718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
5997 lines
202 KiB
C++
5997 lines
202 KiB
C++
// Tencent is pleased to support the open source community by making ncnn available.
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//
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// Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
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//
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// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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// in compliance with the License. You may obtain a copy of the License at
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//
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// https://opensource.org/licenses/BSD-3-Clause
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//
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// Unless required by applicable law or agreed to in writing, software distributed
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// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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// CONDITIONS OF ANY KIND, either express or implied. See the License for the
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// specific language governing permissions and limitations under the License.
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#include "onnx.pb.h"
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#include <algorithm>
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#include <float.h>
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#include <fstream>
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#include <google/protobuf/io/coded_stream.h>
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#include <google/protobuf/io/zero_copy_stream_impl.h>
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#include <google/protobuf/message.h>
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#include <google/protobuf/text_format.h>
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#include <iostream>
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#include <limits.h>
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#include <limits>
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#include <set>
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#include <stdio.h>
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static bool read_proto_from_binary(const char* filepath, onnx::ModelProto* message)
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{
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std::ifstream fs(filepath, std::ifstream::in | std::ifstream::binary);
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if (!fs.is_open())
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{
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fprintf(stderr, "open failed %s\n", filepath);
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return false;
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}
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google::protobuf::io::IstreamInputStream input(&fs);
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google::protobuf::io::CodedInputStream codedstr(&input);
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#if GOOGLE_PROTOBUF_VERSION >= 3011000
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codedstr.SetTotalBytesLimit(INT_MAX);
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#else
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codedstr.SetTotalBytesLimit(INT_MAX, INT_MAX / 2);
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#endif
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bool success = message->ParseFromCodedStream(&codedstr);
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fs.close();
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return success;
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}
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static std::vector<int> get_node_attr_ai(const onnx::NodeProto& node, const char* key)
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{
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std::vector<int> v;
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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v.resize(attr.ints_size());
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for (int j = 0; j < attr.ints_size(); j++)
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{
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v[j] = std::max(std::min(attr.ints(j), (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
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}
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break;
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}
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}
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return v;
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}
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static std::vector<float> get_node_attr_af(const onnx::NodeProto& node, const char* key)
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{
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std::vector<float> v;
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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v.resize(attr.floats_size());
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for (int j = 0; j < attr.floats_size(); j++)
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{
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v[j] = attr.floats(j);
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}
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break;
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}
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}
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return v;
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}
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static int get_node_attr_i(const onnx::NodeProto& node, const char* key, int def = 0)
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{
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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return std::max(std::min(attr.i(), (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
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}
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}
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return def;
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}
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static float get_node_attr_f(const onnx::NodeProto& node, const char* key, float def = 0.f)
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{
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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return attr.f();
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}
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}
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return def;
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}
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static std::string get_node_attr_s(const onnx::NodeProto& node, const char* key, const std::string& def = std::string())
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{
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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return attr.s();
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}
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}
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return def;
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}
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static onnx::TensorProto get_node_attr_tensor(const onnx::NodeProto& node, const char* key)
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{
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for (int i = 0; i < node.attribute_size(); i++)
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{
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const onnx::AttributeProto& attr = node.attribute(i);
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if (attr.name() == key)
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{
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return attr.t();
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}
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}
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return onnx::TensorProto();
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}
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static float get_node_attr_from_input_f(const onnx::TensorProto& tp)
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{
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float v = 0.f;
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// float
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if (tp.data_type() == 1)
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{
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const float* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const float*)tp.raw_data().data();
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}
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else
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{
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shape_data = tp.float_data().data();
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}
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v = shape_data[0];
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}
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// double
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else if (tp.data_type() == 11)
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{
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const double* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const double*)tp.raw_data().data();
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}
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else
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{
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shape_data = tp.double_data().data();
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}
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v = shape_data[0];
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}
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// int64
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else if (tp.data_type() == 7)
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{
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const int64_t* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const int64_t*)tp.raw_data().data();
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}
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else
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{
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shape_data = tp.int64_data().data();
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}
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v = std::max(std::min(shape_data[0], (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
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}
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// int32
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else if (tp.data_type() == 6)
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{
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const int32_t* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const int32_t*)tp.raw_data().data();
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}
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else
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{
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shape_data = tp.int32_data().data();
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}
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v = shape_data[0];
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}
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else
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{
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fprintf(stderr, "Unknown data type %d\n", tp.data_type());
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abort();
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}
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return v;
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}
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static std::vector<int> get_node_attr_from_input_ai(const onnx::TensorProto& tp)
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{
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int size = 0;
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std::vector<int> v;
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// int64
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if (tp.data_type() == 7)
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{
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const int64_t* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const int64_t*)tp.raw_data().data();
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size = (int)(tp.raw_data().size() / 8);
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}
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else
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{
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shape_data = tp.int64_data().data();
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size = tp.int64_data_size();
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}
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for (int j = 0; j < size; j++)
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{
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int vi = std::max(std::min(shape_data[j], (::google::protobuf::int64)INT_MAX), (::google::protobuf::int64)INT_MIN);
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v.push_back(vi);
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}
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}
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// int32
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else if (tp.data_type() == 6)
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{
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const int32_t* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const int32_t*)tp.raw_data().data();
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size = (int)(tp.raw_data().size() / 4);
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}
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else
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{
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shape_data = tp.int32_data().data();
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size = tp.int32_data_size();
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}
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for (int j = 0; j < size; j++)
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{
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v.push_back(shape_data[j]);
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}
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}
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else
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{
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fprintf(stderr, "Unknown data type %d\n", tp.data_type());
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}
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return v;
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}
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static std::vector<float> get_node_attr_from_input_af(const onnx::TensorProto& tp)
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{
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int size = 0;
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std::vector<float> v;
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// float
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if (tp.data_type() == 1)
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{
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const float* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const float*)tp.raw_data().data();
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size = (int)(tp.raw_data().size() / 4);
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}
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else
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{
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shape_data = tp.float_data().data();
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size = tp.float_data_size();
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}
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for (int j = 0; j < size; j++)
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{
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v.push_back(shape_data[j]);
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}
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}
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// double
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else if (tp.data_type() == 11)
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{
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const double* shape_data = 0;
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if (tp.has_raw_data())
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{
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shape_data = (const double*)tp.raw_data().data();
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size = (int)(tp.raw_data().size() / 8);
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}
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else
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{
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shape_data = tp.double_data().data();
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size = tp.double_data_size();
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}
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for (int j = 0; j < size; j++)
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{
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v.push_back((float)shape_data[j]);
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}
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}
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else
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{
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fprintf(stderr, "Unknown data type %d\n", tp.data_type());
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}
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return v;
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}
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static int get_tensor_proto_data_size(const onnx::TensorProto& tp)
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{
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if (tp.has_raw_data())
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{
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const std::string& raw_data = tp.raw_data();
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int size = (int)raw_data.size() / 4;
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return size;
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}
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else if (tp.data_type() == 1)
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{
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return tp.float_data_size();
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}
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return 0;
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}
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static void fwrite_tensor_proto_data(const onnx::TensorProto& tp, FILE* bp)
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{
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int size = get_tensor_proto_data_size(tp);
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if (tp.has_raw_data())
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{
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const std::string& raw_data = tp.raw_data();
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fwrite(raw_data.data(), sizeof(float), size, bp);
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}
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else if (tp.data_type() == 1)
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{
