mirror of https://github.com/dragonflyoss/api.git
113 lines
3.9 KiB
Protocol Buffer
113 lines
3.9 KiB
Protocol Buffer
/*
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* Copyright 2023 The Dragonfly Authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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syntax = "proto3";
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package tfserving.v1;
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import "pkg/apis/tfserving/v1/resource_handle.proto";
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import "pkg/apis/tfserving/v1/tensor_shape.proto";
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import "pkg/apis/tfserving/v1/types.proto";
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option go_package = "d7y.io/api/pkg/apis/tfserving/v1;tfserving";
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// Protocol buffer representing a tensor.
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message TensorProto {
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tfserving.v1.DataType dtype = 1;
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// Shape of the tensor. TODO(touts): sort out the 0-rank issues.
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tfserving.v1.TensorShapeProto tensor_shape = 2;
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// Only one of the representations below is set, one of "tensor_contents" and
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// the "xxx_val" attributes. We are not using oneof because as oneofs cannot
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// contain repeated fields it would require another extra set of messages.
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// Version number.
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//
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// In version 0, if the "repeated xxx" representations contain only one
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// element, that element is repeated to fill the shape. This makes it easy
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// to represent a constant Tensor with a single value.
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int32 version_number = 3;
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// Serialized raw tensor content from either Tensor::AsProtoTensorContent or
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// memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
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// can be used for all tensor types. The purpose of this representation is to
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// reduce serialization overhead during RPC call by avoiding serialization of
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// many repeated small items.
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bytes tensor_content = 4;
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// Type specific representations that make it easy to create tensor protos in
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// all languages. Only the representation corresponding to "dtype" can
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// be set. The values hold the flattened representation of the tensor in
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// row major order.
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// DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll
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// have some pointless zero padding for each value here.
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repeated int32 half_val = 13 [packed = true];
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// DT_FLOAT.
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repeated float float_val = 5 [packed = true];
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// DT_DOUBLE.
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repeated double double_val = 6 [packed = true];
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// DT_INT32, DT_INT16, DT_UINT16, DT_INT8, DT_UINT8.
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repeated int32 int_val = 7 [packed = true];
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// DT_STRING
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repeated bytes string_val = 8;
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// DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
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// and imaginary parts of i-th single precision complex.
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repeated float scomplex_val = 9 [packed = true];
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// DT_INT64
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repeated int64 int64_val = 10 [packed = true];
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// DT_BOOL
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repeated bool bool_val = 11 [packed = true];
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// DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
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// and imaginary parts of i-th double precision complex.
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repeated double dcomplex_val = 12 [packed = true];
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// DT_RESOURCE
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repeated tfserving.v1.ResourceHandleProto resource_handle_val = 14;
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// DT_VARIANT
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repeated VariantTensorDataProto variant_val = 15;
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// DT_UINT32
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repeated uint32 uint32_val = 16 [packed = true];
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// DT_UINT64
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repeated uint64 uint64_val = 17 [packed = true];
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// DT_FLOAT8_*, use variable-sized set of bytes
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// (i.e. the equivalent of repeated uint8, if such a thing existed).
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bytes float8_val = 18;
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}
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// Protocol buffer representing the serialization format of DT_VARIANT tensors.
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message VariantTensorDataProto {
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// Name of the type of objects being serialized.
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string type_name = 1;
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// Portions of the object that are not Tensors.
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bytes metadata = 2;
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// Tensors contained within objects being serialized.
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repeated tfserving.v1.TensorProto tensors = 3;
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}
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