diff --git a/.gitignore b/.gitignore index 556c02c4..a6391ada 100644 --- a/.gitignore +++ b/.gitignore @@ -357,3 +357,6 @@ project/ios/ios_32 project/ios/MNN.framework pymnn_build/ + +# mnncompress generated +MNN_compression_pb2.py diff --git a/CMakeLists.txt b/CMakeLists.txt index c16ac5a6..ab3e787f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,28 +1,17 @@ cmake_minimum_required(VERSION 3.0) # Versioning stuff -if(NOT DEFINED MNN_VERSION_MAJOR) - set(MNN_VERSION_MAJOR 0) -endif() -if(NOT DEFINED MNN_VERSION_MINOR) - set(MNN_VERSION_MINOR 2) -endif() -if(NOT DEFINED MNN_VERSION_PATCH) - set(MNN_VERSION_PATCH 1) -endif() -if(NOT DEFINED MNN_VERSION_BUILD) - set(MNN_VERSION_BUILD 5) -endif() -if(NOT DEFINED MNN_VERSION_SUFFIX) - set(MNN_VERSION_SUFFIX git) -endif() -if (NOT PACKAGE_VERSION) - set(PACKAGE_VERSION - "${MNN_VERSION_MAJOR}.${MNN_VERSION_MINOR}.${MNN_VERSION_PATCH}.${MNN_VERSION_BUILD}${MNN_VERSION_SUFFIX}") -endif() -add_definitions("-DMNN_VERSION=\"${PACKAGE_VERSION}\"") -add_definitions("-DMNN_VERSION_MAJOR=${MNN_VERSION_MAJOR}") -add_definitions("-DMNN_VERSION_MINOR=${MNN_VERSION_MINOR}") -add_definitions("-DMNN_VERSION_PATCH=${MNN_VERSION_PATCH}") +file(STRINGS "${CMAKE_CURRENT_LIST_DIR}/include/MNN/MNNDefine.h" MNN_DEFINE) +string(REGEX MATCH "MNN_VERSION_MAJOR [0-9]+" MNN_VERSION_MAJOR_DEFINE ${MNN_DEFINE}) +string(REGEX MATCH "[0-9]+" MNN_VERSION_MAJOR ${MNN_VERSION_MAJOR_DEFINE}) +string(REGEX MATCH "MNN_VERSION_MINOR [0-9]+" MNN_VERSION_MINOR_DEFINE ${MNN_DEFINE}) +string(REGEX MATCH "[0-9]+" MNN_VERSION_MINOR ${MNN_VERSION_MINOR_DEFINE}) +string(REGEX MATCH "MNN_VERSION_PATCH [0-9]+" MNN_VERSION_PATCH_DEFINE ${MNN_DEFINE}) +string(REGEX MATCH "[0-9]+" MNN_VERSION_PATCH ${MNN_VERSION_PATCH_DEFINE}) +set(MNN_VERSION ${MNN_VERSION_MAJOR}.${MNN_VERSION_MINOR}.${MNN_VERSION_PATCH}) +# add_definitions("-DMNN_VERSION=\"${MNN_VERSION}\"") +# add_definitions("-DMNN_VERSION_MAJOR=${MNN_VERSION_MAJOR}") +# add_definitions("-DMNN_VERSION_MINOR=${MNN_VERSION_MINOR}") +# add_definitions("-DMNN_VERSION_PATCH=${MNN_VERSION_PATCH}") # Clear VERSION variables when no VERSION is given to project() if(POLICY CMP0048) @@ -32,7 +21,7 @@ endif() if(POLICY CMP0091) cmake_policy(SET CMP0091 NEW) endif() -project(MNN VERSION ${MNN_VERSION_MAJOR}.${MNN_VERSION_MINOR}.${MNN_VERSION_PATCH}.${MNN_VERSION_BUILD} LANGUAGES C CXX ASM) +project(MNN VERSION ${MNN_VERSION} LANGUAGES C CXX ASM) # complier options set(CMAKE_C_STANDARD 99) set(CMAKE_CXX_STANDARD 11) @@ -225,11 +214,11 @@ ELSE() set(MNN_HIDDEN TRUE) ENDIF(CMAKE_BUILD_TYPE MATCHES Debug) - message(STATUS ">>>>>>>>>>>>>") message(STATUS "MNN BUILD INFO:") message(STATUS "\tSystem: ${CMAKE_SYSTEM_NAME}") message(STATUS "\tProcessor: ${CMAKE_SYSTEM_PROCESSOR}") +message(STATUS "\tVersion: ${MNN_VERSION}") message(STATUS "\tMetal: ${MNN_METAL}") message(STATUS "\tOpenCL: ${MNN_OPENCL}") message(STATUS "\tOpenGL: ${MNN_OPENGL}") diff --git a/README.md b/README.md index 081fa693..4d914c0a 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [MNN Homepage](http://www.mnn.zone) ## Intro -MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. +MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 30 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. The design principles and performance data of MNN has been published in an MLSys 2020 paper [here](https://arxiv.org/pdf/2002.12418.pdf). Please cite MNN in your publications if it helps your research: @@ -16,44 +16,89 @@ The design principles and performance data of MNN has been published in an MLSys year = {2020} } -## Documentation and Tools +![image.png](doc/workflow.png) + +## Documentation and Workbench MNN's docs are in placed in [Yuque docs here](https://www.yuque.com/mnn/en). MNN Workbench could be downloaded from [MNN's homepage](http://www.mnn.zone), which provides pretrained models, visualized training tools, and one-click deployment of models to devices. ## Key Features -### High performance -- Implements core computing with lots of optimized assembly code to make full use of the ARM CPU. -- For iOS, GPU acceleration (Metal) can be turned on, which is faster than Apple's native CoreML. -- For Android, `OpenCL`, `Vulkan`, and `OpenGL` are available and deep tuned for mainstream GPUs (`Adreno` and `Mali`). -- Convolution and transposition convolution algorithms are efficient and stable. The Winograd convolution algorithm is widely used to better symmetric convolutions such as 3x3 -> 7x7. -- Twice speed increase for the new architecture ARM v8.2 with FP16 half-precision calculation support. - ### Lightweight - Optimized for devices, no dependencies, can be easily deployed to mobile devices and a variety of embedded devices. -- iOS platform: static library size for armv7+arm64 platforms is about 5MB, size increase of linked executables is about 620KB, and metallib file is about 600KB. -- Android platform: core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. +- iOS platform: static library size will full option for armv7+arm64 platforms is about 12MB, size increase of linked executables is about 2M. +- Android platform: core so size is about 800KB (armv7a - c++_shared). +- Use MNN_BUILD_MINI can reduce package size about 25% , with limit of fix model input size +- Support FP16 / Int8 qunatize, can reduce model size 50%-70% ### Versatility -- Supports `Tensorflow`, `Caffe`, `ONNX`, and supports common neural networks such as `CNN`, `RNN`, `GAN`. -- MNN model converter supports 149 `Tensorflow` OPs, 58 `TFLite` OPs, 47 `Caffe` OPs and 74 `ONNX` OPs; Number of OPs by different MNN hardware backends: 111 for CPU, 6 for ARM V8.2, 55 for Metal, 43 for OpenCL, and 32 for Vulkan. +- Supports `Tensorflow`, `Caffe`, `ONNX`,`Torchscripts` and supports common neural networks such as `CNN`, `RNN`, `GAN`, `Transformork`. +- Supports AI model with multi-inputs or multi-outputs, every kind of dimenstion format, dynamic inputs, controlflow. +- MNN supports approximate full OPs used for AI Model. The converter supports 178 `Tensorflow` OPs, 52 `Caffe` OPs, 163 `Torchscripts` OPs, 158 `ONNX` OPs. - Supports iOS 8.0+, Android 4.3+ and embedded devices with POSIX interface. - Supports hybrid computing on multiple devices. Currently supports CPU and GPU. + +### High performance +- Implements core computing with lots of optimized assembly code to make full use of the ARM / x64 CPU. +- Use Metal / OpenCL / Vulkan to support GPU inference on mobile. +- Use CUDA and tensorcore to support NVIDIA GPU for better performance +- Convolution and transposition convolution algorithms are efficient and stable. The Winograd convolution algorithm is widely used to better symmetric convolutions such as 3x3,4x4,5x5,6x6,7x7. +- Twice speed increase for the new architecture ARM v8.2 with FP16 half-precision calculation support. 2.5 faster to use sdot for ARM v8.2 and VNNI. + ### Ease of use -- Efficient image processing module, speeding up affine transform and color space transform without libyuv or opencv. -- Provides callbacks throughout the workflow to extract data or control the execution precisely. -- Provides options for selecting inference branch and paralleling branches on CPU and GPU. -- (BETA) MNN Python API helps ML engineers to easily use MNN to build a model, train it and quantize it, without dipping their toes in C++ code. +- Support use MNN's OP to do numerical calculating like numpy. +- Support lightweight image process module like OpenCV, which is only 100k. +- Support build model and train it on PC / mobile. +- MNN Python API helps ML engineers to easily use MNN to inference, train, process image, without dipping their toes in C++ code. + + +- S ：Support and work well, deeply optimized, recommend to use +- A ：Support and work well, can use +- B ：Support but has bug or not optimized, no recommend to use +- C ：Not Support + +| Architecture / Precision | | Normal | FP16 | BF16 | Int8 | +| --- | --- | --- | --- | --- | --- | +| CPU | Native | B | C | B | B | +| | x86/x64-SSE4.1 | A | B | B | A | +| | x86/x64-AVX2 | S | B | B | A | +| | x86/x64-AVX512 | S | B | B | S | +| | ARMv7a | S | S (ARMv8.2) | S | S | +| | ARMv8 | S | S (ARMv8.2) | S | S | +| GPU | OpenCL | A | S | C | C | +| | Vulkan | A | A | C | C | +| | Metal | A | S | C | C | +| | CUDA | A | S | C | C | +| NPU | CoreML | B | C | C | C | +| | HIAI | B | C | C | B | + + ## Architecture ![architecture](doc/architecture.png) -MNN can be divided into two parts: Converter and Interpreter. +MNN can be divided into two parts: Inference Engine and Tools. -Converter consists of Frontends and Graph Optimize. The former is responsible for supporting different training frameworks. MNN currently supports Tensorflow, Tensorflow Lite, Caffe and ONNX (PyTorch/MXNet); the latter optimizes graphs by operator fusion, operator substitution, and layout adjustment. +### Inference Engine -Interpreter consists of Engine and Backends. The former is responsible for the loading of the model and the scheduling of the calculation graph; the latter includes the memory allocation and the Op implementation under each computing device. In Engine and Backends, MNN applies a variety of optimization schemes, including applying Winograd algorithm in convolution and deconvolution, applying Strassen algorithm in matrix multiplication, low-precision calculation, Neon optimization, hand-written assembly, multi-thread optimization, memory reuse, heterogeneous computing, etc. +The input of Inference Engine, AI model is a Directed Acyclic Graph(DAG), each node in model is an operator, which describe a kind of tensor compute function. Inference Engine will load and execute the graph. It can seperate into schedule and execute: +![runflow.png](doc/runflow.png) + +- Schedule: Load Graph and Pretreat it + - Decompose OP, reduce kinds of OPs + - Search best compute stratagy + - Find best resource allocation +- Execute: Implete OP, use algorithm and hardware feature to optimize + - Algorithm: Winograd Convolution, Strassen Matrix Multiply, Low Precision Compute + - Hardware: SIMD for CPU (SSE/NEON/AVX), GPU API (OpenCL / CUDA / Metal) + +### Tools +- MNN-Converter: Convert other model to MNN model, such as Tensorflow(lite), Caffe, ONNX, Torchscripts. And do graph optimization to reduce computation. +- MNN-Compress: Compress model to reduce size and increase performance / speed +- MNN-Express: Support model with controlflow, use MNN's OP to do general-purpose compute. +- MNN-CV: A OpenCV liked library, but based on MNN and then much more lightweight. +- MNN-Train: Support train MNN model. ## How to Discuss and Get Help From MNN Community diff --git a/README_CN.md b/README_CN.md index 0092ec55..0a8faecf 100644 --- a/README_CN.md +++ b/README_CN.md @@ -2,67 +2,109 @@ [English Version](README.md) -[MNN官网](http://www.mnn.zone) +[MNN Homepage](http://www.mnn.zone) -## 简介 -MNN是一个高效、轻量的深度学习框架。它支持深度模型推理与训练，尤其在端侧的推理与训练性能在业界处于领先地位。目前，MNN已经在阿里巴巴的手机淘宝、手机天猫、优酷、钉钉、闲鱼等20多个App中使用，覆盖直播、短视频、搜索推荐、商品图像搜索、互动营销、权益发放、安全风控等70多个场景。此外，IoT等场景下也有若干应用。 +[MNN](https://github.com/alibaba/MNN)是一个轻量级的深度神经网络推理引擎，加载深度神经网络模型进行推理预测。适用于服务器/个人电脑/手机/嵌入式各类设备。目前，MNN已经在阿里巴巴的手机淘宝、手机天猫、优酷等30多个App中使用，覆盖直播、短视频、搜索推荐、商品图像搜索、互动营销、权益发放、安全风控等场景。 MNN的架构设计理念与性能数据在MLSys 2020上面发表。Paper [在此处](https://arxiv.org/pdf/2002.12418.pdf)。如果MNN对你的研究有所助益，欢迎引用MNN的论文： - @inproceedings{alibaba2020mnn, + @inproceedings{alibaba2020mnn, author = {Jiang, Xiaotang and Wang, Huan and Chen, Yiliu and Wu, Ziqi and Wang, Lichuan and Zou, Bin and Yang, Yafeng and Cui, Zongyang and Cai, Yu and Yu, Tianhang and Lv, Chengfei and Wu, Zhihua}, title = {MNN: A Universal and Efficient Inference Engine}, booktitle = {MLSys}, year = {2020} } -## 文档与工具 +![image.png](doc/workflow.png) + +## 文档与工作台 MNN的使用文档统一放在语雀，请移步至[语雀文档](https://www.yuque.com/mnn/cn)。 [MNN官网](http://www.mnn.zone)上还可以下载MNN团队全新力作MNN工作台，涵盖开箱即用模型、可视化训练等工具，更可以一键部署到多端设备。 -## MNN 特色 +## 整体特点 + +### 轻量性 + +- 主体功能（模型推理CPU+GPU）无任何依赖，代码精简，可以方便地部署到移动设备和各种嵌入式设备中。 + - iOS平台：功能全开的MNN静态库 armv7+arm64大小12MB左右，链接生成可执行文件增加大小2M左右。可裁剪主体功能后静态库大小6.1M ，链接生成可执行文件增加大小 600 KB。 + - Android平台：主体功能 armv7a - c++_shared 动态库大小800KB左右。 +- 支持采用 Mini 编辑选项进一步降低包大小，大约能在上述库体积基础上进一步降低 25% 左右。 +- 支持模型FP16/Int8压缩与量化，可减少模型50% - 75% 的体积 + +### 通用性 + +- 支持 Tensorflow、Caffe、ONNX、Torchscripts 等主流模型文件格式，支持CNN / RNN / GAN / Transformer 等主流网络结构。 +- 支持多输入多输出，支持任意维度的输入输出，支持动态输入（输入大小可变），支持带控制流的模型 +- 算子丰富，支持 178 个Tensorflow Op、52个 Caffe Op、163个 Torchscipts Op、158 个 ONNX Op（ONNX 基本完整支持） +- 支持 服务器 / 个人电脑 / 手机 及具有POSIX接口的嵌入式设备，支持使用设备的 CPU / GPU 计算，支持部分设备的 NPU 计算（IOS 11 + CoreML / Huawei + HIAI） +- 支持 Windows / iOS 8.0+ / Android 4.3+ / Linux  及具有POSIX接口的操作系统 + ### 高性能 -- 不依赖任何第三方计算库，依靠大量手写汇编实现核心运算，充分发挥ARM CPU的算力。 -- iOS设备上可以开启GPU加速（Metal），常用模型上快于苹果原生的CoreML。 -- Android上提供了`OpenCL`、`Vulkan`、`OpenGL`三套方案，尽可能多地满足设备需求，针对主流GPU（`Adreno`和`Mali`）做了深度调优。 -- 卷积、转置卷积算法高效稳定，对于任意形状的卷积均能高效运行，广泛运用了 Winograd 卷积算法，对3x3 -> 7x7之类的对称卷积有高效的实现。 -- 针对ARM v8.2的新架构额外作了优化，新设备可利用FP16半精度计算的特性获得两倍提速。 -### 轻量性 -- 针对端侧设备特点深度定制和裁剪，无任何依赖，可以方便地部署到移动设备和各种嵌入式设备中。 -- iOS平台：armv7+arm64静态库大小5MB左右，链接生成可执行文件增加大小620KB左右，metallib文件600KB左右。 -- Android平台：so大小400KB左右，OpenCL库400KB左右，Vulkan库400KB左右。 - -### 通用性 -- 支持`Tensorflow`、`Caffe`、`ONNX`等主流模型文件格式，支持`CNN`、`RNN`、`GAN`等常用网络。 -- 转换器支持149个`Tensorflow`OP、58个`TFLite` OP、47个`Caffe` OP、74个`ONNX` OP；各计算设备后端支持的MNN OP数：CPU 111个，ARM V8.2 6个，Metal 55个，OpenCL 43个，Vulkan 32个。 -- 支持iOS 8.0+、Android 4.3+和具有POSIX接口的嵌入式设备。 -- 支持异构设备混合计算，目前支持CPU和GPU。 +- 对iOS / Android / PC / Server 的CPU架构进行了适配，编写SIMD代码或手写汇编以实现核心运算，充分发挥 CPU的算力，单线程下运行常见CV模型接近设备算力峰值 +- 支持基于 Metal / OpenCL / Vulkan 使用移动端设备上的GPU进行推理 +- 支持基于 CUDA 使用 PC / Server 上的 NVIDIA GPU 实现更快速的推理 +- 广泛运用了 Winograd 卷积算法提升卷积性能，首次在业界工程实践中实现转置卷积的Winograd算法优化与矩阵乘的Strassen算法优化，并取得加速效果 +- 支持低精度计算（ int8 / fp16 / bf16）以提升推理性能。并对 ARMv8.2 和 AVX512架构的相关指令进行了适配，这两种架构下有更好的加速效果 ### 易用性 -- 有高效的图像处理模块，覆盖常见的形变、转换等需求，一般情况下，无需额外引入libyuv或opencv库处理图像。 -- 支持回调机制，可以在网络运行中插入回调，提取数据或者控制运行走向。 -- 支持只运行网络中的一部分，或者指定CPU和GPU间并行运行。 -- （BETA）MNN Python API，让算法工程师可以轻松地使用MNN构图、训练、量化训练，无需编写C++。 + +- 支持使用 MNN 的算子进行常用的数值计算，覆盖 numpy 常用功能 +- 提供 MNN CV 模块，支持图像仿射变换与归一化等 MNN_CV 库，支持常用的图像处理（armv7a 架构下小于 100 k ） +- 支持各平台下的模型训练，尤其是移动端上的模型训练 +- 支持 python 调用 + +MNN适配的硬件架构与精度详见下表： + +- S ：支持，深度优化并已有应用场景，推荐使用 +- A ：支持，有初步优化或已有应用场景，可以使用 +- B ：支持，无优化或在实验状态，不推荐使用 +- C ：不支持 + +| Architecture / Precision | | Normal | FP16 | BF16 | Int8 | +| --- | --- | --- | --- | --- | --- | +| CPU | Native | B | C | B | B | +| | x86/x64-SSE4.1 | A | B | B | A | +| | x86/x64-AVX2 | S | B | B | A | +| | x86/x64-AVX512 | S | B | B | S | +| | ARMv7a | S | S (ARMv8.2) | S | S | +| | ARMv8 | S | S (ARMv8.2) | S | S | +| GPU | OpenCL | A | S | C | C | +| | Vulkan | A | A | C | C | +| | Metal | A | S | C | C | +| | CUDA | A | S | C | C | +| NPU | CoreML | B | C | C | C | +| | HIAI | B | C | C | B | + + ## 架构设计 -![architecture](doc/architecture.png) -MNN可以分为Converter和Interpreter两部分。 +![架构图](doc/architecture.png) -Converter由Frontends和Graph Optimize构成。前者负责支持不同的训练框架，MNN当前支持Tensorflow(Lite)、Caffe和ONNX(PyTorch/MXNet的模型可先转为ONNX模型再转到MNN)；后者通过算子融合、算子替代、布局调整等方式优化图。 +MNN可以分为主体（推理引擎）和工具两大部分。 -Interpreter由Engine和Backends构成。前者负责模型的加载、计算图的调度；后者包含各计算设备下的内存分配、Op实现。在Engine和Backends中，MNN应用了多种优化方案，包括在卷积和反卷积中应用Winograd算法、在矩阵乘法中应用Strassen算法、低精度计算、Neon优化、手写汇编、多线程优化、内存复用、异构计算等。 +### 主体 +MNN 的输入（AI推理模型）是一个有向无环图（DAG），图中每个节点称为算子，描述一种张量计算函数。推理引擎负责这个图的加载与执行，可分为调度（预推理）与执行（推理）两层： +![runflow.png](doc/runflow.png) -## 社区交流与反馈 -钉钉讨论群： +- 调度：加载计算图并做预处理，以使执行过程高效 + - 对模型中的算子进行预处理，降低算子数 + - 搜索最优计算方案 + - 进行资源分配 +- 执行：实现算子，基于各类算法与不同硬件提供的并行接口进行优化，降低执行耗时 + - 算法层面，采用 Winograd 卷积 / Strassen 矩阵乘 / 分段线性 / 低精度等方案 + - 硬件层面，使用 CPU 的 SIMD指令 (SSE / NEON / AVX / AVX512) ，各类 GPU 计算 API 进行优化 -一群（已满）：23329087 +### 工具 -二群（已满）：23350225 +- MNN-Converter：模型转换工具，由Frontends和Graph Optimize构成。前者负责支持不同的训练框架，MNN当前支持Tensorflow(Lite)、Caffe、ONNX(PyTorch/MXNet的模型可先转为ONNX模型再转到MNN)和Torchscripts；后者通过算子融合、算子替代、布局调整等方式优化图，一般离线运行。 +- MNN-Compress: 模型压缩工具，在一定的精度误差许可下，对MNN模型进行压缩，减少模型体积，提升运行性能。 +- MNN-Express ：支持带控制流的模型运行，支持调用 MNN 的算子进行自定义的计算。 +- MNN-CV ：类似 OpenCV ，但核心计算功能基于 MNN 实现的图像处理算法库 +- MNN-Train ：MNN 训练模块，支持各平台训练 -三群: https://h5.dingtalk.com/circle/healthCheckin.html?dtaction=os&corpId=ding2c1d5c85a81030b9a483726330e8af54&574b2bb2-c53a-4=497bad6b-25a5-4&cbdbhh=qwertyuiop ## License Apache 2.0 @@ -71,6 +113,7 @@ Apache 2.0 MNN参与人员：淘宝技术部、搜索工程团队、达摩院团队、优酷等集团员工。 MNN参考、借鉴了下列项目： + - [Caffe](https://github.com/BVLC/caffe) - [flatbuffer](https://github.com/google/flatbuffers) - [gemmlowp](https://github.com/google/gemmlowp) @@ -91,3 +134,4 @@ MNN参考、借鉴了下列项目： - [libyuv](https://chromium.googlesource.com/libyuv/libyuv) - [libjpeg](https://github.com/libjpeg-turbo/libjpeg-turbo) - [opencv](https://github.com/opencv/opencv) + diff --git a/doc/DingTalkQR1.png b/doc/DingTalkQR1.png deleted file mode 100644 index e9e6f8d9..00000000 Binary files a/doc/DingTalkQR1.png and /dev/null differ diff --git a/doc/DingTalkQR2.png b/doc/DingTalkQR2.png deleted file mode 100644 index 7fe2e2af..00000000 Binary files a/doc/DingTalkQR2.png and /dev/null differ diff --git a/doc/DingTalkQR3.png b/doc/DingTalkQR3.png deleted file mode 100644 index e324dc73..00000000 Binary files a/doc/DingTalkQR3.png and /dev/null differ diff --git a/doc/architecture.png b/doc/architecture.png index 414cff89..15fa673f 100644 Binary files a/doc/architecture.png and b/doc/architecture.png differ diff --git a/doc/runflow.png b/doc/runflow.png new file mode 100644 index 00000000..de3117fa Binary files /dev/null and b/doc/runflow.png differ diff --git a/doc/workflow.png b/doc/workflow.png new file mode 100644 index 00000000..8b87d51c Binary files /dev/null and b/doc/workflow.png differ diff --git a/express/Executor.cpp b/express/Executor.cpp index 2947b308..4f1d6d8e 100644 --- a/express/Executor.cpp +++ b/express/Executor.cpp @@ -73,14 +73,6 @@ void Executor::Profiler::addFlops(const std::string& opType, float flops) { } #endif -struct Executor::Cache{ - AutoStorage modelBuffer; - AutoStorage cacheBuffer; - size_t cacheOffset = 0; - std::string cacheFile; - size_t lastCacheSize = 0; -}; - void Executor::setGlobalExecutorConfig(MNNForwardType type, const BackendConfig& config, int numberThread) { std::lock_guard _l(mMutex); mFirstType = std::make_pair(type, numberThread); @@ -237,7 +229,7 @@ static std::pair getCache(std::shared_ptr &rt) { return std::make_pair(nullptr, 0); } -static void writeCacheFile(std::shared_ptr cache, std::pair buffer) { +static void writeCacheFile(std::shared_ptr cache, std::pair buffer) { auto verifyInfo = std::make_pair((const void*)cache->modelBuffer.get(), cache->cacheOffset); bool res = FileLoader::write(cache->cacheFile.c_str(), buffer); if (!res) { @@ -273,9 +265,9 @@ bool Executor::RuntimeManager::getInfo(Interpreter::SessionInfoCode code, void* switch (code) { case Interpreter::MEMORY: { auto dst = (float*)ptr; - float summer = mRuntime.second->onGetMemoryInMB(); - for (auto& r : mRuntime.first) { - if (r.second.get() != mRuntime.second.get()) { + float summer = mInside->mRuntime.second->onGetMemoryInMB(); + for (auto& r : mInside->mRuntime.first) { + if (r.second.get() != mInside->mRuntime.second.get()) { summer += r.second->onGetMemoryInMB(); } } @@ -326,21 +318,33 @@ Executor::RuntimeManager* Executor::RuntimeManager::createRuntimeManager(const S } originRt.insert(std::make_pair(std::make_pair(compute.type, compute.numThread), std::shared_ptr(newBn))); } - res->mRuntime.second = originRt[DEFAULT_BACKUP_RUNTIME_KEY]; - res->mRuntime.first.insert(std::make_pair(compute.type, originRt[std::make_pair(compute.type, compute.numThread)])); - res->mInfo = originRt[std::make_pair(compute.type, compute.numThread)]; + res->mInside->mRuntime.second = originRt[DEFAULT_BACKUP_RUNTIME_KEY]; + res->mInside->mRuntime.first.insert(std::make_pair(compute.type, originRt[std::make_pair(compute.type, compute.numThread)])); + res->mInside->mInfo = originRt[std::make_pair(compute.type, compute.numThread)]; + if (nullptr != config.backendConfig) { + res->mInside->mConfig = *config.backendConfig; + res->mInside->mUserConfig = true; + } else { + res->mInside->mUserConfig = false; + } return res; } +BackendConfig* Executor::RuntimeManager::getBnConfig() { + if (mInside->mUserConfig) { + return &mInside->mConfig; + } + return nullptr; +} void Executor::RuntimeManager::setCache(std::string cacheName) { - mCache.reset(new Cache); - mCache->cacheFile = cacheName; - if (nullptr == mCache->cacheFile.c_str()) { + mInside->mCache.reset(new Cache); + mInside->mCache->cacheFile = cacheName; + if (nullptr == mInside->mCache->cacheFile.c_str()) { MNN_ERROR("Empty cacheFile\n"); return; } - std::unique_ptr loader(new FileLoader(mCache->cacheFile.c_str())); + std::unique_ptr loader(new FileLoader(mInside->mCache->cacheFile.c_str())); if (!loader->valid()) { MNN_ERROR("Load Cache file error.\n"); return; @@ -354,36 +358,36 @@ void Executor::RuntimeManager::setCache(std::string cacheName) { MNN_ERROR("Load Cache file error.\n"); return; } - bool success = loader->merge(mCache->cacheBuffer); + bool success = loader->merge(mInside->mCache->cacheBuffer); if (!success) { MNN_ERROR("Alloc memory for Cache error.\n"); return; } // load cache - bool valid = loadCache(mInfo, mCache->cacheBuffer.get() + mCache->cacheOffset, - mCache->cacheBuffer.size() - mCache->cacheOffset); + bool valid = loadCache(mInside->mInfo, mInside->mCache->cacheBuffer.get() + mInside->mCache->cacheOffset, + mInside->mCache->cacheBuffer.size() - mInside->mCache->cacheOffset); if(!valid) { // Reset cache - loadCache(mInfo, nullptr, 0); + loadCache(mInside->mInfo, nullptr, 0); MNN_PRINT("Cache invalid, will be reset\n"); } - mCache->lastCacheSize = mCache->cacheBuffer.size() - mCache->cacheOffset; + mInside->mCache->lastCacheSize = mInside->mCache->cacheBuffer.size() - mInside->mCache->cacheOffset; } void Executor::RuntimeManager::updateCache() { - mInfo->waitAsyncWork(); - auto buffer = getCache(mInfo); + mInside->mInfo->waitAsyncWork(); + auto buffer = getCache(mInside->mInfo); //When current cacheSize bigger than previous, update - if (buffer.first != nullptr && buffer.second > mCache->lastCacheSize) { - MNN_PRINT("Update cache to %s, size = %zu\n", mCache->cacheFile.c_str(), buffer.second); - writeCacheFile(mCache, buffer); - mCache->lastCacheSize = buffer.second; + if (buffer.first != nullptr && buffer.second > mInside->mCache->lastCacheSize) { + MNN_PRINT("Update cache to %s, size = %zu\n", mInside->mCache->cacheFile.c_str(), buffer.second); + writeCacheFile(mInside->mCache, buffer); + mInside->mCache->lastCacheSize = buffer.second; } // Reset cache - loadCache(mInfo, nullptr, 0); + loadCache(mInside->mInfo, nullptr, 0); } std::vector Executor::RuntimeManager::isBackendSupport(const std::vector types) { diff --git a/express/Expr.cpp b/express/Expr.cpp index 6f204390..0ba4bb42 100644 --- a/express/Expr.cpp +++ b/express/Expr.cpp @@ -978,6 +978,9 @@ void Variable::save(const std::vector& vars, NetT* dest) { } } } + // add version number + dest->extraInfo.reset(new ExtraInfoT); + dest->extraInfo->version = MNN_VERSION; } void Variable::save(const std::vector& vars, const char* fileName) { std::unique_ptr net(new NetT); diff --git a/express/MathOp.cpp b/express/MathOp.cpp index db97e0e9..0677b6ce 100644 --- a/express/MathOp.cpp +++ b/express/MathOp.cpp @@ -1221,5 +1221,26 @@ VARP _Mod(VARP x, VARP y) { return _Binary(x, y, BinaryOpOperation_MOD); } +VARP _CumSum(VARP x, int axis, bool exclusive, bool reverse) { + std::unique_ptr op(new OpT); + op->type = OpType_CumSum; + op->main.type = OpParameter_CumSum; + auto param = new CumSumT; + param->exclusive = exclusive; + param->reverse = reverse; + op->main.value = param; + return (Variable::create(Expr::create(std::move(op), {x ,_Scalar(axis)}))); +} + +VARP _CumProd(VARP x, int axis) { + std::unique_ptr op(new OpT); + op->type = OpType_CumProd; + op->main.type = OpParameter_Axis; + auto param = new AxisT; + param->axis = axis; + op->main.value = param; + return (Variable::create(Expr::create(std::move(op), {x}))); +} + } // namespace Express } // namespace MNN diff --git a/express/RuntimeAttr.hpp b/express/RuntimeAttr.hpp index 858c2045..efa97c49 100644 --- a/express/RuntimeAttr.hpp +++ b/express/RuntimeAttr.hpp @@ -3,8 +3,21 @@ #include "core/Session.hpp" namespace MNN{ namespace Express { +struct Cache { + AutoStorage modelBuffer; + AutoStorage cacheBuffer; + size_t cacheOffset = 0; + std::string cacheFile; + size_t lastCacheSize = 0; +}; struct RuntimeAttr { Session::ModeGroup modes; + RuntimeInfo mRuntime; + std::shared_ptr mInfo; + std::shared_ptr mCache; + RuntimeAttr* mInside; + BackendConfig mConfig; + bool mUserConfig; }; }; }; diff --git a/express/module/Module.cpp b/express/module/Module.cpp index a9bd2225..dbca86f9 100644 --- a/express/module/Module.cpp +++ b/express/module/Module.cpp @@ -12,6 +12,7 @@ #include "core/FileLoader.hpp" #include "MNN_generated.h" #include "Utils.hpp" +#include "RuntimeAttr.hpp" #ifdef MNN_INTERNAL_ENABLED #include "internal/auth/ModelAuth.hpp" @@ -252,7 +253,7 @@ Module* Module::load(const std::vector& inputs, const std::vector& inputs, const std::vector& outputs, const uint8_t* buffer, size_t length, const std::shared_ptr _rtMgr, const Module::Config* config, bool enforceAuth) { // Check if runtime is valid - if (nullptr != _rtMgr && _rtMgr->getRuntimeInfo().first.empty()) { + if (nullptr != _rtMgr && _rtMgr->getInside()->mRuntime.first.empty()) { MNN_ERROR("Invalid runtime\n"); return nullptr; } diff --git a/express/module/StaticModule.cpp b/express/module/StaticModule.cpp index 9d99aee1..3813b3b4 100644 --- a/express/module/StaticModule.cpp +++ b/express/module/StaticModule.cpp @@ -175,14 +175,16 @@ StaticModule::StaticModule(const void* buffer, size_t length, const std::vector< sche_config.backendConfig = moduleconfig.backend->config; rtMgr.reset(Executor::RuntimeManager::createRuntimeManager(sche_config)); } + const BackendConfig* userConfig = nullptr; if (nullptr == rtMgr) { rt = Executor::getRuntime(); } else { mResource->mModes = rtMgr->getInside()->modes; - rt = rtMgr->getRuntimeInfo(); + rt = rtMgr->getInside()->mRuntime; + userConfig = &rtMgr->getInside()->mConfig; } if (moduleconfig.rearrange) { - mResourceBackend.reset(rt.first.begin()->second->onCreate()); + mResourceBackend.reset(rt.first.begin()->second->onCreate(userConfig)); if (mResourceBackend->type() == MNN_FORWARD_CPU) { mBackupResourceBackend = mResourceBackend; } else { @@ -236,6 +238,7 @@ StaticModule::StaticModule(const void* buffer, size_t length, const std::vector< mResource->mConfig.path.outputs = outputs; mResource->mConfig.saveTensors = outputs; mResource->mConfig.path.inputs = inputs; + mResource->mConfig.backendConfig = (BackendConfig*)userConfig; Schedule::ScheduleInfo scheduleInfo; // Copy Const if (nullptr != mResource->mSharedConst) { diff --git a/include/MNN/Interpreter.hpp b/include/MNN/Interpreter.hpp index 29185a51..9ff5f150 100644 --- a/include/MNN/Interpreter.hpp +++ b/include/MNN/Interpreter.hpp @@ -96,6 +96,12 @@ typedef std::function&, const std::string& /*opN typedef std::function&, const OperatorInfo*)> TensorCallBackWithInfo; typedef std::pair>, std::shared_ptr> RuntimeInfo; +/** + * @brief get mnn version info. + * @return mnn version string. + */ +MNN_PUBLIC const char* getVersion(); + /** net data holder. multiple sessions could share same net. */ class MNN_PUBLIC Interpreter { public: @@ -240,6 +246,13 @@ public: */ std::pair getModelBuffer() const; + /** + * @brief Get the model's version info. + * @return const char* of model's version info like "2.0.0"; + * If model is not loaded or model no version info, return "version info not found". + */ + const char* getModelVersion() const; + /** * @brief update Session's Tensor to model's Const Op * @param session given session. diff --git a/include/MNN/MNNDefine.h b/include/MNN/MNNDefine.h index be6cc17d..7124d5ac 100644 --- a/include/MNN/MNNDefine.h +++ b/include/MNN/MNNDefine.h @@ -65,5 +65,10 @@ MNN_ERROR("Check failed: %s ==> %s\n", #success, #log); \ #else #define MNN_PUBLIC __attribute__((visibility("default"))) #endif - +#define STR_IMP(x) #x +#define STR(x) STR_IMP(x) +#define MNN_VERSION_MAJOR 2 +#define MNN_VERSION_MINOR 0 +#define MNN_VERSION_PATCH 0 +#define MNN_VERSION STR(MNN_VERSION_MAJOR) "." STR(MNN_VERSION_MINOR) "." STR(MNN_VERSION_PATCH) #endif /* MNNDefine_h */ diff --git a/include/MNN/MNNForwardType.h b/include/MNN/MNNForwardType.h index 37c7677a..37ea3926 100644 --- a/include/MNN/MNNForwardType.h +++ b/include/MNN/MNNForwardType.h @@ -104,7 +104,6 @@ struct BackendConfig { STATUS_COUNT }; - }; // namespace MNN #endif #endif /* MNNForwardType_h */ diff --git a/include/MNN/expr/Executor.hpp b/include/MNN/expr/Executor.hpp index 1a833615..2b284063 100644 --- a/include/MNN/expr/Executor.hpp +++ b/include/MNN/expr/Executor.hpp @@ -69,7 +69,6 @@ public: const DebugTools* getDebugTools() const { return mDebug.get(); } - struct Cache; class MNN_PUBLIC RuntimeManager { public: ~RuntimeManager(); @@ -100,22 +99,18 @@ public: */ void updateCache(); std::vector isBackendSupport(const std::vector type); - RuntimeInfo getRuntimeInfo() { - return mRuntime; - } friend class Executor; void setMode(Interpreter::SessionMode mode); void setHint(Interpreter::HintMode mode, int value); bool getInfo(Interpreter::SessionInfoCode code, void* ptr); + BackendConfig* getBnConfig(); const RuntimeAttr* getInside() const { return mInside; } private: - RuntimeManager(); - RuntimeInfo mRuntime; - std::shared_ptr mInfo; - std::shared_ptr mCache; RuntimeAttr* mInside; + friend class StaticModule; + RuntimeManager(); }; private: void _makeCache(const std::vector& outputs, bool forceCPU); diff --git a/include/MNN/expr/MathOp.hpp b/include/MNN/expr/MathOp.hpp index 27170f17..d6496b0b 100644 --- a/include/MNN/expr/MathOp.hpp +++ b/include/MNN/expr/MathOp.hpp @@ -130,6 +130,8 @@ MNN_PUBLIC VARP _BroadcastTo(VARP a, VARP shape); MNN_PUBLIC VARP _LinSpace(VARP start, VARP stop, VARP num); MNN_PUBLIC VARP _RandomUnifom(VARP shape, halide_type_t dtype, float low = 0.0f, float high = 1.0f, int seed0 = 0, int seed1 = 0); +MNN_PUBLIC VARP _CumSum(VARP x, int axis, bool exclusive = false, bool reverse = false); +MNN_PUBLIC VARP _CumProd(VARP x, int axis); }; // namespace Express }; // namespace 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4D4DAE67263905390060D37E /* CoreMLDefine.h */, 4DDE2018263809920085AC8F /* CoreMLExecutorWrapper.h */, 4DDE2017263809920085AC8F /* CoreMLExecutorWrapper.mm */, @@ -2778,6 +2802,7 @@ 1F501F892397BA5B004E8721 /* MNNForwardType.h in Headers */, 92FF027323AA0B5A00AC97F6 /* CPUPoolInt8.hpp in Headers */, 1F501F802397BA5B004E8721 /* MNNDefine.h in Headers */, + 19D0FE76285C66F200B74B1A /* MetalLayerNorm.hpp in Headers */, 489D7A682550FDC800AD896A /* MetalReduction.hpp in Headers */, 1F501F7F2397BA5B004E8721 /* HalideRuntime.h in Headers */, 92FF029E23AA0B5A00AC97F6 /* CPUDeconvolutionDepthwise.hpp in Headers */, @@ -2836,6 +2861,7 @@ EBECA39524643D320062C7A3 /* Arm82Backend.hpp in Headers */, 92FF04C323AA0BFB00AC97F6 /* Session.hpp in Headers */, 48FA474423AA127B00172C3B /* MergeOptimizer.hpp in Headers */, + 19D0FE7128534C4500B74B1A /* MetalSoftmax.hpp in Headers */, 92FF039F23AA0B5A00AC97F6 /* CommonOptFunction.h in Headers */, 4D9A935A26255BDA00F9B43C /* Parameters.pb-c.h in Headers */, 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b/pymnn/examples/MNNEngineDemo/gpu_session_demo.py index 6e39d18a..55db8e47 100644 --- a/pymnn/examples/MNNEngineDemo/gpu_session_demo.py +++ b/pymnn/examples/MNNEngineDemo/gpu_session_demo.py @@ -2,6 +2,7 @@ import os import sys import MNN import numpy as np +import cv2 def createTensor(tensor): shape = tensor.getShape() @@ -28,12 +29,28 @@ def modelTest(modelPath): session = net.createSession(config) print("Run on backendtype: %d \n" % net.getSessionInfo(session, 2)) + + image = cv2.imread(sys.argv[2]) + #cv2 read as bgr format + image = image[..., ::-1] + #change to rgb format + image = cv2.resize(image, (224, 224)) + #resize to mobile_net tensor size + image = image - (103.94, 116.78, 123.68) + image = image * (0.017, 0.017, 0.017) + #preprocess it + image = image.transpose((2, 0, 1)) + #change numpy data type as np.float32 to match tensor's format + image = image.astype(np.float32) + #cv2 read shape is NHWC, Tensor's need is NCHW,transpose it + tmp_input = MNN.Tensor((1, 3, 224, 224), MNN.Halide_Type_Float,\ + image, MNN.Tensor_DimensionType_Caffe) - allInput = net.getSessionInputAll(session) # input inputTensor = net.getSessionInput(session) - inputHost = createTensor(inputTensor) - inputTensor.copyFrom(inputHost) + net.resizeTensor(inputTensor, (1, 3, 224, 224)) + net.resizeSession(session) + inputTensor.copyFrom(tmp_input) # infer net.runSession(session) outputTensor = net.getSessionOutput(session) @@ -43,6 +60,8 @@ def modelTest(modelPath): outputTensor.copyToHostTensor(outputHost) net.updateCacheFile(session, 0) + print("expect 983") + print("output belong to class: {}".format(np.argmax(outputHost.getData()))) if __name__ == '__main__': modelName = sys.argv[1] # model path diff --git a/pymnn/examples/MNNEngineDemo/mobilenet_demo.py b/pymnn/examples/MNNEngineDemo/mobilenet_demo.py index 067cbca9..d12d9551 100644 --- a/pymnn/examples/MNNEngineDemo/mobilenet_demo.py +++ b/pymnn/examples/MNNEngineDemo/mobilenet_demo.py @@ -5,15 +5,20 @@ from __future__ import print_function import numpy as np import MNN import cv2 +import sys + def inference(): """ inference mobilenet_v1 using a specific picture """ - interpreter = MNN.Interpreter("mobilenet_v1.mnn") + interpreter = MNN.Interpreter(sys.argv[1]) interpreter.setCacheFile('.tempcache') config = {} config['precision'] = 'low' session = interpreter.createSession() input_tensor = interpreter.getSessionInput(session) - image = cv2.imread('0000.jpg') + interpreter.resizeTensor(input_tensor, (1, 3, 224, 224)) + interpreter.resizeSession(session) + + image = cv2.imread(sys.argv[2]) #cv2 read as bgr format image = image[..., ::-1] #change to rgb format diff --git a/pymnn/examples/MNNEngineDemo/mobilenet_demo_2.py b/pymnn/examples/MNNEngineDemo/mobilenet_demo_2.py index bb2dc063..fcda5ec1 100644 --- a/pymnn/examples/MNNEngineDemo/mobilenet_demo_2.py +++ b/pymnn/examples/MNNEngineDemo/mobilenet_demo_2.py @@ -5,9 +5,11 @@ from __future__ import print_function import numpy as np import MNN import cv2 +import sys + def inference(): """ inference mobilenet_v1 using a specific picture """ - interpreter = MNN.Interpreter("mobilenet_v1.mnn") + interpreter = MNN.Interpreter(sys.argv[1]) interpreter.setCacheFile('.tempcache') config = {} config['precision'] = 'low' @@ -23,7 +25,10 @@ def inference(): print('backend_info: %d' % interpreter.getSessionInfo(session, 2)) input_tensor = interpreter.getSessionInput(session) - image = cv2.imread('ILSVRC2012_val_00049999.JPEG') + interpreter.resizeTensor(input_tensor, (1, 3, 224, 224)) + interpreter.resizeSession(session) + + image = cv2.imread(sys.argv[2]) #cv2 read as bgr format image = image[..., ::-1] #change to rgb format @@ -46,5 +51,6 @@ def inference(): output_tensor.copyToHostTensor(tmp_output) print("expect 983") print("output belong to class: {}".format(np.argmax(tmp_output.getData()))) + if __name__ == "__main__": inference() diff --git a/pymnn/examples/MNNExpr/gpu_express_demo.py b/pymnn/examples/MNNExpr/gpu_express_demo.py index 7bf2c1c8..03fc0631 100644 --- a/pymnn/examples/MNNExpr/gpu_express_demo.py +++ b/pymnn/examples/MNNExpr/gpu_express_demo.py @@ -4,13 +4,15 @@ from __future__ import print_function import numpy as np import MNN import sys +import cv2 + def inference(): """ inference mobilenet_v1 using a specific picture """ config = {} config['precision'] = 'low' config['backend'] = 3 - config['numThread'] = 4 + config['numThread'] = 4 rt = MNN.nn.create_runtime_manager((config,)) rt.set_cache(".cachefile") @@ -19,13 +21,26 @@ def inference(): # set_hint(type, value) //type 0 for "tune_num" rt.set_hint(0, 20) - net = MNN.nn.load_module_from_file(sys.argv[1], ["data"], ["prob"], runtime_manager=rt) + net = MNN.nn.load_module_from_file(sys.argv[1], ["input"], ["MobilenetV1/Predictions/Reshape_1"], runtime_manager=rt) + image = cv2.imread(sys.argv[2]) + #cv2 read as bgr format + image = image[..., ::-1] + #change to rgb format + image = cv2.resize(image, (224, 224)) + #resize to mobile_net tensor size + image = image - (103.94, 116.78, 123.68) + image = image * (0.017, 0.017, 0.017) + #change numpy data type as np.float32 to match tensor's format + image = image.astype(np.float32) input_var = MNN.expr.placeholder([1, 224, 224, 3], MNN.expr.NHWC) + input_var.write(image) input_var = MNN.expr.convert(input_var, MNN.expr.NC4HW4) #inference output_var = net.forward(input_var) - #output_var = MNN.expr.convert(output_var, MNN.expr.NHWC) + output_var = MNN.expr.convert(output_var, MNN.expr.NHWC) + print("expect 983") + print("output belong to class: {}".format(np.argmax(output_var.read()))) rt.update_cache() if __name__ == "__main__": diff --git a/pymnn/examples/MNNExpr/mnn_numpy_cv_demo.py b/pymnn/examples/MNNExpr/mnn_numpy_cv_demo.py index 34a8ba64..d4ada12f 100644 --- a/pymnn/examples/MNNExpr/mnn_numpy_cv_demo.py +++ b/pymnn/examples/MNNExpr/mnn_numpy_cv_demo.py @@ -6,9 +6,10 @@ import MNN.numpy as np import MNN import MNN.cv as cv2 import sys + def inference(): """ inference mobilenet_v1 using a specific picture """ - net = MNN.nn.load_module_from_file(sys.argv[1], ["data"], ["prob"]) + net = MNN.nn.load_module_from_file(sys.argv[1], ["input"], ["MobilenetV1/Predictions/Reshape_1"]) image = cv2.imread(sys.argv[2]) #cv2 read as bgr format image = image[..., ::-1] @@ -27,6 +28,8 @@ def inference(): output_var = net.forward(input_var) #the output from net may be NC4HW4, turn to linear layout output_var = MNN.expr.convert(output_var, MNN.expr.NHWC) + print("expect 983") print("output belong to class: {}".format(np.argmax(output_var))) + if __name__ == "__main__": inference() diff --git a/pymnn/examples/MNNExpr/mobilenet_demo.py b/pymnn/examples/MNNExpr/mobilenet_demo.py index a19e2b2c..a914bd6b 100644 --- a/pymnn/examples/MNNExpr/mobilenet_demo.py +++ b/pymnn/examples/MNNExpr/mobilenet_demo.py @@ -6,9 +6,10 @@ import numpy as np import MNN import cv2 import sys + def inference(): """ inference mobilenet_v1 using a specific picture """ - net = MNN.nn.load_module_from_file(sys.argv[1], ["data"], ["prob"]) + net = MNN.nn.load_module_from_file(sys.argv[1], ["input"], ["MobilenetV1/Predictions/Reshape_1"]) image = cv2.imread(sys.argv[2]) #cv2 read as bgr format image = image[..., ::-1] @@ -28,6 +29,8 @@ def inference(): output_var = net.forward(input_var) #the output from net may be NC4HW4, turn to linear layout output_var = MNN.expr.convert(output_var, MNN.expr.NHWC) + print("expect 983") print("output belong to class: {}".format(np.argmax(output_var.read()))) + if __name__ == "__main__": inference() diff --git a/pymnn/examples/MNNQuant/test_mnn_offline_quant.py b/pymnn/examples/MNNQuant/test_mnn_offline_quant.py new file mode 100644 index 00000000..88451a50 --- /dev/null +++ b/pymnn/examples/MNNQuant/test_mnn_offline_quant.py @@ -0,0 +1,200 @@ +from __future__ import print_function +import time +import argparse +import numpy as np +import tqdm +import os +import MNN +from PIL import Image + +nn = MNN.nn +F = MNN.expr + + +# adapted from pycaffe +def load_image(filename, color=True): + """ + Load an image converting from grayscale or alpha as needed. + + Parameters + ---------- + filename : string + color : boolean + flag for color format. True (default) loads as RGB while False + loads as intensity (if image is already grayscale). + + Returns + ------- + image : an image with type np.float32 in range [0, 1] + of size (H x W x 3) in RGB or + of size (H x W x 1) in grayscale. + """ + img = Image.open(filename) + img = np.array(img) + if img.ndim == 2: + img = img[:, :, np.newaxis] + if color: + img = np.tile(img, (1, 1, 3)) + elif img.shape[2] == 4: + img = img[:, :, :3] + return img + + +def center_crop(image_data, crop_factor): + height, width, channels = image_data.shape + + h_size = int(height * crop_factor) + h_start = int((height - h_size) / 2) + h_end = h_start + h_size + + w_size = int(width * crop_factor) + w_start = int((width - w_size) / 2) + w_end = w_start + w_size + + cropped_image = image_data[h_start:h_end, w_start:w_end, :] + + return cropped_image + + +def resize_image(image, shape): + im = Image.fromarray(image) + im = im.resize(shape) + resized_image = np.array(im) + + return resized_image + + +class CalibrationDataset(MNN.data.Dataset): + ''' + This is demo for Imagenet calibration dataset. like pytorch, you need to overload __getiterm__ and __len__ methods + __getiterm__ should return a sample in F.const, and you should not use batch dimension here + __len__ should return the number of total samples in the calibration dataset + ''' + def __init__(self, image_folder): + super(CalibrationDataset, self).