         = 9;
        repeated GraphDef g          = 10;
 	    repeated NamedAttrs na       = 11;
 	    repeated int64 dt            = 12; // list ge::DataType

 	    ListValueType val_type       = 20;
    }

    message ListListInt{
        message ListInt{
            repeated int64 list_i             = 1; // list int
        }
        repeated ListInt list_list_i             = 1; // list list int
    }

    oneof value
    {
        bytes            s    = 2;  // "string"
        int64            i    = 3;  // "int"
        float            f    = 4;  // "float"
        bool             b    = 5;  // "bool"
        bytes            bt   = 7;
        ListValue        list = 1;   // any "list(...)"
        NamedAttrs       func = 10;  // Used to support attr nesting
        TensorDescriptor td   = 11;  // GeTensorDesc type
        TensorDef        t    = 12;  // GeTensor type
        GraphDef         g    = 13;  // Graph type
        ListListInt      list_list_int  = 14;  // List List Int type
        int64            dt   = 15; // ge::DataType
    }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs
 {
    string               name = 1;
    map<string, AttrDef> attr = 2;
 }

 // Shape / dimension description, using row-major order
 message ShapeDef
 {
    repeated int64 dim = 1;  // Size of each dimension
 }

 // Multidimensional data description
 message TensorDescriptor
 {
    string   name   = 1;  // Optional parameter, tensor name

    DataType dtype  = 2;  // tensor datatype
    ShapeDef shape  = 3;  // Shape / dimension
    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"

    bool has_out_attr = 9;
    int64 size = 10;
    int64 weight_size = 11;
    bool reuse_input = 12;
    bool output_tensor = 13;
    string device_type = 14;
    bool input_tensor =15;
    int64 real_dim_cnt = 16;
    int64 reuse_input_index = 17;
    int64 data_offset = 18;
    int64 cmps_size = 19;
    string cmps_tab = 20;
    int64 cmps_tab_offset = 21;

 	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
 }

 // GeTensor definition
 message TensorDef
 {
    TensorDescriptor desc = 1;  // Tensor description
    bytes            data = 2;  // Tensor data
 }


 // Operator description
 message OpDef
 {
    string name = 1;  // name
    string type = 2;  // type

    repeated string input = 5;  // input original op name + outgoing index. op_name：index

    map<string, AttrDef> attr = 10;  // Set of operator parameter fields

    bool has_out_attr = 20;
    int64 id = 21;
    int64 stream_id =22;
    repeated string input_name = 23;
    repeated string src_name = 24;
    repeated int64 src_index = 25;
    repeated string dst_name = 26;
    repeated int64 dst_index = 27;
    repeated int64 input_i = 28;
    repeated int64 output_i = 29;
    repeated int64 workspace = 30;
    repeated int64 workspace_bytes = 31;
    repeated bool is_input_const = 32;
    repeated TensorDescriptor input_desc = 33;
    repeated TensorDescriptor output_desc = 34;
    repeated string subgraph_name = 35;
 }

 // Graph definition
 message GraphDef
 {
    string name   = 1;   //  name

    repeated string input  = 4;  // Graph input
    repeated string output = 5;  // Graph output

    repeated OpDef op      = 6;  // List of operators

 	map<string, AttrDef> attr = 11;  // Extended field
 }

 // model definition
 message ModelDef
 {
 	string name         = 1;  // name
 	uint32 version      = 2;  // IR Proto verion
 	string custom_version = 3;  // User model version number, passed in by user

    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef

    map<string, AttrDef> attr = 11;  // Extended field
 }

--- a/parser/proto/insert_op.proto
+++ b/parser/proto/insert_op.proto
@@ -1,140 +0,0 @@
 syntax = "proto3";

 package domi;

 message InsertNewOps {
 	repeated AippOpParams aipp_op = 1;
 	repeated MultiShapeOpParams multi_shape_op = 2;
 }

 message AippOpParams {
 	enum InputFormat {
 		UNDEFINED = 0;
 		YUV420SP_U8 = 1;
 		XRGB8888_U8 = 2;
 		RGB888_U8   = 3;
 		YUV400_U8   = 4;
 		NC1HWC0DI_FP16 = 5;
 		NC1HWC0DI_S8 = 6;
 		ARGB8888_U8 = 7;
 		YUYV_U8 = 8;
 		YUV422SP_U8 = 9;
 		AYUV444_U8 = 10;
 		RAW10 = 11;
 		RAW12 = 12;
 		RAW16 = 13;
 		RAW24 = 14;
 		RGB16 = 15;
 		RGB20 = 16;
 		RGB24 = 17;
 		RGB8_IR = 18;
 		RGB16_IR = 19;
 		RGB24_IR = 20;
 	}

 	enum AippMode {
 		undefined = 0;
 		static = 1;
 		dynamic = 2;
 	}

 	// AIPP模式，区分静态AIPP和动态AIPP
 	AippMode aipp_mode = 1;

 	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
 	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
 	uint32 related_input_rank = 2;

        // related_input_name is optional and the top name of data node which inserts aipp
        string related_input_name = 6;

 	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
 	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
 	// 配置值 <= Data算子输出边的个数。
 	repeated uint32 input_edge_idx = 3;

 	// [Begin] 动态AIPP参数，配置静态AIPP时无效
 	uint32 max_src_image_size = 4;

 	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
 	bool support_rotation = 5;

 	// [End] 动态AIPP参数


 	// [Begin] 静态AIPP参数，配置动态AIPP时无效
 	InputFormat input_format = 51;
 	bool csc_switch = 52;
 	float cpadding_value = 53;
 	bool rbuv_swap_switch = 54;
 	bool ax_swap_switch = 55;
 	bool single_line_mode = 56;

 	int32 src_image_size_w = 57;
 	int32 src_image_size_h = 58;

 	bool crop = 59;
 	int32 load_start_pos_w = 60;
 	int32 load_start_pos_h = 61;
 	int32 crop_size_w = 62;
 	int32 crop_size_h = 63;

 	bool resize = 64;
 	int32 resize_output_w = 65;
 	int32 resize_output_h = 66;

 	bool padding = 67;
 	int32 left_padding_size = 68;
 	int32 right_padding_size = 69;
 	int32 top_padding_size = 70;
 	int32 bottom_padding_size = 71;
 	float padding_value = 72;

 	int32 mean_chn_0 = 10;
 	int32 mean_chn_1 = 11;
 	int32 mean_chn_2 = 12;
 	int32 mean_chn_3 = 19;
 	float min_chn_0 = 13;
 	float min_chn_1 = 14;
 	float min_chn_2 = 15;
 	float min_chn_3 = 20;
 	repeated float var_reci_chn_0 = 16;
 	repeated float var_reci_chn_1 = 17;
 	repeated float var_reci_chn_2 = 18;
 	repeated float var_reci_chn_3 = 21;

 	repeated int32 matrix_r0c0 = 30;
 	repeated int32 matrix_r0c1 = 31;
 	repeated int32 matrix_r0c2 = 32;
 	repeated int32 matrix_r1c0 = 33;
 	repeated int32 matrix_r1c1 = 34;
 	repeated int32 matrix_r1c2 = 35;
 	repeated int32 matrix_r2c0 = 36;
 	repeated int32 matrix_r2c1 = 37;
 	repeated int32 matrix_r2c2 = 38;
 	repeated int32 output_bias_0 = 39;
 	repeated int32 output_bias_1 = 40;
 	repeated int32 output_bias_2 = 41;
 	repeated int32 input_bias_0 = 42;
 	repeated int32 input_bias_1 = 43;
 	repeated int32 input_bias_2 = 44;

 	// [End] 静态AIPP参数

       // The n number that is used for raw/rgbir data into f16 transformation.
       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
       uint32 raw_rgbir_to_f16_n = 45;
 }

 message MultiShapeOpParams {
 	enum MultiShapeMode {
 		batch      = 0;             //动态batch
 		resolution = 1;             //动态分辨率，扩展用
 	}

 	MultiShapeMode    mode                      = 1;        //算子模式
 	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入


 	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
 }
--- a/parser/proto/om.proto
+++ b/parser/proto/om.proto
@@ -1,396 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 enum TargetType
 {
    MINI = 0;
    TINY = 1;
    LITE = 2;
 }

 // offline model
 message ModelDef {
    string name = 1;
    uint32 version = 2;

    uint64 memory_size = 10;
    uint32 stream_num = 11;
    uint32 event_num = 12;
    uint64 weight_size = 13;
    uint32 label_num = 15;
    repeated OpDef op = 20;
    TargetType target_type = 23;

    map<string, AttrDef> attr = 30;
 };

 // operator define
 message OpDef {
    string name = 1;
    string type = 2;

    uint32 id = 3;
    uint32 stream_id = 4;

    repeated string input_name = 5;

    repeated string src_name = 8;
    repeated int32 src_index = 9;
    repeated int64 input = 10;
    repeated int64 output = 11;
    repeated TensorDescriptor input_desc = 12;
    repeated TensorDescriptor output_desc = 13;
    repeated WeightDef weights = 14;
    repeated string dst_name = 15;
    repeated int32 dst_index = 16;

    repeated int64 workspace = 20;
    repeated uint32 workspace_bytes = 21;

    repeated string weight_name = 22;
    repeated bool is_input_const = 23;

    map<string, AttrDef> attr = 30;

    QuantizeFactorParams quantize_factor = 31;

    oneof op_params {
        // start at 100 here
        SendOpParams sender_param = 100;
        RecvOpParams receiver_param = 200;
        ConvolutionOpParams convolution_param = 300;
        PoolingOpParams pooling_param = 400;
        EltwiseOpParams eltwise_param = 500;
        BatchNormOpParams batchnorm_param = 600;
        ScaleOpParams scale_param = 700;
        FullConnectionOpParams full_connection_param = 800;
        SoftmaxOpParams softmax_param = 900;
        ActivationOpParams activation_param = 1000;
        ReshapeOpParams reshape_param = 1100;
    }
 };

 message SendOpParams {
    uint32 event_id = 1;
 };

 message RecvOpParams {
    uint32 event_id = 1;
 };

 enum QuantizeScaleType
 {
    VECTOR_SCALE = 0;
    SCALAR_SCALE = 1;
 }

 enum QuantizeScaleMode
 {
    NORMAL_MODE = 0;
    SQRT_MODE = 1;
 }

 enum QuantizeAlgorithm
 {
    NON_OFFSET_ALGO = 0;
    HALF_OFFSET_ALGO = 1;
    ALL_OFFSET_ALGO = 2;
 }
 message QuantizeFactor
 {
    QuantizeScaleMode scale_mode = 1;
    bytes scale_value = 2;
    int64 scale_offset = 3;
    bytes offset_data_value = 4;
    int64 offset_data_offset = 5;
    bytes offset_weight_value = 6;
    int64 offset_weight_offset = 7;
    bytes offset_pad_value = 8;
    int64 offset_pad_offset = 9;
 };

 message QuantizeCalcFactor
 {
    bytes offsetw = 1;
    int64 offsetw_offset = 2;
    bytes offsetd = 3;
    int64 offsetd_offset = 4;
    bytes scalereq = 5;
    int64 scaledreq_offset = 6;
    bytes offsetdnext = 7;
    int64 offsetdnext_offset = 8;
 }

 message QuantizeFactorParams
 {
    QuantizeAlgorithm quantize_algo = 1;
    QuantizeScaleType scale_type = 2;
    QuantizeFactor quantize_param = 3;
    QuantizeFactor dequantize_param = 4;
    QuantizeFactor requantize_param = 5;
    QuantizeCalcFactor quantizecalc_param = 6;
 };

 message ConvolutionOpParams {
    int32 mode = 1;
    int32 algo = 2;
    int32 pad_mode = 3;
    uint32 group = 4;
    uint32 num_output = 5;

    repeated uint32 pad = 10;
    repeated uint32 stride = 11;
    repeated uint32 dilation = 12;
    repeated uint32 kernel = 13;

    float alpha = 20;
    float beta = 21;

