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// |
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// WARNING: This file is automatically generated! Please edit onnx.in.proto. |
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// |
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// Copyright (c) ONNX Project Contributors. |
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// Licensed under the MIT license. |
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syntax = "proto2"; |
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package onnx; |
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// Overview |
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// |
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// ONNX is an open specification that is comprised of the following components: |
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// |
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// 1) A definition of an extensible computation graph model. |
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// 2) Definitions of standard data types. |
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// 3) Definitions of built-in operators. |
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// |
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// This document describes the syntax of models and their computation graphs, |
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// as well as the standard data types. Together, they are referred to as the ONNX |
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// Intermediate Representation, or 'IR' for short. |
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// |
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// The normative semantic specification of the ONNX IR is found in docs/IR.md. |
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// Definitions of the built-in neural network operators may be found in docs/Operators.md. |
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// Notes |
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// |
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// Release |
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// |
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// We are still in the very early stage of defining ONNX. The current |
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// version of ONNX is a starting point. While we are actively working |
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// towards a complete spec, we would like to get the community involved |
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// by sharing our working version of ONNX. |
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// |
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// Protobuf compatibility |
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// |
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// To simplify framework compatibility, ONNX is defined using the subset of protobuf |
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// that is compatible with both protobuf v2 and v3. This means that we do not use any |
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// protobuf features that are only available in one of the two versions. |
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// |
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// Here are the most notable contortions we have to carry out to work around |
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// these limitations: |
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// |
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// - No 'map' (added protobuf 3.0). We instead represent mappings as lists |
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// of key-value pairs, where order does not matter and duplicates |
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// are not allowed. |
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// Versioning |
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// |
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// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md |
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// |
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// To be compatible with both proto2 and proto3, we will use a version number |
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// that is not defined by the default value but an explicit enum number. |
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enum Version { |
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// proto3 requires the first enum value to be zero. |
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// We add this just to appease the compiler. |
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_START_VERSION = 0; |
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// The version field is always serialized and we will use it to store the |
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// version that the graph is generated from. This helps us set up version |
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// control. |
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// For the IR, we are using simple numbers starting with with 0x00000001, |
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// which was the version we published on Oct 10, 2017. |
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IR_VERSION_2017_10_10 = 0x0000000000000001; |
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// IR_VERSION 2 published on Oct 30, 2017 |
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// - Added type discriminator to AttributeProto to support proto3 users |
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IR_VERSION_2017_10_30 = 0x0000000000000002; |
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// IR VERSION 3 published on Nov 3, 2017 |
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// - For operator versioning: |
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// - Added new message OperatorSetIdProto |
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// - Added opset_import in ModelProto |
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// - For vendor extensions, added domain in NodeProto |
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IR_VERSION_2017_11_3 = 0x0000000000000003; |
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// IR VERSION 4 published on Jan 22, 2019 |
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// - Relax constraint that initializers should be a subset of graph inputs |
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// - Add type BFLOAT16 |
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IR_VERSION_2019_1_22 = 0x0000000000000004; |
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// IR VERSION 5 published on March 18, 2019 |
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// - Add message TensorAnnotation. |
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// - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters. |
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IR_VERSION_2019_3_18 = 0x0000000000000005; |
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// IR VERSION 6 published on Sep 19, 2019 |
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// - Add support for sparse tensor constants stored in model. |
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// - Add message SparseTensorProto |
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// - Add sparse initializers |
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IR_VERSION = 0x0000000000000006; |
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} |
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// Attributes |
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// |