__init__() + self.image_folder = image_folder + self.image_list = os.listdir(image_folder)[0:64] + + def __getitem__(self, index): + image_name = os.path.join(self.image_folder, self.image_list[index].split(' ')[0]) + + + # preprocess your data here, the following code are for tensorflow mobilenets + image_data = load_image(image_name) + image_data = center_crop(image_data, 0.875) + image_data = resize_image(image_data, (224, 224)) + image_data = (image_data - 127.5) / 127.5 + + + # after preprocessing the data, convert it to MNN data structure + dv = F.const(image_data.flatten().tolist(), [224, 224, 3], F.data_format.NHWC, F.dtype.float) + + ''' + first list for inputs, and may have many inputs, so it's a list + if your model have more than one inputs, add the preprocessed MNN const data to the input list + + second list for targets, also, there may be more than one targets + for calibration dataset, we don't need labels, so leave it blank + + Note that, the input order in the first list should be the same in your 'config.yaml' file. + ''' + + return [dv], [] + + def __len__(self): + # size of the dataset + return len(self.image_list) + + +def get_mnn_format(format_str): + fmt = str.lower(format_str) + if fmt == 'nchw': + return F.NCHW + elif fmt == 'nhwc': + return F.NHWC + elif fmt == 'nc4hw4': + return F.NC4HW4 + else: + raise ValueError("unknown format:", format_str) + +def quant_func(net, dataloader, opt): + net.train(True) + dataloader.reset() + + t0 = time.time() + for i in tqdm.trange(dataloader.iter_number): + example = dataloader.next() + input_data = example[0] + predicts = net.forward(input_data) + # fake update + opt.step(F.const([0.0], [])) + for predict in predicts: + predict.read() + + t1 = time.time() + cost = t1 - t0 + print("Epoch cost: %.3f s." % cost) + + return cost + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--mnn_model", type=str, required=True,\ + help="original float MNN model file") + parser.add_argument("--quant_imgs", type=str, required=True, \ + help="path of quant images") + parser.add_argument("--quant_model", type=str, required=True, \ + help="name of quantized model to save") + parser.add_argument("--batch_size", type=int, required=False, default=32,\ + help="calibration batch size") + + args = parser.parse_args() + + mnn_model = args.mnn_model + quant_imgs = args.quant_imgs + quant_model = args.quant_model + batch_size = args.batch_size + + calibration_dataset = CalibrationDataset(image_folder=quant_imgs) + + dataloader = MNN.data.DataLoader(calibration_dataset, batch_size=batch_size, shuffle=True) + + m = F.load_as_dict(mnn_model) + + inputs_outputs = F.get_inputs_and_outputs(m) + for key in inputs_outputs[0].keys(): + print('input names:\t', key) + for key in inputs_outputs[1].keys(): + print('output names:\t', key) + + # set inputs and outputs + inputs = [m['input']] + outputs = [m['MobilenetV2/Predictions/Reshape_1']] + input_placeholders = [] + for i in range(len(inputs)): + shape = [1, 3, 224, 224] + fmt = 'nchw' + nnn_format = get_mnn_format(fmt) + placeholder = F.placeholder(shape, nnn_format) + placeholder.name = 'input' + input_placeholders.append(placeholder) + + net = nn.load_module(inputs, outputs, True) + + # no use optimizer + opt = MNN.optim.SGD(net, 0.01, 0.9, 0.0005) + + nn.compress.train_quant(net, quant_bits=8) + + used_time = quant_func(net, dataloader, opt) + + # save model + net.train(False) + predicts = net.forward(input_placeholders) + print("quantized model save to " + quant_model) + F.save(predicts, quant_model) + + +if __name__ == "__main__": + main() diff --git a/pymnn/examples/MNNTrain/mnist/train_mnist.py b/pymnn/examples/MNNTrain/mnist/train_mnist.py index e6bf85d2..eb78cc72 100644 --- a/pymnn/examples/MNNTrain/mnist/train_mnist.py +++ b/pymnn/examples/MNNTrain/mnist/train_mnist.py @@ -6,7 +6,6 @@ from dataset import MnistDataset nn = MNN.nn F = MNN.expr - class Net(nn.Module): """construct a lenet 5 model""" def __init__(self): @@ -51,7 +50,6 @@ def test_func(net, test_dataloader): print("test acc: ", correct * 100.0 / test_dataloader.size, "%") - def train_func(net, train_dataloader, opt): """train function""" net.train(True) @@ -99,8 +97,7 @@ def demo(): opt = MNN.optim.SGD(model, 0.01, 0.9, 0.0005) F.set_thread_number(4) - - for epoch in range(0, 10): + for epoch in range(0, 1): opt.learning_rate = learning_rate_scheduler(opt.learning_rate, epoch) train_func(model, train_dataloader, opt) diff --git a/pymnn/src/MNN.cc b/pymnn/src/MNN.cc index f7981d99..a9296f76 100644 --- a/pymnn/src/MNN.cc +++ b/pymnn/src/MNN.cc @@ -204,6 +204,7 @@ static PyObject* PyMNNInterpreter_setSessionHint(PyMNNInterpreter *self, PyObjec static PyObject* PyMNNInterpreter_cache(PyMNNInterpreter *self, PyObject *args); static PyObject* PyMNNInterpreter_removeCache(PyMNNInterpreter *self, PyObject *args); static PyObject* PyMNNInterpreter_updateSessionToModel(PyMNNInterpreter *self, PyObject *args); +static PyObject* PyMNNInterpreter_getModelVersion(PyMNNInterpreter *self, PyObject *args); static PyObject* PyMNNInterpreter_new(struct _typeobject *type, PyObject *args, PyObject *kwds); static int PyMNNInterpreter_init(PyMNNInterpreter *self, PyObject *args, PyObject *kwds); static void PyMNNInterpreter_dealloc(PyMNNInterpreter *); @@ -232,6 +233,7 @@ static PyMethodDef PyMNNInterpreter_methods[] = { {"cache", (PyCFunction)PyMNNInterpreter_cache, METH_VARARGS, "cache current net instance"}, {"removeCache", (PyCFunction)PyMNNInterpreter_removeCache, METH_VARARGS, "remove cache with given path"}, {"updateSessionToModel", (PyCFunction)PyMNNInterpreter_updateSessionToModel, METH_VARARGS, "updateSessionToModel"}, + {"getModelVersion", (PyCFunction)PyMNNInterpreter_getModelVersion, METH_VARARGS, "getModelVersion"}, #ifdef PYMNN_INTERNAL_SERVING {"createSessionWithToken", (PyCFunction)PyMNNInterpreter_createSessionWithToken, METH_VARARGS, "create session with token"}, #endif @@ -1410,6 +1412,10 @@ static PyObject* PyMNNInterpreter_updateSessionToModel(PyMNNInterpreter *self, P Py_RETURN_NONE; } +static PyObject* PyMNNInterpreter_getModelVersion(PyMNNInterpreter *self, PyObject *args) { + return toPyObj(self->interpreter->getModelVersion()); +} + static void PyMNNInterpreter_dealloc(PyMNNInterpreter *self) { if (!self->modelPath) { return; @@ -2522,7 +2528,7 @@ static void PyMNNOpInfo_dealloc(PyMNNOpInfo *self) { } #ifdef PYMNN_TRAIN_API -static PyObject* PyMN_get_model_uuid(PyObject *self, PyObject *args) { +static PyObject* PyMNN_get_model_uuid(PyObject *self, PyObject *args) { const char* modelFile; if (!PyArg_ParseTuple(args, "s", &modelFile)) { printf("PyArg_ParseTuple Error\n"); @@ -2531,11 +2537,15 @@ static PyObject* PyMN_get_model_uuid(PyObject *self, PyObject *args) { return toPyObj(HelperFuncs::getModelUUID(modelFile)); } #endif +static PyObject* PyMNN_version(PyObject *self, PyObject *args) { + return toPyObj(MNN::getVersion()); +} /// module init static PyMethodDef module_methods[] = { #ifdef PYMNN_TRAIN_API - {"get_model_uuid", (PyCFunction)PyMN_get_model_uuid, METH_VARARGS, "get model's uuid"}, + {"get_model_uuid", (PyCFunction)PyMNN_get_model_uuid, METH_VARARGS, "get model's uuid"}, #endif + {"version", (PyCFunction)PyMNN_version, METH_VARARGS, "get MNN version number"}, {NULL, NULL, 0, NULL} }; diff --git a/pymnn/src/compress.h b/pymnn/src/compress.h index a5a6f18f..f2a108c5 100644 --- a/pymnn/src/compress.h +++ b/pymnn/src/compress.h @@ -7,21 +7,23 @@ def_enum(Scale_Update_Method, NN::ScaleUpdateMethod, NN::Maximum, "MAXIMUM", NN::MovingAverage, "MOVING_AVERAGE" ) -static PyObject* PyMNNCompress_train_quant(PyMNNOptimizer *self, PyObject *args) { - PyObject *module, - *feature_scale_method = toPyObj(NN::PerTensor), - *scale_update_method = toPyObj(NN::MovingAverage); +static PyObject* PyMNNCompress_train_quant(PyMNNOptimizer *self, PyObject *args, PyObject *kwargs) { + PyObject *module = nullptr, + *feature_scale_method = nullptr /* PerTensor */, + *scale_update_method = nullptr /* MovingAverage */; int quant_bits = 8; - if (!PyArg_ParseTuple(args, "O|iOO", &module, &quant_bits, - &feature_scale_method, &scale_update_method)) { - Py_RETURN_NONE; + static char *kwlist[] = { "module", "quant_bits", "feature_scale_method", "scale_update_method", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|iOO", kwlist, &module, &quant_bits, &feature_scale_method, &scale_update_method)) { + PyMNN_ERROR("train_quant require args: (Module, |int, Feature_Scale_Method, Scale_Update_Method)"); } - auto feature_scale_method_ = toEnum(feature_scale_method); - auto scale_update_method_ = toEnum(scale_update_method); - return toPyObj(NN::turnQuantize(to_Module(module), quant_bits, feature_scale_method_, scale_update_method_)); + auto feature_scale_method_ = feature_scale_method == nullptr ? NN::PerTensor : + toEnum(feature_scale_method); + auto scale_update_method_ = scale_update_method == nullptr ? NN::MovingAverage : + toEnum(scale_update_method); + return toPyObj(NN::turnQuantize(to_Module(module)->get(), quant_bits, feature_scale_method_, scale_update_method_)); } static PyMethodDef PyMNNCompress_methods[] = { - register_methods(Compress, + register_methods_kw(Compress, train_quant, "train_quant" ) }; diff --git a/pymnn/src/data.h b/pymnn/src/data.h index 05853166..70e6e1a8 100644 --- a/pymnn/src/data.h +++ b/pymnn/src/data.h @@ -7,26 +7,118 @@ static PyObject* toPyObj(Example example) { PyList_SetItem(ret, 1, toPyObj(example.second)); return ret; } -def_class_start(Dataset, Dataset) -def_class_without_getset(Dataset) -def_class_methods(Dataset, - __getitem__, "get item: []", - __len__, "get length: len()" -) -def_class_end(Dataset, Dataset) -// class DataSet impl -class_basic_new_impl(Dataset) -static PyObject* PyMNNDataset___getitem__(PyMNNDataset *self, PyObject *args) { - int index; - if (!PyArg_ParseTuple(args, "i", &index)) { - Py_RETURN_NONE; +class DatasetWrapper : public Dataset { +public: + using Dataset::Dataset; + DatasetWrapper(PyObject* py_dataset) { + Py_INCREF(py_dataset); + this->py_dataset = py_dataset; } - return toPyObj(self->ptr->get(index)); + ~DatasetWrapper() { + if (py_dataset) { + Py_DECREF(py_dataset); + } + } + Example get(size_t index) override { + auto getfunc = PyObject_GetAttrString(py_dataset, "__getitem__"); + auto arg = PyTuple_New(1); + PyTuple_SetItem(arg, 0, PyLong_FromLong(index)); + auto res = PyEval_CallObject(getfunc, arg); + Py_DECREF(arg); + Py_DECREF(getfunc); + // res to Example + auto py_example = PyTuple_GetItem(res, 0); + auto py_example_second = PyTuple_GetItem(res, 1); + auto example = std::make_pair( + toVars(py_example), + toVars(py_example_second) + ); + Py_DECREF(res); + return example; + } + size_t size() override { + auto sizefunc = PyObject_GetAttrString(py_dataset, "__len__"); + auto res = PyEval_CallObject(sizefunc, NULL); + Py_DECREF(sizefunc); + auto size = toInt(res); + Py_DECREF(res); + return size; + } +private: + PyObject *py_dataset = nullptr; +}; + +typedef struct { + PyObject_HEAD + std::shared_ptr* ptr; +} PyMNNDataset; + +static PyObject* PyMNNDataset_new(struct _typeobject *type, PyObject *args, PyObject *kwds) { + PyMNNDataset* self = (PyMNNDataset *)type->tp_alloc(type, 0); + return (PyObject*)self; } -static PyObject* PyMNNDataset___len__(PyMNNDataset *self, PyObject *args) { - return toPyObj((int)self->ptr->size()); + +static int PyMNNDataset_init(PyMNNDataset *self, PyObject *args, PyObject *kwds) { + self->ptr = new std::shared_ptr(new DatasetWrapper((PyObject*)self)); + return 0; } +static void PyMNNDataset_dealloc(PyMNNDataset *self) { + if (self->ptr) { + // delete self->ptr; + self->ptr->reset(); + } + Py_TYPE(self)->tp_free((PyObject *)self); +} + +static PyTypeObject PyMNNDatasetType = { + PyVarObject_HEAD_INIT(NULL, 0) + "Dataset", /*tp_name*/ + sizeof(PyMNNDataset), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + (destructor)PyMNNDataset_dealloc, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + 0, /*tp_compare*/ + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash */ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + // Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HEAPTYPE, /*tp_flags*/ + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/ + "Dataset", /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ + 0, /* tp_iter */ + 0, /* tp_iternext */ + 0, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + (initproc)PyMNNDataset_init, /* tp_init */ + 0, /* tp_alloc */ + PyMNNDataset_new, /* tp_new */ +}; + +static std::shared_ptr toDataset(PyObject* m) { + return *((PyMNNDataset*)m)->ptr; +} + +def_class_register(Dataset) + // class DataLoader def def_class_start(DataLoader, DataLoader) def_class_getset( @@ -40,14 +132,17 @@ def_class_methods(DataLoader, ) def_class_end(DataLoader, DataLoader) // class DataLoader impl -static PyObject* PyMNNDataLoader_new(PyTypeObject *type, PyObject *args, PyObject *kwds) { - PyObject* dataset; +class_basic_call_impl(DataLoader) +class_basic_init_impl(DataLoader) +static PyObject* PyMNNDataLoader_new(PyTypeObject *type, PyObject *args, PyObject *kwargs) { + PyObject* dataset = nullptr; int batch_size, num_workers = 0; int shuffle = 1; - if (!PyArg_ParseTuple(args, "Oi|ii", &dataset, &batch_size, &shuffle, &num_workers)) { - Py_RETURN_NONE; + static char *kwlist[] = { "dataset", "batch_size", "shuffle", "num_workers", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "Oi|ii", kwlist, &dataset, &batch_size, &shuffle, &num_workers)) { + PyMNN_ERROR("DataLoader require args: Dataset, int, |int, int)"); } - std::shared_ptr dataset_(toDataset(dataset)); + std::shared_ptr dataset_ = std::move(toDataset(dataset)); PyMNNDataLoader *self = (PyMNNDataLoader *)type->tp_alloc(type, 0); self->ptr = DataLoader::makeDataLoader(dataset_, batch_size, true, shuffle, num_workers); return (PyObject*)self; diff --git a/pymnn/src/expr.h b/pymnn/src/expr.h index a408af15..a262bca8 100644 --- a/pymnn/src/expr.h +++ b/pymnn/src/expr.h @@ -630,8 +630,11 @@ static PyObject* PyMNNVar_repr(PyObject *self) { #else auto content = PyMNNVar_read_as_tuple((PyMNNVar*)self, NULL); #endif - auto repr = PyObject_GetAttrString(content, "__repr__"); - return PyEval_CallObject(repr, NULL); + auto reprfunc = PyObject_GetAttrString(content, "__repr__"); + auto str = PyEval_CallObject(reprfunc, NULL); + Py_DECREF(content); + Py_DECREF(reprfunc); + return str; } // PyMNNVar getter/setter functions impl static PyObject* PyMNNVar_getshape(PyMNNVar *self, void *closure) { @@ -921,6 +924,22 @@ static PyObject* PyMNNVar_write(PyMNNVar *self, PyObject *args) { Py_RETURN_NONE; } // Expr methods +static PyObject* PyMNNExpr_set_thread_number(PyObject *self, PyObject *args) { + int numberThread; + if (!PyArg_ParseTuple(args, "i", &numberThread)) { + Py_RETURN_NONE; + } + if (numberThread < 1) { + numberThread = 1; + } + if (numberThread > 8) { + numberThread = 8; + } + auto exe = Executor::getGlobalExecutor(); + BackendConfig config; + exe->setGlobalExecutorConfig(MNN_FORWARD_CPU, config, numberThread); + Py_RETURN_NONE; +} static PyObject* PyMNNExpr_load_as_list(PyObject *self, PyObject *args) { const char *fileName; if (!PyArg_ParseTuple(args, "s", &fileName)) { @@ -1558,6 +1577,7 @@ static PyMethodDef PyMNNExpr_methods[] = { ) register_methods(Expr, // Var methods + set_thread_number, "set threan number of expr.", load_as_list, "load file as var list.", save, "save vars to file.", load_as_dict, "load file as var dict.", diff --git a/pymnn/src/nn.h b/pymnn/src/nn.h index 2aaaa939..3f2cf9a1 100644 --- a/pymnn/src/nn.h +++ b/pymnn/src/nn.h @@ -11,7 +11,7 @@ #endif // NN Module Start -def_class_start(_Module, Module) +def_class_smart_start(_Module, Module) def_class_getset( _Module, name, 0, @@ -28,7 +28,7 @@ def_class_methods(_Module, _register_submodules, "register submodules", _add_parameter, "add parameter" ) -def_class_end(_Module, Module) +def_class_smart_end(_Module, Module) // NN RuntimeManager Start def_class_smart_start(RuntimeManager, Executor::RuntimeManager) @@ -40,6 +40,7 @@ def_class_methods(RuntimeManager, ) def_class_without_getset(RuntimeManager) def_class_smart_end(RuntimeManager, Executor::RuntimeManager) +class_basic_call_impl(RuntimeManager) static PyObject* load_module(PyObject *runtime_manager, PyObject *inputs, PyObject *outputs, MNNForwardType backend, MemoryMode memory_mode, PowerMode power_mode, PrecisionMode precision_mode, @@ -84,9 +85,10 @@ static PyObject* load_module(PyObject *runtime_manager, PyObject *inputs, PyObje return toPyObj(m_ptr); } +class_basic_init_impl(_Module) static PyObject* PyMNN_Module_new(PyTypeObject *type, PyObject *args, PyObject *kwds) { PyMNN_Module *self = (PyMNN_Module *)type->tp_alloc(type, 0); - self->ptr = Module::createEmpty({}); + self->ptr = new std::shared_ptr(Module::createEmpty({})); return (PyObject*)self; } @@ -99,19 +101,19 @@ static PyObject* PyMNNRuntimeManager_new(PyTypeObject *type, PyObject *args, PyO // PyMNN_Module getter/setter impl static PyObject* PyMNN_Module_getname(PyMNN_Module *self, void *closure) { if (self->ptr) { - return toPyObj(self->ptr->name()); + return toPyObj((*(self->ptr))->name()); } Py_RETURN_NONE; } static PyObject* PyMNN_Module_getis_training(PyMNN_Module *self, void *closure) { if (self->ptr) { - return toPyObj(self->ptr->getIsTraining()); + return toPyObj((*(self->ptr))->getIsTraining()); } Py_RETURN_NONE; } static PyObject* PyMNN_Module_getparameters(PyMNN_Module *self, void *closure) { if (self->ptr) { - return toPyObj(self->ptr->parameters()); + return toPyObj((*(self->ptr))->parameters()); } Py_RETURN_NONE; } @@ -134,7 +136,7 @@ static PyObject* PyMNN_Module_forward(PyMNN_Module *self, PyObject *args) { (void) MonitorService::GetInstance().EventTrack(self->ptr, timer, status, "PyMNN_Module_forward"); return toPyObj(vars); #else - return toPyObj(self->ptr->onForward(toVars(input))); + return toPyObj((*(self->ptr))->onForward(toVars(input))); #endif } if (isVar(input)) { @@ -145,7 +147,7 @@ static PyObject* PyMNN_Module_forward(PyMNN_Module *self, PyObject *args) { (void) MonitorService::GetInstance().EventTrack(self->ptr, timer, status, "PyMNN_Module_forward"); return toPyObj(var); #else - return toPyObj(self->ptr->forward(toVar(input))); + return toPyObj((*(self->ptr))->forward(toVar(input))); #endif } PyMNN_ERROR("PyMNN_Module_forward: args must be Var/[Var]."); @@ -158,25 +160,29 @@ static PyObject* PyMNN_Module_onForward(PyMNN_Module *self, PyObject *args) { #ifdef PYMNN_INTERNAL_SERVING int status = 0; Timer timer; - auto vars = self->ptr->onForward(toVars(inputs)); + auto vars = (*(self->ptr))->onForward(toVars(inputs)); if (vars.empty()) { PyMNN_ERROR("module onForward occur error."); status = -1; } - (void) MonitorService::GetInstance().EventTrack(self->ptr, timer, status, "PyMNN_Module_onForward"); + (void) MonitorService::GetInstance().EventTrack(self->ptr->get(), timer, status, "PyMNN_Module_onForward"); return toPyObj(vars); #else - return toPyObj(self->ptr->onForward(toVars(inputs))); + return toPyObj((*(self->ptr))->onForward(toVars(inputs))); #endif } +static PyObject* PyMNN_Module_call(PyObject *self, PyObject *args, PyObject *kwds) { + return PyMNN_Module_forward((PyMNN_Module*)self, args); +} + static PyObject* PyMNN_Module_set_name(PyMNN_Module *self, PyObject *args) { const char* name; if (!PyArg_ParseTuple(args, "s", &name)) { Py_RETURN_NONE; } - self->ptr->setName(name); + (*(self->ptr))->setName(name); Py_RETURN_NONE; } static PyObject* PyMNN_Module_train(PyMNN_Module *self, PyObject *args) { @@ -184,7 +190,7 @@ static PyObject* PyMNN_Module_train(PyMNN_Module *self, PyObject *args) { if (!PyArg_ParseTuple(args, "i", &isTraining)) { Py_RETURN_NONE; } - self->ptr->setIsTraining(isTraining); + (*(self->ptr))->setIsTraining(isTraining); Py_RETURN_NONE; } static PyObject* PyMNN_Module_load_parameters(PyMNN_Module *self, PyObject *args) { @@ -192,21 +198,21 @@ static PyObject* PyMNN_Module_load_parameters(PyMNN_Module *self, PyObject *args if (!PyArg_ParseTuple(args, "O", ¶meters)) { Py_RETURN_NONE; } - return toPyObj(self->ptr->loadParameters(toVars(parameters))); + return toPyObj((*(self->ptr))->loadParameters(toVars(parameters))); } static PyObject* PyMNN_Module_clear_cache(PyMNN_Module *self, PyObject *args) { - self->ptr->clearCache(); + (*(self->ptr))->clearCache(); Py_RETURN_NONE; } std::shared_ptr toSharedModule(PyObject* obj) { - return std::shared_ptr(to_Module(obj), [](Module*){}); + return *to_Module(obj); } static PyObject* PyMNN_Module__register_submodules(PyMNN_Module *self, PyObject *args) { PyObject *children; if (!PyArg_ParseTuple(args, "O", &children)) { Py_RETURN_NONE; } - self->ptr->registerModel(toVec, toSharedModule>(children)); + (*(self->ptr))->registerModel(toVec, toSharedModule>(children)); Py_RETURN_NONE; } static PyObject* PyMNN_Module__add_parameter(PyMNN_Module *self, PyObject *args) { @@ -214,7 +220,7 @@ static PyObject* PyMNN_Module__add_parameter(PyMNN_Module *self, PyObject *args) if (!PyArg_ParseTuple(args, "O", ¶meter)) { Py_RETURN_NONE; } - return toPyObj(self->ptr->addParameter(toVar(parameter))); + return toPyObj((*(self->ptr))->addParameter(toVar(parameter))); } // NN methods static PyObject* PyMNNNN_load_module(PyObject *self, PyObject *args) { @@ -465,7 +471,7 @@ static PyObject* PyMNNNN_load_module_from_file_with_token(PyObject *self, PyObje #endif #ifdef PYMNN_TRAIN_API -static PyObject* PyMNNNN_conv(PyObject *self, PyObject *args) { +static PyObject* PyMNNNN_conv(PyObject *self, PyObject *args, PyObject* kwargs) { INTS default_1 = {1, 1}, default_0 = {0, 0}; int in_channel, out_channel; PyObject *kernel_size, @@ -474,10 +480,11 @@ static PyObject* PyMNNNN_conv(PyObject *self, PyObject *args) { *dilation = nullptr /* default_1 */, *padding_mode = nullptr /* PaddingMode::VALID */; int depthwise = 0, bias = 1; - if (!PyArg_ParseTuple(args, "iiO|OOOiiO", &in_channel, &out_channel, &kernel_size, + static char *kwlist[] = { "in_channels", "out_channels", "kernel_size", "stride", "padding", + "dilation", "depthwise", "bias", "padding_mode", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "iiO|OOOiiO", kwlist, &in_channel, &out_channel, &kernel_size, &stride, &padding, &dilation, &depthwise, &bias, &padding_mode)) { - printf("PyArg_ParseTuple Error\n"); - return NULL; + PyMNN_ERROR("conv require args: int, int, [int], |[int], [int], [int], bool, bool, PaddingMode)"); } NN::ConvOption option; option.channel = {in_channel, out_channel}; @@ -498,29 +505,29 @@ static PyObject* PyMNNNN_conv(PyObject *self, PyObject *args) { option.depthwise = depthwise; return toPyObj(NN::Conv(std::move(option), bias)); } -static PyObject* PyMNNNN_linear(PyObject *self, PyObject *args) { +static PyObject* PyMNNNN_linear(PyObject *self, PyObject *args, PyObject* kwargs) { int in_channel, out_channel; int bias = 1; - if (!PyArg_ParseTuple(args, "ii|i", &in_channel, &out_channel, &bias)) { - printf("PyArg_ParseTuple Error\n"); - return NULL; + static char *kwlist[] = { "in_channels", "out_channels", "bias", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "ii|i", kwlist, &in_channel, &out_channel, &bias)) { + PyMNN_ERROR("linear require args: int, int, |bool)"); } return toPyObj(NN::Linear(in_channel, out_channel, bias)); } -static PyObject* PyMNNNN_batch_norm(PyObject *self, PyObject *args) { +static PyObject* PyMNNNN_batch_norm(PyObject *self, PyObject *args, PyObject* kwargs) { int channels, dims = 4; float momentum = 0.99, epsilon = 1e-5; - if (!PyArg_ParseTuple(args, "i|iff", &channels, &dims, &momentum, &epsilon)) { - printf("PyArg_ParseTuple Error\n"); - return NULL; + static char *kwlist[] = { "channels", "dims", "momentum", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "i|iff", kwlist, &channels, &dims, &momentum, &epsilon)) { + PyMNN_ERROR("batch_norm require args: int, |int, float, float)"); } return toPyObj(NN::BatchNorm(channels, dims, momentum, epsilon)); } -static PyObject* PyMNNNN_dropout(PyObject *self, PyObject *args) { +static PyObject* PyMNNNN_dropout(PyObject *self, PyObject *args, PyObject* kwargs) { float dropout_ratio; - if (!PyArg_ParseTuple(args, "f", &dropout_ratio)) { - printf("PyArg_ParseTuple Error\n"); - return NULL; + static char *kwlist[] = { "dropout_ratio", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "f", kwlist, &dropout_ratio)) { + PyMNN_ERROR("dropout require args: float)"); } return toPyObj(NN::Dropout(dropout_ratio)); } @@ -540,7 +547,7 @@ static PyMethodDef PyMNNNN_methods[] = { ) #endif #ifdef PYMNN_TRAIN_API - register_methods(NN, + register_methods_kw(NN, conv, "conv Module", linear, "linear Module", batch_norm, "batch_norm Module", diff --git a/pymnn/src/optim.h b/pymnn/src/optim.h index ec4cda62..699ca4f8 100644 --- a/pymnn/src/optim.h +++ b/pymnn/src/optim.h @@ -23,6 +23,8 @@ def_class_methods(Optimizer, def_class_end(Optimizer, ParameterOptimizer) // impl class_basic_new_impl(Optimizer) +class_basic_init_impl(Optimizer) +class_basic_call_impl(Optimizer) // PyMNNOptimizer getter/setter functions impl static PyObject* PyMNNOptimizer_getlearning_rate(PyMNNOptimizer *self, void *closure) { if (self->ptr) { @@ -110,33 +112,35 @@ static PyObject* PyMNNOptimizer_step(PyMNNOptimizer *self, PyObject *args) { } return toPyObj(self->ptr->step(toVar(loss))); } -static PyObject* PyMNNOptim_SGD(PyObject *self, PyObject *args) { - PyObject *module, *method = toPyObj(RegularizationMethod::L2); +static PyObject* PyMNNOptim_SGD(PyObject *self, PyObject *args, PyObject *kwargs) { + PyObject *module = nullptr, *method = nullptr /* L2 */; float learning_rate = 1e-3, momentum = 0.9, weight_decay = 0.0; - if (!PyArg_ParseTuple(args, "O|fffO", &module, &learning_rate, + static char *kwlist[] = { "module", "learning_rate", "momentum", "weight_decay", "regularization_method", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|fffO", kwlist, &module, &learning_rate, &momentum, &weight_decay, &method)) { - return NULL; + PyMNN_ERROR("SGD require args: Module, |float, float, float, RegularizationMethod)"); } - auto method_ = toEnum(method); - std::shared_ptr m(to_Module(module)); + auto method_ = method == nullptr ? RegularizationMethod::L2 : toEnum(method); + std::shared_ptr m = *to_Module(module); return toPyObj(ParameterOptimizer::createSGD(m, learning_rate, momentum, weight_decay, method_)); } -static PyObject* PyMNNOptim_ADAM(PyObject *self, PyObject *args) { - PyObject *module, *method = toPyObj(RegularizationMethod::L2); +static PyObject* PyMNNOptim_ADAM(PyObject *self, PyObject *args, PyObject *kwargs) { + PyObject *module = nullptr, *method = nullptr /* L2 */; float learning_rate = 1e-3, momentum = 0.9, momentum2 = 0.999, weight_decay = 0.0, eps = 1e-8; - if (!PyArg_ParseTuple(args, "O|fffffO", &module, &learning_rate, &momentum, + static char *kwlist[] = { "module", "learning_rate", "momentum", "momentum2", "weight_decay", "eps", "regularization_method", NULL }; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|fffffO", kwlist, &module, &learning_rate, &momentum, &momentum2, &weight_decay, &eps, &method)) { - return NULL; + PyMNN_ERROR("ADAM require args: Module, |float, float, float, float, float, RegularizationMethod)"); } - auto method_ = toEnum(method); - std::shared_ptr m(to_Module(module)); + auto method_ = method == nullptr ? RegularizationMethod::L2 : toEnum(method); + std::shared_ptr m = *to_Module(module); return toPyObj(ParameterOptimizer::createADAM(m, learning_rate, momentum, momentum2, weight_decay, eps, method_)); } static PyMethodDef PyMNNOptim_methods[] = { - register_methods(Optim, + register_methods_kw(Optim, SGD, "SGD Optimizer", ADAM, "ADAM Optimizer" ) diff --git a/pymnn/src/util.h b/pymnn/src/util.h index e3fc998d..251724a5 100644 --- a/pymnn/src/util.h +++ b/pymnn/src/util.h @@ -1037,6 +1037,8 @@ static PyMethodDef PyMNN##NAME##_methods[] = { \ }; #define def_class_end(NAME, TYPE) \ static PyObject* PyMNN##NAME##_new(PyTypeObject *type, PyObject *args, PyObject *kwds); \ +static int PyMNN##NAME##_init(PyTypeObject *self, PyObject *args, PyObject *kwds); \ +static PyObject* PyMNN##NAME##_call(PyObject *self, PyObject *args, PyObject *kwds); \ static void PyMNN##NAME##_dealloc(PyMNN##NAME *self) { \ if (self->ptr) { \ delete self->ptr; \ @@ -1058,7 +1060,7 @@ static PyTypeObject PyMNN##NAME##Type = { \ 0, /*tp_as_sequence*/\ 0, /*tp_as_mapping*/\ 0, /*tp_hash*/\ - 0, /*tp_call*/\ + PyMNN##NAME##_call, /*tp_call*/\ 0, /*tp_str*/\ 0, /*tp_getattro*/\ 0, /*tp_setattro*/\ @@ -1079,7 +1081,7 @@ static PyTypeObject PyMNN##NAME##Type = { \ 0, /*tp_descr_get*/\ 0, /*tp_descr_set*/\ 0, /*tp_dictoffset*/\ - 0, /*tp_init*/\ + (initproc)PyMNN##NAME##_init, /*tp_init*/\ 0, /*tp_alloc*/\ PyMNN##NAME##_new /*tp_new*/\ };\ @@ -1107,6 +1109,14 @@ static PyObject* PyMNN##NAME##_new(PyTypeObject *type, PyObject *args, PyObject PyMNN##NAME *self = (PyMNN##NAME *)type->tp_alloc(type, 0); \ return (PyObject*)self; \ } +#define class_basic_init_impl(NAME) \ +static int PyMNN##NAME##_init(PyTypeObject *self, PyObject *args, PyObject *kwds) { \ + return 0; \ +} +#define class_basic_call_impl(NAME) \ +static PyObject* PyMNN##NAME##_call(PyObject *self, PyObject *args, PyObject *kwds) { \ + return (PyObject*)self; \ +} // ------------------------ class start ------------------------ // ------------------------ capsule start ------------------------ @@ -1118,6 +1128,7 @@ typedef struct { \ } PyMNN##NAME; #define def_class_smart_end(NAME, TYPE) \ static PyObject* PyMNN##NAME##_new(PyTypeObject *type, PyObject *args, PyObject *kwds); \ +static PyObject* PyMNN##NAME##_call(PyObject *self, PyObject *args, PyObject *kwds); \ static void PyMNN##NAME##_dealloc(PyMNN##NAME *self) { \ Py_TYPE(self)->tp_free((PyObject *) self); \ } \ @@ -1136,7 +1147,7 @@ static PyTypeObject PyMNN##NAME##Type = { \ 0, /*tp_as_sequence*/\ 0, /*tp_as_mapping*/\ 0, /*tp_hash*/\ - 0, /*tp_call*/\ + PyMNN##NAME##_call, /*tp_call*/\ 0, /*tp_str*/\ 0, /*tp_getattro*/\ 0, /*tp_setattro*/\ diff --git a/pymnn/test/README.md b/pymnn/test/README.md index 253b64ed..08b4a2d2 100644 --- a/pymnn/test/README.md +++ b/pymnn/test/README.md @@ -1,4 +1,38 @@ -拷贝AliNNModel所有模型和测试数据到MNN工作台工程下 +# Pymnn Test Cases + +# 1. Unit Test +```bash +python3 unit_test.py +``` + +# 2. Model Test +```bash +python3 model_test.py ~/AliNNModel +``` + +# 3. Train Test +```bash +./train_test.sh +``` + +# 4. Quant Test +```bash +python3 ../examples/MNNQuant/test_mnn_offline_quant.py \ + --mnn_model your_model.mnn \ + --quant_imgs \ + --quant_model ./quant_model.mnn +``` + +# 5. Benchmark MNN.numpy +```bash +pip install prettytable +python3 benchmark.py +``` + +# 6. Playgroud Test (just internal usage) +```bash +# 拷贝AliNNModel所有模型和测试数据到MNN工作台工程下 python scripts/pullTestModel.py --alinnmodel_path ../../../AliNNModel --playground_path playground/playground -拷贝AliNNModel指定模型（mobilenet, Ranfa）和测试数据到MNN工作台工程下 +# 拷贝AliNNModel指定模型（mobilenet, Ranfa）和测试数据到MNN工作台工程下 python scripts/pullTestModel.py --alinnmodel_path ../../../AliNNModel --playground_path playground/playground --models mobilenet Ranfa +``` diff --git a/pymnn/test/benchmark.py b/pymnn/test/benchmark.py new file mode 100644 index 00000000..4f9a04e4 --- /dev/null +++ b/pymnn/test/benchmark.py @@ -0,0 +1,143 @@ +# -*- coding: UTF-8 -*- +import os +os.environ["MKL_NUM_THREADS"] = "1" +os.environ["NUMEXPR_NUM_THREADS"] = "1" +os.environ["OMP_NUM_THREADS"] = "1" +import time +import MNN.numpy as mp +import numpy as np +from prettytable import PrettyTable + +res = PrettyTable() +res.field_names = ["function", "numpy", "MNN.numpy"] +x = {'shape':[64000],'dtype':'float32'} + +def gen_data(args): + np_args = [] + mp_args = [] + for arg in args: + if type(arg) == type({'a':1}): + shape = arg['shape'] + dtype = arg['dtype'] + np_x = np.random.rand(*shape).astype(dtype) + mp_x = mp.random.random(shape).astype(getattr(mp, dtype)) + mp_x.fix_as_const() + else: + np_x = arg + mp_x = arg + np_args.append(np_x) + mp_args.append(mp_x) + return np_args, mp_args + +def np_eval(func, args, loop, mode): + if mode == 3: + t1 = time.time() + for i in range(loop): + np_res = func(args) + np_res.__str__() + t2 = time.time() + else: + t1 = time.time() + for i in range(loop): + np_res = func(*args) + np_res.__str__() + t2 = time.time() + return round((t2 - t1) * 1000 / loop, 3) + +def mnn_eval(func, args, loop, mode): + if mode == 0: + t1 = time.time() + for i in range(loop): + mp_res = func(*args) + mp_res.__str__() + t2 = time.time() + elif mode == 1: + t1 = time.time() + for i in range(loop): + mp_res = func(*args) + t2 = time.time() + elif mode == 2: + t1 = time.time() + for i in range(loop): + mp_res = func(*args) + for r in mp_res: r.__str__() + t2 = time.time() + elif mode == 3: + t1 = time.time() + for i in range(loop): + mp_res = func(args) + for r in mp_res: r.__str__() + t2 = time.time() + return round((t2 - t1) * 1000 / loop, 3) + +def bench_funcs(funcs, args, mode=0): + loop = 10 + np_args, mp_args = gen_data(args) + for func in funcs: + np_func = getattr(np, func) + mp_func = getattr(mp, func) + np_time = np_eval(np_func, np_args, loop, mode) + mp_time = mnn_eval(mp_func, mp_args, loop, mode) + # np_sum += np_time + # mp_sum += mp_time + # count += 1 + res.add_row([func, np_time, mp_time]) + +def unary(): + inputs = [x] + maths = ['sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh', 'around', + 'floor', 'ceil', 'trunc', 'exp', 'expm1', 'exp2', 'log', 'log2', 'log10', 'log1p', 'sinc', 'signbit', 'positive', 'cbrt', + 'negative', 'reciprocal', 'sqrt', 'cbrt', 'square', 'sign', 'argwhere', 'flatnonzero', 'sort', 'argsort', 'copy'] + bench_funcs(maths, inputs) + bench_funcs(['modf'], inputs, 2) + +def binary(): + inputs = [x] * 2 + funcs = ['greater', 'greater_equal', 'less', 'less_equal', 'equal', 'not_equal', 'multiply', 'add', 'divide', 'power', + 'subtract', 'true_divide', 'floor_divide', 'mod', 'maximum', 'minimum', 'hypot', 'logaddexp', 'logaddexp2', + 'copysign' ] + bench_funcs(funcs, inputs) + bench_funcs(['divmod'], inputs, 2) + bench_funcs(['ldexp'], [x, 2], 1) + bench_funcs(['dot', 'vdot', 'inner', 'matmul'], [{'shape':[1024, 1024], 'dtype':'float32'}]*2) + bench_funcs(['array_equal', 'array_equiv'], [x, x], 1) + bench_funcs(['bitwise_and', 'bitwise_or', 'bitwise_xor'], [{'shape':[64000], 'dtype':'int32'}]*2) + bench_funcs(['where'], [{'shape':[64000], 'dtype':'int32'}, x, x]) + +def reduce(): + inputs = [x] + reduce = ['prod', 'sum', 'argmax', 'nonzero', 'count_nonzero', 'max', 'min', 'ptp', 'mean', 'var', 'std'] + bench_funcs(reduce, inputs, 1) + bench_funcs(['all', 'any'], [{'shape':[64000], 'dtype':'int32'}], 1) + +def memory(): + y = {'shape':[4, 16, 10, 100],'dtype':'float32'} + bench_funcs(['reshape'], [y, [10, 64, 100]]) + bench_funcs(['ravel', 'transpose', 'atleast_1d', 'atleast_2d', 'atleast_3d', 'squeeze'], [y]) + bench_funcs(['moveaxis', 'rollaxis', 'swapaxes'], [y, 0, 3]) + bench_funcs(['broadcast_to'], [y, [3, 4, 16, 10, 100]]) + bench_funcs(['expand_dims'], [y, 0]) + bench_funcs(['concatenate', 'stack', 'vstack', 'hstack', 'dstack', 'column_stack', 'row_stack'], [y, y], 3) + bench_funcs(['split', 'dsplit', 'hsplit', 'vsplit'], [y, 2], 2) + bench_funcs(['pad'], [x, 2]) + bench_funcs(['tile', 'repeat'], [x, 2]) + +def all(): + unary() + binary() + reduce() + memory() + +def log(): + np_sum = 0 + mp_sum = 0 + count = len(res.rows) + for row in res.rows: + np_sum += row[1] + mp_sum += row[2] + res.add_row(['avg', round(np_sum/count, 3), round(mp_sum/count, 3)]) + print(res) + +if __name__ == '__main__': + all() + log() diff --git a/pymnn/test/train_test.sh b/pymnn/test/train_test.sh new file mode 100755 index 00000000..4e4a12fd --- /dev/null +++ b/pymnn/test/train_test.sh @@ -0,0 +1,4 @@ +python3 ../examples/MNNTrain/mnist/train_mnist.py +rm ./0.mnist.mnn +train_wrong=$[$? > 0] +printf "TEST_NAME_TRAIN_TEST: pymnn训练测试\nTEST_CASE_AMOUNT_TRAIN_TEST: {\"blocked\":0,\"failed\":%d,\"passed\":%d,\"skipped\":0}\n" $train_wrong $[1 - $train_wrong] diff --git a/schema/current/ExtraInfo_generated.h b/schema/current/ExtraInfo_generated.h index df408ed7..6ab3a97e 100644 --- a/schema/current/ExtraInfo_generated.h +++ b/schema/current/ExtraInfo_generated.h @@ -19,6 +19,7 @@ struct ExtraInfoT : public flatbuffers::NativeTable { typedef ExtraInfo TableType; std::vector buffer; std::string name; + std::string version; ExtraInfoT() { } }; @@ -34,12 +35,17 @@ struct ExtraInfo FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const flatbuffers::String *name() const { return GetPointer(6); } + const flatbuffers::String *version() const { + return GetPointer(8); + } bool Verify(flatbuffers::Verifier &verifier) const { return VerifyTableStart(verifier) && VerifyOffset(verifier, 4) && verifier.VerifyVector(buffer()) && VerifyOffset(verifier, 6) && verifier.VerifyString(name()) && + VerifyOffset(verifier, 8) && + verifier.VerifyString(version()) && verifier.EndTable(); } ExtraInfoT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; @@ -56,6 +62,9 @@ struct ExtraInfoBuilder { void add_name(flatbuffers::Offset name) { fbb_.AddOffset(6, name); } + void add_version(flatbuffers::Offset version) { + fbb_.AddOffset(8, version); + } explicit ExtraInfoBuilder(flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb) { start_ = fbb_.StartTable(); @@ -71,8 +80,10 @@ struct ExtraInfoBuilder { inline flatbuffers::Offset CreateExtraInfo( flatbuffers::FlatBufferBuilder &_fbb, flatbuffers::Offset> buffer = 0, - flatbuffers::Offset name = 0) { + flatbuffers::Offset name = 0, + flatbuffers::Offset version = 0) { ExtraInfoBuilder builder_(_fbb); + builder_.add_version(version); builder_.add_name(name); builder_.add_buffer(buffer); return builder_.Finish(); @@ -91,6 +102,7 @@ inline void ExtraInfo::UnPackTo(ExtraInfoT *_o, const flatbuffers::resolver_func (void)_resolver; { auto _e = buffer(); if (_e) { _o->buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->buffer[_i] = _e->Get(_i); } } }; { auto _e = name(); if (_e) _o->name = _e->str(); }; + { auto _e = version(); if (_e) _o->version = _e->str(); }; } inline flatbuffers::Offset ExtraInfo::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExtraInfoT* _o, const flatbuffers::rehasher_function_t *_rehasher) { @@ -103,23 +115,27 @@ inline flatbuffers::Offset CreateExtraInfo(flatbuffers::FlatBufferBui struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExtraInfoT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; auto _buffer = _o->buffer.size() ? _fbb.CreateVector(_o->buffer) : 0; auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name); + auto _version = _o->version.empty() ? 