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Resync protobuf def

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Changming Sun 2019-02-01 13:49:52 -08:00
parent f85cd520c0
commit ebfed60741
7 changed files with 1512 additions and 48 deletions
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2
csharp/test/Microsoft.ML.OnnxRuntime.Tests/Microsoft.ML.OnnxRuntime.Tests.csproj
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@ -8,7 +8,7 @@
<OutputPath>bin\$(Configuration)\</OutputPath>
<OnnxRuntimeBuildDirectory Condition="'$(OnnxRuntimeBuildDirectory)'==''">$(OnnxRuntimeCsharpRoot)\..\build\Windows</OnnxRuntimeBuildDirectory>
<ProtocDirectory>$(OnnxRuntimeBuildDirectory)\$(Configuration)\external\protobuf\cmake\$(Configuration)</ProtocDirectory>
<ProtoSrc>$(OnnxRuntimeCsharpRoot)\..\cmake\external\onnx\onnx</ProtoSrc>
<ProtoSrc>$(OnnxRuntimeCsharpRoot)\..\onnxruntime\core\protobuf</ProtoSrc>
<NativeBuildOutputDir>$(OnnxRuntimeBuildDirectory)\$(Configuration)\$(Configuration)</NativeBuildOutputDir>
</PropertyGroup>

104
onnxruntime/core/protobuf/onnx-ml.proto
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@ -2,6 +2,7 @@
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
@ -19,11 +20,11 @@ package onnx;
//
// 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.
// 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.
// Definitions of the built-in classical machine learning operators may be found in
// Definitions of the built-in classical machine learning operators may be found in
// docs/Operators-ml.md.
// Notes
@ -36,8 +37,8 @@ package onnx;
// by sharing our working version of ONNX.
//
// Protobuf compatibility
//
// To simplify framework compatibility, ONNX is defined using the subset of protobuf
//
// 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.
//
@ -48,6 +49,7 @@ package onnx;
// 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
@ -60,8 +62,8 @@ enum Version {
_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,
// 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;
@ -74,7 +76,12 @@ enum Version {
// - Added new message OperatorSetIdProto
// - Added opset_import in ModelProto
// - For vendor extensions, added domain in NodeProto
IR_VERSION = 0x0000000000000003;
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 = 0x0000000000000004;
}
// Attributes
@ -84,6 +91,7 @@ enum Version {
// 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 {
@ -102,8 +110,8 @@ message AttributeProto {
}
// The name field MUST be present for this version of the IR.
optional string name = 1; // namespace Attribute
optional 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.
@ -119,14 +127,14 @@ message AttributeProto {
// 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.
optional AttributeType type = 20; // discriminator that indicates which field below is in use
optional 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
optional float f = 2; // 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 float f = 2; // float
optional int64 i = 3; // int
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
// Do not use field below, it's deprecated.
// optional ValueProto v = 12; // value - subsumes everything but graph
@ -141,7 +149,7 @@ message AttributeProto {
// the shape of the value.
message ValueInfoProto {
// This field MUST be present in this version of the IR.
optional string name = 1; // namespace Value
optional string name = 1; // namespace Value
// This field MUST be present in this version of the IR.
optional TypeProto type = 2;
// A human-readable documentation for this value. Markdown is allowed.
@ -153,20 +161,20 @@ message ValueInfoProto {
// 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
// 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
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.
optional string name = 3; // namespace Node
optional string name = 3; // namespace Node
// The symbolic identifier of the Operator to execute.
optional string op_type = 4; // namespace Operator
// The domain of the OperatorSet that specifies the operator named by op_type.
optional string domain = 7; // namespace Domain
optional string domain = 7; // namespace Domain
// Additional named attributes.
repeated AttributeProto attribute = 5;
@ -233,7 +241,7 @@ message ModelProto {
// See https://developers.google.com/protocol-buffers/docs/proto3#maps
message StringStringEntryProto {
optional string key = 1;
optional string value = 2;
optional string value= 2;
};
message QuantInfo {
@ -246,7 +254,7 @@ message QuantInfo {
// Graphs
//
// A graph defines the computational logic of a model and is comprised of a parameterized
// 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.
@ -255,11 +263,11 @@ message GraphProto {
repeated NodeProto node = 1;
// The name of the graph.
optional string name = 2; // namespace Graph
optional 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
// also appears in the input list.
// MAY also appear in the input list.
repeated TensorProto initializer = 5;
// A human-readable documentation for this graph. Markdown is allowed.
@ -312,8 +320,8 @@ message TensorProto {
DOUBLE = 11;
UINT32 = 12;
UINT64 = 13;
COMPLEX64 = 14; // complex with float32 real and imaginary components
COMPLEX128 = 15; // complex with float64 real and imaginary components
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.
@ -327,6 +335,7 @@ message TensorProto {
repeated int64 dims = 1;
// The data type of the tensor.
// This field MUST have a valid TensorProto.DataType value
optional int32 data_type = 2;
// For very large tensors, we may want to store them in chunks, in which
@ -371,7 +380,7 @@ message TensorProto {
repeated int64 int64_data = 7 [packed = true];
// Optionally, a name for the tensor.
optional string name = 8; // namespace Value
optional string name = 8; // namespace Value
// A human-readable documentation for this tensor. Markdown is allowed.
optional string doc_string = 12;
@ -396,13 +405,12 @@ message TensorProto {
// 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) - file path relative to the filesystem directory where the ONNX
// - "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:
@ -438,7 +446,7 @@ message TensorShapeProto {
message Dimension {
oneof value {
int64 dim_value = 1;
string dim_param = 2; // namespace Shape
string dim_param = 2; // namespace Shape
};
// Standard denotation can optionally be used to denote tensor
// dimensions with standard semantic descriptions to ensure
@ -454,13 +462,16 @@ message TensorShapeProto {
//
// 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.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// repeated T
message Sequence {
// The type and optional shape of each element of the sequence.
@ -470,6 +481,7 @@ message TypeProto {
// 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
optional int32 key_type = 1;
@ -487,17 +499,20 @@ message TypeProto {
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// This field MUST NOT have the value of UNDEFINED
// This field MUST be present for this version of the IR.
message SparseTensor {
// 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 int32 elem_type = 1;
optional TensorShapeProto shape = 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
@ -512,10 +527,11 @@ message TypeProto {
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
}
// An optional denotation can be used to denote the whole
// type with a standard semantic description as to what is
// 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.
optional string denotation = 6;
@ -536,22 +552,20 @@ message OperatorSetIdProto {
optional int64 version = 2;
}
// Following messages are copied from onnx-operators-ml.proto in ONNX.
//
// Operator/function status.
enum OperatorStatus {
EXPERIMENTAL = 0;
STABLE = 1;
EXPERIMENTAL = 0;
STABLE = 1;
}
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
optional string name = 1;
// 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.
// the updated function will be changed to the updated function set version.
optional int64 since_version = 2;
// This field indicates whether the syntax, semantics, or presence
@ -569,10 +583,10 @@ message FunctionProto {
repeated string output = 5;
// The attributes of the function.
repeated string attribute = 6;
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
optional string doc_string = 8;
}
}