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// A named attribute containing either singular float, integer, string, graph, |
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// and tensor values, or repeated float, integer, string, graph, and tensor values. |
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// An AttributeProto MUST contain the name field, and *only one* of the |
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// following content fields, effectively enforcing a C/C++ union equivalent. |
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message AttributeProto { |
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// Note: this enum is structurally identical to the OpSchema::AttrType |
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// enum defined in schema.h. If you rev one, you likely need to rev the other. |
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enum AttributeType { |
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UNDEFINED = 0; |
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FLOAT = 1; |
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INT = 2; |
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STRING = 3; |
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TENSOR = 4; |
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GRAPH = 5; |
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SPARSE_TENSOR = 11; |
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FLOATS = 6; |
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INTS = 7; |
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STRINGS = 8; |
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TENSORS = 9; |
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GRAPHS = 10; |
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SPARSE_TENSORS = 12; |
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} |
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// The name field MUST be present for this version of the IR. |
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optional string name = 1; // namespace Attribute |
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// if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function. |
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// In this case, this AttributeProto does not contain data, and it's a reference of attribute |
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// in parent scope. |
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// NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph. |
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optional string ref_attr_name = 21; |
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// A human-readable documentation for this attribute. Markdown is allowed. |
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optional string doc_string = 13; |
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// The type field MUST be present for this version of the IR. |
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// For 0.0.1 versions of the IR, this field was not defined, and |
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// implementations needed to use has_field hueristics to determine |
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// which value field was in use. For IR_VERSION 0.0.2 or later, this |
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// field MUST be set and match the f|i|s|t|... field in use. This |
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// change was made to accomodate proto3 implementations. |
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optional AttributeType type = 20; // discriminator that indicates which field below is in use |
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// Exactly ONE of the following fields must be present for this version of the IR |
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optional float f = 2; // float |
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optional int64 i = 3; // int |
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optional bytes s = 4; // UTF-8 string |
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optional TensorProto t = 5; // tensor value |
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optional GraphProto g = 6; // graph |
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optional SparseTensorProto sparse_tensor = 22; // sparse tensor value |
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// Do not use field below, it's deprecated. |
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// optional ValueProto v = 12; // value - subsumes everything but graph |
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repeated float floats = 7; // list of floats |
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repeated int64 ints = 8; // list of ints |
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repeated bytes strings = 9; // list of UTF-8 strings |
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repeated TensorProto tensors = 10; // list of tensors |
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repeated GraphProto graphs = 11; // list of graph |
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repeated SparseTensorProto sparse_tensors = 23; // list of sparse tensors |
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} |
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// Defines information on value, including the name, the type, and |
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// the shape of the value. |
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message ValueInfoProto { |
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// This field MUST be present in this version of the IR. |
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optional string name = 1; // namespace Value |
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// This field MUST be present in this version of the IR for |
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// inputs and outputs of the top-level graph. |
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optional TypeProto type = 2; |
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// A human-readable documentation for this value. Markdown is allowed. |
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optional string doc_string = 3; |
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} |
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// Nodes |
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// |
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// Computation graphs are made up of a DAG of nodes, which represent what is |
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// commonly called a "layer" or "pipeline stage" in machine learning frameworks. |
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// |
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// For example, it can be a node of type "Conv" that takes in an image, a filter |
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// tensor and a bias tensor, and produces the convolved output. |
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message NodeProto { |
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repeated string input = 1; // namespace Value |
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repeated string output = 2; // namespace Value |
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// An optional identifier for this node in a graph. |
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// This field MAY be absent in ths version of the IR. |
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optional string name = 3; // namespace Node |
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// The symbolic identifier of the Operator to execute. |