0 : _fbb.CreateString(_o->version); return MNN::CreateExtraInfo( _fbb, _buffer, - _name); + _name, + _version); } inline const flatbuffers::TypeTable *ExtraInfoTypeTable() { static const flatbuffers::TypeCode type_codes[] = { { flatbuffers::ET_CHAR, 1, -1 }, + { flatbuffers::ET_STRING, 0, -1 }, { flatbuffers::ET_STRING, 0, -1 } }; static const char * const names[] = { "buffer", - "name" + "name", + "version" }; static const flatbuffers::TypeTable tt = { - flatbuffers::ST_TABLE, 2, type_codes, nullptr, nullptr, names + flatbuffers::ST_TABLE, 3, type_codes, nullptr, nullptr, names }; return &tt; } diff --git a/schema/current/MNN_generated.h b/schema/current/MNN_generated.h index 565217a8..9efda016 100644 --- a/schema/current/MNN_generated.h +++ b/schema/current/MNN_generated.h @@ -230,6 +230,7 @@ enum OpType { OpType_EyeLike = 147, OpType_CumSum = 148, OpType_Det = 149, + OpType_CumProd = 150, OpType_Plugin = 256, OpType_Select = 257, OpType_ZerosLike = 258, @@ -258,7 +259,7 @@ enum OpType { OpType_MAX = OpType_GridSample }; -inline const OpType (&EnumValuesOpType())[168] { +inline const OpType (&EnumValuesOpType())[169] { static const OpType values[] = { OpType_AbsVal, OpType_QuantizedAdd, @@ -404,6 +405,7 @@ inline const OpType (&EnumValuesOpType())[168] { OpType_EyeLike, OpType_CumSum, OpType_Det, + OpType_CumProd, OpType_Plugin, OpType_Select, OpType_ZerosLike, @@ -584,7 +586,7 @@ inline const char * const *EnumNamesOpType() { "EyeLike", "CumSum", "Det", - "", + "CumProd", "", "", "", @@ -7394,12 +7396,13 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 }, + { flatbuffers::ET_INT, 0, 0 }, { flatbuffers::ET_INT, 0, 0 } }; static const flatbuffers::TypeFunction type_refs[] = { OpTypeTypeTable }; - static const int64_t values[] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 512, 513, 514, 515, 516, 517, 518, 600, 601, 603, 604 }; + static const int64_t values[] = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 512, 513, 514, 515, 516, 517, 518, 600, 601, 603, 604 }; static const char * const names[] = { "AbsVal", "QuantizedAdd", @@ -7545,6 +7548,7 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { "EyeLike", "CumSum", "Det", + "CumProd", "Plugin", "Select", "ZerosLike", @@ -7571,7 +7575,7 @@ inline const flatbuffers::TypeTable *OpTypeTypeTable() { "GridSample" }; static const flatbuffers::TypeTable tt = { - flatbuffers::ST_ENUM, 168, type_codes, type_refs, values, names + flatbuffers::ST_ENUM, 169, type_codes, type_refs, values, names }; return &tt; } diff --git a/schema/default/ExtraInfo.fbs b/schema/default/ExtraInfo.fbs index 04eb0d7f..843e0993 100644 --- a/schema/default/ExtraInfo.fbs +++ b/schema/default/ExtraInfo.fbs @@ -11,4 +11,5 @@ namespace MNN; table ExtraInfo { buffer:[int8]; name: string; + version: string; } diff --git a/schema/default/MNN.fbs b/schema/default/MNN.fbs index 84125d0c..5ed892e0 100644 --- a/schema/default/MNN.fbs +++ b/schema/default/MNN.fbs @@ -161,6 +161,7 @@ enum OpType : int { EyeLike = 147, CumSum = 148, Det = 149, + CumProd = 150, Plugin = 256, //The Type load from plugin //Training Op Start from 257 diff --git a/source/backend/coreml/backend/CoreMLBackend.cpp b/source/backend/coreml/backend/CoreMLBackend.cpp index 2559d6b0..8165849e 100644 --- a/source/backend/coreml/backend/CoreMLBackend.cpp +++ b/source/backend/coreml/backend/CoreMLBackend.cpp @@ -15,6 +15,7 @@ extern bool isAvailable(); namespace MNN { + void registerCoreMLOps(); static inline std::map* getCreatorMap() { static std::once_flag of; static std::map* ret = nullptr; @@ -298,6 +299,7 @@ namespace MNN { if (!isAvailable()) { return; } + registerCoreMLOps(); MNNInsertExtraRuntimeCreator(MNN_FORWARD_NN, new CoreMLBackendCreator, true); } } diff --git a/source/backend/coreml/backend/CoreMLBackend.hpp b/source/backend/coreml/backend/CoreMLBackend.hpp index e3a17cda..0b64fb73 100644 --- a/source/backend/coreml/backend/CoreMLBackend.hpp +++ b/source/backend/coreml/backend/CoreMLBackend.hpp @@ -146,6 +146,12 @@ namespace MNN { } }; +#define REGISTER_COREML_OP_CREATOR(name, opType) \ + void ___##name##__##opType##__() { \ + static TypedCreator _temp;\ + CoreMLBackend::addCreator(opType, &_temp); \ + } + } #endif //MNN_COREMLBACKEND_H diff --git a/source/backend/coreml/backend/CoreMLExecutor.mm b/source/backend/coreml/backend/CoreMLExecutor.mm index d6ec2999..c17332fe 100644 --- a/source/backend/coreml/backend/CoreMLExecutor.mm +++ b/source/backend/coreml/backend/CoreMLExecutor.mm @@ -156,32 +156,37 @@ id getRasterPipeline() { if (_model == nil) { return NO; } - NSError* error = nil; - MultiArrayFeatureProvider* inputFeature = [[MultiArrayFeatureProvider alloc] initWithInputs:&inputs coreMlVersion:[self coreMlVersion]]; - if (inputFeature == nil) { - NSLog(@"inputFeature is not initialized."); - return NO; - } - MLPredictionOptions* options = [[MLPredictionOptions alloc] init]; - // options.usesCPUOnly = true; - id outputFeature = [_model predictionFromFeatures:inputFeature - options:options - error:&error]; - if (error != nil) { - NSLog(@"Error executing model: %@", [error localizedDescription]); - return NO; - } - NSSet* outputFeatureNames = [outputFeature featureNames]; - for (auto& output : outputs) { - NSString* outputName = [NSString stringWithCString:output.second.c_str() - encoding:[NSString defaultCStringEncoding]]; - MLFeatureValue* outputValue = [outputFeature featureValueForName:[outputFeatureNames member:outputName]]; - auto* data = [outputValue multiArrayValue]; - float* outputData = (float*)data.dataPointer; - if (outputData == nullptr) { + @autoreleasepool { + NSError* error = nil; + MultiArrayFeatureProvider* inputFeature = [[MultiArrayFeatureProvider alloc] initWithInputs:&inputs coreMlVersion:[self coreMlVersion]]; + if (inputFeature == nil) { + NSLog(@"inputFeature is not initialized."); return NO; } - memcpy(output.first->host(), outputData, output.first->size()); + MLPredictionOptions* options = [[MLPredictionOptions alloc] init]; + // options.usesCPUOnly = true; + //NSDate* timeStartData = [NSDate date]; + id outputFeature = [_model predictionFromFeatures:inputFeature + options:options + error:&error]; + //float deltaTime = [[NSDate date] timeIntervalSinceDate:timeStartData]; + //NSLog(@"cost time = %f", deltaTime * 1000); + if (error != nil) { + NSLog(@"Error executing model: %@", [error localizedDescription]); + return NO; + } + NSSet* outputFeatureNames = [outputFeature featureNames]; + for (auto& output : outputs) { + NSString* outputName = [NSString stringWithCString:output.second.c_str() + encoding:[NSString defaultCStringEncoding]]; + MLFeatureValue* outputValue = [outputFeature featureValueForName:[outputFeatureNames member:outputName]]; + auto* data = [outputValue multiArrayValue]; + float* outputData = (float*)data.dataPointer; + if (outputData == nullptr) { + return NO; + } + memcpy(output.first->host(), outputData, output.first->size()); + } } return YES; } @@ -451,6 +456,13 @@ id getRasterPipeline() { - (NSArray *> *)outputShapesForInputShapes:(NSArray *> *)inputShapes error:(NSError * _Nullable *)error { + for (int i = 0; i < inputShapes.count; i++) { + printf("### shape_%d : { ", i); + for (int j = 0; j < inputShapes[i].count; j++) { + printf("%d, ", inputShapes[i][j].intValue); + } + printf(" }\n"); + } return inputShapes; } diff --git a/source/backend/coreml/backend/CoreMLOPRegister.cpp b/source/backend/coreml/backend/CoreMLOPRegister.cpp new file mode 100644 index 00000000..45ea5da3 --- /dev/null +++ b/source/backend/coreml/backend/CoreMLOPRegister.cpp @@ -0,0 +1,42 @@ +// This file is generated by Shell for ops register +namespace MNN { +extern void ___CoreMLReduction__OpType_Reduction__(); +extern void ___CoreMLBinary__OpType_BinaryOp__(); +extern void ___CoreMLBinary__OpType_Eltwise__(); +extern void ___CoreMLArgMax__OpType_ArgMax__(); +extern void ___CoreMLConvolution__OpType_Convolution__(); +extern void ___CoreMLConvolution__OpType_ConvolutionDepthwise__(); +extern void ___CoreMLConvolution__OpType_Deconvolution__(); +extern void ___CoreMLInterp__OpType_Interp__(); +extern void ___CoreMLUnary__OpType_UnaryOp__(); +extern void ___CoreMLScale__OpType_Scale__(); +extern void ___CoreMLPool__OpType_Pooling__(); +extern void ___CoreMLRaster__OpType_Raster__(); +extern void ___CoreMLActivation__OpType_ReLU__(); +extern void ___CoreMLActivation__OpType_ReLU6__(); +extern void ___CoreMLActivation__OpType_ELU__(); +extern void ___CoreMLActivation__OpType_PReLU__(); +extern void ___CoreMLActivation__OpType_Sigmoid__(); +extern void ___CoreMLActivation__OpType_Softmax__(); + +void registerCoreMLOps() { +___CoreMLReduction__OpType_Reduction__(); +___CoreMLBinary__OpType_BinaryOp__(); +___CoreMLBinary__OpType_Eltwise__(); +___CoreMLArgMax__OpType_ArgMax__(); +___CoreMLConvolution__OpType_Convolution__(); +___CoreMLConvolution__OpType_ConvolutionDepthwise__(); +___CoreMLConvolution__OpType_Deconvolution__(); +___CoreMLInterp__OpType_Interp__(); +___CoreMLUnary__OpType_UnaryOp__(); +___CoreMLScale__OpType_Scale__(); +___CoreMLPool__OpType_Pooling__(); +___CoreMLRaster__OpType_Raster__(); +___CoreMLActivation__OpType_ReLU__(); +___CoreMLActivation__OpType_ReLU6__(); +___CoreMLActivation__OpType_ELU__(); +___CoreMLActivation__OpType_PReLU__(); +___CoreMLActivation__OpType_Sigmoid__(); +___CoreMLActivation__OpType_Softmax__(); +} +} diff --git a/source/backend/coreml/execution/CoreMLActivation.cpp b/source/backend/coreml/execution/CoreMLActivation.cpp index bcf4de53..db3f9dcf 100644 --- a/source/backend/coreml/execution/CoreMLActivation.cpp +++ b/source/backend/coreml/execution/CoreMLActivation.cpp @@ -41,6 +41,7 @@ ErrorCode CoreMLActivation::onResize(const std::vector &inputs, const auto reluLayer = mCoreMLBackend->create(); core_ml__specification__neural_network_layer__init(reluLayer); mCoreMLBackend->setLayerName(reluLayer, "relu6-relu"); + reluLayer->activation = mCoreMLBackend->create(); reluLayer->activation->nonlinearity_type_case = CORE_ML__SPECIFICATION__ACTIVATION_PARAMS__NONLINEARITY_TYPE_RE_LU; reluLayer->activation->relu = mCoreMLBackend->create(); core_ml__specification__activation_re_lu__init(reluLayer->activation->relu); @@ -98,12 +99,10 @@ ErrorCode CoreMLActivation::onResize(const std::vector &inputs, const return NO_ERROR; } -CoreMLCreatorRegister> __relu_op(OpType_ReLU); -CoreMLCreatorRegister> __relu6_op(OpType_ReLU6); -CoreMLCreatorRegister> __elu_op(OpType_ELU); -CoreMLCreatorRegister> __prelu_op(OpType_PReLU); -CoreMLCreatorRegister> __sigmoid_op(OpType_Sigmoid); -CoreMLCreatorRegister> __softmax_op(OpType_Softmax); - - +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_ReLU) +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_ReLU6) +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_ELU) +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_PReLU) +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_Sigmoid) +REGISTER_COREML_OP_CREATOR(CoreMLActivation, OpType_Softmax) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLArgMax.cpp b/source/backend/coreml/execution/CoreMLArgMax.cpp index 520bdda3..f6ac34b4 100644 --- a/source/backend/coreml/execution/CoreMLArgMax.cpp +++ b/source/backend/coreml/execution/CoreMLArgMax.cpp @@ -31,5 +31,5 @@ ErrorCode CoreMLArgMax::onResize(const std::vector &inputs, const std: return NO_ERROR; } -CoreMLCreatorRegister> __argmax_op(OpType_ArgMax); +REGISTER_COREML_OP_CREATOR(CoreMLArgMax, OpType_ArgMax) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLBinary.cpp b/source/backend/coreml/execution/CoreMLBinary.cpp index 8b91f247..36d43df5 100644 --- a/source/backend/coreml/execution/CoreMLBinary.cpp +++ b/source/backend/coreml/execution/CoreMLBinary.cpp @@ -192,8 +192,7 @@ ErrorCode CoreMLBinary::onResize(const std::vector &inputs, const std: } -CoreMLCreatorRegister> __bianry_op(OpType_BinaryOp); -CoreMLCreatorRegister> __elemwise_op(OpType_Eltwise); - +REGISTER_COREML_OP_CREATOR(CoreMLBinary, OpType_BinaryOp) +REGISTER_COREML_OP_CREATOR(CoreMLBinary, OpType_Eltwise) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLConvolution.cpp b/source/backend/coreml/execution/CoreMLConvolution.cpp index d94c7da3..ef3a490d 100644 --- a/source/backend/coreml/execution/CoreMLConvolution.cpp +++ b/source/backend/coreml/execution/CoreMLConvolution.cpp @@ -12,6 +12,7 @@ namespace MNN { CoreMLConvolution::CoreMLConvolution(MNN::Backend *b, const MNN::Op *op, const std::vector &inputs, const std::vector &outputs) : CoreMLCommonExecution(b, op) { + isDeconv = op->type() == OpType_Deconvolution; initLayer(); } @@ -111,6 +112,7 @@ ErrorCode CoreMLConvolution::onResize(const std::vector &inputs, const mLayer_->convolution = mCoreMLBackend->create(); core_ml__specification__convolution_layer_params__init(mLayer_->convolution); mLayer_->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_CONVOLUTION; + mLayer_->convolution->isdeconvolution = isDeconv; mLayer_->convolution->ngroups = group; mLayer_->convolution->n_stride = 2; mLayer_->convolution->stride = mCoreMLBackend->create(mLayer_->convolution->n_stride); @@ -183,10 +185,7 @@ ErrorCode CoreMLConvolution::onResize(const std::vector &inputs, const return NO_ERROR; } - -CoreMLCreatorRegister> __convolution_op(OpType_Convolution); -CoreMLCreatorRegister> __convdepwise_op(OpType_ConvolutionDepthwise); -// CoreMLCreatorRegister> __deconvolution_op(OpType_Deconvolution); - - +REGISTER_COREML_OP_CREATOR(CoreMLConvolution, OpType_Convolution) +REGISTER_COREML_OP_CREATOR(CoreMLConvolution, OpType_ConvolutionDepthwise) +REGISTER_COREML_OP_CREATOR(CoreMLConvolution, OpType_Deconvolution) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLConvolution.hpp b/source/backend/coreml/execution/CoreMLConvolution.hpp index cc502f70..26e55170 100644 --- a/source/backend/coreml/execution/CoreMLConvolution.hpp +++ b/source/backend/coreml/execution/CoreMLConvolution.hpp @@ -27,6 +27,7 @@ private: std::shared_ptr quanCommon; const float *weightPtr, *biasPtr; int weightSize, biasSize; + bool isDeconv = false; }; } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLInterp.cpp b/source/backend/coreml/execution/CoreMLInterp.cpp index e75c82ec..2690220b 100644 --- a/source/backend/coreml/execution/CoreMLInterp.cpp +++ b/source/backend/coreml/execution/CoreMLInterp.cpp @@ -72,5 +72,5 @@ ErrorCode CoreMLInterp::onResize(const std::vector &inputs, const std: return NO_ERROR; } -CoreMLCreatorRegister> __interp_op(OpType_Interp); +REGISTER_COREML_OP_CREATOR(CoreMLInterp, OpType_Interp) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLPool.cpp b/source/backend/coreml/execution/CoreMLPool.cpp index e7061377..e84fcd03 100644 --- a/source/backend/coreml/execution/CoreMLPool.cpp +++ b/source/backend/coreml/execution/CoreMLPool.cpp @@ -15,8 +15,56 @@ CoreMLPool::CoreMLPool(MNN::Backend *b, const MNN::Op *op, const std::vectorpadType() == PoolPadType_CAFFE); + int top, left, bottom, right; + if (nullptr != common->pads()) { + MNN_ASSERT(common->pads()->size() >= 4); + top = common->pads()->Get(0); + left = common->pads()->Get(1); + bottom = common->pads()->Get(2); + right = common->pads()->Get(3); + } else { + top = common->padY(); + left = common->padX(); + bottom = common->padY(); + right = common->padX(); + } + if (top == 0 && left == 0 && bottom == 0 && right == 0) { + return; + } + auto paddingLayer = mCoreMLBackend->create(); + core_ml__specification__neural_network_layer__init(paddingLayer); + paddingLayer->layer_case = CORE_ML__SPECIFICATION__NEURAL_NETWORK_LAYER__LAYER_PADDING; + mCoreMLBackend->setLayerName(paddingLayer, "PoolPadding"); + paddingLayer->padding = mCoreMLBackend->create(); + core_ml__specification__padding_layer_params__init(paddingLayer->padding); + paddingLayer->padding->padding_type_case = CORE_ML__SPECIFICATION__PADDING_LAYER_PARAMS__PADDING_TYPE_CONSTANT; + paddingLayer->padding->constant = mCoreMLBackend->create(); + core_ml__specification__padding_layer_params__padding_constant__init(paddingLayer->padding->constant); + paddingLayer->padding->constant->value = 0; + paddingLayer->padding->paddingamounts = mCoreMLBackend->create(); + core_ml__specification__border_amounts__init(paddingLayer->padding->paddingamounts); + paddingLayer->padding->paddingamounts->n_borderamounts = 2; + paddingLayer->padding->paddingamounts->borderamounts = mCoreMLBackend->create(2); + paddingLayer->padding->paddingamounts->borderamounts[0] = mCoreMLBackend->create(); + core_ml__specification__border_amounts__edge_sizes__init(paddingLayer->padding->paddingamounts->borderamounts[0]); + paddingLayer->padding->paddingamounts->borderamounts[0]->startedgesize = top; + paddingLayer->padding->paddingamounts->borderamounts[0]->endedgesize = bottom; + paddingLayer->padding->paddingamounts->borderamounts[1] = mCoreMLBackend->create(); + core_ml__specification__border_amounts__edge_sizes__init(paddingLayer->padding->paddingamounts->borderamounts[1]); + paddingLayer->padding->paddingamounts->borderamounts[1]->startedgesize = left; + paddingLayer->padding->paddingamounts->borderamounts[1]->endedgesize = right; + auto inputName = mPoolInputName; + mPoolInputName = mPoolInputName + "-" + mPoolOutputName + "-Padding"; + setLayerInputsAndOutputs(paddingLayer, {inputName}, {mPoolInputName}); + mCoreMLBackend->addLayer(paddingLayer); +} + ErrorCode CoreMLPool::onResize(const std::vector &inputs, const std::vector &outputs) { MNN_ASSERT(inputs.size() == 1 && outputs.size() == 1); + mPoolInputName = mCoreMLBackend->getTensorName(inputs[0]); + mPoolOutputName = mCoreMLBackend->getTensorName(outputs[0]); auto pool = mOp->main_as_Pool(); auto strideX = pool->strideX(); auto strideY = pool->strideY(); @@ -48,10 +96,16 @@ ErrorCode CoreMLPool::onResize(const std::vector &inputs, const std::v core_ml__specification__valid_padding__init(mLayer_->pooling->valid); break; case PoolPadType_CAFFE: - // TODO: deal caffe pad mode - mLayer_->pooling->pooling_padding_type_case = CORE_ML__SPECIFICATION__POOLING_LAYER_PARAMS__POOLING_PADDING_TYPE_INCLUDE_LAST_PIXEL; - mLayer_->pooling->includelastpixel = mCoreMLBackend->create(); - core_ml__specification__pooling_layer_params__valid_complete_padding__init(mLayer_->pooling->includelastpixel); + if (pool->pads()->size() > 0 || pool->padX() > 0) { + addPadLayer(inputs[0], pool); + mLayer_->pooling->pooling_padding_type_case = CORE_ML__SPECIFICATION__POOLING_LAYER_PARAMS__POOLING_PADDING_TYPE_VALID; + mLayer_->pooling->valid = mCoreMLBackend->create(); + core_ml__specification__valid_padding__init(mLayer_->pooling->valid); + } else { + mLayer_->pooling->pooling_padding_type_case = CORE_ML__SPECIFICATION__POOLING_LAYER_PARAMS__POOLING_PADDING_TYPE_INCLUDE_LAST_PIXEL; + mLayer_->pooling->includelastpixel = mCoreMLBackend->create(); + core_ml__specification__pooling_layer_params__valid_complete_padding__init(mLayer_->pooling->includelastpixel); + } break; default: break; @@ -62,13 +116,11 @@ ErrorCode CoreMLPool::onResize(const std::vector &inputs, const std::v } else { mLayer_->pooling->type = CORE_ML__SPECIFICATION__POOLING_LAYER_PARAMS__POOLING_TYPE__MAX; } - setLayerInputsAndOutputs(mLayer_, {mCoreMLBackend->getTensorName(inputs[0])}, {mCoreMLBackend->getTensorName(outputs[0])}); + setLayerInputsAndOutputs(mLayer_, {mPoolInputName}, {mPoolOutputName}); mCoreMLBackend->addLayer(mLayer_); return NO_ERROR; } -CoreMLCreatorRegister> __pool_op(OpType_Pooling); - - +REGISTER_COREML_OP_CREATOR(CoreMLPool, OpType_Pooling) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLPool.hpp b/source/backend/coreml/execution/CoreMLPool.hpp index 5cdc01fa..a1b1e174 100644 --- a/source/backend/coreml/execution/CoreMLPool.hpp +++ b/source/backend/coreml/execution/CoreMLPool.hpp @@ -19,6 +19,9 @@ public: CoreMLPool(Backend *b, const Op *op, const std::vector &inputs, const std::vector &outputs); ErrorCode onResize(const std::vector &inputs, const std::vector &outputs); virtual ~CoreMLPool() = default; +private: + void addPadLayer(const Tensor * input, const Pool* common); + std::string mPoolInputName, mPoolOutputName; }; } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLRaster.cpp b/source/backend/coreml/execution/CoreMLRaster.cpp index e3923bcf..9d29592a 100644 --- a/source/backend/coreml/execution/CoreMLRaster.cpp +++ b/source/backend/coreml/execution/CoreMLRaster.cpp @@ -366,5 +366,5 @@ ErrorCode CoreMLRaster::onResize(const std::vector &inputs, const std: return NO_ERROR; } -CoreMLCreatorRegister> __raster_op(OpType_Raster); +REGISTER_COREML_OP_CREATOR(CoreMLRaster, OpType_Raster) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLReduction.cpp b/source/backend/coreml/execution/CoreMLReduction.cpp index 76c57dba..8195abe2 100644 --- a/source/backend/coreml/execution/CoreMLReduction.cpp +++ b/source/backend/coreml/execution/CoreMLReduction.cpp @@ -67,5 +67,5 @@ ErrorCode CoreMLReduction::onResize(const std::vector &inputs, const s return NO_ERROR; } -CoreMLCreatorRegister> __reduction_op(OpType_Reduction); +REGISTER_COREML_OP_CREATOR(CoreMLReduction, OpType_Reduction) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLScale.cpp b/source/backend/coreml/execution/CoreMLScale.cpp index c9734df3..7a7b8d3e 100644 --- a/source/backend/coreml/execution/CoreMLScale.cpp +++ b/source/backend/coreml/execution/CoreMLScale.cpp @@ -47,5 +47,5 @@ ErrorCode CoreMLScale::onResize(const std::vector &inputs, const std:: return NO_ERROR; } -CoreMLCreatorRegister> __scale_op(OpType_Scale); +REGISTER_COREML_OP_CREATOR(CoreMLScale, OpType_Scale) } // namespace MNN diff --git a/source/backend/coreml/execution/CoreMLUnary.cpp b/source/backend/coreml/execution/CoreMLUnary.cpp index b6e0ddce..7f980164 100644 --- a/source/backend/coreml/execution/CoreMLUnary.cpp +++ b/source/backend/coreml/execution/CoreMLUnary.cpp @@ -234,5 +234,5 @@ ErrorCode CoreMLUnary::onResize(const std::vector &inputs, const std:: return NO_ERROR; } -CoreMLCreatorRegister> __unary_op(OpType_UnaryOp); +REGISTER_COREML_OP_CREATOR(CoreMLUnary, OpType_UnaryOp) } // namespace MNN diff --git a/source/backend/cpu/CPUBackend.cpp b/source/backend/cpu/CPUBackend.cpp index d85eb523..c8c78589 100644 --- a/source/backend/cpu/CPUBackend.cpp +++ b/source/backend/cpu/CPUBackend.cpp @@ -220,6 +220,8 @@ Backend::MemObj* CPUBackend::allocBuffer(int size, Tensor* dest, StorageType sto if (nullptr != originMem) { if (static_cast(originMem)->getSize() >= size) { return originMem; + } else { + TensorUtils::getDescribe(dest)->mem.reset(nullptr); } } // MNN_PRINT("Acquire size = %d\n", size); diff --git a/source/backend/cpu/CPUGridSample.cpp b/source/backend/cpu/CPUGridSample.cpp index 4e92c64d..5696093b 100644 --- a/source/backend/cpu/CPUGridSample.cpp +++ b/source/backend/cpu/CPUGridSample.cpp @@ -30,9 +30,17 @@ ErrorCode CPUGridSample::onResize(const std::vector &inputs, const std int numberThread = static_cast(backend())->threadNumber(); auto core = static_cast(backend())->functions(); auto outputTensor = outputs[0]; - auto outH = outputTensor->buffer().dim[2].extent; - auto outW = outputTensor->buffer().dim[3].extent; - mTempCordBuffer.reset(Tensor::createDevice({1, outH * outW * 2 * core->bytes})); + int outD, outH, outW; + if (outputTensor->dimensions() == 4) { + outH = outputTensor->buffer().dim[2].extent; + outW = outputTensor->buffer().dim[3].extent; + mTempCordBuffer.reset(Tensor::createDevice({1, outH * outW * 2 * core->bytes})); + } else { + outD = outputTensor->buffer().dim[2].extent; + outH = outputTensor->buffer().dim[3].extent; + outW = outputTensor->buffer().dim[4].extent; + mTempCordBuffer.reset(Tensor::createDevice({1, outD * outH * outW * 3 * core->bytes})); + } auto res = backend()->onAcquireBuffer(mTempCordBuffer.get(), Backend::DYNAMIC); if (!res) { return OUT_OF_MEMORY; @@ -52,35 +60,71 @@ ErrorCode CPUGridSample::onExecute(const std::vector &inputs, const st auto batches = inputTensor->buffer().dim[0].extent; auto channels = inputTensor->buffer().dim[1].extent; auto channelC4 = UP_DIV(channels, core->pack); - auto inH = inputTensor->buffer().dim[2].extent; - auto inW = inputTensor->buffer().dim[3].extent; - auto outH = outputTensor->buffer().dim[2].extent; - auto outW = outputTensor->buffer().dim[3].extent; - auto threadCount = static_cast(backend())->threadNumber(); - auto tileCount = outH; - auto inOffset = batches * inH * inW * core->pack; - auto outOffset = batches * outH * outW * core->pack; - auto cordPtr = mTempCordBuffer->host(); - for (auto b = 0; b < batches; ++b) { - auto _inputPtr = inputPtr + b * inH * inW * core->pack * core->bytes; - auto _gridPtr = gridPtr + b * gridTensor->buffer().dim[0].stride * core->bytes; - auto _outputPtr = outputPtr + b * outH * outW * core->pack * core->bytes; - core->MNNGridSampleComputeCord((float *)cordPtr, (const float *)_gridPtr, inH, inW, outH, outW, gridTensor->buffer().dim[1].stride, mAlignCorners); - // Compute cord - MNN_CONCURRENCY_BEGIN(tId, threadCount) { - for (int index=tId; index < tileCount; index += threadCount) { - auto c = index / outH; - auto h = index % outH; - auto inputC = _inputPtr + c * inW * inH * batches * core->pack * core->bytes; - auto outputC = _outputPtr + c * outW * outH * batches * core->pack * core->bytes; - auto cordH = cordPtr + h * outW * 2 * core->bytes; - auto outputH = outputC + h * outW * core->pack * core->bytes; - core->MNNGridSampleInterp((float *)outputH, (const float *)inputC, (const float *)cordH, inH, inW, outW, channelC4, inOffset, outOffset, (mMode == SampleMode_NEAREST), (mPaddingMode == BorderMode_ZEROS)); + if (outputs[0]->dimensions() == 4) { + auto inH = inputTensor->buffer().dim[2].extent; + auto inW = inputTensor->buffer().dim[3].extent; + auto outH = outputTensor->buffer().dim[2].extent; + auto outW = outputTensor->buffer().dim[3].extent; + auto threadCount = static_cast(backend())->threadNumber(); + auto tileCount = outH; + auto inOffset = batches * inH * inW * core->pack; + auto outOffset = batches * outH * outW * core->pack; + auto cordPtr = mTempCordBuffer->host(); + for (auto b = 0; b < batches; ++b) { + auto _inputPtr = inputPtr + b * inH * inW * core->pack * core->bytes; + auto _gridPtr = gridPtr + b * gridTensor->buffer().dim[0].stride * core->bytes; + auto _outputPtr = outputPtr + b * outH * outW * core->pack * core->bytes; + core->MNNGridSampleComputeCord((float *)cordPtr, (const float *)_gridPtr, inH, inW, outH, outW, gridTensor->buffer().dim[1].stride, mAlignCorners); + // Compute cord + MNN_CONCURRENCY_BEGIN(tId, threadCount) { + for (int index=tId; index < tileCount; index += threadCount) { + auto c = index / outH; + auto h = index % outH; + auto inputC = _inputPtr + c * inW * inH * batches * core->pack * core->bytes; + auto outputC = _outputPtr + c * outW * outH * batches * core->pack * core->bytes; + auto cordH = cordPtr + h * outW * 2 * core->bytes; + auto outputH = outputC + h * outW * core->pack * core->bytes; + core->MNNGridSampleInterp((float *)outputH, (const float *)inputC, (const float *)cordH, inH, inW, outW, channelC4, inOffset, outOffset, (mMode == SampleMode_NEAREST), (mPaddingMode == BorderMode_ZEROS)); + } } + MNN_CONCURRENCY_END(); + } + } else { + auto inD = inputTensor->buffer().dim[2].extent; + auto inH = inputTensor->buffer().dim[3].extent; + auto inW = inputTensor->buffer().dim[4].extent; + auto outD = outputTensor->buffer().dim[2].extent; + auto outH = outputTensor->buffer().dim[3].extent; + auto outW = outputTensor->buffer().dim[4].extent; + auto threadCount = static_cast(backend())->threadNumber(); + auto tileCount = outD; + auto inOffset = batches * inH * inW * core->pack; + auto outOffset = batches * outH * outW * core->pack; + auto cordPtr = mTempCordBuffer->host(); + for (auto b = 0; b < batches; ++b) { + auto _inputPtr = inputPtr + b * inD * inH * inW * core->pack * core->bytes; + auto _gridPtr = gridPtr + b * gridTensor->buffer().dim[0].stride * core->bytes; + auto _outputPtr = outputPtr + b * outD * outH * outW * core->pack * core->bytes; + core->MNNGridSampleComputeCord3D((float *)cordPtr, (const float *)_gridPtr, inD, inH, inW, outD, outH, outW, gridTensor->buffer().dim[1].stride, gridTensor->buffer().dim[2].stride, mAlignCorners); + // Compute cord + MNN_CONCURRENCY_BEGIN(tId, threadCount) { + for (int index=tId; index < tileCount; index += threadCount) { + auto c = index / outD; + auto d = index % outD; + auto inputC = _inputPtr + c * inD * inW * inH * batches * core->pack * core->bytes; + auto outputC = _outputPtr + c * outD * outW * outH * batches * core->pack * core->bytes; + auto cordD = cordPtr + d * outH * outW * 3 * core->bytes; + auto outputD = outputC + d * outH * outW * core->pack * core->bytes; + for (int h = 0; h < outH; h++) { + auto cordH = cordD + h * outW * 3 * core->bytes; + auto outputH = outputD + h * outW * core->pack * core->bytes; + core->MNNGridSampleInterp3D((float *)outputH, (const float *)inputC, (const float *)cordH, inD, inH, inW, outW, channelC4, inOffset, outOffset, (mMode == SampleMode_NEAREST), (mPaddingMode == BorderMode_ZEROS)); + } + } + } + MNN_CONCURRENCY_END(); } - MNN_CONCURRENCY_END(); } - return NO_ERROR; } diff --git a/source/backend/cpu/CPUInterp.cpp b/source/backend/cpu/CPUInterp.cpp index 1d36c951..61baa014 100644 --- a/source/backend/cpu/CPUInterp.cpp +++ b/source/backend/cpu/CPUInterp.cpp @@ -74,12 +74,6 @@ ErrorCode CPUInterp::onResize(const std::vector &inputs, const std::ve const int inH = inputs[0]->buffer().dim[2].extent; const int outW = outputs[0]->buffer().dim[3].extent; const int outH = outputs[0]->buffer().dim[2].extent; - if (mInit && mResizeType == 2) { - backend()->onReleaseBuffer(&mWidthPosition, Backend::STATIC); - backend()->onReleaseBuffer(&mWidthFactor, Backend::STATIC); - backend()->onReleaseBuffer(&mHeightPosition, Backend::STATIC); - backend()->onReleaseBuffer(&mHeightFactor, Backend::STATIC); - } const float xScaling = mWidthScale; const float yScaling = mHeightScale; @@ -105,8 +99,6 @@ ErrorCode CPUInterp::onResize(const std::vector &inputs, const std::ve if (!res) { return OUT_OF_MEMORY; } - mInit = true; - auto _wPosition = mWidthPosition.host(); auto _wFactor = mWidthFactor.host(); diff --git a/source/backend/cpu/CPULayerNorm.cpp b/source/backend/cpu/CPULayerNorm.cpp index 51e927e7..a021162d 100644 --- a/source/backend/cpu/CPULayerNorm.cpp +++ b/source/backend/cpu/CPULayerNorm.cpp @@ -29,7 +29,7 @@ public: const std::vector &outputs) override; private: - std::vector axis_; + int axis_size = 0; int inner_size_ = 1; int outter_size_ = 1; int group_ = 1; @@ -43,11 +43,7 @@ private: CPULayerNorm::CPULayerNorm(const MNN::Op* op, Backend* backend) : Execution(backend) { const auto* layer_norm_param = op->main_as_LayerNorm(); - int axis_size = layer_norm_param->axis()->size(); - axis_.resize(axis_size); - for (int i = 0; i < axis_size; ++i) { - axis_[i] = layer_norm_param->axis()->Get(i); - } + axis_size = layer_norm_param->axis()->size(); group_ = layer_norm_param->group(); epsilon_ = layer_norm_param->epsilon(); @@ -104,18 +100,10 @@ ErrorCode CPULayerNorm::onResize(const std::vector &inputs, inner_size_ /= group_; return NO_ERROR; } - std::vector axis(axis_.size()); - for (int i = 0; i < axis_.size(); ++i) { - if (axis_[i] < 0) { - axis[i] += rank; - } - } - std::sort(axis.begin(), axis.end()); - - for (int i = 0; i < rank - axis.size(); ++i) { + for (int i = 0; i < rank - axis_size; ++i) { outter_size_ *= inputs.at(0)->length(i); } - for (int i = rank - axis.size(); i < rank; ++i) { + for (int i = rank - axis_size; i < rank; ++i) { inner_size_ *= inputs.at(0)->length(i); } return NO_ERROR; diff --git a/source/backend/cpu/CPURaster.cpp b/source/backend/cpu/CPURaster.cpp index 946ff646..89fc74fe 100644 --- a/source/backend/cpu/CPURaster.cpp +++ b/source/backend/cpu/CPURaster.cpp @@ -35,6 +35,15 @@ static void getBatchChannelArea(const Tensor* t, int& batch, int& channel, int& channel = t->length(1); area = t->length(2); } + } else if (t->dimensions() == 5) { + auto format = TensorUtils::getDescribe(t)->dimensionFormat; + if (format == MNN_DATA_FORMAT_NHWC) { + channel = t->length(4); + area = t->length(1) * t->length(2) * t->length(3); + } else { + channel = t->length(1); + area = t->length(2) * t->length(3) * t->length(4); + } } else { auto format = TensorUtils::getDescribe(t)->dimensionFormat; if (format == MNN_DATA_FORMAT_NHWC) { diff --git a/source/backend/cpu/CPUResize.cpp b/source/backend/cpu/CPUResize.cpp index 3fc75d3c..c269a20b 100644 --- a/source/backend/cpu/CPUResize.cpp +++ b/source/backend/cpu/CPUResize.cpp @@ -153,65 +153,63 @@ void CPUResizeCommon::CPUResizeBilinearC4(halide_buffer_t& input, halide_buffer_ const int outW = output.dim[3].extent; const int outH = output.dim[2].extent; - int depthQuad = UP_DIV(input.dim[1].extent, 4); + int depthQuad = UP_DIV(input.dim[1].extent, 4) * batches; - for (int b = 0; b < batches; ++b) { - auto threadFunction = [&](size_t tId) { - for (int n = (int)tId; n < depthQuad; n += threadNumber) { - auto _lineBuffer = lineBuffer + 2 * 4 * outW * tId; - auto _line0 = _lineBuffer + 4 * outW * 0; - auto _line1 = _lineBuffer + 4 * outW * 1; - int yUsed[2] = {0, 0}; - int yCache[2] = {-1, -1}; + auto threadFunction = [&](size_t tId) { + for (int n = (int)tId; n < depthQuad; n += threadNumber) { + auto _lineBuffer = lineBuffer + 2 * 4 * outW * tId; + auto _line0 = _lineBuffer + 4 * outW * 0; + auto _line1 = _lineBuffer + 4 * outW * 1; + int yUsed[2] = {0, 0}; + int yCache[2] = {-1, -1}; - float* yCacheLine[2] = {_line0, _line1}; - float* const yCacheStorage[2] = {_line0, _line1}; + float* yCacheLine[2] = {_line0, _line1}; + float* const yCacheStorage[2] = {_line0, _line1}; - auto bottomData = - reinterpret_cast(input.host) + b * inputBatchSize + (int)n * 4 * inW * inH; - auto topData = reinterpret_cast(output.host) + b * outputBatchSize + (int)n * 4 * outW * outH; - for (int dy = 0; dy < outH; dy++) { - int yp[2]; - yp[0] = heightPosition[2 * dy + 0]; - yp[1] = heightPosition[2 * dy + 1]; - // Search cache - for (int j = 0; j < 2; ++j) { - yUsed[j] = 0; + auto bottomData = + reinterpret_cast(input.host) + (int)n * 4 * inW * inH; + auto topData = reinterpret_cast(output.host) + (int)n * 4 * outW * outH; + for (int dy = 0; dy < outH; dy++) { + int yp[2]; + yp[0] = heightPosition[2 * dy + 0]; + yp[1] = heightPosition[2 * dy + 1]; + // Search cache + for (int j = 0; j < 2; ++j) { + yUsed[j] = 0; + } + for (int j = 0; j < 2; ++j) { + int find = 0; + for (int k = 0; k < 2; ++k) { + if (yp[j] == yCache[k]) { + yUsed[k] = 1; + yCacheLine[j] = yCacheStorage[k]; + find = 1; + break; + } } - for (int j = 0; j < 2; ++j) { - int find = 0; + if (!find) { + const float* bottomY0 = bottomData + yp[j] * inW * 4; for (int k = 0; k < 2; ++k) { - if (yp[j] == yCache[k]) { + if (!yUsed[k]) { + yCache[k] = yp[j]; yUsed[k] = 1; yCacheLine[j] = yCacheStorage[k]; - find = 1; + CPUBilinearSampleC4(bottomY0, yCacheLine[j], widthPosition, widthFactor, outW); break; } } - if (!find) { - const float* bottomY0 = bottomData + yp[j] * inW * 4; - for (int k = 0; k < 2; ++k) { - if (!yUsed[k]) { - yCache[k] = yp[j]; - yUsed[k] = 1; - yCacheLine[j] = yCacheStorage[k]; - CPUBilinearSampleC4(bottomY0, yCacheLine[j], widthPosition, widthFactor, outW); - break; - } - } - } } - auto topY = topData + outW * 4 * dy; - // Sample Input - CPUBilinearLineC4(topY, yCacheLine[0], yCacheLine[1], &heightFactor[dy], outW); } + auto topY = topData + outW * 4 * dy; + // Sample Input + CPUBilinearLineC4(topY, yCacheLine[0], yCacheLine[1], &heightFactor[dy], outW); } - }; - MNN_CONCURRENCY_BEGIN(tId, threadNumber) { - threadFunction(tId); } - MNN_CONCURRENCY_END(); + }; + MNN_CONCURRENCY_BEGIN(tId, threadNumber) { + threadFunction(tId); } + MNN_CONCURRENCY_END(); } void CPUResizeCommon::CPUResizeNearestneighborRoundC4(halide_buffer_t &input, halide_buffer_t &output, float wScale, float hScale, float wOffset, float hOffset) { diff --git a/source/backend/cpu/CPUScatterNd.cpp b/source/backend/cpu/CPUScatterNd.cpp index 94ac0e49..9b867c0a 100644 --- a/source/backend/cpu/CPUScatterNd.cpp +++ b/source/backend/cpu/CPUScatterNd.cpp @@ -18,6 +18,9 @@ void ScatterNdImpl(const Tensor* indices, const Tensor* updates, const Tensor* s auto outputPtr = output->host(); const int indicesDimension = indices->dimensions(); const int indicesLastDim = indices->length(indicesDimension - 1); + if (indicesLastDim == 0) { + return; + } const int indexes = indices->elementSize() / indicesLastDim; int accNumber = 1; for (int i = indicesDimension - 1; i < updates->dimensions(); ++i) { diff --git a/source/backend/cpu/compute/CommonOptFunction.cpp b/source/backend/cpu/compute/CommonOptFunction.cpp index f06aa1e4..5c53342d 100644 --- a/source/backend/cpu/compute/CommonOptFunction.cpp +++ b/source/backend/cpu/compute/CommonOptFunction.cpp @@ -1543,6 +1543,24 @@ void MNNGridSampleComputeCord(float* dst, const float* src, size_t inH, size_t i } } } +void MNNGridSampleComputeCord3D(float* dst, const float* src, size_t inD, size_t inH, size_t inW, size_t outD, size_t outH, size_t outW, size_t strideD, size_t strideH, bool alignCorners) { + float a = alignCorners ? 1.0f : 0.0f; + float b = alignCorners ? 0.0f : 1.0f; + for (auto d = 0; d < outD; ++d) { + for (auto h = 0; h < outH; ++h) { + auto __gridPtr = src + d * strideD + h * strideH; + auto cordH = dst + (d * outH + h) * outW * 3; + for (auto w = 0; w < outW; ++w) { + auto x = __gridPtr[3 * w + 0]; + auto y = __gridPtr[3 * w + 1]; + auto z = __gridPtr[3 * w + 2]; + cordH[3 * w + 0] = ((1 + x) * (inW - a) - b) * 0.5f; + cordH[3 * w + 1] = ((1 + y) * (inH - a) - b) * 0.5f; + cordH[3 * w + 2] = ((1 + z) * (inD - a) - b) * 0.5f; + } + } + } +} #ifndef MNN_USE_SSE void MNNNorm(float *dst, const float *src, const float *gamma, const float *beta, float epsilon, size_t size) { @@ -1585,6 +1603,22 @@ size_t MNNGridSampleComputeOffset(int h, int w, int height, int width, bool padM return h * width * 4 + w * 4; } +size_t MNNGridSampleComputeOffset3D(int d, int h, int w, int depth, int height, int width, bool padMode) { + if (padMode == true) { //padMode == BorderMode_ZEROS + if (h < 0 || h >= height || w < 0 || w >= width || d < 0 || d >= depth) { + return -1; + } + } else { + // Clearly, CLAMP is the right way to go for GridSamplePaddingMode_BORDER + // For GridSamplePaddingMode_REFLECTION, since we have reflected the values into (-1, 1), + // the leftover reflections degrade to GridSamplePaddingMode_BORDER + d = d < 0 ? 0 : (d > (depth - 1) ? (depth - 1) : d); + h = h < 0 ? 0 : ( h > (height - 1) ? (height - 1) : h); + w = w < 0 ? 