    WeightDef filter = 40;
    WeightDef bias = 41;

    bool relu_flag = 62;
    repeated uint32 adj = 70;
    repeated uint32 target_shape = 71;
    repeated uint32 before_pad = 72;
 };

 message PoolingOpParams {
    int32 mode = 1;
    int32 nan_opt = 2;
    int32 pad_mode = 3;
    bool global_pooling = 4;

    repeated uint32 window = 10;
    repeated uint32 pad = 11;
    repeated uint32 stride = 12;
    bool ceil_mode = 13;
    int32 data_mode  = 14;

    float alpha = 20;
    float beta = 21;
    repeated uint32 before_pad = 22;
 };

 message EltwiseOpParams {
    int32 mode = 1;
    repeated float coeff = 2;
    float alpha = 3;
    float beta = 4;
    repeated WeightDef weight = 5;
    bool relu_flag = 6;
 };

 message ActivationOpParams {
    int32 mode = 1;
    float coef = 2;
    float alpha = 3;
    float beta = 4;
 };

 message BatchNormOpParams {
    int32 mode = 1;

    float alpha = 2;
    float beta = 3;
    double epsilon = 4;//optinal,[default = 1e-5]
    bool use_global_stats = 5; //optinal,by default true,testing mode
    float  moving_average_fraction = 6; //optinal,[default = .999];

    WeightDef estimated_mean = 7;
    WeightDef estimated_variance = 8;

    WeightDef scale = 9;
    WeightDef bias = 10;
 };

 message ScaleOpParams {
    WeightDef scale = 1;
    WeightDef bias = 2;
 };

 message ReshapeOpParams {
    float alpha = 1;
    float beta = 2;
    ShapeDef shape = 3;
    int32 axis = 4;
    int32 num_axes = 5;
    int32 format = 6;
 };

 message SoftmaxOpParams {
    int32 algo = 1;
    int32 mode = 2;
    float alpha = 3;
    float beta = 4;
 };

 message FullConnectionOpParams {
    WeightDef filter = 1;
    WeightDef bias = 2;
    uint32 num_output = 3;
    bool relu_flag = 12;
 };

 message FlattenOpParams {
    float alpha = 1;
    float beta = 2;
    int32 start_axis = 3;
    int32 end_axis = 4;
 }

 message AddLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message MulLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message AddOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message MulOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message SubOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message BiasAddOpParams {
    float alpha = 1;
    float beta = 2;

    WeightDef bias = 10;
 };

 message MatMulOpParams {
    float alpha = 1;
    float beta = 2;
    bool transposeX = 3;
    bool transposeW = 4;

    WeightDef filter = 10;
    WeightDef bias = 12;
 };

 message RsqrtOpParams {
    float alpha = 1;
    float beta = 2;
 };


 message WeightDef {
    int32 format = 1;
    int32 data_type = 2;
    ShapeDef shape = 3;
    bytes data = 4;
    int64 data_offset = 5;
    uint32 cmps_size = 6;
    bytes cmps_tab = 7;
    int64 cmps_tab_offset = 10;
    CompressInfo cmps_info = 8;
    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
 }

 message ShapeDef {
    repeated int64 dim = 1;
 }

 enum DeviceType {
  NPU = 0;                   // In default, we will use NPU.
  CPU = 1;                   // CPU
 }

 message AllOffsetQuantizeInfo {
 	float scale  = 1;
 	int32 offset = 2;
 } 

 message TensorDescriptor {
    int32 format = 1;
    int32 data_type = 2;
    repeated int64 dim = 3;
    uint32 size = 4;
    bool reuse_input = 5;
    bool output_tensor = 7;
    DeviceType device_type = 8;
    bool input_tensor = 9;
    uint32 real_dim_cnt = 10;
    uint32 reuse_input_index = 11;
    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
 }

 message CompressInfo {
    int32 blockRow = 1;                             // block row
    int32 blockCol = 2;                             // block col
    int32 fractalK = 3;                             // fractal K
    int32 fractalN = 4;                             // fractal N
    int32 lastFractalK = 5;                         // K of last fractal
    int32 lastFractalN = 6;                         // N of last fractal
    int32 cubeSize = 7;                             // cube's length
    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
 }

 message AttrDef {
  message ListValue {
    repeated string s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated uint32 u = 6 [packed = true];         // "list(uint)"
    repeated bytes bt = 7;
  }

  oneof value {
    string s = 2;                // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    uint32 u = 6;                // "uint32"
    bytes bt = 7;
    ListValue list = 1;          // any "list(...)"
    NamedAttrs func = 10;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs {
  string name = 1;
  map<string, AttrDef> attr = 2;
 }

--- a/parser/proto/task.proto
+++ b/parser/proto/task.proto
@@ -1,179 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 message ModelTaskDef {
    string version = 1;

    map<string, string> attr = 9; // Extended field
    repeated TaskDef task = 10;

    uint64 memory_size = 11;
    uint32 stream_num = 12;
    uint32 event_num = 13;
    uint64 weight_size = 14;

    repeated bytes op = 15; // input/output opdef in bytes

    uint64 base_addr = 16;    // base addr
    uint64 weight_addr = 17;  // weight addr
    uint32 batch_num = 18;
 }


 message TaskDef {
    uint32 id = 1;
    uint32 type = 2;

    uint32 stream_id = 10;
    uint32 event_id = 11;

    KernelDef kernel = 20;
    KernelExDef kernel_ex = 21;
    KernelHcclDef kernel_hccl = 25;
    EventExDef event_ex = 26;
    LogTimeStampDef log_timestamp = 28;

    uint32 label_id = 30;

    MemcpyAsyncDef memcpy_async = 31;
    StreamSwitchDef stream_switch = 32;
    StreamActiveDef stream_active = 33;
    bytes private_def = 34;
    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
    StreamSwitchNDef stream_switch_n = 36;

    LabelSetDef label_set = 37;
    LabelGotoExDef label_goto_ex = 38;
    LabelSwitchByIndexDef label_switch_by_index = 39;
    KernelDefWithHandle kernel_with_handle = 40;
 }

 message KernelDef {
    KernelContext context = 1;

    string stub_func = 10;
    uint32 block_dim = 11;
    uint32 args_size = 12;
    bytes args = 13;
    bytes sm_desc = 14;
    bytes flowtable = 15;
    string so_name = 16;
    string kernel_name = 17;
    bytes kernel_ext_info = 18;
    uint32 kernel_ext_info_size = 19;
 }

 message KernelDefWithHandle {
    KernelContext context = 1;

    uint64 handle = 10;
    string dev_func = 11;
    uint32 block_dim = 12;
    uint32 args_size = 13;
    bytes args = 14;
    bytes sm_desc = 15;
    string original_kernel_key = 16;
    string node_info = 17;
 }

 message KernelContext {
    uint32 kernel_type = 1;
    uint32 op_id = 2;                              // OP type in CCE
    uint32 kernel_func_id = 3;
    uint32 op_index = 4;                           // TE/Custom operator
    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
    bytes args_offset = 6;                         // args offset information
    uint32 args_count = 7;                         // args count
    repeated uint32 origin_op_index = 8;
 }


 message KernelExDef {
    uint32 flags = 1;

    uint32 op_index = 4;
    uint32 args_size = 12;
    bytes args = 13;
    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
    uint32 task_info_size = 15;
    bytes kernel_ext_info = 16;
    uint32 kernel_ext_info_size = 17;
 }


 message KernelHcclDef {
    uint32 op_index = 8;
    string hccl_type = 9;
 }


 message EventExDef {
    uint32 op_index = 1;
    uint32 event_type = 2;
 }

 message LogTimeStampDef {
    uint64 logid = 1;
    bool notify = 2;
    uint32 flat = 3;
 }

 message MemcpyAsyncDef {
    uint64 dst = 1;
    uint64 dst_max = 2;
    uint64 src = 3;
    uint64 count = 4;
    uint32 kind = 5;
    uint32 op_index = 6;
 }

 message StreamSwitchDef {
    uint32 op_index = 1;
    uint32 true_stream_id = 2;
    int64 value = 3;
    uint64 value_ptr = 4;
    uint32 data_type = 5;
 }

 message StreamActiveDef {
    uint32 op_index = 1;
    uint32 active_stream_id = 2;
 }

 message StreamSwitchNDef {
    uint32 op_index = 1;
    uint32 size = 2;
    repeated int64 target_value = 3;
    repeated uint32 true_stream_id = 4;
    uint32 element_size = 5;
    uint32 data_type = 6;
 }

 message LabelSetDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelGotoExDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelSwitchByIndexDef {
    uint32 op_index = 1;
    uint32 label_max = 2;
 }
--- a/parser/proto/tensorflow/attr_value.proto
+++ b/parser/proto/tensorflow/attr_value.proto
@@ -1,62 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "AttrValueProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "tensor.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing the value for an attr used to configure an Op.
 // Comment indicates the corresponding attr type.  Only the field matching the
 // attr type may be filled.
 message AttrValue {
  // LINT.IfChange
  message ListValue {
    repeated bytes s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated DataType type = 6 [packed = true];  // "list(type)"
    repeated TensorShapeProto shape = 7;         // "list(shape)"
    repeated TensorProto tensor = 8;             // "list(tensor)"
    repeated NameAttrList func = 9;              // "list(attr)"
  }
  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)

  oneof value {
    bytes s = 2;                 // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    DataType type = 6;           // "type"
    TensorShapeProto shape = 7;  // "shape"
    TensorProto tensor = 8;      // "tensor"
    ListValue list = 1;          // any "list(...)"