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optional string op_type = 4; // namespace Operator |
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// The domain of the OperatorSet that specifies the operator named by op_type. |
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optional string domain = 7; // namespace Domain |
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// Additional named attributes. |
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repeated AttributeProto attribute = 5; |
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// A human-readable documentation for this node. Markdown is allowed. |
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optional string doc_string = 6; |
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} |
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// Models |
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// |
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// ModelProto is a top-level file/container format for bundling a ML model and |
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// associating its computation graph with metadata. |
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// |
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// The semantics of the model are described by the associated GraphProto. |
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message ModelProto { |
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// The version of the IR this model targets. See Version enum above. |
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// This field MUST be present. |
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optional int64 ir_version = 1; |
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// The OperatorSets this model relies on. |
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// All ModelProtos MUST have at least one entry that |
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// specifies which version of the ONNX OperatorSet is |
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// being imported. |
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// |
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// All nodes in the ModelProto's graph will bind against the operator |
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// with the same-domain/same-op_type operator with the HIGHEST version |
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// in the referenced operator sets. |
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repeated OperatorSetIdProto opset_import = 8; |
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// The name of the framework or tool used to generate this model. |
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// This field SHOULD be present to indicate which implementation/tool/framework |
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// emitted the model. |
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optional string producer_name = 2; |
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// The version of the framework or tool used to generate this model. |
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// This field SHOULD be present to indicate which implementation/tool/framework |
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// emitted the model. |
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optional string producer_version = 3; |
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// Domain name of the model. |
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// We use reverse domain names as name space indicators. For example: |
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// `com.facebook.fair` or `com.microsoft.cognitiveservices` |
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// |
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// Together with `model_version` and GraphProto.name, this forms the unique identity of |
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// the graph. |
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optional string domain = 4; |
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// The version of the graph encoded. See Version enum below. |
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optional int64 model_version = 5; |
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// A human-readable documentation for this model. Markdown is allowed. |
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optional string doc_string = 6; |
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// The parameterized graph that is evaluated to execute the model. |
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optional GraphProto graph = 7; |
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// Named metadata values; keys should be distinct. |
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repeated StringStringEntryProto metadata_props = 14; |
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}; |
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// StringStringEntryProto follows the pattern for cross-proto-version maps. |
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// See https://developers.google.com/protocol-buffers/docs/proto3#maps |
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message StringStringEntryProto { |
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optional string key = 1; |
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optional string value= 2; |
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}; |
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message TensorAnnotation { |
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optional string tensor_name = 1; |
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// <key, value> pairs to annotate tensor specified by <tensor_name> above. |
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// The keys used in the mapping below must be pre-defined in ONNX spec. |
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// For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as |
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// quantization parameter keys. |
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repeated StringStringEntryProto quant_parameter_tensor_names = 2; |
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} |
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// Graphs |
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// |
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// A graph defines the computational logic of a model and is comprised of a parameterized |
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// list of nodes that form a directed acyclic graph based on their inputs and outputs. |
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// This is the equivalent of the "network" or "graph" in many deep learning |
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// frameworks. |
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message GraphProto { |
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// The nodes in the graph, sorted topologically. |
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repeated NodeProto node = 1; |
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// The name of the graph. |
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optional string name = 2; // namespace Graph |
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// A list of named tensor values, used to specify constant inputs of the graph. |
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// Each TensorProto entry must have a distinct name (within the list) that |
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// MAY also appear in the input list. |
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repeated TensorProto initializer = 5; |