592
onnxruntime/core/protobuf/onnx-ml.proto3 Normal file
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@ -0,0 +1,592 @@
//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
syntax = "proto3";
package 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.
// Definitions of the built-in classical machine learning operators may be found in
// docs/Operators-ml.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 = 0x0000000000000004;
}
// 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;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
}
// 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
// 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
}
// 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.
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;
// kezhan: This field is not in ONNX, and will be pushed into ONNX with good use cases in microsoft.
repeated FunctionProto functions = 100;
// 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 QuantInfo {
string tensor_name = 1;
// The keys used in the mapping below will be pre-defined in ONNX spec.
// For example, for linear case, “SCALE”, “ZERO_POINT” 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;
// 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:
// A tensor “a” and its quantization parameter tensor are “scale_a” and
// “zero_point_a” (in linear case).
repeated QuantInfo quantization_annotation = 100;
// 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];
}
// 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;
};
message Opaque {
// When missing, the domain is the same as the model's.
string domain = 1;
// The name is optional but significant when provided.
string name = 2;
// parameters that help defining the type
// DEPRECATED do not use.
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// 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;
}
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;
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
}
// 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;
}
// Operator/function status.
enum OperatorStatus {
EXPERIMENTAL = 0;
STABLE = 1;
}
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
string name = 1;
// 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.
int64 since_version = 2;
// 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.
OperatorStatus status = 3;
// The inputs and outputs of the function.
repeated string input = 4;
repeated string output = 5;
// The attributes of the function.
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
string doc_string = 8;
}