    // "func" represents a function. func.name is a function's name or
    // a primitive op's name. func.attr.first is the name of an attr
    // defined for that function. func.attr.second is the value for
    // that attr in the instantiation.
    NameAttrList func = 10;

    // This is a placeholder only used in nodes defined inside a
    // function.  It indicates the attr value will be supplied when
    // the function is instantiated.  For example, let us suppose a
    // node "N" in function "FN". "N" has an attr "A" with value
    // placeholder = "foo". When FN is instantiated with attr "foo"
    // set to "bar", the instantiated node N's attr A will have been
    // given the value "bar".
    string placeholder = 9;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NameAttrList {
  string name = 1;
  map<string, AttrValue> attr = 2;
 }
--- a/parser/proto/tensorflow/function.proto
+++ b/parser/proto/tensorflow/function.proto
@@ -1,100 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "FunctionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "node_def.proto";
 import "op_def.proto";

 // A library is a set of named functions.
 message FunctionDefLibrary {
  repeated FunctionDef function = 1;
  repeated GradientDef gradient = 2;
 }

 // A function can be instantiated when the runtime can bind every attr
 // with a value. When a GraphDef has a call to a function, it must
 // have binding for every attr defined in the signature.
 //   * device spec, etc.
 message FunctionDef {
  // The definition of the function's name, arguments, return values,
  // attrs etc.
  OpDef signature = 1;

  // Attributes specific to this function definition.
  map<string, AttrValue> attr = 5;

  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
  reserved 2;

  // In both of the following fields, there is the need to specify an
  // output that is used as either the input to another node (in
  // `node_def`) or as a return value of the function (in `ret`).
  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
  // list in some cases (instead of just single outputs).  Also, we
  // need to be able to deal with lists of unknown length (so the
  // output index may not be known at function definition time).  So
  // we use the following format instead:
  // * "fun_in" where "fun_in" is the name of a function input arg in
  //   the `signature` field above.  This represents that input, whether
  //   it is a single tensor or a list.
  // * "fun_in:0" gives the first element of a function input arg (a
  //   non-list input is considered a list of length 1 for these
  //   purposes).
  // * "node:out" where "node" is the name of a node in `node_def` and
  //   "out" is the name one of its op's output arguments (the name
  //   comes from the OpDef of the node's op). This represents that
  //   node's output, whether it is a single tensor or a list.
  //   Note: We enforce that an op's output arguments are never
  //   renamed in the backwards-compatibility test.
  // * "node:out:0" gives the first element of a node output arg (a
  //   non-list output is considered a list of length 1 for these
  //   purposes).
  //
  // NOT CURRENTLY SUPPORTED (but may be in the future):
  // * "node:out:-1" gives last element in a node output list
  // * "node:out:1:" gives a list with all but the first element in a
  //   node output list
  // * "node:out::-1" gives a list with all but the last element in a
  //   node output list

  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
  // may have values of type `placeholder` and the `input` field uses
  // the "output" format above.

  // By convention, "op" in node_def is resolved by consulting with a
  // user-defined library first. If not resolved, "func" is assumed to
  // be a builtin op.
  repeated NodeDef node_def = 3;

  // A mapping from the output arg names from `signature` to the
  // outputs from `node_def` that should be returned by the function.
  map<string, string> ret = 4;
 }

 // GradientDef defines the gradient function of a function defined in
 // a function library.
 //
 // A gradient function g (specified by gradient_func) for a function f
 // (specified by function_name) must follow the following:
 //
 // The function 'f' must be a numerical function which takes N inputs
 // and produces M outputs. Its gradient function 'g', which is a
 // function taking N + M inputs and produces N outputs.
 //
 // I.e. if we have
 //    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
 // then, g is
 //    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
 //                                      dL/dy1, dL/dy2, ..., dL/dy_M),
 // where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
 // loss function). dL/dx_i is the partial derivative of L with respect
 // to x_i.
 message GradientDef {
  string function_name = 1;  // The function name.
  string gradient_func = 2;  // The gradient function's name.
 }
--- a/parser/proto/tensorflow/graph.proto
+++ b/parser/proto/tensorflow/graph.proto
@@ -1,56 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "node_def.proto";
 import "function.proto";
 import "versions.proto";

 // Represents the graph of operations
 message GraphDef {
  repeated NodeDef node = 1;

  // Compatibility versions of the graph.  See core/public/version.h for version
  // history.  The GraphDef version is distinct from the TensorFlow version, and
  // each release of TensorFlow will support a range of GraphDef versions.
  VersionDef versions = 4;

  // Deprecated single version field; use versions above instead.  Since all
  // GraphDef changes before "versions" was introduced were forward
  // compatible, this field is entirely ignored.
  int32 version = 3 [deprecated = true];

  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
  //
  // "library" provides user-defined functions.
  //
  // Naming:
  //   * library.function.name are in a flat namespace.
  //     NOTE: We may need to change it to be hierarchical to support
  //     different orgs. E.g.,
  //     { "/google/nn", { ... }},
  //     { "/google/vision", { ... }}
  //     { "/org_foo/module_bar", { ... }}
  //     map<string, FunctionDefLib> named_lib;
  //   * If node[i].op is the name of one function in "library",
  //     node[i] is deemed as a function call. Otherwise, node[i].op
  //     must be a primitive operation supported by the runtime.
  //
  //
  // Function call semantics:
  //
  //   * The callee may start execution as soon as some of its inputs
  //     are ready. The caller may want to use Tuple() mechanism to
  //     ensure all inputs are ready in the same time.
  //
  //   * The consumer of return values may start executing as soon as
  //     the return values the consumer depends on are ready.  The
  //     consumer may want to use Tuple() mechanism to ensure the
  //     consumer does not start until all return values of the callee
  //     function are ready.
  FunctionDefLibrary library = 2;
 };
--- a/parser/proto/tensorflow/graph_library.proto
+++ b/parser/proto/tensorflow/graph_library.proto
@@ -1,14 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;

 import "graph.proto";

 message GeGraphDef {
  string name = 1;
  GraphDef graph = 2;
 }

 message GraphDefLibrary {
  repeated GeGraphDef graph_def = 1;
 };
--- a/parser/proto/tensorflow/node_def.proto
+++ b/parser/proto/tensorflow/node_def.proto
@@ -1,63 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "NodeProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";

 message NodeDef {
  // The name given to this operator. Used for naming inputs,
  // logging, visualization, etc.  Unique within a single GraphDef.
  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
  string name = 1;

  // The operation name.  There may be custom parameters in attrs.
  // Op names starting with an underscore are reserved for internal use.
  string op = 2;

  // Each input is "node:src_output" with "node" being a string name and
  // "src_output" indicating which output tensor to use from "node". If
  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
  // may optionally be followed by control inputs that have the format
  // "^node".
  repeated string input = 3;

  // A (possibly partial) specification for the device on which this
  // node should be placed.
  // The expected syntax for this string is as follows:
  //
  // DEVICE_SPEC ::= PARTIAL_SPEC
  //
  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
  // CONSTRAINT ::= ("job:" JOB_NAME)
  //              | ("replica:" [1-9][0-9]*)
  //              | ("task:" [1-9][0-9]*)
  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
  //
  // Valid values for this string include:
  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
  // * "/job:worker/device:GPU:3"                   (partial specification)
  // * ""                                    (no specification)
  //
  // If the constraints do not resolve to a single device (or if this
  // field is empty or not present), the runtime will attempt to
  // choose a device automatically.
  string device = 4;

  // Operation-specific graph-construction-time configuration.
  // Note that this should include all attrs defined in the
  // corresponding OpDef, including those with a value matching
  // the default -- this allows the default to change and makes
  // NodeDefs easier to interpret on their own.  However, if
  // an attr with a default is not specified in this list, the
  // default will be used.
  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
  // one of the names from the corresponding OpDef's attr field).
  // The values must have a type matching the corresponding OpDef
  // attr's type field.
  // Add some examples here showing best practices.
  map<string, AttrValue> attr = 5;
 };
--- a/parser/proto/tensorflow/op_def.proto
+++ b/parser/proto/tensorflow/op_def.proto
@@ -1,164 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "OpDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "types.proto";

 // Defines an operation. A NodeDef in a GraphDef specifies an Op by
 // using the "op" field which should match the name of a OpDef.
 // LINT.IfChange
 message OpDef {
  // Op names starting with an underscore are reserved for internal use.
  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
  string name = 1;

  // For describing inputs and outputs.
  message ArgDef {
    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
    string name = 1;

    // Human readable description.
    string description = 2;

    // Describes the type of one or more tensors that are accepted/produced
    // by this input/output arg.  The only legal combinations are:
    // * For a single tensor: either the "type" field is set or the
    //   "type_attr" field is set to the name of an attr with type "type".
    // * For a sequence of tensors with the same type: the "number_attr"
    //   field will be set to the name of an attr with type "int", and
    //   either the "type" or "type_attr" field will be set as for
    //   single tensors.
    // * For a sequence of tensors, the "type_list_attr" field will be set
    //   to the name of an attr with type "list(type)".
    DataType type = 3;
    string type_attr = 4;    // if specified, attr must have type "type"
    string number_attr = 5;  // if specified, attr must have type "int"
    // If specified, attr must have type "list(type)", and none of
    // type, type_attr, and number_attr may be specified.
    string type_list_attr = 6;

    // For inputs: if true, the inputs are required to be refs.
    //   By default, inputs can be either refs or non-refs.
    // For outputs: if true, outputs are refs, otherwise they are not.
    bool is_ref = 16;
  };

  // Description of the input(s).
  repeated ArgDef input_arg = 2;

  // Description of the output(s).
  repeated ArgDef output_arg = 3;

  // Description of the graph-construction-time configuration of this
  // Op.  That is to say, this describes the attr fields that will
  // be specified in the NodeDef.
  message AttrDef {
    // A descriptive name for the argument.  May be used, e.g. by the
    // Python client, as a keyword argument name, and so should match
    // the regexp "[a-z][a-z0-9_]+".
    string name = 1;

    // One of the type names from attr_value.proto ("string", "list(string)",
    // "int", etc.).
    string type = 2;

    // A reasonable default for this attribute if the user does not supply
    // a value.  If not specified, the user must supply a value.
    AttrValue default_value = 3;

    // Human-readable description.
    string description = 4;


    // --- Constraints ---
    // These constraints are only in effect if specified.  Default is no
    // constraints.

    // For type == "int", this is a minimum value.  For "list(___)"
    // types, this is the minimum length.
    bool has_minimum = 5;
    int64 minimum = 6;

    // The set of allowed values.  Has type that is the "list" version
    // of the "type" field above (uses the "list" field of AttrValue).
    // If type == "type" or "list(type)" above, then the "type" field
    // of "allowed_values.list" has the set of allowed DataTypes.
    // If type == "string" or "list(string)", then the "s" field of
    // "allowed_values.list" has the set of allowed strings.
    AttrValue allowed_values = 7;
  }
  repeated AttrDef attr = 4;

  // Optional deprecation based on GraphDef versions.
  OpDeprecation deprecation = 8;

  // One-line human-readable description of what the Op does.
  string summary = 5;

  // Additional, longer human-readable description of what the Op does.
  string description = 6;

  // -------------------------------------------------------------------------
  // Which optimizations this operation can participate in.

  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
  bool is_commutative = 18;

  // If is_aggregate is true, then this operation accepts N >= 2
  // inputs and produces 1 output all of the same type.  Should be
  // associative and commutative, and produce output with the same
  // shape as the input.  The optimizer may replace an aggregate op
  // taking input from multiple devices with a tree of aggregate ops
  // that aggregate locally within each device (and possibly within
  // groups of nearby devices) before communicating.
  bool is_aggregate = 16;  // for things like add

  // Other optimizations go here, like
  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.

  // -------------------------------------------------------------------------
  // Optimization constraints.

