From 40d2a16f0e8ecc2fad3354b4fec65bb539f3aa2e Mon Sep 17 00:00:00 2001 From: scorebot Date: Wed, 9 Oct 2024 22:00:42 -0700 Subject: [PATCH] Fix the build error --- src/main/protobuf/onnx-ml.proto | 553 ------------------ .../serving/deploy/OnnxModel.scala | 12 + .../serving/protobuf/package.scala | 3 +- 3 files changed, 13 insertions(+), 555 deletions(-) delete mode 100644 src/main/protobuf/onnx-ml.proto diff --git a/src/main/protobuf/onnx-ml.proto b/src/main/protobuf/onnx-ml.proto deleted file mode 100644 index 081bb3c..0000000 --- a/src/main/protobuf/onnx-ml.proto +++ /dev/null @@ -1,553 +0,0 @@ -// -// WARNING: This file is automatically generated! Please edit onnx.in.proto. -// - -// Copyright (c) ONNX Project Contributors. -// 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 getMetadata the community involved -// by sharing our working version of ONNX. -// -// Protobuf compatibility -// -// To simplify framework compatibility, ONNX is defined using the subset of protobuf -// that is compatible with both protobuf v2 and v3. This means that we do not use any -// protobuf features that are only available in one of the two versions. -// -// Here are the most notable contortions we have to carry out to work around -// these limitations: -// -// - No 'map' (added protobuf 3.0). We instead represent mappings as lists -// of key-value pairs, where order does not matter and duplicates -// are not allowed. - -// Versioning -// -// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md -// -// To be compatible with both proto2 and proto3, we will use a version number -// that is not defined by the default value but an explicit enum number. -enum Version { - // proto3 requires the first enum value to be zero. - // We add this just to appease the compiler. - _START_VERSION = 0; - // The version field is always serialized and we will use it to store the - // version that the graph is generated from. This helps us set up version - // control. - // For the IR, we are using simple numbers starting with with 0x00000001, - // which was the version we published on Oct 10, 2017. - IR_VERSION_2017_10_10 = 0x0000000000000001; - - // IR_VERSION 2 published on Oct 30, 2017 - // - Added type discriminator to AttributeProto to support proto3 users - IR_VERSION_2017_10_30 = 0x0000000000000002; - - // IR VERSION 3 published on Nov 3, 2017 - // - For operator versioning: - // - Added new message OperatorSetIdProto - // - Added opset_import in ModelProto - // - For vendor extensions, added domain in NodeProto - IR_VERSION_2017_11_3 = 0x0000000000000003; - - // IR VERSION 4 published on Jan 22, 2019 - // - Relax constraint that initializers should be a subset of graph inputs - // - Add type BFLOAT16 - IR_VERSION_2019_1_22 = 0x0000000000000004; - - // IR VERSION 5 published on March 18, 2019 - // - Add message TensorAnnotation. - // - Add quantization annotation in GraphProto to map tensor with its scale and zero point quantization parameters. - IR_VERSION = 0x0000000000000005; -} - -// 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; - - // Named metadata values; keys should be distinct. - repeated StringStringEntryProto metadata_props = 14; -}; - -// StringStringEntryProto follows the pattern for cross-proto-version maps. -// See https://developers.google.com/protocol-buffers/docs/proto3#maps -message StringStringEntryProto { - string key = 1; - string value = 2; -}; - -message TensorAnnotation { - string tensor_name = 1; - // pairs to annotate tensor specified by above. - // The keys used in the mapping below must be pre-defined in ONNX spec. - // For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as - // quantization parameter keys. - repeated StringStringEntryProto quant_parameter_tensor_names = 2; -} - -// Graphs -// -// A graph defines the computational logic of a model and is comprised of a parameterized -// list of nodes that form a directed acyclic graph based on their inputs and outputs. -// This is the equivalent of the "network" or "graph" in many deep learning -// frameworks. -message GraphProto { - // The nodes in the graph, sorted topologically. - repeated NodeProto node = 1; - - // The name of the graph. - string name = 2; // namespace Graph - - // A list of named tensor values, used to specify constant inputs of the graph. - // Each TensorProto entry must have a distinct name (within the list) that - // MAY also