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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.

Used in: NodeProto

• optional string name = 1

  The name field MUST be present for this version of the IR.

  namespace Attribute

• optional string ref_attr_name = 21

  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.

• optional string doc_string = 13

  A human-readable documentation for this attribute. Markdown is allowed.

• optional AttributeProto.AttributeType type = 20

  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 heuristics 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 accommodate proto3 implementations.

  discriminator that indicates which field below is in use

• optional float f = 2

  Exactly ONE of the following fields must be present for this version of the IR

  float

• optional int64 i = 3

  int

• optional bytes s = 4

  UTF-8 string

• optional TensorProto t = 5

  tensor value

• optional GraphProto g = 6

  graph

• optional SparseTensorProto sparse_tensor = 22

  sparse tensor value

• 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

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.

Used in: AttributeProto

• 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

Used in: OperatorSetProto

• optional string name = 1

  The name of the function, similar usage of op_type in OperatorProto.

• optional int64 since_version = 2

  The first version of a function set which contains this function. When there's any breaking change for this function, the function set contains the function needs to bump its version, and since_version of the updated function will be changed to the updated function set version.

• optional OperatorStatus status = 3

  This field indicates whether the syntax, semantics, or presence of this function is in an experimental or stable stage. Once an function is published as STABLE, its syntax and semantics MUST NOT change in subsequent versions of the operator set. When a function is published as EXPERIMENTAL, the syntax and semantics of the function MAY change across operator set versions. Functions "become" stable by deprecating the experimental version and introducing a new stable function with the same name.

• repeated string input = 4

  The inputs and outputs of the function.

• repeated string output = 5

• repeated string attribute = 6

  The attributes of the function.

• repeated NodeProto node = 7

  The nodes in the function.

• optional string doc_string = 8

  A human-readable documentation for this function. Markdown is allowed.

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.

Used in: AttributeProto, ModelProto, TrainingInfoProto

• repeated NodeProto node = 1

  The nodes in the graph, sorted topologically.

• optional string name = 2

  The name of the graph.

  namespace Graph

• repeated TensorProto initializer = 5

  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 SparseTensorProto sparse_initializer = 15

  Initializers (see above) stored in sparse format.

• optional string doc_string = 10

  A human-readable documentation for this graph. Markdown is allowed.

• repeated ValueInfoProto input = 11

  The inputs and outputs of the graph.

• repeated ValueInfoProto output = 12

• repeated ValueInfoProto value_info = 13

  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 TensorAnnotation quantization_annotation = 14

  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.

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's.

• optional int64 ir_version = 1

  The version of the IR this model targets. See Version enum above. This field MUST be present.

• repeated OperatorSetIdProto opset_import = 8

  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.

• optional string producer_name = 2

  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.

• optional string producer_version = 3

  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.

• optional string domain = 4

  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.

• optional int64 model_version = 5

  The version of the graph encoded. See Version enum below.

• optional string doc_string = 6

  A human-readable documentation for this model. Markdown is allowed.

• optional GraphProto graph = 7

  The parameterized graph that is evaluated to execute the model.

• repeated StringStringEntryProto metadata_props = 14

  Named metadata values; keys should be distinct.

• repeated TrainingInfoProto training_info = 20

  Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.

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.

Used in: FunctionProto, GraphProto

• repeated string input = 1

  namespace Value

• repeated string output = 2

  namespace Value

• optional string name = 3

  An optional identifier for this node in a graph. This field MAY be absent in ths version of the IR.

  namespace Node

• optional string op_type = 4

  The symbolic identifier of the Operator to execute.

  namespace Operator

• optional string domain = 7

  The domain of the OperatorSet that specifies the operator named by op_type.

  namespace Domain

• repeated AttributeProto attribute = 5

  Additional named attributes.

• optional string doc_string = 6

  A human-readable documentation for this node. Markdown is allowed.