0 : ( w > (width - 1) ? (width - 1) : w); + } + return ((d * height + h) * width + w) * 4; +} + void MNNGridSampleInterp(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode) { for (auto ow = 0; ow < outW; ++ow) { auto w = cordPtr[2 * ow + 0]; @@ -1695,6 +1729,70 @@ void MNNRoiAlignAvg(float* dst, const float* src, const std::vectorMNNScaleAndAddBias = MNNScaleAndAddBias; gCoreFunction->MNNGridSampleComputeCord = MNNGridSampleComputeCord; gCoreFunction->MNNGridSampleInterp = MNNGridSampleInterp; + gCoreFunction->MNNGridSampleComputeCord3D = MNNGridSampleComputeCord3D; + gCoreFunction->MNNGridSampleInterp3D = MNNGridSampleInterp3D; gCoreFunction->MNNRoiPoolingMax = MNNRoiPoolingMax; gCoreFunction->MNNRoiAlignMax = MNNRoiAlignMax; gCoreFunction->MNNRoiAlignAvg = MNNRoiAlignAvg; diff --git a/source/backend/cpu/compute/CommonOptFunction.h b/source/backend/cpu/compute/CommonOptFunction.h index 754c61c2..76baaf24 100644 --- a/source/backend/cpu/compute/CommonOptFunction.h +++ b/source/backend/cpu/compute/CommonOptFunction.h @@ -149,10 +149,13 @@ void MNNInt8ToInt16(int16_t* dest, const int8_t* source, size_t count); void MNNGridSampleComputeCord(float* dst, const float* src, size_t inH, size_t inW, size_t outH, size_t outW, size_t stride, bool alignCorners); void MNNGridSampleInterp(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode); +void MNNGridSampleComputeCord3D(float* dst, const float* src, size_t inD, size_t inH, size_t inW, size_t outD, size_t outH, size_t outW, size_t stride, bool alignCorners); +void MNNGridSampleInterp3D(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inD, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode); void MNNRoiPoolingMax(float* dst, const float* src, int hLen, int wLen, int iw); void MNNRoiAlignMax(float* dst, const float* src, const std::vector> &vecPos, const std::vector> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth); void MNNRoiAlignAvg(float* dst, const float* src, const std::vector> &vecPos, const std::vector> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth); + typedef void(*MNNBinaryExecute)(void* outputRaw, const void* inputRaw0, const void* inputRaw1, int elementSize, int broadcastIndex); typedef void(*MNNUnaryExecute)(void* outputRaw, const void* inputRaw, int elementSize); typedef void(*MNNCopyWithStride)(uint8_t* dstO, const uint8_t* srcO, int size, int stride, int ds); @@ -237,6 +240,8 @@ struct CoreFunctions { void(*MNNScaleAndAddBias)(float* dst, const float* src, const float* bias, const float* alpha, size_t planeNumber, size_t biasNumber); void(*MNNGridSampleComputeCord)(float* dst, const float* src, size_t inH, size_t inW, size_t outH, size_t outW, size_t stride, bool alignCorners); void(*MNNGridSampleInterp)(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode); + void(*MNNGridSampleComputeCord3D)(float* dst, const float* src, size_t inD, size_t inH, size_t inW, size_t outD, size_t outH, size_t outW, size_t stride1, size_t stride2, bool alignCorners); + void(*MNNGridSampleInterp3D)(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inD, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode); void(*MNNRoiPoolingMax)(float* dst, const float* src, int hLen, int wLen, int iw); void(*MNNRoiAlignMax)(float* dst, const float* src, const std::vector> &vecPos, const std::vector> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth); void(*MNNRoiAlignAvg)(float* dst, const float* src, const std::vector> &vecPos, const std::vector> &vecArea, int samplingRatioArea, int pooledHeight, int pooledWidth); diff --git a/source/backend/cpu/x86_x64/avx/PackedFunction.cpp b/source/backend/cpu/x86_x64/avx/PackedFunction.cpp index e7a5cb2c..991f0cb0 100644 --- a/source/backend/cpu/x86_x64/avx/PackedFunction.cpp +++ b/source/backend/cpu/x86_x64/avx/PackedFunction.cpp @@ -438,6 +438,154 @@ void _AVX_MNNRoiAlignAvg(float* dst, const float* src, const std::vector 0) { + float flag[PACK_UNIT] = {0.f}; + __m256i mask; + if (areaRemain < 3) { + for (int i = 0; i < areaRemain * 3; i++) { + flag[i] = -0.f; + } + mask = _mm256_loadu_si256((__m256i*)flag); + __m256 cord0 = _mm256_loadu_ps(src); + cord0 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord0), _mm256_sub_ps(in0, a)), b)); + _mm256_maskstore_ps(dst, mask, cord0); + } else if (areaRemain < 6) { + for (int i = 0; i < areaRemain * 3 - 8; i++) { + flag[i] = -0.f;; + } + mask = _mm256_loadu_si256((__m256i*)flag); + __m256 cord0 = _mm256_loadu_ps(src); + __m256 cord1 = _mm256_maskload_ps(src + PACK_UNIT, mask); + cord0 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord0), _mm256_sub_ps(in0, a)), b)); + cord1 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord1), _mm256_sub_ps(in1, a)), b)); + _mm256_storeu_ps(dst, cord0); + _mm256_maskstore_ps(dst + PACK_UNIT, mask, cord1); + } else { + for (int i = 0; i < areaRemain * 3 - 16; i++) { + flag[i] = -0.f;; + } + mask = _mm256_loadu_si256((__m256i*)flag); + __m256 cord0 = _mm256_loadu_ps(src); + __m256 cord1 = _mm256_loadu_ps(src + PACK_UNIT); + __m256 cord2 = _mm256_maskload_ps(src + PACK_UNIT * 2, mask); + cord0 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord0), _mm256_sub_ps(in0, a)), b)); + cord1 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord1), _mm256_sub_ps(in1, a)), b)); + cord2 = _mm256_mul_ps(half, _mm256_sub_ps(_mm256_mul_ps(_mm256_add_ps(one, cord2), _mm256_sub_ps(in2, a)), b)); + _mm256_storeu_ps(dst, cord0); + _mm256_storeu_ps(dst + PACK_UNIT, cord1); + _mm256_maskstore_ps(dst + PACK_UNIT * 2, mask, cord2); + } + } +} + +static size_t _AVX_MNNGridSampleComputeOffset3D(int d, int h, int w, int depth, int height, int width, bool padMode) { + if (padMode == true) { //padMode == BorderMode_ZEROS + if (h < 0 || h >= height || w < 0 || w >= width) { + return -1; + } + } else { + // Clearly, CLAMP is the right way to go for GridSamplePaddingMode_BORDER + // For GridSamplePaddingMode_REFLECTION, since we have reflected the values into (-1, 1), + // the leftover reflections degrade to GridSamplePaddingMode_BORDER + d = d < 0 ? 0 : (d > (depth - 1) ? (depth - 1) : d); + h = h < 0 ? 0 : ( h > (height - 1) ? (height - 1) : h); + w = w < 0 ? 0 : ( w > (width - 1) ? (width - 1) : w); + } + return ((d * height + h) * width + w) * PACK_UNIT; +} + +void _AVX_MNNGridSampleInterp3D(float* outputPtr, const float* inputPtr, const float* cordPtr, size_t inD, size_t inH, size_t inW, size_t outW, size_t channelCUnit, size_t inOffset, size_t outOffset, bool sampleMode, bool padMode) { + for (auto ow = 0; ow < outW; ++ow) { + auto w = cordPtr[3 * ow + 0]; + auto h = cordPtr[3 * ow + 1]; + auto d = cordPtr[3 * ow + 2]; + __m256 interp; + + if (sampleMode == true) { //sampleMode == SampleMode_NEAREST + int nd = ::floor(d + 0.5f); + int nh = ::floor(h + 0.5f); + int nw = ::floor(w + 0.5f); + size_t ns = _AVX_MNNGridSampleComputeOffset3D(nd, nh, nw, inD, inH, inW, padMode); + for (int k = 0; k < channelCUnit; ++k) { + interp = ns == -1 ? _mm256_set1_ps(0.f) : _mm256_loadu_ps(inputPtr + k * inOffset + ns); + _mm256_storeu_ps(outputPtr + k * outOffset + PACK_UNIT * ow, interp); + } + } else { //sampleMode == GridSampleMode_BILINEAR + int w0_d = ::floor(d); + int w0_h = ::floor(h); + int w0_w = ::floor(w); + int w1_d = ::ceil(d); + int w1_h = ::ceil(h); + int w1_w = ::ceil(w); + auto oneV = _mm256_set1_ps(1.0f); + + auto f0 = _mm256_set1_ps((float)w1_w - w); + auto f1 = _mm256_sub_ps(oneV, f0); + auto h0 = _mm256_set1_ps((float)w1_h - h); + auto h1 = _mm256_sub_ps(oneV, h0); + auto d0 = _mm256_set1_ps((float)w1_d - d); + auto d1 = _mm256_sub_ps(oneV, d0); + + size_t s000 = _AVX_MNNGridSampleComputeOffset3D(w0_d, w0_h, w0_w, inD, inH, inW, padMode); + size_t s001 = _AVX_MNNGridSampleComputeOffset3D(w0_d, w0_h, w1_w, inD, inH, inW, padMode); + size_t s010 = _AVX_MNNGridSampleComputeOffset3D(w0_d, w1_h, w0_w, inD, inH, inW, padMode); + size_t s011 = _AVX_MNNGridSampleComputeOffset3D(w0_d, w1_h, w1_w, inD, inH, inW, padMode); + size_t s100 = _AVX_MNNGridSampleComputeOffset3D(w1_d, w0_h, w0_w, inD, inH, inW, padMode); + size_t s101 = _AVX_MNNGridSampleComputeOffset3D(w1_d, w0_h, w1_w, inD, inH, inW, padMode); + size_t s110 = _AVX_MNNGridSampleComputeOffset3D(w1_d, w1_h, w0_w, inD, inH, inW, padMode); + size_t s111 = _AVX_MNNGridSampleComputeOffset3D(w1_d, w1_h, w1_w, inD, inH, inW, padMode); + + for (int k = 0; k < channelCUnit; ++k) { + __m256 i000 = s000 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s000); + __m256 i001 = s001 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s001); + __m256 i010 = s010 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s010); + __m256 i011 = s011 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s011); + __m256 i100 = s100 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s100); + __m256 i101 = s101 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s101); + __m256 i110 = s110 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s110); + __m256 i111 = s111 == -1 ? _mm256_setzero_ps() : _mm256_loadu_ps(inputPtr + k * inOffset + s111); + + __m256 i00 = _mm256_add_ps(_mm256_mul_ps(i000, f0), _mm256_mul_ps(i001, f1)); + __m256 i01 = _mm256_add_ps(_mm256_mul_ps(i010, f0), _mm256_mul_ps(i011, f1)); + __m256 i0 = _mm256_add_ps(_mm256_mul_ps(i00, h0), _mm256_mul_ps(i01, h1)); + __m256 i10 = _mm256_add_ps(_mm256_mul_ps(i100, f0), _mm256_mul_ps(i101, f1)); + __m256 i11 = _mm256_add_ps(_mm256_mul_ps(i110, f0), _mm256_mul_ps(i111, f1)); + __m256 i1 = _mm256_add_ps(_mm256_mul_ps(i10, h0), _mm256_mul_ps(i11, h1)); + + interp = _mm256_add_ps(_mm256_mul_ps(i0, d0), _mm256_mul_ps(i1, d1)); + _mm256_storeu_ps(outputPtr + k * outOffset + PACK_UNIT * ow, interp); + } + } + } +} + void _AVX_MNNMatrixAdd(float* C, const float* A, const float* B, size_t widthC4, size_t cStride, size_t aStride, size_t bStride, size_t height) { for (int y = 0; y < height; ++y) { @@ -727,6 +875,8 @@ void _AVX_ExtraInit(void* functions) { coreFunction->MNNDeconvRunForUnitDepthWise = _AVX_MNNDeconvRunForUnitDepthWise; coreFunction->MNNGridSampleComputeCord = _AVX_MNNGridSampleComputeCord; coreFunction->MNNGridSampleInterp = _AVX_MNNGridSampleInterp; + coreFunction->MNNGridSampleComputeCord3D = _AVX_MNNGridSampleComputeCord3D; + coreFunction->MNNGridSampleInterp3D = _AVX_MNNGridSampleInterp3D; coreFunction->MNNRoiPoolingMax = _AVX_MNNRoiPoolingMax; coreFunction->MNNRoiAlignMax = _AVX_MNNRoiAlignMax; coreFunction->MNNRoiAlignAvg = _AVX_MNNRoiAlignAvg; diff --git a/source/backend/metal/MNNMetalLib.h b/source/backend/metal/MNNMetalLib.h index dc0a3bf2..848ae05c 100644 --- a/source/backend/metal/MNNMetalLib.h +++ b/source/backend/metal/MNNMetalLib.h @@ -1,12 +1,12 @@ unsigned char MNNMetalLib[] = { 0x4d, 0x54, 0x4c, 0x42, 0x01, 0x80, 0x02, 0x00, 0x02, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0xdc, 0xaf, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0xdc, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0xd8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb4, 0x43, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x00, 0xd8, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, - 0x8c, 0x47, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x50, 0x68, 0x06, 0x00, - 0x00, 0x00, 0x00, 0x00, 0x7b, 0x00, 0x00, 0x00, 0x7d, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0xf5, 0x19, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x58, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb9, 0x43, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x15, 0x44, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2d, 0x48, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x18, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x45, 0x4c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb0, 0xcd, 0x06, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x83, 0x00, 0x00, 0x00, 0x7d, 0x00, 0x00, 0x00, 0x4e, 0x41, 0x4d, 0x45, 0x08, 0x00, 0x72, 0x65, 0x6c, 0x75, 0x5f, 0x78, 0x31, 0x00, 0x54, 0x59, 0x50, 0x45, 0x01, 0x00, 0x02, 0x48, 0x41, 0x53, 0x48, 0x20, 0x00, 0xfe, 0x7a, 0x99, 0x60, 0xb9, 0x6a, 0x40, 0xd8, 0xa1, @@ -27,2409 +27,3127 @@ unsigned char MNNMetalLib[] = { 0x00, 0x00, 0x30, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0x45, 0x52, 0x53, 0x08, 0x00, 0x01, 0x00, 0x08, 0x00, 0x01, 0x00, 0x01, 0x00, 0x4d, 0x44, 0x53, 0x5a, 0x08, 0x00, 0x40, 0x0a, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x45, 0x4e, 0x44, 0x54, 0x81, 0x00, 0x00, 0x00, 0x4e, 0x41, - 0x4d, 0x45, 0x0c, 0x00, 0x63, 0x6f, 0x6e, 0x76, 0x5f, 0x69, 0x6d, 0x32, - 0x63, 0x6f, 0x6c, 0x00, 0x54, 0x59, 0x50, 0x45, 0x01, 0x00, 0x02, 0x48, - 0x41, 0x53, 0x48, 0x20, 0x00, 0x7c, 0x5b, 0xfd, 0xcf, 0x59, 0x65, 0x4d, - 0xe2, 0x86, 0x09, 0x66, 0xfb, 0x1e, 0x6b, 0x5e, 0xed, 0x07, 0x48, 0x1b, - 0x23, 0xa4, 0xad, 0x6d, 0x19, 0xa6, 0x4b, 0x57, 0x34, 0x50, 0xec, 0xdb, - 0x3c, 0x4f, 0x46, 0x46, 0x54, 0x18, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, - 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x70, - 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x56, 0x45, 0x52, 0x53, 0x08, - 0x00, 0x01, 0x00, 0x08, 0x00, 0x01, 0x00, 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0x26, 0x82, 0x50, 0x28, + 0x82, 0x50, 0x2a, 0xc5, 0x52, 0x2e, 0x65, 0xe3, 0x13, 0x96, 0x26, 0xe7, 0x22, 0x56, 0x67, 0x66, 0x56, 0x26, 0xf7, 0x35, 0x97, 0xa6, 0x57, 0x46, 0x2a, 0x2c, 0x4d, 0xce, 0x65, 0x8e, 0x4e, 0xae, 0x6e, 0x8c, 0xee, 0x8b, 0x2e, 0x0f, 0xae, 0xec, 0x2b, 0xcd, 0xcd, 0xec, 0x8d, 0x43, 0x9a, 0x1b, @@ -2450,9355 +3168,8115 @@ unsigned char MNNMetalLib[] = { 0x1e, 0x15, 0xb2, 0x34, 0xb6, 0x30, 0xba, 0xb4, 0x37, 0xb7, 0x2f, 0x3c, 0x2a, 0x64, 0x69, 0x6c, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x79, 0x54, 0xc2, 0xc6, 0xe8, 0xd2, 0xec, 0xc2, 0xe8, 0xd2, 0xde, 0xdc, 0x86, 0xf8, - 0x05, 0x31, 0x10, 0x04, 0x31, 0x38, 0x61, 0xe0, 0x88, 0x01, 0x41, 0x10, - 0x04, 0x31, 0x38, 0x61, 0xe0, 0x8c, 0x01, 0x61, 0x10, 0x04, 0x31, 0x38, - 0x61, 0xe0, 0x90, 0x01, 0xa1, 0x10, 0x04, 0x31, 0x38, 0x61, 0xe0, 0x94, - 0x01, 0xb1, 0x10, 0x04, 0x31, 0x38, 0x61, 0xe0, 0x98, 0x01, 0xc1, 0x10, - 0x04, 0x31, 0x38, 0x61, 0xe0, 0x9c, 0x01, 0xd1, 0x10, 0x04, 0x31, 0x38, - 0x61, 0xe0, 0xa0, 0x01, 0xe1, 0x10, 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0xe6, 0xc0, 0x0e, 0xe1, 0x70, 0x0e, 0xf3, 0x30, 0x45, 0x08, @@ -14388,2516 +14813,246 @@ unsigned char MNNMetalLib[] = { 0x3b, 0xc0, 0x03, 0x3d, 0xa4, 0xc3, 0x3b, 0xb8, 0xc3, 0x3c, 0x4c, 0x19, 0x14, 0xc6, 0x19, 0xa1, 0x84, 0x43, 0x3a, 0xc8, 0x83, 0x1b, 0xac, 0x43, 0x39, 0xc8, 0x83, 0x3b, 0x94, 0x03, 0x3b, 0x4c, 0x09, 0xdc, 0x00, 0x00, - 0x79, 0x18, 0x00, 0x00, 0x7b, 0x00, 0x00, 0x00, 0x33, 0x08, 0x80, 0x1c, - 0xc4, 0xe1, 0x1c, 0x66, 0x14, 0x01, 0x3d, 0x88, 0x43, 0x38, 0x84, 0xc3, - 0x8c, 0x42, 0x80, 0x07, 0x79, 0x78, 0x07, 0x73, 0x98, 0x71, 0x0c, 0xe6, - 0x00, 0x0f, 0xed, 0x10, 0x0e, 0xf4, 0x80, 0x0e, 0x33, 0x0c, 0x42, 0x1e, - 0xc2, 0xc1, 0x1d, 0xce, 0xa1, 0x1c, 0x66, 0x30, 0x05, 0x3d, 0x88, 0x43, - 0x38, 0x84, 0x83, 0x1b, 0xcc, 0x03, 0x3d, 0xc8, 0x43, 0x3d, 0x8c, 0x03, - 0x3d, 0xcc, 0x78, 0x8c, 0x74, 0x70, 0x07, 0x7b, 0x08, 0x07, 0x79, 0x48, - 0x87, 0x70, 0x70, 0x07, 0x7a, 0x70, 0x03, 0x76, 0x78, 0x87, 0x70, 0x20, - 0x87, 0x19, 0xcc, 0x11, 0x0e, 0xec, 0x90, 0x0e, 0xe1, 0x30, 0x0f, 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0x0a, 0x3b, 0xb0, 0x83, 0x3d, 0xb4, 0x83, 0x1b, 0xa4, @@ -16911,11225 +15066,245 @@ unsigned char MNNMetalLib[] = { 0xc2, 0x3b, 0xc0, 0x03, 0x3d, 0xa4, 0xc3, 0x3b, 0xb8, 0xc3, 0x3c, 0x4c, 0x19, 0x14, 0xc6, 0x19, 0xa1, 0x84, 0x43, 0x3a, 0xc8, 0x83, 0x1b, 0xac, 0x43, 0x39, 0xc8, 0x83, 0x3b, 0x94, 0x03, 0x3b, 0x4c, 0x09, 0xdc, 0x00, - 0x79, 0x18, 0x00, 0x00, 0x7b, 0x00, 0x00, 0x00, 0x33, 0x08, 0x80, 0x1c, - 0xc4, 0xe1, 0x1c, 0x66, 0x14, 0x01, 0x3d, 0x88, 0x43, 0x38, 0x84, 0xc3, - 0x8c, 0x42, 0x80, 0x07, 0x79, 0x78, 0x07, 0x73, 0x98, 0x71, 0x0c, 0xe6, - 0x00, 0x0f, 0xed, 0x10, 0x0e, 0xf4, 0x80, 0x0e, 0x33, 0x0c, 0x42, 0x1e, - 0xc2, 0xc1, 0x1d, 0xce, 0xa1, 0x1c, 0x66, 0x30, 0x05, 0x3d, 0x88, 0x43, - 0x38, 0x84, 0x83, 0x1b, 0xcc, 0x03, 0x3d, 0xc8, 0x43, 0x3d, 0x8c, 0x03, - 0x3d, 0xcc, 0x78, 0x8c, 0x74, 0x70, 0x07, 0x7b, 0x08, 0x07, 0x79, 0x48, - 0x87, 0x70, 0x70, 0x07, 0x7a, 0x70, 0x03, 0x76, 0x78, 0x87, 0x70, 0x20, - 0x87, 0x19, 0xcc, 0x11, 0x0e, 0xec, 0x90, 0x0e, 0xe1, 0x30, 0x0f, 0x6e, - 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-28143,2874 +15318,3930 @@ unsigned char MNNMetalLib[] = { 0x3a, 0xb0, 0x43, 0x39, 0xfc, 0xc2, 0x3b, 0xc0, 0x03, 0x3d, 0xa4, 0xc3, 0x3b, 0xb8, 0xc3, 0x3c, 0x4c, 0x19, 0x14, 0xc6, 0x19, 0xa1, 0x84, 0x43, 0x3a, 0xc8, 0x83, 0x1b, 0xac, 0x43, 0x39, 0xc8, 0x83, 0x3b, 0x94, 0x03, - 0x3b, 0x4c, 0x09, 0xec, 0x00, 0x00, 0x00, 0x00, 0x79, 0x18, 0x00, 0x00, - 0x7b, 0x00, 0x00, 0x00, 0x33, 0x08, 0x80, 0x1c, 0xc4, 0xe1, 0x1c, 0x66, - 0x14, 0x01, 0x3d, 0x88, 0x43, 0x38, 0x84, 0xc3, 0x8c, 0x42, 0x80, 0x07, - 0x79, 0x78, 0x07, 0x73, 0x98, 0x71, 0x0c, 0xe6, 0x00, 0x0f, 0xed, 0x10, - 0x0e, 0xf4, 0x80, 0x0e, 0x33, 0x0c, 0x42, 0x1e, 0xc2, 0xc1, 0x1d, 0xce, - 0xa1, 0x1c, 0x66, 0x30, 0x05, 0x3d, 0x88, 0x43, 0x38, 0x84, 0x83, 0x1b, - 0xcc, 0x03, 0x3d, 0xc8, 0x43, 0x3d, 0x8c, 0x03, 0x3d, 0xcc, 0x78, 0x8c, - 0x74, 0x70, 0x07, 0x7b, 0x08, 0x07, 0x79, 0x48, 0x87, 0x70, 0x70, 0x07, - 0x7a, 0x70, 0x03, 0x76, 0x78, 0x87, 0x70, 0x20, 0x87, 0x19, 0xcc, 0x11, - 0x0e, 0xec, 0x90, 0x0e, 0xe1, 0x30, 0x0f, 0x6e, 0x30, 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0x0e, 0x6e, @@ -31024,2813 +19255,4468 @@ unsigned char MNNMetalLib[] = { 0xf0, 0x0b, 0xef, 0x00, 0x0f, 0xf4, 0x90, 0x0e, 0xef, 0xe0, 0x0e, 0xf3, 0x30, 0x65, 0x50, 0x18, 0x67, 0x84, 0x12, 0x0e, 0xe9, 0x20, 0x0f, 0x6e, 0xb0, 0x0e, 0xe5, 0x20, 0x0f, 0xee, 0x50, 0x0e, 0xec, 0x30, 0x25, 0x68, - 0x03, 0x00, 0x00, 0x00, 0x79, 0x18, 0x00, 0x00, 0x7b, 0x00, 0x00, 0x00, - 0x33, 0x08, 0x80, 0x1c, 0xc4, 0xe1, 0x1c, 0x66, 0x14, 0x01, 0x3d, 0x88, - 0x43, 0x38, 0x84, 0xc3, 0x8c, 0x42, 0x80, 0x07, 0x79, 0x78, 0x07, 0x73, - 0x98, 0x71, 0x0c, 0xe6, 0x00, 0x0f, 0xed, 0x10, 0x0e, 0xf4, 0x80, 0x0e, - 0x33, 0x0c, 0x42, 0x1e, 0xc2, 0xc1, 0x1d, 0xce, 0xa1, 0x1c, 0x66, 0x30, - 0x05, 0x3d, 0x88, 0x43, 0x38, 0x84, 0x83, 0x1b, 0xcc, 0x03, 0x3d, 0xc8, - 0x43, 0x3d, 0x8c, 0x03, 0x3d, 0xcc, 0x78, 0x8c, 0x74, 0x70, 0x07, 0x7b, - 0x08, 0x07, 0x79, 0x48, 0x87, 0x70, 0x70, 0x07, 0x7a, 0x70, 0x03, 0x76, - 0x78, 0x87, 0x70, 0x20, 0x87, 0x19, 0xcc, 0x11, 0x0e, 0xec, 0x90, 0x0e, - 0xe1, 0x30, 0x0f, 0x6e, 0x30, 0x0f, 0xe3, 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0x0d, 0x86, 0x18, 0x1d, + 0x2d, 0x74, 0xb3, 0xf0, 0xdd, 0xc1, 0x88, 0x88, 0x1d, 0xd8, 0xc1, 0x1e, 0xda, 0xc1, 0x0d, 0xda, 0xe1, 0x1d, 0xc8, 0xa1, 0x1e, 0xd8, 0xa1, 0x1c, 0xdc, 0xc0, 0x1c, 0xd8, 0x21, 0x1c, 0xce, 0x61, 0x1e, 0xa6, 0x08, 0xc1, 0x30, 0x42, 0x61, 0x07, 0x76, 0xb0, 0x87, 0x76, 0x70, 0x83, 0x74, 0x20, @@ -35949,575 +25520,13268 @@ unsigned char MNNMetalLib[] = { 0x87, 0x77, 0x68, 0x07, 0x78, 0x48, 0x07, 0x76, 0x28, 0x87, 0x5f, 0x78, 0x07, 0x78, 0xa0, 0x87, 0x74, 0x78, 0x07, 0x77, 0x98, 0x87, 0x29, 0x83, 0xc2, 0x38, 0x23, 0x94, 0x70, 0x48, 0x07, 0x79, 0x70, 0x83, 0x75, 0x28, - 0x07, 0x79, 0x70, 0x87, 0x72, 0x60, 0x87, 0x29, 0x81, 0x19, 0x00, 0x00, - 0x79, 0x18, 0x00, 0x00, 0x7b, 0x00, 0x00, 0x00, 0x33, 0x08, 0x80, 0x1c, - 0xc4, 0xe1, 0x1c, 0x66, 0x14, 0x01, 0x3d, 0x88, 0x43, 0x38, 0x84, 0xc3, - 0x8c, 0x42, 0x80, 0x07, 0x79, 0x78, 0x07, 0x73, 0x98, 0x71, 0x0c, 0xe6, - 0x00, 0x0f, 0xed, 0x10, 0x0e, 0xf4, 0x80, 0x0e, 0x33, 0x0c, 0x42, 0x1e, - 0xc2, 0xc1, 0x1d, 0xce, 0xa1, 0x1c, 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0x40, 0x70, 0x41, 0x94, 0xf1, 0x04, 0x2b, 0xa0, 0x80, 0x9c, 0x11, 0x65, + 0x3c, 0xc1, 0x0a, 0x28, 0x28, 0x63, 0x08, 0x81, 0x19, 0x8c, 0x18, 0x10, + 0x40, 0xa0, 0x06, 0xc1, 0x1c, 0xc3, 0x15, 0xa0, 0xc1, 0x18, 0x02, 0xb1, + 0x06, 0x23, 0x06, 0x04, 0x10, 0xb4, 0x41, 0x30, 0xc7, 0x30, 0x04, 0x6d, + 0x30, 0x86, 0x70, 0xa4, 0xc1, 0x88, 0x01, 0x01, 0x04, 0x70, 0x10, 0xcc, + 0x31, 0x0c, 0xc1, 0x1a, 0x8c, 0x21, 0x28, 0x6a, 0x30, 0x62, 0x40, 0x00, + 0xc1, 0x1c, 0x04, 0x73, 0x0c, 0x43, 0xc0, 0x06, 0x16, 0x40, 0xf2, 0x19, + 0x4f, 0x20, 0x03, 0x88, 0x82, 0x32, 0x86, 0x10, 0xd4, 0xc1, 0x88, 0x01, + 0x01, 0x04, 0x79, 0x10, 0xcc, 0x31, 0x98, 0x41, 0x70, 0x07, 0x63, 0x08, + 0x84, 0x1e, 0x8c, 0x18, 0x10, 0x40, 0xc0, 0x07, 0xc1, 0x1c, 0xc3, 0x10, + 0xf0, 0xc1, 0x18, 0xc2, 0x81, 0x07, 0x23, 0x06, 0x04, 0x10, 0xfc, 0x41, + 0x30, 0xc7, 0x30, 0x04, 0x7a, 0x30, 0x86, 0xa0, 0xe4, 0xc1, 0x88, 0x01, + 0x01, 0x04, 0xa2, 0x10, 0xcc, 0x31, 0x0c, 0xc1, 0x1e, 0x58, 0xf0, 0xc0, + 0x67, 0x3c, 0xc1, 0x0e, 0xc4, 0x60, 0x30, 0x22, 0x10, 0x08, 0x60, 0x96, + 0x60, 0xc0, 0x80, 0x18, 0x00, 0x09, 0x00, 0x00, 0x00, 0x5b, 0x06, 0x21, + 0xc8, 0x83, 0x2d, 0xc3, 0x11, 0xe8, 0xc1, 0x96, 0x21, 0x09, 0xf6, 0x60, + 0xcb, 0x20, 0x05, 0x7c, 0xb0, 0x65, 0xa8, 0x02, 0x3e, 0xd8, 0x32, 0x90, + 0x41, 0xc0, 0x07, 0x5b, 0x86, 0x39, 0x08, 0xf8, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00 }; -unsigned int MNNMetalLib_len = 438236; +unsigned int MNNMetalLib_len = 465397; diff --git a/source/backend/metal/MetalBackend.metal b/source/backend/metal/MetalBackend.metal index affd5de0..5e2473e5 100644 --- a/source/backend/metal/MetalBackend.metal +++ b/source/backend/metal/MetalBackend.metal @@ -282,3 +282,58 @@ kernel void blit_int64(const device short4 *in [[buffer(0)]], out[int(dstOffset)] = in[int(srcOffset)]; } } + +template +static inline void template_NHWC_to_NCHW(const device IType* in, + device OType* out, constant tensor_shape &s, uint2 gid) { + int b = gid.y / s.slice; + int c4 = gid.y % s.slice; + + auto in_off = (b * s.size + gid.x) * s.channel + c4 * 4; + auto out_off = (b * s.channel + c4 * 4) * s.size + gid.x; + + out[out_off] = in[in_off]; + if(c4 * 4 + 1 < s.channel) { + out[out_off + s.size] = in[in_off + 1]; + } + if(c4 * 4 + 2 < s.channel) { + out[out_off + s.size * 2] = in[in_off + 2]; + } + if(c4 * 4 + 3 < s.channel) { + out[out_off + s.size * 3] = in[in_off + 3]; + } +} + +kernel void upcast_f_NHWC_to_NCHW(const device ftype *in [[buffer(0)]], + device float *out [[buffer(1)]], + constant tensor_shape &s [[buffer(2)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x < s.size && (int)gid.y < s.batch_slices) template_NHWC_to_NCHW(in, out, s, gid); +} + +template +static inline void template_NCHW_to_NHWC(const device IType* in, + device OType* out, constant tensor_shape &s, uint2 gid) { + int b = gid.y / s.slice; + int c4 = gid.y % s.slice; + + auto in_off = (b * s.channel + c4 * 4) * s.size + gid.x; + auto out_off = (b * s.size + gid.x) * s.channel + c4 * 4; + + out[out_off] = in[in_off]; + if(c4 * 4 + 1 < s.channel) { + out[out_off + 1] = in[in_off + s.size]; + } + if(c4 * 4 + 2 < s.channel) { + out[out_off + 2] = in[in_off + s.size * 2]; + } + if(c4 * 4 + 3 < s.channel) { + out[out_off + 3] = in[in_off + s.size * 3]; + } +} +kernel void downcast_f_NCHW_to_NHWC(const device float *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant tensor_shape &s [[buffer(2)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x < s.size && (int)gid.y < s.batch_slices) template_NCHW_to_NHWC(in, out, s, gid); +} diff --git a/source/backend/metal/MetalBackend.mm b/source/backend/metal/MetalBackend.mm index 1b0747e8..97843d64 100644 --- a/source/backend/metal/MetalBackend.mm +++ b/source/backend/metal/MetalBackend.mm @@ -282,7 +282,7 @@ static NSString *kernelForConvert(halide_type_t type, MNN_DATA_FORMAT from, MNN_ // from MNN_DATA_FORMAT_NCHW {nil, nil, @"upcast_f_NCHW_to_NC4HW4", nil, nil}, // from MNN_DATA_FORMAT_NHWC - {nil, nil, @"upcast_f_NHWC_to_NC4HW4", nil, nil}, + {@"upcast_f_NHWC_to_NCHW", nil, @"upcast_f_NHWC_to_NC4HW4", nil, nil}, // from MNN_DATA_FORMAT_NC4HW4 {@"upcast_f_NC4HW4_to_NCHW", @"upcast_f_NC4HW4_to_NHWC", nil, nil, nil}, // from MNN_DATA_FORMAT_NHWC4 @@ -293,7 +293,7 @@ static NSString *kernelForConvert(halide_type_t type, MNN_DATA_FORMAT from, MNN_ // down { // from MNN_DATA_FORMAT_NCHW - {nil, nil, @"downcast_f_NCHW_to_NC4HW4", nil, nil}, + {nil, @"downcast_f_NCHW_to_NHWC", @"downcast_f_NCHW_to_NC4HW4", nil, nil}, // from MNN_DATA_FORMAT_NHWC {nil, nil, @"downcast_f_NHWC_to_NC4HW4", nil, nil}, // from MNN_DATA_FORMAT_NC4HW4 diff --git a/source/backend/metal/MetalCodeGen.py b/source/backend/metal/MetalCodeGen.py index 6e55944a..a1f52dac 100644 --- a/source/backend/metal/MetalCodeGen.py +++ b/source/backend/metal/MetalCodeGen.py @@ -35,7 +35,7 @@ def genSchema(): FLATC = shaderPath + "/../../../3rd_party/flatbuffers/tmp/flatc" sourceFile = shaderPath + "/schema/MetalCache.fbs" destFile = shaderPath + "/" - cmd = FLATC + " -c " + sourceFile +" --gen-object-api" + cmd = FLATC + " -c " + sourceFile +" --gen-object-api" +" --reflect-names" print(cmd) print(os.popen(cmd).read()) return diff --git a/source/backend/metal/MetalLayerNorm.hpp b/source/backend/metal/MetalLayerNorm.hpp new file mode 100644 index 00000000..05da6620 --- /dev/null +++ b/source/backend/metal/MetalLayerNorm.hpp @@ -0,0 +1,44 @@ +// +// MetalLayerNorm.hpp +// MNN +// +// Created by MNN on 2019/01/30. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef MetalLayerNorm_hpp +#define MetalLayerNorm_hpp + +#import "core/Execution.hpp" +#import "MNN_generated.h" +#import "MetalDefine.h" + +#if MNN_METAL_ENABLED +namespace MNN { + +class MetalLayerNorm : public Execution { +public: + MetalLayerNorm(Backend *backend, const LayerNorm *layernorm); + virtual ~MetalLayerNorm() = default; + virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; + virtual ErrorCode onExecute(const std::vector &inputs, const std::vector &outputs) override; + +private: + int mOutside; + std::vector mAxis; + int mInside; + int mGroup = 1; + float mEps; + + bool has_gamma_beta_ = false; + id mGammaBuffer = nil; + id mBetaBuffer = nil; + id mShapeBuffer; + id mPipeline; + std::pair mThreads; + +}; + +} // namespace MNN +#endif /* MNN_METAL_ENABLED */ +#endif /* MetalLayerNorm_hpp */ diff --git a/source/backend/metal/MetalLayerNorm.metal b/source/backend/metal/MetalLayerNorm.metal new file mode 100644 index 00000000..b18565a7 --- /dev/null +++ b/source/backend/metal/MetalLayerNorm.metal @@ -0,0 +1,99 @@ +// +// MetalLayerNorm.metal +// MNN +// +// Created by MNN on 2022/06/14. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include "MetalDefine.metal" + +using namespace metal; + +struct layernorm_constants { + int inside; + int outside; + float eps; + int has_gamma_beta; +}; + +kernel void layernorm_x1(const device ftype *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float *gamma [[buffer(3)]], + const device float *beta [[buffer(4)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x >= cst.inside || (int)gid.y >= cst.outside) { + return; + } + auto in_data = in + gid.y * cst.inside; + auto out_data = out + gid.y * cst.inside; + + float mean; + float sum = 0.0f; + float square_sum = 0.0f; + + for(int i = 0; i < cst.inside; i++) { + sum += in_data[i]; + } + mean = sum / cst.inside; + + for(int i = 0; i < cst.inside; i++) { + float dis = (in_data[i] - mean); + square_sum += dis * dis; + } + float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); + + float norm = var * ((float)in_data[gid.x] - mean); + if(cst.has_gamma_beta) { + out_data[gid.x] = (ftype)(norm * gamma[gid.x] + beta[gid.x]); + } else { + out_data[gid.x] = (ftype)(norm); + } +} + + +kernel void layernorm_x4(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant layernorm_constants& cst [[buffer(2)]], + const device float4 *gamma [[buffer(3)]], + const device float4 *beta [[buffer(4)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x >= cst.inside/4 || (int)gid.y >= cst.outside) { + return; + } + auto in_data = in + gid.y * cst.inside/4; + auto out_data = out + gid.y * cst.inside/4; + + float mean; + float sum = 0.0f; + float square_sum = 0.0f; + + for(int i = 0; i < cst.inside/4; i++) { + sum += in_data[i].x; + sum += in_data[i].y; + sum += in_data[i].z; + sum += in_data[i].w; + } + mean = sum / cst.inside; + + for(int i = 0; i < cst.inside/4; i++) { + float dis = (in_data[i].x - mean); + square_sum += dis * dis; + dis = (in_data[i].y - mean); + square_sum += dis * dis; + dis = (in_data[i].z - mean); + square_sum += dis * dis; + dis = (in_data[i].w - mean); + square_sum += dis * dis; + } + float var = 1.0 / sqrt(square_sum / cst.inside + cst.eps); + + float4 norm = var * ((float4)in_data[gid.x] - mean); + if(cst.has_gamma_beta) { + out_data[gid.x] = (ftype4)(norm * gamma[gid.x] + beta[gid.x]); + } else { + out_data[gid.x] = (ftype4)(norm); + } +} diff --git a/source/backend/metal/MetalLayerNorm.mm b/source/backend/metal/MetalLayerNorm.mm new file mode 100755 index 00000000..162c7791 --- /dev/null +++ b/source/backend/metal/MetalLayerNorm.mm @@ -0,0 +1,135 @@ +// +// MetalLayerNorm.mm +// MNN +// +// Created by MNN on 2022/06/14. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#import "backend/metal/MetalLayerNorm.hpp" +#import "backend/metal/MNNMetalContext.h" +#import "backend/metal/MetalBackend.hpp" + +#if MNN_METAL_ENABLED +namespace MNN { + +MetalLayerNorm::MetalLayerNorm(Backend *backend, const LayerNorm *layernorm) + : Execution(backend), mGroup(layernorm->group()), + mEps(layernorm->epsilon()) { + auto context = (__bridge MNNMetalContext *)static_cast(backend)->context(); + + int axis_size = layernorm->axis()->size(); + mAxis.resize(axis_size); + for (int i = 0; i < axis_size; ++i) { + mAxis[i] = layernorm->axis()->Get(i); + } + + if (layernorm->gamma() && layernorm->beta()) { + has_gamma_beta_ = true; + int gamma_size = layernorm->gamma()->size(); + const float* gamma_data = layernorm->gamma()->data(); + mGammaBuffer = + [context newDeviceBuffer:gamma_size * sizeof(float) access:CPUWriteOnly]; + + memcpy(mGammaBuffer.contents, (const void *)gamma_data, gamma_size * sizeof(float)); + + if (layernorm->beta()->size() != gamma_size) { + MNN_ERROR("Size of gamma and beta are not match in MetalLayerNorm.\n"); + } + + const float* beta_data = layernorm->beta()->data(); + mBetaBuffer = + [context newDeviceBuffer:gamma_size * sizeof(float) access:CPUWriteOnly]; + memcpy(mBetaBuffer.contents, (const void *)beta_data, gamma_size * sizeof(float)); + } +} + +ErrorCode MetalLayerNorm::onResize(const std::vector &inputs, const std::vector &outputs) { + auto backend = static_cast(this->backend()); + auto context = (__bridge MNNMetalContext *)backend->context(); + + auto input = inputs[0], output = outputs[0]; + + mOutside = 1; + mInside = 1; + int rank = input->dimensions(); + if (mGroup > 1) { + mOutside = input->length(0) * mGroup; + for (int i = 1; i < rank; i++) { + mInside *= input->length(i); + } + mInside /= mGroup; + return NO_ERROR; + } + std::vector axis(mAxis.size()); + for (int i = 0; i < mAxis.size(); ++i) { + if (mAxis[i] < 0) { + mAxis[i] += rank; + } + } + std::sort(mAxis.begin(), mAxis.end()); + + for (int i = 0; i < rank - axis.size(); ++i) { + mOutside *= input->length(i); + } + for (int i = rank - axis.size(); i < rank; ++i) { + mInside *= input->length(i); + } + + mShapeBuffer = [context newDeviceBuffer:3 * sizeof(int) + sizeof(float) access:CPUWriteOnly]; + ((int *)mShapeBuffer.contents)[0] = mInside; + ((int *)mShapeBuffer.contents)[1] = mOutside; + ((float *)mShapeBuffer.contents)[2] = mEps; + ((int *)mShapeBuffer.contents)[3] = (int)has_gamma_beta_; + + + bool parallel = (mInside > 32) && ((mInside & 3) == 0); + mPipeline = [context pipelineWithName:parallel ? @"layernorm_x4" : @"layernorm_x1"]; + + auto inside = parallel ? mInside/4 : mInside; + mThreads = [context computeBestGroupAndLocal:mPipeline threads:MTLSizeMake((NSUInteger)inside, (NSUInteger)mOutside, 1)]; + return NO_ERROR; +} + +ErrorCode MetalLayerNorm::onExecute(const std::vector &inputs, const std::vector &outputs) { + auto backend = static_cast(this->backend()); + auto context = (__bridge MNNMetalContext *)backend->context(); + + if(backend->isCommandEncoderSet()) { + return NO_ERROR; + } + + auto func = [=](){ + auto input = inputs[0], output = outputs[0]; + + auto encoder = backend->encoder(); + [encoder setComputePipelineState:mPipeline]; + [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; + [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; + [encoder setBuffer:mShapeBuffer offset:0 atIndex:2]; + [encoder setBuffer:mGammaBuffer offset:0 atIndex:3]; + [encoder setBuffer:mBetaBuffer offset:0 atIndex:4]; + + [encoder dispatchThreadgroups:mThreads.first threadsPerThreadgroup:mThreads.second]; + MNN_PRINT_ENCODER(context, encoder); + + auto context = (__bridge MNNMetalContext *)backend->context(); + if(backend->isCmdBufferCommit()) { + backend->flushEncoder(); + [context commit_net]; + } + }; + func(); + backend->addOpEncoder(func); + return NO_ERROR; +} + +class MetalLayerNormCreator : public MetalBackend::Creator { +public: + virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector &outputs) const { + return new MetalLayerNorm(backend, op->main_as_LayerNorm()); + } +}; +REGISTER_METAL_OP_CREATOR(MetalLayerNormCreator, OpType_LayerNorm); +} // namespace MNN +#endif /* MNN_METAL_ENABLED */ diff --git a/source/backend/metal/MetalOPRegister.mm b/source/backend/metal/MetalOPRegister.mm index eb45d540..c84a7ee5 100644 --- a/source/backend/metal/MetalOPRegister.mm +++ b/source/backend/metal/MetalOPRegister.mm @@ -2,10 +2,12 @@ #import "backend/metal/MetalDefine.h" namespace MNN { #if MNN_METAL_ENABLED + extern void ___MetalSoftmaxCreator__OpType_Softmax__(); extern void ___MetalCastCreator__OpType_Cast__(); extern void ___MetalReductionCreator__OpType_Reduction__(); extern void ___MetalEltwiseCreator__OpType_Eltwise__(); extern void ___MetalConvolutionCreator__OpType_Convolution__(); + extern void ___MetalLayerNormCreator__OpType_LayerNorm__(); extern void ___MetalMatMulCreator__OpType_MatMul__(); extern void ___MetalBinaryCreator__OpType_BinaryOp__(); extern void ___MetalConvolutionDepthwiseCreator__OpType_ConvolutionDepthwise__(); @@ -24,10 +26,12 @@ extern void ___MetalPReLUCreator__OpType_PReLU__(); extern void ___MetalReLU6Creator__OpType_ReLU6__(); void registerMetalOps() { + ___MetalSoftmaxCreator__OpType_Softmax__(); ___MetalCastCreator__OpType_Cast__(); ___MetalReductionCreator__OpType_Reduction__(); ___MetalEltwiseCreator__OpType_Eltwise__(); ___MetalConvolutionCreator__OpType_Convolution__(); + ___MetalLayerNormCreator__OpType_LayerNorm__(); ___MetalMatMulCreator__OpType_MatMul__(); ___MetalBinaryCreator__OpType_BinaryOp__(); ___MetalConvolutionDepthwiseCreator__OpType_ConvolutionDepthwise__(); diff --git a/source/backend/metal/MetalSoftmax.hpp b/source/backend/metal/MetalSoftmax.hpp new file mode 100644 index 00000000..ad885309 --- /dev/null +++ b/source/backend/metal/MetalSoftmax.hpp @@ -0,0 +1,34 @@ +// +// MetalSoftmax.hpp +// MNN +// +// Created by MNN on 2019/01/30. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#ifndef MetalSoftmax_hpp +#define MetalSoftmax_hpp + +#import "core/Execution.hpp" + +#if MNN_METAL_ENABLED +namespace MNN { + +class MetalSoftmax : public Execution { +public: + MetalSoftmax(Backend *backend, int32_t axis); + virtual ~MetalSoftmax() = default; + virtual ErrorCode onResize(const std::vector &inputs, const std::vector &outputs) override; + virtual ErrorCode onExecute(const std::vector &inputs, const std::vector &outputs) override; + +private: + int32_t mAxis; + int32_t mAxisLen; + id mShapeBuffer; + id mPipeline; + std::pair mThreads; +}; + +} // namespace MNN +#endif /* MNN_METAL_ENABLED */ +#endif /* MetalSoftmax_hpp */ diff --git a/source/backend/metal/MetalSoftmax.metal b/source/backend/metal/MetalSoftmax.metal new file mode 100644 index 00000000..12083223 --- /dev/null +++ b/source/backend/metal/MetalSoftmax.metal @@ -0,0 +1,117 @@ +// +// MetalSoftmax.metal +// MNN +// +// Created by MNN on 2018/08/08. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include "MetalDefine.metal" + +using namespace metal; + +struct softmax_shape { + int inside_size; + int axis_length; + int outside_size; + int flat_length; +}; + +static inline float softmax_max4(float4 value) { + return max(max(value[0], value[1]), max(value[2], value[3])); +} + +static inline float softmax_sum4(float4 value) { + return value[0] + value[1] + value[2] + value[3]; +} + +static inline float4 softmax_filter(float4 value, int z, int limit) { + return select(0, value, z * 4 + int4(0, 1, 2, 3) < limit); +} + +kernel void softmax_plane(const device ftype *in [[buffer(0)]], + device ftype *out [[buffer(1)]], + constant softmax_shape& s [[buffer(2)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x >= s.inside_size || (int)gid.y >= s.outside_size) return; + + auto axis_off = gid.y * s.axis_length * s.inside_size + gid.x; + auto axis_in = in + axis_off; + auto axis_out = out + axis_off; + + // get max + auto max1 = axis_in[0]; + for (int i = 1; i < s.axis_length; i++) { + max1 = max(max1, axis_in[i * s.inside_size]); + } + + // get sum + float sum1 = 0; + for (int i = 0; i < s.axis_length; i++) { + sum1 += float(exp(axis_in[i * s.inside_size] - max1)); + } + + // output + for (int i = 0; i < s.axis_length; i++) { + axis_out[i * s.inside_size] = ftype(exp(float(axis_in[i * s.inside_size] - max1)) / sum1); + } +} + +kernel void softmax_on_reorder(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant softmax_shape& s [[buffer(2)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x >= s.inside_size || (int)gid.y >= s.outside_size) return; + + auto axis_off = gid.y * s.axis_length * s.inside_size + gid.x; + auto axis_in = in + axis_off; + auto axis_out = out + axis_off; + + // get max + auto max4 = softmax_filter(float4(axis_in[0]), 0, s.flat_length); + for (int i = 1; i < s.axis_length; i++) { + max4 = max(max4, softmax_filter(float4(axis_in[i * s.inside_size]), i, s.flat_length)); + } + float max1 = softmax_max4(max4); + + // get sum + float4 sum4 = 0; + for (int i = 0; i < s.axis_length; i++) { + sum4 += softmax_filter(exp(float4(axis_in[i * s.inside_size] - max1)), i, s.flat_length); + } + float sum1 = softmax_sum4(sum4); + + // output + for (int i = 0; i < s.axis_length; i++) { + axis_out[i * s.inside_size] = ftype4(exp(float4(axis_in[i * s.inside_size]) - max1) / sum1); + } +} + +kernel void softmax_off_reorder(const device ftype4 *in [[buffer(0)]], + device ftype4 *out [[buffer(1)]], + constant softmax_shape& s [[buffer(2)]], + uint2 gid [[thread_position_in_grid]]) { + if ((int)gid.x >= s.inside_size || (int)gid.y >= s.outside_size) return; + + auto axis_off = gid.y * s.axis_length * s.inside_size + gid.x; + auto axis_in = in + axis_off; + auto axis_out = out + axis_off; + + // get max + auto max4 = axis_in[0]; + for (int i = 1; i < s.axis_length; i++) { + max4 = max(max4, axis_in[i * s.inside_size]); + } + + // get sum + float4 sum4 = 0; + for (int i = 0; i < s.axis_length; i++) { + sum4 += exp(float4(axis_in[i * s.inside_size] - max4)); + } + + // output + for (int i = 0; i < s.axis_length; i++) { + axis_out[i * s.inside_size] = ftype4(exp(float4(axis_in[i * s.inside_size] - max4)) / sum4); + } +} diff --git a/source/backend/metal/MetalSoftmax.mm b/source/backend/metal/MetalSoftmax.mm new file mode 100644 index 00000000..424c5037 --- /dev/null +++ b/source/backend/metal/MetalSoftmax.mm @@ -0,0 +1,110 @@ +// +// MetalSoftmax.mm +// MNN +// +// Created by MNN on 2019/01/30. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#import "backend/metal/MNNMetalContext.h" +#import "backend/metal/MetalSoftmax.hpp" +#import "core/Macro.h" +#import "backend/metal/MetalBackend.hpp" +#import "core/TensorUtils.hpp" + +#if MNN_METAL_ENABLED +namespace MNN { + +MetalSoftmax::MetalSoftmax(Backend *backend, int32_t axis) : Execution(backend), mAxis(axis) { + // nothing to do +} + +ErrorCode MetalSoftmax::onResize(const std::vector &inputs, const std::vector &outputs) { + auto backend = static_cast(this->backend()); + auto context = (__bridge MNNMetalContext *)backend->context(); + auto input = inputs[0], output = outputs[0]; + auto dimensions = input->buffer().dimensions; + auto realAxis = mAxis < 0 ? dimensions + mAxis : mAxis; + auto c4 = TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4; // even dims != 4 + auto reorder = realAxis == 1 && c4; + // shape + auto inside = 1; + auto flat = input->length(realAxis); + auto axis = flat; + auto outside = 1; + + for (int i = 0; i < realAxis; i++) { + auto length = input->length(i); + if (1 == i && c4) { + length = UP_DIV(length, 4); + } + outside *= length; + } + for (int i = realAxis + 1; i < input->dimensions(); i++) { + auto length = input->length(i); + if (1 == i && c4) { + length = UP_DIV(length, 4); + } + inside *= length; + } + if (reorder) { + axis = UP_DIV(axis, 4); + } + mShapeBuffer = [context newDeviceBuffer:4 * sizeof(int) access:CPUWriteOnly]; + ((int *)mShapeBuffer.contents)[0] = inside; + ((int *)mShapeBuffer.contents)[1] = axis; + ((int *)mShapeBuffer.contents)[2] = outside; + ((int *)mShapeBuffer.contents)[3] = flat; + + // encode + auto plane = !