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fwrite(tp.float_data().data(), sizeof(float), size, bp);
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}
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}
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static void fuse_weight_reshape(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
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{
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int node_count = mutable_graph->node_size();
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for (int i = 0; i < node_count; i++)
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{
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onnx::NodeProto* node = mutable_graph->mutable_node(i);
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// weight <= Reshape(weight)
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if (node->op_type() == "Reshape")
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{
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// check weight
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if (weights.find(node->input(0)) == weights.end())
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continue;
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weights[node->output(0)] = weights[node->input(0)];
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// set weight shape directly
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std::vector<int> shape;
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if (node->input_size() == 1)
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{
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shape = get_node_attr_ai(*node, "shape");
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}
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else if (node->input_size() == 2)
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{
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// opset 5
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shape = get_node_attr_from_input_ai(weights[node->input(1)]);
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}
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weights[node->output(0)].clear_dims();
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for (int j = 0; j < shape.size(); j++)
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{
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weights[node->output(0)].add_dims(shape[j]);
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}
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// reduce
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node->set_op_type("noop_reducedncnn");
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node_reference[node->input(0)] -= 1;
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if (node->input_size() == 2)
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{
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node_reference[node->input(1)] -= 1;
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}
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reduced_node_count += 1;
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i += 1;
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}
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}
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}
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static void fuse_weight_transpose(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
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{
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int node_count = mutable_graph->node_size();
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for (int i = 0; i < node_count; i++)
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{
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onnx::NodeProto* node = mutable_graph->mutable_node(i);
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// weight <= Transpose(weight)
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if (node->op_type() == "Transpose")
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{
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// check weight
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if (weights.find(node->input(0)) == weights.end())
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continue;
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if (weights[node->input(0)].dims_size() != 2)
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continue;
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// perm = (1, 0)
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std::vector<int> perm = get_node_attr_ai(*node, "perm");
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if (perm.size() != 2)
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continue;
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if (perm[0] != 1 || perm[1] != 0)
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continue;
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weights[node->output(0)] = weights[node->input(0)];
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// permute weight
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{
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onnx::TensorProto& B = weights[node->output(0)];
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const int h = B.dims(0);
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const int w = B.dims(1);
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std::vector<float> permuted_data;
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permuted_data.reserve((size_t)h * w);
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const float* bptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
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for (int j = 0; j < w; j++)
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{
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for (int k = 0; k < h; k++)
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{
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float vb = bptr[k * w + j];
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permuted_data.push_back(vb);
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}
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}
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B.set_dims(0, w);
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B.set_dims(1, h);
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if (B.has_raw_data())
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{
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B.set_raw_data(permuted_data.data(), permuted_data.size() * sizeof(float));
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}
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else
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{
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for (int j = 0; j < (int)permuted_data.size(); j++)
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B.set_float_data(j, permuted_data[j]);
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}
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}
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// reduce
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node->set_op_type("noop_reducedncnn");
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node_reference[node->input(0)] -= 1;
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reduced_node_count += 1;
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i += 1;
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}
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}
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}
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static void fuse_shufflechannel(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
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{
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int node_count = mutable_graph->node_size();
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for (int i = 0; i < node_count; i++)
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{
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onnx::NodeProto* node = mutable_graph->mutable_node(i);
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// ShuffleChannel <= Reshape - Transpose - Reshape
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// ShuffleChannel <= Reshape - Transpose - Constant - Reshape
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if (node->op_type() == "Reshape")
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{
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if (node_reference[node->output(0)] != 1)
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continue;
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std::vector<int> shape;
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if (node->input_size() == 1)
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{
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shape = get_node_attr_ai(*node, "shape");
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}
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else
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{
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// skip weight reshape
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if (weights.find(node->input(1)) == weights.end())
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continue;
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shape = get_node_attr_from_input_ai(weights[node->input(1)]);
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}
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// 1 groups channels_per_group, height, width
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// reverse style = channels_per_group, groups, height * width
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if (shape.size() != 5 && shape.size() != 3)
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continue;
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if (shape.size() == 5 && shape[0] != 1)
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continue;
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if (i + 2 >= node_count)
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continue;
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onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
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onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
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if (node3->op_type() == "Constant")
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{
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if (i + 3 >= node_count)
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continue;
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node3 = mutable_graph->mutable_node(i + 3);
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}
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if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
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continue;
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|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
// 0 2 1 3 4
|
|
// reverse style = 1 0 2
|
|
std::vector<int> perm = get_node_attr_ai(*node2, "perm");
|
|
if (perm.size() != 5 && perm.size() != 3)
|
|
continue;
|
|
|
|
if (perm.size() == 5 && (perm[0] != 0 || perm[1] != 2 || perm[2] != 1 || perm[3] != 3 || perm[4] != 4))
|
|
continue;
|
|
|
|
if (perm.size() == 3 && (perm[0] != 1 || perm[1] != 0 || perm[2] != 2))
|
|
continue;
|
|
|
|
std::vector<int> shape3;
|
|
if (node3->input_size() == 1)
|
|
{
|
|
shape3 = get_node_attr_ai(*node3, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape3 = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
}
|
|
|
|
// 1, -1, height, width
|
|
// reverse style = group, -1, channels_per_group, height, width
|
|
if (shape3.size() != 4 && shape3.size() != 5)
|
|
continue;
|
|
|
|
if (shape3.size() == 4 && (shape3[0] != 1 || (shape3[1] != -1 && shape3[1] != shape[1] * shape[2])))
|
|
continue;
|
|
|
|
if (shape3.size() == 5 && (shape3[0] != shape[1] || shape3[2] != shape[0] || shape3[3] * shape3[4] != shape[2]))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
if (node->input_size() == 2)
|
|
{
|
|
node_reference[node->input(1)] -= 1;
|
|
}
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node3->set_op_type("ShuffleChannel");
|
|
node3->set_input(0, node->input(0));
|
|
|
|
onnx::AttributeProto* attr_group = node3->add_attribute();
|
|
attr_group->set_name("group");
|
|
attr_group->set_i(shape[1]);
|
|
|
|
onnx::AttributeProto* attr_reverse = node3->add_attribute();
|
|
attr_reverse->set_name("reverse");
|
|
attr_reverse->set_i(shape.size() == 3);
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_shufflechannel_split(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Split <= ShuffleChannel(reverse type) - Gather(0) - Gather(1)
|
|
if (node->op_type() == "ShuffleChannel")
|
|
{
|
|
// reverse = 1
|
|
int reverse = get_node_attr_i(*node, "reverse");
|
|
if (reverse != 1)
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node2->op_type() != "Gather" || node3->op_type() != "Gather")
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node->output(0))
|
|
continue;
|
|
|
|
// axis = 0
|
|
int gather2_axis = get_node_attr_i(*node2, "axis");
|
|
if (gather2_axis != 0)
|
|
continue;
|
|
|
|
// indices = 0
|
|
if (weights.find(node2->input(1)) == weights.end())
|
|
continue;
|
|
|
|
std::vector<int> gather2_indices = get_node_attr_from_input_ai(weights[node2->input(1)]);
|
|
if (gather2_indices.size() != 1 || gather2_indices[0] != 0)
|
|
continue;
|
|
|
|
// axis = 0
|
|
int gather3_axis = get_node_attr_i(*node3, "axis");
|
|
if (gather3_axis != 0)
|
|
continue;
|
|
|
|
// indices = 1
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
std::vector<int> gather3_indices = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
if (gather3_indices.size() != 1 || gather3_indices[0] != 1)
|
|
continue;
|
|
|
|
// reduce
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 2;
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node3->input(1)] -= 1;
|
|
|
|
node3->set_op_type("Split");
|
|
node3->clear_input();
|
|
node3->add_input(node->output(0));
|
|
node3->add_output(node3->output(0));
|
|
node3->set_output(0, node2->output(0));
|
|
|
|
node3->clear_attribute();
|
|
onnx::AttributeProto* attr_axis = node3->add_attribute();
|
|
attr_axis->set_name("axis");
|
|
attr_axis->set_i(1);
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_hardswish(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Div(/6)
|
|
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Mul(*(1/6))
|
|
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Constant - Div(/6)
|
|
// HardSwish <= Add(+3) - Clip(0,6) - Mul(X,) - Constant - Mul(*(1/6))
|
|
// out = x * F.relu6(x + 3, inplace=True) / 6
|
|
if (node->op_type() == "Add")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 3 >= node_count)
|
|
continue;
|
|
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& add_three = weights[node->input(1)];
|
|
if (add_three.dims_size() != 0 || get_tensor_proto_data_size(add_three) != 1)
|
|
continue;
|
|
|
|
float constant_add_three = get_node_attr_from_input_f(add_three);
|
|
if (constant_add_three != 3.f)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
|
|
if (node4->op_type() == "Constant")
|
|
{
|
|
if (i + 4 >= node_count)
|
|
continue;
|
|
|
|
node4 = mutable_graph->mutable_node(i + 4);
|
|
}
|
|
|
|
if (node2->op_type() != "Clip" || node3->op_type() != "Mul" || (node4->op_type() != "Div" && node4->op_type() != "Mul"))
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
float relu6_min;
|
|
float relu6_max;
|
|
if (node2->input_size() == 1)
|
|
{
|
|
relu6_min = get_node_attr_f(*node2, "min", -FLT_MAX);
|
|
relu6_max = get_node_attr_f(*node2, "max", FLT_MAX);
|
|
}
|
|
else
|
|
{
|
|
const onnx::TensorProto& min_tp = weights[node2->input(1)];
|
|
const onnx::TensorProto& max_tp = weights[node2->input(2)];
|
|
|
|
relu6_min = get_node_attr_from_input_f(min_tp);
|
|
relu6_max = get_node_attr_from_input_f(max_tp);
|
|
}
|
|
if (relu6_min != 0.f || relu6_max != 6.f)
|
|
continue;
|
|
|
|
if (node_reference[node3->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node3->input(0) != node->input(0) || node3->input(1) != node2->output(0))
|
|
continue;
|
|
|
|
if (weights.find(node4->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& div_six = weights[node4->input(1)];
|
|
if (div_six.dims_size() != 0 || get_tensor_proto_data_size(div_six) != 1)
|
|
continue;
|
|
|
|
float constant_div_six = get_node_attr_from_input_f(div_six);
|
|
if (node4->op_type() == "Div" && constant_div_six != 6.f)
|
|
continue;
|
|
if (node4->op_type() == "Mul" && constant_div_six != 1 / 6.f)
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
node_reference[node->input(1)] -= 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
if (node2->input_size() == 3)
|
|
{
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node2->input(2)] -= 1;
|
|
}
|
|
node_reference[node2->output(0)] -= 1;
|
|
node_reference[node3->output(0)] -= 1;
|
|
node_reference[node4->input(1)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
blob_names.erase(node3->output(0));
|
|
|
|
node4->set_op_type("HardSwish");
|
|
node4->clear_input();
|
|
node4->add_input(node->input(0));
|
|
|
|
onnx::AttributeProto* attr_alpha = node4->add_attribute();
|
|
attr_alpha->set_name("alpha");
|
|
attr_alpha->set_f(1.f / 6.f);
|
|
|
|
onnx::AttributeProto* attr_beta = node4->add_attribute();
|
|
attr_beta->set_name("beta");
|
|
attr_beta->set_f(3.f / 6.f);
|
|
|
|
reduced_node_count += 3;
|
|
i += 3;
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// HardSwish <= HardSigmoid - Mul
|
|
// out = x * hsigmoid(x)
|
|
if (node->op_type() == "HardSigmoid")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
float alpha = get_node_attr_f(*node, "alpha", 0.2f);
|
|
float beta = get_node_attr_f(*node, "beta", 0.5f);
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "Mul")
|
|
continue;
|
|
|
|
if (node2->input(0) != node->input(0) || node2->input(1) != node->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node2->set_op_type("HardSwish");
|
|
node2->clear_input();
|
|
node2->add_input(node->input(0));
|
|
|
|
onnx::AttributeProto* attr_alpha = node2->add_attribute();
|
|
attr_alpha->set_name("alpha");
|
|
attr_alpha->set_f(alpha);
|
|
|
|
onnx::AttributeProto* attr_beta = node2->add_attribute();
|
|
attr_beta->set_name("beta");
|
|
attr_beta->set_f(beta);
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_hardsigmoid(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// HardSigmoid <= Add(+3) - Clip(0,6) - Div(/6)
|
|
// HardSigmoid <= Add(+3) - Clip(0,6) - Mul(*(1/6))
|
|
// HardSigmoid <= Add(+3) - Clip(0,6) - Constant - Div(/6)
|
|
// HardSigmoid <= Add(+3) - Clip(0,6) - Constant - Mul(*(1/6))
|
|
// out = F.relu6(x + 3, inplace=True) / 6
|
|
if (node->op_type() == "Add")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& add_three = weights[node->input(1)];
|
|
if (add_three.dims_size() != 0 || get_tensor_proto_data_size(add_three) != 1)
|
|
continue;
|
|
|
|
float constant_add_three = get_node_attr_from_input_f(add_three);
|
|
if (constant_add_three != 3.f)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node3->op_type() == "Constant")
|
|
{
|
|
if (i + 3 >= node_count)
|
|
continue;
|
|
|
|
node3 = mutable_graph->mutable_node(i + 3);
|
|
}
|
|
|
|
if (node2->op_type() != "Clip" || (node3->op_type() != "Div" && node3->op_type() != "Mul"))
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
float relu6_min;
|
|
float relu6_max;
|
|
if (node2->input_size() == 1)
|
|
{
|
|
relu6_min = get_node_attr_f(*node2, "min", -FLT_MAX);
|
|
relu6_max = get_node_attr_f(*node2, "max", FLT_MAX);
|
|
}
|
|
else
|
|
{
|
|
const onnx::TensorProto& min_tp = weights[node2->input(1)];
|
|
const onnx::TensorProto& max_tp = weights[node2->input(2)];
|
|
|
|
relu6_min = get_node_attr_from_input_f(min_tp);
|
|
relu6_max = get_node_attr_from_input_f(max_tp);
|
|
}
|
|
if (relu6_min != 0.f || relu6_max != 6.f)
|
|
continue;
|
|
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& div_six = weights[node3->input(1)];
|
|
if (div_six.dims_size() != 0 || get_tensor_proto_data_size(div_six) != 1)
|
|
continue;
|
|
|
|
float constant_div_six = get_node_attr_from_input_f(div_six);
|
|
if (node3->op_type() == "Div" && constant_div_six != 6.f)
|
|
continue;
|
|
if (node3->op_type() == "Mul" && constant_div_six != 1 / 6.f)
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(1)] -= 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
if (node2->input_size() == 3)
|
|
{
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node2->input(2)] -= 1;
|
|
}
|
|
node_reference[node2->output(0)] -= 1;
