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397 lines
8.9 KiB
397 lines
8.9 KiB
/* Copyright (C) 2018. Huawei Technologies Co., Ltd. All rights reserved.
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*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the Apache License Version 2.0.You may not use this file except in compliance with the License.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* Apache License for more details at
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* http://www.apache.org/licenses/LICENSE-2.0
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*/
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syntax = "proto3";
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package domi;
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enum TargetType
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{
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MINI = 0;
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TINY = 1;
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LITE = 2;
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}
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// offline model
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message ModelDef {
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string name = 1;
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uint32 version = 2;
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uint64 memory_size = 10;
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uint32 stream_num = 11;
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uint32 event_num = 12;
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uint64 weight_size = 13;
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uint32 label_num = 15;
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repeated OpDef op = 20;
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TargetType target_type = 23;
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map<string, AttrDef> attr = 30;
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};
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// operator define
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message OpDef {
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string name = 1;
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string type = 2;
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uint32 id = 3;
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uint32 stream_id = 4;
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repeated string input_name = 5;
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repeated string src_name = 8;
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repeated int32 src_index = 9;
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repeated int64 input = 10;
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repeated int64 output = 11;
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repeated TensorDescriptor input_desc = 12;
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repeated TensorDescriptor output_desc = 13;
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repeated WeightDef weights = 14;
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repeated string dst_name = 15;
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repeated int32 dst_index = 16;
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repeated int64 workspace = 20;
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repeated uint32 workspace_bytes = 21;
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repeated string weight_name = 22;
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repeated bool is_input_const = 23;
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map<string, AttrDef> attr = 30;
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QuantizeFactorParams quantize_factor = 31;
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oneof op_params {
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// start at 100 here
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SendOpParams sender_param = 100;
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RecvOpParams receiver_param = 200;
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ConvolutionOpParams convolution_param = 300;
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PoolingOpParams pooling_param = 400;
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EltwiseOpParams eltwise_param = 500;
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BatchNormOpParams batchnorm_param = 600;
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ScaleOpParams scale_param = 700;
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FullConnectionOpParams full_connection_param = 800;
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SoftmaxOpParams softmax_param = 900;
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ActivationOpParams activation_param = 1000;
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ReshapeOpParams reshape_param = 1100;
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}
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};
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message SendOpParams {
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uint32 event_id = 1;
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};
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message RecvOpParams {
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uint32 event_id = 1;
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};
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enum QuantizeScaleType
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{
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VECTOR_SCALE = 0;
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SCALAR_SCALE = 1;
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}
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enum QuantizeScaleMode
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{
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NORMAL_MODE = 0;
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SQRT_MODE = 1;
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}
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enum QuantizeAlgorithm
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{
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NON_OFFSET_ALGO = 0;
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HALF_OFFSET_ALGO = 1;
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ALL_OFFSET_ALGO = 2;
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}
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message QuantizeFactor
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{
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QuantizeScaleMode scale_mode = 1;
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bytes scale_value = 2;
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int64 scale_offset = 3;
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bytes offset_data_value = 4;
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int64 offset_data_offset = 5;
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bytes offset_weight_value = 6;
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int64 offset_weight_offset = 7;
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bytes offset_pad_value = 8;
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int64 offset_pad_offset = 9;
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};
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message QuantizeCalcFactor
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{
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bytes offsetw = 1;
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int64 offsetw_offset = 2;
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bytes offsetd = 3;
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int64 offsetd_offset = 4;
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bytes scalereq = 5;
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int64 scaledreq_offset = 6;
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bytes offsetdnext = 7;
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int64 offsetdnext_offset = 8;
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}
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message QuantizeFactorParams
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{
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QuantizeAlgorithm quantize_algo = 1;
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QuantizeScaleType scale_type = 2;
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QuantizeFactor quantize_param = 3;
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QuantizeFactor dequantize_param = 4;
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QuantizeFactor requantize_param = 5;
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QuantizeCalcFactor quantizecalc_param = 6;
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};
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message ConvolutionOpParams {
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int32 mode = 1;
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int32 algo = 2;
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int32 pad_mode = 3;
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uint32 group = 4;
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uint32 num_output = 5;
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repeated uint32 pad = 10;
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repeated uint32 stride = 11;
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repeated uint32 dilation = 12;
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repeated uint32 kernel = 13;
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float alpha = 20;
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float beta = 21;
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WeightDef filter = 40;
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WeightDef bias = 41;
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bool relu_flag = 62;
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repeated uint32 adj = 70;
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repeated uint32 target_shape = 71;
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repeated uint32 before_pad = 72;
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};
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message PoolingOpParams {
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int32 mode = 1;
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int32 nan_opt = 2;
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int32 pad_mode = 3;
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bool global_pooling = 4;
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repeated uint32 window = 10;
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repeated uint32 pad = 11;
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repeated uint32 stride = 12;
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bool ceil_mode = 13;
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int32 data_mode = 14;
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float alpha = 20;
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float beta = 21;
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repeated uint32 before_pad = 22;
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};
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message EltwiseOpParams {
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int32 mode = 1;
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repeated float coeff = 2;
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float alpha = 3;
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float beta = 4;
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repeated WeightDef weight = 5;
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bool relu_flag = 6;
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};
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message ActivationOpParams {
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int32 mode = 1;
