meta_graph.proto

/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.*/
// Modification Copyright (C) 2018-2023 Intel Corporation

syntax = "proto3";

package tensorflow;

import "any.proto";
import "graph.proto";
import "op_def.proto";
import "tensor_shape.proto";
import "types.proto";
import "saved_object_graph.proto";
import "saver.proto";
import "struct.proto";

option cc_enable_arenas = true;
option java_outer_classname = "MetaGraphProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto";

// Protocol buffer containing the following which are necessary to restart
// training, run inference. It can be used to serialize/de-serialize memory
// objects necessary for running computation in a graph when crossing the
// process boundary. It can be used for long term storage of graphs,
// cross-language execution of graphs, etc.
//   MetaInfoDef
//   GraphDef
//   SaverDef
//   CollectionDef
//   TensorInfo
//   SignatureDef
message MetaGraphDef {
  // Meta information regarding the graph to be exported.  To be used by users
  // of this protocol buffer to encode information regarding their meta graph.
  message MetaInfoDef {
    // User specified Version string. Can be the name of the model and revision,
    // steps this model has been trained to, etc.
    string meta_graph_version = 1;

    // A copy of the OpDefs used by the producer of this graph_def.
    // Descriptions and Ops not used in graph_def are stripped out.
    OpList stripped_op_list = 2;

    // A serialized protobuf. Can be the time this meta graph is created, or
    // modified, or name of the model.
    google.protobuf.Any any_info = 3;

    // User supplied tag(s) on the meta_graph and included graph_def.
    //
    // MetaGraphDefs should be tagged with their capabilities or use-cases.
    // Examples: "train", "serve", "gpu", "tpu", etc.
    // These tags enable loaders to access the MetaGraph(s) appropriate for a
    // specific use-case or runtime environment.
    repeated string tags = 4;

    // The __version__ string of the tensorflow build used to write this graph.
    // This will be populated by the framework, which will overwrite any user
    // supplied value.
    string tensorflow_version = 5;

    // The __git_version__ string of the tensorflow build used to write this
    // graph. This will be populated by the framework, which will overwrite any
    // user supplied value.
    string tensorflow_git_version = 6;

    // A flag to denote whether default-valued attrs have been stripped from
    // the nodes in this graph_def.
    bool stripped_default_attrs = 7;

    // FunctionDef name to aliases mapping.
    map<string, string> function_aliases = 8;
  }
  MetaInfoDef meta_info_def = 1;

  // GraphDef.
  GraphDef graph_def = 2;

  // SaverDef.
  SaverDef saver_def = 3;

  // collection_def: Map from collection name to collections.
  // See CollectionDef section for details.
  map<string, CollectionDef> collection_def = 4;

  // signature_def: Map from user supplied key for a signature to a single
  // SignatureDef.
  map<string, SignatureDef> signature_def = 5;

  // Asset file def to be used with the defined graph.
  repeated AssetFileDef asset_file_def = 6;

  // Extra information about the structure of functions and stateful objects.
  SavedObjectGraph object_graph_def = 7;
}

// CollectionDef should cover most collections.
// To add a user-defined collection, do one of the following:
// 1. For simple data types, such as string, int, float:
//      tf.add_to_collection("your_collection_name", your_simple_value)
//    strings will be stored as bytes_list.
//
// 2. For Protobuf types, there are three ways to add them:
//    1) tf.add_to_collection("your_collection_name",
//         your_proto.SerializeToString())
//
//       collection_def {
//         key: "user_defined_bytes_collection"
//         value {
//           bytes_list {
//             value: "queue_name: \"test_queue\"\n"
//           }
//         }
//       }
//
//  or
//
//    2) tf.add_to_collection("your_collection_name", str(your_proto))
//
//       collection_def {
//         key: "user_defined_string_collection"
//         value {
//          bytes_list {
//             value: "\n\ntest_queue"
//           }
//         }
//       }
//
//  or
//
//    3) any_buf = any_pb2.Any()
//       tf.add_to_collection("your_collection_name",
//         any_buf.Pack(your_proto))
//
//       collection_def {
//         key: "user_defined_any_collection"
//         value {
//           any_list {
//             value {
//               type_url: "type.googleapis.com/tensorflow.QueueRunnerDef"
//               value: "\n\ntest_queue"
//             }
//           }
//         }
//       }
//
// 3. For Python objects, implement to_proto() and from_proto(), and register
//    them in the following manner:
//    ops.register_proto_function("your_collection_name",
//                                proto_type,
//                                to_proto=YourPythonObject.to_proto,
//                                from_proto=YourPythonObject.from_proto)
//    These functions will be invoked to serialize and de-serialize the
//    collection. For example,
//    ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES,
//                                proto_type=variable_pb2.VariableDef,
//                                to_proto=Variable.to_proto,
//                                from_proto=Variable.from_proto)
message CollectionDef {
  // NodeList is used for collecting nodes in graph. For example
  // collection_def {
  //   key: "summaries"
  //   value {
  //     node_list {
  //       value: "input_producer/ScalarSummary:0"
  //       value: "shuffle_batch/ScalarSummary:0"
  //       value: "ImageSummary:0"
  //     }
  //   }
  message NodeList {
    repeated string value = 1;
  }