  // Ops are marked as stateful if their behavior depends on some state beyond
  // their input tensors (e.g. variable reading op) or if they have
  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
  // must always produce the same output for the same input and have
  // no side-effects.
  //
  // By default Ops may be moved between devices.  Stateful ops should
  // either not be moved, or should only be moved if that state can also
  // be moved (e.g. via some sort of save / restore).
  // Stateful ops are guaranteed to never be optimized away by Common
  // Subexpression Elimination (CSE).
  bool is_stateful = 17;  // for things like variables, queue

  // -------------------------------------------------------------------------
  // Non-standard options.

  // By default, all inputs to an Op must be initialized Tensors.  Ops
  // that may initialize tensors for the first time should set this
  // field to true, to allow the Op to take an uninitialized Tensor as
  // input.
  bool allows_uninitialized_input = 19;  // for Assign, etc.
 };
 // LINT.ThenChange(
 //     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)

 // Information about version-dependent deprecation of an op
 message OpDeprecation {
  // First GraphDef version at which the op is disallowed.
  int32 version = 1;

  // Explanation of why it was deprecated and what to use instead.
  string explanation = 2;
 };

 // A collection of OpDefs
 message OpList {
  repeated OpDef op = 1;
 };
--- a/parser/proto/tensorflow/resource_handle.proto
+++ b/parser/proto/tensorflow/resource_handle.proto
@@ -1,29 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
 message ResourceHandleProto {
  // Unique name for the device containing the resource.
  string device = 1;

  // Container in which this resource is placed.
  string container = 2;

  // Unique name of this resource.
  string name = 3;

  // Hash code for the type of the resource. Is only valid in the same device
  // and in the same execution.
  uint64 hash_code = 4;

  // For debug-only, the name of the type pointed to by this handle, if
  // available.
  string maybe_type_name = 5;
 };
--- a/parser/proto/tensorflow/tensor.proto
+++ b/parser/proto/tensorflow/tensor.proto
@@ -1,94 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "resource_handle.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing a tensor.
 message TensorProto {
  DataType dtype = 1;

  // Shape of the tensor.
  TensorShapeProto tensor_shape = 2;

  // Only one of the representations below is set, one of "tensor_contents" and
  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
  // contain repeated fields it would require another extra set of messages.

  // Version number.
  //
  // In version 0, if the "repeated xxx" representations contain only one
  // element, that element is repeated to fill the shape.  This makes it easy
  // to represent a constant Tensor with a single value.
  int32 version_number = 3;

  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
  // can be used for all tensor types. The purpose of this representation is to
  // reduce serialization overhead during RPC call by avoiding serialization of
  // many repeated small items.
  bytes tensor_content = 4;

  // Type specific representations that make it easy to create tensor protos in
  // all languages.  Only the representation corresponding to "dtype" can
  // be set.  The values hold the flattened representation of the tensor in
  // row major order.

  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
  // have some pointless zero padding for each value here.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];
 };

 // Protocol buffer representing the serialization format of DT_VARIANT tensors.
 message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
 }
--- a/parser/proto/tensorflow/tensor_shape.proto
+++ b/parser/proto/tensorflow/tensor_shape.proto
@@ -1,45 +0,0 @@
 // Protocol buffer representing the shape of tensors.

 syntax = "proto3";
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorShapeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 package domi.tensorflow;

 // Dimensions of a tensor.
 message TensorShapeProto {
  // One dimension of the tensor.
  message Dim {
    // Size of the tensor in that dimension.
    // This value must be >= -1, but values of -1 are reserved for "unknown"
    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
    // that work with TensorShapeProto may fail at runtime when deserializing
    // a TensorShapeProto containing a dim value of -1.
    int64 size = 1;

    // Optional name of the tensor dimension.
    string name = 2;
  };

  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
  // for a 30 x 40 2D tensor.  If an entry has size -1, this
  // corresponds to a dimension of unknown size. The names are
  // optional.
  //
  // The order of entries in "dim" matters: It indicates the layout of the
  // values in the tensor in-memory representation.
  //
  // The first entry in "dim" is the outermost dimension used to layout the
  // values, the last entry is the innermost dimension.  This matches the
  // in-memory layout of RowMajor Eigen tensors.
  //
  // If "dim.size()" > 0, "unknown_rank" must be false.
  repeated Dim dim = 2;

  // If true, the number of dimensions in the shape is unknown.
  //
  // If true, "dim.size()" must be 0.
  bool unknown_rank = 3;
 };
--- a/parser/proto/tensorflow/types.proto
+++ b/parser/proto/tensorflow/types.proto
@@ -1,74 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // LINT.IfChange
 enum DataType {
  // Not a legal value for DataType.  Used to indicate a DataType field
  // has not been set.
  DT_INVALID = 0;

  // Data types that all computation devices are expected to be
  // capable to support.
  DT_FLOAT = 1;
  DT_DOUBLE = 2;
  DT_INT32 = 3;
  DT_UINT8 = 4;
  DT_INT16 = 5;
  DT_INT8 = 6;
  DT_STRING = 7;
  DT_COMPLEX64 = 8;  // Single-precision complex
  DT_INT64 = 9;
  DT_BOOL = 10;
  DT_QINT8 = 11;     // Quantized int8
  DT_QUINT8 = 12;    // Quantized uint8
  DT_QINT32 = 13;    // Quantized int32
  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
  DT_QINT16 = 15;    // Quantized int16
  DT_QUINT16 = 16;   // Quantized uint16
  DT_UINT16 = 17;
  DT_COMPLEX128 = 18;  // Double-precision complex
  DT_HALF = 19;
  DT_RESOURCE = 20;
  DT_VARIANT = 21;  // Arbitrary C++ data types
  DT_UINT32 = 22;
  DT_UINT64 = 23;

  // Do not use!  These are only for parameters.  Every enum above
  // should have a corresponding value below (verified by types_test).
  DT_FLOAT_REF = 101;
  DT_DOUBLE_REF = 102;
  DT_INT32_REF = 103;
  DT_UINT8_REF = 104;
  DT_INT16_REF = 105;
  DT_INT8_REF = 106;
  DT_STRING_REF = 107;
  DT_COMPLEX64_REF = 108;
  DT_INT64_REF = 109;
  DT_BOOL_REF = 110;
  DT_QINT8_REF = 111;
  DT_QUINT8_REF = 112;
  DT_QINT32_REF = 113;
  DT_BFLOAT16_REF = 114;
  DT_QINT16_REF = 115;
  DT_QUINT16_REF = 116;
  DT_UINT16_REF = 117;
  DT_COMPLEX128_REF = 118;
  DT_HALF_REF = 119;
  DT_RESOURCE_REF = 120;
  DT_VARIANT_REF = 121;
  DT_UINT32_REF = 122;
  DT_UINT64_REF = 123;
 }
 // LINT.ThenChange(
 //    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
 //    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)
--- a/parser/proto/tensorflow/versions.proto
+++ b/parser/proto/tensorflow/versions.proto
@@ -1,31 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "VersionsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Version information for a piece of serialized data
 //
 // There are different types of versions for each type of data
 // (GraphDef, etc.), but they all have the same common shape
 // described here.
 //
 // Each consumer has "consumer" and "min_producer" versions (specified
 // elsewhere).  A consumer is allowed to consume this data if
 //
 //   producer >= min_producer
 //   consumer >= min_consumer
 //   consumer not in bad_consumers
 //
 message VersionDef {
  // The version of the code that produced this data.
  int32 producer = 1;

  // Any consumer below this version is not allowed to consume this data.
  int32 min_consumer = 2;

  // Specific consumer versions which are disallowed (e.g. due to bugs).
  repeated int32 bad_consumers = 3;
 };
--- a/parser/tensorflow/proto/ge_ir.proto
+++ b/parser/tensorflow/proto/ge_ir.proto
@@ -1,193 +0,0 @@
 syntax = "proto3";

 package ge.proto;

 enum DataType
 {
    DT_UNDEFINED = 0;  // Used to indicate a DataType field has not been set.
    DT_FLOAT     = 1;  // float type
    DT_FLOAT16   = 2;  // fp16 type
    DT_INT8      = 3;  // int8 type
    DT_UINT8     = 4;  // uint8 type
    DT_INT16     = 5;  // int16 type
    DT_UINT16    = 6;  // uint16 type
    DT_INT32     = 7;  //
    DT_INT64     = 8;  // int64 type
    DT_UINT32    = 9;  // unsigned int32
    DT_UINT64    = 10;  // unsigned int64
    DT_BOOL      = 11;  // bool type
    DT_DOUBLE    = 12; // double type
    DT_STRING = 13;            // string type
    DT_DUAL_SUB_INT8 = 14;    /**< dual output int8 type */
    DT_DUAL_SUB_UINT8 = 15;    /**< dual output uint8 type */
    DT_COMPLEX64 = 16;         // complex64 type
    DT_COMPLEX128 = 17;        // complex128 type
    DT_QINT8 = 18;             // qint8 type
    DT_QINT16 = 19;            // qint16 type
    DT_QINT32 = 20;            // qint32 type
    DT_QUINT8 = 21;            // quint8 type
    DT_QUINT16 = 22;           // quint16 type
    DT_RESOURCE  = 23;         // resource type
    DT_STRING_REF = 24;        // string_ref type
    DT_DUAL      = 25;              /**< dual output type */
    DT_VARIANT = 26;           // variant type
    DT_BF16 = 27;              // bf16 type
    DT_INT4 = 28;              // int4 type
 }

 message AttrDef
 {
    message ListValue
    {
        enum ListValueType{
          VT_LIST_NONE = 0;
          VT_LIST_STRING = 1;
          VT_LIST_INT = 2;
          VT_LIST_FLOAT = 3;
          VT_LIST_BOOL = 4;
          VT_LIST_BYTES = 5;
          VT_LIST_TENSOR_DESC = 6;
          VT_LIST_TENSOR = 7;
          VT_LIST_GRAPH = 8;
          VT_LIST_NAMED_ATTRS = 9;
          VT_LIST_DATA_TYPE = 10;
        }
        repeated bytes s             = 2;                    // "list(string)"
        repeated int64 i             = 3;  // "list(int)"
        repeated float f             = 4;   // "list(float)"
        repeated bool  b             = 5;  // "list(bool)"
        repeated bytes bt            = 7;
        repeated TensorDescriptor td = 8;
        repeated TensorDef t         = 9;
        repeated GraphDef g          = 10;
 	    repeated NamedAttrs na       = 11;
 	    repeated int64 dt            = 12; // list ge::DataType

 	    ListValueType val_type       = 20;
    }

    message ListListInt{
        message ListInt{
            repeated int64 list_i             = 1; // list int
        }
        repeated ListInt list_list_i             = 1; // list list int
    }

    oneof value
    {
        bytes            s    = 2;  // "string"
        int64            i    = 3;  // "int"
        float            f    = 4;  // "float"
        bool             b    = 5;  // "bool"
        bytes            bt   = 7;
        ListValue        list = 1;   // any "list(...)"
        NamedAttrs       func = 10;  // Used to support attr nesting
        TensorDescriptor td   = 11;  // GeTensorDesc type
        TensorDef        t    = 12;  // GeTensor type
        GraphDef         g    = 13;  // Graph type
        ListListInt      list_list_int  = 14;  // List List Int type
        int64            dt   = 15; // ge::DataType
    }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs
 {
    string               name = 1;
    map<string, AttrDef> attr = 2;
 }