appear in the input list. - repeated TensorProto initializer = 5; - - // A human-readable documentation for this graph. Markdown is allowed. - string doc_string = 10; - - // The inputs and outputs of the graph. - repeated ValueInfoProto input = 11; - repeated ValueInfoProto output = 12; - - // Information for the values in the graph. The ValueInfoProto.name's - // must be distinct. It is optional for a value to appear in value_info list. - repeated ValueInfoProto value_info = 13; - - // This field carries information to indicate the mapping among a tensor and its - // quantization parameter tensors. For example: - // For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated, - // which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model. - repeated TensorAnnotation quantization_annotation = 14; - - // DO NOT USE the following fields, they were deprecated from earlier versions. - // repeated string input = 3; - // repeated string output = 4; - // optional int64 ir_version = 6; - // optional int64 producer_version = 7; - // optional string producer_tag = 8; - // optional string domain = 9; -} - -// Tensors -// -// A serialized tensor value. -message TensorProto { - enum DataType { - UNDEFINED = 0; - // Basic types. - FLOAT = 1; // float - UINT8 = 2; // uint8_t - INT8 = 3; // int8_t - UINT16 = 4; // uint16_t - INT16 = 5; // int16_t - INT32 = 6; // int32_t - INT64 = 7; // int64_t - STRING = 8; // string - BOOL = 9; // bool - - // IEEE754 half-precision floating-point format (16 bits wide). - // This format has 1 sign bit, 5 exponent bits, and 10 mantissa bits. - FLOAT16 = 10; - - DOUBLE = 11; - UINT32 = 12; - UINT64 = 13; - COMPLEX64 = 14; // complex with float32 real and imaginary components - COMPLEX128 = 15; // complex with float64 real and imaginary components - - // Non-IEEE floating-point format based on IEEE754 single-precision - // floating-point number truncated to 16 bits. - // This format has 1 sign bit, 8 exponent bits, and 7 mantissa bits. - BFLOAT16 = 16; - - // Future extensions go here. - } - - // The shape of the tensor. - repeated int64 dims = 1; - - // The data type of the tensor. - // This field MUST have a valid TensorProto.DataType value - int32 data_type = 2; - - // For very large tensors, we may want to store them in chunks, in which - // case the following fields will specify the segment that is stored in - // the current TensorProto. - message Segment { - int64 begin = 1; - int64 end = 2; - } - Segment segment = 3; - - // Tensor content must be organized in row-major order. - // - // Depending on the data_type field, exactly one of the fields below with - // name ending in _data is used to store the elements of the tensor. - - // For float and complex64 values - // Complex64 tensors are encoded as a single array of floats, - // with the real components appearing in odd numbered positions, - // and the corresponding imaginary component apparing in the - // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - // is encoded as [1.0, 2.0 ,3.0 ,4.0] - // When this field is present, the data_type field MUST be FLOAT or COMPLEX64. - repeated float float_data = 4 [packed = true]; - - // For int32, uint8, int8, uint16, int16, bool, and float16 values - // float16 values must be bit-wise converted to an uint16_t prior - // to writing to the buffer. - // When this field is present, the data_type field MUST be - // INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16 - repeated int32 int32_data = 5 [packed = true]; - - // For strings. - // Each element of string_data is a UTF-8 encoded Unicode - // string. No trailing null, no leading BOM. The protobuf "string" - // scalar type is not used to match ML community conventions. - // When this field is present, the data_type field MUST be STRING - repeated bytes string_data = 6; - - // For int64. - // When this field is present, the data_type field MUST be INT64 - repeated int64 int64_data = 7 [packed = true]; - - // Optionally, a name for the tensor. - string name = 8; // namespace Value - - // A human-readable documentation for this tensor. Markdown is allowed. - string doc_string = 12; - - // Serializations can either use one of the fields above, or use this - // raw bytes field. The only exception is the string case, where one is - // required to store the content in the repeated bytes string_data field. - // - // When this raw_data field is