(TensorUtils::getDescribe(input)->dimensionFormat == MNN_DATA_FORMAT_NC4HW4); + auto kernel = plane ? @"softmax_plane" : reorder ? @"softmax_on_reorder" : @"softmax_off_reorder"; + mPipeline = [context pipelineWithName:kernel]; + + mThreads = [context computeBestGroupAndLocal:mPipeline threads:MTLSizeMake((NSUInteger)inside, (NSUInteger)outside, 1)]; + return NO_ERROR; +} + +ErrorCode MetalSoftmax::onExecute(const std::vector &inputs, const std::vector &outputs) { + auto backend = static_cast(this->backend()); + auto context = (__bridge MNNMetalContext *)backend->context(); + + if(backend->isCommandEncoderSet()) { + return NO_ERROR; + } + + auto func = [=](){ + + auto input = inputs[0], output = outputs[0]; + + auto encoder = backend->encoder(); + [encoder setComputePipelineState:mPipeline]; + [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)input->deviceId())->getBuffer() offset:TensorUtils::getDescribe(input)->extra.offset atIndex:0]; + [encoder setBuffer:(id)((MetalRuntimeAllocator::MetalBufferAlloc *)output->deviceId())->getBuffer() offset:TensorUtils::getDescribe(output)->extra.offset atIndex:1]; + [encoder setBuffer:mShapeBuffer offset:0 atIndex:2]; + + [encoder dispatchThreadgroups:mThreads.first threadsPerThreadgroup:mThreads.second]; + MNN_PRINT_ENCODER(context, encoder); + + auto context = (__bridge MNNMetalContext *)backend->context(); + if(backend->isCmdBufferCommit()) { + backend->flushEncoder(); + [context commit_net]; + } + }; + func(); + backend->addOpEncoder(func); + return NO_ERROR; + +} + +class MetalSoftmaxCreator : public MetalBackend::Creator { +public: + virtual Execution *onCreate(const std::vector &inputs, const MNN::Op *op, Backend *backend, const std::vector &outputs) const { + auto softmax = op->main_as_Axis(); + return new MetalSoftmax(backend, softmax->axis()); + } +}; +REGISTER_METAL_OP_CREATOR(MetalSoftmaxCreator, OpType_Softmax); +} // namespace MNN +#endif /* MNN_METAL_ENABLED */ diff --git a/source/core/Interpreter.cpp b/source/core/Interpreter.cpp index 5cf22eb6..854c1776 100644 --- a/source/core/Interpreter.cpp +++ b/source/core/Interpreter.cpp @@ -42,6 +42,10 @@ struct Content { std::string uuid; }; +const char* getVersion() { + return MNN_VERSION; +} + static void writeCacheFile(const Content *net, std::pair buffer) { bool res = FileLoader::write(net->cacheFile.c_str(), buffer); if (!res) { @@ -554,6 +558,13 @@ ErrorCode Interpreter::updateSessionToModel(Session* session) { return session->updateToModel((Net*)mNet->net); } +const char* Interpreter::getModelVersion() const { + if (mNet && mNet->net && mNet->net->extraInfo() && mNet->net->extraInfo()->version()) { + return mNet->net->extraInfo()->version()->c_str(); + } + return "version info not found"; +} + bool Interpreter::getSessionInfo(const Session* session, SessionInfoCode code, void* ptr) { std::unique_lock _l(mNet->lock); if (nullptr == session || nullptr == ptr) { diff --git a/source/core/OpCommonUtils.cpp b/source/core/OpCommonUtils.cpp index 538150b3..e8c0e5fe 100644 --- a/source/core/OpCommonUtils.cpp +++ b/source/core/OpCommonUtils.cpp @@ -360,6 +360,9 @@ std::vector> OpCommonUtils::computeReduceDims(const st for (int i = 0; i < axises.size(); ++i) { if (axises[i] < 0) { axises[i] = inputs[0]->dimensions() + axises[i]; + if (axises[i] < 0) { + return {std::make_tuple(1, totalSize, 1)}; + } } } // Cache for input's dims diff --git a/source/geometry/GeometryCumSum.cpp b/source/geometry/GeometryCumSum.cpp index 4b3a942d..5ff0c799 100644 --- a/source/geometry/GeometryCumSum.cpp +++ b/source/geometry/GeometryCumSum.cpp @@ -22,7 +22,7 @@ public: int outside = std::accumulate(shape.begin(), shape.begin() + axis, 1, [](int a, int b) { return a * b; }); int inside = std::accumulate(shape.begin() + axis + 1, shape.end(), 1, [](int a, int b) { return a * b; }); bool exclusive = op->main_as_CumSum()->exclusive(), reverse = op->main_as_CumSum()->reverse(); - + auto outDes = TensorUtils::getDescribe(outputs[0]); outDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; if (!exclusive) { @@ -38,7 +38,7 @@ public: if (shape[axis] == 1) { return true; } - + flatbuffers::FlatBufferBuilder builder; BinaryOpBuilder binaryOpParamBuilder(builder); binaryOpParamBuilder.add_opType(BinaryOpOperation_ADD); @@ -48,7 +48,7 @@ public: cmdOpBuilder.add_main(binaryOpParamOffset.Union()); cmdOpBuilder.add_main_type(OpParameter_BinaryOp); auto cmdOpOffset = cmdOpBuilder.Finish(); - + auto viewStride = builder.CreateVector(std::vector{shape[axis] * inside, 1, 1}); int step = inside, offset = inside; if (reverse) { @@ -65,13 +65,13 @@ public: view1.add_offset(offset - step); views[1] = view1.Finish(); views[2] = views[exclusive ? 1 : 0]; - + auto viewsOffset = builder.CreateVector>(views); auto sizeOffset = builder.CreateVector(std::vector{outside, inside, 1}); auto stepOffset = builder.CreateVector(std::vector{step, step, step}); auto iterIndexesOffset = builder.CreateVector(std::vector{-1, -1, -1}); auto indexesOffset = builder.CreateVector(std::vector{2, 0, 1}); - + RegionCommandBuilder cmdBuilder(builder); cmdBuilder.add_op(cmdOpOffset); cmdBuilder.add_view(viewsOffset); @@ -79,7 +79,7 @@ public: cmdBuilder.add_steps(stepOffset); cmdBuilder.add_iterIndexes(iterIndexesOffset); cmdBuilder.add_indexes(indexesOffset); - + std::vector> regionCommands; regionCommands.emplace_back(cmdBuilder.Finish()); auto rcmdAllOffset = builder.CreateVector>(regionCommands); @@ -107,14 +107,111 @@ public: builder.Finish(finishBuilder.Finish()); auto cmd = GeometryComputerUtils::makeCommand(builder, {outputs[0], inputs[0]}, outputs); res.command.emplace_back(std::move(cmd)); - + return true; } }; +class GeometryCumProd : public GeometryComputer { +public: + virtual bool onCompute(const Op* op, const std::vector& inputs, const std::vector& outputs, + Context& context, CommandBuffer& res) const override { + auto shape = inputs[0]->shape(); + int axis = op->main_as_Axis()->axis(); + if (axis < 0) { + axis += shape.size(); + } + int outside = std::accumulate(shape.begin(), shape.begin() + axis, 1, [](int a, int b) { return a * b; }); + int inside = std::accumulate(shape.begin() + axis + 1, shape.end(), 1, [](int a, int b) { return a * b; }); + auto outDes = TensorUtils::getDescribe(outputs[0]); + outDes->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + outDes->regions.resize(1); + auto& reg = outDes->regions[0]; + reg.origin = inputs[0]; + reg.src.offset = reg.dst.offset = 0; + reg.src.stride[0] = reg.dst.stride[0] = inside * shape[axis]; + reg.size[0] = outside; + reg.size[1] = inside; + + if (shape[axis] == 1) { + return true; + } + + flatbuffers::FlatBufferBuilder builder; + BinaryOpBuilder binaryOpParamBuilder(builder); + binaryOpParamBuilder.add_opType(BinaryOpOperation_MUL); + auto binaryOpParamOffset = binaryOpParamBuilder.Finish(); + OpBuilder cmdOpBuilder(builder); + cmdOpBuilder.add_type(OpType_BinaryOp); + cmdOpBuilder.add_main(binaryOpParamOffset.Union()); + cmdOpBuilder.add_main_type(OpParameter_BinaryOp); + auto cmdOpOffset = cmdOpBuilder.Finish(); + + auto viewStride = builder.CreateVector(std::vector{shape[axis] * inside, 1, 1}); + int step = inside, offset = inside; + std::vector> views(3); + ViewBuilder view0(builder); + view0.add_stride(viewStride); + view0.add_offset(offset); + views[0] = view0.Finish(); + ViewBuilder view1(builder); + view1.add_stride(viewStride); + view1.add_offset(offset - step); + views[1] = view1.Finish(); + views[2] = views[0]; + + auto viewsOffset = builder.CreateVector>(views); + auto sizeOffset = builder.CreateVector(std::vector{outside, inside, 1}); + auto stepOffset = builder.CreateVector(std::vector{step, step, step}); + auto iterIndexesOffset = builder.CreateVector(std::vector{-1, -1, -1}); + auto indexesOffset = builder.CreateVector(std::vector{2, 0, 1}); + + RegionCommandBuilder cmdBuilder(builder); + cmdBuilder.add_op(cmdOpOffset); + cmdBuilder.add_view(viewsOffset); + cmdBuilder.add_size(sizeOffset); + cmdBuilder.add_steps(stepOffset); + cmdBuilder.add_iterIndexes(iterIndexesOffset); + cmdBuilder.add_indexes(indexesOffset); + + std::vector> regionCommands; + regionCommands.emplace_back(cmdBuilder.Finish()); + auto rcmdAllOffset = builder.CreateVector>(regionCommands); + auto inputIndexesOffset = builder.CreateVector(std::vector{0, 1}); + auto outputIndexesOffset = builder.CreateVector(std::vector{2}); + LoopParamBuilder loopBuilder(builder); + loopBuilder.add_parallel(false); // cumprod(i) = cumprod(i-1) * x(i), so can't do outside parallel + loopBuilder.add_commands(rcmdAllOffset); + loopBuilder.add_loopNumber(shape[axis] - 1); + loopBuilder.add_tensorNumber(3); + loopBuilder.add_inputIndexes(inputIndexesOffset); + loopBuilder.add_outputIndexes(outputIndexesOffset); + auto loopOffset = loopBuilder.Finish(); + flatbuffers::Offset nameOffset; + if (nullptr != op->name()) { + nameOffset = builder.CreateString(op->name()->c_str()); + } + OpBuilder finishBuilder(builder); + finishBuilder.add_main(loopOffset.Union()); + finishBuilder.add_main_type(OpParameter_LoopParam); + finishBuilder.add_type(OpType_While); + if (nullptr != op->name()) { + finishBuilder.add_name(nameOffset); + } + builder.Finish(finishBuilder.Finish()); + auto cmd = GeometryComputerUtils::makeCommand(builder, {outputs[0], inputs[0]}, outputs); + res.command.emplace_back(std::move(cmd)); + + return true; + } +}; + + static void _create() { std::shared_ptr comp(new GeometryCumSum); GeometryComputer::registerGeometryComputer(comp, {OpType_CumSum}); + std::shared_ptr comp1(new GeometryCumProd); + GeometryComputer::registerGeometryComputer(comp1, {OpType_CumProd}); } REGISTER_GEOMETRY(GeometryCumSum, _create); diff --git a/source/geometry/GeometryGather.cpp b/source/geometry/GeometryGather.cpp index 6a2f3b06..290d2635 100644 --- a/source/geometry/GeometryGather.cpp +++ b/source/geometry/GeometryGather.cpp @@ -161,8 +161,8 @@ public: enum MID_POSITION { P_constStride = 0, P_reshapeIndice = 1, - P_reshapeIndiceFloat = 2, - P_indiceFloat = 3, + P_broadcastStride = 2, + P_mulIndice = 3, P_indiceOneLine = 4, P_MAX }; @@ -236,18 +236,20 @@ public: auto paramSize = params->elementSize(); auto constStride = cmd.extras[P_constStride]; auto reshapeIndice = cmd.extras[P_reshapeIndice]; - auto reshapeIndiceFloat = cmd.extras[P_reshapeIndiceFloat]; - auto indiceFloat = cmd.extras[P_indiceFloat]; + auto broadcastStride = cmd.extras[P_broadcastStride]; + auto mulIndice = cmd.extras[P_mulIndice]; auto indiceOneLine = cmd.extras[P_indiceOneLine]; // Set length bool needAlloc = constStride->length(0) < indiceNd; constStride->setLength(0, indiceNd); reshapeIndice->setLength(0, mSliceN); reshapeIndice->setLength(1, indiceNd); - reshapeIndiceFloat->setLength(0, mSliceN); - reshapeIndiceFloat->setLength(1, indiceNd); - indiceFloat->setLength(0, mSliceN); + broadcastStride->setLength(0, mSliceN); + broadcastStride->setLength(1, indiceNd); + mulIndice->setLength(0, mSliceN); + mulIndice->setLength(1, indiceNd); indiceOneLine->setLength(0, mSliceN); + indiceOneLine->setLength(1, 1); if (needAlloc) { if (!context.allocTensor(constStride.get())) { @@ -256,9 +258,10 @@ public: } for (int i=0; ilength(i); - constStride->host()[i] = (float)dimCount; + constStride->host()[i] = dimCount; paramSize = dimCount; } + // recompute reshape reshapeIndice->buffer().device = 0; reshapeIndice->buffer().host = 0; auto des = TensorUtils::getDescribe(reshapeIndice.get()); @@ -266,7 +269,21 @@ public: des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; des->backend = nullptr; des->regions = {GeometryComputerUtils::makeRawAddressRef(indice, 0, mSliceN * indiceNd)}; - + // recompute broadcast + broadcastStride->buffer().device = 0; + broadcastStride->buffer().host = 0; + des = TensorUtils::getDescribe(broadcastStride.get()); + des->extra.offset = 0; + des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + des->backend = nullptr; + des->regions[0].origin = constStride.get(); + des->regions[0].size[0] = 1; + des->regions[0].size[1] = mSliceN; + des->regions[0].size[2] = indiceNd; + des->regions[0].dst.stride[0] = indiceNd*mSliceN; + des->regions[0].dst.stride[1] = indiceNd; + des->regions[0].dst.stride[2] = 1; + // recompute loop auto loopCmd = cmd.command[cmd.command.size() - 1]; auto param = loopCmd->op->main_as_LoopParam(); // Reset parameters for last command @@ -304,14 +321,14 @@ public: } auto paramSize = params->elementSize(); std::array, 5> midTensors; - std::shared_ptr constStride(Tensor::createDevice({indiceNd, 1})); + std::shared_ptr constStride(Tensor::createDevice({indiceNd})); if (!context.allocTensor(constStride.get())) { return false; } midTensors[P_constStride] = constStride; for (int i=0; ilength(i); - constStride->host()[i] = (float)dimCount; + constStride->host()[i] = dimCount; paramSize = dimCount; } std::shared_ptr reshapeIndice(Tensor::createDevice({mSliceN, indiceNd})); @@ -321,41 +338,36 @@ public: des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; des->regions = {GeometryComputerUtils::makeRawAddressRef(indice, 0, mSliceN * indiceNd)}; } - std::shared_ptr reshapeIndiceFloat(Tensor::createDevice({mSliceN, indiceNd})); - midTensors[P_reshapeIndiceFloat] = reshapeIndiceFloat; + std::shared_ptr broadcastStride(Tensor::createDevice({mSliceN, indiceNd})); + midTensors[P_broadcastStride] = broadcastStride; { - flatbuffers::FlatBufferBuilder builder; - CastParamBuilder builder_(builder); - builder_.add_dstT(DataType_DT_FLOAT); - auto mainOffset = builder_.Finish().Union(); - OpBuilder opB(builder); - opB.add_type(OpType_Cast); - opB.add_main(mainOffset); - opB.add_main_type(OpParameter_CastParam); - builder.Finish(opB.Finish()); - auto cmd = GeometryComputerUtils::makeCommand(builder, {reshapeIndice.get()}, {reshapeIndiceFloat.get()}); - res.command.emplace_back(std::move(cmd)); + // [D] => [N, D] + auto des = TensorUtils::getDescribe(broadcastStride.get()); + des->memoryType = Tensor::InsideDescribe::MEMORY_VIRTUAL; + des->regions.resize(1); + des->regions[0].origin = constStride.get(); + des->regions[0].size[0] = 1; + des->regions[0].size[1] = mSliceN; + des->regions[0].size[2] = indiceNd; + des->regions[0].dst.stride[0] = indiceNd*mSliceN; + des->regions[0].dst.stride[1] = indiceNd; + des->regions[0].dst.stride[2] = 1; + des->regions[0].src.stride[0] = 0; + des->regions[0].src.stride[1] = 0; + des->regions[0].src.stride[2] = 1; } - std::shared_ptr indiceFloat(Tensor::createDevice({mSliceN, 1})); - midTensors[P_indiceFloat] = indiceFloat; + std::shared_ptr mulIndice(Tensor::createDevice({mSliceN, indiceNd})); + midTensors[P_mulIndice] = mulIndice; { - // MatMul - auto cmd = GeometryComputerUtils::makeMatMul(reshapeIndiceFloat.get(), constStride.get(), indiceFloat.get()); + // [N, D] * [N, D] => [N, D] + auto cmd = GeometryComputerUtils::makeBinary(BinaryOpOperation_MUL, reshapeIndice.get(), broadcastStride.get(), mulIndice.get()); res.command.emplace_back(std::move(cmd)); } std::shared_ptr indiceOneLine(Tensor::createDevice({mSliceN, 1})); midTensors[P_indiceOneLine] = indiceOneLine; { - flatbuffers::FlatBufferBuilder builder; - CastParamBuilder builder_(builder); - builder_.add_dstT(DataType_DT_INT32); - auto mainOffset = builder_.Finish().Union(); - OpBuilder opB(builder); - opB.add_type(OpType_Cast); - opB.add_main(mainOffset); - opB.add_main_type(OpParameter_CastParam); - builder.Finish(opB.Finish()); - auto cmd = GeometryComputerUtils::makeCommand(builder, {indiceFloat.get()}, {indiceOneLine.get()}); + // [N, D] => [N, 1] + auto cmd = GeometryComputerUtils::makeReduce(ReductionType_SUM, mulIndice.get(), indiceOneLine.get()); res.command.emplace_back(std::move(cmd)); } diff --git a/source/shape/ShapeGatherND.cpp b/source/shape/ShapeGatherND.cpp index 47cbb8b8..1b12229c 100644 --- a/source/shape/ShapeGatherND.cpp +++ b/source/shape/ShapeGatherND.cpp @@ -24,6 +24,14 @@ public: MNN_ERROR("params->dimensions() < 1 || indices->dimensions() < 1\n"); return false; } + if (indices->elementSize() == 0) { + outputs[0]->buffer().type = params->buffer().type; + TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat; + outputs[0]->buffer().dimensions = 2; + outputs[0]->setLength(0, 0); + outputs[0]->setLength(1, params->shape().back()); + return true; + } auto indiceNd = indices->length(indices->dimensions()-1); if (indiceNd > params->dimensions()) { MNN_ERROR("indiceNd > params->dimensions()\n"); diff --git a/source/shape/ShapeGridSample.cpp b/source/shape/ShapeGridSample.cpp index 7a24f8ad..fbc22f6a 100644 --- a/source/shape/ShapeGridSample.cpp +++ b/source/shape/ShapeGridSample.cpp @@ -17,9 +17,11 @@ class GridSampleSizeComputer : public SizeComputer { // inputs[0] is input, inputs[1] is grid MNN_ASSERT(2 == inputs.size()); MNN_ASSERT(1 == outputs.size()); - MNN_ASSERT(4 == inputs[0]->buffer().dimensions && 4 == inputs[1]->buffer().dimensions); + int input_dim = inputs[0]->buffer().dimensions; + int grid_dim = inputs[1]->buffer().dimensions; + MNN_ASSERT((4 == input_dim && 4 == grid_dim) || (5 == input_dim && 5 == grid_dim)); MNN_ASSERT(inputs[0]->buffer().dim[0].extent == inputs[1]->buffer().dim[0].extent); - MNN_ASSERT(2 == inputs[1]->buffer().dim[3].extent); + MNN_ASSERT(grid_dim - 2 == inputs[1]->buffer().dim[grid_dim - 1].extent); auto &ibInput0 = inputs[0]->buffer(); auto &ibInput1 = inputs[1]->buffer(); @@ -30,6 +32,9 @@ class GridSampleSizeComputer : public SizeComputer { ob.dim[1].extent = ibInput0.dim[1].extent; ob.dim[2].extent = ibInput1.dim[1].extent; ob.dim[3].extent = ibInput1.dim[2].extent; + if (grid_dim == 5) { + ob.dim[4].extent = ibInput1.dim[3].extent; + } ob.type = ibInput0.type; TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe( diff --git a/source/shape/ShapeReshape.cpp b/source/shape/ShapeReshape.cpp index 5f61c983..87073a50 100644 --- a/source/shape/ShapeReshape.cpp +++ b/source/shape/ShapeReshape.cpp @@ -21,21 +21,43 @@ public: return false; } auto axis = flatten->axis(); + auto endAxis = flatten->endAxis(); auto dim = inputs[0]->dimensions(); if (axis < 0) { axis += dim; } + if (endAxis < 0) { + endAxis += dim; + } int inside = 1; + int middle = 1; int outside = 1; - for (int i=0; ilength(i); + if (endAxis == 0) { + for (int i=0; ilength(i); + } + for (int i=axis; ilength(i); + } + outputs[0]->buffer().dimensions = 2; + outputs[0]->setLength(0, outside); + outputs[0]->setLength(1, inside); + } else { + // [ 0 - axis, 1, endAxis - lastDim] + outputs[0]->buffer().dimensions = dim - endAxis + axis; + for (int i = 0; i < axis; ++i) { + outputs[0]->setLength(i, inputs[0]->length(i)); + } + for (int i = axis; i <= endAxis; ++i) { + outside *= inputs[0]->length(i); + } + outputs[0]->setLength(axis, outside); + if (dim > endAxis + 1) { + for (int i = endAxis + 1; i < dim; ++i) { + outputs[0]->setLength(i, inputs[0]->length(i)); + } + } } - for (int i=axis; ilength(i); - } - outputs[0]->buffer().dimensions = 2; - outputs[0]->setLength(0, outside); - outputs[0]->setLength(1, inside); outputs[0]->buffer().type = inputs[0]->getType(); TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat; return true; diff --git a/source/shape/ShapeTopKV2.cpp b/source/shape/ShapeTopKV2.cpp index 3ef467fd..3d025a87 100644 --- a/source/shape/ShapeTopKV2.cpp +++ b/source/shape/ShapeTopKV2.cpp @@ -22,7 +22,8 @@ class TopKV2SizeComputer : public SizeComputer { MNN_ASSERT(kTensor->getType().code == halide_type_int); const int k = kTensor->host()[0]; const int inputDimension = input->buffer().dimensions; - const int axis = (inputs.size() == 3 ? inputs[2]->host()[0] : inputDimension - 1); + int axis = (inputs.size() == 3 ? inputs[2]->host()[0] : inputDimension - 1); + if (axis < 0) axis += input->dimensions(); // outputs: 0 --> data, 1 --> index auto outputData = outputs[0]; diff --git a/test.sh b/test.sh index 2d91cc7d..857ab4ea 100755 --- a/test.sh +++ b/test.sh @@ -293,7 +293,18 @@ pymnn_test() { echo '### PYMNN模型测试失败，测试终止！' failed fi - # 4. uninstall pymnn + # 4. train test + ./train_test.sh + # 5. quant test + python3 ../examples/MNNQuant/test_mnn_offline_quant.py \ + --mnn_model ~/AliNNModel/TestQuant/mobilenet_v2_tfpb_train_withBN.mnn \ + --quant_imgs ~/AliNNModel/TestQuant/quant_imgs \ + --quant_model ./quant_model.mnn + rm ./quant_model.mnn + quant_wrong=$[$? > 0] + printf "TEST_NAME_QUANT_TEST: pymnn量化测试\nTEST_CASE_AMOUNT_QUANT_TEST: {\"blocked\":0,\"failed\":%d,\"passed\":%d,\"skipped\":0}\n" \ + $quant_wrong $[1 - $quant_wrong] + # 6. uninstall pymnn pip uninstall --yes MNN-Internal popd popd diff --git a/test/op/CumTest.cpp b/test/op/CumTest.cpp new file mode 100644 index 00000000..00876ab2 --- /dev/null +++ b/test/op/CumTest.cpp @@ -0,0 +1,83 @@ +// +// CumTest.cpp +// MNNTests +// +// Created by MNN on 2022/05/10. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include +#include "MNNTestSuite.h" +#include "TestUtils.h" + +using namespace MNN::Express; +class CumProdTest : public MNNTestCase { +public: + virtual ~CumProdTest() = default; + virtual bool run(int precision) { + auto input = _Input({2, 2, 2},NCHW); + input->setName("input_tensor"); + const float inpudata[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}; + auto inputPtr = input->writeMap(); + memcpy(inputPtr, inpudata, 8 * sizeof(float)); + auto output0 = _CumProd(input, 0); + const std::vector expectedOutput0 = {1., 2., 3., 4., 5., 12., 21., 32.}; + auto gotOutput0 = output0->readMap(); + if (!checkVector(gotOutput0, expectedOutput0.data(), 8, 0.01)) { + MNN_ERROR("CumProdTest axis=0 test failed!\n"); + return false; + } + auto output1 = _CumProd(input, 1); + const std::vector expectedOutput1 = {1., 2., 3., 8., 5., 6., 35., 48.}; + auto gotOutput1 = output1->readMap(); + if (!checkVector(gotOutput1, expectedOutput1.data(), 8, 0.01)) { + MNN_ERROR("CumProdTest axis=1 test failed!\n"); + return false; + } + auto output2 = _CumProd(input, 2); + const std::vector expectedOutput2 = {1., 2., 3., 12., 5., 30., 7., 56.}; + auto gotOutput2 = output2->readMap(); + if (!checkVector(gotOutput2, expectedOutput2.data(), 8, 0.01)) { + MNN_ERROR("CumProdTest axis=2 test failed!\n"); + return false; + } + return true; + } +}; +MNNTestSuiteRegister(CumProdTest, "op/cumprod"); + +class CumSumTest : public MNNTestCase { +public: + virtual ~CumSumTest() = default; + virtual bool run(int precision) { + auto input = _Input({2, 2, 2},NCHW); + input->setName("input_tensor"); + const float inpudata[] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0}; + auto inputPtr = input->writeMap(); + memcpy(inputPtr, inpudata, 8 * sizeof(float)); + auto output0 = _CumSum(input, 0); + const std::vector expectedOutput0 = {1., 2., 3., 4., 6., 8., 10., 12.}; + auto gotOutput0 = output0->readMap(); + if (!checkVector(gotOutput0, expectedOutput0.data(), 8, 0.01)) { + MNN_ERROR("CumSumTest axis=0 test failed!\n"); + return false; + } + auto output1 = _CumSum(input, 1); + const std::vector expectedOutput1 = {1., 2., 4., 6., 5., 6., 12., 14.}; + auto gotOutput1 = output1->readMap(); + if (!checkVector(gotOutput1, expectedOutput1.data(), 8, 0.01)) { + MNN_ERROR("CumSumTest axis=1 test failed!\n"); + return false; + } + auto output2 = _CumSum(input, 2); + const std::vector expectedOutput2 = {1., 3., 3., 7., 5., 11., 7., 15.}; + auto gotOutput2 = output2->readMap(); + if (!checkVector(gotOutput2, expectedOutput2.data(), 8, 0.01)) { + MNN_ERROR("CumSumTest axis=2 test failed!\n"); + return false; + } + return true; + } +}; +MNNTestSuiteRegister(CumSumTest, "op/cumsum"); diff --git a/tools/converter/CMakeLists.txt b/tools/converter/CMakeLists.txt index 064f56cc..a00b7596 100644 --- a/tools/converter/CMakeLists.txt +++ b/tools/converter/CMakeLists.txt @@ -36,8 +36,6 @@ IF(MNN_BUILD_CONVERTER) endif() file(GLOB COMMON_SRC ${CMAKE_CURRENT_LIST_DIR}/source/common/*.cpp) - add_executable(MNNDump2Json ${CMAKE_CURRENT_LIST_DIR}/source/MNNDump2Json.cpp) - add_executable(MNNRevert2Buffer ${CMAKE_CURRENT_LIST_DIR}/source/MNNRevert2Buffer.cpp) add_executable(MNNConvert ${CMAKE_CURRENT_LIST_DIR}/source/MNNConverter.cpp ) @@ -74,6 +72,10 @@ IF(MNN_BUILD_CONVERTER) if (MNN_BUILD_TORCH) target_link_libraries(MNNConvertDeps PUBLIC ${TORCH_LIBRARIES}) endif() + add_executable(MNNRevert2Buffer ${CMAKE_CURRENT_LIST_DIR}/source/MNNRevert2Buffer.cpp) + target_link_libraries(MNNRevert2Buffer MNNConvertDeps) + add_executable(MNNDump2Json ${CMAKE_CURRENT_LIST_DIR}/source/MNNDump2Json.cpp) + target_link_libraries(MNNDump2Json MNNConvertDeps) add_executable(TestConvertResult ${CMAKE_CURRENT_LIST_DIR}/source/TestConvertResult.cpp) target_link_libraries(TestConvertResult MNNConvertDeps) add_executable(TestPassManager ${CMAKE_CURRENT_LIST_DIR}/source/TestPassManager.cpp) diff --git a/tools/converter/include/cli.hpp b/tools/converter/include/cli.hpp index db151e4b..11ea6020 100644 --- a/tools/converter/include/cli.hpp +++ b/tools/converter/include/cli.hpp @@ -17,6 +17,8 @@ public: static bool initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv); static bool convertModel(modelConfig& modelPath); static int testconvert(const std::string& defaultCacheFile, const std::string& directName, float maxErrorRate); + static bool mnn2json(const char* modelFile, const char* jsonFile, int flag = 3); + static bool json2mnn(const char* jsonFile, const char* modelFile); }; }; diff --git a/tools/converter/include/config.hpp b/tools/converter/include/config.hpp index af9e98a7..64d07fac 100644 --- a/tools/converter/include/config.hpp +++ b/tools/converter/include/config.hpp @@ -22,7 +22,7 @@ public: benchmarkModel(false), saveHalfFloat(false){ } - enum MODEL_SOURCE { TENSORFLOW = 0, CAFFE, ONNX, MNN, TFLITE, TORCH, MAX_SOURCE }; + enum MODEL_SOURCE { TENSORFLOW = 0, CAFFE, ONNX, MNN, TFLITE, TORCH, JSON, MAX_SOURCE }; // MNN model path std::string MNNModel; @@ -55,6 +55,7 @@ public: std::string authCode = ""; std::string testDir = ""; float testThredhold = 0.01; + bool mnn2json = false; }; #endif // CONFIG_HPP diff --git a/tools/converter/source/MNNDump2Json.cpp b/tools/converter/source/MNNDump2Json.cpp index e81373b4..161f51e8 100644 --- a/tools/converter/source/MNNDump2Json.cpp +++ b/tools/converter/source/MNNDump2Json.cpp @@ -7,113 +7,13 @@ // #include -#include -#include -#include "MNN_generated.h" -#include "flatbuffers/idl.h" -#include "flatbuffers/minireflect.h" -#include "flatbuffers/util.h" -#include +#include "cli.hpp" int main(int argc, const char** argv) { if (argc <= 2) { printf("Usage: ./MNNDump2Json.out XXX.MNN XXX.json\n"); return 0; } - std::ifstream inputFile(argv[1], std::ios::binary); - inputFile.seekg(0, std::ios::end); - auto size = inputFile.tellg(); - inputFile.seekg(0, std::ios::beg); - - char* buffer = new char[size]; - - inputFile.read((char*)buffer, size); - std::ofstream output(argv[2]); - - if (argc > 3) { - printf("Dont't add convweight\n"); - auto netT = MNN::UnPackNet((void*)buffer); - auto treatFunction = [&](MNN::OpT* opParam) { - auto type = opParam->main.type; - if (type == MNN::OpParameter::OpParameter_Convolution2D) { - auto param = opParam->main.AsConvolution2D(); - param->weight.clear(); - param->bias.clear(); - if (param->symmetricQuan) { - param->symmetricQuan->weight.clear(); - } - if (param->quanParameter) { - param->quanParameter->buffer.clear(); - } - } else if (type == MNN::OpParameter::OpParameter_Blob) { - auto blobT = opParam->main.AsBlob(); - blobT->float32s.clear(); - blobT->int8s.clear(); - blobT->uint8s.clear(); - blobT->int32s.clear(); - blobT->int64s.clear(); - } else if (type == MNN::OpParameter::OpParameter_Convolution2D) { - opParam->main.AsConvolution2D()->weight.clear(); - opParam->main.AsConvolution2D()->bias.clear(); - } else if (type == MNN::OpParameter::OpParameter_MatMul) { - opParam->main.AsMatMul()->weight.clear(); - opParam->main.AsMatMul()->bias.clear(); - } else if (type == MNN::OpParameter::OpParameter_PRelu) { - opParam->main.AsPRelu()->slope.clear(); - } else if (type == MNN::OpParameter::OpParameter_Extra) { - auto extra = opParam->main.AsExtra(); - extra->info.clear(); - } else if(type == MNN::OpParameter::OpParameter_LSTM){ - auto param = opParam->main.AsLSTM(); - if (param->weightH) { - param->weightH->float32s.clear(); - } - if (param->weightI) { - param->weightI->float32s.clear(); - } - if (param->bias) { - param->bias->float32s.clear(); - } - } - }; - for (int i = 0; i < netT->oplists.size(); ++i) { - treatFunction(netT->oplists[i].get()); - } - for (int i = 0; i < netT->subgraphs.size(); ++i) { - for (int j=0; jsubgraphs[i]->nodes.size(); ++j) { - treatFunction(netT->subgraphs[i]->nodes[j].get()); - } - } - if (argc > 4) { - printf("Separate dump subgraph\n"); - for (int i=0; isubgraphs.size(); ++i) { - auto& g = netT->subgraphs[i]; - flatbuffers::FlatBufferBuilder newBuilder(1024); - auto root = MNN::SubGraphProto::Pack(newBuilder, g.get()); - newBuilder.Finish(root); - auto content = newBuilder.GetBufferPointer(); - char subGraphNameStr[128]; - sprintf(subGraphNameStr, "%s_%d", argv[2], i); - printf("Dump subgraph %s to %s\n", g->name.c_str(), subGraphNameStr); - std::ofstream tempOutput(subGraphNameStr); - auto s = flatbuffers::FlatBufferToString((const uint8_t*)content, MNN::SubGraphProtoTypeTable()); - tempOutput << s; - } - netT->subgraphs.clear(); - } - flatbuffers::FlatBufferBuilder newBuilder(1024); - auto root = MNN::Net::Pack(newBuilder, netT.get()); - MNN::FinishNetBuffer(newBuilder, root); - { - auto content = newBuilder.GetBufferPointer(); - auto s = flatbuffers::FlatBufferToString((const uint8_t*)content, MNN::NetTypeTable()); - output << s; - } - } else { - auto s = flatbuffers::FlatBufferToString((const uint8_t*)buffer, MNN::NetTypeTable()); - output << s; - } - - delete[] buffer; + MNN::Cli::mnn2json(argv[1], argv[2], argc); return 0; } diff --git a/tools/converter/source/MNNRevert2Buffer.cpp b/tools/converter/source/MNNRevert2Buffer.cpp index aeb7a5d1..d332f274 100644 --- a/tools/converter/source/MNNRevert2Buffer.cpp +++ b/tools/converter/source/MNNRevert2Buffer.cpp @@ -7,253 +7,13 @@ // #include -#include -#include -#include -#include "MNN_generated.h" -#include "flatbuffers/idl.h" -#include "flatbuffers/minireflect.h" -#include "flatbuffers/util.h" -#include -#include -#include "rapidjson/document.h" -#include "rapidjson/stringbuffer.h" -#include "rapidjson/prettywriter.h" - -#define VECTOR_EXTRACT(FLATBUFFER_TYPE, CPP_TYPE, JSON_TYPE)\ -case flatbuffers::ET_##FLATBUFFER_TYPE:\ -{\ - std::vector data(array.Size());\ - for (int i=0; ivalue.JSON_TYPE()), (CPP_TYPE)0);\ -break;\ -}\ - -static flatbuffers::Offset _writeJsonToFlatbuffer(const flatbuffers::TypeTable * table, flatbuffers::FlatBufferBuilder& builder, const rapidjson::GenericObject>>& object) { - std::vector>> indexes; - // Load union type for easy to use - std::map unionNames; - for (int i=0; inum_elems; ++i) { - if (table->type_codes[i].sequence_ref == -1) { - continue; - } - const flatbuffers::TypeTable *ref = table->type_refs[table->type_codes[i].sequence_ref](); - if (ref->st == flatbuffers::ST_UNION) { - unionNames.insert(std::make_pair(std::string(table->names[i]) + "_type", i)); - } - } - // Find index and cache - std::map unionTypes; - for (auto iter = object.begin(); iter !=object.end(); iter++) { - auto name = iter->name.GetString(); - int index = -1; - for (int i=0; inum_elems; ++i) { - if (0 == ::strcmp(table->names[i], name)) { - index = i; - break; - } - } - auto uiter = unionNames.find(name); - if (uiter != unionNames.end()) { - // Find union type id - auto value = iter->value.GetString(); - int typePos = -1; - auto unionIndex = uiter->second; - auto ref = table->type_refs[table->type_codes[unionIndex].sequence_ref](); - for (int j=0; jnum_elems; ++j) { - if (0 == ::strcmp(ref->names[j], value)) { - typePos = j; - break; - } - } - if (-1 == typePos) { - MNN_ERROR("Can't find union type\n"); - continue; - } - if (typePos > 0) { - // First is None - unionTypes.insert(std::make_pair(unionIndex, typePos-1)); - } - } - if (index == -1) { - MNN_PRINT("Invalid: %s, Skip it\n", name); - } - indexes.emplace_back(std::make_pair(index, 0)); - } - - // resolve single object - int pos = 0; - for (auto iter = object.begin(); iter !=object.end(); iter++, pos++) { - int index = indexes[pos].first; - if (-1 == index) { - continue; - } - auto code = table->type_codes[index]; - if (code.is_vector) { - continue; - } - if (code.sequence_ref != -1 && code.base_type == flatbuffers::ET_SEQUENCE) { - const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); - if (ref->st == flatbuffers::ST_TABLE) { - indexes[pos].second = _writeJsonToFlatbuffer(ref, builder, iter->value.GetObject()); - } else if (ref->st == flatbuffers::ST_UNION) { - auto unionInd = unionTypes.find(index)->second; - ref = ref->type_refs[unionInd](); - indexes[pos].second = _writeJsonToFlatbuffer(ref, builder, iter->value.GetObject()); - } - } - } - - // Resolve Vector and String - pos = 0; - for (auto iter = object.begin(); iter !=object.end(); iter++, pos++) { - int index = indexes[pos].first; - if (-1 == index) { - continue; - } - auto code = table->type_codes[index]; - if (!code.is_vector) { - if (code.base_type == flatbuffers::ET_STRING) { - indexes[pos].second = builder.CreateString(iter->value.GetString()).Union(); - } - continue; - } - auto array = iter->value.GetArray(); - if (code.sequence_ref != -1) { - const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); - std::vector> offsets(array.Size()); - for (int i=0; i data(array.Size()); - for (int i=0; itype_codes[index]; - if (code.sequence_ref != -1) { - const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); - int value = -1; - if (ref->st == flatbuffers::ST_UNION || ref->st == flatbuffers::ST_ENUM) { - auto type = iter->value.GetString(); - for (int i=0; inum_elems; ++i) { - if (0 == ::strcmp(type, ref->names[i])) { - if (nullptr == ref->values) { - value = i; - } else { - value = ref->values[i]; - } - } - } - switch (code.base_type) { - case flatbuffers::ET_UTYPE: - case flatbuffers::ET_UINT: - builder.AddElement(field, (uint32_t)value, (uint32_t)0); - break; - case flatbuffers::ET_INT: - builder.AddElement(field, (int32_t)value, (int32_t)-1); - break; - case flatbuffers::ET_UCHAR: - builder.AddElement(field, (uint8_t)value, (uint8_t)0); - break; - case flatbuffers::ET_CHAR: - builder.AddElement(field, (int8_t)value, (int8_t)0); - break; - default: - break; - } - continue; - } - } - switch (code.base_type) { - SCALAR_EXTRACT(BOOL, bool, GetBool); - SCALAR_EXTRACT(CHAR, char, GetInt); - SCALAR_EXTRACT(UCHAR, uint8_t, GetInt); - SCALAR_EXTRACT(SHORT, int16_t, GetInt); - SCALAR_EXTRACT(USHORT, uint16_t, GetInt); - SCALAR_EXTRACT(INT, int, GetInt); - SCALAR_EXTRACT(UINT, uint32_t, GetUint); - SCALAR_EXTRACT(LONG, int64_t, GetInt64); - SCALAR_EXTRACT(ULONG, uint64_t, GetUint64); - SCALAR_EXTRACT(FLOAT, float, GetFloat); - SCALAR_EXTRACT(DOUBLE, double, GetDouble); - default: - break; - } - } - return builder.EndTable(start); -} +#include "cli.hpp" int main(int argc, const char** argv) { if (argc <= 2) { printf("Usage: ./MNNRevert2Buffer.out XXX.json XXX.mnn\n"); return 0; } - rapidjson::Document document; - { - std::ifstream fileNames(argv[1]); - std::ostringstream output; - output << fileNames.rdbuf(); - auto outputStr = output.str(); - document.Parse(outputStr.c_str()); - if (document.HasParseError()) { - MNN_ERROR("Invalid json\n"); - return 0; - } - } - auto object = document.GetObject(); - flatbuffers::FlatBufferBuilder builder; - builder.ForceDefaults(true); - auto table = MNN::NetTypeTable(); - auto offset = _writeJsonToFlatbuffer(table, builder, object); - builder.Finish(offset); - std::ofstream outputOs(argv[2]); - outputOs.write((char*)builder.GetBufferPointer(), builder.GetSize()); + MNN::Cli::json2mnn(argv[1], argv[2]); return 0; } diff --git a/tools/converter/source/caffe/caffeConverter.cpp b/tools/converter/source/caffe/caffeConverter.cpp index 688ff9a3..ded9ae22 100644 --- a/tools/converter/source/caffe/caffeConverter.cpp +++ b/tools/converter/source/caffe/caffeConverter.cpp @@ -26,8 +26,12 @@ int caffe2MNNNet(const std::string prototxtFile, const std::string modelFile, co bool succ = read_proto_from_text(prototxtFile.c_str(), &caffeProtxt); DCHECK(succ) << "read_proto_from_text failed"; - succ = read_proto_from_binary(modelFile.c_str(), &caffeModel); + succ &= read_proto_from_binary(modelFile.c_str(), &caffeModel); DCHECK(succ) << "read_proto_from_binary failed"; + if (!succ) { + MNN_ERROR("[ERROR] Model file is not caffe model.\n"); + return 1; + } std::map tensorName; // Load Parameters diff --git a/tools/converter/source/common/cli.cpp b/tools/converter/source/common/cli.cpp index 2415f3a1..977e5f9b 100644 --- a/tools/converter/source/common/cli.cpp +++ b/tools/converter/source/common/cli.cpp @@ -38,7 +38,6 @@ #include #include "common/MemoryFormater.h" namespace MNN { -static float gMNNVersion = 1.2f; bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv) { cxxopts::Options options("MNNConvert"); @@ -49,9 +48,9 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv std::make_pair("v", "version"), "show current version") (std::make_pair("f", "framework"), #ifdef MNN_BUILD_TORCH - "model type, ex: [TF,CAFFE,ONNX,TFLITE,MNN,TORCH]", + "model type, ex: [TF,CAFFE,ONNX,TFLITE,MNN,TORCH,JSON]", #else - "model type, ex: [TF,CAFFE,ONNX,TFLITE,MNN]", + "model type, ex: [TF,CAFFE,ONNX,TFLITE,MNN,JSON]", #endif cxxopts::value()) ( @@ -165,6 +164,11 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv "if set test dir, thredhold mean the max rate permit for run MNN model and origin error", cxxopts::value() ) + ( + "JsonFile", + "if input model is MNN and give jsonfile, while Dump MNN model to the JsonFile.", + cxxopts::value() + ) ( "alignDenormalizedValue", "if 1, converter would align denormalized float(|x| < 1.18e-38) as zero, because of in ubuntu/protobuf or android/flatbuf, system behaviors are different. default: 1, range: {0, 1}", @@ -179,7 +183,7 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv } if (result.count("version")) { - std::cout << gMNNVersion << std::endl; + std::cout << MNN_VERSION << std::endl; return false; } @@ -202,13 +206,15 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv } else if ("TORCH" == frameWork) { modelPath.model = modelConfig::TORCH; #endif + } else if ("JSON" == frameWork) { + modelPath.model = modelConfig::JSON; } else { std::cout << "Framework Input ERROR or Not Support This Model Type Now!" << std::endl; return false; } } else { std::cout << options.help({""}) << std::endl; - DLOG(INFO) << "framework Invalid, use -f CAFFE/MNN/ONNX/TFLITE/TORCH !"; + DLOG(INFO) << "framework Invalid, use -f CAFFE/MNN/ONNX/TFLITE/TORCH/JSON !"; return false; } if (result.count("OP")) { @@ -266,6 +272,10 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv if (result.count("MNNModel")) { const std::string MNNModelPath = result["MNNModel"].as(); modelPath.MNNModel = MNNModelPath; + } else if (result.count("JsonFile")) { + const std::string JsonFilePath = result["JsonFile"].as(); + modelPath.mnn2json = true; + modelPath.MNNModel = JsonFilePath; } else { DLOG(INFO) << "MNNModel File Not Set, use --MNNModel XXX.prototxt to set it!"; return false; @@ -340,32 +350,50 @@ bool Cli::initializeMNNConvertArgs(modelConfig &modelPath, int argc, char **argv if (result.count("thredhold")) { modelPath.testThredhold = result["thredhold"].as(); } - return true; } bool Cli::convertModel(modelConfig& modelPath) { std::cout << "Start to Convert Other Model Format To MNN Model..." << std::endl; std::unique_ptr netT = std::unique_ptr(new MNN::NetT()); + int parseRes = 1; if (modelPath.model == modelConfig::CAFFE) { - caffe2MNNNet(modelPath.prototxtFile, modelPath.modelFile, modelPath.bizCode, netT); + parseRes = caffe2MNNNet(modelPath.prototxtFile, modelPath.modelFile, modelPath.bizCode, netT); } else if (modelPath.model == modelConfig::TENSORFLOW) { - tensorflow2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); + parseRes = tensorflow2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); } else if (modelPath.model == modelConfig::MNN) { - addBizCode(modelPath.modelFile, modelPath.bizCode, netT); + if (modelPath.mnn2json) { + if (mnn2json(modelPath.modelFile.c_str(), modelPath.MNNModel.c_str())) { + MNN_PRINT("MNNModel %s has convert to JsonFile %s.