 // Shape / dimension description, using row-major order
 message ShapeDef
 {
    repeated int64 dim = 1;  // Size of each dimension
 }

 // Multidimensional data description
 message TensorDescriptor
 {
    string   name   = 1;  // Optional parameter, tensor name

    DataType dtype  = 2;  // tensor datatype
    ShapeDef shape  = 3;  // Shape / dimension
    string   layout = 4;  // Tensor format, eg: "NCHW", "NHWC", "CHW", "ND"

    bool has_out_attr = 9;
    int64 size = 10;
    int64 weight_size = 11;
    bool reuse_input = 12;
    bool output_tensor = 13;
    string device_type = 14;
    bool input_tensor =15;
    int64 real_dim_cnt = 16;
    int64 reuse_input_index = 17;
    int64 data_offset = 18;
    int64 cmps_size = 19;
    string cmps_tab = 20;
    int64 cmps_tab_offset = 21;

 	map<string, AttrDef> attr = 5;  // Set of extra parameter fields
 }

 // GeTensor definition
 message TensorDef
 {
    TensorDescriptor desc = 1;  // Tensor description
    bytes            data = 2;  // Tensor data
 }


 // Operator description
 message OpDef
 {
    string name = 1;  // name
    string type = 2;  // type

    repeated string input = 5;  // input original op name + outgoing index. op_name：index

    map<string, AttrDef> attr = 10;  // Set of operator parameter fields

    bool has_out_attr = 20;
    int64 id = 21;
    int64 stream_id =22;
    repeated string input_name = 23;
    repeated string src_name = 24;
    repeated int64 src_index = 25;
    repeated string dst_name = 26;
    repeated int64 dst_index = 27;
    repeated int64 input_i = 28;
    repeated int64 output_i = 29;
    repeated int64 workspace = 30;
    repeated int64 workspace_bytes = 31;
    repeated bool is_input_const = 32;
    repeated TensorDescriptor input_desc = 33;
    repeated TensorDescriptor output_desc = 34;
    repeated string subgraph_name = 35;
 }

 // Graph definition
 message GraphDef
 {
    string name   = 1;   //  name

    repeated string input  = 4;  // Graph input
    repeated string output = 5;  // Graph output

    repeated OpDef op      = 6;  // List of operators

 	map<string, AttrDef> attr = 11;  // Extended field
 }

 // model definition
 message ModelDef
 {
 	string name         = 1;  // name
 	uint32 version      = 2;  // IR Proto verion
 	string custom_version = 3;  // User model version number, passed in by user

    repeated GraphDef graph = 7;  // Graph definition，graph[0] represents the main diagram in modeldef

    map<string, AttrDef> attr = 11;  // Extended field
 }

--- a/parser/tensorflow/proto/insert_op.proto
+++ b/parser/tensorflow/proto/insert_op.proto
@@ -1,140 +0,0 @@
 syntax = "proto3";

 package domi;

 message InsertNewOps {
 	repeated AippOpParams aipp_op = 1;
 	repeated MultiShapeOpParams multi_shape_op = 2;
 }

 message AippOpParams {
 	enum InputFormat {
 		UNDEFINED = 0;
 		YUV420SP_U8 = 1;
 		XRGB8888_U8 = 2;
 		RGB888_U8   = 3;
 		YUV400_U8   = 4;
 		NC1HWC0DI_FP16 = 5;
 		NC1HWC0DI_S8 = 6;
 		ARGB8888_U8 = 7;
 		YUYV_U8 = 8;
 		YUV422SP_U8 = 9;
 		AYUV444_U8 = 10;
 		RAW10 = 11;
 		RAW12 = 12;
 		RAW16 = 13;
 		RAW24 = 14;
 		RGB16 = 15;
 		RGB20 = 16;
 		RGB24 = 17;
 		RGB8_IR = 18;
 		RGB16_IR = 19;
 		RGB24_IR = 20;
 	}

 	enum AippMode {
 		undefined = 0;
 		static = 1;
 		dynamic = 2;
 	}

 	// AIPP模式，区分静态AIPP和动态AIPP
 	AippMode aipp_mode = 1;

 	// related_input_rank参数为必填，类型为整型，配置范围>=0, <=输入Data算子的个数，默认值为0。
 	// 标识对模型的第几个输入做AIPP处理，例如模型有两个输入，需要对第2个输入做AIPP，则配置related_input_rank为1。
 	uint32 related_input_rank = 2;

        // related_input_name is optional and the top name of data node which inserts aipp
        string related_input_name = 6;

 	// input_edge_idx参数为可选，类型为整型，配置范围为>=0。
 	// 配置该参数的作用，在于对Data算子不同的输出做不同的AIPP处理，如果该参数没有配置，默认对related_input_rank指定的模型输入的所有输出边做AIPP。
 	// 配置值 <= Data算子输出边的个数。
 	repeated uint32 input_edge_idx = 3;

 	// [Begin] 动态AIPP参数，配置静态AIPP时无效
 	uint32 max_src_image_size = 4;

 	// 是否支持旋转。默认不支持，开启支持旋转时，会有额外的空间和性能损失
 	bool support_rotation = 5;

 	// [End] 动态AIPP参数


 	// [Begin] 静态AIPP参数，配置动态AIPP时无效
 	InputFormat input_format = 51;
 	bool csc_switch = 52;
 	float cpadding_value = 53;
 	bool rbuv_swap_switch = 54;
 	bool ax_swap_switch = 55;
 	bool single_line_mode = 56;

 	int32 src_image_size_w = 57;
 	int32 src_image_size_h = 58;

 	bool crop = 59;
 	int32 load_start_pos_w = 60;
 	int32 load_start_pos_h = 61;
 	int32 crop_size_w = 62;
 	int32 crop_size_h = 63;

 	bool resize = 64;
 	int32 resize_output_w = 65;
 	int32 resize_output_h = 66;

 	bool padding = 67;
 	int32 left_padding_size = 68;
 	int32 right_padding_size = 69;
 	int32 top_padding_size = 70;
 	int32 bottom_padding_size = 71;
 	float padding_value = 72;

 	int32 mean_chn_0 = 10;
 	int32 mean_chn_1 = 11;
 	int32 mean_chn_2 = 12;
 	int32 mean_chn_3 = 19;
 	float min_chn_0 = 13;
 	float min_chn_1 = 14;
 	float min_chn_2 = 15;
 	float min_chn_3 = 20;
 	repeated float var_reci_chn_0 = 16;
 	repeated float var_reci_chn_1 = 17;
 	repeated float var_reci_chn_2 = 18;
 	repeated float var_reci_chn_3 = 21;

 	repeated int32 matrix_r0c0 = 30;
 	repeated int32 matrix_r0c1 = 31;
 	repeated int32 matrix_r0c2 = 32;
 	repeated int32 matrix_r1c0 = 33;
 	repeated int32 matrix_r1c1 = 34;
 	repeated int32 matrix_r1c2 = 35;
 	repeated int32 matrix_r2c0 = 36;
 	repeated int32 matrix_r2c1 = 37;
 	repeated int32 matrix_r2c2 = 38;
 	repeated int32 output_bias_0 = 39;
 	repeated int32 output_bias_1 = 40;
 	repeated int32 output_bias_2 = 41;
 	repeated int32 input_bias_0 = 42;
 	repeated int32 input_bias_1 = 43;
 	repeated int32 input_bias_2 = 44;

 	// [End] 静态AIPP参数

       // The n number that is used for raw/rgbir data into f16 transformation.
       // The transformation equation is x/(2^n). If set to 0, no transform is performed.
       uint32 raw_rgbir_to_f16_n = 45;
 }

 message MultiShapeOpParams {
 	enum MultiShapeMode {
 		batch      = 0;             //动态batch
 		resolution = 1;             //动态分辨率，扩展用
 	}

 	MultiShapeMode    mode                      = 1;        //算子模式
 	uint32            related_input_rank        = 2;        //新增算子插入到哪个输入


 	repeated uint32 batch_list   = 11; //batch_list值，batch_list的个数是2到8之间
 }
--- a/parser/tensorflow/proto/om.proto
+++ b/parser/tensorflow/proto/om.proto
@@ -1,396 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 enum TargetType
 {
    MINI = 0;
    TINY = 1;
    LITE = 2;
 }

 // offline model
 message ModelDef {
    string name = 1;
    uint32 version = 2;

    uint64 memory_size = 10;
    uint32 stream_num = 11;
    uint32 event_num = 12;
    uint64 weight_size = 13;
    uint32 label_num = 15;
    repeated OpDef op = 20;
    TargetType target_type = 23;

    map<string, AttrDef> attr = 30;
 };

 // operator define
 message OpDef {
    string name = 1;
    string type = 2;

    uint32 id = 3;
    uint32 stream_id = 4;

    repeated string input_name = 5;

    repeated string src_name = 8;
    repeated int32 src_index = 9;
    repeated int64 input = 10;
    repeated int64 output = 11;
    repeated TensorDescriptor input_desc = 12;
    repeated TensorDescriptor output_desc = 13;
    repeated WeightDef weights = 14;
    repeated string dst_name = 15;
    repeated int32 dst_index = 16;

    repeated int64 workspace = 20;
    repeated uint32 workspace_bytes = 21;

    repeated string weight_name = 22;
    repeated bool is_input_const = 23;

    map<string, AttrDef> attr = 30;

    QuantizeFactorParams quantize_factor = 31;

    oneof op_params {
        // start at 100 here
        SendOpParams sender_param = 100;
        RecvOpParams receiver_param = 200;
        ConvolutionOpParams convolution_param = 300;
        PoolingOpParams pooling_param = 400;
        EltwiseOpParams eltwise_param = 500;
        BatchNormOpParams batchnorm_param = 600;
        ScaleOpParams scale_param = 700;
        FullConnectionOpParams full_connection_param = 800;
        SoftmaxOpParams softmax_param = 900;
        ActivationOpParams activation_param = 1000;
        ReshapeOpParams reshape_param = 1100;
    }
 };

 message SendOpParams {
    uint32 event_id = 1;
 };

 message RecvOpParams {
    uint32 event_id = 1;
 };

 enum QuantizeScaleType
 {
    VECTOR_SCALE = 0;
    SCALAR_SCALE = 1;
 }

 enum QuantizeScaleMode
 {
    NORMAL_MODE = 0;
    SQRT_MODE = 1;
 }

 enum QuantizeAlgorithm
 {
    NON_OFFSET_ALGO = 0;
    HALF_OFFSET_ALGO = 1;
    ALL_OFFSET_ALGO = 2;
 }
 message QuantizeFactor
 {
    QuantizeScaleMode scale_mode = 1;
    bytes scale_value = 2;
    int64 scale_offset = 3;
    bytes offset_data_value = 4;
    int64 offset_data_offset = 5;
    bytes offset_weight_value = 6;
    int64 offset_weight_offset = 7;
    bytes offset_pad_value = 8;
    int64 offset_pad_offset = 9;
 };

 message QuantizeCalcFactor
 {
    bytes offsetw = 1;
    int64 offsetw_offset = 2;
    bytes offsetd = 3;
    int64 offsetd_offset = 4;
    bytes scalereq = 5;
    int64 scaledreq_offset = 6;
    bytes offsetdnext = 7;
    int64 offsetdnext_offset = 8;
 }

 message QuantizeFactorParams
 {
    QuantizeAlgorithm quantize_algo = 1;
    QuantizeScaleType scale_type = 2;
    QuantizeFactor quantize_param = 3;
    QuantizeFactor dequantize_param = 4;
    QuantizeFactor requantize_param = 5;
    QuantizeCalcFactor quantizecalc_param = 6;
 };

 message ConvolutionOpParams {
    int32 mode = 1;
    int32 algo = 2;
    int32 pad_mode = 3;
    uint32 group = 4;
    uint32 num_output = 5;

    repeated uint32 pad = 10;
    repeated uint32 stride = 11;
    repeated uint32 dilation = 12;
    repeated uint32 kernel = 13;

    float alpha = 20;
    float beta = 21;