|
|
node_reference[node3->input(1)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node3->set_op_type("HardSigmoid");
|
|
node3->clear_input();
|
|
node3->add_input(node->input(0));
|
|
|
|
onnx::AttributeProto* attr_alpha = node3->add_attribute();
|
|
attr_alpha->set_name("alpha");
|
|
attr_alpha->set_f(1.f / 6.f);
|
|
|
|
onnx::AttributeProto* attr_beta = node3->add_attribute();
|
|
attr_beta->set_name("beta");
|
|
attr_beta->set_f(3.f / 6.f);
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_swish(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Swish <= Sigmoid - Mul
|
|
// x * torch.sigmoid(x)
|
|
if (node->op_type() == "Sigmoid")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "Mul")
|
|
continue;
|
|
|
|
if (node2->input(0) != node->input(0) || node2->input(1) != node->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node2->set_op_type("Swish");
|
|
node2->clear_input();
|
|
node2->add_input(node->input(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_batchnorm1d_squeeze_unsqueeze(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// BatchNormalization <= Unsqueeze - BatchNormalization - Squeeze
|
|
if (node->op_type() == "Unsqueeze")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node2->op_type() != "BatchNormalization" || node3->op_type() != "Squeeze")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node2->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node2->set_input(0, node->input(0));
|
|
node2->set_output(0, node3->output(0));
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_unsqueeze_prelu(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// PReLU <= Unsqueeze - PReLU
|
|
if (node->op_type() == "Unsqueeze")
|
|
{
|
|
// check weight
|
|
if (weights.find(node->input(0)) == weights.end())
|
|
continue;
|
|
|
|
onnx::TensorProto& B = weights[node->input(0)];
|
|
if (B.dims_size() != 1)
|
|
continue;
|
|
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
// axes = (1, 2)
|
|
std::vector<int> axes = get_node_attr_ai(*node, "axes");
|
|
if (axes.size() != 2)
|
|
continue;
|
|
if (axes[0] != 1 || axes[1] != 2)
|
|
continue;
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "PRelu")
|
|
continue;
|
|
|
|
if (node2->input(1) != node->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node2->set_input(1, node->input(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_normalize(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Normalize <= X - ReduceL2 - Clip - Expand - Div
|
|
// Normalize <= X - ReduceL2 - Clip - Shape - Expand - Div
|
|
if (node->op_type() == "ReduceL2")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
// axes = (1)
|
|
std::vector<int> axes = get_node_attr_ai(*node, "axes");
|
|
if (axes.size() != 1)
|
|
continue;
|
|
if (axes[0] != 1)
|
|
continue;
|
|
|
|
if (i + 3 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
|
|
bool has_shape_node = node3->op_type() == "Shape";
|
|
onnx::NodeProto* node_shape = 0;
|
|
if (has_shape_node)
|
|
{
|
|
if (i + 4 >= node_count)
|
|
continue;
|
|
|
|
node_shape = node3;
|
|
node3 = mutable_graph->mutable_node(i + 3);
|
|
node4 = mutable_graph->mutable_node(i + 4);
|
|
}
|
|
|
|
if (node2->op_type() != "Clip" || node3->op_type() != "Expand" || node4->op_type() != "Div")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node3->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node2->output(0)
|
|
|| node4->input(0) != node->input(0) || node4->input(1) != node3->output(0))
|
|
continue;
|
|
|
|
if (has_shape_node)
|
|
{
|
|
if (node_shape->input(0) != node->input(0) || node3->input(1) != node_shape->output(0))
|
|
continue;
|
|
}
|
|
|
|
// +eps
|
|
float clip_min;
|
|
if (node2->input_size() == 1)
|
|
{
|
|
clip_min = get_node_attr_f(*node2, "min", -FLT_MAX);
|
|
}
|
|
else
|
|
{
|
|
const onnx::TensorProto& min_tp = weights[node2->input(1)];
|
|
|
|
clip_min = get_node_attr_from_input_f(min_tp);
|
|
}
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
if (has_shape_node)
|
|
{
|
|
node_shape->set_op_type("noop_reducedncnn");
|
|
}
|
|
node3->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= has_shape_node ? 2 : 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (has_shape_node)
|
|
{
|
|
node_reference[node_shape->output(0)] -= 1;
|
|
}
|
|
node_reference[node3->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
if (has_shape_node)
|
|
{
|
|
blob_names.erase(node_shape->output(0));
|
|
}
|
|
blob_names.erase(node3->output(0));
|
|
|
|
node4->set_op_type("Normalize");
|
|
node4->clear_input();
|
|
node4->add_input(node->input(0));
|
|
|
|
onnx::AttributeProto* attr_alpha = node4->add_attribute();
|
|
attr_alpha->set_name("eps");
|
|
attr_alpha->set_f(clip_min);
|
|
|
|
reduced_node_count += has_shape_node ? 4 : 3;
|
|
i += has_shape_node ? 4 : 3;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_groupnorm(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// GroupNorm <= X - Reshape - InstanceNormalization - Reshape - Mul - Add
|
|
if (node->op_type() == "Reshape")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
std::vector<int> shape;
|
|
if (node->input_size() == 1)
|
|
{
|
|
shape = get_node_attr_ai(*node, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape = get_node_attr_from_input_ai(weights[node->input(1)]);
|
|
}
|
|
|
|
// 0, group, -1
|
|
if (shape.size() != 3)
|
|
continue;
|
|
|
|
if (shape[0] != 0 || shape[2] != -1)
|
|
continue;
|
|
|
|
int groups = shape[1];
|
|
|
|
if (i + 4 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
onnx::NodeProto* node5 = mutable_graph->mutable_node(i + 4);
|
|
|
|
if (node2->op_type() != "InstanceNormalization" || node3->op_type() != "Reshape" || node4->op_type() != "Mul" || node5->op_type() != "Add")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node3->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node4->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node2->output(0)
|
|
|| node4->input(0) != node3->output(0) || node5->input(0) != node4->output(0))
|
|
continue;
|
|
|
|
// +eps
|
|
float eps = get_node_attr_f(*node2, "epsilon", 1e-05f);
|
|
|
|
// InstanceNormalization S=1 B=0
|
|
std::vector<float> S = get_node_attr_from_input_af(weights[node2->input(1)]);
|
|
std::vector<float> B = get_node_attr_from_input_af(weights[node2->input(2)]);
|
|
if ((int)S.size() != groups || (int)B.size() != groups)
|
|
continue;
|
|
|
|
bool instancenorm_affine = false;
|
|
for (int j = 0; j < groups; j++)
|
|
{
|
|
if (S[j] != 1.f || B[j] != 0.f)
|
|
{
|
|
instancenorm_affine = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (instancenorm_affine)
|
|
continue;
|
|
|
|
std::vector<int> shape2;
|
|
if (node3->input_size() == 1)
|
|
{
|
|
shape2 = get_node_attr_ai(*node3, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape2 = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
}
|
|
|
|
// 1, channels, w, h
|
|
if (shape2.size() != 4)
|
|
continue;
|
|
|
|
if (shape2[0] != 1)
|
|
continue;
|
|
|
|
int channels = shape2[1];
|
|
|
|
// affine
|
|
std::vector<float> affine_S = get_node_attr_from_input_af(weights[node4->input(1)]);
|
|
std::vector<float> affine_B = get_node_attr_from_input_af(weights[node5->input(1)]);
|
|
if (affine_S.size() == 1 && affine_S[0] == 1.f && affine_B.size() == 1 && affine_B[0] == 0.f)
|
|
{
|
|
// no affine
|
|
}
|
|
else if ((int)affine_S.size() != channels && (int)affine_B.size() != channels)
|
|
{
|
|
// we only allow per-channel affine
|
|
continue;
|
|
}
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
node4->set_op_type("noop_reducedncnn");
|
|
|
|
if (node->input_size() == 2)
|
|
{
|
|
node_reference[node->input(1)] -= 1;
|
|
}
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node2->input(2)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
node_reference[node3->output(0)] -= 1;
|
|
node_reference[node4->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
blob_names.erase(node3->output(0));
|
|
blob_names.erase(node4->output(0));
|
|
|
|
std::string affine_scale = node4->input(1);
|
|
std::string affine_bias = node5->input(1);
|
|
|
|
node5->set_op_type("GroupNorm");
|
|
node5->clear_input();
|
|
node5->add_input(node->input(0));
|
|
node5->add_input(affine_scale);
|
|
node5->add_input(affine_bias);
|
|
|
|
onnx::AttributeProto* attr_groups = node5->add_attribute();
|
|
attr_groups->set_name("groups");
|
|
attr_groups->set_i(groups);
|
|
|
|
onnx::AttributeProto* attr_channels = node5->add_attribute();
|
|
attr_channels->set_name("channels");
|
|
attr_channels->set_i(channels);
|
|
|
|
onnx::AttributeProto* attr_eps = node5->add_attribute();
|
|
attr_eps->set_name("epsilon");
|
|
attr_eps->set_f(eps);
|
|
|
|
onnx::AttributeProto* attr_affine = node5->add_attribute();
|
|
attr_affine->set_name("affine");
|
|
attr_affine->set_i(1);
|
|
|
|
reduced_node_count += 4;
|
|
i += 4;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_layernorm(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// LayerNorm <= X - ReduceMean - Sub - Pow - ReduceMean - Add - Sqrt - Div
|
|
// LayerNorm <= X - ReduceMean - Sub - Pow - ReduceMean - Add - Sqrt - Div - Mul - Add
|
|
if (node->op_type() == "ReduceMean")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
std::vector<int> axes = get_node_attr_ai(*node, "axes");
|
|
|
|
// -1
|
|
// -2 -1
|
|
if (axes.size() != 1 && axes.size() != 2)
|
|
continue;
|
|
|
|
int normed_axes = (int)axes.size();
|
|
if (normed_axes == 1 && axes[0] != -1)
|
|
continue;
|
|
if (normed_axes == 2 && (axes[0] != -2 || axes[1] != -1))
|
|
continue;
|
|
|
|
if (i + 6 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
onnx::NodeProto* node5 = mutable_graph->mutable_node(i + 4);
|
|
onnx::NodeProto* node6 = mutable_graph->mutable_node(i + 5);
|
|
onnx::NodeProto* node7 = mutable_graph->mutable_node(i + 6);
|
|
|
|
if (node2->op_type() != "Sub" || node3->op_type() != "Pow" || node4->op_type() != "ReduceMean" || node5->op_type() != "Add" || node6->op_type() != "Sqrt" || node7->op_type() != "Div")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 2)
|
|
continue;
|
|
|
|
if (node_reference[node3->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node4->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node5->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node6->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->input(0) || node2->input(1) != node->output(0)
|
|
|| node3->input(0) != node2->output(0) || node4->input(0) != node3->output(0)
|
|
|| node5->input(0) != node4->output(0) || node6->input(0) != node5->output(0)
|
|
|| node7->input(0) != node2->output(0) || node7->input(1) != node6->output(0))
|
|
continue;
|
|
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& pow_two = weights[node3->input(1)];
|
|
if (pow_two.dims_size() != 0 || get_tensor_proto_data_size(pow_two) != 1)
|
|
continue;
|
|
|
|
float constant_pow_two = get_node_attr_from_input_f(pow_two);
|
|
if (constant_pow_two != 2.f)
|
|
continue;
|
|
|
|
std::vector<int> axes4 = get_node_attr_ai(*node4, "axes");
|
|
|
|
// -1
|
|
// -2 -1
|
|
if ((int)axes4.size() != normed_axes)
|
|
continue;
|
|
|
|
if (normed_axes == 1 && axes4[0] != -1)
|
|
continue;
|
|
if (normed_axes == 2 && (axes4[0] != -2 || axes4[1] != -1))
|
|
continue;
|
|
|
|
if (weights.find(node5->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& add_eps = weights[node5->input(1)];
|
|
if (add_eps.dims_size() != 0 || get_tensor_proto_data_size(add_eps) != 1)
|
|
continue;
|
|
|
|
float eps = get_node_attr_from_input_f(add_eps);
|
|
|
|
int affine = 0;
|
|
while (i + 8 < node_count)
|
|
{
|
|
onnx::NodeProto* node8 = mutable_graph->mutable_node(i + 7);
|
|
onnx::NodeProto* node9 = mutable_graph->mutable_node(i + 8);
|
|
|
|
if (node8->op_type() != "Mul" || node9->op_type() != "Add")
|
|
break;
|
|
|
|
if (node_reference[node7->output(0)] != 1)
|
|
break;
|
|
|
|
if (node_reference[node8->output(0)] != 1)
|
|
break;
|
|
|
|
if (node8->input(0) != node7->output(0) || node9->input(0) != node8->output(0))
|
|
break;
|
|
|
|
// affine
|
|
std::vector<float> affine_S = get_node_attr_from_input_af(weights[node8->input(1)]);
|
|
std::vector<float> affine_B = get_node_attr_from_input_af(weights[node9->input(1)]);
|
|
if (affine_S.size() != affine_B.size())
|
|
break;
|
|
|
|
affine = 1;
|
|
break;
|
|
}
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
node4->set_op_type("noop_reducedncnn");
|
|
node5->set_op_type("noop_reducedncnn");
|
|
node6->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
node_reference[node2->input(0)] -= 1;
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node3->input(0)] -= 1;
|
|
node_reference[node3->input(1)] -= 1;
|
|
node_reference[node4->input(0)] -= 1;
|
|
node_reference[node5->input(0)] -= 1;
|
|
node_reference[node5->input(1)] -= 1;
|
|
node_reference[node6->input(0)] -= 1;
|
|
node_reference[node7->input(0)] -= 1;
|
|
node_reference[node7->input(1)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
blob_names.erase(node3->output(0));
|
|
blob_names.erase(node4->output(0));
|
|
blob_names.erase(node5->output(0));
|
|
blob_names.erase(node6->output(0));
|
|
|
|
node_reference[node->input(0)] += 1;
|
|
|
|
if (affine == 0)
|
|
{
|
|
node7->set_op_type("LayerNorm");
|
|
node7->clear_input();
|
|
node7->add_input(node->input(0));
|
|
|
|
onnx::AttributeProto* attr_eps = node7->add_attribute();
|
|
attr_eps->set_name("epsilon");
|
|
attr_eps->set_f(eps);
|
|
|
|
onnx::AttributeProto* attr_affine = node7->add_attribute();
|
|
attr_affine->set_name("affine");
|
|
attr_affine->set_i(affine);
|
|
|
|
reduced_node_count += 6;
|
|
i += 6;
|
|
}
|
|
else // if (affine == 1)
|
|
{
|
|
onnx::NodeProto* node8 = mutable_graph->mutable_node(i + 7);
|
|
onnx::NodeProto* node9 = mutable_graph->mutable_node(i + 8);
|
|
|
|
node7->set_op_type("noop_reducedncnn");
|
|
node8->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node8->input(0)] -= 1;
|
|
node_reference[node9->input(0)] -= 1;
|
|
|
|
blob_names.erase(node7->output(0));
|
|
blob_names.erase(node8->output(0));
|
|
|
|
std::string affine_scale = node8->input(1);
|
|
std::string affine_bias = node9->input(1);
|
|
|
|
node9->set_op_type("LayerNorm");
|
|
node9->clear_input();
|
|
node9->add_input(node->input(0));
|
|
node9->add_input(affine_scale);
|
|
node9->add_input(affine_bias);
|
|
|
|
onnx::AttributeProto* attr_eps = node9->add_attribute();
|
|
attr_eps->set_name("epsilon");
|
|
attr_eps->set_f(eps);
|
|
|
|
onnx::AttributeProto* attr_affine = node9->add_attribute();
|
|
attr_affine->set_name("affine");
|
|
attr_affine->set_i(affine);
|
|
|
|
reduced_node_count += 8;
|
|
i += 8;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_flatten(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Flatten <= X - Shape - Gather - Constant - Unsqueeze - Unsqueeze - Concat - Reshape
|
|
if (node->op_type() == "Shape")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 6 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
onnx::NodeProto* node5 = mutable_graph->mutable_node(i + 4);
|
|
onnx::NodeProto* node6 = mutable_graph->mutable_node(i + 5);
|
|
onnx::NodeProto* node7 = mutable_graph->mutable_node(i + 6);
|
|
|
|
if (node2->op_type() != "Gather" || node3->op_type() != "Constant" || node4->op_type() != "Unsqueeze" || node5->op_type() != "Unsqueeze"
|
|
|| node6->op_type() != "Concat" || node7->op_type() != "Reshape")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
// if (node_reference[node3->output(0)] != 1)
|
|
// continue;
|
|
|
|
if (node_reference[node4->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node5->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node_reference[node6->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node4->input(0) != node2->output(0) || node5->input(0) != node3->output(0)
|
|
|| node6->input(0) != node4->output(0) || node6->input(1) != node5->output(0)
|
|
|| node7->input(0) != node->input(0) || node7->input(1) != node6->output(0))
|
|
continue;
|
|
|
|
// axis = 0
|
|
int gather_axis = get_node_attr_i(*node2, "axis");
|
|
if (gather_axis != 0)
|
|
continue;
|
|
|
|
// indices = 0
|
|
if (weights.find(node2->input(1)) == weights.end())
|
|
continue;
|
|
|
|
std::vector<int> gather_indices = get_node_attr_from_input_ai(weights[node2->input(1)]);
|
|
if (gather_indices.size() != 1 || gather_indices[0] != 0)
|
|
continue;
|
|
|
|
// axes = (0)
|
|
std::vector<int> unsqueeze_axes = get_node_attr_ai(*node4, "axes");
|
|
if (unsqueeze_axes.size() != 1)
|
|
continue;
|
|
if (unsqueeze_axes[0] != 0)
|
|
continue;
|
|
|
|
// axes = (0)
|
|
std::vector<int> unsqueeze2_axes = get_node_attr_ai(*node5, "axes");
|
|
if (unsqueeze2_axes.size() != 1)
|
|
continue;
|
|
if (unsqueeze2_axes[0] != 0)
|
|
continue;
|
|
|
|
// data = -1
|
|
if (weights.find(node5->input(0)) == weights.end())
|
|
continue;
|
|
|
|
std::vector<int> unsqueeze2_data = get_node_attr_from_input_ai(weights[node5->input(0)]);
|
|
if (unsqueeze2_data.size() != 1 || unsqueeze2_data[0] != -1)
|
|
continue;
|
|
|
|
// axis = 0
|
|
int concat_axis = get_node_attr_i(*node6, "axis");
|
|
if (concat_axis != 0)
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
// node3->set_op_type("noop_reducedncnn");
|
|
node4->set_op_type("noop_reducedncnn");
|
|
node5->set_op_type("noop_reducedncnn");
|
|
node6->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->input(1)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
// node_reference[node3->output(0)] -= 1;
|
|
node_reference[node4->output(0)] -= 1;
|
|
node_reference[node5->input(0)] -= 1;
|
|
node_reference[node5->output(0)] -= 1;
|
|
node_reference[node6->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
// blob_names.erase(node3->output(0));
|
|
blob_names.erase(node4->output(0));
|
|
blob_names.erase(node5->output(0));
|
|
blob_names.erase(node6->output(0));
|
|
|
|
node7->set_op_type("Flatten");
|
|
node7->clear_input();
|
|
node7->add_input(node->input(0));
|
|
|
|
reduced_node_count += 5;
|
|
i += 5;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_pixelshuffle(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// PixelShuffle <= Reshape - Transpose - Reshape
|
|
// PixelShuffle <= Reshape - Transpose - Constant - Reshape
|
|
if (node->op_type() == "Reshape")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
std::vector<int> shape;
|
|
if (node->input_size() == 1)
|
|
{
|
|
shape = get_node_attr_ai(*node, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape = get_node_attr_from_input_ai(weights[node->input(1)]);
|
|
}
|
|
|
|
// -1, 3, upscale_factor, upscale_factor, height, width
|
|
if (shape.size() != 6)
|
|
continue;
|
|
|
|
if (shape[0] != 1 && shape[0] != -1)
|
|
continue;
|
|
|
|
if (shape[2] != shape[3])
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node3->op_type() == "Constant")
|
|
{
|
|
if (i + 3 >= node_count)
|
|
continue;
|
|
|
|
node3 = mutable_graph->mutable_node(i + 3);
|
|
}
|
|
|
|
if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
// 0 1 4 2 5 3
|
|
std::vector<int> perm = get_node_attr_ai(*node2, "perm");
|
|
if (perm.size() != 6)
|
|
continue;
|
|
|
|
if (perm[0] != 0 || perm[1] != 1 || perm[2] != 4 || perm[3] != 2 || perm[4] != 5 || perm[5] != 3)
|
|
continue;
|
|
|
|
std::vector<int> shape3;
|
|
if (node3->input_size() == 1)
|
|
{
|
|
shape3 = get_node_attr_ai(*node3, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape3 = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
}
|
|
|
|
// -1, 3, height, width
|
|
if (shape3.size() != 4)
|
|
continue;
|
|
|
|
if (shape3[0] != 1 && shape3[0] != -1)
|
|
continue;
|
|
|
|
if (shape3[1] != shape[1] || shape3[2] != shape[2] * shape[4] || shape3[3] != shape[3] * shape[5])
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
if (node->input_size() == 2)
|
|
{
|
|
node_reference[node->input(1)] -= 1;
|
|
}
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node3->set_op_type("PixelShuffle");
|
|
node3->set_input(0, node->input(0));
|
|
|
|
onnx::AttributeProto* attr_group = node3->add_attribute();
|
|
attr_group->set_name("scale_factor");
|
|
attr_group->set_i(shape[2]);
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_reorg(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// PixelShuffle <= Reshape - Transpose - Reshape
|
|
// PixelShuffle <= Reshape - Transpose - Constant - Reshape
|
|
if (node->op_type() == "Reshape")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
std::vector<int> shape;
|
|
if (node->input_size() == 1)
|
|
{
|
|
shape = get_node_attr_ai(*node, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape = get_node_attr_from_input_ai(weights[node->input(1)]);
|
|
}
|
|
|
|
// -1, 3, out_height, block_size, out_width, block_size
|
|
if (shape.size() != 6)
|
|
continue;
|
|
|
|
if (shape[0] != 1 && shape[0] != -1)
|
|
continue;
|
|
|
|
if (shape[3] != shape[5])
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node3->op_type() == "Constant")
|
|
{
|
|
if (i + 3 >= node_count)
|
|
continue;
|
|
|
|
node3 = mutable_graph->mutable_node(i + 3);
|
|
}
|
|
|
|