\n", modelPath.modelFile.c_str(), modelPath.MNNModel.c_str()); + return true; + } else { + MNN_ERROR("[ERROR] MNN to Json failed.\n"); + return false; + } + } else { + parseRes = addBizCode(modelPath.modelFile, modelPath.bizCode, netT); + } } else if (modelPath.model == modelConfig::ONNX) { - onnx2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); + parseRes = onnx2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); } else if (modelPath.model == modelConfig::TFLITE) { - tflite2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); + parseRes = tflite2MNNNet(modelPath.modelFile, modelPath.bizCode, netT); #ifdef MNN_BUILD_TORCH } else if (modelPath.model == modelConfig::TORCH) { - torch2MNNNet(modelPath.modelFile, modelPath.bizCode, netT, modelPath.customOpLibs); + parseRes = torch2MNNNet(modelPath.modelFile, modelPath.bizCode, netT, modelPath.customOpLibs); #endif + } else if (modelPath.model == modelConfig::JSON) { + if (json2mnn(modelPath.modelFile.c_str(), modelPath.MNNModel.c_str())) { + MNN_PRINT("JsonFile %s has convert to MNNModel %s.\n", modelPath.modelFile.c_str(), modelPath.MNNModel.c_str()); + return true; + } else { + MNN_ERROR("[ERROR] Json to MNN failed.\n"); + return false; + } } else { - std::cout << "Not Support Model Type" << std::endl; + MNN_ERROR("[ERROR] Not Support Model Type.\n"); } - if (netT.get() == nullptr) { - MNN_ERROR("Convert error\n"); + if (netT.get() == nullptr || parseRes) { + MNN_ERROR("[ERROR] Convert error, please check your file format.\n"); return false; } int error = 0; @@ -457,12 +485,6 @@ static bool compareOutput(MNN::Express::VARP output, const std::string& directNa auto diffAbsMaxV = diffAbsMax->readMap()[0]; if (absMaxV * maxError < diffAbsMaxV || std::isnan(absMaxV)) { MNN_ERROR("TESTERROR %s value error : absMaxV:%f - DiffMax %f\n", name.c_str(), absMaxV, diffAbsMaxV); - - MNN_PRINT("expected value\n"); - formatMatrix(targetValue->readMap(), targetValue->getInfo()->dim); - MNN_PRINT("real value\n"); - formatMatrix(output->readMap(), output->getInfo()->dim); - return false; } return true; @@ -650,6 +672,336 @@ int Cli::testconvert(const std::string& defaultCacheFile, const std::string& dir return 0; } +bool Cli::mnn2json(const char* modelFile, const char* jsonFile, int flag) { + std::ifstream inputFile(modelFile, std::ios::binary); + inputFile.seekg(0, std::ios::end); + auto size = inputFile.tellg(); + inputFile.seekg(0, std::ios::beg); + + char* buffer = new char[size]; + + inputFile.read((char*)buffer, size); + std::ofstream output(jsonFile); + + if (flag > 3) { + MNN_PRINT("Dont't add convweight\n"); + auto netT = MNN::UnPackNet((void*)buffer); + auto treatFunction = [&](MNN::OpT* opParam) { + auto type = opParam->main.type; + if (type == MNN::OpParameter::OpParameter_Convolution2D) { + auto param = opParam->main.AsConvolution2D(); + param->weight.clear(); + param->bias.clear(); + if (param->symmetricQuan) { + param->symmetricQuan->weight.clear(); + } + if (param->quanParameter) { + param->quanParameter->buffer.clear(); + } + } else if (type == MNN::OpParameter::OpParameter_Blob) { + auto blobT = opParam->main.AsBlob(); + blobT->float32s.clear(); + blobT->int8s.clear(); + blobT->uint8s.clear(); + blobT->int32s.clear(); + blobT->int64s.clear(); + } else if (type == MNN::OpParameter::OpParameter_Convolution2D) { + opParam->main.AsConvolution2D()->weight.clear(); + opParam->main.AsConvolution2D()->bias.clear(); + } else if (type == MNN::OpParameter::OpParameter_MatMul) { + opParam->main.AsMatMul()->weight.clear(); + opParam->main.AsMatMul()->bias.clear(); + } else if (type == MNN::OpParameter::OpParameter_PRelu) { + opParam->main.AsPRelu()->slope.clear(); + } else if (type == MNN::OpParameter::OpParameter_Extra) { + auto extra = opParam->main.AsExtra(); + extra->info.clear(); + } else if(type == MNN::OpParameter::OpParameter_LSTM){ + auto param = opParam->main.AsLSTM(); + if (param->weightH) { + param->weightH->float32s.clear(); + } + if (param->weightI) { + param->weightI->float32s.clear(); + } + if (param->bias) { + param->bias->float32s.clear(); + } + } + }; + for (int i = 0; i < netT->oplists.size(); ++i) { + treatFunction(netT->oplists[i].get()); + } + for (int i = 0; i < netT->subgraphs.size(); ++i) { + for (int j=0; jsubgraphs[i]->nodes.size(); ++j) { + treatFunction(netT->subgraphs[i]->nodes[j].get()); + } + } + if (flag > 4) { + printf("Separate dump subgraph\n"); + for (int i=0; isubgraphs.size(); ++i) { + auto& g = netT->subgraphs[i]; + flatbuffers::FlatBufferBuilder newBuilder(1024); + auto root = MNN::SubGraphProto::Pack(newBuilder, g.get()); + newBuilder.Finish(root); + auto content = newBuilder.GetBufferPointer(); + char subGraphNameStr[128]; + sprintf(subGraphNameStr, "%s_%d", jsonFile, i); + printf("Dump subgraph %s to %s\n", g->name.c_str(), subGraphNameStr); + std::ofstream tempOutput(subGraphNameStr); + auto s = flatbuffers::FlatBufferToString((const uint8_t*)content, MNN::SubGraphProtoTypeTable()); + tempOutput << s; + } + netT->subgraphs.clear(); + } + flatbuffers::FlatBufferBuilder newBuilder(1024); + auto root = MNN::Net::Pack(newBuilder, netT.get()); + MNN::FinishNetBuffer(newBuilder, root); + { + auto content = newBuilder.GetBufferPointer(); + auto s = flatbuffers::FlatBufferToString((const uint8_t*)content, MNN::NetTypeTable()); + output << s; + } + } else { + auto s = flatbuffers::FlatBufferToString((const uint8_t*)buffer, MNN::NetTypeTable()); + output << s; + } + + delete[] buffer; + return true; +} + +#define VECTOR_EXTRACT(FLATBUFFER_TYPE, CPP_TYPE, JSON_TYPE)\ +case flatbuffers::ET_##FLATBUFFER_TYPE:\ +{\ + std::vector data(array.Size());\ + for (int i=0; ivalue.JSON_TYPE()), (CPP_TYPE)0);\ +break;\ +} +static flatbuffers::Offset _writeJsonToFlatbuffer(const flatbuffers::TypeTable * table, flatbuffers::FlatBufferBuilder& builder, const rapidjson::GenericObject>>& object) { + std::vector>> indexes; + // Load union type for easy to use + std::map unionNames; + for (int i=0; inum_elems; ++i) { + if (table->type_codes[i].sequence_ref == -1) { + continue; + } + const flatbuffers::TypeTable *ref = table->type_refs[table->type_codes[i].sequence_ref](); + if (ref->st == flatbuffers::ST_UNION) { + unionNames.insert(std::make_pair(std::string(table->names[i]) + "_type", i)); + } + } + // Find index and cache + std::map unionTypes; + for (auto iter = object.begin(); iter !=object.end(); iter++) { + auto name = iter->name.GetString(); + int index = -1; + for (int i=0; inum_elems; ++i) { + if (0 == ::strcmp(table->names[i], name)) { + index = i; + break; + } + } + auto uiter = unionNames.find(name); + if (uiter != unionNames.end()) { + // Find union type id + auto value = iter->value.GetString(); + int typePos = -1; + auto unionIndex = uiter->second; + auto ref = table->type_refs[table->type_codes[unionIndex].sequence_ref](); + for (int j=0; jnum_elems; ++j) { + if (0 == ::strcmp(ref->names[j], value)) { + typePos = j; + break; + } + } + if (-1 == typePos) { + MNN_ERROR("Can't find union type\n"); + continue; + } + if (typePos > 0) { + // First is None + unionTypes.insert(std::make_pair(unionIndex, typePos-1)); + } + } + if (index == -1) { + MNN_PRINT("Invalid: %s, Skip it\n", name); + } + indexes.emplace_back(std::make_pair(index, 0)); + } + + // resolve single object + int pos = 0; + for (auto iter = object.begin(); iter !=object.end(); iter++, pos++) { + int index = indexes[pos].first; + if (-1 == index) { + continue; + } + auto code = table->type_codes[index]; + if (code.is_vector) { + continue; + } + if (code.sequence_ref != -1 && code.base_type == flatbuffers::ET_SEQUENCE) { + const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); + if (ref->st == flatbuffers::ST_TABLE) { + indexes[pos].second = _writeJsonToFlatbuffer(ref, builder, iter->value.GetObject()); + } else if (ref->st == flatbuffers::ST_UNION) { + auto unionInd = unionTypes.find(index)->second; + ref = ref->type_refs[unionInd](); + indexes[pos].second = _writeJsonToFlatbuffer(ref, builder, iter->value.GetObject()); + } + } + } + + // Resolve Vector and String + pos = 0; + for (auto iter = object.begin(); iter !=object.end(); iter++, pos++) { + int index = indexes[pos].first; + if (-1 == index) { + continue; + } + auto code = table->type_codes[index]; + if (!code.is_vector) { + if (code.base_type == flatbuffers::ET_STRING) { + indexes[pos].second = builder.CreateString(iter->value.GetString()).Union(); + } + continue; + } + auto array = iter->value.GetArray(); + if (code.sequence_ref != -1) { + const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); + std::vector> offsets(array.Size()); + for (int i=0; i data(array.Size()); + for (int i=0; itype_codes[index]; + if (code.sequence_ref != -1) { + const flatbuffers::TypeTable *ref = table->type_refs[code.sequence_ref](); + int value = -1; + if (ref->st == flatbuffers::ST_UNION || ref->st == flatbuffers::ST_ENUM) { + auto type = iter->value.GetString(); + for (int i=0; inum_elems; ++i) { + if (0 == ::strcmp(type, ref->names[i])) { + if (nullptr == ref->values) { + value = i; + } else { + value = ref->values[i]; + } + } + } + switch (code.base_type) { + case flatbuffers::ET_UTYPE: + case flatbuffers::ET_UINT: + builder.AddElement(field, (uint32_t)value, (uint32_t)0); + break; + case flatbuffers::ET_INT: + builder.AddElement(field, (int32_t)value, (int32_t)-1); + break; + case flatbuffers::ET_UCHAR: + builder.AddElement(field, (uint8_t)value, (uint8_t)0); + break; + case flatbuffers::ET_CHAR: + builder.AddElement(field, (int8_t)value, (int8_t)0); + break; + default: + break; + } + continue; + } + } + switch (code.base_type) { + SCALAR_EXTRACT(BOOL, bool, GetBool); + SCALAR_EXTRACT(CHAR, char, GetInt); + SCALAR_EXTRACT(UCHAR, uint8_t, GetInt); + SCALAR_EXTRACT(SHORT, int16_t, GetInt); + SCALAR_EXTRACT(USHORT, uint16_t, GetInt); + SCALAR_EXTRACT(INT, int, GetInt); + SCALAR_EXTRACT(UINT, uint32_t, GetUint); + SCALAR_EXTRACT(LONG, int64_t, GetInt64); + SCALAR_EXTRACT(ULONG, uint64_t, GetUint64); + SCALAR_EXTRACT(FLOAT, float, GetFloat); + SCALAR_EXTRACT(DOUBLE, double, GetDouble); + default: + break; + } + } + return builder.EndTable(start); +} +bool Cli::json2mnn(const char* jsonFile, const char* modelFile) { + rapidjson::Document document; + { + std::ifstream fileNames(jsonFile); + std::ostringstream output; + output << fileNames.rdbuf(); + auto outputStr = output.str(); + document.Parse(outputStr.c_str()); + if (document.HasParseError()) { + MNN_ERROR("Invalid json\n"); + return 0; + } + } + auto object = document.GetObject(); + flatbuffers::FlatBufferBuilder builder; + builder.ForceDefaults(true); + auto table = MNN::NetTypeTable(); + auto offset = _writeJsonToFlatbuffer(table, builder, object); + builder.Finish(offset); + std::ofstream outputOs(modelFile); + outputOs.write((char*)builder.GetBufferPointer(), builder.GetSize()); + return true; +} }; diff --git a/tools/converter/source/common/convertToStaticModel.cpp b/tools/converter/source/common/convertToStaticModel.cpp index 6ef20b4d..2f7253c8 100644 --- a/tools/converter/source/common/convertToStaticModel.cpp +++ b/tools/converter/source/common/convertToStaticModel.cpp @@ -192,8 +192,9 @@ void converToStaticModel(const Net* net, std::map>& auto name = net->tensorName()->GetAsString(i)->str(); if (inputConfig.find(name) != inputConfig.end()) { auto& dims = inputConfig[name]; + allTensors[i]->buffer().dimensions = dims.size(); for (int j = 0; j < dims.size(); j++) { - allTensors[i]->buffer().dim[j].extent = dims[j]; + allTensors[i]->setLength(j, dims[j]); } } } diff --git a/tools/converter/source/common/writeFb.cpp b/tools/converter/source/common/writeFb.cpp index 5bbf1175..cc8d8b62 100644 --- a/tools/converter/source/common/writeFb.cpp +++ b/tools/converter/source/common/writeFb.cpp @@ -37,8 +37,11 @@ int writeFb(std::unique_ptr& netT, const std::string& MNNModelFile, c addUUID(netT, proto); + // add version info to model + netT->extraInfo.reset(new ExtraInfoT); + netT->extraInfo->version = MNN_VERSION; if (!config.authCode.empty()) { - netT->extraInfo.reset(new ExtraInfoT); + // add auth code to model netT->extraInfo->name = config.authCode; } diff --git a/tools/converter/source/onnx/onnxConverter.cpp b/tools/converter/source/onnx/onnxConverter.cpp index c21827d9..9aa439db 100644 --- a/tools/converter/source/onnx/onnxConverter.cpp +++ b/tools/converter/source/onnx/onnxConverter.cpp @@ -26,6 +26,10 @@ int onnx2MNNNet(const std::string inputModel, const std::string bizCode, // read ONNX Model bool success = onnx_read_proto_from_binary(inputModel.c_str(), &onnxModel); DCHECK(success) << "read onnx model failed: " << inputModel; + if (!success) { + MNN_ERROR("[ERROR] Model file is not onnx model.\n"); + return 1; + } LOG(INFO) << "ONNX Model ir version: " << onnxModel.ir_version(); diff --git a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp index d3b52538..e6e956c5 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxBatchNormMerge.cpp @@ -11,6 +11,16 @@ #include "OnnxExtraManager.hpp" namespace MNN { namespace Express { +static VARP _ReshapeF(VARP x, VARP shape, MNN::MNN_DATA_FORMAT format) { + MNN_ASSERT(nullptr != x); + std::unique_ptr reshape(new OpT); + reshape->type = OpType_Reshape; + reshape->main.type = OpParameter_Reshape; + reshape->main.value = new ReshapeT; + reshape->main.AsReshape()->dimType = format; + return (Variable::create(Expr::create(reshape.get(), {x, shape}))); +} + class OnnxBatchNormTransform : public OnnxExtraManager::Transform { virtual EXPRP onExecute(EXPRP expr) const override { auto inputs = expr->inputs(); @@ -142,19 +152,51 @@ class OnnxInstanceNormalTransform : public OnnxExtraManager::Transform { } } } - - auto compatShape = _Concat({_Shape(inputs[1], true), _Fill(_Unsqueeze(_Size(_Shape(input, true)) - _Scalar(2), {0}), _Scalar(1))}, 0); - auto scale = _OnnxReshape(inputs[1], compatShape); - auto bias = _OnnxReshape(inputs[2], compatShape); - auto epsilonVar = _Scalar(epsilon); - auto mean = _ReduceMean(input, {2, 3}, true); - auto temp = input - mean; - temp = temp * temp; - auto var = _ReduceMean(temp, {2, 3}, true); - auto varRev = _Rsqrt(var + epsilonVar); - auto alpha = scale * varRev; - auto beta = bias - alpha * mean; - auto res = input * alpha + beta; + bool needScale = true; + do { + auto biasPtr = inputs[2]->readMap(); + auto scalePtr = inputs[1]->readMap(); + if (nullptr == biasPtr || nullptr == scalePtr) { + break; + } + auto oneVar = _Scalar(1.0f); + auto scaleOff = inputs[1] - oneVar; + auto scaleSum = _ReduceSum(scaleOff * scaleOff); + if (scaleSum->readMap()[0] > 0.000001f) { + break; + } + auto biasSum = _ReduceSum(inputs[2] * inputs[2]); + if (biasSum->readMap()[0] > 0.000001f) { + break; + } + needScale = false; + } while (false); + auto originShape = _Shape(inputs[0], NCHW); + auto inputDim3 = _Reshape(inputs[0], {0, 0, -1}, NCHW); + + // Turn to layernorm + std::unique_ptr layerNormOp(new MNN::OpT); + layerNormOp->type = OpType_LayerNorm; + layerNormOp->main.value = new LayerNormT; + layerNormOp->main.type = OpParameter_LayerNorm; + { + auto param = layerNormOp->main.AsLayerNorm(); + param->axis = {2}; // Layernorm only need axis's size as 1 + param->epsilon = epsilon; +// param->beta.resize(inputs[2]->getInfo()->size); +// ::memcpy(param->beta.data(), biasPtr, param->beta.size() * sizeof(float)); +// param->gamma.resize(inputs[1]->getInfo()->size); +// ::memcpy(param->gamma.data(), scalePtr, param->beta.size() * sizeof(float)); + param->group = 1; + } + auto res = Variable::create(Expr::create(layerNormOp.get(), {inputDim3})); + res = _ReshapeF(res, originShape, MNN_DATA_FORMAT_NCHW); + if (needScale) { + auto compatShape = _Concat({_Shape(inputs[1], true), _Fill(_Unsqueeze(_Size(_Shape(input, true)) - _Scalar(2), {0}), _Scalar(1))}, 0); + auto scale = _OnnxReshape(inputs[1], compatShape); + auto bias = _OnnxReshape(inputs[2], compatShape); + res = res * scale + bias; + } res->setName(expr->name()); return res->expr().first; } diff --git a/tools/converter/source/optimizer/onnxextra/OnnxSlice.cpp b/tools/converter/source/optimizer/onnxextra/OnnxSlice.cpp index 762cf21b..8deef934 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxSlice.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxSlice.cpp @@ -113,7 +113,7 @@ public: auto adjustIndexIfNeg = [=](VARP index) { return _Select(_Less(index, zero), index + shape, index); }; - startVar = _Maximum(_Minimum(adjustIndexIfNeg(startVar), shape - _Scalar(1)), zero); + startVar = adjustIndexIfNeg(startVar); endVar = adjustIndexIfNeg(endVar); std::unique_ptr sliceOp(new OpT); diff --git a/tools/converter/source/optimizer/onnxextra/OnnxTopK.cpp b/tools/converter/source/optimizer/onnxextra/OnnxTopK.cpp index f25e321d..c8e1361b 100644 --- a/tools/converter/source/optimizer/onnxextra/OnnxTopK.cpp +++ b/tools/converter/source/optimizer/onnxextra/OnnxTopK.cpp @@ -54,6 +54,9 @@ public: onnxTopKOp->main.type = OpParameter_TopKV2; onnxTopKOp->main.value = onnxTopKParam.release(); + if (axis < 0 && inputs[0]->getInfo()) { + axis += inputs[0]->getInfo()->dim.size(); + } EXPRP output = Expr::create(onnxTopKOp.get(), {inputs[0], kVar, _Scalar(axis)}, 2); output->setName(expr->name()); Variable::create(output, 0)->setName(expr->outputName(0)); diff --git a/tools/converter/source/optimizer/torchextra/TorchFlip.cpp b/tools/converter/source/optimizer/torchextra/TorchFlip.cpp index b3c57737..dda21817 100644 --- a/tools/converter/source/optimizer/torchextra/TorchFlip.cpp +++ b/tools/converter/source/optimizer/torchextra/TorchFlip.cpp @@ -31,10 +31,12 @@ public: } } auto input = expr->inputs()[0]; - auto shape = _Shape(input); - auto dim0 = _Unsqueeze(_GatherV2(shape, _Scalar(0), _Scalar(0)), {0}); - auto dim1 = _GatherV2(shape, _Scalar(1), _Scalar(0)); - auto newExpr = _ReverseSequence(input, _Fill(dim0, dim1), 0, dim)->expr().first; + auto end = _Shape(input); + auto begin = _ZerosLike(end); + auto dims = _Scalar(dim); + auto index = _Add(dims, _Multiply(_Rank(input), _Less(dims, _Scalar(0)))); + auto strided = _ScatterNd(_Unsqueeze(index, {0}), _Unsqueeze(_Scalar(-1), {0}), _Shape(end), begin + _Scalar(1)); + auto newExpr = _StridedSlice(input, begin, end, strided, 31, 31, 0, 0, 0)->expr().first; newExpr->setName(expr->name()); return newExpr; } diff --git a/tools/converter/source/optimizer/torchextra/TorchIndex.cpp b/tools/converter/source/optimizer/torchextra/TorchIndex.cpp new file mode 100644 index 00000000..55385bff --- /dev/null +++ b/tools/converter/source/optimizer/torchextra/TorchIndex.cpp @@ -0,0 +1,89 @@ +// +// TorchIndex.cpp +// MNNConverter +// +// Created by MNN on 2022/04/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "MNN_generated.h" +#include "TorchExtraManager.hpp" +#include "logkit.h" + +namespace MNN { +namespace Express { + +class TorchSelectTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 3); + auto input = inputs[0]; + auto axis = inputs[1]; + auto index = inputs[2]; + auto output = _GatherV2(input, _Squeeze(index), axis); + output->setName(opName); + return output->expr().first; + } +}; + +class TorchIndexTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 2); + auto input = inputs[0]; + auto mask = inputs[1]; + auto index = _GatherND(input, _Where(mask)); + index->setName(opName); + return index->expr().first; + } +}; + +class TorchIndexStridedSliceTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + // TODO + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 2); + auto input = inputs[0]; + auto mask = inputs[1]; + auto index = _GatherND(input, _Where(mask)); + index->setName(opName); + return index->expr().first; + } +}; + +class TorchIndexPutTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() >= 3); + auto input = inputs[0]; + auto mask = inputs[1]; + auto value = inputs[2]; + auto idx = _Where(mask); + auto index_put = _ScatterNd(idx, value, _Shape(input), input); + index_put->setName(opName); + return index_put->expr().first; + } +}; + +static auto gRegister = []() { + TorchExtraManager::get()->insert("select", std::shared_ptr(new TorchSelectTransform)); + TorchExtraManager::get()->insert("index", std::shared_ptr(new TorchIndexTransform)); + TorchExtraManager::get()->insert("index_stridedslice", std::shared_ptr(new TorchIndexStridedSliceTransform)); + TorchExtraManager::get()->insert("index_put", std::shared_ptr(new TorchIndexPutTransform)); + return true; +}(); + +} // namespace Express +} // namespace MNN diff --git a/tools/converter/source/optimizer/torchextra/TorchSilu.cpp b/tools/converter/source/optimizer/torchextra/TorchSilu.cpp index d38688a3..5d1ea45a 100644 --- a/tools/converter/source/optimizer/torchextra/TorchSilu.cpp +++ b/tools/converter/source/optimizer/torchextra/TorchSilu.cpp @@ -1,5 +1,5 @@ // -// TorchSize.cpp +// TorchSilu.cpp // MNNConverter // // Created by MNN on 2021/08/12. diff --git a/tools/converter/source/optimizer/torchextra/TorchSoftplus.cpp b/tools/converter/source/optimizer/torchextra/TorchSoftplus.cpp new file mode 100644 index 00000000..673319f6 --- /dev/null +++ b/tools/converter/source/optimizer/torchextra/TorchSoftplus.cpp @@ -0,0 +1,69 @@ +// +// TorchSoftplus.cpp +// MNNConverter +// +// Created by MNN on 2022/04/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "MNN_generated.h" +#include "TorchExtraManager.hpp" +#include "logkit.h" + +namespace MNN { +namespace Express { + +class TorchSoftplusTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 1); + auto info = op->main_as_Extra(); + int beta = 1; + if (nullptr != info->attr()) { + for (int i = 0; i < info->attr()->size(); ++i) { + const auto attr = info->attr()->GetAs(i); + const auto attributeName = attr->key()->str(); + if (attributeName == "beta") { + beta = attr->i(); + } + } + } + VARP softplus; + auto x = inputs[0]; + if (beta == 1) { + softplus = _Softplus(x); + } else { + auto beta_ = _Const(beta); + softplus = _Log(_Add(_Exp(x * beta_), _Const(1))) / beta_; + } + softplus->setName(opName); + return softplus->expr().first; + } +}; + +class TorchBitwiseNotTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 1); + auto info = op->main_as_Extra(); + auto x = inputs[0]; + VARP bitwise_not = _Scalar(1) - x; + bitwise_not->setName(opName); + return bitwise_not->expr().first; + } +}; + +static auto gRegister = []() { + TorchExtraManager::get()->insert("softplus", std::shared_ptr(new TorchSoftplusTransform)); + TorchExtraManager::get()->insert("bitwise_not", std::shared_ptr(new TorchBitwiseNotTransform)); + return true; +}(); + +} // namespace Express +} // namespace MNN diff --git a/tools/converter/source/optimizer/torchextra/TorchZeros.cpp b/tools/converter/source/optimizer/torchextra/TorchZeros.cpp new file mode 100644 index 00000000..25535cf0 --- /dev/null +++ b/tools/converter/source/optimizer/torchextra/TorchZeros.cpp @@ -0,0 +1,77 @@ +// +// TorchZeros.cpp +// MNNConverter +// +// Created by MNN on 2022/04/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include "MNN_generated.h" +#include "TorchExtraManager.hpp" +#include "logkit.h" + +namespace MNN { +namespace Express { + +class TorchZerosTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 1); + auto zeros = _Fill(inputs[0], _Const(0.)); + zeros->setName(opName); + return zeros->expr().first; + } +}; + +class TorchOnesTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 1); + auto zeros = _Fill(inputs[0], _Const(1.)); + zeros->setName(opName); + return zeros->expr().first; + } +}; + +class TorchOnesLikeTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 1); + auto zeros = _Fill(_Shape(inputs[0]), _Const(1.)); + zeros->setName(opName); + return zeros->expr().first; + } +}; + +class TorchFullLikeTransform : public TorchExtraManager::Transform { +public: + virtual EXPRP onExecute(EXPRP expr) const override { + auto inputs = expr->inputs(); + auto op = expr->get(); + auto opName = op->name()->str(); + MNN_ASSERT(inputs.size() == 2); + auto full = _Fill(_Shape(inputs[0]), inputs[1]); + full->setName(opName); + return full->expr().first; + } +}; + +static auto gRegister = []() { + TorchExtraManager::get()->insert("zeros", std::shared_ptr(new TorchZerosTransform)); + TorchExtraManager::get()->insert("ones", std::shared_ptr(new TorchOnesTransform)); + TorchExtraManager::get()->insert("ones_like", std::shared_ptr(new TorchOnesLikeTransform)); + TorchExtraManager::get()->insert("full_like", std::shared_ptr(new TorchFullLikeTransform)); + return true; +}(); + +} // namespace Express +} // namespace MNN diff --git a/tools/converter/source/tensorflow/tensorflowConverter.cpp b/tools/converter/source/tensorflow/tensorflowConverter.cpp index 8a4e1914..3903dfae 100644 --- a/tools/converter/source/tensorflow/tensorflowConverter.cpp +++ b/tools/converter/source/tensorflow/tensorflowConverter.cpp @@ -18,7 +18,11 @@ int tensorflow2MNNNet(const std::string inputModel, const std::string bizCode, // Load tensorflow model. tensorflow::GraphDef tfGraph; bool success = tf_read_proto_from_binary(inputModel.c_str(), &tfGraph); - DCHECK(success) << "read_proto_from_binary failed"; + DCHECK(success) << "read_proto_from_binary failed!"; + if (!success) { + MNN_ERROR("[ERROR] MNNConvert just support tensorflow frozen graph model. Model file is not tf frozen graph model.\n"); + return 1; + } TFGraphResolver resolver(tfGraph); for (int i = 0; i < resolver.graph_size(); ++i) { diff --git a/tools/converter/source/torch/BinaryTorch.cpp b/tools/converter/source/torch/BinaryTorch.cpp index 6697917b..edfc6833 100644 --- a/tools/converter/source/torch/BinaryTorch.cpp +++ b/tools/converter/source/torch/BinaryTorch.cpp @@ -40,7 +40,10 @@ void BinaryTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* {"eq", MNN::BinaryOpOperation_EQUAL}, {"__is__", MNN::BinaryOpOperation_EQUAL}, {"mode", MNN::BinaryOpOperation_MOD}, {"remainder", MNN::BinaryOpOperation_MOD}, {"atan2", MNN::BinaryOpOperation_ATAN2}, - {"logical_or", MNN::BinaryOpOperation_LOGICALOR}, {"__or__", MNN::BinaryOpOperation_LOGICALOR}, + {"logical_or", MNN::BinaryOpOperation_LOGICALOR}, + {"__or__", MNN::BinaryOpOperation_BITWISE_OR}, {"__ior__", MNN::BinaryOpOperation_BITWISE_OR}, + {"__and__", MNN::BinaryOpOperation_BITWISE_AND}, {"__iand__", MNN::BinaryOpOperation_BITWISE_AND}, + {"__xor__", MNN::BinaryOpOperation_BITWISE_XOR}, {"__ixor__", MNN::BinaryOpOperation_BITWISE_XOR}, {"ne", MNN::BinaryOpOperation_NOTEQUAL}, {"__isnot__", MNN::BinaryOpOperation_NOTEQUAL} }; auto param = new MNN::BinaryOpT; @@ -56,14 +59,14 @@ void BinaryTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* } REGISTER_CONVERTER(BinaryTorch, add); -REGISTER_CONVERTER(BinaryTorch, sum); +REGISTER_CONVERTER(BinaryTorch, sum_binary); REGISTER_CONVERTER(BinaryTorch, sub); REGISTER_CONVERTER(BinaryTorch, mul); REGISTER_CONVERTER(BinaryTorch, pow); REGISTER_CONVERTER(BinaryTorch, div); -REGISTER_CONVERTER(BinaryTorch, min_compare); +REGISTER_CONVERTER(BinaryTorch, min_binary); REGISTER_CONVERTER(BinaryTorch, minimum); -REGISTER_CONVERTER(BinaryTorch, max_compare); +REGISTER_CONVERTER(BinaryTorch, max_binary); REGISTER_CONVERTER(BinaryTorch, maximum); REGISTER_CONVERTER(BinaryTorch, gt); REGISTER_CONVERTER(BinaryTorch, greater); @@ -78,8 +81,13 @@ REGISTER_CONVERTER(BinaryTorch, mode); REGISTER_CONVERTER(BinaryTorch, remainder); REGISTER_CONVERTER(BinaryTorch, atan2); REGISTER_CONVERTER(BinaryTorch, logical_or); -REGISTER_CONVERTER(BinaryTorch, __or__); REGISTER_CONVERTER(BinaryTorch, ne); REGISTER_CONVERTER(BinaryTorch, rsub); REGISTER_CONVERTER(BinaryTorch, __is__); REGISTER_CONVERTER(BinaryTorch, __isnot__); +REGISTER_CONVERTER(BinaryTorch, __or__); +REGISTER_CONVERTER(BinaryTorch, __ior__); +REGISTER_CONVERTER(BinaryTorch, __and__); +REGISTER_CONVERTER(BinaryTorch, __iand__); +REGISTER_CONVERTER(BinaryTorch, __xor__); +REGISTER_CONVERTER(BinaryTorch, __ixor__); diff --git a/tools/converter/source/torch/ConstantTorch.cpp b/tools/converter/source/torch/ConstantTorch.cpp index 98245224..d8699266 100644 --- a/tools/converter/source/torch/ConstantTorch.cpp +++ b/tools/converter/source/torch/ConstantTorch.cpp @@ -99,6 +99,13 @@ void ConstantTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScop break; } case at::ScalarType::Bool: + param->dataType = MNN::DataType_DT_INT32; + param->int32s = std::move(getValue(output, param->dims)); + if (param->dims.empty() && param->int32s.empty()) { + param->int32s.push_back(0); + param->dims.push_back(1); + } + break; case at::ScalarType::BFloat16: case at::ScalarType::Short: case at::ScalarType::Half: diff --git a/tools/converter/source/torch/CumProdTorch.cpp b/tools/converter/source/torch/CumProdTorch.cpp new file mode 100644 index 00000000..12643fbd --- /dev/null +++ b/tools/converter/source/torch/CumProdTorch.cpp @@ -0,0 +1,30 @@ +// +// CumPordTorch.cpp +// MNNConverter +// +// Created by MNN on 2022/05/10. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include "torchOpConverter.hpp" + +DECLARE_OP_CONVERTER(CumPordTorch); + +MNN::OpType CumPordTorch::opType() { + return MNN::OpType_CumProd; +} +MNN::OpParameter CumPordTorch::type() { + return MNN::OpParameter_Axis; +} +std::vector CumPordTorch::inputTensorIdx() { + return {0}; +} + +void CumPordTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto param = new MNN::AxisT; + param->axis = getValue(node->input(1)); + dstOp->main.value = param; +} + +REGISTER_CONVERTER(CumPordTorch, cumprod); diff --git a/tools/converter/source/torch/FlattenTorch.cpp b/tools/converter/source/torch/FlattenTorch.cpp index 64ebcb07..d7382124 100644 --- a/tools/converter/source/torch/FlattenTorch.cpp +++ b/tools/converter/source/torch/FlattenTorch.cpp @@ -28,7 +28,6 @@ void FlattenTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope const auto end_dim = inputs[2]; param->axis = getValue(start_dim); param->endAxis = getValue(end_dim); - param->endAxis = 0; dstOp->main.value = param; } diff --git a/tools/converter/source/torch/GatherTorch.cpp b/tools/converter/source/torch/GatherTorch.cpp index 3f6d924a..1fc88a70 100644 --- a/tools/converter/source/torch/GatherTorch.cpp +++ b/tools/converter/source/torch/GatherTorch.cpp @@ -33,19 +33,20 @@ REGISTER_CONVERTER(GatherTorch, embedding); DECLARE_OP_CONVERTER(SelectTorch); MNN::OpType SelectTorch::opType() { - return MNN::OpType_Gather; + return MNN::OpType_Extra; } MNN::OpParameter SelectTorch::type() { - return MNN::OpParameter_Axis; + return MNN::OpParameter_Extra; } std::vector SelectTorch::inputTensorIdx() { - return {0, 2}; + return {-1}; } void SelectTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { - auto param = new MNN::AxisT; - param->axis = getValue(node->input(1)); - dstOp->main.value = param; + auto extra = new MNN::ExtraT; + dstOp->main.value = extra; + extra->engine = "Torch"; + extra->type = getRealOpType(node); } REGISTER_CONVERTER(SelectTorch, select); diff --git a/tools/converter/source/torch/GridSampleTorch.cpp b/tools/converter/source/torch/GridSampleTorch.cpp new file mode 100644 index 00000000..b6e39a0e --- /dev/null +++ b/tools/converter/source/torch/GridSampleTorch.cpp @@ -0,0 +1,42 @@ +// +// GridSampleTorch.cpp +// MNNConverter +// +// Created by MNN on 2022/04/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include "torchOpConverter.hpp" + +DECLARE_OP_CONVERTER(GridSampleTorch); + +MNN::OpType GridSampleTorch::opType() { + return MNN::OpType_GridSample; +} +MNN::OpParameter GridSampleTorch::type() { + return MNN::OpParameter_GridSample; +} +std::vector GridSampleTorch::inputTensorIdx() { + return {0, 1}; +} + +void GridSampleTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto gridSampleParam = new MNN::GridSampleT; + int mode = getValue(node->input(2)); + if (mode == 0 || mode == 1) { + gridSampleParam->mode = static_cast(mode); + } else { + LOG(FATAL) << "Unknown mode for " << dstOp->name << "!"; + } + int padding_mode = getValue(node->input(3)); + if (padding_mode == 0 || padding_mode == 1 || padding_mode == 2) { + gridSampleParam->paddingMode = static_cast(mode); + } else { + LOG(FATAL) << "Unknown padding for " << dstOp->name << "!"; + } + gridSampleParam->alignCorners = getValue(node->input(4)); + dstOp->main.value = gridSampleParam; +} + +REGISTER_CONVERTER(GridSampleTorch, grid_sampler); diff --git a/tools/converter/source/torch/IndexTorch.cpp b/tools/converter/source/torch/IndexTorch.cpp new file mode 100644 index 00000000..4dd2148a --- /dev/null +++ b/tools/converter/source/torch/IndexTorch.cpp @@ -0,0 +1,33 @@ +// +// IndexTorch.cpp +// MNNConverter +// +// Created by MNN on 2022/04/28. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include "torchOpConverter.hpp" + +DECLARE_OP_CONVERTER(IndexTorch); + +MNN::OpType IndexTorch::opType() { + return MNN::OpType_Extra; +} +MNN::OpParameter IndexTorch::type() { + return MNN::OpParameter_Extra; +} +std::vector IndexTorch::inputTensorIdx() { + return {-1}; +} + +void IndexTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto extra = new MNN::ExtraT; + dstOp->main.value = extra; + extra->engine = "Torch"; + extra->type = getRealOpType(node); +} + +REGISTER_CONVERTER(IndexTorch, index); +REGISTER_CONVERTER(IndexTorch, index_stridedslice); +REGISTER_CONVERTER(IndexTorch, index_put); diff --git a/tools/converter/source/torch/ReduceTorch.cpp b/tools/converter/source/torch/ReduceTorch.cpp index 30652a7d..58387e3d 100644 --- a/tools/converter/source/torch/ReduceTorch.cpp +++ b/tools/converter/source/torch/ReduceTorch.cpp @@ -25,10 +25,12 @@ std::vector ReduceTorch::inputTensorIdx() { void ReduceTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { static std::map gMaps{ - {"sum", MNN::ReductionType_SUM}, + {"sum_reduce", MNN::ReductionType_SUM}, {"mean", MNN::ReductionType_MEAN}, {"max_reduce", MNN::ReductionType_MAXIMUM}, + {"amax", MNN::ReductionType_MAXIMUM}, {"min_reduce", MNN::ReductionType_MINIMUM}, + {"amin", MNN::ReductionType_MINIMUM}, {"prod", MNN::ReductionType_PROD}, {"all", MNN::ReductionType_ALL}, {"any", MNN::ReductionType_ANY}, @@ -38,15 +40,22 @@ void ReduceTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* std::string opType = getRealOpType(node); param->operation = gMaps[opType]; - if (opType == "mean") { + if (opType == "sum_reduce" || opType == "mean" || opType == "amax" || opType == "amin") { const auto dims = getValue>(inputs[1]); for (int i : dims) { param->dim.push_back(i); } param->keepDims = getValue(inputs[2]); } else { - const auto dim = getValue(inputs[1]); - param->dim.push_back(dim); + if (inputs[1]->type()->kind() == c10::TypeKind::IntType) { + const auto dim = getValue(inputs[1]); + param->dim.push_back(dim); + } else { + const auto dims = getValue>(inputs[1]); + for (auto dim : dims) { + param->dim.push_back(dim); + } + } } if (dstOp->outputIndexes.size() > 1) { int realOutput = dstOp->outputIndexes[0]; @@ -56,13 +65,15 @@ void ReduceTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* dstOp->main.value = param; } -REGISTER_CONVERTER(ReduceTorch, sum); +REGISTER_CONVERTER(ReduceTorch, sum_reduce); REGISTER_CONVERTER(ReduceTorch, mean); REGISTER_CONVERTER(ReduceTorch, max_reduce); REGISTER_CONVERTER(ReduceTorch, min_reduce); REGISTER_CONVERTER(ReduceTorch, prod); REGISTER_CONVERTER(ReduceTorch, all); REGISTER_CONVERTER(ReduceTorch, any); +REGISTER_CONVERTER(ReduceTorch, amin); +REGISTER_CONVERTER(ReduceTorch, amax); DECLARE_OP_CONVERTER(NormTorch); @@ -81,23 +92,32 @@ void NormTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* s dstOp->main.value = extra; extra->engine = "Torch"; extra->type = "norm"; + auto type = getRealOpType(node); extra->attr.resize(3); extra->attr[0].reset(new MNN::AttributeT); extra->attr[0]->key = "ord"; - auto ord = node->input(1); - auto kind = ord->type()->kind(); - if (kind == c10::TypeKind::FloatType) { - extra->attr[0]->i = getValue(node->input(1)); - } else { - extra->attr[0]->i = getValue(node->input(1)); - } extra->attr[1].reset(new MNN::AttributeT); extra->attr[1]->key = "dim"; - auto dims = getValue>(node->input(2)); - extra->attr[1]->i = dims[0]; extra->attr[2].reset(new MNN::AttributeT); extra->attr[2]->key = "keepDim"; - extra->attr[2]->i = getValue(node->input(3)); + if (type == "frobenius_norm") { + extra->attr[0]->i = 2; + auto dims = getValue>(node->input(1)); + extra->attr[1]->i = dims[0]; + extra->attr[2]->i = getValue(node->input(2)); + } else { + auto ord = node->input(1); + auto kind = ord->type()->kind(); + if (kind == c10::TypeKind::FloatType) { + extra->attr[0]->i = getValue(node->input(1)); + } else { + extra->attr[0]->i = getValue(node->input(1)); + } + auto dims = getValue>(node->input(2)); + extra->attr[1]->i = dims[0]; + extra->attr[2]->i = getValue(node->input(3)); + } } REGISTER_CONVERTER(NormTorch, norm); +REGISTER_CONVERTER(NormTorch, frobenius_norm); diff --git a/tools/converter/source/torch/ReluTorch.cpp b/tools/converter/source/torch/ReluTorch.cpp index 11dabad0..c6c005de 100644 --- a/tools/converter/source/torch/ReluTorch.cpp +++ b/tools/converter/source/torch/ReluTorch.cpp @@ -50,15 +50,29 @@ std::vector Relu6Torch::inputTensorIdx() { void Relu6Torch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { auto param = new MNN::Relu6T; + bool isFloat = node->input(1)->type()->kind() == c10::TypeKind::FloatType; if (getRealOpType(node) == "clamp") { - param->minValue = getValue(node->input(1)); - param->maxValue = getValue(node->input(2)); + if (isFloat) { + param->minValue = getValue(node->input(1)); + param->maxValue = getValue(node->input(2)); + } else { + param->minValue = getValue(node->input(1)); + param->maxValue = getValue(node->input(2)); + } } else if (getRealOpType(node) == "clamp_min") { - param->minValue = getValue(node->input(1)); + if (isFloat) { + param->minValue = getValue(node->input(1)); + } else { + param->minValue = getValue(node->input(1)); + } param->maxValue = std::numeric_limits::max(); } else if (getRealOpType(node) == "clamp_max") { param->minValue = std::numeric_limits::min(); - param->maxValue = getValue(node->input(1)); + if (isFloat) { + param->maxValue = getValue(node->input(1)); + } else { + param->maxValue = getValue(node->input(1)); + } } dstOp->main.value = param; } diff --git a/tools/converter/source/torch/TransposeTorch.cpp b/tools/converter/source/torch/TransposeTorch.cpp index 4a4d9984..a17f6123 100644 --- a/tools/converter/source/torch/TransposeTorch.cpp +++ b/tools/converter/source/torch/TransposeTorch.cpp @@ -23,15 +23,21 @@ std::vector PermuteTorch::inputTensorIdx() { void PermuteTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { auto param = new MNN::PermuteT; - auto dims = getValue>(node->input(1)); - param->dims.resize(dims.size()); - for (int i = 0; i < dims.size(); i++) { - param->dims[i] = dims[i]; + auto type = getRealOpType(node); + if (type == "numpy_T") { + param->dims = {1, 0}; + } else { + auto dims = getValue>(node->input(1)); + param->dims.resize(dims.size()); + for (int i = 0; i < dims.size(); i++) { + param->dims[i] = dims[i]; + } } dstOp->main.value = param; } REGISTER_CONVERTER(PermuteTorch, permute); +REGISTER_CONVERTER(PermuteTorch, numpy_T); DECLARE_OP_CONVERTER(TransposeTorch); diff --git a/tools/converter/source/torch/UnaryTorch.cpp b/tools/converter/source/torch/UnaryTorch.cpp index 5eb9c3d2..e1b15872 100644 --- a/tools/converter/source/torch/UnaryTorch.cpp +++ b/tools/converter/source/torch/UnaryTorch.cpp @@ -99,25 +99,36 @@ REGISTER_CONVERTER(UnaryTorch, sigmoid); REGISTER_CONVERTER(UnaryTorch, hardswish); REGISTER_CONVERTER(UnaryTorch, gelu); - // TODO: silu will impl as unary ? -DECLARE_OP_CONVERTER(SiluTorch); +DECLARE_OP_CONVERTER(ExtraUnaryTorch); -MNN::OpType SiluTorch::opType() { +MNN::OpType ExtraUnaryTorch::opType() { return MNN::OpType_Extra; } -MNN::OpParameter SiluTorch::type() { +MNN::OpParameter ExtraUnaryTorch::type() { return MNN::OpParameter_Extra; } -std::vector SiluTorch::inputTensorIdx() { +std::vector ExtraUnaryTorch::inputTensorIdx() { return {0}; } -void SiluTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { +void ExtraUnaryTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { auto extra = new MNN::ExtraT; extra->engine = "Torch"; - extra->type = getRealOpType(node); + auto type = getRealOpType(node); + extra->type = type; dstOp->main.value = extra; + if (type == "softplus") { + extra->attr.resize(2); + extra->attr[0].reset(new MNN::AttributeT); + extra->attr[0]->key = "beta"; + extra->attr[0]->i = getValue(node->input(1)); + extra->attr[1].reset(new MNN::AttributeT); + extra->attr[1]->key = "threshold"; + extra->attr[1]->i = getValue(node->input(2)); + } } -REGISTER_CONVERTER(SiluTorch, silu); +REGISTER_CONVERTER(ExtraUnaryTorch, silu); +REGISTER_CONVERTER(ExtraUnaryTorch, softplus); +REGISTER_CONVERTER(ExtraUnaryTorch, bitwise_not); diff --git a/tools/converter/source/torch/ZerosLikeTorch.cpp b/tools/converter/source/torch/ZerosLikeTorch.cpp index f9c39b4e..3f33a2df 100644 --- a/tools/converter/source/torch/ZerosLikeTorch.cpp +++ b/tools/converter/source/torch/ZerosLikeTorch.cpp @@ -26,3 +26,70 @@ void ZerosLikeTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchSco } REGISTER_CONVERTER(ZerosLikeTorch, zeros_like); + +DECLARE_OP_CONVERTER(FullLikeTorch); + +MNN::OpType FullLikeTorch::opType() { + return MNN::OpType_Extra; +} +MNN::OpParameter FullLikeTorch::type() { + return MNN::OpParameter_Extra; +} +std::vector FullLikeTorch::inputTensorIdx() { + return {0, 1}; +} + +void FullLikeTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto extra = new MNN::ExtraT; + extra->engine = "Torch"; + extra->type = "full_like"; + dstOp->main.value = extra; + return; +} + +REGISTER_CONVERTER(FullLikeTorch, full_like); + +DECLARE_OP_CONVERTER(OnesLikeTorch); + +MNN::OpType OnesLikeTorch::opType() { + return MNN::OpType_Extra; +} +MNN::OpParameter OnesLikeTorch::type() { + return MNN::OpParameter_Extra; +} +std::vector OnesLikeTorch::inputTensorIdx() { + return {0}; +} + +void OnesLikeTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto extra = new MNN::ExtraT; + extra->engine = "Torch"; + extra->type = "ones_like"; + dstOp->main.value = extra; + return; +} + +REGISTER_CONVERTER(OnesLikeTorch, ones_like); + +DECLARE_OP_CONVERTER(ZerosTorch); + +MNN::OpType ZerosTorch::opType() { + return MNN::OpType_Extra; +} +MNN::OpParameter ZerosTorch::type() { + return MNN::OpParameter_Extra; +} +std::vector ZerosTorch::inputTensorIdx() { + return {0}; +} + +void ZerosTorch::run(MNN::OpT* dstOp, const torch::jit::Node* node, TorchScope* scope) { + auto extra = new MNN::ExtraT; + extra->engine = "Torch"; + extra->type = getRealOpType(node); + dstOp->main.value = extra; + return; +} + +REGISTER_CONVERTER(ZerosTorch, zeros); +REGISTER_CONVERTER(ZerosTorch, ones); diff --git a/tools/converter/source/torch/torchConverter.cpp b/tools/converter/source/torch/torchConverter.cpp index 841647b9..da8c376f 100644 --- a/tools/converter/source/torch/torchConverter.cpp +++ b/tools/converter/source/torch/torchConverter.cpp @@ -36,7 +36,13 @@ MNN_PUBLIC int torch2MNNNet(const std::string inputModel, const std::string bizC loadCustomOp(customTorchOps); // Deserialize the ScriptModule from a file, set to eval mode and freeze c10::Device device("cpu"); - auto module = torch::jit::load(inputModel.c_str(), device); + torch::jit::Module module; + try { + module = torch::jit::load(inputModel.c_str(), device); + } catch (std::exception e) { + MNN_ERROR("[ERROR] TorchScript model can't load. Please using `torch.jit.script` or `torch.jit.trace` save model.