    WeightDef filter = 40;
    WeightDef bias = 41;

    bool relu_flag = 62;
    repeated uint32 adj = 70;
    repeated uint32 target_shape = 71;
    repeated uint32 before_pad = 72;
 };

 message PoolingOpParams {
    int32 mode = 1;
    int32 nan_opt = 2;
    int32 pad_mode = 3;
    bool global_pooling = 4;

    repeated uint32 window = 10;
    repeated uint32 pad = 11;
    repeated uint32 stride = 12;
    bool ceil_mode = 13;
    int32 data_mode  = 14;

    float alpha = 20;
    float beta = 21;
    repeated uint32 before_pad = 22;
 };

 message EltwiseOpParams {
    int32 mode = 1;
    repeated float coeff = 2;
    float alpha = 3;
    float beta = 4;
    repeated WeightDef weight = 5;
    bool relu_flag = 6;
 };

 message ActivationOpParams {
    int32 mode = 1;
    float coef = 2;
    float alpha = 3;
    float beta = 4;
 };

 message BatchNormOpParams {
    int32 mode = 1;

    float alpha = 2;
    float beta = 3;
    double epsilon = 4;//optinal,[default = 1e-5]
    bool use_global_stats = 5; //optinal,by default true,testing mode
    float  moving_average_fraction = 6; //optinal,[default = .999];

    WeightDef estimated_mean = 7;
    WeightDef estimated_variance = 8;

    WeightDef scale = 9;
    WeightDef bias = 10;
 };

 message ScaleOpParams {
    WeightDef scale = 1;
    WeightDef bias = 2;
 };

 message ReshapeOpParams {
    float alpha = 1;
    float beta = 2;
    ShapeDef shape = 3;
    int32 axis = 4;
    int32 num_axes = 5;
    int32 format = 6;
 };

 message SoftmaxOpParams {
    int32 algo = 1;
    int32 mode = 2;
    float alpha = 3;
    float beta = 4;
 };

 message FullConnectionOpParams {
    WeightDef filter = 1;
    WeightDef bias = 2;
    uint32 num_output = 3;
    bool relu_flag = 12;
 };

 message FlattenOpParams {
    float alpha = 1;
    float beta = 2;
    int32 start_axis = 3;
    int32 end_axis = 4;
 }

 message AddLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message MulLimitedOpParams {
    float alpha = 1;
    float beta = 2;
    int32 axis = 3;
    bool broadcast = 4;

    repeated WeightDef weight = 10;
 };

 message AddOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message MulOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message SubOpParams {
    float alpha = 1;
    float beta = 2;

    repeated WeightDef weight = 10;
 };

 message BiasAddOpParams {
    float alpha = 1;
    float beta = 2;

    WeightDef bias = 10;
 };

 message MatMulOpParams {
    float alpha = 1;
    float beta = 2;
    bool transposeX = 3;
    bool transposeW = 4;

    WeightDef filter = 10;
    WeightDef bias = 12;
 };

 message RsqrtOpParams {
    float alpha = 1;
    float beta = 2;
 };


 message WeightDef {
    int32 format = 1;
    int32 data_type = 2;
    ShapeDef shape = 3;
    bytes data = 4;
    int64 data_offset = 5;
    uint32 cmps_size = 6;
    bytes cmps_tab = 7;
    int64 cmps_tab_offset = 10;
    CompressInfo cmps_info = 8;
    AllOffsetQuantizeInfo alloffset_quantize_info = 11;
 }

 message ShapeDef {
    repeated int64 dim = 1;
 }

 enum DeviceType {
  NPU = 0;                   // In default, we will use NPU.
  CPU = 1;                   // CPU
 }

 message AllOffsetQuantizeInfo {
 	float scale  = 1;
 	int32 offset = 2;
 } 

 message TensorDescriptor {
    int32 format = 1;
    int32 data_type = 2;
    repeated int64 dim = 3;
    uint32 size = 4;
    bool reuse_input = 5;
    bool output_tensor = 7;
    DeviceType device_type = 8;
    bool input_tensor = 9;
    uint32 real_dim_cnt = 10;
    uint32 reuse_input_index = 11;
    AllOffsetQuantizeInfo alloffset_quantize_info = 12;
 }

 message CompressInfo {
    int32 blockRow = 1;                             // block row
    int32 blockCol = 2;                             // block col
    int32 fractalK = 3;                             // fractal K
    int32 fractalN = 4;                             // fractal N
    int32 lastFractalK = 5;                         // K of last fractal
    int32 lastFractalN = 6;                         // N of last fractal
    int32 cubeSize = 7;                             // cube's length
    int32 loadDir = 8;                              // data load directtiono 0:col load 1:row load
 }

 message AttrDef {
  message ListValue {
    repeated string s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated uint32 u = 6 [packed = true];         // "list(uint)"
    repeated bytes bt = 7;
  }

  oneof value {
    string s = 2;                // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    uint32 u = 6;                // "uint32"
    bytes bt = 7;
    ListValue list = 1;          // any "list(...)"
    NamedAttrs func = 10;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NamedAttrs {
  string name = 1;
  map<string, AttrDef> attr = 2;
 }

--- a/parser/tensorflow/proto/task.proto
+++ b/parser/tensorflow/proto/task.proto
@@ -1,179 +0,0 @@
 /* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * Apache License for more details at
 * http://www.apache.org/licenses/LICENSE-2.0
 */
 syntax = "proto3";

 package domi;

 message ModelTaskDef {
    string version = 1;

    map<string, string> attr = 9; // Extended field
    repeated TaskDef task = 10;

    uint64 memory_size = 11;
    uint32 stream_num = 12;
    uint32 event_num = 13;
    uint64 weight_size = 14;

    repeated bytes op = 15; // input/output opdef in bytes

    uint64 base_addr = 16;    // base addr
    uint64 weight_addr = 17;  // weight addr
    uint32 batch_num = 18;
 }


 message TaskDef {
    uint32 id = 1;
    uint32 type = 2;

    uint32 stream_id = 10;
    uint32 event_id = 11;

    KernelDef kernel = 20;
    KernelExDef kernel_ex = 21;
    KernelHcclDef kernel_hccl = 25;
    EventExDef event_ex = 26;
    LogTimeStampDef log_timestamp = 28;

    uint32 label_id = 30;

    MemcpyAsyncDef memcpy_async = 31;
    StreamSwitchDef stream_switch = 32;
    StreamActiveDef stream_active = 33;
    bytes private_def = 34;
    uint64 ops_kernel_store_ptr = 35;      // adjustments to other fields in the future
    StreamSwitchNDef stream_switch_n = 36;

    LabelSetDef label_set = 37;
    LabelGotoExDef label_goto_ex = 38;
    LabelSwitchByIndexDef label_switch_by_index = 39;
    KernelDefWithHandle kernel_with_handle = 40;
 }

 message KernelDef {
    KernelContext context = 1;

    string stub_func = 10;
    uint32 block_dim = 11;
    uint32 args_size = 12;
    bytes args = 13;
    bytes sm_desc = 14;
    bytes flowtable = 15;
    string so_name = 16;
    string kernel_name = 17;
    bytes kernel_ext_info = 18;
    uint32 kernel_ext_info_size = 19;
 }

 message KernelDefWithHandle {
    KernelContext context = 1;

    uint64 handle = 10;
    string dev_func = 11;
    uint32 block_dim = 12;
    uint32 args_size = 13;
    bytes args = 14;
    bytes sm_desc = 15;
    string original_kernel_key = 16;
    string node_info = 17;
 }

 message KernelContext {
    uint32 kernel_type = 1;
    uint32 op_id = 2;                              // OP type in CCE
    uint32 kernel_func_id = 3;
    uint32 op_index = 4;                           // TE/Custom operator
    bool is_flowtable = 5;                         // Identify whether args is a flowtable structure
    bytes args_offset = 6;                         // args offset information
    uint32 args_count = 7;                         // args count
    repeated uint32 origin_op_index = 8;
 }


 message KernelExDef {
    uint32 flags = 1;

    uint32 op_index = 4;
    uint32 args_size = 12;
    bytes args = 13;
    bytes task_info = 14;                 // serialized nodeDef, funcDef, inputoutput
    uint32 task_info_size = 15;
    bytes kernel_ext_info = 16;
    uint32 kernel_ext_info_size = 17;
 }


 message KernelHcclDef {
    uint32 op_index = 8;
    string hccl_type = 9;
 }


 message EventExDef {
    uint32 op_index = 1;
    uint32 event_type = 2;
 }

 message LogTimeStampDef {
    uint64 logid = 1;
    bool notify = 2;
    uint32 flat = 3;
 }

 message MemcpyAsyncDef {
    uint64 dst = 1;
    uint64 dst_max = 2;
    uint64 src = 3;
    uint64 count = 4;
    uint32 kind = 5;
    uint32 op_index = 6;
 }

 message StreamSwitchDef {
    uint32 op_index = 1;
    uint32 true_stream_id = 2;
    int64 value = 3;
    uint64 value_ptr = 4;
    uint32 data_type = 5;
 }

 message StreamActiveDef {
    uint32 op_index = 1;
    uint32 active_stream_id = 2;
 }

 message StreamSwitchNDef {
    uint32 op_index = 1;
    uint32 size = 2;
    repeated int64 target_value = 3;
    repeated uint32 true_stream_id = 4;
    uint32 element_size = 5;
    uint32 data_type = 6;
 }

 message LabelSetDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelGotoExDef {
    uint32 op_index = 1;
    uint32 label_id = 2;
    uint32 model_id = 3;
 }

 message LabelSwitchByIndexDef {
    uint32 op_index = 1;
    uint32 label_max = 2;
 }
--- a/parser/tensorflow/proto/tensorflow/attr_value.proto
+++ b/parser/tensorflow/proto/tensorflow/attr_value.proto
@@ -1,62 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "AttrValueProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "tensor.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing the value for an attr used to configure an Op.
 // Comment indicates the corresponding attr type.  Only the field matching the
 // attr type may be filled.
 message AttrValue {
  // LINT.IfChange
  message ListValue {
    repeated bytes s = 2;                        // "list(string)"
    repeated int64 i = 3 [packed = true];        // "list(int)"
    repeated float f = 4 [packed = true];        // "list(float)"
    repeated bool b = 5 [packed = true];         // "list(bool)"
    repeated DataType type = 6 [packed = true];  // "list(type)"
    repeated TensorShapeProto shape = 7;         // "list(shape)"
    repeated TensorProto tensor = 8;             // "list(tensor)"
    repeated NameAttrList func = 9;              // "list(attr)"
  }
  // LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.cc)

  oneof value {
    bytes s = 2;                 // "string"
    int64 i = 3;                 // "int"
    float f = 4;                 // "float"
    bool b = 5;                  // "bool"
    DataType type = 6;           // "type"
    TensorShapeProto shape = 7;  // "shape"
    TensorProto tensor = 8;      // "tensor"
    ListValue list = 1;          // any "list(...)"