if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
// 0 1 3 5 2 4
|
|
std::vector<int> perm = get_node_attr_ai(*node2, "perm");
|
|
if (perm.size() != 6)
|
|
continue;
|
|
|
|
if (perm[0] != 0 || perm[1] != 1 || perm[2] != 3 || perm[3] != 5 || perm[4] != 2 || perm[5] != 4)
|
|
continue;
|
|
|
|
std::vector<int> shape3;
|
|
if (node3->input_size() == 1)
|
|
{
|
|
shape3 = get_node_attr_ai(*node3, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape3 = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
}
|
|
|
|
// -1, out_channels, out_height, out_width
|
|
if (shape3.size() != 4)
|
|
continue;
|
|
|
|
if (shape3[0] != 1 && shape3[0] != -1)
|
|
continue;
|
|
|
|
if (shape3[1] != shape[1] * shape[3] * shape[5] || shape3[2] != shape[2] || shape3[3] != shape[4])
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
if (node->input_size() == 2)
|
|
{
|
|
node_reference[node->input(1)] -= 1;
|
|
}
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node3->set_op_type("Reorg");
|
|
node3->set_input(0, node->input(0));
|
|
|
|
onnx::AttributeProto* attr_group = node3->add_attribute();
|
|
attr_group->set_name("stride");
|
|
attr_group->set_i(shape[3]);
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_expand_broadcast(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Add/Sub/Mul/Div/Min/Max <= Expand - Add/Sub/Mul/Div/Min/Max
|
|
if (node->op_type() == "Expand")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "Add" && node2->op_type() != "Sub" && node2->op_type() != "Mul" && node2->op_type() != "Div" && node2->op_type() != "Min" && node2->op_type() != "Max")
|
|
continue;
|
|
|
|
if (node2->input(1) != node->output(0) && node2->input(0) != node->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
if (node->input_size() == 2)
|
|
{
|
|
node_reference[node->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
if (node2->input(0) == node->output(0))
|
|
{
|
|
node2->set_input(0, node->input(0));
|
|
}
|
|
else
|
|
{
|
|
node2->set_input(1, node->input(0));
|
|
}
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_lstm_gru_rnn(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// LSTM(bi) <= LSTM(bi) - Transpose - Reshape - Transpose
|
|
if (node->op_type() == "LSTM" || node->op_type() == "GRU" || node->op_type() == "RNN")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 2 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
|
|
if (node2->op_type() != "Transpose" || node3->op_type() != "Reshape")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node2->output(0))
|
|
continue;
|
|
|
|
std::string direction = get_node_attr_s(*node, "direction");
|
|
if (direction != "bidirectional")
|
|
continue;
|
|
|
|
// 0 2 1 3
|
|
std::vector<int> perm = get_node_attr_ai(*node2, "perm");
|
|
if (perm.size() != 4)
|
|
continue;
|
|
|
|
if (perm[0] != 0 || perm[1] != 2 || perm[2] != 1 || perm[3] != 3)
|
|
continue;
|
|
|
|
std::vector<int> shape;
|
|
if (node3->input_size() == 1)
|
|
{
|
|
shape = get_node_attr_ai(*node3, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node3->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape = get_node_attr_from_input_ai(weights[node3->input(1)]);
|
|
}
|
|
|
|
// 0 0 -1
|
|
if (shape.size() != 3)
|
|
continue;
|
|
|
|
if (shape[0] != 0 || shape[1] != 0 || shape[2] != -1)
|
|
continue;
|
|
|
|
// reduce
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
node_reference[node2->output(0)] -= 1;
|
|
if (node3->input_size() == 2)
|
|
{
|
|
node_reference[node3->input(1)] -= 1;
|
|
}
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
|
|
node->set_output(0, node3->output(0));
|
|
|
|
reduced_node_count += 2;
|
|
i += 2;
|
|
|
|
if (i + 1 < node_count)
|
|
{
|
|
if (node_reference[node3->output(0)] != 1)
|
|
continue;
|
|
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node4->op_type() != "Transpose")
|
|
continue;
|
|
|
|
if (node4->input(0) != node->output(0))
|
|
continue;
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm4 = get_node_attr_ai(*node4, "perm");
|
|
if (perm4.size() != 3)
|
|
continue;
|
|
|
|
if (perm4[0] != 1 || perm4[1] != 0 || perm4[2] != 2)
|
|
continue;
|
|
|
|
// reduce
|
|
node4->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node->set_output(0, node4->output(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// LSTM(uni) <= LSTM(uni) - Squeeze - Transpose
|
|
if (node->op_type() == "LSTM" || node->op_type() == "GRU" || node->op_type() == "RNN")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "Squeeze")
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0))
|
|
continue;
|
|
|
|
std::string direction = get_node_attr_s(*node, "direction");
|
|
if (direction == "bidirectional")
|
|
continue;
|
|
|
|
// 1
|
|
std::vector<int> axes = get_node_attr_ai(*node2, "axes");
|
|
if (axes.size() != 1)
|
|
continue;
|
|
|
|
if (axes[0] != 1)
|
|
continue;
|
|
|
|
// reduce
|
|
node2->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node->set_output(0, node2->output(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
|
|
if (i + 1 < node_count)
|
|
{
|
|
if (node_reference[node2->output(0)] != 1)
|
|
continue;
|
|
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node3->op_type() != "Transpose")
|
|
continue;
|
|
|
|
if (node3->input(0) != node->output(0))
|
|
continue;
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm4 = get_node_attr_ai(*node3, "perm");
|
|
if (perm4.size() != 3)
|
|
continue;
|
|
|
|
if (perm4[0] != 1 || perm4[1] != 0 || perm4[2] != 2)
|
|
continue;
|
|
|
|
// reduce
|
|
node3->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node->set_output(0, node3->output(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// LSTM <= Transpose - LSTM
|
|
if (node->op_type() == "Transpose")
|
|
{
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm = get_node_attr_ai(*node, "perm");
|
|
if (perm.size() != 3)
|
|
continue;
|
|
|
|
if (perm[0] != 1 || perm[1] != 0 || perm[2] != 2)
|
|
continue;
|
|
|
|
if (i + 1 >= node_count)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
|
|
if (node2->op_type() != "LSTM" && node->op_type() != "GRU" && node->op_type() != "RNN")
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0))
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node->output(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
|
|
node2->set_input(0, node->input(0));
|
|
|
|
reduced_node_count += 1;
|
|
i += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_multiheadattention(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// MultiHeadAttention <= MatMul(q) - Add
|
|
// - MatMul(k) - Add
|
|
// - MatMul(v) - Add
|
|
// - Mul
|
|
// - Reshape - Transpose
|
|
// - Reshape - Reshape - Transpose - Transpose
|
|
// - Gemm - Softmax - Gemm - Transpose - Reshape - MatMul - Add
|
|
if (node->op_type() == "MatMul")
|
|
{
|
|
if (i + 19 >= node_count)
|
|
continue;
|
|
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
onnx::NodeProto* node5 = mutable_graph->mutable_node(i + 4);
|
|
onnx::NodeProto* node6 = mutable_graph->mutable_node(i + 5);
|
|
onnx::NodeProto* node7 = mutable_graph->mutable_node(i + 6);
|
|
onnx::NodeProto* node8 = mutable_graph->mutable_node(i + 7);
|
|
onnx::NodeProto* node9 = mutable_graph->mutable_node(i + 8);
|
|
onnx::NodeProto* node10 = mutable_graph->mutable_node(i + 9);
|
|
onnx::NodeProto* node11 = mutable_graph->mutable_node(i + 10);
|
|
onnx::NodeProto* node12 = mutable_graph->mutable_node(i + 11);
|
|
onnx::NodeProto* node13 = mutable_graph->mutable_node(i + 12);
|
|
onnx::NodeProto* node14 = mutable_graph->mutable_node(i + 13);
|
|
onnx::NodeProto* node15 = mutable_graph->mutable_node(i + 14);
|
|
onnx::NodeProto* node16 = mutable_graph->mutable_node(i + 15);
|
|
onnx::NodeProto* node17 = mutable_graph->mutable_node(i + 16);
|
|
onnx::NodeProto* node18 = mutable_graph->mutable_node(i + 17);
|
|
onnx::NodeProto* node19 = mutable_graph->mutable_node(i + 18);
|
|
onnx::NodeProto* node20 = mutable_graph->mutable_node(i + 19);
|
|
|
|
if (node2->op_type() != "Add" || node3->op_type() != "MatMul" || node4->op_type() != "Add" || node5->op_type() != "MatMul" || node6->op_type() != "Add" || node7->op_type() != "Mul" || node8->op_type() != "Reshape" || node9->op_type() != "Transpose" || node10->op_type() != "Reshape" || node11->op_type() != "Reshape" || node12->op_type() != "Transpose" || node13->op_type() != "Transpose" || node14->op_type() != "MatMul" || node15->op_type() != "Softmax" || node16->op_type() != "MatMul" || node17->op_type() != "Transpose" || node18->op_type() != "Reshape" || node19->op_type() != "MatMul" || node20->op_type() != "Add")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1 || node_reference[node3->output(0)] != 1 || node_reference[node4->output(0)] != 1 || node_reference[node5->output(0)] != 1 || node_reference[node6->output(0)] != 1 || node_reference[node7->output(0)] != 1 || node_reference[node8->output(0)] != 1 || node_reference[node9->output(0)] != 1 || node_reference[node10->output(0)] != 1 || node_reference[node11->output(0)] != 1 || node_reference[node12->output(0)] != 1 || node_reference[node13->output(0)] != 1 || node_reference[node14->output(0)] != 1 || node_reference[node15->output(0)] != 1 || node_reference[node16->output(0)] != 1 || node_reference[node17->output(0)] != 1 || node_reference[node18->output(0)] != 1 || node_reference[node19->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node4->input(0) != node3->output(0) || node6->input(0) != node5->output(0) || node7->input(0) != node2->output(0) || node8->input(0) != node7->output(0) || node9->input(0) != node8->output(0) || node10->input(0) != node4->output(0) || node11->input(0) != node6->output(0) || node12->input(0) != node11->output(0) || node13->input(0) != node10->output(0) || node14->input(0) != node9->output(0) || node14->input(1) != node13->output(0) || node15->input(0) != node14->output(0) || node16->input(0) != node15->output(0) || node16->input(1) != node12->output(0) || node17->input(0) != node16->output(0) || node18->input(0) != node17->output(0) || node19->input(0) != node18->output(0) || node20->input(0) != node19->output(0))
|
|
continue;
|
|
|
|
std::vector<float> q_B = get_node_attr_from_input_af(weights[node2->input(1)]);
|
|
std::vector<float> k_B = get_node_attr_from_input_af(weights[node4->input(1)]);
|
|
std::vector<float> v_B = get_node_attr_from_input_af(weights[node6->input(1)]);
|
|
std::vector<float> o_B = get_node_attr_from_input_af(weights[node20->input(1)]);
|
|
|
|
if (q_B.size() != k_B.size() || q_B.size() != v_B.size() || q_B.size() != o_B.size())
|
|
continue;
|
|
|
|
int embed_dim = q_B.size();
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm9 = get_node_attr_ai(*node9, "perm");
|
|
std::vector<int> perm12 = get_node_attr_ai(*node12, "perm");
|
|
if (perm9.size() != 3 || perm12.size() != 3)
|
|
continue;
|
|
|
|
if (perm9[0] != 1 || perm9[1] != 0 || perm9[2] != 2 || perm12[0] != 1 || perm12[1] != 0 || perm12[2] != 2)
|
|
continue;
|
|
|
|
// 1 2 0
|
|
std::vector<int> perm13 = get_node_attr_ai(*node13, "perm");
|
|
if (perm13.size() != 3)
|
|
continue;
|
|
|
|
if (perm13[0] != 1 || perm13[1] != 2 || perm13[2] != 0)
|
|
continue;
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm17 = get_node_attr_ai(*node17, "perm");
|
|
if (perm17.size() != 3)
|
|
continue;
|
|
|
|
if (perm17[0] != 1 || perm17[1] != 0 || perm17[2] != 2)
|
|
continue;
|
|
|
|
int softmax_axis = get_node_attr_i(*node15, "axis");
|
|
if (softmax_axis != 2)
|
|
continue;
|
|
|
|
// 1/-1, seqlen * num_heads, embed_dim / num_heads
|
|
std::vector<int> shape8;
|
|
std::vector<int> shape10;
|
|
std::vector<int> shape11;
|
|
if (node8->input_size() == 1)
|
|
{
|
|
shape8 = get_node_attr_ai(*node8, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node8->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape8 = get_node_attr_from_input_ai(weights[node8->input(1)]);
|
|
}
|
|
if (node10->input_size() == 1)
|
|
{
|
|
shape10 = get_node_attr_ai(*node10, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node10->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape10 = get_node_attr_from_input_ai(weights[node10->input(1)]);
|
|
}
|
|
if (node11->input_size() == 1)
|
|
{
|
|
shape11 = get_node_attr_ai(*node11, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node11->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape11 = get_node_attr_from_input_ai(weights[node11->input(1)]);
|
|
}
|
|
|
|
if (shape8.size() != 3 || shape10.size() != 3 || shape11.size() != 3)
|
|
continue;
|
|
|
|
if (shape8[1] != shape10[1] || shape8[1] != shape11[1] || shape8[2] != shape10[2] || shape8[2] != shape11[2])
|
|
continue;
|
|
|
|
int num_heads = embed_dim / shape8[2];
|
|
|
|
// 1, seqlen, embed_dim
|
|
std::vector<int> shape18;
|
|
if (node18->input_size() == 1)
|
|
{
|
|
shape18 = get_node_attr_ai(*node18, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node18->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape18 = get_node_attr_from_input_ai(weights[node18->input(1)]);
|
|
}
|
|
|
|
if (shape18.size() != 3)
|
|
continue;
|
|
|
|
if (shape18[2] != embed_dim || shape18[1] * num_heads != shape8[1])
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
node4->set_op_type("noop_reducedncnn");
|
|
node5->set_op_type("noop_reducedncnn");
|
|
node6->set_op_type("noop_reducedncnn");
|
|
node7->set_op_type("noop_reducedncnn");
|
|
node8->set_op_type("noop_reducedncnn");
|
|
node9->set_op_type("noop_reducedncnn");
|
|
node10->set_op_type("noop_reducedncnn");
|
|
node11->set_op_type("noop_reducedncnn");
|
|
node12->set_op_type("noop_reducedncnn");
|
|
node13->set_op_type("noop_reducedncnn");
|
|
node14->set_op_type("noop_reducedncnn");
|
|
node15->set_op_type("noop_reducedncnn");
|
|
node16->set_op_type("noop_reducedncnn");
|
|
node17->set_op_type("noop_reducedncnn");
|
|
node18->set_op_type("noop_reducedncnn");
|
|
node19->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node2->input(0)] -= 1;
|
|
node_reference[node4->input(0)] -= 1;
|
|
node_reference[node6->input(0)] -= 1;
|
|
node_reference[node7->input(0)] -= 1;
|
|
node_reference[node7->input(1)] -= 1;
|
|
node_reference[node8->input(0)] -= 1;
|
|
if (node8->input_size() == 2)
|
|
{
|
|
node_reference[node8->input(1)] -= 1;
|
|
}
|
|
node_reference[node9->input(0)] -= 1;
|
|
node_reference[node10->input(0)] -= 1;
|
|
if (node10->input_size() == 2)
|
|
{
|
|
node_reference[node10->input(1)] -= 1;
|
|
}
|
|
node_reference[node11->input(0)] -= 1;
|
|
if (node11->input_size() == 2)
|
|
{
|
|
node_reference[node11->input(1)] -= 1;
|
|
}
|
|
node_reference[node12->input(0)] -= 1;
|
|
node_reference[node13->input(0)] -= 1;
|
|
node_reference[node14->input(0)] -= 1;
|
|
node_reference[node14->input(1)] -= 1;
|
|
node_reference[node15->input(0)] -= 1;
|
|
node_reference[node16->input(0)] -= 1;
|
|
node_reference[node16->input(1)] -= 1;
|
|
node_reference[node17->input(0)] -= 1;
|
|
node_reference[node18->input(0)] -= 1;
|
|
if (node18->input_size() == 2)
|
|
{
|
|
node_reference[node18->input(1)] -= 1;
|
|
}
|
|
node_reference[node19->input(0)] -= 1;
|
|
node_reference[node20->input(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
blob_names.erase(node3->output(0));
|
|
blob_names.erase(node4->output(0));
|
|
blob_names.erase(node5->output(0));
|
|
blob_names.erase(node6->output(0));
|
|
blob_names.erase(node7->output(0));
|
|
blob_names.erase(node8->output(0));
|
|
blob_names.erase(node9->output(0));
|
|
blob_names.erase(node10->output(0));
|
|
blob_names.erase(node11->output(0));
|
|
blob_names.erase(node12->output(0));
|
|
blob_names.erase(node13->output(0));
|
|
blob_names.erase(node14->output(0));
|
|
blob_names.erase(node15->output(0));
|
|
blob_names.erase(node16->output(0));
|
|
blob_names.erase(node17->output(0));
|
|
blob_names.erase(node18->output(0));
|
|
blob_names.erase(node19->output(0));
|
|
|
|
std::string qw = node->input(1);
|
|
std::string qb = node2->input(1);
|
|
std::string kw = node3->input(1);
|
|
std::string kb = node4->input(1);
|
|
std::string vw = node5->input(1);
|
|
std::string vb = node6->input(1);
|
|
std::string ow = node19->input(1);
|
|
std::string ob = node20->input(1);
|
|
|
|
node20->set_op_type("MultiHeadAttention");
|
|
node20->clear_input();
|
|
node20->add_input(node->input(0));
|
|
node20->add_input(node3->input(0));
|
|
node20->add_input(node5->input(0));
|
|
// q
|
|
node20->add_input(qw);
|
|
node20->add_input(qb);
|
|
// k
|
|
node20->add_input(kw);
|
|
node20->add_input(kb);
|
|
// v
|
|
node20->add_input(vw);
|
|
node20->add_input(vb);
|
|
// out linear
|
|
node20->add_input(ow);
|
|
node20->add_input(ob);
|
|
|
|
onnx::AttributeProto* attr_embed_dim = node20->add_attribute();
|
|
attr_embed_dim->set_name("embed_dim");
|
|
attr_embed_dim->set_i(embed_dim);
|
|
|
|
onnx::AttributeProto* attr_num_heads = node20->add_attribute();
|
|
attr_num_heads->set_name("num_heads");
|
|
attr_num_heads->set_i(num_heads);
|
|
|
|
reduced_node_count += 19;
|
|
i += 19;
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// MultiHeadAttention <= MatMul(qkv) - Add - Split
|
|
// - Mul
|
|
// - Reshape - Transpose
|
|
// - Reshape - Reshape - Transpose - Transpose
|
|
// - Gemm - Softmax - Gemm - Transpose - Reshape - MatMul - Add
|
|
if (node->op_type() == "MatMul")
|
|
{
|
|
if (i + 16 >= node_count)
|
|
continue;
|
|
|
|
if (node_reference[node->output(0)] != 1)
|
|
continue;
|
|
|
|
onnx::NodeProto* node2 = mutable_graph->mutable_node(i + 1);
|
|
onnx::NodeProto* node3 = mutable_graph->mutable_node(i + 2);
|
|
onnx::NodeProto* node4 = mutable_graph->mutable_node(i + 3);
|
|
onnx::NodeProto* node5 = mutable_graph->mutable_node(i + 4);
|
|
onnx::NodeProto* node6 = mutable_graph->mutable_node(i + 5);
|
|
onnx::NodeProto* node7 = mutable_graph->mutable_node(i + 6);
|
|
onnx::NodeProto* node8 = mutable_graph->mutable_node(i + 7);
|
|
onnx::NodeProto* node9 = mutable_graph->mutable_node(i + 8);
|
|
onnx::NodeProto* node10 = mutable_graph->mutable_node(i + 9);
|
|
onnx::NodeProto* node11 = mutable_graph->mutable_node(i + 10);
|
|
onnx::NodeProto* node12 = mutable_graph->mutable_node(i + 11);
|
|
onnx::NodeProto* node13 = mutable_graph->mutable_node(i + 12);
|
|
onnx::NodeProto* node14 = mutable_graph->mutable_node(i + 13);
|
|
onnx::NodeProto* node15 = mutable_graph->mutable_node(i + 14);
|
|
onnx::NodeProto* node16 = mutable_graph->mutable_node(i + 15);
|
|
onnx::NodeProto* node17 = mutable_graph->mutable_node(i + 16);
|
|
|
|
if (node2->op_type() != "Add" || node3->op_type() != "Split" || node4->op_type() != "Mul" || node5->op_type() != "Reshape" || node6->op_type() != "Transpose" || node7->op_type() != "Reshape" || node8->op_type() != "Reshape" || node9->op_type() != "Transpose" || node10->op_type() != "Transpose" || node11->op_type() != "MatMul" || node12->op_type() != "Softmax" || node13->op_type() != "MatMul" || node14->op_type() != "Transpose" || node15->op_type() != "Reshape" || node16->op_type() != "MatMul" || node17->op_type() != "Add")
|
|
continue;
|
|
|
|
if (node_reference[node2->output(0)] != 1 || node_reference[node3->output(0)] != 1 || node_reference[node3->output(1)] != 1 || node_reference[node3->output(2)] != 1 || node_reference[node4->output(0)] != 1 || node_reference[node5->output(0)] != 1 || node_reference[node6->output(0)] != 1 || node_reference[node7->output(0)] != 1 || node_reference[node8->output(0)] != 1 || node_reference[node9->output(0)] != 1 || node_reference[node10->output(0)] != 1 || node_reference[node11->output(0)] != 1 || node_reference[node12->output(0)] != 1 || node_reference[node13->output(0)] != 1 || node_reference[node14->output(0)] != 1 || node_reference[node15->output(0)] != 1 || node_reference[node16->output(0)] != 1)
|
|
continue;
|
|
|
|
if (node2->input(0) != node->output(0) || node3->input(0) != node2->output(0) || node4->input(0) != node3->output(0) || node5->input(0) != node4->output(0) || node6->input(0) != node5->output(0) || node7->input(0) != node3->output(1) || node8->input(0) != node3->output(2) || node9->input(0) != node8->output(0) || node10->input(0) != node7->output(0) || node11->input(0) != node6->output(0) || node11->input(1) != node10->output(0) || node12->input(0) != node11->output(0) || node13->input(0) != node12->output(0) || node13->input(1) != node9->output(0) || node14->input(0) != node13->output(0) || node15->input(0) != node14->output(0) || node16->input(0) != node15->output(0) || node17->input(0) != node16->output(0))
|
|
continue;
|
|
|
|
std::vector<float> qkv_B = get_node_attr_from_input_af(weights[node2->input(1)]);
|
|
std::vector<float> o_B = get_node_attr_from_input_af(weights[node17->input(1)]);
|
|
|
|
if (qkv_B.size() != o_B.size() * 3)
|
|
continue;
|
|
|
|
int embed_dim = o_B.size();
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm6 = get_node_attr_ai(*node6, "perm");
|
|
std::vector<int> perm9 = get_node_attr_ai(*node9, "perm");
|
|
if (perm6.size() != 3 || perm9.size() != 3)
|
|
continue;
|
|
|
|