An OperatorProto represents the immutable specification of the signature and semantics of an operator. Operators are declared as part of an OperatorSet, which also defines the domain name for the set. Operators are uniquely identified by a three part identifier (domain, op_type, since_version) where *domain* is the domain of an operator set that contains this operator specification. *op_type* is the name of the operator as referenced by a NodeProto.op_type *since_version* is the version of the operator set that this operator was initially declared in.

Used in: OperatorSetProto

• optional string op_type = 1

  The name of the operator within a domain. This field MUST be present in this version of the IR.

• optional int64 since_version = 2

  The version of the operator set that first introduced this operator. This value MUST be the same value as the opset_version of the operator set that first published this operator. Subsequent versions of the operator set MUST NOT alter the signature or semantics of the operator once published as STABLE. This field MUST be present in this version of the IR.

• optional OperatorStatus status = 3

  This field indicates whether the syntax, semantics, or presence of this operator is in an experimental or stable stage. Once an operator is published as STABLE, it's syntax and semantics MUST NOT change in subsequent versions of the operator set. When an operator is published as EXPERIMENTAL, the syntax and semantics of the operator MAY change across operator set versions. Operators "become" stable by deprecating the experimental version and introducing a new stable operator with the same op_type.

• optional string doc_string = 10

  A human-readable documentation for this operator. Markdown is allowed.

Operator Sets OperatorSets are uniquely identified by a (domain, opset_version) pair.

Used in: ModelProto

• optional string domain = 1

  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.

• optional int64 version = 2

  The version of the operator set being identified. This field MUST be present in this version of the IR.

An OperatorSetProto represents an immutable set of immutable operator specifications. The domain of the set (OperatorSetProto.domain) is a reverse-DNS name that disambiguates operator sets defined by independent entities. The version of the set (opset_version) is a monotonically increasing integer that indicates changes to the membership of the operator set. Operator sets are uniquely identified by a two part identifier (domain, opset_version) Like ModelProto, OperatorSetProto is intended as a top-level file/wire format, and thus has the standard format headers in addition to the operator set information.

• optional string magic = 1

  All OperatorSetProtos start with a distingushed byte sequence to disambiguate protobuf files containing OperatorSets from other content. This field MUST be "ONNXOPSET" This field MUST be present in this version of the IR

• optional int32 ir_version = 2

  All OperatorSetProtos indicate the version of the IR syntax and semantics they adhere to. It is always IR_VERSION. This field MUST be present in this version of the IR

• optional string ir_version_prerelease = 3

  The prerelease component of the SemVer of the IR. This field MAY be absent in this version of the IR

• optional string ir_build_metadata = 7

  The build metadata component of the SemVer of the IR. This field MAY be absent in this version of the IR

• optional string domain = 4

  Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).

• optional int64 opset_version = 5

  The version of the set of operators. This is a simple int value that is monotonically increasing as new versions of operator set are published. All operators in this set MUST have version numbers no greater than opset_version.

• optional string doc_string = 6

  A human-readable documentation for this set of operators. Markdown is allowed.

• repeated OperatorProto operator = 8

  The operators specified by this operator set. The (name, version) MUST be unique across all OperatorProtos in operator

• repeated FunctionProto functions = 9

  The functions specified by this operator set. The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions

Operator/function status.

Used in: FunctionProto, OperatorProto

• EXPERIMENTAL = 0

• STABLE = 1

A serialized sparse-tensor value

Used in: AttributeProto, GraphProto

• optional TensorProto values = 1

  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.

• optional TensorProto indices = 2

  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]

• repeated int64 dims = 3

  The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]

StringStringEntryProto follows the pattern for cross-proto-version maps. See https://developers.google.com/protocol-buffers/docs/proto3#maps

Used in: ModelProto, TensorAnnotation, TensorProto, TrainingInfoProto

• optional string key = 1

• optional string value = 2

Used in: GraphProto

• optional string tensor_name = 1

• repeated StringStringEntryProto quant_parameter_tensor_names = 2

  <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.