6
onnxruntime/core/protobuf/onnx-operators-ml.proto
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@ -2,6 +2,7 @@
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
@ -44,7 +45,8 @@ import "onnx-ml.proto";
// *since_version* is the version of the operator set that
// this operator was initially declared in.
//
message OperatorProto {
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
optional string op_type = 1;
@ -123,7 +125,7 @@ message OperatorSetProto {
// The operators specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos in operator
repeated OperatorProto operator = 8;
// The functions specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
repeated FunctionProto functions = 9;

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//
// WARNING: This file is automatically generated! Please edit onnx.in.proto.
//
// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
syntax = "proto3";
package onnx;
import "onnx-ml.proto3";
//
// This file contains the proto definitions for OperatorSetProto and
// OperatorProto. OperatorSetProtos are used to describe a versioned
// set of operators that can be used by a ModelProto.
//
// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
// format, however their usage is different.
//
// ModelProto files are used to describe executable graphs that can be
// executed directly by a framework, runtime, or engine.
//
// OperatorSetProto files are used to describe a set of operators that are
// available in a given environment. The file TBD.TBD is the OperatorSetProto
// that describes the ONNX standard operators.
//
// 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.
//
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
string op_type = 1;
// 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.
int64 since_version = 2;
// 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.
OperatorStatus status = 3;
// Eventually we will declare the signature of the operator here
// A human-readable documentation for this operator. Markdown is allowed.
string doc_string = 10;
}
// 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.
//
message OperatorSetProto {
// 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
string magic = 1;
// 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
int32 ir_version = 2;
// The prerelease component of the SemVer of the IR.
// This field MAY be absent in this version of the IR
string ir_version_prerelease = 3;
// The build metadata component of the SemVer of the IR.
// This field MAY be absent in this version of the IR
string ir_build_metadata = 7;
// Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
string domain = 4;
// 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.
int64 opset_version = 5;
// A human-readable documentation for this set of operators. Markdown is allowed.
string doc_string = 6;
// The operators specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos in operator
repeated OperatorProto operator = 8;
// The functions specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
repeated FunctionProto functions = 9;
}

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// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
syntax = "proto2";
package {PACKAGE_NAME};
// #if ONNX-ML
import "onnx/onnx-ml.proto";
// #else
import "onnx/onnx.proto";
// #endif
//
// This file contains the proto definitions for OperatorSetProto and
// OperatorProto. OperatorSetProtos are used to describe a versioned
// set of operators that can be used by a ModelProto.
//
// Like ModelProto, OperatorSetProto is defined as a top-level file/wire
// format, however their usage is different.
//
// ModelProto files are used to describe executable graphs that can be
// executed directly by a framework, runtime, or engine.
//
// OperatorSetProto files are used to describe a set of operators that are
// available in a given environment. The file TBD.TBD is the OperatorSetProto
// that describes the ONNX standard operators.
//
// 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.
//
message OperatorProto {
// The name of the operator within a domain.
// This field MUST be present in this version of the IR.
optional string op_type = 1;
// 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 int64 since_version = 2;
// 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 OperatorStatus status = 3;
// Eventually we will declare the signature of the operator here
// A human-readable documentation for this operator. Markdown is allowed.
optional string doc_string = 10;
}
// 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.
//
message OperatorSetProto {
// 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 string magic = 1;
// 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 int32 ir_version = 2;
// The prerelease component of the SemVer of the IR.
// This field MAY be absent in this version of the IR
optional string ir_version_prerelease = 3;
// The build metadata component of the SemVer of the IR.
// This field MAY be absent in this version of the IR
optional string ir_build_metadata = 7;
// Domain name of the operator set, in reverse DNS form (e.g., com.acme.dnnops).
optional string domain = 4;
// 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 int64 opset_version = 5;
// A human-readable documentation for this set of operators. Markdown is allowed.
optional string doc_string = 6;
// The operators specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos in operator
repeated OperatorProto operator = 8;
// The functions specified by this operator set.
// The (name, version) MUST be unique across all OperatorProtos/FunctionProtos in operator/functions
repeated FunctionProto functions = 9;
}