if (perm6[0] != 1 || perm6[1] != 0 || perm6[2] != 2 || perm9[0] != 1 || perm9[1] != 0 || perm9[2] != 2)
|
|
continue;
|
|
|
|
// 1 2 0
|
|
std::vector<int> perm10 = get_node_attr_ai(*node10, "perm");
|
|
if (perm10.size() != 3)
|
|
continue;
|
|
|
|
if (perm10[0] != 1 || perm10[1] != 2 || perm10[2] != 0)
|
|
continue;
|
|
|
|
// 1 0 2
|
|
std::vector<int> perm14 = get_node_attr_ai(*node14, "perm");
|
|
if (perm14.size() != 3)
|
|
continue;
|
|
|
|
if (perm14[0] != 1 || perm14[1] != 0 || perm14[2] != 2)
|
|
continue;
|
|
|
|
int softmax_axis = get_node_attr_i(*node12, "axis");
|
|
if (softmax_axis != 2)
|
|
continue;
|
|
|
|
// 1/-1, seqlen * num_heads, embed_dim / num_heads
|
|
std::vector<int> shape5;
|
|
std::vector<int> shape7;
|
|
std::vector<int> shape8;
|
|
if (node5->input_size() == 1)
|
|
{
|
|
shape5 = get_node_attr_ai(*node5, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node5->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape5 = get_node_attr_from_input_ai(weights[node5->input(1)]);
|
|
}
|
|
if (node7->input_size() == 1)
|
|
{
|
|
shape7 = get_node_attr_ai(*node7, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node7->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape7 = get_node_attr_from_input_ai(weights[node7->input(1)]);
|
|
}
|
|
if (node8->input_size() == 1)
|
|
{
|
|
shape8 = get_node_attr_ai(*node8, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node8->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape8 = get_node_attr_from_input_ai(weights[node8->input(1)]);
|
|
}
|
|
|
|
if (shape5.size() != 3 || shape7.size() != 3 || shape8.size() != 3)
|
|
continue;
|
|
|
|
if (shape5[1] != shape7[1] || shape5[1] != shape8[1] || shape5[2] != shape7[2] || shape5[2] != shape8[2])
|
|
continue;
|
|
|
|
int num_heads = embed_dim / shape5[2];
|
|
|
|
// 1, seqlen, embed_dim
|
|
std::vector<int> shape15;
|
|
if (node15->input_size() == 1)
|
|
{
|
|
shape15 = get_node_attr_ai(*node15, "shape");
|
|
}
|
|
else
|
|
{
|
|
// skip weight reshape
|
|
if (weights.find(node15->input(1)) == weights.end())
|
|
continue;
|
|
|
|
shape15 = get_node_attr_from_input_ai(weights[node15->input(1)]);
|
|
}
|
|
|
|
if (shape15.size() != 3)
|
|
continue;
|
|
|
|
if (shape15[2] != embed_dim || shape15[1] * num_heads != shape8[1])
|
|
continue;
|
|
|
|
// reduce
|
|
node->set_op_type("noop_reducedncnn");
|
|
node2->set_op_type("noop_reducedncnn");
|
|
node3->set_op_type("noop_reducedncnn");
|
|
node4->set_op_type("noop_reducedncnn");
|
|
node5->set_op_type("noop_reducedncnn");
|
|
node6->set_op_type("noop_reducedncnn");
|
|
node7->set_op_type("noop_reducedncnn");
|
|
node8->set_op_type("noop_reducedncnn");
|
|
node9->set_op_type("noop_reducedncnn");
|
|
node10->set_op_type("noop_reducedncnn");
|
|
node11->set_op_type("noop_reducedncnn");
|
|
node12->set_op_type("noop_reducedncnn");
|
|
node13->set_op_type("noop_reducedncnn");
|
|
node14->set_op_type("noop_reducedncnn");
|
|
node15->set_op_type("noop_reducedncnn");
|
|
node16->set_op_type("noop_reducedncnn");
|
|
|
|
node_reference[node2->input(0)] -= 1;
|
|
node_reference[node3->input(0)] -= 1;
|
|
node_reference[node4->input(0)] -= 1;
|
|
node_reference[node4->input(1)] -= 1;
|
|
node_reference[node5->input(0)] -= 1;
|
|
if (node5->input_size() == 2)
|
|
{
|
|
node_reference[node5->input(1)] -= 1;
|
|
}
|
|
node_reference[node6->input(0)] -= 1;
|
|
node_reference[node7->input(0)] -= 1;
|
|
if (node7->input_size() == 2)
|
|
{
|
|
node_reference[node7->input(1)] -= 1;
|
|
}
|
|
node_reference[node8->input(0)] -= 1;
|
|
if (node8->input_size() == 2)
|
|
{
|
|
node_reference[node8->input(1)] -= 1;
|
|
}
|
|
node_reference[node9->input(0)] -= 1;
|
|
node_reference[node10->input(0)] -= 1;
|
|
node_reference[node11->input(0)] -= 1;
|
|
node_reference[node11->input(1)] -= 1;
|
|
node_reference[node12->input(0)] -= 1;
|
|
node_reference[node13->input(0)] -= 1;
|
|
node_reference[node13->input(1)] -= 1;
|
|
node_reference[node14->input(0)] -= 1;
|
|
node_reference[node15->input(0)] -= 1;
|
|
if (node15->input_size() == 2)
|
|
{
|
|
node_reference[node15->input(1)] -= 1;
|
|
}
|
|
node_reference[node16->input(0)] -= 1;
|
|
node_reference[node17->input(0)] -= 1;
|
|
|
|
blob_names.erase(node->output(0));
|
|
blob_names.erase(node2->output(0));
|
|
blob_names.erase(node3->output(0));
|
|
blob_names.erase(node3->output(1));
|
|
blob_names.erase(node3->output(2));
|
|
blob_names.erase(node4->output(0));
|
|
blob_names.erase(node5->output(0));
|
|
blob_names.erase(node6->output(0));
|
|
blob_names.erase(node7->output(0));
|
|
blob_names.erase(node8->output(0));
|
|
blob_names.erase(node9->output(0));
|
|
blob_names.erase(node10->output(0));
|
|
blob_names.erase(node11->output(0));
|
|
blob_names.erase(node12->output(0));
|
|
blob_names.erase(node13->output(0));
|
|
blob_names.erase(node14->output(0));
|
|
blob_names.erase(node15->output(0));
|
|
blob_names.erase(node16->output(0));
|
|
|
|
std::string qkvw = node->input(1);
|
|
std::string qkvb = node2->input(1);
|
|
std::string ow = node16->input(1);
|
|
std::string ob = node17->input(1);
|
|
|
|
node17->set_op_type("MultiHeadAttention");
|
|
node17->clear_input();
|
|
node17->add_input(node->input(0));
|
|
// qkv
|
|
node17->add_input(qkvw);
|
|
node17->add_input(qkvb);
|
|
// out linear
|
|
node17->add_input(ow);
|
|
node17->add_input(ob);
|
|
|
|
onnx::AttributeProto* attr_embed_dim = node17->add_attribute();
|
|
attr_embed_dim->set_name("embed_dim");
|
|
attr_embed_dim->set_i(embed_dim);
|
|
|
|
onnx::AttributeProto* attr_num_heads = node17->add_attribute();
|
|
attr_num_heads->set_name("num_heads");
|
|
attr_num_heads->set_i(num_heads);
|
|
|
|
reduced_node_count += 16;
|
|
i += 16;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void fuse_binaryop_with_scalar(onnx::GraphProto* mutable_graph, std::map<std::string, onnx::TensorProto>& weights, std::map<std::string, int>& node_reference, std::set<std::string>& blob_names, int& reduced_node_count)
|
|
{
|
|
int node_count = mutable_graph->node_size();
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Add/Sub/Mul/Div/Min/Max/Pow(a, x)
|
|
if (node->op_type() == "Add" || node->op_type() == "Sub" || node->op_type() == "Mul" || node->op_type() == "Div" || node->op_type() == "Max" || node->op_type() == "Min" || node->op_type() == "Pow")
|
|
{
|
|
if (weights.find(node->input(0)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& scalar_b = weights[node->input(0)];
|
|
if (scalar_b.dims_size() != 0 || get_tensor_proto_data_size(scalar_b) != 1)
|
|
continue;
|
|
|
|
if (node->op_type() == "Sub")
|
|
{
|
|
node->set_op_type("RSub");
|
|
}
|
|
else if (node->op_type() == "Div")
|
|
{
|
|
node->set_op_type("RDiv");
|
|
}
|
|
|
|
float b = get_node_attr_from_input_f(scalar_b);
|
|
|
|
node_reference[node->input(0)] -= 1;
|
|
|
|
std::string input = node->input(1);
|
|
|
|
node->clear_input();
|
|
node->add_input(input);
|
|
|
|
onnx::AttributeProto* attr_with_scalar = node->add_attribute();
|
|
attr_with_scalar->set_name("with_scalar");
|
|
attr_with_scalar->set_i(1);
|
|
|
|
onnx::AttributeProto* attr_b = node->add_attribute();
|
|
attr_b->set_name("b");
|
|
attr_b->set_f(b);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
// Add/Sub/Mul/Div/Min/Max/Pow(x, b)
|
|
if (node->op_type() == "Add" || node->op_type() == "Sub" || node->op_type() == "Mul" || node->op_type() == "Div" || node->op_type() == "Max" || node->op_type() == "Min" || node->op_type() == "Pow")
|
|
{
|
|
if (weights.find(node->input(1)) == weights.end())
|
|
continue;
|
|
|
|
const onnx::TensorProto& scalar_b = weights[node->input(1)];
|
|
if (scalar_b.dims_size() != 0 || get_tensor_proto_data_size(scalar_b) != 1)
|
|
continue;
|
|
|
|
float b = get_node_attr_from_input_f(scalar_b);
|
|
|
|
node_reference[node->input(1)] -= 1;
|
|
|
|
std::string input = node->input(0);
|
|
|
|
node->clear_input();
|
|
node->add_input(input);
|
|
|
|
onnx::AttributeProto* attr_with_scalar = node->add_attribute();
|
|
attr_with_scalar->set_name("with_scalar");
|
|
attr_with_scalar->set_i(1);
|
|
|
|
onnx::AttributeProto* attr_b = node->add_attribute();
|
|
attr_b->set_name("b");
|
|
attr_b->set_f(b);
|
|
}
|
|
}
|
|
}
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
if (!(argc == 2 || argc == 4))
|
|
{
|
|
fprintf(stderr, "Usage: %s [onnxpb] [ncnnparam] [ncnnbin]\n", argv[0]);
|
|
return -1;
|
|
}
|
|
|
|
const char* onnxpb = argv[1];
|
|
const char* ncnn_prototxt = argc == 4 ? argv[2] : "ncnn.param";
|
|
const char* ncnn_modelbin = argc == 4 ? argv[3] : "ncnn.bin";
|
|
|
|
onnx::ModelProto model;
|
|
|
|
// load
|
|
bool s1 = read_proto_from_binary(onnxpb, &model);
|
|
if (!s1)
|
|
{
|
|
fprintf(stderr, "read_proto_from_binary failed\n");
|
|
return -1;
|
|
}
|
|
|
|
FILE* pp = fopen(ncnn_prototxt, "wb");
|
|
FILE* bp = fopen(ncnn_modelbin, "wb");
|
|
|
|
// magic
|
|
fprintf(pp, "7767517\n");
|
|
|
|
const onnx::GraphProto& graph = model.graph();
|
|
onnx::GraphProto* mutable_graph = model.mutable_graph();
|
|
|
|
int node_count = graph.node_size();
|
|
|
|
// node reference
|
|
std::map<std::string, int> node_reference;
|
|
|
|
// weight node and weight reshape node
|
|
std::map<std::string, onnx::TensorProto> weights;
|
|
|
|
for (int j = 0; j < graph.initializer_size(); j++)
|
|
{
|
|
const onnx::TensorProto& initializer = graph.initializer(j);
|
|
|
|
// fprintf(stderr, "weight = %s %d\n", initializer.name().c_str(), initializer.data_type());
|
|
|
|
weights[initializer.name()] = initializer;
|
|
}
|
|
|
|
// topological sort
|
|
{
|
|
// name -> producer node index
|
|
std::set<std::string> producers;
|
|
for (int j = 0; j < graph.input_size(); j++)
|
|
{
|
|
const std::string& input_name = graph.input(j).name();
|
|
producers.insert(input_name);
|
|
}
|
|
|
|
for (int i = 0; i < node_count;)
|
|
{
|
|
onnx::NodeProto* node = mutable_graph->mutable_node(i);
|
|
|
|
bool swapnode = false;
|
|
std::string missing_input_name;
|
|
for (int j = 0; j < (int)node->input_size(); j++)
|
|
{
|
|
const std::string& input_name = node->input(j);
|
|
if (input_name.empty())
|
|
continue;
|
|
|
|
if (producers.find(input_name) == producers.end() && weights.find(input_name) == weights.end())
|
|
{
|
|
swapnode = true;
|
|
missing_input_name = input_name;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (!swapnode)
|
|
{
|
|
for (int j = 0; j < (int)node->output_size(); j++)
|
|
{
|
|
const std::string& output_name = node->output(j);
|
|
if (output_name.empty())
|
|
continue;
|
|
|
|
producers.insert(output_name);
|
|
}
|
|
|
|
i++;
|
|
continue;
|
|
}
|
|
|
|
// find node that produce missing_input_name
|
|
int q = i + 1;
|
|
for (; q < node_count; q++)
|
|
{
|
|
onnx::NodeProto* nodeq = mutable_graph->mutable_node(q);
|
|
bool found = false;
|
|
for (int j = 0; j < (int)nodeq->output_size(); j++)
|
|
{
|
|
const std::string& output_name = nodeq->output(j);
|
|
if (output_name == missing_input_name)
|
|
{
|
|
found = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (found)
|
|
break;
|
|
}
|
|
|
|
if (q == node_count)
|
|
{
|
|
fprintf(stderr, "cannot find node produces %s but node %d requires it\n", missing_input_name.c_str(), i);
|
|
return -1;
|
|
}
|
|
|
|
// fprintf(stderr, "swap %d %d\n", i, q);
|
|
// swap this node with q
|
|
onnx::NodeProto* nodeq = mutable_graph->mutable_node(q);
|
|
onnx::NodeProto tmp = *node;
|
|
*node = *nodeq;
|
|
*nodeq = tmp;
|
|
}
|
|
}
|
|
|
|
// global definition line
|
|
// [layer count] [blob count]
|
|
std::set<std::string> blob_names;
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
const onnx::NodeProto& node = graph.node(i);
|
|
|
|
const std::string& op = node.op_type();
|
|
|
|
std::string name = node.name();
|
|
if (name.empty())
|
|
{
|
|
name = node.output(0);
|
|
}
|
|
|
|
if (op == "Constant")
|
|
{
|
|
onnx::TensorProto tensor = get_node_attr_tensor(node, "value");
|
|
weights[node.output(0)] = tensor;
|
|
}
|
|
|
|
for (int j = 0; j < (int)node.input_size(); j++)
|
|
{
|
|
const std::string& input_name = node.input(j);
|
|
|
|
blob_names.insert(input_name);
|
|
|
|
if (node_reference.find(input_name) == node_reference.end())
|
|
{
|
|
node_reference[input_name] = 1;
|
|
}
|
|
else
|
|
{
|
|
node_reference[input_name] = node_reference[input_name] + 1;
|
|
}
|
|
}
|
|
|
|
if (op == "Dropout")
|
|
{
|
|
const std::string& output_name = node.output(0);
|
|
blob_names.insert(output_name);
|
|
node_reference[output_name] = 0;
|
|
continue;
|
|
}
|
|
|
|
for (int j = 0; j < (int)node.output_size(); j++)
|
|
{
|
|
const std::string& output_name = node.output(j);
|
|
|
|
blob_names.insert(output_name);
|
|
|
|
node_reference[output_name] = 0;
|
|
}
|
|
}
|
|
|
|
// include Input node
|
|
int input_node_count = 0;
|
|
for (int j = 0; j < graph.input_size(); j++)
|
|
{
|
|
const std::string& input_name = graph.input(j).name();
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end())
|
|
continue;
|
|
|
|
blob_names.insert(input_name);
|
|
|
|
input_node_count++;
|
|
}
|
|
|
|
// for (auto a: node_reference)
|
|
// {
|
|
// fprintf(stderr, "a = %s %d\n", a.first.c_str(), a.second);
|
|
// }
|
|
|
|
// op chain fusion
|
|
int reduced_node_count = 0;
|
|
fuse_weight_reshape(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_weight_transpose(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_shufflechannel(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_shufflechannel_split(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_hardsigmoid(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_hardswish(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_swish(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_batchnorm1d_squeeze_unsqueeze(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_unsqueeze_prelu(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_normalize(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_groupnorm(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_layernorm(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_flatten(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_pixelshuffle(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_reorg(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_expand_broadcast(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_lstm_gru_rnn(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_multiheadattention(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
fuse_binaryop_with_scalar(mutable_graph, weights, node_reference, blob_names, reduced_node_count);
|
|
|
|
// reduce common const weight node_reference
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
const onnx::NodeProto& node = graph.node(i);
|
|
|
|
const std::string& op = node.op_type();
|
|
|
|
if (op == "BatchNormalization")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
node_reference[node.input(3)] -= 1;
|
|
node_reference[node.input(4)] -= 1;
|
|
}
|
|
else if (op == "BiasGelu")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
else if (op == "Clip")
|
|
{
|
|
if (node.input_size() == 3)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "Conv")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
if (node.input_size() == 3)
|
|
{
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "ConvTranspose")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
if (node.input_size() == 3)
|
|
{
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "EmbedLayerNormalization")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
node_reference[node.input(3)] -= 1;
|
|
node_reference[node.input(4)] -= 1;
|
|
node_reference[node.input(5)] -= 1;
|
|
node_reference[node.input(6)] -= 1;
|
|
}
|
|
else if (op == "Gemm")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 1.f);
|
|
float beta = get_node_attr_f(node, "beta", 1.f);
|
|
int transA = get_node_attr_i(node, "transA", 0);
|
|
int transB = get_node_attr_i(node, "transB", 0);
|
|
|
|
if (alpha == 1.f && beta == 1.f && transA == 0 && transB == 1)
|
|
{
|
|
// InnerProduct-like A * B + C
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "GroupNorm")
|
|
{
|
|
int affine = get_node_attr_i(node, "affine", 1);
|
|
if (affine)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "GRU")
|
|
{
|
|
for (int j = 1; j < node.input_size(); j++)
|
|
{
|
|
node_reference[node.input(j)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "InstanceNormalization")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
else if (op == "LayerNorm")
|
|
{
|
|
int affine = get_node_attr_i(node, "affine", 1);
|
|
if (affine)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "LSTM")
|
|
{
|
|
for (int j = 1; j < node.input_size(); j++)
|
|
{
|
|
node_reference[node.input(j)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "MatMul")
|
|
{
|
|
if (weights.find(node.input(1)) != weights.end() && weights[node.input(1)].dims_size() == 2)
|
|
{
|
|
// InnerProduct
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "MultiHeadAttention")
|
|
{
|
|
if (node.input_size() == 5)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
node_reference[node.input(3)] -= 1;
|
|
node_reference[node.input(4)] -= 1;
|
|
}
|
|
else
|
|
{
|
|
node_reference[node.input(3)] -= 1;
|
|
node_reference[node.input(4)] -= 1;
|
|
node_reference[node.input(5)] -= 1;
|
|
node_reference[node.input(6)] -= 1;
|
|
node_reference[node.input(7)] -= 1;
|
|
node_reference[node.input(8)] -= 1;
|
|
node_reference[node.input(9)] -= 1;
|
|
node_reference[node.input(10)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "Pad")
|
|
{
|
|
if (node.input_size() >= 2)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "PRelu")
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
else if (op == "Reshape")
|
|
{
|
|
if (node.input_size() >= 2)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "Resize")
|
|
{
|
|
if (node.input_size() == 2)
|
|
{
|
|
// opset 10
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
else
|
|
{
|
|
// opset 11+
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
if (node.input_size() >= 4)
|
|
{
|
|
node_reference[node.input(3)] -= 1;
|
|
}
|
|
}
|
|
}
|
|
else if (op == "RNN")
|
|
{
|
|
for (int j = 1; j < node.input_size(); j++)
|
|
{
|
|
node_reference[node.input(j)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "SkipLayerNormalization")
|
|
{
|
|
node_reference[node.input(2)] -= 1;
|
|
node_reference[node.input(3)] -= 1;
|
|
node_reference[node.input(4)] -= 1;
|
|
}
|
|
else if (op == "Slice")
|
|
{
|
|
if (node.input_size() >= 2)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
node_reference[node.input(2)] -= 1;
|
|
if (node.input_size() >= 4)
|
|
node_reference[node.input(3)] -= 1;
|
|
if (node.input_size() >= 5)
|
|
node_reference[node.input(4)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "Upsample")
|
|
{
|
|
if (node.input_size() >= 2)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
}
|
|
else if (op == "adaptive_avg_pool2d" || op == "adaptive_max_pool2d")
|
|
{
|
|
if (node.input_size() >= 2)
|
|
{
|
|
node_reference[node.input(1)] -= 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
// for (auto a: node_reference)
|
|
// {
|
|
// fprintf(stderr, "b = %s %d\n", a.first.c_str(), a.second);
|
|
// }
|
|
|
|
// count all weight node with zero reference
|
|
int zero_reference_weight_node_count = 0;
|
|
for (std::map<std::string, onnx::TensorProto>::iterator it = weights.begin(); it != weights.end(); it++)
|
|
{
|
|
const std::string& input_name = it->first;
|
|
|
|
// there may be some weight nodes in initializer but none of the graph node use them
|
|
// add them to blob_names so we could get proper blob count later
|
|
blob_names.insert(input_name);
|
|
|
|
int refcount = node_reference[input_name];
|
|
if (refcount == 0)
|
|
zero_reference_weight_node_count++;
|
|
}
|
|
|
|
// we always treat constant node as weight or binaryop_weights
|
|
// do not count it twice for layer_count
|
|
int constant_node_count_moved_to_weight = 0;
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
const onnx::NodeProto& node = graph.node(i);
|
|
|
|
const std::string& op = node.op_type();
|
|
|
|
if (op == "Constant")
|
|
{
|
|
constant_node_count_moved_to_weight++;
|
|
}
|
|
}
|
|
|
|
// some op may have anonymous input
|
|
// LSTM sequence_lens
|
|
blob_names.erase("");
|
|
node_reference.erase("");
|
|
|
|
// remove node_reference entry with reference equals to one
|
|
int split_layer_count = 0;
|
|
int splitncnn_blob_count = 0;
|
|
// split node reference
|
|
std::map<std::string, int> split_node_reference;
|
|
for (std::map<std::string, int>::iterator it = node_reference.begin(); it != node_reference.end(); it++)
|
|
{
|
|
if (it->second > 1)
|
|
{
|
|
split_layer_count++;
|
|
splitncnn_blob_count += it->second;
|
|
|
|
split_node_reference[it->first] = it->second;
|
|
}
|
|
}
|
|
|
|
fprintf(pp, "%zu %zu\n", node_count - constant_node_count_moved_to_weight + weights.size() - zero_reference_weight_node_count - reduced_node_count + input_node_count + split_layer_count, blob_names.size() - zero_reference_weight_node_count + splitncnn_blob_count);
|
|