    // "func" represents a function. func.name is a function's name or
    // a primitive op's name. func.attr.first is the name of an attr
    // defined for that function. func.attr.second is the value for
    // that attr in the instantiation.
    NameAttrList func = 10;

    // This is a placeholder only used in nodes defined inside a
    // function.  It indicates the attr value will be supplied when
    // the function is instantiated.  For example, let us suppose a
    // node "N" in function "FN". "N" has an attr "A" with value
    // placeholder = "foo". When FN is instantiated with attr "foo"
    // set to "bar", the instantiated node N's attr A will have been
    // given the value "bar".
    string placeholder = 9;
  }
 }

 // A list of attr names and their values. The whole list is attached
 // with a string name.  E.g., MatMul[T=float].
 message NameAttrList {
  string name = 1;
  map<string, AttrValue> attr = 2;
 }
--- a/parser/tensorflow/proto/tensorflow/function.proto
+++ b/parser/tensorflow/proto/tensorflow/function.proto
@@ -1,100 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "FunctionProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "node_def.proto";
 import "op_def.proto";

 // A library is a set of named functions.
 message FunctionDefLibrary {
  repeated FunctionDef function = 1;
  repeated GradientDef gradient = 2;
 }

 // A function can be instantiated when the runtime can bind every attr
 // with a value. When a GraphDef has a call to a function, it must
 // have binding for every attr defined in the signature.
 //   * device spec, etc.
 message FunctionDef {
  // The definition of the function's name, arguments, return values,
  // attrs etc.
  OpDef signature = 1;

  // Attributes specific to this function definition.
  map<string, AttrValue> attr = 5;

  // NOTE: field id 2 deleted on Jan 11, 2017, GraphDef version 21.
  reserved 2;

  // In both of the following fields, there is the need to specify an
  // output that is used as either the input to another node (in
  // `node_def`) or as a return value of the function (in `ret`).
  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
  // list in some cases (instead of just single outputs).  Also, we
  // need to be able to deal with lists of unknown length (so the
  // output index may not be known at function definition time).  So
  // we use the following format instead:
  // * "fun_in" where "fun_in" is the name of a function input arg in
  //   the `signature` field above.  This represents that input, whether
  //   it is a single tensor or a list.
  // * "fun_in:0" gives the first element of a function input arg (a
  //   non-list input is considered a list of length 1 for these
  //   purposes).
  // * "node:out" where "node" is the name of a node in `node_def` and
  //   "out" is the name one of its op's output arguments (the name
  //   comes from the OpDef of the node's op). This represents that
  //   node's output, whether it is a single tensor or a list.
  //   Note: We enforce that an op's output arguments are never
  //   renamed in the backwards-compatibility test.
  // * "node:out:0" gives the first element of a node output arg (a
  //   non-list output is considered a list of length 1 for these
  //   purposes).
  //
  // NOT CURRENTLY SUPPORTED (but may be in the future):
  // * "node:out:-1" gives last element in a node output list
  // * "node:out:1:" gives a list with all but the first element in a
  //   node output list
  // * "node:out::-1" gives a list with all but the last element in a
  //   node output list

  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
  // may have values of type `placeholder` and the `input` field uses
  // the "output" format above.

  // By convention, "op" in node_def is resolved by consulting with a
  // user-defined library first. If not resolved, "func" is assumed to
  // be a builtin op.
  repeated NodeDef node_def = 3;

  // A mapping from the output arg names from `signature` to the
  // outputs from `node_def` that should be returned by the function.
  map<string, string> ret = 4;
 }

 // GradientDef defines the gradient function of a function defined in
 // a function library.
 //
 // A gradient function g (specified by gradient_func) for a function f
 // (specified by function_name) must follow the following:
 //
 // The function 'f' must be a numerical function which takes N inputs
 // and produces M outputs. Its gradient function 'g', which is a
 // function taking N + M inputs and produces N outputs.
 //
 // I.e. if we have
 //    (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
 // then, g is
 //    (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
 //                                      dL/dy1, dL/dy2, ..., dL/dy_M),
 // where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
 // loss function). dL/dx_i is the partial derivative of L with respect
 // to x_i.
 message GradientDef {
  string function_name = 1;  // The function name.
  string gradient_func = 2;  // The gradient function's name.
 }
--- a/parser/tensorflow/proto/tensorflow/graph.proto
+++ b/parser/tensorflow/proto/tensorflow/graph.proto
@@ -1,56 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "node_def.proto";
 import "function.proto";
 import "versions.proto";

 // Represents the graph of operations
 message GraphDef {
  repeated NodeDef node = 1;

  // Compatibility versions of the graph.  See core/public/version.h for version
  // history.  The GraphDef version is distinct from the TensorFlow version, and
  // each release of TensorFlow will support a range of GraphDef versions.
  VersionDef versions = 4;

  // Deprecated single version field; use versions above instead.  Since all
  // GraphDef changes before "versions" was introduced were forward
  // compatible, this field is entirely ignored.
  int32 version = 3 [deprecated = true];

  // EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
  //
  // "library" provides user-defined functions.
  //
  // Naming:
  //   * library.function.name are in a flat namespace.
  //     NOTE: We may need to change it to be hierarchical to support
  //     different orgs. E.g.,
  //     { "/google/nn", { ... }},
  //     { "/google/vision", { ... }}
  //     { "/org_foo/module_bar", { ... }}
  //     map<string, FunctionDefLib> named_lib;
  //   * If node[i].op is the name of one function in "library",
  //     node[i] is deemed as a function call. Otherwise, node[i].op
  //     must be a primitive operation supported by the runtime.
  //
  //
  // Function call semantics:
  //
  //   * The callee may start execution as soon as some of its inputs
  //     are ready. The caller may want to use Tuple() mechanism to
  //     ensure all inputs are ready in the same time.
  //
  //   * The consumer of return values may start executing as soon as
  //     the return values the consumer depends on are ready.  The
  //     consumer may want to use Tuple() mechanism to ensure the
  //     consumer does not start until all return values of the callee
  //     function are ready.
  FunctionDefLibrary library = 2;
 };
--- a/parser/tensorflow/proto/tensorflow/graph_library.proto
+++ b/parser/tensorflow/proto/tensorflow/graph_library.proto
@@ -1,14 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;

 import "graph.proto";

 message GeGraphDef {
  string name = 1;
  GraphDef graph = 2;
 }

 message GraphDefLibrary {
  repeated GeGraphDef graph_def = 1;
 };
--- a/parser/tensorflow/proto/tensorflow/node_def.proto
+++ b/parser/tensorflow/proto/tensorflow/node_def.proto
@@ -1,63 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "NodeProto";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";

 message NodeDef {
  // The name given to this operator. Used for naming inputs,
  // logging, visualization, etc.  Unique within a single GraphDef.
  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
  string name = 1;

  // The operation name.  There may be custom parameters in attrs.
  // Op names starting with an underscore are reserved for internal use.
  string op = 2;

  // Each input is "node:src_output" with "node" being a string name and
  // "src_output" indicating which output tensor to use from "node". If
  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
  // may optionally be followed by control inputs that have the format
  // "^node".
  repeated string input = 3;

  // A (possibly partial) specification for the device on which this
  // node should be placed.
  // The expected syntax for this string is as follows:
  //
  // DEVICE_SPEC ::= PARTIAL_SPEC
  //
  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
  // CONSTRAINT ::= ("job:" JOB_NAME)
  //              | ("replica:" [1-9][0-9]*)
  //              | ("task:" [1-9][0-9]*)
  //              | ("device:" [A-Za-z]* ":" ([1-9][0-9]* | "*") )
  //
  // Valid values for this string include:
  // * "/job:worker/replica:0/task:1/device:GPU:3"  (full specification)
  // * "/job:worker/device:GPU:3"                   (partial specification)
  // * ""                                    (no specification)
  //
  // If the constraints do not resolve to a single device (or if this
  // field is empty or not present), the runtime will attempt to
  // choose a device automatically.
  string device = 4;

  // Operation-specific graph-construction-time configuration.
  // Note that this should include all attrs defined in the
  // corresponding OpDef, including those with a value matching
  // the default -- this allows the default to change and makes
  // NodeDefs easier to interpret on their own.  However, if
  // an attr with a default is not specified in this list, the
  // default will be used.
  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
  // one of the names from the corresponding OpDef's attr field).
  // The values must have a type matching the corresponding OpDef
  // attr's type field.
  // Add some examples here showing best practices.
  map<string, AttrValue> attr = 5;
 };
--- a/parser/tensorflow/proto/tensorflow/op_def.proto
+++ b/parser/tensorflow/proto/tensorflow/op_def.proto
@@ -1,164 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "OpDefProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "attr_value.proto";
 import "types.proto";

 // Defines an operation. A NodeDef in a GraphDef specifies an Op by
 // using the "op" field which should match the name of a OpDef.
 // LINT.IfChange
 message OpDef {
  // Op names starting with an underscore are reserved for internal use.
  // Names should be CamelCase and match the regexp "[A-Z][a-zA-Z0-9_]*".
  string name = 1;

  // For describing inputs and outputs.
  message ArgDef {
    // Name for the input/output.  Should match the regexp "[a-z][a-z0-9_]*".
    string name = 1;

    // Human readable description.
    string description = 2;

    // Describes the type of one or more tensors that are accepted/produced
    // by this input/output arg.  The only legal combinations are:
    // * For a single tensor: either the "type" field is set or the
    //   "type_attr" field is set to the name of an attr with type "type".
    // * For a sequence of tensors with the same type: the "number_attr"
    //   field will be set to the name of an attr with type "int", and
    //   either the "type" or "type_attr" field will be set as for
    //   single tensors.
    // * For a sequence of tensors, the "type_list_attr" field will be set
    //   to the name of an attr with type "list(type)".
    DataType type = 3;
    string type_attr = 4;    // if specified, attr must have type "type"
    string number_attr = 5;  // if specified, attr must have type "int"
    // If specified, attr must have type "list(type)", and none of
    // type, type_attr, and number_attr may be specified.
    string type_list_attr = 6;

    // For inputs: if true, the inputs are required to be refs.
    //   By default, inputs can be either refs or non-refs.
    // For outputs: if true, outputs are refs, otherwise they are not.
    bool is_ref = 16;
  };

  // Description of the input(s).
  repeated ArgDef input_arg = 2;

  // Description of the output(s).
  repeated ArgDef output_arg = 3;

  // Description of the graph-construction-time configuration of this
  // Op.  That is to say, this describes the attr fields that will
  // be specified in the NodeDef.
  message AttrDef {
    // A descriptive name for the argument.  May be used, e.g. by the
    // Python client, as a keyword argument name, and so should match
    // the regexp "[a-z][a-z0-9_]+".
    string name = 1;

    // One of the type names from attr_value.proto ("string", "list(string)",
    // "int", etc.).
    string type = 2;

    // A reasonable default for this attribute if the user does not supply
    // a value.  If not specified, the user must supply a value.
    AttrValue default_value = 3;

    // Human-readable description.
    string description = 4;


    // --- Constraints ---
    // These constraints are only in effect if specified.  Default is no
    // constraints.