593
onnxruntime/core/protobuf/onnx.in.proto Normal file
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// Copyright (c) Facebook Inc. and Microsoft Corporation.
// Licensed under the MIT license.
syntax = "proto2";
package {PACKAGE_NAME};
// 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.
// #if ONNX-ML
// Definitions of the built-in classical machine learning operators may be found in
// docs/Operators-ml.md.
// #endif
// 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 = 0x0000000000000004;
}
// 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;
FLOATS = 6;
INTS = 7;
STRINGS = 8;
TENSORS = 9;
GRAPHS = 10;
}
// The name field MUST be present for this version of the IR.
optional 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.
optional string ref_attr_name = 21;
// A human-readable documentation for this attribute. Markdown is allowed.
optional 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.
optional 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
optional float f = 2; // float
optional int64 i = 3; // int
optional bytes s = 4; // UTF-8 string
optional TensorProto t = 5; // tensor value
optional GraphProto g = 6; // graph
// 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
}
// 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.
optional string name = 1; // namespace Value
// This field MUST be present in this version of the IR.
optional TypeProto type = 2;
// A human-readable documentation for this value. Markdown is allowed.
optional 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.
optional string name = 3; // namespace Node
// The symbolic identifier of the Operator to execute.
optional string op_type = 4; // namespace Operator
// The domain of the OperatorSet that specifies the operator named by op_type.
optional string domain = 7; // namespace Domain
// Additional named attributes.
repeated AttributeProto attribute = 5;
// A human-readable documentation for this node. Markdown is allowed.
optional 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.
optional 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.
optional 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.
optional 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.
optional string domain = 4;
// The version of the graph encoded. See Version enum below.
optional int64 model_version = 5;
// A human-readable documentation for this model. Markdown is allowed.
optional string doc_string = 6;
// The parameterized graph that is evaluated to execute the model.
optional GraphProto graph = 7;
// kezhan: This field is not in ONNX, and will be pushed into ONNX with good use cases in microsoft.
repeated FunctionProto functions = 100;
// 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 {
optional string key = 1;
optional string value= 2;
};
message QuantInfo {
optional string tensor_name = 1;
// The keys used in the mapping below will be pre-defined in ONNX spec.
// For example, for linear case, “SCALE”, “ZERO_POINT” 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.
optional 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;
// A human-readable documentation for this graph. Markdown is allowed.
optional 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:
// A tensor “a” and its quantization parameter tensor are “scale_a” and
// “zero_point_a” (in linear case).
repeated QuantInfo quantization_annotation = 100;
// 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
optional 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 {
optional int64 begin = 1;
optional int64 end = 2;
}
optional 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.
optional string name = 8; // namespace Value
// A human-readable documentation for this tensor. Markdown is allowed.
optional 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
optional 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.
optional 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];
}
// 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.
optional 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.
optional int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// #if ONNX-ML
// 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.
optional 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
optional int32 key_type = 1;
// This field MUST be present for this version of the IR.
optional TypeProto value_type = 2;
};
message Opaque {
// When missing, the domain is the same as the model's.
optional string domain = 1;
// The name is optional but significant when provided.
optional string name = 2;
// parameters that help defining the type
// DEPRECATED do not use.
// repeated TypeProto parameters = 3;
}
message SparseTensor {
// 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 int32 elem_type = 1;
optional TensorShapeProto shape = 2;
}
// #endif
oneof value {
// The type of a tensor.
Tensor tensor_type = 1;
// #if ONNX-ML
// 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;
Opaque opaque_type = 7;
SparseTensor sparse_tensor_type = 8;
// #endif
}
// 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.
optional 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.
optional string domain = 1;
// The version of the operator set being identified.
// This field MUST be present in this version of the IR.
optional int64 version = 2;
}
// Operator/function status.
enum OperatorStatus {
EXPERIMENTAL = 0;
STABLE = 1;
}
message FunctionProto {
// The name of the function, similar usage of op_type in OperatorProto.
optional string name = 1;
// 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 int64 since_version = 2;
// 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.
optional OperatorStatus status = 3;
// The inputs and outputs of the function.
repeated string input = 4;
repeated string output = 5;
// The attributes of the function.
repeated string attribute= 6;
// The nodes in the function.
repeated NodeProto node = 7;
// A human-readable documentation for this function. Markdown is allowed.
optional string doc_string = 8;
}