|
|
int internal_split = 0;
|
|
|
|
// place Input at the beginning
|
|
for (int j = 0; j < graph.input_size(); j++)
|
|
{
|
|
const std::string& input_name = graph.input(j).name();
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end())
|
|
continue;
|
|
|
|
fprintf(pp, "%-16s %-24s 0 1 %s\n", "Input", input_name.c_str(), input_name.c_str());
|
|
|
|
int refcount = node_reference[input_name];
|
|
if (refcount <= 1)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
char splitname[256];
|
|
sprintf(splitname, "splitncnn_input%d", j);
|
|
fprintf(pp, "%-16s %-24s %d %d", "Split", splitname, 1, refcount);
|
|
fprintf(pp, " %s", input_name.c_str());
|
|
|
|
for (int k = 0; k < refcount; k++)
|
|
{
|
|
fprintf(pp, " %s_splitncnn_%d", input_name.c_str(), k);
|
|
}
|
|
fprintf(pp, "\n");
|
|
}
|
|
|
|
// place MemoryData next
|
|
for (std::map<std::string, onnx::TensorProto>::iterator weight_it = weights.begin(); weight_it != weights.end(); weight_it++)
|
|
{
|
|
const std::string& input_name = weight_it->first;
|
|
|
|
int refcount = node_reference[input_name];
|
|
if (refcount == 0)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
fprintf(pp, "%-16s %-24s 0 1 %s", "MemoryData", input_name.c_str(), input_name.c_str());
|
|
|
|
const onnx::TensorProto& M = weights[input_name];
|
|
|
|
if (M.dims_size() == 0)
|
|
{
|
|
fprintf(pp, " 0=%d", get_tensor_proto_data_size(M));
|
|
}
|
|
else if (M.dims_size() == 1)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)M.dims(0));
|
|
}
|
|
else if (M.dims_size() == 2)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)M.dims(1));
|
|
if (M.dims(0) != 1)
|
|
{
|
|
fprintf(pp, " 1=%d", (int)M.dims(0));
|
|
}
|
|
}
|
|
else if (M.dims_size() == 3)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)M.dims(2));
|
|
fprintf(pp, " 1=%d", (int)M.dims(1));
|
|
if (M.dims(0) != 1)
|
|
{
|
|
fprintf(pp, " 2=%d", (int)M.dims(0));
|
|
}
|
|
}
|
|
else if (M.dims_size() == 4)
|
|
{
|
|
fprintf(pp, " 0=%d", (int)M.dims(3));
|
|
fprintf(pp, " 1=%d", (int)M.dims(2));
|
|
fprintf(pp, " 2=%d", (int)M.dims(1));
|
|
}
|
|
|
|
fprintf(pp, "\n");
|
|
|
|
fwrite_tensor_proto_data(M, bp);
|
|
|
|
if (refcount <= 1)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
char splitname[256];
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
|
fprintf(pp, "%-16s %-24s %d %d", "Split", splitname, 1, refcount);
|
|
|
|
fprintf(pp, " %s", input_name.c_str());
|
|
|
|
for (int k = 0; k < refcount; k++)
|
|
{
|
|
fprintf(pp, " %s_splitncnn_%d", input_name.c_str(), k);
|
|
}
|
|
fprintf(pp, "\n");
|
|
|
|
internal_split++;
|
|
}
|
|
|
|
for (int i = 0; i < node_count; i++)
|
|
{
|
|
const onnx::NodeProto& node = graph.node(i);
|
|
|
|
const std::string& op = node.op_type();
|
|
|
|
// fprintf(stderr, "op = %s\n", op.c_str());
|
|
|
|
if (op == "noop_reducedncnn")
|
|
{
|
|
continue;
|
|
}
|
|
|
|
std::string name = node.name();
|
|
if (name.empty())
|
|
{
|
|
name = node.output(0);
|
|
}
|
|
|
|
int input_size = node.input_size();
|
|
int output_size = node.output_size();
|
|
|
|
for (int j = 0; j < (int)node.input_size(); j++)
|
|
{
|
|
const std::string& input_name = node.input(j);
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end() && node_reference[input_name] == 0)
|
|
{
|
|
input_size--;
|
|
}
|
|
|
|
if (input_name.empty())
|
|
{
|
|
input_size--;
|
|
}
|
|
|
|
// fprintf(stderr, " input = %s\n", input_name.c_str());
|
|
}
|
|
/*
|
|
for (int j=0; j<(int)node.output_size(); j++)
|
|
{
|
|
const std::string& output_name = node.output(j);
|
|
fprintf(stderr, " output = %s\n", output_name.c_str());
|
|
}
|
|
*/
|
|
|
|
if (op == "Abs")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Acos")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Add")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "Asin")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Atan")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "AveragePool" || op == "MaxPool")
|
|
{
|
|
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
|
|
if (kernel_shape.size() == 1)
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling1D");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
}
|
|
else if (op == "BatchNormalization")
|
|
{
|
|
fprintf(pp, "%-16s", "BatchNorm");
|
|
}
|
|
else if (op == "BiasGelu")
|
|
{
|
|
fprintf(pp, "%-16s", "BiasGelu");
|
|
}
|
|
else if (op == "Ceil")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Clip")
|
|
{
|
|
fprintf(pp, "%-16s", "Clip");
|
|
}
|
|
else if (op == "Concat")
|
|
{
|
|
fprintf(pp, "%-16s", "Concat");
|
|
}
|
|
else if (op == "Constant")
|
|
{
|
|
continue;
|
|
}
|
|
else if (op == "Conv")
|
|
{
|
|
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
|
|
if (kernel_shape.size() == 1)
|
|
{
|
|
fprintf(pp, "%-16s", "Convolution1D");
|
|
}
|
|
else
|
|
{
|
|
int group = get_node_attr_i(node, "group", 1);
|
|
if (group > 1)
|
|
{
|
|
fprintf(pp, "%-16s", "ConvolutionDepthWise");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Convolution");
|
|
}
|
|
}
|
|
}
|
|
else if (op == "ConvTranspose")
|
|
{
|
|
int group = get_node_attr_i(node, "group", 1);
|
|
if (group > 1)
|
|
{
|
|
fprintf(pp, "%-16s", "DeconvolutionDepthWise");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Deconvolution");
|
|
}
|
|
}
|
|
else if (op == "Cos")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "DepthToSpace")
|
|
{
|
|
fprintf(pp, "%-16s", "PixelShuffle");
|
|
}
|
|
else if (op == "Div")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "Dropout")
|
|
{
|
|
fprintf(pp, "%-16s", "Dropout");
|
|
output_size = 1;
|
|
}
|
|
else if (op == "Elu")
|
|
{
|
|
fprintf(pp, "%-16s", "ELU");
|
|
}
|
|
else if (op == "EmbedLayerNormalization")
|
|
{
|
|
fprintf(pp, "%-16s", "EmbedLayerNormalization");
|
|
}
|
|
else if (op == "Exp")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Flatten")
|
|
{
|
|
fprintf(pp, "%-16s", "Flatten");
|
|
}
|
|
else if (op == "Floor")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Gemm")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 1.f);
|
|
float beta = get_node_attr_f(node, "beta", 1.f);
|
|
int transA = get_node_attr_i(node, "transA", 0);
|
|
int transB = get_node_attr_i(node, "transB", 0);
|
|
|
|
if (alpha == 1.f && beta == 1.f && transA == 0 && transB == 1)
|
|
{
|
|
// InnerProduct-like A * B + C
|
|
fprintf(pp, "%-16s", "InnerProduct");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Gemm");
|
|
}
|
|
}
|
|
else if (op == "GlobalAveragePool")
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else if (op == "GlobalMaxPool")
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else if (op == "adaptive_avg_pool2d" || op == "adaptive_max_pool2d")
|
|
{
|
|
fprintf(pp, "%-16s", "Pooling");
|
|
}
|
|
else if (op == "GroupNorm")
|
|
{
|
|
fprintf(pp, "%-16s", "GroupNorm");
|
|
}
|
|
else if (op == "GRU")
|
|
{
|
|
fprintf(pp, "%-16s", "GRU");
|
|
}
|
|
else if (op == "HardSigmoid")
|
|
{
|
|
fprintf(pp, "%-16s", "HardSigmoid");
|
|
}
|
|
else if (op == "HardSwish")
|
|
{
|
|
fprintf(pp, "%-16s", "HardSwish");
|
|
}
|
|
else if (op == "ImageScaler")
|
|
{
|
|
fprintf(pp, "%-16s", "Scale");
|
|
}
|
|
else if (op == "InstanceNormalization")
|
|
{
|
|
fprintf(pp, "%-16s", "InstanceNorm");
|
|
}
|
|
else if (op == "LayerNorm")
|
|
{
|
|
fprintf(pp, "%-16s", "LayerNorm");
|
|
}
|
|
else if (op == "LeakyRelu")
|
|
{
|
|
fprintf(pp, "%-16s", "ReLU");
|
|
}
|
|
else if (op == "Log")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "LRN")
|
|
{
|
|
fprintf(pp, "%-16s", "LRN");
|
|
}
|
|
else if (op == "LSTM")
|
|
{
|
|
fprintf(pp, "%-16s", "LSTM");
|
|
}
|
|
else if (op == "MatMul")
|
|
{
|
|
if (weights.find(node.input(1)) != weights.end() && weights[node.input(1)].dims_size() == 2)
|
|
{
|
|
fprintf(pp, "%-16s", "InnerProduct");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, "%-16s", "Gemm");
|
|
}
|
|
}
|
|
else if (op == "Max")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "Min")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "Mul")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "MultiHeadAttention")
|
|
{
|
|
fprintf(pp, "%-16s", "MultiHeadAttention");
|
|
}
|
|
else if (op == "Neg")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Normalize")
|
|
{
|
|
fprintf(pp, "%-16s", "Normalize");
|
|
}
|
|
else if (op == "Pad")
|
|
{
|
|
fprintf(pp, "%-16s", "Padding");
|
|
}
|
|
else if (op == "PixelShuffle")
|
|
{
|
|
fprintf(pp, "%-16s", "PixelShuffle");
|
|
}
|
|
else if (op == "Pow")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "PRelu")
|
|
{
|
|
fprintf(pp, "%-16s", "PReLU");
|
|
}
|
|
else if (op == "Reciprocal")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "ReduceMax" || op == "ReduceMin" || op == "ReduceMean" || op == "ReduceProd" || op == "ReduceSum" || op == "ReduceSumSquare" || op == "ReduceL1" || op == "ReduceL2" || op == "ReduceLogSum" || op == "ReduceLogSumExp")
|
|
{
|
|
fprintf(pp, "%-16s", "Reduction");
|
|
}
|
|
else if (op == "Relu")
|
|
{
|
|
fprintf(pp, "%-16s", "ReLU");
|
|
}
|
|
else if (op == "Reorg")
|
|
{
|
|
fprintf(pp, "%-16s", "Reorg");
|
|
}
|
|
else if (op == "Reshape")
|
|
{
|
|
fprintf(pp, "%-16s", "Reshape");
|
|
}
|
|
else if (op == "RNN")
|
|
{
|
|
fprintf(pp, "%-16s", "RNN");
|
|
}
|
|
else if (op == "RDiv")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "RSub")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "ShuffleChannel")
|
|
{
|
|
fprintf(pp, "%-16s", "ShuffleChannel");
|
|
}
|
|
else if (op == "Sigmoid")
|
|
{
|
|
fprintf(pp, "%-16s", "Sigmoid");
|
|
}
|
|
else if (op == "Sin")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "SkipLayerNormalization")
|
|
{
|
|
fprintf(pp, "%-16s", "SkipLayerNormalization");
|
|
}
|
|
else if (op == "Slice")
|
|
{
|
|
fprintf(pp, "%-16s", "Crop");
|
|
}
|
|
else if (op == "Softmax")
|
|
{
|
|
fprintf(pp, "%-16s", "Softmax");
|
|
}
|
|
else if (op == "Softplus")
|
|
{
|
|
fprintf(pp, "%-16s", "Softplus");
|
|
}
|
|
else if (op == "Split")
|
|
{
|
|
fprintf(pp, "%-16s", "Slice");
|
|
}
|
|
else if (op == "Sqrt")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Squeeze")
|
|
{
|
|
fprintf(pp, "%-16s", "Squeeze");
|
|
}
|
|
else if (op == "Sub")
|
|
{
|
|
fprintf(pp, "%-16s", "BinaryOp");
|
|
}
|
|
else if (op == "Sum")
|
|
{
|
|
fprintf(pp, "%-16s", "Eltwise");
|
|
}
|
|
else if (op == "Swish")
|
|
{
|
|
fprintf(pp, "%-16s", "Swish");
|
|
}
|
|
else if (op == "Tan")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Tanh")
|
|
{
|
|
fprintf(pp, "%-16s", "UnaryOp");
|
|
}
|
|
else if (op == "Transpose")
|
|
{
|
|
fprintf(pp, "%-16s", "Permute");
|
|
}
|
|
else if (op == "Upsample" || op == "Resize")
|
|
{
|
|
fprintf(pp, "%-16s", "Interp");
|
|
}
|
|
else if (op == "Unsqueeze")
|
|
{
|
|
fprintf(pp, "%-16s", "ExpandDims");
|
|
}
|
|
else
|
|
{
|
|
// TODO
|
|
fprintf(stderr, "%s not supported yet!\n", op.c_str());
|
|
fprintf(pp, "%-16s", op.c_str());
|
|
}
|
|
|
|
fprintf(pp, " %-24s %d %d", name.c_str(), input_size, output_size);
|
|
|
|
for (int j = 0; j < (int)node.input_size(); j++)
|
|
{
|
|
std::string input_name = node.input(j);
|
|
|
|
// check weight
|
|
if (weights.find(input_name) != weights.end() && node_reference[input_name] == 0)
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (input_name.empty())
|
|
{
|
|
continue;
|
|
}
|
|
|
|
if (split_node_reference.find(input_name) != split_node_reference.end())
|
|
{
|
|
int refidx = split_node_reference[input_name] - 1;
|
|
split_node_reference[input_name] = refidx;
|
|
|
|
char splitsuffix[256];
|
|
sprintf(splitsuffix, "_splitncnn_%d", refidx);
|
|
input_name = input_name + splitsuffix;
|
|
}
|
|
|
|
fprintf(pp, " %s", input_name.c_str());
|
|
}
|
|
|
|
for (int j = 0; j < output_size; j++)
|
|
{
|
|
const std::string& output_name = node.output(j);
|
|
|
|
fprintf(pp, " %s", output_name.c_str());
|
|
}
|
|
|
|
if (op == "Abs")
|
|
{
|
|
int op_type = 0;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Acos")
|
|
{
|
|
int op_type = 13;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Add")
|
|
{
|
|
int op_type = 0;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "Asin")
|
|
{
|
|
int op_type = 12;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Atan")
|
|
{
|
|
int op_type = 14;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "AveragePool" || op == "MaxPool")
|
|
{
|
|
std::string auto_pad = get_node_attr_s(node, "auto_pad");
|
|
int ceil_mode = get_node_attr_i(node, "ceil_mode", 0);
|
|
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
|
|
std::vector<int> strides = get_node_attr_ai(node, "strides");
|
|
std::vector<int> pads = get_node_attr_ai(node, "pads");
|
|
|
|
int pool = op == "AveragePool" ? 1 : 0;
|
|
int pad_mode = 1;
|
|
|
|
if (auto_pad == "SAME_UPPER")
|
|
{
|
|
pad_mode = 2;
|
|
}
|
|
else if (auto_pad == "SAME_LOWER")
|
|
{
|
|
pad_mode = 3;
|
|
}
|
|
|
|
if (ceil_mode == 1)
|
|
{
|
|
pad_mode = 0;
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", pool);
|
|
|
|
if (kernel_shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[0]);
|
|
}
|
|
else if (kernel_shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[1]);
|
|
fprintf(pp, " 11=%d", kernel_shape[0]);
|
|
}
|
|
|
|
if (strides.size() == 1)
|
|
{
|
|
fprintf(pp, " 2=%d", strides[0]);
|
|
}
|
|
else if (strides.size() == 2)
|
|
{
|
|
fprintf(pp, " 2=%d", strides[1]);
|
|
fprintf(pp, " 12=%d", strides[0]);
|
|
}
|
|
|
|
if (pads.size() == 1)
|
|
{
|
|
fprintf(pp, " 3=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 2)
|
|
{
|
|
fprintf(pp, " 3=%d", pads[1]);
|
|
fprintf(pp, " 13=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 4)
|
|
{
|
|
fprintf(pp, " 3=%d", pads[1]);
|
|
fprintf(pp, " 13=%d", pads[0]);
|
|
fprintf(pp, " 14=%d", pads[3]);
|
|
fprintf(pp, " 15=%d", pads[2]);
|
|
}
|
|
|
|
fprintf(pp, " 5=%d", pad_mode);
|
|
|
|
if (op == "AveragePool")
|
|
{
|
|
int avgpool_count_include_pad = get_node_attr_i(node, "count_include_pad", 0);
|
|
fprintf(pp, " 6=%d", avgpool_count_include_pad);
|
|
}
|
|
}
|
|
else if (op == "BatchNormalization")
|
|
{
|
|
float epsilon = get_node_attr_f(node, "epsilon", 1e-5f);
|
|
|
|
const onnx::TensorProto& scale = weights[node.input(1)];
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
const onnx::TensorProto& mean = weights[node.input(3)];
|
|
const onnx::TensorProto& var = weights[node.input(4)];
|
|
|
|
int channels = get_tensor_proto_data_size(scale);
|
|
|
|
fprintf(pp, " 0=%d", channels);
|
|
|
|
fwrite_tensor_proto_data(scale, bp);
|
|
fwrite_tensor_proto_data(mean, bp);
|
|
// apply epsilon to var
|
|
{
|
|
const float* v = var.has_raw_data() ? (const float*)var.raw_data().data() : var.float_data().data();
|
|
|
|
for (int j = 0; j < channels; j++)
|
|
{
|
|
float ve = v[j] + epsilon;
|
|
fwrite(&ve, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
else if (op == "BiasGelu")
|
|
{
|
|
const onnx::TensorProto& B = weights[node.input(1)];
|
|
|
|
fprintf(pp, " 0=%d", get_tensor_proto_data_size(B));
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
else if (op == "Ceil")
|
|
{
|
|
int op_type = 3;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Clip")
|
|
{
|
|
float min;
|
|
float max;
|
|
if (node.input_size() == 1)
|
|
{
|
|
min = get_node_attr_f(node, "min", -FLT_MAX);
|
|
max = get_node_attr_f(node, "max", FLT_MAX);
|
|
}
|
|
else
|
|
{
|
|
min = weights.find(node.input(1)) != weights.end() ? get_node_attr_from_input_f(weights[node.input(1)]) : -FLT_MAX;
|
|
max = weights.find(node.input(2)) != weights.end() ? get_node_attr_from_input_f(weights[node.input(2)]) : FLT_MAX;
|
|
}
|
|
|
|
fprintf(pp, " 0=%e", min);
|
|
fprintf(pp, " 1=%e", max);
|
|
}
|
|
else if (op == "Concat")
|
|
{
|
|
int axis = get_node_attr_i(node, "axis", 1);
|
|
fprintf(pp, " 0=%d", axis > 0 ? axis - 1 : axis);
|
|
}
|
|
else if (op == "Constant")
|
|
{
|
|
// never reach here
|
|
}
|
|
else if (op == "Conv")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(1)];
|
|
|
|
int num_filter = W.dims(0);
|
|
int has_bias = node.input_size() == 3 ? 1 : 0;
|
|
|
|
std::string auto_pad = get_node_attr_s(node, "auto_pad");
|
|
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
|
|
std::vector<int> dilations = get_node_attr_ai(node, "dilations");
|
|
std::vector<int> strides = get_node_attr_ai(node, "strides");
|
|
std::vector<int> pads = get_node_attr_ai(node, "pads");
|
|
int group = get_node_attr_i(node, "group", 1);
|
|
|
|
fprintf(pp, " 0=%d", num_filter);
|
|
|
|
if (kernel_shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[0]);
|
|
}
|
|
else if (kernel_shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[1]);
|
|
fprintf(pp, " 11=%d", kernel_shape[0]);
|
|
}
|
|
|
|
if (dilations.size() == 1)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[0]);
|
|
}
|
|
else if (dilations.size() == 2)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[1]);
|
|
fprintf(pp, " 12=%d", dilations[0]);
|
|
}
|
|
|
|
if (strides.size() == 1)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[0]);
|
|
}
|
|
else if (strides.size() == 2)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[1]);
|
|
fprintf(pp, " 13=%d", strides[0]);
|
|
}
|
|
|
|
if (auto_pad == "SAME_UPPER")
|
|
{
|
|
fprintf(pp, " 4=-233");
|
|
}
|
|
else if (auto_pad == "SAME_LOWER")
|
|
{
|
|
fprintf(pp, " 4=-234");
|
|
}
|
|
else
|
|
{
|
|
if (pads.size() == 1)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 2)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[1]);
|
|
fprintf(pp, " 14=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 4)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[1]);
|
|
fprintf(pp, " 14=%d", pads[0]);
|
|
fprintf(pp, " 15=%d", pads[3]);
|
|
fprintf(pp, " 16=%d", pads[2]);
|
|
}
|
|
}
|
|
|
|
fprintf(pp, " 5=%d", has_bias);
|
|
|
|
fprintf(pp, " 6=%d", get_tensor_proto_data_size(W));
|
|
|
|
if (group > 1)
|
|
{
|
|
fprintf(pp, " 7=%d", group);
|
|
}
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(W, bp);
|
|
|
|
if (has_bias)
|
|
{
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
}
|
|
else if (op == "ConvTranspose")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(1)];
|
|
|
|
int has_bias = node.input_size() == 3 ? 1 : 0;
|
|
|
|
std::string auto_pad = get_node_attr_s(node, "auto_pad");
|
|
std::vector<int> kernel_shape = get_node_attr_ai(node, "kernel_shape");
|
|
std::vector<int> dilations = get_node_attr_ai(node, "dilations");
|
|
std::vector<int> strides = get_node_attr_ai(node, "strides");
|
|
std::vector<int> output_padding = get_node_attr_ai(node, "output_padding");
|
|
std::vector<int> output_shape = get_node_attr_ai(node, "output_shape");
|
|
std::vector<int> pads = get_node_attr_ai(node, "pads");
|
|
int group = get_node_attr_i(node, "group", 1);
|
|
int num_filter = W.dims(1) * group;
|
|
|
|
fprintf(pp, " 0=%d", num_filter);
|
|
|
|
if (kernel_shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[0]);
|
|
}
|
|
else if (kernel_shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 1=%d", kernel_shape[1]);
|
|
fprintf(pp, " 11=%d", kernel_shape[0]);
|
|
}
|
|
|
|
if (dilations.size() == 1)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[0]);
|
|
}
|
|
else if (dilations.size() == 2)
|
|
{
|
|
fprintf(pp, " 2=%d", dilations[1]);
|
|
fprintf(pp, " 12=%d", dilations[0]);
|
|
}
|
|
|
|
if (strides.size() == 1)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[0]);
|
|
}
|
|
else if (strides.size() == 2)
|
|
{
|
|
fprintf(pp, " 3=%d", strides[1]);
|
|
fprintf(pp, " 13=%d", strides[0]);
|
|
}
|
|
|
|
if (auto_pad == "SAME_UPPER")
|
|
{
|
|
fprintf(pp, " 4=-233");
|
|
}
|
|
else if (auto_pad == "SAME_LOWER")
|
|
{
|
|
fprintf(pp, " 4=-234");
|
|
}
|
|
else
|
|
{
|
|
if (pads.size() == 1)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 2)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[1]);
|
|
fprintf(pp, " 14=%d", pads[0]);
|
|
}
|
|
else if (pads.size() == 4)
|
|
{
|
|
fprintf(pp, " 4=%d", pads[1]);
|
|
fprintf(pp, " 14=%d", pads[0]);
|
|
fprintf(pp, " 15=%d", pads[3]);
|
|
fprintf(pp, " 16=%d", pads[2]);
|
|
}
|
|
}
|
|
|
|
if (output_padding.size() == 1)
|
|
{
|
|
fprintf(pp, " 18=%d", output_padding[0]);
|
|
}
|
|
else if (output_padding.size() == 2)
|
|
{
|
|
fprintf(pp, " 18=%d", output_padding[1]);
|
|
fprintf(pp, " 19=%d", output_padding[0]);
|
|
}
|
|
|
|
if (output_shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 20=%d", output_shape[0]);
|
|
}
|
|
else if (output_shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 20=%d", output_shape[1]);
|
|
fprintf(pp, " 21=%d", output_shape[0]);
|
|
}
|
|
|
|
fprintf(pp, " 5=%d", has_bias);
|
|
|
|
fprintf(pp, " 6=%d", get_tensor_proto_data_size(W));
|
|
|
|
if (group > 1)
|
|
{
|
|
fprintf(pp, " 7=%d", group);
|
|
}
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int maxk = 0;
|
|
if (kernel_shape.size() == 2)
|
|
{
|
|
maxk = kernel_shape[1] * kernel_shape[0];
|
|
}
|
|
else
|
|
{
|
|
maxk = kernel_shape[0] * kernel_shape[0];
|
|
}
|
|
int weight_data_size = get_tensor_proto_data_size(W);
|
|
const float* weight_data = 0;