\n"); + return 1; + } auto graph = torch::jit::torchOptPass(module); std::unique_ptr scope(new TorchScope(netT.get())); for (const auto input : graph->inputs()) { diff --git a/tools/converter/source/torch/torchOptimize.cpp b/tools/converter/source/torch/torchOptimize.cpp index dc9ecfb8..c2c2d624 100644 --- a/tools/converter/source/torch/torchOptimize.cpp +++ b/tools/converter/source/torch/torchOptimize.cpp @@ -154,6 +154,37 @@ void removeListAppend(Graph* graph, Block* block) { node->destroy(); } } +/* + We remove all ListConstruct op with only one input and not used by aten::cat, like below: + %116 : Tensor?[] = prim::ListConstruct(%115) + %alpha0.1 : Tensor = aten::index_put_(%alpha.1, %116, %x.1, %16) + ListConstruct used by aten::cat will be reserved like below: + %features.2 : Tensor[] = prim::ListConstruct(%input3.4) + %concated_features.380 : Tensor = aten::cat(%features.2, %5) + Attention: Runing this pass after removeListAppend + */ +void removeListConstructOps(Block* block) { + for (auto it = block->nodes().begin(), end = block->nodes().end(); it != end; ++it) { + for (auto b : it->blocks()) { + removeUselessOps(b); + } + if (it->kind() == prim::ListConstruct && it->inputs().size() == 1) { + bool remove = true; + for (auto use : it->output()->uses()) { + if (use.user->kind() == aten::cat) { + remove = false; + break; + } + } + if (remove) { + it->output()->replaceAllUsesWith(it->input(0)); + it->removeInput(0); + it.destroyCurrent(); + } + } + } +} + /* We rewrite something like: y = chunk(x) @@ -403,11 +434,18 @@ void overloadDistinguish(Block* block) { // min/max(Tensor, int) is reduce case aten::min: case aten::max: + case aten::sum: if (node->inputs().size() > 1 && - node->input(1)->type()->kind() == c10::TypeKind::IntType) { + (node->input(1)->type()->kind() == c10::TypeKind::IntType || + node->input(1)->type()->kind() == c10::TypeKind::ListType)) { node->s_(symb, "reduce"); } else { - node->s_(symb, "compare"); + node->s_(symb, "binary"); + } + break; + case aten::index: + if (node->input(1)->node()->kind() == prim::ListConstruct) { + node->s_(symb, "stridedslice"); } break; default: @@ -459,6 +497,7 @@ std::shared_ptr torchOptPass(Module& module) { // Example: x = {v0}; x.append(v1); -> x = {v0, v1}; // RemoveListMutation(graph); removeListAppend(graph.get(), graph->block()); + removeListConstructOps(graph->block()); //RemoveTensorMutation(graph); // elimate dead code EliminateDeadCode(graph, DCESideEffectPolicy::ALLOW_DELETING_NODES_WITH_SIDE_EFFECTS); diff --git a/tools/cpp/ModuleBasic.cpp b/tools/cpp/ModuleBasic.cpp index d57262ff..f9bc2b74 100644 --- a/tools/cpp/ModuleBasic.cpp +++ b/tools/cpp/ModuleBasic.cpp @@ -288,6 +288,7 @@ int main(int argc, char *argv[]) { if (argc > 8) { cacheFileName = argv[8]; } + FUNC_PRINT(precision); FUNC_PRINT_ALL(cacheFileName, s); // create session MNN::ScheduleConfig config; diff --git a/tools/cpp/backendTest.cpp b/tools/cpp/backendTest.cpp index 0a3b8ce0..5763ae54 100644 --- a/tools/cpp/backendTest.cpp +++ b/tools/cpp/backendTest.cpp @@ -33,6 +33,20 @@ inline T stringConvert(const char* number) { using namespace MNN; +static void _zeroInputs(const Interpreter* net, const Session* session) { + // Set Other Inputs to Zero + auto allInput = net->getSessionInputAll(session); + for (auto& iter : allInput) { + auto inputTensor = iter.second; + auto size = inputTensor->size(); + if (size <= 0) { + continue; + } + MNN::Tensor tempTensor(inputTensor, inputTensor->getDimensionType()); + ::memset(tempTensor.host(), 0, tempTensor.size()); + inputTensor->copyFromHostTensor(&tempTensor); + } +} static void compareForwadType(Interpreter* net, MNNForwardType expectType, MNNForwardType compareType, float tolerance, const std::map>& inputs, const std::string& stopOp, BackendConfig::PrecisionMode precision, int modeNum) { std::vector> correctResult; @@ -46,7 +60,8 @@ static void compareForwadType(Interpreter* net, MNNForwardType expectType, MNNFo compareConfig.mode = modeNum; auto expectSession = net->createSession(expectConfig); auto compareSession = net->createSession(compareConfig); - + _zeroInputs(net, expectSession); + _zeroInputs(net, compareSession); bool allCorrect = true; MNN::TensorCallBackWithInfo beginCallBack = [&](const std::vector& t, const OperatorInfo* op) { @@ -118,6 +133,7 @@ static void compareForwadType(Interpreter* net, MNNForwardType expectType, MNNFo } else { return; } + _zeroInputs(net, compareSession); index = 0; for (auto& iter : inputs) { Tensor* compareInput = net->getSessionInput(compareSession, iter.first.empty() ? NULL : iter.first.c_str()); @@ -260,6 +276,7 @@ int main(int argc, const char* argv[]) { stopOp = argv[6]; } FUNC_PRINT_ALL(stopOp.c_str(), s); + net->releaseSession(session); compareForwadType(net.get(), MNN_FORWARD_CPU, type, tolerance, inputs, stopOp, precision, modeNum); return 0; diff --git a/tools/cv/CMakeLists.txt b/tools/cv/CMakeLists.txt index c26a7283..e794aa15 100644 --- a/tools/cv/CMakeLists.txt +++ b/tools/cv/CMakeLists.txt @@ -7,6 +7,7 @@ IF(MNN_BUILD_OPENCV) option(MNN_IMGPROC_STRUCTRAL "Enable imgproc structral" ON) option(MNN_IMGCODECS "Enable imgcodecs" OFF) option(MNN_OPENCV_TEST "Enable opencv test" OFF) + option(MNN_OPENCV_BENCH "Enable opencv benchmark" OFF) SET(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/../../) include_directories(${CMAKE_CURRENT_LIST_DIR}/include) @@ -17,6 +18,15 @@ IF(MNN_BUILD_OPENCV) add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/test) endif() + if(${MNN_OPENCV_BENCH}) + add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/benchmark) + endif() + + if(${MNN_OPENCV_TEST} OR ${MNN_OPENCV_BENCH}) + # copy test images + FILE(COPY imgs DESTINATION ${CMAKE_BINARY_DIR}/) + endif() + # file(GLOB_RECURSE IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgproc/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgproc/*.hpp) set(IMGPROC_SRC "") # imgproc color functions @@ -45,6 +55,7 @@ IF(MNN_BUILD_OPENCV) endif() # imgcodecs functions if(${MNN_IMGCODECS}) + add_definitions(-DMNN_IMGCODECS) file(GLOB_RECURSE IMGCODECS_SRC ${CMAKE_CURRENT_LIST_DIR}/source/imgcodecs/*.cpp ${CMAKE_CURRENT_LIST_DIR}/include/cv/imgcodecs/*.hpp) endif() diff --git a/tools/cv/README.md b/tools/cv/README.md new file mode 100644 index 00000000..1abcb230 --- /dev/null +++ b/tools/cv/README.md @@ -0,0 +1,30 @@ +# MNN cv + +MNN cv is a warpper of MNN's expr functions and some cv functions, which provides the APIs like OpenCV. + +## Usage +Compile MNN with cv, using below command: +```bash +cmake -DMNN_BUILD_OPENCV=ON .. && make -j8 +``` + +## Macros +### MNN_IMGPROC_COLOR +`MNN_IMGPROC_COLOR` default is ON, this macro control the color function like: cvtColor. +### MNN_IMGPROC_GEOMETRIC +`MNN_IMGPROC_GEOMETRIC` default is ON, this macro control the geometric function like: resize. +### MNN_IMGPROC_DRAW +`MNN_IMGPROC_DRAW` default is ON, this macro control the draw function like: line. +### MNN_IMGPROC_FILTER +`MNN_IMGPROC_FILTER` default is ON, this macro control the filter function like: blur. +### MNN_IMGPROC_MISCELLANEOUS +`MNN_IMGPROC_MISCELLANEOUS` default is ON, this macro control the miscellaneous function like: threshold. +### MNN_IMGPROC_STRUCTRAL +`MNN_IMGPROC_STRUCTRAL` default is ON, this macro control the structral function like: findContours. +### MNN_IMGCODECS +`MNN_IMGCODECS` default is OFF, this macro control the imgcodecs function like: imread. +### MNN_OPENCV_TEST +`MNN_OPENCV_TEST` default is OFF, this macro control the unit test. +### MNN_OPENCV_BENCH +`MNN_OPENCV_BENCH` default is OFF, this macro control the benchmark of MNN cv with OpenCV. + diff --git a/tools/cv/benchmark/CMakeLists.txt b/tools/cv/benchmark/CMakeLists.txt new file mode 100644 index 00000000..9a264a68 --- /dev/null +++ b/tools/cv/benchmark/CMakeLists.txt @@ -0,0 +1,9 @@ +# using opencv +find_package(OpenCV REQUIRED) +include_directories(${OpenCV_INCLUDE_DIRS}) +link_directories(${OpenCV_LIBRARY_DIRS}) + +include_directories(${CMAKE_CURRENT_LIST_DIR}/) + +add_executable(opencv_bench ${CMAKE_CURRENT_LIST_DIR}/opencv_benchmark.cpp) +target_link_libraries(opencv_bench MNNOpenCV ${OpenCV_LIBS}) diff --git a/tools/cv/benchmark/opencv_benchmark.cpp b/tools/cv/benchmark/opencv_benchmark.cpp new file mode 100644 index 00000000..fb6d3a3c --- /dev/null +++ b/tools/cv/benchmark/opencv_benchmark.cpp @@ -0,0 +1,347 @@ +// +// opencv_benchmark.cpp +// MNN +// +// Created by MNN on 2022/06/21. +// Copyright © 2018, Alibaba Group Holding Limited +// + +#include +#include +#include +#include +#include +#include "cv/imgproc/imgproc.hpp" +#ifdef MNN_IMGCODECS +#include "cv/imgcodecs.hpp" +#endif + +using namespace MNN; +using namespace Express; +using namespace CV; + +constexpr int LOOP = 10; + +static std::vector functions; +static std::vector times; +constexpr const char* path = "./imgs/cat.jpg"; + +template +VARP cv2mnn(const cv::Mat& src) { + VARP dst = _Input({ src.rows, src.cols, src.channels() }, NHWC, halide_type_of()); + auto inputPtr = dst->writeMap(); + memcpy(inputPtr, src.ptr(0), dst->getInfo()->size * sizeof(T)); + return dst; +} + +#define arg_concat_impl(x, y) x ## y +#define arg_concat(x, y) arg_concat_impl(x, y) +#define arg_switch_0(CASE0, CASE1, CASE2, CASE3) CASE0 +#define arg_switch_1(CASE0, CASE1, CASE2, CASE3) CASE1 +#define arg_switch_2(CASE0, CASE1, CASE2, CASE3) CASE2 +#define arg_switch_3(CASE0, CASE1, CASE2, CASE3) CASE3 +// just support COND = [0, 1, 2, 3] +#define arg_switch(COND, CASE0, CASE1, CASE2, CASE3) arg_concat(arg_switch_, COND)(CASE0, CASE1, CASE2, CASE3) + +#define BENCH_IMPL(mode, func, ...)\ + auto t1 = std::chrono::high_resolution_clock::now();\ + for (int i = 0; i < LOOP; i++) {\ +arg_switch(mode, cv::func(__VA_ARGS__);, auto dst = func(__VA_ARGS__);dst->readMap();, auto dst = func(__VA_ARGS__);dst[0]->readMap();, func(__VA_ARGS__);)\ + }\ + auto t2 = std::chrono::high_resolution_clock::now();\ + auto duration = std::chrono::duration_cast(t2 - t1).count() / (1000. * LOOP);\ + times.push_back(duration); \ + +#define BENCHMARK_NAME(mode, name, func, ...) \ +do {\ + BENCH_IMPL(mode, func, __VA_ARGS__)\ + functions.emplace_back(#name);\ +} while(0); + +#define BENCHMARK(mode, func, ...) \ +do {\ + BENCH_IMPL(mode, func, __VA_ARGS__)\ + functions.emplace_back(#func); \ +} while(0); + +#define BENCHMARK_CV(func, ...) BENCHMARK(0, func, __VA_ARGS__) +#define BENCHMARK_MNN(func, ...) BENCHMARK(1, func, __VA_ARGS__) + +void color(cv::Mat cvimg, VARP mnnimg) { + cv::Mat dst; +#define CVTCOLOR(code)\ + BENCHMARK_NAME(0, code, cvtColor, cvimg, dst, cv::COLOR_##code)\ + BENCHMARK_NAME(1, code, cvtColor, mnnimg, COLOR_##code) + CVTCOLOR(RGB2BGR) + CVTCOLOR(RGB2GRAY) + CVTCOLOR(RGB2RGBA) + CVTCOLOR(RGB2BGRA) + CVTCOLOR(RGB2YUV) + CVTCOLOR(RGB2XYZ) + CVTCOLOR(RGB2HSV) + CVTCOLOR(RGB2HSV_FULL) + CVTCOLOR(RGB2BGR555) + CVTCOLOR(RGB2BGR565) +} + +void filter(cv::Mat cvimg, VARP mnnimg) { + cv::Mat dst; + // blur + BENCHMARK_CV(blur, cvimg, dst, {3, 3}); + BENCHMARK_MNN(blur, mnnimg, {3, 3}); + // boxFilter + BENCHMARK_CV(boxFilter, cvimg, dst, -1, {3, 3}); + BENCHMARK_MNN(boxFilter, mnnimg, -1, {3, 3}); + // dilate + BENCHMARK_CV(dilate, cvimg, dst, cv::getStructuringElement(0, {3, 3})); + BENCHMARK_MNN(dilate, mnnimg, getStructuringElement(0, {3, 3})); + // filter2D + std::vector kernel { 0, -1, 0, -1, 5, -1, 0, -1, 0 }; + cv::Mat cvKernel = cv::Mat(3, 3, CV_32FC1); + memcpy(cvKernel.data, kernel.data(), kernel.size() * sizeof(float)); + VARP mnnKernel = _Const(kernel.data(), {3, 3}); + BENCHMARK_CV(filter2D, cvimg, dst, -1, cvKernel); + BENCHMARK_MNN(filter2D, mnnimg, -1, mnnKernel); + // boxFilter + BENCHMARK_CV(GaussianBlur, cvimg, dst, {3, 3}, 10); + BENCHMARK_MNN(GaussianBlur, mnnimg, {3, 3}, 10); + // getDerivKernels + BENCHMARK_CV(getDerivKernels, dst, dst, 1, 2, 1); + BENCHMARK(3, getDerivKernels, 1, 2, 1); + // getGaborKernel + BENCHMARK_CV(getGaborKernel, {3, 3}, 10, 5, 5, 5, CV_PI*0.5, CV_32F); + BENCHMARK_MNN(getGaborKernel, {3, 3}, 10, 5, 5, 5); + // getGaussianKernel + BENCHMARK_CV(getGaussianKernel, 3, 5, CV_32F); + BENCHMARK_MNN(getGaussianKernel, 3, 5); + // getStructuringElement + BENCHMARK_CV(getStructuringElement, 0, {3, 3}); + BENCHMARK_MNN(getStructuringElement, 0, {3, 3}); + // Laplacian + BENCHMARK_CV(Laplacian, cvimg, dst, -1); + BENCHMARK_MNN(Laplacian, mnnimg, -1); + // pyrDown + BENCHMARK_CV(pyrDown, cvimg, dst); + BENCHMARK_MNN(pyrDown, mnnimg); + // pyrUp + BENCHMARK_CV(pyrUp, cvimg, dst); + BENCHMARK_MNN(pyrUp, mnnimg); + // Scharr + BENCHMARK_CV(Scharr, cvimg, dst, -1, 1, 0); + BENCHMARK_MNN(Scharr, mnnimg, -1, 1, 0); + // sepFilter2D + std::vector kernelX { 0, -1, 0 }, kernelY { -1, 0, -1 }; + cv::Mat cvKernelX = cv::Mat(1, 3, CV_32FC1); + cv::Mat cvKernelY = cv::Mat(1, 3, CV_32FC1); + memcpy(cvKernelX.data, kernelX.data(), kernelX.size() * sizeof(float)); + memcpy(cvKernelY.data, kernelY.data(), kernelY.size() * sizeof(float)); + VARP mnnKernelX = _Const(kernelX.data(), {1, 3}); + VARP mnnKernelY = _Const(kernelY.data(), {1, 3}); + BENCHMARK_CV(sepFilter2D, cvimg, dst, -1, cvKernelX, cvKernelY); + BENCHMARK_MNN(sepFilter2D, mnnimg, -1, mnnKernelX, mnnKernelY); + // Sobel + BENCHMARK_CV(Sobel, cvimg, dst, -1, 1, 0); + BENCHMARK_MNN(Sobel, mnnimg, -1, 1, 0); + // sqrBoxFilter + BENCHMARK_CV(sqrBoxFilter, cvimg, dst, -1, {1, 1}, {-1, -1}); + BENCHMARK_MNN(sqrBoxFilter, mnnimg, -1, {1, 1}); +} + +void geometric(cv::Mat cvimg, VARP mnnimg) { + cv::Mat dst; + // getAffineTransform + float points[] = { 50, 50, 200, 50, 50, 200, 10, 100, 200, 20, 100, 250 }; + cv::Point2f cvSrc[3], cvDst[3]; + memcpy(cvSrc, points, 6 * sizeof(float)); + memcpy(cvDst, points + 6, 6 * sizeof(float)); + Point mnnSrc[3], mnnDst[3]; + memcpy(mnnSrc, points, 6 * sizeof(float)); + memcpy(mnnDst, points + 6, 6 * sizeof(float)); + BENCHMARK_CV(getAffineTransform, cvSrc, cvDst); + BENCHMARK(3, getAffineTransform, mnnSrc, mnnDst); + // getPerspectiveTransform + BENCHMARK_CV(getPerspectiveTransform, cvSrc, cvDst); + BENCHMARK(3, getPerspectiveTransform, mnnSrc, mnnDst); + // getRotationMatrix2D + cv::Point2f cvCenter {10, 10}; + Point mnnCenter {10, 10}; + BENCHMARK_CV(getRotationMatrix2D, cvCenter, 50, 0.6); + BENCHMARK(3, getRotationMatrix2D, mnnCenter, 50, 0.6); + // getRectSubPix + BENCHMARK_CV(getRectSubPix, cvimg, {11, 11}, cvCenter, dst); + BENCHMARK_MNN(getRectSubPix, mnnimg, {11, 11}, mnnCenter); + // invertAffineTransform + std::vector M { 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 }; + cv::Mat cvM = cv::Mat(2, 3, CV_32FC1), cvMDst = cv::Mat(2, 3, CV_32FC1); + memcpy(cvM.data, M.data(), M.size() * sizeof(float)); + Matrix mnnM; + for (int i = 0; i < M.size(); i++) mnnM.set(i, M[i]); + BENCHMARK_CV(invertAffineTransform, cvM, cvMDst); + BENCHMARK(3, invertAffineTransform, mnnM); + // resize + BENCHMARK_CV(resize, cvimg, dst, cv::Size(), 2, 2); + BENCHMARK_MNN(resize, mnnimg, {}, 2, 2); + // warpAffine + BENCHMARK_CV(warpAffine, cvimg, dst, cvM, {480, 360}); + BENCHMARK_MNN(warpAffine, mnnimg, mnnM, {480, 360}); +} + +void miscellaneous(cv::Mat cvimg, VARP mnnimg) { + cv::Mat dst; + // blendLinear + int weightSize = cvimg.rows * cvimg.cols; + std::vector weight1(weightSize, 0.6), weight2(weightSize, 0.7); + cv::Mat cvWeight1 = cv::Mat(cvimg.rows, cvimg.cols, CV_32FC1); + cv::Mat cvWeight2 = cv::Mat(cvimg.rows, cvimg.cols, CV_32FC1); + memcpy(cvWeight1.data, weight1.data(), weight1.size() * sizeof(float)); + memcpy(cvWeight2.data, weight2.data(), weight2.size() * sizeof(float)); + auto mnnWeight1 = cv2mnn(cvWeight1); + auto mnnWeight2 = cv2mnn(cvWeight2); + BENCHMARK_CV(blendLinear, cvimg, cvimg, cvWeight1, cvWeight2, dst); + BENCHMARK_MNN(blendLinear, mnnimg, mnnimg, mnnWeight1, mnnWeight2); + // threshold + BENCHMARK_CV(threshold, cvimg, dst, 50, 20, cv::THRESH_BINARY); + BENCHMARK_MNN(threshold, mnnimg, 50, 20, cv::THRESH_BINARY); +} + +void structral(cv::Mat cvimg, VARP mnnimg) { + static std::vector img = { + 0,0,0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,1,0,0,0,0,0,0,0,0,0, + 0,0,1,1,1,1,1,1,1,0,0,0,0, + 0,0,1,0,0,1,0,0,0,1,1,0,0, + 0,0,1,0,0,1,0,0,1,0,0,0,0, + 0,0,1,0,0,1,0,0,1,0,0,0,0, + 0,0,1,1,1,1,1,1,1,0,0,0,0, + 0,0,0,1,0,0,1,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0,0,0, + 0,0,0,0,0,0,0,0,0,0,0,0,0 + }; + // findContours + std::vector> cv_contours; + std::vector hierarchy; + VARP x = _Const(img.data(), {1, 11, 13, 1}, NHWC); + cv::Mat mask = cv::Mat(11, 13, CV_8UC1); + ::memcpy(mask.data, img.data(), img.size() * sizeof(uchar)); + BENCHMARK_CV(findContours, mask, cv_contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_NONE); + BENCHMARK(2, findContours, x, RETR_EXTERNAL, CHAIN_APPROX_NONE); + // contourArea + std::vector cv_contour = { {0, 0}, {10, 0}, {10, 10}, {5, 4}}; + VARP mnn_contour = _Const(cv_contour.data(), {4, 2}, NHWC, halide_type_of()); + BENCHMARK_CV(contourArea, cv_contour); + BENCHMARK(3, contourArea, mnn_contour); + // convexHull + std::vector y; + BENCHMARK_CV(convexHull, cv_contour, y, false, false); + BENCHMARK(3, convexHull, mnn_contour, false, false); + // minAreaRect + BENCHMARK_CV(minAreaRect, cv_contour); + BENCHMARK(3, minAreaRect, mnn_contour); + // boundingRect + BENCHMARK_CV(boundingRect, cv_contour); + BENCHMARK(3, boundingRect, mnn_contour); + // connectedComponentsWithStats + cv::Mat cv_label, cv_statsv, cv_centroids; + VARP mnn_label, mnn_statsv, mnn_centroids; + BENCHMARK_CV(connectedComponentsWithStats, mask, cv_label, cv_statsv, cv_centroids); + BENCHMARK(3, connectedComponentsWithStats, x, mnn_label, mnn_statsv, mnn_centroids); + // boxPoints + BENCHMARK_CV(boxPoints, cv::minAreaRect(cv_contour), cv_label); + BENCHMARK(3, boxPoints, minAreaRect(mnn_contour)); +} + +void draw(cv::Mat cvimg, VARP mnnimg) { +#define DRAW(func, ...)\ + BENCHMARK_CV(func, cvimg, __VA_ARGS__)\ + BENCHMARK(3, func, mnnimg, __VA_ARGS__) + // arrowedLine + DRAW(arrowedLine, {10, 10}, {300, 200}, {0, 0, 255}, 1) + // circle + DRAW(circle, {50, 50}, 10, {0, 0, 255}, 1) + // line + DRAW(line, {10, 10}, {50, 50}, {0, 0, 255}, 5) + // rectangle + DRAW(rectangle, {10, 10}, {200, 300}, {0, 0, 255}, 1) + // drawContours + cv::Mat gray, binary; + cv::cvtColor(cvimg, gray, cv::COLOR_BGR2GRAY); + cv::threshold(gray, binary, 127, 255, cv::THRESH_BINARY); + std::vector> cv_contours; + std::vector hierarchy; + cv::findContours(binary, cv_contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE); + std::vector> mnn_contours(cv_contours.size()); + for (int i = 0; i < cv_contours.size(); i++) { + for (int j = 0; j < cv_contours[i].size(); j++) { + Point p; + p.set(cv_contours[i][j].x, cv_contours[i][j].y); + mnn_contours[i].push_back(p); + } + } + BENCHMARK_CV(drawContours, cvimg, cv_contours, -1, {0, 0, 255}, -1) + BENCHMARK(3, drawContours, mnnimg, mnn_contours, -1, {0, 0, 255}, -1) + // fillPoly + BENCHMARK_CV(fillPoly, cvimg, cv_contours, {0, 0, 255}) + BENCHMARK(3, fillPoly, mnnimg, mnn_contours, {0, 0, 255}) +} + +void codecs(cv::Mat cvimg, VARP mnnimg) { +#ifdef MNN_IMGCODECS + // imread + BENCHMARK_CV(imread, path) + BENCHMARK_MNN(imread, path) + // imwrite + BENCHMARK_CV(imwrite, "cv.jpg", cvimg) + BENCHMARK(3, imwrite, "mnn.jpg", mnnimg) +#endif +} + +void printLine() { + std::cout << "+----------------------------+----------+----------+" << std::endl; +} +void printLine(const char* col0, const char* col1, const char* col2) { + std::cout << std::setiosflags(std::ios::left) << "|" << std::setw(28) << col0 << "|" << std::setw(10) << col1 << "|" << std::setw(10) << col2 << "|" << std::endl; +} +void printLine(const std::string& func, double t1, double t2) { + std::cout << std::setiosflags(std::ios::left) << "|" << std::setw(28) << func << "|" << std::setw(10) << t1 << "|" << std::setw(10) << t2 << "|" << std::endl; +} +void log() { + int count = times.size() / 2; + printLine(); + printLine("function", "opencv", "MNN.cv"); + printLine(); + double cv_sum = 0., mnn_sum = 0.; + for (int i = 0; i < count; i++) { + auto func = functions[i * 2]; + double cv = times[i * 2]; + double mnn = times[i * 2 + 1]; + cv_sum += cv; + mnn_sum += mnn; + printLine(func, cv, mnn); + } + printLine(); + printLine("avg", cv_sum/count, mnn_sum/count); + printLine(); +} + +int main(int argc, char** argv) { + printf("opencv benchmark\n"); + cv::setNumThreads(1); + // uint8 + auto img_uchar = cv::imread(path); + auto mnn_uchar = cv2mnn(img_uchar); + // fp32 + cv::Mat img_fp32; + img_uchar.convertTo(img_fp32, CV_32FC3); + auto mnn_fp32 = cv2mnn(img_fp32); + + color(img_uchar, mnn_uchar); + filter(img_fp32, mnn_fp32); + geometric(img_uchar, mnn_uchar); + miscellaneous(img_fp32, mnn_fp32); + structral(img_uchar, mnn_uchar); + draw(img_uchar, mnn_uchar); + codecs(img_uchar, mnn_uchar); + log(); + return 0; +} diff --git a/tools/cv/test/imgs/cat.jpg b/tools/cv/imgs/cat.jpg similarity index 100% rename from tools/cv/test/imgs/cat.jpg rename to tools/cv/imgs/cat.jpg diff --git a/tools/cv/test/imgs/cat__ b/tools/cv/imgs/cat__ similarity index 100% rename from tools/cv/test/imgs/cat__ rename to tools/cv/imgs/cat__ diff --git a/tools/cv/test/imgs/fake.jpg b/tools/cv/imgs/fake.jpg similarity index 100% rename from tools/cv/test/imgs/fake.jpg rename to tools/cv/imgs/fake.jpg diff --git a/tools/cv/test/CMakeLists.txt b/tools/cv/test/CMakeLists.txt index 51054c68..6a30438c 100644 --- a/tools/cv/test/CMakeLists.txt +++ b/tools/cv/test/CMakeLists.txt @@ -12,9 +12,7 @@ FetchContent_Declare( set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) -# copy test images -FILE(COPY imgs DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) -FILE(COPY imgs DESTINATION ${CMAKE_BINARY_DIR}/) +FILE(COPY ../imgs DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/) include_directories(${CMAKE_CURRENT_LIST_DIR}/) @@ -24,7 +22,7 @@ file(GLOB_RECURSE IMGPROC_SRC ${CMAKE_CURRENT_LIST_DIR}/imgproc/*.cpp) if(MNN_IMGCODECS) file(GLOB_RECURSE IMGCODECS_SRC ${CMAKE_CURRENT_LIST_DIR}/imgcodecs/*.cpp) endif() -add_executable(opencv_test ${IMGPROC_SRC} ${IMGCODECS_SRC} ./main.cpp) +add_executable(opencv_test ${IMGPROC_SRC} ${IMGCODECS_SRC} ./opencv_test.cpp) target_link_libraries(opencv_test MNNOpenCV gtest_main ${OpenCV_LIBS}) include(GoogleTest) diff --git a/tools/cv/test/main.cpp b/tools/cv/test/opencv_test.cpp similarity index 78% rename from tools/cv/test/main.cpp rename to tools/cv/test/opencv_test.cpp index e23c0b2a..e44dd123 100644 --- a/tools/cv/test/main.cpp +++ b/tools/cv/test/opencv_test.cpp @@ -1,3 +1,11 @@ +// +// opencv_test.cpp +// MNN +// +// Created by MNN on 2021/08/18. +// Copyright © 2018, Alibaba Group Holding Limited +// + #include "gtest/gtest.h" int main(int argc, char** argv) { diff --git a/tools/mnncompress/README.md b/tools/mnncompress/README.md new file mode 100644 index 00000000..57464bf4 --- /dev/null +++ b/tools/mnncompress/README.md @@ -0,0 +1,6 @@ +MNN模型压缩工具箱 +安装 + 源码安装：python setup.py install/develop + pip安装：pip install mnncompress + +文档：https://www.yuque.com/mnn/cn/ng8qc1 diff --git a/tools/mnncompress/mnncompress/__init__.py b/tools/mnncompress/mnncompress/__init__.py new file mode 100644 index 00000000..bb133bbe --- /dev/null +++ b/tools/mnncompress/mnncompress/__init__.py @@ -0,0 +1 @@ +from mnncompress.version import __version__ diff --git a/tools/mnncompress/mnncompress/common/MNN_compression.proto b/tools/mnncompress/mnncompress/common/MNN_compression.proto new file mode 100644 index 00000000..48a1f276 --- /dev/null +++ b/tools/mnncompress/mnncompress/common/MNN_compression.proto @@ -0,0 +1,100 @@ +syntax = "proto2"; + +package MNN.Compression; + +message QuantizeParams { + enum RoundMode { + ROUND_TOWARDS_ZERO = 0; + ROUND_AWAY_FROM_ZERO = 1; + ROUND_HALF_TO_EVEN = 2; + } + optional RoundMode round_mode = 1 [default = ROUND_AWAY_FROM_ZERO]; + + // Quantization parameters for each layer that needs to be quantized. + // For a block composed of several operators, such as + // `Convolution` + `BatchNorm` + `Relu`, it should be considered as a + // single layer. + repeated LayerQuantizeParams layer = 4; +} + +message LayerQuantizeParams { + // Activation quantization parameters. + // Both symmetric and asymmetric mode are supported for activation, + // and `zero_point` should always be zero if symmetric mode. + message ActivationParams { + // Activation tensor name. + required string name = 1; + optional int32 bits = 2 [default = 8]; + repeated float scales = 3; + optional int32 zero_point = 4 [default = 0]; + optional int32 clamp_min = 5 [default = -128]; + optional int32 clamp_max = 6 [default = 127]; + } + + // Weight quantization parameters. + // Only symmetric mode is supported. Both channel-wise and tensor-wise + // quantization are supported, depending on whether `scales` length is + // equal to output channels. + message WeightParams { + // Weight tensor name. + required string name = 1; + optional int32 bits = 2 [default = 8]; + repeated float scales = 3; + optional int32 clamp_min = 4 [default = -128]; + optional int32 clamp_max = 5 [default = 127]; + } + + enum QuantMethod { + QAT = 0; + OverflowAware = 1; + WinogradAware = 2; + } + + repeated ActivationParams input = 1; + repeated WeightParams weight = 2; + repeated ActivationParams output = 3; + optional QuantMethod method = 4 [default = QAT]; +} + +message LevelPrunerParams { + repeated string weight_tensor_names = 1; + repeated float prune_ratios = 2; + repeated string layer_names = 3; +} + +message SIMDOCPrunerParams { + repeated string weight_tensor_names = 1; + repeated float prune_ratios = 2; + repeated string layer_names = 3; + repeated int32 oc_blocks = 4; +} + +message PruneParams { + enum PruneType { + RANDOM = 0; + SIMD_OC = 1; + FILTER = 2; + } + optional PruneType type = 1 [default = RANDOM]; + optional LevelPrunerParams level_pruner_params = 2; + optional SIMDOCPrunerParams simd_oc_pruner_params = 3; +} + +message CompressionAlgo { + enum CompressionType { + QUANTIZE = 0; + PRUNE = 1; + } + optional CompressionType type = 1 [default = QUANTIZE]; + + optional QuantizeParams quant_params = 2; + optional PruneParams prune_params = 3; +} + +// Model compression algorithm pipeline. +message Pipeline { + required string version = 1 [default = "0.0.0"]; + + repeated CompressionAlgo algo = 2; + optional string mnn_uuid = 3; +} diff --git a/tools/mnncompress/mnncompress/common/VBMF.py b/tools/mnncompress/mnncompress/common/VBMF.py new file mode 100644 index 00000000..02ad3a2e --- /dev/null +++ b/tools/mnncompress/mnncompress/common/VBMF.py @@ -0,0 +1,290 @@ +from __future__ import print_function +from __future__ import division + +import numpy as np +from scipy.sparse.linalg import svds +from scipy.optimize import minimize_scalar + +def VBMF(Y, cacb, sigma2=None, H=None): + """Implementation of the analytical solution to Variational Bayes Matrix Factorization. + + This function can be used to calculate the analytical solution to VBMF. + This is based on the paper and MatLab code by Nakajima et al.: + "Global analytic solution of fully-observed variational Bayesian matrix factorization." + + Notes + ----- + If sigma2 is unspecified, it is estimated by minimizing the free energy. + If H is unspecified, it is set to the smallest of the sides of the input Y. + To estimate cacb, use the function EVBMF(). + + Attributes + ---------- + Y : numpy-array + Input matrix that is to be factorized. Y has shape (L,M), where L<=M. + + cacb : int + Product of the prior variances of the matrices that factorize the input. + + sigma2 : int or None (default=None) + Variance of the noise on Y. + + H : int or None (default = None) + Maximum rank of the factorized matrices. + + Returns + ------- + U : numpy-array + Left-singular vectors. + + S : numpy-array + Diagonal matrix of singular values. + + V : numpy-array + Right-singular vectors. + + post : dictionary + Dictionary containing the computed posterior values. + + + References + ---------- + .. [1] Nakajima, Shinichi, et al. "Global analytic solution of fully-observed variational Bayesian matrix factorization." Journal of Machine Learning Research 14.Jan (2013): 1-37. + + .. [2] Nakajima, Shinichi, et al. "Perfect dimensionality recovery by variational Bayesian PCA." Advances in Neural Information Processing Systems. 2012. + """ + + L,M = Y.shape #has to be L<=M + + if H is None: + H = L + + #SVD of the input matrix, max rank of H + U,s,V = np.linalg.svd(Y) + U = U[:,:H] + s = s[:H] + V = V[:H].T + + #Calculate residual + residual = 0. + if Hthreshold + pos = np.sum(s>threshold) + + #Formula (10) from [2] + d = np.multiply(s[:pos], + 1 - np.multiply(sigma2/(2*s[:pos]**2), + L+M+np.sqrt( (M-L)**2 + 4*s[:pos]**2/cacb**2 ))) + + #Computation of the posterior + post = {} + zeta = sigma2/(2*L*M) * (L+M+sigma2/cacb**2 - np.sqrt((L+M+sigma2/cacb**2)**2 - 4*L*M)) + post['ma'] = np.zeros(H) + post['mb'] = np.zeros(H) + post['sa2'] = cacb * (1-L*zeta/sigma2) * np.ones(H) + post['sb2'] = cacb * (1-M*zeta/sigma2) * np.ones(H) + + delta = cacb/sigma2 * (s[:pos]-d- L*sigma2/s[:pos]) + post['ma'][:pos] = np.sqrt(np.multiply(d, delta)) + post['mb'][:pos] = np.sqrt(np.divide(d, delta)) + post['sa2'][:pos] = np.divide(sigma2*delta, s[:pos]) + post['sb2'][:pos] = np.divide(sigma2, np.multiply(delta, s[:pos])) + post['sigma2'] = sigma2 + post['F'] = 0.5*(L*M*np.log(2*np.pi*sigma2) + (residual+np.sum(s**2))/sigma2 - (L+M)*H + + np.sum(M*np.log(cacb/post['sa2']) + L*np.log(cacb/post['sb2']) + + (post['ma']**2 + M*post['sa2'])/cacb + (post['mb']**2 + L*post['sb2'])/cacb + + (-2 * np.multiply(np.multiply(post['ma'], post['mb']), s) + + np.multiply(post['ma']**2 + M*post['sa2'],post['mb']**2 + L*post['sb2']))/sigma2)) + + return U[:,:pos], np.diag(d), V[:,:pos], post + + +def VBsigma2(sigma2,L,M,cacb,s,residual): + H = len(s) + + thresh_term = (L+M + sigma2/cacb**2)/2 + threshold = np.sqrt( sigma2 * (thresh_term + np.sqrt(thresh_term**2 - L*M) )) + pos = np.sum(s>threshold) + + d = np.multiply(s[:pos], + 1 - np.multiply(sigma2/(2*s[:pos]**2), + L+M+np.sqrt( (M-L)**2 + 4*s[:pos]**2/cacb**2 ))) + + zeta = sigma2/(2*L*M) * (L+M+sigma2/cacb**2 - np.sqrt((L+M+sigma2/cacb**2)**2 - 4*L*M)) + post_ma = np.zeros(H) + post_mb = np.zeros(H) + post_sa2 = cacb * (1-L*zeta/sigma2) * np.ones(H) + post_sb2 = cacb * (1-M*zeta/sigma2) * np.ones(H) + + delta = cacb/sigma2 * (s[:pos]-d- L*sigma2/s[:pos]) + post_ma[:pos] = np.sqrt(np.multiply(d, delta)) + post_mb[:pos] = np.sqrt(np.divide(d, delta)) + post_sa2[:pos] = np.divide(sigma2*delta, s[:pos]) + post_sb2[:pos] = np.divide(sigma2, np.multiply(delta, s[:pos])) + + F = 0.5*(L*M*np.log(2*np.pi*sigma2) + (residual+np.sum(s**2))/sigma2 - (L+M)*H + + np.sum(M*np.log(cacb/post_sa2) + L*np.log(cacb/post_sb2) + + (post_ma**2 + M*post_sa2)/cacb + (post_mb**2 + L*post_sb2)/cacb + + (-2 * np.multiply(np.multiply(post_ma, post_mb), s) + + np.multiply(post_ma**2 + M*post_sa2,post_mb**2 + L*post_sb2))/sigma2)) + return F + + + +def EVBMF(Y, sigma2=None, H=None): + """Implementation of the analytical solution to Empirical Variational Bayes Matrix Factorization. + + This function can be used to calculate the analytical solution to empirical VBMF. + This is based on the paper and MatLab code by Nakajima et al.: + "Global analytic solution of fully-observed variational Bayesian matrix factorization." + + Notes + ----- + If sigma2 is unspecified, it is estimated by minimizing the free energy. + If H is unspecified, it is set to the smallest of the sides of the input Y. + + Attributes + ---------- + Y : numpy-array + Input matrix that is to be factorized. Y has shape (L,M), where L<=M. + + sigma2 : int or None (default=None) + Variance of the noise on Y. + + H : int or None (default = None) + Maximum rank of the factorized matrices. + + Returns + ------- + U : numpy-array + Left-singular vectors. + + S : numpy-array + Diagonal matrix of singular values. + + V : numpy-array + Right-singular vectors. + + post : dictionary + Dictionary containing the computed posterior values. + + + References + ---------- + .. [1] Nakajima, Shinichi, et al. "Global analytic solution of fully-observed variational Bayesian matrix factorization." Journal of Machine Learning Research 14.Jan (2013): 1-37. + + .. [2] Nakajima, Shinichi, et al. "Perfect dimensionality recovery by variational Bayesian PCA." Advances in Neural Information Processing Systems. 