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// Initializers (see above) stored in sparse format. |
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repeated SparseTensorProto sparse_initializer = 15; |
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// A human-readable documentation for this graph. Markdown is allowed. |
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optional string doc_string = 10; |
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// The inputs and outputs of the graph. |
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repeated ValueInfoProto input = 11; |
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repeated ValueInfoProto output = 12; |
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// Information for the values in the graph. The ValueInfoProto.name's |
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// must be distinct. It is optional for a value to appear in value_info list. |
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repeated ValueInfoProto value_info = 13; |
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// This field carries information to indicate the mapping among a tensor and its |
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// quantization parameter tensors. For example: |
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// For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated, |
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// which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model. |
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repeated TensorAnnotation quantization_annotation = 14; |
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// DO NOT USE the following fields, they were deprecated from earlier versions. |
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// repeated string input = 3; |
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// repeated string output = 4; |
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// optional int64 ir_version = 6; |
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// optional int64 producer_version = 7; |
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// optional string producer_tag = 8; |
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// optional string domain = 9; |
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} |
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// Tensors |
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// |
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// A serialized tensor value. |
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message TensorProto { |
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enum DataType { |
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UNDEFINED = 0; |
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// Basic types. |
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FLOAT = 1; // float |
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UINT8 = 2; // uint8_t |
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INT8 = 3; // int8_t |
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UINT16 = 4; // uint16_t |
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INT16 = 5; // int16_t |
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INT32 = 6; // int32_t |
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INT64 = 7; // int64_t |
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STRING = 8; // string |
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BOOL = 9; // bool |
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// IEEE754 half-precision floating-point format (16 bits wide). |
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// This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits. |
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FLOAT16 = 10; |
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DOUBLE = 11; |
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UINT32 = 12; |
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UINT64 = 13; |
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COMPLEX64 = 14; // complex with float32 real and imaginary components |
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COMPLEX128 = 15; // complex with float64 real and imaginary components |
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// Non-IEEE floating-point format based on IEEE754 single-precision |
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// floating-point number truncated to 16 bits. |
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// This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits. |
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BFLOAT16 = 16; |
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// Future extensions go here. |
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} |
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// The shape of the tensor. |
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repeated int64 dims = 1; |
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// The data type of the tensor. |
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// This field MUST have a valid TensorProto.DataType value |
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optional int32 data_type = 2; |
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// For very large tensors, we may want to store them in chunks, in which |
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// case the following fields will specify the segment that is stored in |
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// the current TensorProto. |
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message Segment { |
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optional int64 begin = 1; |
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optional int64 end = 2; |
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} |
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optional Segment segment = 3; |
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// Tensor content must be organized in row-major order. |
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// |
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// Depending on the data_type field, exactly one of the fields below with |
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// name ending in _data is used to store the elements of the tensor. |
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// For float and complex64 values |
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// Complex64 tensors are encoded as a single array of floats, |
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// with the real components appearing in odd numbered positions, |
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// and the corresponding imaginary component apparing in the |
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// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] |
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// is encoded as [1.0, 2.0 ,3.0 ,4.0] |
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// When this field is present, the data_type field MUST be FLOAT or COMPLEX64. |
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|
repeated float float_data = 4 [packed = true]; |
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// For int32, uint8, int8, uint16, int16, bool, and float16 values |
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|
// float16 values must be bit-wise converted to an uint16_t prior |
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// to writing to the buffer. |
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// When this field is present, the data_type field MUST be |
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|
// INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16 |