|
|
if (W.has_raw_data())
|
|
{
|
|
weight_data = (const float*)W.raw_data().data();
|
|
}
|
|
else if (W.data_type() == 1)
|
|
{
|
|
weight_data = W.float_data().data();
|
|
}
|
|
for (int g = 0; g < group; g++)
|
|
{
|
|
// reorder weight from inch-outch to outch-inch
|
|
int num_filter_g = num_filter / group;
|
|
int num_input = weight_data_size / maxk / num_filter_g / group;
|
|
const float* weight_data_ptr = weight_data + g * maxk * num_filter_g * num_input;
|
|
for (int k = 0; k < num_filter_g; k++)
|
|
{
|
|
for (int j = 0; j < num_input; j++)
|
|
{
|
|
fwrite(weight_data_ptr + (j * num_filter_g + k) * maxk, sizeof(float), maxk, bp);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (has_bias)
|
|
{
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
}
|
|
else if (op == "Cos")
|
|
{
|
|
int op_type = 10;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "DepthToSpace")
|
|
{
|
|
// pixelshuffle
|
|
int scale_factor = get_node_attr_i(node, "blocksize", 1);
|
|
std::string mode = get_node_attr_s(node, "mode");
|
|
fprintf(pp, " 0=%d", scale_factor);
|
|
if (mode == "CRD")
|
|
{
|
|
fprintf(pp, " 1=0");
|
|
}
|
|
else if (mode == "DCR")
|
|
{
|
|
fprintf(pp, " 1=1");
|
|
}
|
|
}
|
|
else if (op == "Div")
|
|
{
|
|
int op_type = 3;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "Dropout")
|
|
{
|
|
// no-op
|
|
}
|
|
else if (op == "Elu")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 1.f);
|
|
fprintf(pp, " 0=%e", alpha);
|
|
}
|
|
else if (op == "EmbedLayerNormalization")
|
|
{
|
|
const onnx::TensorProto& words = weights[node.input(2)];
|
|
const onnx::TensorProto& positions = weights[node.input(3)];
|
|
const onnx::TensorProto& W = weights[node.input(5)];
|
|
const onnx::TensorProto& B = weights[node.input(6)];
|
|
|
|
fprintf(pp, " 0=%d", get_tensor_proto_data_size(B));
|
|
fprintf(pp, " 1=%d", get_tensor_proto_data_size(words));
|
|
fprintf(pp, " 2=%d", get_tensor_proto_data_size(positions));
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(words, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(positions, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(W, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
else if (op == "Exp")
|
|
{
|
|
int op_type = 7;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Flatten")
|
|
{
|
|
int axis = get_node_attr_i(node, "axis", 1);
|
|
if (axis != 1)
|
|
{
|
|
fprintf(stderr, "Unsupported Flatten axis %d!\n", axis);
|
|
}
|
|
}
|
|
else if (op == "Floor")
|
|
{
|
|
int op_type = 2;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Gemm")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 1.f);
|
|
float beta = get_node_attr_f(node, "beta", 1.f);
|
|
int transA = get_node_attr_i(node, "transA", 0);
|
|
int transB = get_node_attr_i(node, "transB", 0);
|
|
|
|
if (alpha == 1.f && beta == 1.f && transA == 0 && transB == 1)
|
|
{
|
|
// InnerProduct-like A * B + C
|
|
const onnx::TensorProto& B = weights[node.input(1)];
|
|
const onnx::TensorProto& C = weights[node.input(2)];
|
|
|
|
fprintf(pp, " 0=%d", get_tensor_proto_data_size(C));
|
|
fprintf(pp, " 1=1");
|
|
fprintf(pp, " 2=%d", get_tensor_proto_data_size(B));
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(B, bp);
|
|
fwrite_tensor_proto_data(C, bp);
|
|
}
|
|
else
|
|
{
|
|
// gemm
|
|
fprintf(pp, " 0=%e", alpha);
|
|
fprintf(pp, " 1=%e", beta);
|
|
fprintf(pp, " 2=%d", transA);
|
|
fprintf(pp, " 3=%d", transB);
|
|
}
|
|
}
|
|
else if (op == "GlobalAveragePool")
|
|
{
|
|
int pool = 1;
|
|
int global_pool = 1;
|
|
|
|
fprintf(pp, " 0=%d", pool);
|
|
fprintf(pp, " 4=%d", global_pool);
|
|
}
|
|
else if (op == "GlobalMaxPool")
|
|
{
|
|
int pool = 0;
|
|
int global_pool = 1;
|
|
|
|
fprintf(pp, " 0=%d", pool);
|
|
fprintf(pp, " 4=%d", global_pool);
|
|
}
|
|
else if (op == "adaptive_avg_pool2d" || op == "adaptive_max_pool2d")
|
|
{
|
|
int pool = 0;
|
|
if (op == "adaptive_avg_pool2d")
|
|
{
|
|
pool = 1;
|
|
}
|
|
int adaptive_pooling = 1;
|
|
const onnx::TensorProto& out_shape_tp = weights[node.input(1)];
|
|
std::vector<int> out_shape = get_node_attr_from_input_ai(out_shape_tp);
|
|
|
|
fprintf(pp, " 0=%d", pool);
|
|
fprintf(pp, " 7=%d", adaptive_pooling);
|
|
if (out_shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 8=%d", out_shape[0]);
|
|
}
|
|
else if (out_shape.size() == 2)
|
|
{
|
|
// out_w
|
|
fprintf(pp, " 8=%d", out_shape[1]);
|
|
// out_h
|
|
fprintf(pp, " 18=%d", out_shape[0]);
|
|
}
|
|
}
|
|
else if (op == "GroupNorm")
|
|
{
|
|
int groups = get_node_attr_i(node, "groups", 1);
|
|
int channels = get_node_attr_i(node, "channels", 1);
|
|
float eps = get_node_attr_f(node, "epsilon", 1e-5f);
|
|
int affine = get_node_attr_i(node, "affine", 1);
|
|
|
|
if (affine)
|
|
{
|
|
// discard affine-less S=1 B=0
|
|
std::vector<float> affine_S = get_node_attr_from_input_af(weights[node.input(1)]);
|
|
std::vector<float> affine_B = get_node_attr_from_input_af(weights[node.input(2)]);
|
|
if (affine_S.size() == 1 && affine_S[0] == 1.f && affine_B.size() == 1 && affine_B[0] == 0.f)
|
|
{
|
|
affine = 0;
|
|
}
|
|
else
|
|
{
|
|
affine = 0;
|
|
{
|
|
for (int j = 0; j < channels; j++)
|
|
{
|
|
if (affine_S[j] != 1.f || affine_B[j] != 0.f)
|
|
{
|
|
affine = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", groups);
|
|
fprintf(pp, " 1=%d", channels);
|
|
fprintf(pp, " 2=%e", eps);
|
|
fprintf(pp, " 3=%d", affine);
|
|
if (affine)
|
|
{
|
|
const onnx::TensorProto& scale = weights[node.input(1)];
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
|
|
fwrite_tensor_proto_data(scale, bp);
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
}
|
|
else if (op == "GRU")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(1)];
|
|
const onnx::TensorProto& R = weights[node.input(2)];
|
|
const onnx::TensorProto& B = weights[node.input(3)];
|
|
|
|
int hidden_size = get_node_attr_i(node, "hidden_size", 0);
|
|
std::string direction = get_node_attr_s(node, "direction");
|
|
|
|
int direction_type = 0;
|
|
if (direction == "forward")
|
|
{
|
|
direction_type = 0;
|
|
}
|
|
else if (direction == "reverse")
|
|
{
|
|
direction_type = 1;
|
|
}
|
|
else if (direction == "bidirectional")
|
|
{
|
|
direction_type = 2;
|
|
}
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(W);
|
|
|
|
fprintf(pp, " 0=%d", hidden_size);
|
|
fprintf(pp, " 1=%d", weight_data_size);
|
|
fprintf(pp, " 2=%d", direction_type);
|
|
|
|
int num_directions = direction_type == 2 ? 2 : 1;
|
|
|
|
int quantize_tag = 0;
|
|
|
|
// reorder num_directions-URN-hidden-size to num_directions-RUN-hidden-size
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int weight_data_size_g = get_tensor_proto_data_size(W) / 3 / num_directions;
|
|
const float* wptr = W.has_raw_data() ? (const float*)W.raw_data().data() : W.float_data().data();
|
|
|
|
const float* uptr = wptr;
|
|
const float* rptr = wptr + weight_data_size_g;
|
|
const float* nptr = wptr + weight_data_size_g * 2;
|
|
fwrite(rptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(uptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(nptr, sizeof(float), weight_data_size_g, bp);
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
uptr += weight_data_size_g * 3;
|
|
rptr += weight_data_size_g * 3;
|
|
nptr += weight_data_size_g * 3;
|
|
fwrite(rptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(uptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(nptr, sizeof(float), weight_data_size_g, bp);
|
|
}
|
|
}
|
|
|
|
// reduce U and R bias except N
|
|
// reorder num_directions-URN-hidden to num_directions-RUN-hidden
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int bias_data_size_g = get_tensor_proto_data_size(B) / 2 / 3 / num_directions;
|
|
const float* bptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
|
|
const float* wuptr = bptr;
|
|
const float* wrptr = bptr + bias_data_size_g;
|
|
const float* wnptr = bptr + bias_data_size_g * 2;
|
|
const float* buptr = bptr + bias_data_size_g * 3;
|
|
const float* brptr = bptr + bias_data_size_g * 4;
|
|
const float* bnptr = bptr + bias_data_size_g * 5;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = wrptr[j] + brptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = wuptr[j] + buptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
fwrite(wnptr, sizeof(float), bias_data_size_g, bp);
|
|
fwrite(bnptr, sizeof(float), bias_data_size_g, bp);
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
wuptr += bias_data_size_g * 6;
|
|
wrptr += bias_data_size_g * 6;
|
|
wnptr += bias_data_size_g * 6;
|
|
buptr += bias_data_size_g * 6;
|
|
brptr += bias_data_size_g * 6;
|
|
bnptr += bias_data_size_g * 6;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = wrptr[j] + brptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = wuptr[j] + buptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
fwrite(wnptr, sizeof(float), bias_data_size_g, bp);
|
|
fwrite(bnptr, sizeof(float), bias_data_size_g, bp);
|
|
}
|
|
}
|
|
|
|
// reorder num_directions-URN-hidden-hidden to num_directions-RUN-hidden-hidden
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int weight_data_size_g = get_tensor_proto_data_size(R) / 3 / num_directions;
|
|
const float* Rptr = R.has_raw_data() ? (const float*)R.raw_data().data() : R.float_data().data();
|
|
|
|
const float* uptr = Rptr;
|
|
const float* rptr = Rptr + weight_data_size_g;
|
|
const float* nptr = Rptr + weight_data_size_g * 2;
|
|
fwrite(rptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(uptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(nptr, sizeof(float), weight_data_size_g, bp);
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
uptr += weight_data_size_g * 3;
|
|
rptr += weight_data_size_g * 3;
|
|
nptr += weight_data_size_g * 3;
|
|
fwrite(rptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(uptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(nptr, sizeof(float), weight_data_size_g, bp);
|
|
}
|
|
}
|
|
}
|
|
else if (op == "HardSigmoid")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 0.2f);
|
|
float beta = get_node_attr_f(node, "beta", 0.5f);
|
|
|
|
fprintf(pp, " 0=%e", alpha);
|
|
fprintf(pp, " 1=%e", beta);
|
|
}
|
|
else if (op == "HardSwish")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 0.2f);
|
|
float beta = get_node_attr_f(node, "beta", 0.5f);
|
|
|
|
fprintf(pp, " 0=%e", alpha);
|
|
fprintf(pp, " 1=%e", beta);
|
|
}
|
|
else if (op == "ImageScaler")
|
|
{
|
|
std::vector<float> bias = get_node_attr_af(node, "bias");
|
|
float scale = get_node_attr_f(node, "scale", 1.f);
|
|
|
|
int channels = (int)bias.size();
|
|
|
|
fprintf(pp, " 0=%d", channels);
|
|
fprintf(pp, " 1=1");
|
|
|
|
for (int j = 0; j < channels; j++)
|
|
{
|
|
fwrite(&scale, sizeof(float), 1, bp);
|
|
}
|
|
fwrite(&bias[0], sizeof(float), channels, bp);
|
|
}
|
|
else if (op == "InstanceNormalization")
|
|
{
|
|
float eps = get_node_attr_f(node, "epsilon", 1e-5f);
|
|
|
|
// discard affine-less S=1 B=0
|
|
std::vector<float> affine_S = get_node_attr_from_input_af(weights[node.input(1)]);
|
|
std::vector<float> affine_B = get_node_attr_from_input_af(weights[node.input(2)]);
|
|
int channels = (int)affine_S.size();
|
|
int affine = 0;
|
|
{
|
|
for (int j = 0; j < channels; j++)
|
|
{
|
|
if (affine_S[j] != 1.f || affine_B[j] != 0.f)
|
|
{
|
|
affine = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", channels);
|
|
fprintf(pp, " 1=%e", eps);
|
|
fprintf(pp, " 2=%d", affine);
|
|
if (affine)
|
|
{
|
|
const onnx::TensorProto& scale = weights[node.input(1)];
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
|
|
fwrite_tensor_proto_data(scale, bp);
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
}
|
|
else if (op == "LayerNorm")
|
|
{
|
|
float eps = get_node_attr_f(node, "epsilon", 1e-5f);
|
|
int affine = get_node_attr_i(node, "affine", 1);
|
|
|
|
if (affine)
|
|
{
|
|
// discard affine-less S=1 B=0
|
|
std::vector<float> affine_S = get_node_attr_from_input_af(weights[node.input(1)]);
|
|
std::vector<float> affine_B = get_node_attr_from_input_af(weights[node.input(2)]);
|
|
int affine_size = (int)affine_S.size();
|
|
affine = 0;
|
|
{
|
|
for (int j = 0; j < affine_size; j++)
|
|
{
|
|
if (affine_S[j] != 1.f || affine_B[j] != 0.f)
|
|
{
|
|
affine = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (affine)
|
|
{
|
|
fprintf(pp, " 0=%d", affine_size);
|
|
}
|
|
}
|
|
|
|
fprintf(pp, " 1=%e", eps);
|
|
fprintf(pp, " 2=%d", affine);
|
|
|
|
if (affine)
|
|
{
|
|
const onnx::TensorProto& scale = weights[node.input(1)];
|
|
const onnx::TensorProto& B = weights[node.input(2)];
|
|
|
|
fwrite_tensor_proto_data(scale, bp);
|
|
fwrite_tensor_proto_data(B, bp);
|
|
}
|
|
}
|
|
else if (op == "LeakyRelu")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 0.01f);
|
|
|
|
fprintf(pp, " 0=%e", alpha);
|
|
}
|
|
else if (op == "Log")
|
|
{
|
|
int op_type = 8;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "LRN")
|
|
{
|
|
float alpha = get_node_attr_f(node, "alpha", 1.f);
|
|
float beta = get_node_attr_f(node, "beta", 0.5f);
|
|
float bias = get_node_attr_f(node, "bias", 1.f);
|
|
int size = get_node_attr_i(node, "size", 1);
|
|
|
|
int norm_region = 0;
|
|
|
|
fprintf(pp, " 0=%d", norm_region);
|
|
fprintf(pp, " 1=%d", size);
|
|
fprintf(pp, " 2=%e", alpha);
|
|
fprintf(pp, " 3=%e", beta);
|
|
fprintf(pp, " 4=%e", bias);
|
|
}
|
|
else if (op == "LSTM")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(1)];
|
|
const onnx::TensorProto& R = weights[node.input(2)];
|
|
const onnx::TensorProto& B = weights[node.input(3)];
|
|
|
|
int hidden_size = get_node_attr_i(node, "hidden_size", 0);
|
|
std::string direction = get_node_attr_s(node, "direction");
|
|
|
|
int direction_type = 0;
|
|
if (direction == "forward")
|
|
{
|
|
direction_type = 0;
|
|
}
|
|
else if (direction == "reverse")
|
|
{
|
|
direction_type = 1;
|
|
}
|
|
else if (direction == "bidirectional")
|
|
{
|
|
direction_type = 2;
|
|
}
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(W);
|
|
|
|
fprintf(pp, " 0=%d", hidden_size);
|
|
fprintf(pp, " 1=%d", weight_data_size);
|
|
fprintf(pp, " 2=%d", direction_type);
|
|
|
|
int num_directions = direction_type == 2 ? 2 : 1;
|
|
|
|
int quantize_tag = 0;
|
|
|
|
// reorder num_directions-IOFG-hidden-size to num_directions-IFOG-hidden-size
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int weight_data_size_g = get_tensor_proto_data_size(W) / 4 / num_directions;
|
|
const float* wptr = W.has_raw_data() ? (const float*)W.raw_data().data() : W.float_data().data();
|
|
|
|
const float* iptr = wptr;
|
|
const float* optr = wptr + weight_data_size_g;
|
|
const float* fptr = wptr + weight_data_size_g * 2;
|
|
const float* gptr = wptr + weight_data_size_g * 3;
|
|
fwrite(iptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(fptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(optr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(gptr, sizeof(float), weight_data_size_g, bp);
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
iptr += weight_data_size_g * 4;
|
|
optr += weight_data_size_g * 4;
|
|
fptr += weight_data_size_g * 4;
|
|
gptr += weight_data_size_g * 4;
|
|
fwrite(iptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(fptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(optr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(gptr, sizeof(float), weight_data_size_g, bp);
|
|
}
|
|
}
|
|
|
|
// reduce xc and hc bias
|
|
// reorder num_directions-IOFG-hidden to num_directions-IFOG-hidden
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int bias_data_size_g = get_tensor_proto_data_size(B) / 2 / 4 / num_directions;
|
|
const float* xcbptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
|
|
const float* xiptr = xcbptr;
|
|
const float* xoptr = xcbptr + bias_data_size_g;
|
|
const float* xfptr = xcbptr + bias_data_size_g * 2;
|
|
const float* xgptr = xcbptr + bias_data_size_g * 3;
|
|
const float* hiptr = xcbptr + bias_data_size_g * 4;
|
|
const float* hoptr = xcbptr + bias_data_size_g * 5;
|
|
const float* hfptr = xcbptr + bias_data_size_g * 6;
|
|
const float* hgptr = xcbptr + bias_data_size_g * 7;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xiptr[j] + hiptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xfptr[j] + hfptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xoptr[j] + hoptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xgptr[j] + hgptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
xiptr += bias_data_size_g * 8;
|
|
xoptr += bias_data_size_g * 8;
|
|
xfptr += bias_data_size_g * 8;
|
|
xgptr += bias_data_size_g * 8;
|
|
hiptr += bias_data_size_g * 8;
|
|
hoptr += bias_data_size_g * 8;
|
|
hfptr += bias_data_size_g * 8;
|
|
hgptr += bias_data_size_g * 8;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xiptr[j] + hiptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xfptr[j] + hfptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xoptr[j] + hoptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xgptr[j] + hgptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
|
|
// reorder num_directions-IOFG-hidden-hidden to num_directions-IFOG-hidden-hidden
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int weight_data_size_g = get_tensor_proto_data_size(R) / 4 / num_directions;
|
|
const float* rptr = R.has_raw_data() ? (const float*)R.raw_data().data() : R.float_data().data();
|
|
|
|
const float* iptr = rptr;
|
|
const float* optr = rptr + weight_data_size_g;
|
|
const float* fptr = rptr + weight_data_size_g * 2;
|
|
const float* gptr = rptr + weight_data_size_g * 3;
|
|
fwrite(iptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(fptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(optr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(gptr, sizeof(float), weight_data_size_g, bp);
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
iptr += weight_data_size_g * 4;
|
|
optr += weight_data_size_g * 4;
|
|
fptr += weight_data_size_g * 4;
|
|
gptr += weight_data_size_g * 4;
|
|
fwrite(iptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(fptr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(optr, sizeof(float), weight_data_size_g, bp);
|
|
fwrite(gptr, sizeof(float), weight_data_size_g, bp);
|
|
}
|
|
}
|
|
}
|
|
else if (op == "MatMul")
|
|
{
|
|
if (weights.find(node.input(1)) != weights.end() && weights[node.input(1)].dims_size() == 2)
|
|
{
|
|
// InnerProduct
|
|
const onnx::TensorProto& B = weights[node.input(1)];
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(B);
|
|
|
|
int num_output = B.dims(B.dims_size() - 1);
|
|
int num_input = weight_data_size / num_output;
|
|
|
|
fprintf(pp, " 0=%d", num_output);
|
|
fprintf(pp, " 1=0");
|
|
fprintf(pp, " 2=%d", weight_data_size);
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
// reorder num_input-num_output to num_output-num_input
|
|
{
|
|
const float* bptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
|
|
|
|
for (int j = 0; j < num_output; j++)
|
|
{
|
|
for (int k = 0; k < num_input; k++)
|
|
{
|
|
float vb = bptr[k * num_output + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
|
|
// fwrite_tensor_proto_data(B, bp)
|
|
}
|
|
else
|
|
{
|
|
// default matrix multiplication
|
|
}
|
|
}
|
|
else if (op == "Max")
|
|
{
|
|
int op_type = 4;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "Min")
|
|
{
|
|
int op_type = 5;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "Mul")
|
|
{
|
|
int op_type = 2;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "MultiHeadAttention")
|
|
{
|
|
int embed_dim = get_node_attr_i(node, "embed_dim", 0);
|
|
int num_heads = get_node_attr_i(node, "num_heads", 0);
|
|
|
|
fprintf(pp, " 0=%d", embed_dim);
|
|
fprintf(pp, " 1=%d", num_heads);
|
|
|
|
if (node.input_size() == 5)
|
|
{
|
|
const onnx::TensorProto& qkvw = weights[node.input(1)];
|
|
const onnx::TensorProto& qkvb = weights[node.input(2)];
|
|
const onnx::TensorProto& ow = weights[node.input(3)];
|
|
const onnx::TensorProto& ob = weights[node.input(4)];
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(ow);
|
|
|
|
fprintf(pp, " 2=%d", weight_data_size);
|
|
|
|
int quantize_tag = 0;
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose qw
|
|
{
|
|
const float* wptr = qkvw.has_raw_data() ? (const float*)qkvw.raw_data().data() : qkvw.float_data().data();
|
|
const float* bptr = qkvb.has_raw_data() ? (const float*)qkvb.raw_data().data() : qkvb.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim * 3 + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
|
|
fwrite(bptr, sizeof(float), embed_dim, bp);
|
|
}
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose kw
|
|
{
|
|
const float* wptr = qkvw.has_raw_data() ? (const float*)qkvw.raw_data().data() : qkvw.float_data().data();
|
|
const float* bptr = qkvb.has_raw_data() ? (const float*)qkvb.raw_data().data() : qkvb.float_data().data();
|
|
bptr += embed_dim;
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim * 3 + j + embed_dim];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
|
|
fwrite(bptr, sizeof(float), embed_dim, bp);