used to store tensor value, elements MUST - // be stored in as fixed-width, little-endian order. - // Floating-point data types MUST be stored in IEEE 754 format. - // Complex64 elements must be written as two consecutive FLOAT values, real component first. - // Complex128 elements must be written as two consecutive DOUBLE values, real component first. - // Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). - // - // Note: the advantage of specific field rather than the raw_data field is - // that in some cases (e.g. int data), protobuf does a better packing via - // variable length storage, and may lead to smaller binary footprint. - // When this field is present, the data_type field MUST NOT be STRING or UNDEFINED - bytes raw_data = 9; - - // Data can be stored inside the protobuf file using type-specific fields or raw_data. - // Alternatively, raw bytes data can be stored in an external file, using the external_data field. - // external_data stores key-value pairs describing data location. Recognized keys are: - // - "location" (required) - POSIX filesystem path relative to the directory where the ONNX - // protobuf model was stored - // - "offset" (optional) - position of byte at which stored data begins. Integer stored as string. - // Offset values SHOULD be multiples 4096 (page size) to enable mmap support. - // - "length" (optional) - number of bytes containing data. Integer stored as string. - // - "checksum" (optional) - SHA1 digest of file specified in under 'location' key. - repeated StringStringEntryProto external_data = 13; - - // Location of the data for this tensor. MUST be one of: - // - DEFAULT - data stored inside the protobuf message. Data is stored in raw_data (if set) otherwise in type-specified field. - // - EXTERNAL - data stored in an external location as described by external_data field. - enum DataLocation { - DEFAULT = 0; - EXTERNAL = 1; - } - - // If value not set, data is stored in raw_data (if set) otherwise in type-specified field. - DataLocation data_location = 14; - - // For double - // Complex128 tensors are encoded as a single array of doubles, - // with the real components appearing in odd numbered positions, - // and the corresponding imaginary component apparing in the - // subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - // is encoded as [1.0, 2.0 ,3.0 ,4.0] - // When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 - repeated double double_data = 10 [packed = true]; - - // For uint64 and uint32 values - // When this field is present, the data_type field MUST be - // UINT32 or UINT64 - repeated uint64 uint64_data = 11 [packed = true]; -} - -// 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 - 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; -} \ No newline at end of file diff --git a/src/main/scala/com/autodeployai/serving/deploy/OnnxModel.scala b/src/main/scala/com/autodeployai/serving/deploy/OnnxModel.scala index 0154d85..4684a6d 100644 --- a/src/main/scala/com/autodeployai/serving/deploy/OnnxModel.scala +++ b/src/main/scala/com/autodeployai/serving/deploy/OnnxModel.scala @@ -202,6 +202,12 @@ class OnnxModel(val session: OrtSession, val env: OrtEnvironment) extends Predic case UINT8 => { OnnxTensor.createTensor(env, buffer, convertedShape, OnnxJavaType.UINT8) } + case FLOAT16 => { + ??? + } + case BFLOAT16 => { + ??? + } case UNKNOWN => { throw UnknownDataTypeException(name) } @@ -256,6 +262,12 @@ class OnnxModel(val session: OrtSession, val env: OrtEnvironment) extends Predic val data = copyToBuffer[Byte](intCount, value) OnnxTensor.createTensor(env, ByteBuffer.wrap(data), convertedShape, OnnxJavaType.UINT8) } + case FLOAT16 => { + ??? + } + case BFLOAT16 => { + ??? + } case UNKNOWN => { throw UnknownDataTypeException(name) } diff --git a/src/main/scala/com/autodeployai/serving/protobuf/package.scala b/src/main/scala/com/autodeployai/serving/protobuf/package.scala index 035eeec..e79d0b6 100644 --- a/src/main/scala/com/autodeployai/serving/protobuf/package.scala +++ b/src/main/scala/com/autodeployai/serving/protobuf/package.scala @@ -23,8 +23,7 @@ import com.autodeployai.serving.utils.Utils import com.google.protobuf.ByteString import com.google.protobuf.timestamp.Timestamp import io.grpc.{Status, StatusRuntimeException} -import onnx._ -import onnx.TensorProto.DataType._ +import protobuf.TensorProto.DataType._ import scala.jdk.CollectionConverters._