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repeated int32 int32_data = 5 [packed = true]; |
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// For strings. |
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|
// Each element of string_data is a UTF-8 encoded Unicode |
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|
// string. No trailing null, no leading BOM. The protobuf "string" |
|
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|
// scalar type is not used to match ML community conventions. |
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|
// When this field is present, the data_type field MUST be STRING |
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|
repeated bytes string_data = 6; |
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|
// For int64. |
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|
// When this field is present, the data_type field MUST be INT64 |
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|
repeated int64 int64_data = 7 [packed = true]; |
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|
// Optionally, a name for the tensor. |
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|
optional string name = 8; // namespace Value |
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|
// A human-readable documentation for this tensor. Markdown is allowed. |
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|
optional string doc_string = 12; |
|
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|
|
// Serializations can either use one of the fields above, or use this |
|
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|
// raw bytes field. The only exception is the string case, where one is |
|
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|
|
// required to store the content in the repeated bytes string_data field. |
|
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|
|
// |
|
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|
|
// When this raw_data field is used to store tensor value, elements MUST |
|
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|
|
// be stored in as fixed-width, little-endian order. |
|
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|
|
// Floating-point data types MUST be stored in IEEE 754 format. |
|
|
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|
|
// Complex64 elements must be written as two consecutive FLOAT values, real component first. |
|
|
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|
|
// Complex128 elements must be written as two consecutive DOUBLE values, real component first. |
|
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|
|
// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). |
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|
|
// |
|
|
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|
|
// Note: the advantage of specific field rather than the raw_data field is |
|
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|
|
// that in some cases (e.g. int data), protobuf does a better packing via |
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|
|
// variable length storage, and may lead to smaller binary footprint. |
|
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|
|
|
// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED |
|
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|
|
optional bytes raw_data = 9; |
|
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|
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|
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|
|
|
// Data can be stored inside the protobuf file using type-specific fields or raw_data. |
|
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|
|
// 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: |
|
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|
|
// - "location" (required) - POSIX filesystem path relative to the directory where the ONNX |
|
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|
|
// protobuf model was stored |
|
|
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|
|
// - "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; |
|
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|
|
|
|
|
|
|
|
|
// Location of the data for this tensor. MUST be one of: |
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|
|
// - 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. |
|
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|
|
|
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]; |
|
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
// A serialized sparse-tensor value |
|
|
|
|
|
message SparseTensorProto { |
|
|
|
|
|
// The sequence of non-default values are encoded as a tensor of shape [NNZ]. |
|
|
|
|
|
// The default-value is zero for numeric tensors, and empty-string for string tensors. |
|
|
|
|
|
optional TensorProto values = 1; |
|
|
|
|
|
|
|
|
|
|
|
// The indices of the non-default values, which may be stored in one of two formats. |
|
|
|
|
|
// (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value |
|
|
|
|
|
// corresponding to the j-th index of the i-th value (in the values tensor). |
|
|
|
|
|
// (b) Indices can be a tensor of shape [NNZ], in which case the i-th value |
|
|
|
|
|
// must be the linearized-index of the i-th value (in the values tensor). |
|
|
|
|
|
// The linearized-index can be converted into an index tuple (k_1,...,k_rank) |
|
|
|
|
|
// using the shape provided below. |
|
|
|
|
|
// The indices must appear in ascending order without duplication. |
|
|
|
|
|
// In the first format, the ordering is lexicographic-ordering: |
|
|
|
|
|
// e.g., index-value [1,4] must appear before [2,1] |
|
|
|
|
|
optional TensorProto indices = 2; |
|
|
|
|
|
|
|
|
|
|
|
// The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank] |
|
|
|
|
|
repeated int64 dims = 3; |
|
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
// Defines a tensor shape. A dimension can be either an integer value |
|
|
|
|
|
// or a symbolic variable. A symbolic variable represents an unknown |
|
|
|
|
|
// dimension. |
|
|
|
|
|
message TensorShapeProto { |
|
|
|
|
|
message Dimension { |
|
|
|
|
|
oneof value { |
|
|
|
|
|
int64 dim_value = 1; |
|
|
|
|
|
string dim_param = 2; // namespace Shape |
|
|
|
|
|
}; |
|
|
|
|
|
// Standard denotation can optionally be used to denote tensor |
|
|
|
|
|
// dimensions with standard semantic descriptions to ensure |
|
|
|
|
|
// that operations are applied to the correct axis of a tensor. |
|
|
|
|
|
// Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition |
|
|
|
|
|
// for pre-defined dimension denotations. |
|
|
|
|
|
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; |
|
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
// 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; |
|
|
|
|
|
}; |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
oneof value { |
|
|
|
|
|
// The type of a tensor. |
|
|
|
|
|
Tensor tensor_type = 1; |
|
|
|
|
|
|
|
|
|
|
|
// NOTE: DNN-only implementations of ONNX MAY elect to not support non-tensor values |
|
|
|
|
|
// as input and output to graphs and nodes. These types are needed to naturally |
|
|
|
|
|
// support classical ML operators. DNN operators SHOULD restrict their input |
|
|
|
|
|
// and output types to tensors. |
|
|
|
|
|
|
|
|
|
|
|
// The type of a sequence. |
|
|
|
|
|
Sequence sequence_type = 4; |
|
|
|
|
|
|
|
|
|
|
|
// The type of a map. |
|
|
|
|
|
Map map_type = 5; |
|
|
|
|
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
// An optional denotation can be used to denote the whole |
|
|
|
|
|
// type with a standard semantic description as to what is |
|
|
|
|
|
// stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition |
|
|
|
|
|
// for pre-defined type denotations. |
|
|
|
|
|
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; |
|
|
|
|
|
} |
mindspore-ci-bot
Gitee
4 years ago