  // BytesList is used for collecting strings and serialized protobufs. For
  // example:
  // collection_def {
  //   key: "trainable_variables"
  //   value {
  //     bytes_list {
  //       value: "\n\017conv1/weights:0\022\024conv1/weights/Assign
  //              \032\024conv1/weights/read:0"
  //       value: "\n\016conv1/biases:0\022\023conv1/biases/Assign\032
  //              \023conv1/biases/read:0"
  //     }
  //   }
  // }
  message BytesList {
    repeated bytes value = 1;
  }

  // Int64List is used for collecting int, int64 and long values.
  message Int64List {
    repeated int64 value = 1 [packed = true];
  }

  // FloatList is used for collecting float values.
  message FloatList {
    repeated float value = 1 [packed = true];
  }

  // AnyList is used for collecting Any protos.
  message AnyList {
    repeated google.protobuf.Any value = 1;
  }

  oneof kind {
    NodeList node_list = 1;
    BytesList bytes_list = 2;
    Int64List int64_list = 3;
    FloatList float_list = 4;
    AnyList any_list = 5;
  }
}

// Information about a Tensor necessary for feeding or retrieval.
message TensorInfo {
  // For sparse tensors, The COO encoding stores a triple of values, indices,
  // and shape.
  message CooSparse {
    // The shape of the values Tensor is [?].  Its dtype must be the dtype of
    // the SparseTensor as a whole, given in the enclosing TensorInfo.
    string values_tensor_name = 1;

    // The indices Tensor must have dtype int64 and shape [?, ?].
    string indices_tensor_name = 2;

    // The dynamic logical shape represented by the SparseTensor is recorded in
    // the Tensor referenced here.  It must have dtype int64 and shape [?].
    string dense_shape_tensor_name = 3;
  }

  // Generic encoding for composite tensors.
  message CompositeTensor {
    // The serialized TypeSpec for the composite tensor.
    TypeSpecProto type_spec = 1;

    // A TensorInfo for each flattened component tensor.
    repeated TensorInfo components = 2;
  }

  oneof encoding {
    // For dense `Tensor`s, the name of the tensor in the graph.
    string name = 1;
    // There are many possible encodings of sparse matrices
    // (https://en.wikipedia.org/wiki/Sparse_matrix).  Currently, TensorFlow
    // uses only the COO encoding.  This is supported and documented in the
    // SparseTensor Python class.
    CooSparse coo_sparse = 4;
    // Generic encoding for CompositeTensors.
    CompositeTensor composite_tensor = 5;
  }
  DataType dtype = 2;
  // The static shape should be recorded here, to the extent that it can
  // be known in advance.  In the case of a SparseTensor, this field describes
  // the logical shape of the represented tensor (aka dense_shape).
  TensorShapeProto tensor_shape = 3;
}

// SignatureDef defines the signature of a computation supported by a TensorFlow
// graph.
//
// For example, a model with two loss computations, sharing a single input,
// might have the following signature_def map, in a MetaGraphDef message.
//
// Note that across the two SignatureDefs "loss_A" and "loss_B", the input key,
// output key, and method_name are identical, and will be used by system(s) that
// implement or rely upon this particular loss method. The output tensor names
// differ, demonstrating how different outputs can exist for the same method.
//
// signature_def {
//   key: "loss_A"
//   value {
//     inputs {
//       key: "input"
//       value {
//         name: "input:0"
//         dtype: DT_STRING
//         tensor_shape: ...
//       }
//     }
//     outputs {
//       key: "loss_output"
//       value {
//         name: "loss_output_A:0"
//         dtype: DT_FLOAT
//         tensor_shape: ...
//       }
//     }
//     method_name: "some/package/compute_loss"
//   }
//   ...
// }
// signature_def {
//   key: "loss_B"
//   value {
//     inputs {
//       key: "input"
//       value {
//         name: "input:0"
//         dtype: DT_STRING
//         tensor_shape: ...
//       }
//     }
//     outputs {
//       key: "loss_output"
//       value {
//         name: "loss_output_B:0"
//         dtype: DT_FLOAT
//         tensor_shape: ...
//       }
//     }
//     method_name: "some/package/compute_loss"
//   }
//   ...
// }
message SignatureDef {
  // Named input parameters.
  map<string, TensorInfo> inputs = 1;
  // Named output parameters.
  map<string, TensorInfo> outputs = 2;
  // Extensible method_name information enabling third-party users to mark a
  // SignatureDef as supporting a particular method. This enables producers and
  // consumers of SignatureDefs, e.g. a model definition library and a serving
  // library to have a clear hand-off regarding the semantics of a computation.
  //
  // Note that multiple SignatureDefs in a single MetaGraphDef may have the same
  // method_name. This is commonly used to support multi-headed computation,
  // where a single graph computation may return multiple results.
  string method_name = 3;
}

// An asset file def for a single file or a set of sharded files with the same
// name.
message AssetFileDef {
  // The tensor to bind the asset filename to.
  TensorInfo tensor_info = 1;
  // The filename within an assets directory. Note: does not include the path
  // prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename
  // would be "vocab.txt".
  string filename = 2;
}