    // For type == "int", this is a minimum value.  For "list(___)"
    // types, this is the minimum length.
    bool has_minimum = 5;
    int64 minimum = 6;

    // The set of allowed values.  Has type that is the "list" version
    // of the "type" field above (uses the "list" field of AttrValue).
    // If type == "type" or "list(type)" above, then the "type" field
    // of "allowed_values.list" has the set of allowed DataTypes.
    // If type == "string" or "list(string)", then the "s" field of
    // "allowed_values.list" has the set of allowed strings.
    AttrValue allowed_values = 7;
  }
  repeated AttrDef attr = 4;

  // Optional deprecation based on GraphDef versions.
  OpDeprecation deprecation = 8;

  // One-line human-readable description of what the Op does.
  string summary = 5;

  // Additional, longer human-readable description of what the Op does.
  string description = 6;

  // -------------------------------------------------------------------------
  // Which optimizations this operation can participate in.

  // True if the operation is commutative ("op(a,b) == op(b,a)" for all inputs)
  bool is_commutative = 18;

  // If is_aggregate is true, then this operation accepts N >= 2
  // inputs and produces 1 output all of the same type.  Should be
  // associative and commutative, and produce output with the same
  // shape as the input.  The optimizer may replace an aggregate op
  // taking input from multiple devices with a tree of aggregate ops
  // that aggregate locally within each device (and possibly within
  // groups of nearby devices) before communicating.
  bool is_aggregate = 16;  // for things like add

  // Other optimizations go here, like
  //   can_alias_input, rewrite_when_output_unused, partitioning_strategy, etc.

  // -------------------------------------------------------------------------
  // Optimization constraints.

  // Ops are marked as stateful if their behavior depends on some state beyond
  // their input tensors (e.g. variable reading op) or if they have
  // a side-effect (e.g. printing or asserting ops). Equivalently, stateless ops
  // must always produce the same output for the same input and have
  // no side-effects.
  //
  // By default Ops may be moved between devices.  Stateful ops should
  // either not be moved, or should only be moved if that state can also
  // be moved (e.g. via some sort of save / restore).
  // Stateful ops are guaranteed to never be optimized away by Common
  // Subexpression Elimination (CSE).
  bool is_stateful = 17;  // for things like variables, queue

  // -------------------------------------------------------------------------
  // Non-standard options.

  // By default, all inputs to an Op must be initialized Tensors.  Ops
  // that may initialize tensors for the first time should set this
  // field to true, to allow the Op to take an uninitialized Tensor as
  // input.
  bool allows_uninitialized_input = 19;  // for Assign, etc.
 };
 // LINT.ThenChange(
 //     https://www.tensorflow.org/code/tensorflow/core/framework/op_def_util.cc)

 // Information about version-dependent deprecation of an op
 message OpDeprecation {
  // First GraphDef version at which the op is disallowed.
  int32 version = 1;

  // Explanation of why it was deprecated and what to use instead.
  string explanation = 2;
 };

 // A collection of OpDefs
 message OpList {
  repeated OpDef op = 1;
 };
--- a/parser/tensorflow/proto/tensorflow/resource_handle.proto
+++ b/parser/tensorflow/proto/tensorflow/resource_handle.proto
@@ -1,29 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "ResourceHandle";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Protocol buffer representing a handle to a tensorflow resource. Handles are
 // not valid across executions, but can be serialized back and forth from within
 // a single run.
 message ResourceHandleProto {
  // Unique name for the device containing the resource.
  string device = 1;

  // Container in which this resource is placed.
  string container = 2;

  // Unique name of this resource.
  string name = 3;

  // Hash code for the type of the resource. Is only valid in the same device
  // and in the same execution.
  uint64 hash_code = 4;

  // For debug-only, the name of the type pointed to by this handle, if
  // available.
  string maybe_type_name = 5;
 };
--- a/parser/tensorflow/proto/tensorflow/tensor.proto
+++ b/parser/tensorflow/proto/tensorflow/tensor.proto
@@ -1,94 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 import "resource_handle.proto";
 import "tensor_shape.proto";
 import "types.proto";

 // Protocol buffer representing a tensor.
 message TensorProto {
  DataType dtype = 1;

  // Shape of the tensor.
  TensorShapeProto tensor_shape = 2;

  // Only one of the representations below is set, one of "tensor_contents" and
  // the "xxx_val" attributes.  We are not using oneof because as oneofs cannot
  // contain repeated fields it would require another extra set of messages.

  // Version number.
  //
  // In version 0, if the "repeated xxx" representations contain only one
  // element, that element is repeated to fill the shape.  This makes it easy
  // to represent a constant Tensor with a single value.
  int32 version_number = 3;

  // Serialized raw tensor content from either Tensor::AsProtoTensorContent or
  // memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
  // can be used for all tensor types. The purpose of this representation is to
  // reduce serialization overhead during RPC call by avoiding serialization of
  // many repeated small items.
  bytes tensor_content = 4;

  // Type specific representations that make it easy to create tensor protos in
  // all languages.  Only the representation corresponding to "dtype" can
  // be set.  The values hold the flattened representation of the tensor in
  // row major order.

  // DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
  // have some pointless zero padding for each value here.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];
 };

 // Protocol buffer representing the serialization format of DT_VARIANT tensors.
 message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
 }
--- a/parser/tensorflow/proto/tensorflow/tensor_shape.proto
+++ b/parser/tensorflow/proto/tensorflow/tensor_shape.proto
@@ -1,45 +0,0 @@
 // Protocol buffer representing the shape of tensors.

 syntax = "proto3";
 option cc_enable_arenas = true;
 option java_outer_classname = "TensorShapeProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 package domi.tensorflow;

 // Dimensions of a tensor.
 message TensorShapeProto {
  // One dimension of the tensor.
  message Dim {
    // Size of the tensor in that dimension.
    // This value must be >= -1, but values of -1 are reserved for "unknown"
    // shapes (values of -1 mean "unknown" dimension).  Certain wrappers
    // that work with TensorShapeProto may fail at runtime when deserializing
    // a TensorShapeProto containing a dim value of -1.
    int64 size = 1;

    // Optional name of the tensor dimension.
    string name = 2;
  };

  // Dimensions of the tensor, such as {"input", 30}, {"output", 40}
  // for a 30 x 40 2D tensor.  If an entry has size -1, this
  // corresponds to a dimension of unknown size. The names are
  // optional.
  //
  // The order of entries in "dim" matters: It indicates the layout of the
  // values in the tensor in-memory representation.
  //
  // The first entry in "dim" is the outermost dimension used to layout the
  // values, the last entry is the innermost dimension.  This matches the
  // in-memory layout of RowMajor Eigen tensors.
  //
  // If "dim.size()" > 0, "unknown_rank" must be false.
  repeated Dim dim = 2;

  // If true, the number of dimensions in the shape is unknown.
  //
  // If true, "dim.size()" must be 0.
  bool unknown_rank = 3;
 };
--- a/parser/tensorflow/proto/tensorflow/types.proto
+++ b/parser/tensorflow/proto/tensorflow/types.proto
@@ -1,74 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // LINT.IfChange
 enum DataType {
  // Not a legal value for DataType.  Used to indicate a DataType field
  // has not been set.
  DT_INVALID = 0;

  // Data types that all computation devices are expected to be
  // capable to support.
  DT_FLOAT = 1;
  DT_DOUBLE = 2;
  DT_INT32 = 3;
  DT_UINT8 = 4;
  DT_INT16 = 5;
  DT_INT8 = 6;
  DT_STRING = 7;
  DT_COMPLEX64 = 8;  // Single-precision complex
  DT_INT64 = 9;
  DT_BOOL = 10;
  DT_QINT8 = 11;     // Quantized int8
  DT_QUINT8 = 12;    // Quantized uint8
  DT_QINT32 = 13;    // Quantized int32
  DT_BFLOAT16 = 14;  // Float32 truncated to 16 bits.  Only for cast ops.
  DT_QINT16 = 15;    // Quantized int16
  DT_QUINT16 = 16;   // Quantized uint16
  DT_UINT16 = 17;
  DT_COMPLEX128 = 18;  // Double-precision complex
  DT_HALF = 19;
  DT_RESOURCE = 20;
  DT_VARIANT = 21;  // Arbitrary C++ data types
  DT_UINT32 = 22;
  DT_UINT64 = 23;

  // Do not use!  These are only for parameters.  Every enum above
  // should have a corresponding value below (verified by types_test).
  DT_FLOAT_REF = 101;
  DT_DOUBLE_REF = 102;
  DT_INT32_REF = 103;
  DT_UINT8_REF = 104;
  DT_INT16_REF = 105;
  DT_INT8_REF = 106;
  DT_STRING_REF = 107;
  DT_COMPLEX64_REF = 108;
  DT_INT64_REF = 109;
  DT_BOOL_REF = 110;
  DT_QINT8_REF = 111;
  DT_QUINT8_REF = 112;
  DT_QINT32_REF = 113;
  DT_BFLOAT16_REF = 114;
  DT_QINT16_REF = 115;
  DT_QUINT16_REF = 116;
  DT_UINT16_REF = 117;
  DT_COMPLEX128_REF = 118;
  DT_HALF_REF = 119;
  DT_RESOURCE_REF = 120;
  DT_VARIANT_REF = 121;
  DT_UINT32_REF = 122;
  DT_UINT64_REF = 123;
 }
 // LINT.ThenChange(
 //    https://www.tensorflow.org/code/tensorflow/c/c_api.h,
 //    https://www.tensorflow.org/code/tensorflow/go/tensor.go,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/tensor.cc,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.h,
 //    https://www.tensorflow.org/code/tensorflow/core/framework/types.cc,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/dtypes.py,
 //    https://www.tensorflow.org/code/tensorflow/python/framework/function.py)
--- a/parser/tensorflow/proto/tensorflow/versions.proto
+++ b/parser/tensorflow/proto/tensorflow/versions.proto
@@ -1,31 +0,0 @@
 syntax = "proto3";

 package domi.tensorflow;
 option cc_enable_arenas = true;
 option java_outer_classname = "VersionsProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";

 // Version information for a piece of serialized data
 //
 // There are different types of versions for each type of data
 // (GraphDef, etc.), but they all have the same common shape
 // described here.
 //
 // Each consumer has "consumer" and "min_producer" versions (specified
 // elsewhere).  A consumer is allowed to consume this data if
 //
 //   producer >= min_producer
 //   consumer >= min_consumer
 //   consumer not in bad_consumers
 //
 message VersionDef {
  // The version of the code that produced this data.
  int32 producer = 1;

  // Any consumer below this version is not allowed to consume this data.
  int32 min_consumer = 2;

  // Specific consumer versions which are disallowed (e.g. due to bugs).
  repeated int32 bad_consumers = 3;
 };