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float coef = 2;
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float alpha = 3;
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float beta = 4;
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};
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message BatchNormOpParams {
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int32 mode = 1;
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float alpha = 2;
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float beta = 3;
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double epsilon = 4;//optinal,[default = 1e-5]
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bool use_global_stats = 5; //optinal,by default true,testing mode
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float moving_average_fraction = 6; //optinal,[default = .999];
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WeightDef estimated_mean = 7;
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WeightDef estimated_variance = 8;
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WeightDef scale = 9;
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WeightDef bias = 10;
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};
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message ScaleOpParams {
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WeightDef scale = 1;
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WeightDef bias = 2;
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};
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message ReshapeOpParams {
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float alpha = 1;
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float beta = 2;
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ShapeDef shape = 3;
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int32 axis = 4;
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int32 num_axes = 5;
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int32 format = 6;
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};
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message SoftmaxOpParams {
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int32 algo = 1;
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int32 mode = 2;
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float alpha = 3;
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float beta = 4;
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};
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message FullConnectionOpParams {
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WeightDef filter = 1;
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WeightDef bias = 2;
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uint32 num_output = 3;
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bool relu_flag = 12;
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};
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message FlattenOpParams {
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float alpha = 1;
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float beta = 2;
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int32 start_axis = 3;
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int32 end_axis = 4;
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}
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message AddLimitedOpParams {
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float alpha = 1;
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float beta = 2;
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int32 axis = 3;
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bool broadcast = 4;
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repeated WeightDef weight = 10;
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};
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message MulLimitedOpParams {
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float alpha = 1;
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float beta = 2;
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int32 axis = 3;
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bool broadcast = 4;
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repeated WeightDef weight = 10;
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};
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message AddOpParams {
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float alpha = 1;
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float beta = 2;
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repeated WeightDef weight = 10;
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};
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message MulOpParams {
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float alpha = 1;
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float beta = 2;
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repeated WeightDef weight = 10;
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};
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message SubOpParams {
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float alpha = 1;
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float beta = 2;
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repeated WeightDef weight = 10;
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};
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message BiasAddOpParams {
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float alpha = 1;
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float beta = 2;
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WeightDef bias = 10;
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};
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message MatMulOpParams {
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float alpha = 1;
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float beta = 2;
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bool transposeX = 3;
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bool transposeW = 4;
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WeightDef filter = 10;
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WeightDef bias = 12;
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};
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message RsqrtOpParams {
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float alpha = 1;
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float beta = 2;
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};
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message WeightDef {
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int32 format = 1;
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int32 data_type = 2;
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ShapeDef shape = 3;
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bytes data = 4;
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int64 data_offset = 5;
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uint32 cmps_size = 6;
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bytes cmps_tab = 7;
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int64 cmps_tab_offset = 10;
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CompressInfo cmps_info = 8;
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AllOffsetQuantizeInfo alloffset_quantize_info = 11;
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}
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message ShapeDef {
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repeated int64 dim = 1;
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}
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enum DeviceType {
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NPU = 0; // In default, we will use NPU.
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CPU = 1; // CPU
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}
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message AllOffsetQuantizeInfo {
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float scale = 1;
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int32 offset = 2;
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}
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message TensorDescriptor {
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int32 format = 1;
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int32 data_type = 2;
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repeated int64 dim = 3;
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uint32 size = 4;
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bool reuse_input = 5;
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bool output_tensor = 7;
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DeviceType device_type = 8;
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bool input_tensor = 9;
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uint32 real_dim_cnt = 10;
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uint32 reuse_input_index = 11;
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AllOffsetQuantizeInfo alloffset_quantize_info = 12;
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}
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message CompressInfo {
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int32 blockRow = 1; // block row
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int32 blockCol = 2; // block col
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int32 fractalK = 3; // fractal K
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int32 fractalN = 4; // fractal N
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int32 lastFractalK = 5; // K of last fractal
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int32 lastFractalN = 6; // N of last fractal
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int32 cubeSize = 7; // cube's length
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int32 loadDir = 8; // data load directtiono 0:col load 1:row load
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}
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message AttrDef {
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message ListValue {
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repeated string s = 2; // "list(string)"
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repeated int64 i = 3 [packed = true]; // "list(int)"
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repeated float f = 4 [packed = true]; // "list(float)"
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repeated bool b = 5 [packed = true]; // "list(bool)"
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repeated uint32 u = 6 [packed = true]; // "list(uint)"
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repeated bytes bt = 7;
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}
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oneof value {
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string s = 2; // "string"
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int64 i = 3; // "int"
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float f = 4; // "float"
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bool b = 5; // "bool"
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uint32 u = 6; // "uint32"
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bytes bt = 7;
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ListValue list = 1; // any "list(...)"
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NamedAttrs func = 10;
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}
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}
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// A list of attr names and their values. The whole list is attached
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// with a string name. E.g., MatMul[T=float].
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message NamedAttrs {
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string name = 1;
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map<string, AttrDef> attr = 2;
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}
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