Tensors A serialized tensor value.

Used in: AttributeProto, GraphProto, SparseTensorProto

• repeated int64 dims = 1

  The shape of the tensor.

• optional int32 data_type = 2

  The data type of the tensor. This field MUST have a valid TensorProto.DataType value

• optional TensorProto.Segment segment = 3

• repeated float float_data = 4

  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 appearing 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 int32 int32_data = 5

  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 bytes string_data = 6

  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 int64 int64_data = 7

  For int64. When this field is present, the data_type field MUST be INT64

• optional string name = 8

  Optionally, a name for the tensor.

  namespace Value

• optional string doc_string = 12

  A human-readable documentation for this tensor. Markdown is allowed.

• optional bytes raw_data = 9

  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

• repeated StringStringEntryProto external_data = 13

  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.

• optional TensorProto.DataLocation data_location = 14

  If value not set, data is stored in raw_data (if set) otherwise in type-specified field.

• repeated double double_data = 10

  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 appearing 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 uint64 uint64_data = 11

  For uint64 and uint32 values When this field is present, the data_type field MUST be UINT32 or UINT64

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.

Used in: TensorProto

• DEFAULT = 0

• EXTERNAL = 1

• UNDEFINED = 0

• FLOAT = 1

  Basic types.

  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

• FLOAT16 = 10

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

• DOUBLE = 11

• UINT32 = 12

• UINT64 = 13

• COMPLEX64 = 14

  complex with float32 real and imaginary components

• COMPLEX128 = 15

  complex with float64 real and imaginary components

• BFLOAT16 = 16

  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.

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.

Used in: TensorProto

• optional int64 begin = 1

• optional int64 end = 2

Defines a tensor shape. A dimension can be either an integer value or a symbolic variable. A symbolic variable represents an unknown dimension.

Used in: TypeProto.Tensor

• repeated TensorShapeProto.Dimension dim = 1

Used in: TensorShapeProto

• oneof value

  • int64 dim_value = 1

  • string dim_param = 2

    namespace Shape

• optional string denotation = 3

  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.

Training information TrainingInfoProto stores information for training a model. In particular, this defines two functionalities: an initialization-step and a training-algorithm-step. Initialization resets the model back to its original state as if no training has been consumed. Training algorithm improves the model based on input data. The semantics of the initialization-step is that the initializers in ModelProto.graph and in TrainingInfoProto.algorithm are first initialized as specified by the initializers in the graph, and then updated by the "initialization_binding" in every instance in ModelProto.training_info. The field "algorithm" defines a computation graph which represents a training algorithm's step. After the execution of a TrainingInfoProto.algorithm, the initializers specified by "update_binding" may be immediately updated. If the targeted training algorithm contains consecutive update stages (such as block coordinate descent methods), the user needs to create a TrainingInfoProto for each stage.

Used in: ModelProto

• optional GraphProto initialization = 1

  This field describes a graph to compute the initial tensors upon starting the training process. Initialization graph has no input and can have multiple outputs. Usually, trainable tensors in neural networks are randomly initialized. To achieve that, for each tensor, the user can put a random number operator such as RandomNormal or RandomUniform in TrainingInfoProto.initialization.node and assign its random output to the specific tensor using "initialization_binding". This graph can also set the initializers in "algorithm" in the same TrainingInfoProto; a use case is resetting the number of training iteration to zero. By default, this field is an empty graph and its evaluation does not produce any output.

• optional GraphProto algorithm = 2

  This field represents a training algorithm step. Given required inputs, it computes outputs to update initializers in its own or inference graph's initializer lists. In general, this graph contains loss node, gradient node, optimizer node, increment of iteration count, and some calls to the inference graph. The field algorithm.node is the only place the user can use GraphCall operator. The only callable graph is the one stored in ModelProto.graph. By default, this field is an empty graph and its evaluation does not produce any output.

• repeated StringStringEntryProto initialization_binding = 3

  This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto. See "update_binding" below for details. By default, this field is empty and no initializer would be changed by the execution of "initialization".