|
|
}
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose vw
|
|
{
|
|
const float* wptr = qkvw.has_raw_data() ? (const float*)qkvw.raw_data().data() : qkvw.float_data().data();
|
|
const float* bptr = qkvb.has_raw_data() ? (const float*)qkvb.raw_data().data() : qkvb.float_data().data();
|
|
bptr += embed_dim * 2;
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim * 3 + j + embed_dim * 2];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
|
|
fwrite(bptr, sizeof(float), embed_dim, bp);
|
|
}
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose ow
|
|
{
|
|
const float* wptr = ow.has_raw_data() ? (const float*)ow.raw_data().data() : ow.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(ob, bp);
|
|
}
|
|
else
|
|
{
|
|
const onnx::TensorProto& qw = weights[node.input(3)];
|
|
const onnx::TensorProto& qb = weights[node.input(4)];
|
|
const onnx::TensorProto& kw = weights[node.input(5)];
|
|
const onnx::TensorProto& kb = weights[node.input(6)];
|
|
const onnx::TensorProto& vw = weights[node.input(7)];
|
|
const onnx::TensorProto& vb = weights[node.input(8)];
|
|
const onnx::TensorProto& ow = weights[node.input(9)];
|
|
const onnx::TensorProto& ob = weights[node.input(10)];
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(qw);
|
|
|
|
fprintf(pp, " 2=%d", weight_data_size);
|
|
|
|
int quantize_tag = 0;
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose qw
|
|
{
|
|
const float* wptr = qw.has_raw_data() ? (const float*)qw.raw_data().data() : qw.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(qb, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose kw
|
|
{
|
|
const float* wptr = kw.has_raw_data() ? (const float*)kw.raw_data().data() : kw.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(kb, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose vw
|
|
{
|
|
const float* wptr = vw.has_raw_data() ? (const float*)vw.raw_data().data() : vw.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(vb, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
// transpose ow
|
|
{
|
|
const float* wptr = ow.has_raw_data() ? (const float*)ow.raw_data().data() : ow.float_data().data();
|
|
|
|
for (int j = 0; j < embed_dim; j++)
|
|
{
|
|
for (int k = 0; k < embed_dim; k++)
|
|
{
|
|
float vb = wptr[k * embed_dim + j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
fwrite_tensor_proto_data(ob, bp);
|
|
}
|
|
}
|
|
else if (op == "Neg")
|
|
{
|
|
int op_type = 1;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Normalize")
|
|
{
|
|
float eps = get_node_attr_f(node, "eps", 0.f);
|
|
int scale_data_size = 1;
|
|
|
|
fprintf(pp, " 1=1"); // channel_shared
|
|
fprintf(pp, " 2=%e", eps);
|
|
fprintf(pp, " 3=%d", scale_data_size);
|
|
fprintf(pp, " 9=1"); // TODO hardcode pytorch style
|
|
|
|
const float scale_data[1] = {1.f};
|
|
fwrite(scale_data, sizeof(float), 1, bp);
|
|
}
|
|
else if (op == "Pad")
|
|
{
|
|
std::string mode = get_node_attr_s(node, "mode");
|
|
float value = get_node_attr_f(node, "value", 0.f);
|
|
|
|
std::vector<int> pads;
|
|
if (node.input_size() == 1)
|
|
{
|
|
pads = get_node_attr_ai(node, "pads");
|
|
}
|
|
else
|
|
{
|
|
pads = get_node_attr_from_input_ai(weights[node.input(1)]);
|
|
}
|
|
|
|
int type = 0;
|
|
if (mode == "constant")
|
|
{
|
|
type = 0;
|
|
}
|
|
else if (mode == "edge")
|
|
{
|
|
type = 1;
|
|
}
|
|
else if (mode == "reflect")
|
|
{
|
|
type = 2;
|
|
}
|
|
|
|
int pad_size = (int)pads.size();
|
|
int top = 0;
|
|
int bottom = 0;
|
|
int left = 0;
|
|
int right = 0;
|
|
int front = 0;
|
|
int behind = 0;
|
|
if (pad_size == 8)
|
|
{
|
|
//NCHW
|
|
top = pads[2];
|
|
bottom = pads[6];
|
|
left = pads[3];
|
|
right = pads[7];
|
|
front = pads[1];
|
|
behind = pads[5];
|
|
}
|
|
else if (pad_size == 6)
|
|
{
|
|
//NHW
|
|
top = pads[1];
|
|
bottom = pads[4];
|
|
left = pads[2];
|
|
right = pads[5];
|
|
}
|
|
else
|
|
{
|
|
//NW
|
|
left = pads[1];
|
|
right = pads[3];
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", top);
|
|
fprintf(pp, " 1=%d", bottom);
|
|
fprintf(pp, " 2=%d", left);
|
|
fprintf(pp, " 3=%d", right);
|
|
fprintf(pp, " 4=%d", type);
|
|
fprintf(pp, " 5=%e", value);
|
|
fprintf(pp, " 7=%d", front);
|
|
fprintf(pp, " 8=%d", behind);
|
|
}
|
|
else if (op == "Pow")
|
|
{
|
|
int op_type = 6;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "PixelShuffle")
|
|
{
|
|
int scale_factor = get_node_attr_i(node, "scale_factor", 1);
|
|
fprintf(pp, " 0=%d", scale_factor);
|
|
}
|
|
else if (op == "PRelu")
|
|
{
|
|
const onnx::TensorProto& slope = weights[node.input(1)];
|
|
|
|
int num_slope = get_tensor_proto_data_size(slope);
|
|
|
|
fprintf(pp, " 0=%d", num_slope);
|
|
|
|
fwrite_tensor_proto_data(slope, bp);
|
|
}
|
|
else if (op == "Reciprocal")
|
|
{
|
|
int op_type = 15;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "ReduceMax" || op == "ReduceMin" || op == "ReduceMean" || op == "ReduceProd" || op == "ReduceSum" || op == "ReduceSumSquare" || op == "ReduceL1" || op == "ReduceL2" || op == "ReduceLogSum" || op == "ReduceLogSumExp")
|
|
{
|
|
int op_type = -233;
|
|
if (op == "ReduceSum")
|
|
op_type = 0;
|
|
else if (op == "ReduceSumSquare")
|
|
op_type = 2;
|
|
else if (op == "ReduceMean")
|
|
op_type = 3;
|
|
else if (op == "ReduceMax")
|
|
op_type = 4;
|
|
else if (op == "ReduceMin")
|
|
op_type = 5;
|
|
else if (op == "ReduceProd")
|
|
op_type = 6;
|
|
else if (op == "ReduceL1")
|
|
op_type = 7;
|
|
else if (op == "ReduceL2")
|
|
op_type = 8;
|
|
else if (op == "ReduceLogSum")
|
|
op_type = 9;
|
|
else if (op == "ReduceLogSumExp")
|
|
op_type = 10;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
std::vector<int> axes = get_node_attr_ai(node, "axes");
|
|
int keepdims = get_node_attr_i(node, "keepdims", 1);
|
|
|
|
if (axes.size() > 0)
|
|
{
|
|
// if axes set, reduce according to axes
|
|
fprintf(pp, " 1=%d", 0);
|
|
fprintf(pp, " -23303=%zu", axes.size());
|
|
for (size_t j = 0; j < axes.size(); j++)
|
|
{
|
|
if (axes[j] == 0 || axes[j] > 4 || axes[j] < -3)
|
|
fprintf(stderr, "Unsupported reduction axes !\n");
|
|
fprintf(pp, ",%d", axes[j] > 0 ? axes[j] - 1 : axes[j]);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// if axes not set, reduce all axes by default
|
|
fprintf(pp, " 1=%d", 1);
|
|
}
|
|
fprintf(pp, " 4=%d", keepdims);
|
|
fprintf(pp, " 5=1");
|
|
}
|
|
else if (op == "Reorg")
|
|
{
|
|
int stride = get_node_attr_i(node, "stride", 1);
|
|
fprintf(pp, " 0=%d", stride);
|
|
}
|
|
else if (op == "Reshape")
|
|
{
|
|
std::vector<int> shape;
|
|
|
|
if (node.input_size() == 1)
|
|
{
|
|
shape = get_node_attr_ai(node, "shape");
|
|
}
|
|
else
|
|
{
|
|
shape = get_node_attr_from_input_ai(weights[node.input(1)]);
|
|
}
|
|
|
|
if (shape.size() == 1)
|
|
{
|
|
fprintf(pp, " 0=%d", shape[0]); // should never reach here
|
|
}
|
|
else if (shape.size() == 2)
|
|
{
|
|
fprintf(pp, " 0=%d", shape[1]);
|
|
}
|
|
else if (shape.size() == 3)
|
|
{
|
|
fprintf(pp, " 0=%d", shape[2]);
|
|
fprintf(pp, " 1=%d", shape[1]);
|
|
}
|
|
else if (shape.size() == 4)
|
|
{
|
|
fprintf(pp, " 0=%d", shape[3]);
|
|
fprintf(pp, " 1=%d", shape[2]);
|
|
fprintf(pp, " 2=%d", shape[1]);
|
|
}
|
|
else if (shape.size() == 5)
|
|
{
|
|
fprintf(pp, " 0=%d", shape[4] * shape[3]);
|
|
fprintf(pp, " 1=%d", shape[2]);
|
|
fprintf(pp, " 2=%d", shape[1]);
|
|
}
|
|
}
|
|
else if (op == "Resize")
|
|
{
|
|
std::string mode = get_node_attr_s(node, "mode");
|
|
std::string align = get_node_attr_s(node, "coordinate_transformation_mode");
|
|
|
|
std::vector<float> scales;
|
|
std::vector<int> sizes;
|
|
if (node.input_size() == 2)
|
|
{
|
|
// opset 10
|
|
scales = get_node_attr_from_input_af(weights[node.input(1)]);
|
|
}
|
|
else
|
|
{
|
|
// opset 11+
|
|
scales = get_node_attr_from_input_af(weights[node.input(2)]);
|
|
if (node.input_size() >= 4)
|
|
{
|
|
sizes = get_node_attr_from_input_ai(weights[node.input(3)]);
|
|
}
|
|
}
|
|
|
|
int resize_type = 1;
|
|
if (mode == "nearest")
|
|
{
|
|
resize_type = 1;
|
|
}
|
|
else if (mode == "linear")
|
|
{
|
|
resize_type = 2;
|
|
}
|
|
else if (mode == "cubic")
|
|
{
|
|
resize_type = 3;
|
|
}
|
|
|
|
if (scales.empty() && sizes.empty())
|
|
{
|
|
fprintf(stderr, "Unsupported Resize scales and sizes are all empty!\n");
|
|
}
|
|
|
|
float h_scale = 1.f;
|
|
float w_scale = 1.f;
|
|
if (scales.size() == 2)
|
|
{
|
|
w_scale = scales[1];
|
|
}
|
|
else if (scales.size() == 3)
|
|
{
|
|
h_scale = scales[1];
|
|
w_scale = scales[2];
|
|
}
|
|
else if (scales.size() == 4)
|
|
{
|
|
h_scale = scales[2];
|
|
w_scale = scales[3];
|
|
|
|
if (scales[1] != 1.f)
|
|
fprintf(stderr, "Unsupported Resize scales !\n");
|
|
}
|
|
|
|
int output_height = 0;
|
|
int output_width = 0;
|
|
if (sizes.size() == 2)
|
|
{
|
|
output_width = sizes[1];
|
|
}
|
|
else if (sizes.size() == 3)
|
|
{
|
|
output_height = sizes[1];
|
|
output_width = sizes[2];
|
|
}
|
|
else if (sizes.size() == 4)
|
|
{
|
|
output_height = sizes[2];
|
|
output_width = sizes[3];
|
|
}
|
|
|
|
int align_corner = 0;
|
|
if (align == "align_corners")
|
|
{
|
|
align_corner = 1;
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", resize_type);
|
|
fprintf(pp, " 1=%e", h_scale);
|
|
fprintf(pp, " 2=%e", w_scale);
|
|
fprintf(pp, " 3=%d", output_height);
|
|
fprintf(pp, " 4=%d", output_width);
|
|
fprintf(pp, " 6=%d", align_corner);
|
|
}
|
|
else if (op == "RNN")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(1)];
|
|
const onnx::TensorProto& R = weights[node.input(2)];
|
|
const onnx::TensorProto& B = weights[node.input(3)];
|
|
|
|
int hidden_size = get_node_attr_i(node, "hidden_size", 0);
|
|
std::string direction = get_node_attr_s(node, "direction");
|
|
|
|
int direction_type = 0;
|
|
if (direction == "forward")
|
|
{
|
|
direction_type = 0;
|
|
}
|
|
else if (direction == "reverse")
|
|
{
|
|
direction_type = 1;
|
|
}
|
|
else if (direction == "bidirectional")
|
|
{
|
|
direction_type = 2;
|
|
}
|
|
|
|
int weight_data_size = get_tensor_proto_data_size(W);
|
|
|
|
fprintf(pp, " 0=%d", hidden_size);
|
|
fprintf(pp, " 1=%d", weight_data_size);
|
|
fprintf(pp, " 2=%d", direction_type);
|
|
|
|
int num_directions = direction_type == 2 ? 2 : 1;
|
|
|
|
int quantize_tag = 0;
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
fwrite_tensor_proto_data(W, bp);
|
|
|
|
// reduce xc and hc bias
|
|
{
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
int bias_data_size_g = get_tensor_proto_data_size(B) / 2 / num_directions;
|
|
const float* bptr = B.has_raw_data() ? (const float*)B.raw_data().data() : B.float_data().data();
|
|
const float* xiptr = bptr;
|
|
const float* hiptr = bptr + bias_data_size_g;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xiptr[j] + hiptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
|
|
if (direction_type == 2)
|
|
{
|
|
xiptr += bias_data_size_g * 2;
|
|
hiptr += bias_data_size_g * 2;
|
|
|
|
for (int j = 0; j < bias_data_size_g; j++)
|
|
{
|
|
float vb = xiptr[j] + hiptr[j];
|
|
fwrite(&vb, sizeof(float), 1, bp);
|
|
}
|
|
}
|
|
}
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
fwrite_tensor_proto_data(R, bp);
|
|
}
|
|
else if (op == "RDiv")
|
|
{
|
|
int op_type = 8;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "RSub")
|
|
{
|
|
int op_type = 7;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "ShuffleChannel")
|
|
{
|
|
int group = get_node_attr_i(node, "group", 1);
|
|
int reverse = get_node_attr_i(node, "reverse", 0);
|
|
fprintf(pp, " 0=%d", group);
|
|
fprintf(pp, " 1=%d", reverse);
|
|
}
|
|
else if (op == "Sigmoid")
|
|
{
|
|
// no param
|
|
}
|
|
else if (op == "Sin")
|
|
{
|
|
int op_type = 9;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "SkipLayerNormalization")
|
|
{
|
|
const onnx::TensorProto& W = weights[node.input(2)];
|
|
const onnx::TensorProto& B = weights[node.input(3)];
|
|
const onnx::TensorProto& B2 = weights[node.input(4)];
|
|
|
|
fprintf(pp, " 0=%d", get_tensor_proto_data_size(B));
|
|
|
|
int quantize_tag = 0;
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(W, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(B, bp);
|
|
|
|
fwrite(&quantize_tag, sizeof(int), 1, bp);
|
|
|
|
fwrite_tensor_proto_data(B2, bp);
|
|
}
|
|
else if (op == "Slice")
|
|
{
|
|
std::vector<int> starts;
|
|
std::vector<int> ends;
|
|
std::vector<int> axes;
|
|
std::vector<int> steps;
|
|
if (node.input_size() == 1)
|
|
{
|
|
starts = get_node_attr_ai(node, "starts");
|
|
ends = get_node_attr_ai(node, "ends");
|
|
axes = get_node_attr_ai(node, "axes");
|
|
steps = get_node_attr_ai(node, "steps"); // TODO
|
|
}
|
|
else
|
|
{
|
|
starts = get_node_attr_from_input_ai(weights[node.input(1)]);
|
|
ends = get_node_attr_from_input_ai(weights[node.input(2)]);
|
|
if (node.input_size() >= 4)
|
|
axes = get_node_attr_from_input_ai(weights[node.input(3)]);
|
|
if (node.input_size() >= 5)
|
|
steps = get_node_attr_from_input_ai(weights[node.input(4)]);
|
|
}
|
|
|
|
// assert step == 1
|
|
for (int i = 0; i < (int)steps.size(); i++)
|
|
{
|
|
if (steps[i] != 1)
|
|
fprintf(stderr, "Unsupported slice step !\n");
|
|
}
|
|
|
|
// filter out N-dim axis
|
|
if (!axes.empty())
|
|
{
|
|
for (int i = 0; i < (int)axes.size(); i++)
|
|
{
|
|
int axis = axes[i];
|
|
if (axis == 0)
|
|
{
|
|
starts.erase(starts.begin() + i);
|
|
ends.erase(ends.begin() + i);
|
|
axes.erase(axes.begin() + i);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
fprintf(pp, " -23309=%d", (int)starts.size());
|
|
for (int i = 0; i < (int)starts.size(); i++)
|
|
{
|
|
fprintf(pp, ",%d", starts[i]);
|
|
}
|
|
fprintf(pp, " -23310=%d", (int)ends.size());
|
|
for (int i = 0; i < (int)ends.size(); i++)
|
|
{
|
|
fprintf(pp, ",%d", ends[i]);
|
|
}
|
|
if (!axes.empty())
|
|
{
|
|
fprintf(pp, " -23311=%d", (int)axes.size());
|
|
for (int i = 0; i < (int)axes.size(); i++)
|
|
{
|
|
int axis = axes[i];
|
|
if (axis == 0 || axis > 3 || axis < -3)
|
|
fprintf(stderr, "Unsupported slice axes !\n");
|
|
|
|
if (axis > 0)
|
|
axis = axis - 1; // -1 for skip N-dim
|
|
|
|
fprintf(pp, ",%d", axis);
|
|
}
|
|
}
|
|
}
|
|
else if (op == "Softmax")
|
|
{
|
|
int axis = get_node_attr_i(node, "axis", 1);
|
|
fprintf(pp, " 0=%d", axis - 1);
|
|
fprintf(pp, " 1=1");
|
|
}
|
|
else if (op == "Split")
|
|
{
|
|
int axis = get_node_attr_i(node, "axis", 0);
|
|
std::vector<int> split = get_node_attr_ai(node, "split");
|
|
if (axis < 1)
|
|
fprintf(stderr, "Unsupported split axis !\n");
|
|
|
|
fprintf(pp, " -23300=%d", output_size);
|
|
if (split.empty())
|
|
{
|
|
for (int i = 0; i < output_size; i++)
|
|
{
|
|
fprintf(pp, ",-233");
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for (size_t i = 0; i < split.size() - 1; i++)
|
|
{
|
|
fprintf(pp, ",%d", split[i]);
|
|
}
|
|
fprintf(pp, ",-233");
|
|
}
|
|
fprintf(pp, " 1=%d", axis - 1);
|
|
}
|
|
else if (op == "Sqrt")
|
|
{
|
|
int op_type = 5;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Squeeze")
|
|
{
|
|
std::vector<int> axes = get_node_attr_ai(node, "axes");
|
|
|
|
if (axes.empty())
|
|
{
|
|
fprintf(pp, " 0=1");
|
|
fprintf(pp, " 1=1");
|
|
fprintf(pp, " 2=1");
|
|
}
|
|
else
|
|
{
|
|
fprintf(pp, " -23303=%zu", axes.size());
|
|
for (int i = 0; i < (int)axes.size(); i++)
|
|
{
|
|
if (axes[i] == 0 || axes[i] > 4 || axes[i] < -3)
|
|
fprintf(stderr, "Unsupported squeeze axes !\n");
|
|
fprintf(pp, ",%d", axes[i] > 0 ? axes[i] - 1 : axes[i]);
|
|
}
|
|
}
|
|
}
|
|
else if (op == "Sub")
|
|
{
|
|
int op_type = 1;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
|
|
int with_scalar = get_node_attr_i(node, "with_scalar", 0);
|
|
float b = get_node_attr_f(node, "b", 0.f);
|
|
if (with_scalar)
|
|
{
|
|
fprintf(pp, " 1=%d", with_scalar);
|
|
fprintf(pp, " 2=%e", b);
|
|
}
|
|
}
|
|
else if (op == "Sum")
|
|
{
|
|
int op_type = 1;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Swish")
|
|
{
|
|
// no param
|
|
}
|
|
else if (op == "Tan")
|
|
{
|
|
int op_type = 11;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Tanh")
|
|
{
|
|
int op_type = 16;
|
|
fprintf(pp, " 0=%d", op_type);
|
|
}
|
|
else if (op == "Transpose")
|
|
{
|
|
std::vector<int> perm = get_node_attr_ai(node, "perm");
|
|
|
|
if (perm.size() == 3)
|
|
{
|
|
if (perm[1] == 1 && perm[2] == 2)
|
|
fprintf(pp, " 0=0"); // w h
|
|
else if (perm[1] == 2 && perm[2] == 1)
|
|
fprintf(pp, " 0=1"); // h w
|
|
else if (perm[0] == 1 && perm[1] == 0 && perm[2] == 2)
|
|
fprintf(pp, " 0=0"); // w h
|
|
else if (perm[0] == 2 && perm[1] == 0 && perm[2] == 1)
|
|
fprintf(pp, " 0=1"); // h w
|
|
}
|
|
else if (perm.size() == 4)
|
|
{
|
|
if (perm[1] == 1 && perm[2] == 2 && perm[3] == 3)
|
|
fprintf(pp, " 0=0"); // w h c
|
|
else if (perm[1] == 1 && perm[2] == 3 && perm[3] == 2)
|
|
fprintf(pp, " 0=1"); // h w c
|
|
else if (perm[1] == 2 && perm[2] == 1 && perm[3] == 3)
|
|
fprintf(pp, " 0=2"); // w c h
|
|
else if (perm[1] == 2 && perm[2] == 3 && perm[3] == 1)
|
|
fprintf(pp, " 0=3"); // c w h
|
|
else if (perm[1] == 3 && perm[2] == 1 && perm[3] == 2)
|
|
fprintf(pp, " 0=4"); // h c w
|
|
else if (perm[1] == 3 && perm[2] == 2 && perm[3] == 1)
|
|
fprintf(pp, " 0=5"); // c h w
|
|
}
|
|
else if (perm.size() == 5)
|
|
{
|
|
if (perm[1] == 1 && perm[2] == 2 && perm[3] == 3 && perm[4] == 4)
|
|
fprintf(pp, " 0=0"); // wx h c
|
|
else if (perm[1] == 1 && perm[2] == 3 && perm[3] == 4 && perm[4] == 2)
|
|
fprintf(pp, " 0=1"); // h wx c
|
|
else if (perm[1] == 2 && perm[2] == 1 && perm[3] == 3 && perm[4] == 4)
|
|
fprintf(pp, " 0=2"); // wx c h
|
|
else if (perm[1] == 2 && perm[2] == 3 && perm[3] == 4 && perm[4] == 1)
|
|
fprintf(pp, " 0=3"); // c wx h
|
|
else if (perm[1] == 3 && perm[2] == 4 && perm[3] == 1 && perm[4] == 2)
|
|
fprintf(pp, " 0=4"); // h c wx
|
|
else if (perm[1] == 3 && perm[2] == 4 && perm[3] == 2 && perm[4] == 1)
|
|
fprintf(pp, " 0=5"); // c h wx
|
|
else
|
|
fprintf(stderr, "Unsupported transpose type !\n");
|
|
}
|
|
}
|
|
else if (op == "Upsample")
|
|
{
|
|
std::string mode = get_node_attr_s(node, "mode");
|
|
std::string align = get_node_attr_s(node, "coordinate_transformation_mode");
|
|
|
|
std::vector<float> scales;
|
|
|
|
if (node.input_size() == 1)
|
|
{
|
|
scales = get_node_attr_af(node, "scales");
|
|
}
|
|
else
|
|
{
|
|
scales = get_node_attr_from_input_af(weights[node.input(1)]);
|
|
}
|
|
|
|
int resize_type = 1;
|
|
if (mode == "nearest")
|
|
{
|
|
resize_type = 1;
|
|
}
|
|
else if (mode == "bilinear" || mode == "linear")
|
|
{
|
|
resize_type = 2;
|
|
}
|
|
else if (mode == "trilinear")
|
|
{
|
|
fprintf(stderr, "Unsupported Upsample mode !\n");
|
|
}
|
|
|
|
float h_scale = 1.f;
|
|
float w_scale = 1.f;
|
|
if (scales.size() == 2)
|
|
{
|
|
w_scale = scales[1];
|
|
}
|
|
else if (scales.size() == 3)
|
|
{
|
|
h_scale = scales[1];
|
|
w_scale = scales[2];
|
|
}
|
|
else if (scales.size() == 4)
|
|
{
|
|
h_scale = scales[2];
|
|
w_scale = scales[3];
|
|
|
|
if (scales[1] != 1.f)
|
|
fprintf(stderr, "Unsupported Upsample scales !\n");
|
|
}
|
|
else
|
|
{
|
|
fprintf(stderr, "Unsupported Upsample scales !\n");
|
|
}
|
|
|
|
int align_corner = 0;
|
|
if (align == "align_corners")
|
|
{
|
|
align_corner = 1;
|
|
}
|
|
|
|
fprintf(pp, " 0=%d", resize_type);
|
|
fprintf(pp, " 1=%e", h_scale);
|
|
fprintf(pp, " 2=%e", w_scale);
|
|
fprintf(pp, " 6=%d", align_corner);
|
|
}
|
|
else if (op == "Unsqueeze")
|
|
{
|
|
std::vector<int> axes = get_node_attr_ai(node, "axes");
|
|
|
|
fprintf(pp, " -23303=%zu", axes.size());
|
|
for (int i = 0; i < (int)axes.size(); i++)
|
|
{
|
|
if (axes[i] == 0 || axes[i] > 4 || axes[i] < -4)
|
|
fprintf(stderr, "Unsupported unsqueeze axes !\n");
|
|
fprintf(pp, ",%d", axes[i] > 0 ? axes[i] - 1 : axes[i]);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// TODO op specific param
|
|
for (int j = 0; j < node.attribute_size(); j++)
|
|
{
|
|
const onnx::AttributeProto& attr = node.attribute(j);
|
|
if (attr.type() == 1)
|
|
{
|
|
fprintf(stderr, " # %s=%g\n", attr.name().c_str(), attr.f());
|
|
}
|
|
else if (attr.type() == 2)
|
|
{
|
|
fprintf(stderr, " # %s=%lld\n", attr.name().c_str(), (long long)attr.i());
|
|
}
|
|
else if (attr.type() == 3)
|
|
{
|
|
fprintf(stderr, " # %s=%s\n", attr.name().c_str(), attr.s().c_str());
|
|
}
|
|
else
|
|
{
|
|
fprintf(stderr, " # %s %d\n", attr.name().c_str(), attr.type());
|
|
}
|
|
}
|
|
}
|
|
|
|
fprintf(pp, "\n");
|
|
|
|
for (int j = 0; j < output_size; j++)
|
|
{
|
|
const std::string& output_name = node.output(j);
|
|
if (node_reference.find(output_name) != node_reference.end())
|
|
{
|
|
int refcount = node_reference[output_name];
|
|
if (refcount > 1)
|
|
{
|
|
char splitname[256];
|
|
sprintf(splitname, "splitncnn_%d", internal_split);
|
|
fprintf(pp, "%-16s %-24s %d %d", "Split", splitname, 1, refcount);
|
|
|
|
fprintf(pp, " %s", output_name.c_str());
|
|
|
|
for (int k = 0; k < refcount; k++)
|
|
{
|
|
fprintf(pp, " %s_splitncnn_%d", output_name.c_str(), k);
|
|
}
|
|
fprintf(pp, "\n");
|
|
|
|
internal_split++;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
fclose(pp);
|
|
fclose(bp);
|
|
|
|
return 0;
|
|
}
|