• repeated StringStringEntryProto update_binding = 4

  Gradient-based training is usually an iterative procedure. In one gradient descent iteration, we apply x = x - r * g where "x" is the optimized tensor, "r" stands for learning rate, and "g" is gradient of "x" with respect to a chosen loss. To avoid adding assignments into the training graph, we split the update equation into y = x - r * g x = y The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To tell that "y" should be assigned to "x", the field "update_binding" may contain a key-value pair of strings, "x" (key of StringStringEntryProto) and "y" (value of StringStringEntryProto). For a neural network with multiple trainable (mutable) tensors, there can be multiple key-value pairs in "update_binding". The initializers appears as keys in "update_binding" are considered mutable and globally-visible variables. This implies some behaviors as described below. 1. We have only unique keys in all "update_binding"s so that two global variables may not have the same name. This ensures that one global variable is assigned up to once. 2. The keys must appear in names of "ModelProto.graph.initializer" or "TrainingInfoProto.algorithm.initializer". 3. The values must be output names of "algorithm". 4. If an optional input of a graph is omitted when using GraphCall, the global variable with the same name may be used. 5. When using GraphCall, the users always can pass values to optional inputs of the called graph even if the associated initializers appears as keys in "update_binding"s. 6. The graphs in TrainingInfoProto's can use global variables as their operator inputs. 7. Mutable variables are initialized to the value specified by the corresponding initializer, and then potentially updated by "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s. This field usually contains names of trainable tensors (in ModelProto.graph), optimizer states such as momentums in advanced stochastic gradient methods (in TrainingInfoProto.graph), and number of training iterations (in TrainingInfoProto.graph). By default, this field is empty and no initializer would be changed by the execution of "algorithm".

Types The standard ONNX data types.

Used in: TypeProto.Map, TypeProto.Sequence, ValueInfoProto

• oneof value

  • TypeProto.Tensor tensor_type = 1

    The type of a tensor.

  • TypeProto.Sequence sequence_type = 4

    The type of a sequence.

  • TypeProto.Map map_type = 5

    The type of a map.

• optional string denotation = 6

  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.

map<K,V>

Used in: TypeProto

• optional int32 key_type = 1

  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

• optional TypeProto value_type = 2

  This field MUST be present for this version of the IR.

repeated T

Used in: TypeProto

• optional TypeProto elem_type = 1

  The type and optional shape of each element of the sequence. This field MUST be present for this version of the IR.

Used in: TypeProto

• optional int32 elem_type = 1

  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.

• optional TensorShapeProto shape = 2

Defines information on value, including the name, the type, and the shape of the value.

Used in: GraphProto

• optional string name = 1

  This field MUST be present in this version of the IR.

  namespace Value

• optional TypeProto type = 2

  This field MUST be present in this version of the IR for inputs and outputs of the top-level graph.

• optional string doc_string = 3

  A human-readable documentation for this value. Markdown is 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.

• _START_VERSION = 0

  proto3 requires the first enum value to be zero. We add this just to appease the compiler.

• IR_VERSION_2017_10_10 = 1

  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 0x00000001, which was the version we published on Oct 10, 2017.

• IR_VERSION_2017_10_30 = 2

  IR_VERSION 2 published on Oct 30, 2017 - Added type discriminator to AttributeProto to support proto3 users

• IR_VERSION_2017_11_3 = 3

  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_2019_1_22 = 4

  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_3_18 = 5

  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_9_19 = 6

  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 = 7

  IR VERSION 7 published on <TBD> - Add support to allow function body graph to rely on multiple external operator sets. - Add a list to promote inference graph's initializers to global and mutable variables. Global variables are visible in all graphs of the stored models. - Add message TrainingInfoProto to store initialization method and training algorithm. The execution of TrainingInfoProto can modify the values of mutable variables. - Make inference graph callable from TrainingInfoProto via GraphCall operator.