2012. + """ + L,M = Y.shape #has to be L<=M + + if H is None: + H = L + + alpha = L/M + tauubar = 2.5129*np.sqrt(alpha) + + #SVD of the input matrix, max rank of H + U,s,V = np.linalg.svd(Y) + U = U[:,:H] + s = s[:H] + V = V[:H].T + + #Calculate residual + residual = 0. + if Hthreshold) + + #Formula (15) from [2] + d = np.multiply(s[:pos]/2, 1-np.divide((L+M)*sigma2, s[:pos]**2) + np.sqrt((1-np.divide((L+M)*sigma2, s[:pos]**2))**2 -4*L*M*sigma2**2/s[:pos]**4) ) + + #Computation of the posterior + post = {} + post['ma'] = np.zeros(H) + post['mb'] = np.zeros(H) + post['sa2'] = np.zeros(H) + post['sb2'] = np.zeros(H) + post['cacb'] = np.zeros(H) + + tau = np.multiply(d, s[:pos])/(M*sigma2) + delta = np.multiply(np.sqrt(np.divide(M*d, L*s[:pos])), 1+alpha/tau) + + post['ma'][:pos] = np.sqrt(np.multiply(d, delta)) + post['mb'][:pos] = np.sqrt(np.divide(d, delta)) + post['sa2'][:pos] = np.divide(sigma2*delta, s[:pos]) + post['sb2'][:pos] = np.divide(sigma2, np.multiply(delta, s[:pos])) + post['cacb'][:pos] = np.sqrt(np.multiply(d, s[:pos])/(L*M)) + post['sigma2'] = sigma2 + post['F'] = 0.5*(L*M*np.log(2*np.pi*sigma2) + (residual+np.sum(s**2))/sigma2 + + np.sum(M*np.log(tau+1) + L*np.log(tau/alpha +1) - M*tau)) + + return U[:,:pos], np.diag(d), V[:,:pos], post + +def EVBsigma2(sigma2,L,M,s,residual,xubar): + H = len(s) + + alpha = L/M + x = s**2/(M*sigma2) + + z1 = x[x>xubar] + z2 = x[x<=xubar] + tau_z1 = tau(z1, alpha) + + term1 = np.sum(z2 - np.log(z2)) + term2 = np.sum(z1 - tau_z1) + term3 = np.sum( np.log( np.divide(tau_z1+1, z1))) + term4 = alpha*np.sum(np.log(tau_z1/alpha+1)) + + obj = term1+term2+term3+term4+ residual/(M*sigma2) + (L-H)*np.log(sigma2) + + return obj + +def phi0(x): + return x-np.log(x) + +def phi1(x, alpha): + return np.log(tau(x,alpha)+1) + alpha*np.log(tau(x,alpha)/alpha + 1) - tau(x,alpha) + +def tau(x, alpha): + return 0.5 * (x-(1+alpha) + np.sqrt((x-(1+alpha))**2 - 4*alpha)) diff --git a/tools/mnncompress/mnncompress/common/__init__.py b/tools/mnncompress/mnncompress/common/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/mnncompress/mnncompress/common/helper.py b/tools/mnncompress/mnncompress/common/helper.py new file mode 100644 index 00000000..02dbc4cf --- /dev/null +++ b/tools/mnncompress/mnncompress/common/helper.py @@ -0,0 +1,47 @@ +from __future__ import print_function +import mnncompress.common.MNN_compression_pb2 as compress_pb + +def get_pipeline_methods(compress_proto): + compress_types = compress_pb.CompressionAlgo.CompressionType.keys() + used_compress_types = [compress_types[algo.type] for algo in compress_proto.algo] + quant_types = compress_pb.LayerQuantizeParams.QuantMethod.keys() + prune_types = compress_pb.PruneParams.PruneType.keys() + + methods = [] + for i in range(len(used_compress_types)): + type = used_compress_types[i] + algo = compress_proto.algo[i] + + if type == "QUANTIZE": + method = quant_types[algo.quant_params.layer[0].method] + for layer in algo.quant_params.layer: + if quant_types[layer.method] == "OverflowAware": + method = quant_types[layer.method] + if quant_types[layer.method] == "WinogradAware": + method = quant_types[layer.method] + + if type == "PRUNE": + method = prune_types[algo.prune_params.type] + + methods.append(type + "." + method) + + return methods + +def get_align_channels(value, max_value, align_channels, minimal_ratio=0.0): + res = value // align_channels * align_channels + if res == 0: + if align_channels <= max_value: + res = align_channels + else: + res = max_value + + if (res / float(max_value)) < minimal_ratio: + res = int(max_value * minimal_ratio) + res = res // align_channels * align_channels + if res == 0: + if align_channels <= max_value: + res = align_channels + else: + res = max_value + + return res diff --git a/tools/mnncompress/mnncompress/common/log.py b/tools/mnncompress/mnncompress/common/log.py new file mode 100644 index 00000000..72fb700c --- /dev/null +++ b/tools/mnncompress/mnncompress/common/log.py @@ -0,0 +1,130 @@ +import urllib.request +import json +import time +import datetime +import os +import base64 + +try: + import aliyun.log +except ImportError: + print("try 'pip install -U aliyun-log-python-sdk'") + os.system("pip install -U aliyun-log-python-sdk") +from aliyun.log import LogClient +from aliyun.log import PutLogsRequest +from aliyun.log.logitem import LogItem + +import platform +import os +import uuid + + +class MNNLogger(object): + def __init__(self): + self._url = base64.urlsafe_b64decode(b'aHR0cHM6Ly8xMDMyMjc3OTQ5NDA5MTkzLmNuLWhhbmd6aG91LmZjLmFsaXl1bmNzLmNvbS8yMDE2LTA4LTE1L3Byb3h5L21ubi1zZXJ2aWNlL3dvcmtzdGF0aW9uLXN0cy8=').decode() + self._endpoint = base64.urlsafe_b64decode(b'aHR0cHM6Ly9jbi1oYW5nemhvdS5sb2cuYWxpeXVuY3MuY29t').decode() + self._log_project = base64.urlsafe_b64decode(b'bW5uLW1vbml0b3I=').decode() + self._log_store = base64.urlsafe_b64decode(b'bW5uLWNvbXByZXNz').decode() + self._network_available = True + self._activate() + + def _activate(self): + try: + req = urllib.request.Request(self._url) + res = urllib.request.urlopen(req) + data = res.read() + temp_credentials = json.loads(data) + + access_key_id = temp_credentials['Credentials']['AccessKeyId'] + access_key = temp_credentials['Credentials']['AccessKeySecret'] + security_token = temp_credentials['Credentials']['SecurityToken'] + self._expire_time = temp_credentials['Credentials']['Expiration'] + + self._client = LogClient(self._endpoint, access_key_id, access_key, security_token) + except: + self._network_available = False + + def _is_token_valid(self): + utc_date = datetime.datetime.strptime(self._expire_time, "%Y-%m-%dT%H:%M:%SZ") + local_date = utc_date + datetime.timedelta(hours=8) + now_time = int(time.time()) + if local_date.timestamp() - now_time < 60: + return False + else: + return True + + def _get_machine_id(self, os_type): + machine_id = "" + + if os_type == "Linux": + if os.path.exists("/etc/machine-id"): + machine_id = os.popen("cat /etc/machine-id").readline().strip().lower() + elif os_type == "Darwin": + res = os.popen("ioreg -rd1 -c IOPlatformExpertDevice | grep UUID").readline().strip().split('"') + if len(res) > 1: + machine_id = res[-2].lower() + elif os_type == "Windows": + res = os.popen("reg query HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Cryptography\ /v MachineGuid").read().strip().split(" ")[-1].lower() + else: + pass + + if machine_id == "": + machine_id = uuid.uuid1().hex[20:] + + return machine_id + + def _collect_basic_logs(self): + basic_logs = {} + from mnncompress.version import __version__ + basic_logs["version"] = __version__ + + os_type = platform.system() + basic_logs["os"] = os_type + basic_logs["machine_id"] = self._get_machine_id(os_type) + + return basic_logs + + def _collect_contents(self, log_dict, contents): + for key, value in log_dict.items(): + key = str(key) + if isinstance(value, dict): + value = json.dumps(value) + else: + value = str(value) + contents.append((key, value)) + + def put_log(self, log_dict, topic=None): + if not self._network_available: + print("network not available...") + return False + + try: + if not self._is_token_valid(): + self._activate() + + contents = [] + self._collect_contents(self._collect_basic_logs(), contents) + self._collect_contents(log_dict, contents) + + log_item = LogItem() + log_item.set_time(int(time.time())) + log_item.set_contents(contents) + + req = PutLogsRequest(self._log_project, self._log_store, topic, '', [log_item,]) + res = self._client.put_logs(req) + return True + except: + return False + + def on_done(self, framework, model_guid, detail): + if framework not in ["tensorflow", "pytorch"]: + raise ValueError("framework should be tensorflow or pytorch") + log_dict = {} + log_dict["framework"] = framework + log_dict["model_guid"] = model_guid + log_dict["detail"] = detail + res = self.put_log(log_dict) + + return res + +mnn_logger = MNNLogger() diff --git a/tools/mnncompress/mnncompress/pytorch/LSQ_quantizer.py b/tools/mnncompress/mnncompress/pytorch/LSQ_quantizer.py new file mode 100644 index 00000000..9dd4ffd1 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/LSQ_quantizer.py @@ -0,0 +1,716 @@ +from __future__ import print_function, with_statement +import copy +import numpy as np +from onnx import numpy_helper +import torch +try: + import torch.fx as fx +except ImportError: + print("need torch version >= 1.8.0, try 'pip install -U torch torchvision torchaudio' to upgrade torch") +from torch.fx import symbolic_trace +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair +import onnx +import mnncompress.common.MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid + + +_Supported_Modules = (torch.nn.Conv2d, torch.nn.Linear) +_MNN_Quant_prefix = "mnn_quant_" +_eps = 1e-6 + + +class _FeatureFakeQuantModule(nn.Module): + def __init__(self, clamp_value = 127.0, mode='online'): + super().__init__() + self.scale = nn.Parameter(torch.rand(1)[0]) + self._initialized = nn.Parameter(torch.BoolTensor([False]), requires_grad=False) + self._clamp_value = clamp_value + if mode not in ['online', 'offline']: + raise ValueError("mode should be 'online' or 'offline'") + self._mode = mode + self._momentum = 0.99 + self.zero_point = nn.Parameter(torch.zeros(1)[0]) + + def forward(self, x): + if not self._initialized: + if self._mode == 'online': + if torch.distributed.is_initialized(): + init_scale = torch.max(torch.abs(x)) / self._clamp_value + _eps + torch.distributed.all_reduce(init_scale, torch.distributed.ReduceOp.MAX) + else: + init_scale = torch.max(torch.abs(x)) / self._clamp_value + _eps + self.scale.data = init_scale.detach() + else: + self.scale.requires_grad = False + self.zero_point.requires_grad = False + self.min = torch.min(x) + self.max = torch.max(x) + self._initialized.data = torch.BoolTensor([True]) + + if self._mode == "online": + temp = self.scale / np.sqrt(x.numel() * self._clamp_value) + s_scale = torch.detach(self.scale - temp) + temp + quanted = x / s_scale + rounded = torch.detach(torch.round(quanted + torch.sign(quanted)*_eps) - quanted) + quanted + clamped = torch.clamp(rounded, -self._clamp_value, self._clamp_value) + dequanted = clamped * s_scale + else: + inst_min = torch.minimum(torch.min(x), torch.Tensor([0.0])[0]).detach() + inst_max = torch.maximum(torch.max(x), torch.Tensor([0.0])[0]).detach() + scale = ((inst_max - inst_min) / (2 * self._clamp_value)).detach() + zero_point = torch.round((0 - inst_min) / scale - self._clamp_value).detach() + + nudge_min = (-self._clamp_value - zero_point) * scale + nudge_max = (self._clamp_value - zero_point) * scale + nudge_min = torch.minimum(nudge_min, torch.Tensor([0.0])[0]).detach() + nudge_max = torch.maximum(nudge_max, torch.Tensor([0.0])[0]).detach() + + self.min = self._momentum * self.min + (1 - self._momentum) * nudge_min + self.max = self._momentum * self.max + (1 - self._momentum) * nudge_max + self.min = torch.minimum(self.min, torch.Tensor([0.0])[0]).detach() + self.max = torch.maximum(self.max, torch.Tensor([0.0])[0]).detach() + + self.scale.data = (self.max - self.min) / (2 * self._clamp_value) + self.scale.detach() + self.zero_point.data = torch.round((0 - self.min) / self.scale - self._clamp_value) + self.zero_point.detach() + + temp = x / self.scale + self.zero_point + quanted = torch.round(temp + torch.sign(temp)*_eps) + clamped = torch.clamp(quanted, -self._clamp_value, self._clamp_value) + dequanted = (clamped - self.zero_point) * self.scale + + return dequanted + +class _Conv2dWeightFakeQuantModule(nn.Module): + def __init__(self, w_num, prune_mask, clamp_value = 127.0): + super().__init__() + self.scale = nn.Parameter(torch.rand(w_num, 1, 1, 1)) + self._initialized = nn.Parameter(torch.BoolTensor([False]), requires_grad=False) + self._clamp_value = clamp_value + self._prune_mask = nn.Parameter(prune_mask, requires_grad=False) + + def forward(self, w): + w.data = w.data * self._prune_mask + + if not self._initialized: + abs_w = torch.abs(w) + max3 = torch.max(abs_w, 3, keepdim=True) + max2 = torch.max(max3.values, 2, keepdim=True) + max1 = torch.max(max2.values, 1, keepdim=True) + init_scale = max1.values / self._clamp_value + _eps + self.scale.data = init_scale + self._initialized.data = torch.BoolTensor([True]) + + temp = self.scale / np.sqrt(w.numel() * self._clamp_value) + s_scale = torch.detach(self.scale - temp) + temp + quanted = w / s_scale + rounded = torch.detach(torch.round(quanted + torch.sign(quanted)*_eps) - quanted) + quanted + clamped = torch.clamp(rounded, -self._clamp_value, self._clamp_value) + dequanted = clamped * s_scale + + return dequanted + +class _LinearWeightFakeQuantModule(nn.Module): + def __init__(self, w_num, prune_mask, clamp_value = 127.0): + super().__init__() + self.scale = nn.Parameter(torch.rand(w_num, 1)) + self._initialized = nn.Parameter(torch.BoolTensor([False]), requires_grad=False) + self._clamp_value = clamp_value + self._prune_mask = nn.Parameter(prune_mask, requires_grad=False) + + def forward(self, w): + w.data = w.data * self._prune_mask + + if not self._initialized: + abs_w = torch.abs(w) + max1 = torch.max(abs_w, 1, keepdim=True) + init_scale = max1.values / self._clamp_value + _eps + self.scale.data = init_scale + self._initialized.data = torch.BoolTensor([True]) + + temp = self.scale / np.sqrt(w.numel() * self._clamp_value) + s_scale = torch.detach(self.scale - temp) + temp + quanted = w / s_scale + rounded = torch.detach(torch.round(quanted + torch.sign(quanted)*_eps) - quanted) + quanted + clamped = torch.clamp(rounded, -self._clamp_value, self._clamp_value) + dequanted = clamped * s_scale + + return dequanted + + +class LSQQuantizer(object): + def __init__(self, model, skip_quant_layers = [], bits = 8, debug_info = False, mode = 'online', retain_sparsity=False): + self._model = copy.deepcopy(model) + self._gm = symbolic_trace(model) + + nm = dict(model.named_modules()) + supported_nm = {n:m for n, m in nm.items() if isinstance(m, _Supported_Modules)} + + for name in skip_quant_layers: + if name not in supported_nm.keys(): + raise ValueError("skip quant layer '" + name + "' is not found in model's named_modules. availables are:", [n for n in supported_nm.keys()]) + + self._skip_quant_layers = skip_quant_layers + + # self._skip_quant_types = skip_quant_types + if bits < 2 or bits > 8: + raise ValueError("bits must be a integer in [2, 8]") + self._bits = bits + self._clamp_value = float(pow(2, bits-1) - 1) + # self._layer_weight_bits_config = layer_weight_bits_config + self._debug = debug_info + + if mode not in ['online', 'offline']: + raise ValueError("mode should be 'online' or 'offline'") + self._mode = mode + self._retain_sparsity = retain_sparsity + + self._pname_mask = {} + self._quant_module_nodes = [] + self._quant_func_nodes = [] + self._init_prune_ratios = {} + self._ori_prune_weights = {} + self._feature_node_ori_args = {} + self._weight_node_ori_args = {} + self._quant_user_nodes_update_args = {} + self._qat_ops_stripped = False + self._weight_quant_info_map = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + self._init() + + def save_compress_params(self, onnx_inference_model_file, filename, append=False): + nm = dict(self._gm.named_modules()) + named_params = dict(self._gm.named_parameters()) + + onnx_model = onnx.load(onnx_inference_model_file) + onnx_model_bn_fused = True + for node in onnx_model.graph.node: + if node.op_type == "Conv": + output_name = node.output[0] + output_nodes = self._find_onnx_output_nodes(output_name, onnx_model) + if len(output_nodes) == 1: + if output_nodes[0].op_type == "BatchNormalization": + onnx_model_bn_fused = False + break + + compress_proto = compress_pb.Pipeline() + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.QUANTIZE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + quant_algorithm = compress_proto.algo.add() + + for weight_name, info in self._weight_quant_info_map.items(): + used_bn_name = info[3] + if onnx_model_bn_fused == False: + used_bn_name = None + + input_name, output_name, bn_info = self._get_input_output_name_bn_info(onnx_model, weight_name, used_bn_name, False) + assert (input_name is not None) and (output_name is not None), "input_name and output_name not found for key: " + weight_name + + l = quant_algorithm.quant_params.layer.add() + + input_params = compress_pb.LayerQuantizeParams.ActivationParams() + input_params.name = input_name + input_params.bits = self._bits + input_params.scales.append(named_params[info[0][0]].detach().cpu().numpy().tolist()) + input_params.zero_point = int(named_params[info[0][1]].detach().cpu().numpy().tolist()) + input_params.clamp_min = -int(self._clamp_value) + input_params.clamp_max = int(self._clamp_value) + l.input.append(input_params) + + output_params = compress_pb.LayerQuantizeParams.ActivationParams() + output_params.name = output_name + output_params.bits = self._bits + output_params.scales.append(named_params[info[1][0]].detach().cpu().numpy().tolist()) + output_params.zero_point = int(named_params[info[1][1]].detach().cpu().numpy().tolist()) + output_params.clamp_min = -int(self._clamp_value) + output_params.clamp_max = int(self._clamp_value) + l.output.append(output_params) + + weight_params = compress_pb.LayerQuantizeParams.WeightParams() + weight_params.name = weight_name + weight_params.bits = self._bits + weight_scales = named_params[info[2]].flatten().detach().cpu().numpy() + gamma = np.ones_like(weight_scales) + var = np.ones_like(weight_scales) + if info[3] is not None: + bn = nm[info[3]] + gamma = bn.weight.detach().cpu().numpy() + var = bn.running_var.detach().cpu().numpy() + weight_scales = weight_scales * gamma / np.sqrt(var + bn.eps) + + weight_scales = weight_scales.tolist() + for ws in weight_scales: + weight_params.scales.append(abs(ws)) + weight_params.clamp_min = -int(self._clamp_value) + weight_params.clamp_max = int(self._clamp_value) + l.weight.append(weight_params) + + if not self._reported: + detail = {"algorithm": "LSQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self._bits, "skip_quant_layers": self._skip_quant_layers, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("pytorch", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _get_input_output_name_bn_info(self, onnx_model, weight_name, bn_name, from_onnx_changed): + input_name = None + output_name = None + bn_info = None + + for node in onnx_model.graph.node: + if weight_name in node.input: + if node.op_type not in ["Conv", "Gemm"]: + raise ValueError("only quantize onnx Conv and Gemm ops, but encountered op type:" + node.op_type) + input_name = node.input[0] + output_name, bn_info = self._get_onnx_output_name_bn_info(node, onnx_model) + return input_name, output_name, bn_info + + if input_name == None and from_onnx_changed == False: + input_name, output_name, bn_info = self._get_input_output_name_onnx_name_changed(onnx_model, weight_name, bn_name) + + return input_name, output_name, bn_info + + def _get_input_output_name_onnx_name_changed(self, onnx_model, weight_name, bn_name): + input_name = None + output_name = None + bn_info = None + + nm = dict(self._gm.named_modules()) + + named_params = dict(self._gm.named_parameters()) + weight = named_params[weight_name].detach().cpu().numpy() + bn_stat_shape = [weight.shape[0]] + [1 for i in range(len(weight.shape)-1)] + gamma = np.ones(bn_stat_shape).astype(weight.dtype) + var = np.ones(bn_stat_shape).astype(weight.dtype) + if bn_name is not None: + bn = nm[bn_name] + gamma = bn.weight.detach().cpu().numpy().reshape(bn_stat_shape) + var = bn.running_var.detach().cpu().numpy().reshape(bn_stat_shape) + weight = weight * (gamma / np.sqrt(var + bn.eps)) + + for iz in onnx_model.graph.initializer: + izv = numpy_helper.to_array(iz) + if weight.shape == izv.shape: + if np.mean(np.abs(weight - izv) < 1e-6) > 0.99: + input_name, output_name, bn_info = self._get_input_output_name_bn_info(onnx_model, iz.name, bn_name, True) + return input_name, output_name, bn_info + + return input_name, output_name, bn_info + + def _node_is_relu6(self, node, onnx_model): + if node.op_type != "Clip": + return False + + if len(node.input) == 1: + clip_min, clip_max = None, None + attrs = node.attribute + for attr in attrs: + if attr.name == "min": + clip_min = attr.f + if attr.name == "max": + clip_max = attr.f + + if clip_min == 0.0 and clip_max == 6.0: + return True + else: + return False + + if len(node.input) == 3: + min_name, max_name = node.input[1], node.input[2] + clip_min, clip_max = None, None + for n in onnx_model.graph.node: + if n.output[0] == min_name and n.op_type == "Constant": + clip_min = numpy_helper.to_array(n.attribute[0].t).tolist() + if n.output[0] == max_name and n.op_type == "Constant": + clip_max = numpy_helper.to_array(n.attribute[0].t).tolist() + + if clip_min == None and clip_max == None: + for iz in onnx_model.graph.initializer: + if iz.name == min_name: + clip_min = numpy_helper.to_array(iz).tolist() + if iz.name == max_name: + clip_max = numpy_helper.to_array(iz).tolist() + + if clip_min == 0.0 and clip_max == 6.0: + return True + else: + return False + + return False + + def _get_onnx_output_name_bn_info(self, conv_or_gemm_node, onnx_model): + assert len(conv_or_gemm_node.output) == 1, "conv or gemm node should only have one output" + output_name = conv_or_gemm_node.output[0] + bn_info = None + bn_weight = None + bn_var = None + + if conv_or_gemm_node.op_type == "Gemm": + return output_name, bn_info + + output_nodes = self._find_onnx_output_nodes(output_name, onnx_model) + if len(output_nodes) != 1: + return output_name, bn_info + if output_nodes[0].op_type not in ["BatchNormalization", "Relu"] and (not self._node_is_relu6(output_nodes[0], onnx_model)): + return output_name, bn_info + + if output_nodes[0].op_type == "BatchNormalization": + if not any([input_name.endswith(".running_var") for input_name in output_nodes[0].input]): + return output_name, bn_info + + output_name = output_nodes[0].output[0] + + for iz in onnx_model.graph.initializer: + if iz.name == output_nodes[0].input[1]: + bn_weight = numpy_helper.to_array(iz) + if iz.name == output_nodes[0].input[4]: + bn_var = numpy_helper.to_array(iz) + + if bn_weight is not None and bn_var is not None: + bn_info = [bn_weight, bn_var] + + output_nodes = self._find_onnx_output_nodes(output_name, onnx_model) + if len(output_nodes) != 1: + return output_name, bn_info + if self._node_is_relu6(output_nodes[0], onnx_model): + output_name = output_nodes[0].output[0] + return output_name, bn_info + + if output_nodes[0].op_type == "Relu": + output_name = output_nodes[0].output[0] + output_nodes = self._find_onnx_output_nodes(output_name, onnx_model) + if len(output_nodes) != 1: + return output_name, bn_info + # in onnx opset > 9, F.relu6 is composed by relu + clip + if self._node_is_relu6(output_nodes[0], onnx_model): + output_name = output_nodes[0].output[0] + return output_name, bn_info + + return output_name, bn_info + + def _find_onnx_output_nodes(self, output_name, onnx_model): + output_nodes = [] + for n in onnx_model.graph.node: + if output_name in n.input: + output_nodes.append(n) + + return output_nodes + + def strip_qat_ops(self): + if self._qat_ops_stripped: + return + + for node, index_feature_node in self._feature_node_ori_args.items(): + ori_args = list(node.args) + for index, ori_node in index_feature_node: + ori_args[index] = ori_node + node.args = tuple(ori_args) + + for node, index_weight_node in self._weight_node_ori_args.items(): + ori_args = list(node.args) + for index, ori_node in index_weight_node: + ori_args[index] = ori_node + node.args = tuple(ori_args) + + self._gm.recompile() + self._qat_ops_stripped = True + + def resume_qat_graph(self): + if not self._qat_ops_stripped: + return + + for node, new_args in self._quant_user_nodes_update_args.items(): + node.args = new_args + + self._gm.recompile() + self._qat_ops_stripped = False + + def _current_prune_ratios(self): + prune_ratios = {} + for n, m in self._ori_prune_weights.items(): + ratio = 1 - np.mean(np.abs(m.cpu().numpy()) > _eps) + prune_ratios[n] = ratio + return prune_ratios + + def _init(self): + for module_name, module in self._model.named_modules(): + if isinstance(module, _Supported_Modules): + for n, p in module.named_parameters(): + if 'weight' == n: + pname = module_name + "." + n + if self._retain_sparsity: + th = _eps + else: + th = -1.0 # so that mask are all 1.0 + mask = (torch.abs(p.data) > th).float() + self._pname_mask[pname] = mask + self._ori_prune_weights[pname] = p.data + self._init_prune_ratios[pname] = 1 - torch.mean(mask).cpu().numpy().tolist() + + print("init pruning ratios:") + for key, value in self._init_prune_ratios.items(): + print(key, ":", value) + + nm = dict(self._gm.named_modules()) + for node in self._gm.graph.nodes: + if node.target in nm.keys() and isinstance(nm[node.target], _Supported_Modules): + self._quant_module_nodes.append(node) + + for node in self._gm.graph.nodes: + if node.target is F.conv2d or node.target is F.linear: + self._quant_func_nodes.append(node) + + for module_name in self._skip_quant_layers: + if module_name in [n.target for n in self._gm.graph.nodes]: + self._total_weight_num += dict(self._model.named_parameters())[module_name + ".weight"].data.numel() + self._remain_weight_num += dict(self._model.named_parameters())[module_name + ".weight"].data.numel() + rn = {n.target : n for n in self._gm.graph.nodes}[module_name] + self._quant_module_nodes.remove(rn) + + self._extract() + + def _extract(self): + gm = self._gm + nm = dict(gm.named_modules()) + for n in self._quant_module_nodes: + if n.target in nm.keys() and isinstance(nm[n.target], torch.nn.Conv2d): + conv_module = nm[n.target] + with gm.graph.inserting_after(n): + w = gm.graph.get_attr(n.target+".weight") + + self._total_weight_num += dict(self._model.named_parameters())[n.target + ".weight"].data.numel() + self._remain_weight_num += (dict(self._model.named_parameters())[n.target + ".weight"].data.numel() / (32.0 / self._bits) * (1 - self._init_prune_ratios[n.target + ".weight"])) + + p = w + b = None + if conv_module.bias is not None: + with gm.graph.inserting_after(w): + b = gm.graph.get_attr(n.target+".bias") + p = b + + pt = n.args[0] + if conv_module.padding_mode != 'zeros': + with gm.graph.inserting_after(p): + pad = gm.graph.call_function(F.pad) + pad.args = (n.args[0],) + pad.kwargs = {'pad':conv_module._reversed_padding_repeated_twice, 'mode':conv_module.padding_mode} + p = pad + pt = pad + + with gm.graph.inserting_after(p): + conv_func = gm.graph.call_function(F.conv2d) + conv_func.args = (pt, w, b) + if conv_module.padding_mode != 'zeros': + conv_func.kwargs = {'stride': conv_module.stride, 'padding': _pair(0), 'dilation': conv_module.dilation, 'groups': conv_module.groups} + else: + conv_func.kwargs = {'stride': conv_module.stride, 'padding': conv_module.padding, 'dilation': conv_module.dilation, 'groups': conv_module.groups} + + n.replace_all_uses_with(conv_func) + self._quant_func_nodes.append(conv_func) + gm.graph.erase_node(n) + + if n.target in nm.keys() and isinstance(nm[n.target], torch.nn.Linear): + linear_module = nm[n.target] + with gm.graph.inserting_after(n): + w = gm.graph.get_attr(n.target+".weight") + + self._total_weight_num += dict(self._model.named_parameters())[n.target + ".weight"].data.numel() + self._remain_weight_num += (dict(self._model.named_parameters())[n.target + ".weight"].data.numel() / (32.0 / self._bits) * (1 - self._init_prune_ratios[n.target + ".weight"])) + + p = w + b = None + if linear_module.bias is not None: + with gm.graph.inserting_after(w): + b = gm.graph.get_attr(n.target+".bias") + p = b + + with gm.graph.inserting_after(p): + linear_func = gm.graph.call_function(F.linear) + linear_func.args = (n.args[0], w, b) + + n.replace_all_uses_with(linear_func) + self._quant_func_nodes.append(linear_func) + gm.graph.erase_node(n) + + gm.recompile() + + def convert(self): + for i, node in zip(range(len(self._quant_func_nodes)), self._quant_func_nodes): + if node.target is F.conv2d or node.target is F.linear: + self._quant_one_layer(node, _MNN_Quant_prefix + str(i) + "_" + node.name, node.target) + + self._gm.recompile() + return self._gm + + def _quant_one_layer(self, node, prefix, type): + input_node = node.args[0] + weight_node = node.args[1] + + weight_node_user_num = 0 + for n in self._gm.graph.nodes: + if n.op == "get_attr" and n.target == weight_node.target: + weight_node_user_num += len(n.users.keys()) + if weight_node_user_num != 1: + print("weight_node:", weight_node.target, "has", weight_node_user_num, "users, skip quant for this layer") + return + + real_input_node = self._find_real_input(input_node) + input_scale_name = self._fake_quant_feature(real_input_node, input_node, prefix + "_input") + weight_scale_name = self._fake_quant_weight(weight_node, prefix + "_weight", type) + output_node, bn_name = self._find_output_node(node) + output_scale_name = self._fake_quant_feature(output_node, output_node, prefix + "_output") + + self._weight_quant_info_map[weight_node.target] = [input_scale_name, output_scale_name, weight_scale_name, bn_name] + + def _fake_quant_feature(self, feature_node, ori_node, name): + if _MNN_Quant_prefix in feature_node.name: + return [feature_node.name + ".scale", feature_node.name + ".zero_point"] + + self._gm.add_module(name, _FeatureFakeQuantModule(self._clamp_value, self._mode)) + + with self._gm.graph.inserting_after(ori_node): + quant_input = self._gm.graph.call_module(name) + quant_input.args = (ori_node,) + + nodes_update_args = [] + for node in ori_node.users.keys(): + if _MNN_Quant_prefix not in node.name: + new_args = tuple() + if node not in self._feature_node_ori_args.keys(): + self._feature_node_ori_args[node] = [] + for i in range(0, len(node.args)): + if node.args[i] == ori_node: + new_args += (quant_input,) + self._feature_node_ori_args[node].append([i, ori_node]) + else: + new_args += (node.args[i],) + nodes_update_args.append((node, new_args)) + self._quant_user_nodes_update_args[node] = new_args + + for node, new_args in nodes_update_args: + node.args = new_args + + return [name + ".scale", name + ".zero_point"] + + def _fake_quant_weight(self, weight_node, name, type): + if _MNN_Quant_prefix in weight_node.name: + return weight_node.name + ".scale" + + nps = dict(self._gm.named_parameters()) + if type is F.conv2d: + p = nps[weight_node.target] + self._gm.add_module(name, _Conv2dWeightFakeQuantModule(p.shape[0], self._pname_mask[weight_node.target], self._clamp_value)) + + if type is F.linear: + p = nps[weight_node.target] + self._gm.add_module(name, _LinearWeightFakeQuantModule(p.shape[0], self._pname_mask[weight_node.target], self._clamp_value)) + + with self._gm.graph.inserting_after(weight_node): + quant_weight = self._gm.graph.call_module(name) + quant_weight.args = (weight_node,) + + nodes_update_args = [] + for node in weight_node.users.keys(): + if _MNN_Quant_prefix not in node.name: + new_args = tuple() + if node not in self._weight_node_ori_args.keys(): + self._weight_node_ori_args[node] = [] + for i in range(0, len(node.args)): + if node.args[i] == weight_node: + new_args += (quant_weight,) + self._weight_node_ori_args[node].append([i, weight_node]) + else: + new_args += (node.args[i],) + nodes_update_args.append((node, new_args)) + self._quant_user_nodes_update_args[node] = new_args + + for node, new_args in nodes_update_args: + node.args = new_args + + return name + ".scale" + + def _find_real_input(self, input_node): + gm = self._gm + nm = dict(gm.named_modules()) + real_input_node = input_node + + is_dropout = input_node.target is torch.dropout or input_node.target is F.dropout + is_dropout = is_dropout or (input_node.target in nm.keys() and isinstance(nm[input_node.target], nn.Dropout)) + if is_dropout: + real_input_node = input_node.args[0] + + return real_input_node + + def _find_output_node(self, conv_or_linear_node): + gm = self._gm + nm = dict(gm.named_modules()) + output_node = conv_or_linear_node + bn_name = None + + # TODO: linear's bn or relu not supported + if conv_or_linear_node.target is F.linear: + return output_node, bn_name + + if len(output_node.users) > 1: + return output_node, bn_name + + node = [n for n in output_node.users.keys()][0] + has_bn = node.target in nm.keys() and isinstance(nm[node.target], torch.nn.BatchNorm2d) + if has_bn: + bn = nm[node.target] + if bn.running_var is None: + return output_node, bn_name + + output_node = node + bn_name = node.target + if len(output_node.users) > 1: + return output_node, bn_name + + node = [n for n in output_node.users.keys()][0] + has_relu = node.target is torch.relu or node.target is F.relu + has_relu = has_relu or (node.op == "call_method" and node.target in ["relu", "relu_"]) + # TODO: mnn not support this relu6 yet + # has_relu6 = node.target is F.relu6 + has_relu6 = False + + if node.op == "call_module": + if isinstance(nm[node.target], nn.ReLU): + has_relu = True + + if isinstance(nm[node.target], nn.ReLU6): + has_relu6 = True + + if has_relu or has_relu6: + output_node = node + return output_node, bn_name + + return output_node, bn_name diff --git a/tools/mnncompress/mnncompress/pytorch/SIMD_OC_pruner.py b/tools/mnncompress/mnncompress/pytorch/SIMD_OC_pruner.py new file mode 100644 index 00000000..2fae1e03 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/SIMD_OC_pruner.py @@ -0,0 +1,355 @@ +from __future__ import print_function +import torch +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid + +class SIMDOCPruner(object): + def __init__(self, model, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info=False, prune_finetune_iterations=0, max_prune_ratio=0.99): + self._model = model + if not isinstance(model, torch.nn.Module): + raise TypeError("model should be a torch.nn.Module instance") + if sparsity < 0 or sparsity > 1: + raise ValueError("sparsity should be a float number in [0, 1]") + if total_pruning_iterations < 1: + raise ValueError("total_pruning_iterations should be an integer >= 1") + + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._current_accumulate_step = 0 + self._total_accumulate_steps = 10 + self._step = 0 + self._finetune_step = 0 + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._debug = debug_info + self._config_file = config_file + self._pname_accgrad = {} + self._pname_gt_reshape_mean = {} + self._pname_gt_remains_reshape_mean = {} + self._pname_mask = {} + self._all_mask_grad_flatten = [] + self._config_dict = {} + self._generated_config_dict = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + def _init(self): + for module_name, module in self._model.named_modules(): + if (isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear)): + for n, p in module.named_parameters(): + if 'weight' == n: + pname = module_name + "." + n + if p.grad is None: + continue + self._pname_accgrad[pname] = torch.zeros_like(p.grad) + self._pname_mask[pname] = torch.ones_like(p.data) + + def current_prune_ratios(self): + prune_ratios = {} + total_weight_num = 0 + pruned_weight_num = 0 + for n, m in self._pname_mask.items(): + ratio = 1 - np.mean(m.cpu().numpy()) + prune_ratios[n] = ratio + total_weight_num += m.numel() + pruned_weight_num += int(m.numel() * ratio) + + prune_ratios['overall_prune_ratio'] = float(pruned_weight_num) / total_weight_num + + return prune_ratios + + def do_pruning(self, result_file = "SIMDOC_found_prune_ratios.yml"): + if self._current_accumulate_step == 0: + self._init() + + if self._current_accumulate_step < self._total_accumulate_steps: + print("SIMDOCPruner: accumulating weight sensitivity...") + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + self._pname_accgrad[n] += torch.abs(p.grad * p.data) + + self._current_accumulate_step += 1 + + elif self._current_accumulate_step == self._total_accumulate_steps: + # named_parameters = dict(self._model.named_parameters()) + # for n, g in self._pname_accgrad.items(): + # self._pname_accgrad[n] = torch.abs(named_parameters[n]) * g + + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + self._generate_prune_ratios(config_dict) + + f = open(result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", result_file) + + self._current_accumulate_step += 1 + + else: + if self._step < self._total_pruning_iterations: + if self._prune_finetune_iterations == 0: + self._step += 1 + else: + if self._finetune_step == 0: + self._step += 1 + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + self._generate_prune_mask() + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + p.data.mul_(self._pname_mask[n]) + + if self._step == self._total_pruning_iterations: + self._step += 1 + + def save_compress_params(self, filename, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.SIMD_OC + weight_tensor_names = algorithm.prune_params.simd_oc_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.simd_oc_pruner_params.layer_names + prune_ratios = algorithm.prune_params.simd_oc_pruner_params.prune_ratios + oc_blocks = algorithm.prune_params.simd_oc_pruner_params.oc_blocks + + for n, m in self._pname_mask.items(): + # layer_names.append(op_name) + weight_tensor_names.append(n) + ratio = 1 - np.mean(m.cpu().numpy()) + self._total_weight_num += m.numel() + self._remain_weight_num += (m.numel() * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + oc_blocks.append(4) + print(n, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "SIMD_OC", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("pytorch", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _generate_prune_ratios(self, config_dict = {}): + all_mask_grad_flatten = [] + for n, g in self._pname_accgrad.items(): + gn = g.detach().cpu().numpy() + dims = len(gn.shape) + trans_dims = [x for x in range(1, dims)] + [0] + g_trans = gn.transpose(trans_dims) + g_trans_shape = g_trans.shape + oc_blocks = g_trans_shape[-1] // 4 + remains = g_trans_shape[-1] % 4 + + if oc_blocks != 0: + block_total_rows = oc_blocks * 4 + gt_reshape = g_trans[..., 0:block_total_rows].reshape((-1, 4)) + gt_reshape_mean = gt_reshape.mean(axis=1, keepdims=True) + self._pname_gt_reshape_mean[n] = gt_reshape_mean + all_mask_grad_flatten.append(gt_reshape_mean.flatten()) + + if remains > 0: + gt_remains_reshape = g_trans[..., block_total_rows:block_total_rows+remains].reshape((-1, remains)) + gt_remains_reshape_mean = gt_remains_reshape.mean(axis=1, keepdims=True) + self._pname_gt_remains_reshape_mean[n] = gt_remains_reshape_mean + all_mask_grad_flatten.append(gt_remains_reshape_mean.flatten()) + + all_mask_grad_flatten = np.concatenate(all_mask_grad_flatten) + all_mask_grad_flatten = np.sort(all_mask_grad_flatten) + threshold_index = int(all_mask_grad_flatten.size * self._target_sparsity) + threshold = all_mask_grad_flatten[threshold_index] + self._all_mask_grad_flatten = all_mask_grad_flatten + + total_weight_num = 0 + total_pruned_weight_num = 0 + nm = dict(self._model.named_modules()) + for n, g in self._pname_accgrad.items(): + if n in self._pname_gt_reshape_mean.keys(): + gt_reshape_mean = self._pname_gt_reshape_mean[n] + mask = gt_reshape_mean > threshold + total_weight_num += g.numel() + pruned_num = np.sum((mask == False)) * 4 + + if n in self._pname_gt_remains_reshape_mean.keys(): + gt_remains_reshape_mean = self._pname_gt_remains_reshape_mean[n] + mask = gt_remains_reshape_mean > threshold + pruned_num += (np.sum((mask == False)) * (g.shape[0] % 4)) + + sparsity = float(pruned_num) / g.numel() + self._generated_config_dict[n] = sparsity + if n in config_dict.keys(): + self._config_dict[n] = config_dict[n] + else: + self._config_dict[n] = sparsity + + if self._config_dict[n] > self._max_prune_ratio: + self._config_dict[n] = self._max_prune_ratio + + module_name = n[:-7] + if isinstance(nm[module_name], torch.nn.Conv2d) and nm[module_name].groups > 1: + self._config_dict[n] = 0.0 + + total_pruned_weight_num += int(g.numel() * self._config_dict[n]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _generate_prune_mask(self): + if self._step > self._total_pruning_iterations: + return + + threshold_index = int(self._all_mask_grad_flatten.size * self._target_sparsity * self._step / self._total_pruning_iterations) + threshold = self._all_mask_grad_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + config_dict = {} + + for n, g in self._pname_accgrad.items(): + mask = None + if n in self._pname_gt_reshape_mean.keys(): + gt_reshape_mean = self._pname_gt_reshape_mean[n] + mask = gt_reshape_mean > threshold + if np.abs(self._generated_config_dict[n] - self._config_dict[n]) > 0.01: + argsorted = np.argsort(gt_reshape_mean.flatten()) + local_threshold_index = int(gt_reshape_mean.size * self._config_dict[n] * self._step / self._total_pruning_iterations) + mask = np.ones_like(gt_reshape_mean.flatten()) + mask[argsorted[0:local_threshold_index]] = 0. + mask = mask.reshape(gt_reshape_mean.shape) + mask = np.concatenate([mask, mask, mask, mask], axis=1) + oc_blocks = g.shape[0] // 4 + block_total_rows = oc_blocks * 4 + mask = mask.reshape((-1, block_total_rows)) + + if n in self._pname_gt_remains_reshape_mean.keys(): + gt_remains_reshape_mean = self._pname_gt_remains_reshape_mean[n] + remain_mask = gt_remains_reshape_mean > threshold + if np.abs(self._generated_config_dict[n] - self._config_dict[n]) > 0.01: + argsorted = np.argsort(gt_remains_reshape_mean.flatten()) + local_threshold_index = int(gt_remains_reshape_mean.size * self._config_dict[n] * self._step / self._total_pruning_iterations) + remain_mask = np.ones_like(gt_remains_reshape_mean.flatten()) + remain_mask[argsorted[0:local_threshold_index]] = 0. + remain_mask = remain_mask.reshape(gt_remains_reshape_mean.shape) + remains = g.shape[0] % 4 + remain_mask = np.concatenate([remain_mask for x in range(0, remains)], axis=1) + if mask is not None: + mask = np.concatenate([mask, remain_mask], axis=1) + else: + mask = remain_mask + + g_shape = np.array([x for x in g.shape]) + reshape_shape = np.concatenate([g_shape[1:], g_shape[0:1]]) + mask = mask.reshape(reshape_shape) + dims = len(g.shape) + trans_dims = [dims-1] + [x for x in range(0, dims-1)] + mask = mask.transpose(trans_dims) + + if self._debug: + total_weight_num += mask.size + total_pruned_weight_num += np.sum((mask == False)) + sparsity = 1.0 - np.mean(mask) + config_dict[n] = sparsity.tolist() + self._pname_mask[n] = torch.Tensor(mask).to(g.device) + + if self._debug: + print("prune step:", self._step, "total prune steps:", self._total_pruning_iterations) + print("overall prune ratio:", float(total_pruned_weight_num) / total_weight_num) + print("pruning config:") + for key, value in config_dict.items(): + print(key, ":", value) + + # def _generate_prune_mask(self): + # total_weight_num = 0 + # total_pruned_weight_num = 0 + # config_dict = {} + + # for n, g in self._pname_accgrad.items(): + # mask = None + # if n in self._pname_gt_reshape_mean.keys(): + # gt_reshape_mean = self._pname_gt_reshape_mean[n] + # gt_reshape_mean_argsort = np.argsort(gt_reshape_mean.flatten()) + # final_threshold_index = int(gt_reshape_mean.size * self._config_dict[n]) + # threshold_index = int(gt_reshape_mean.size * (self._config_dict[n] * self._step / self._total_pruning_iterations)) + + # mask = np.ones(gt_reshape_mean_argsort.shape) + # mask[0:threshold_index] = 0. + # mask = mask.reshape(gt_reshape_mean.shape) + # mask = np.concatenate([mask, mask, mask, mask], axis=1) + # oc_blocks = g.shape[0] // 4 + # block_total_rows = oc_blocks * 4 + # mask = mask.reshape((-1, block_total_rows)) + + # if n in self._pname_gt_remains_reshape_mean.keys(): + # gt_remains_reshape_mean = self._pname_gt_remains_reshape_mean[n] + # gt_remains_reshape_mean_argsort = np.argsort(gt_remains_reshape_mean.flatten()) + # final_threshold_index = int(gt_remains_reshape_mean.size * self._config_dict[n]) + # threshold_index = int(gt_remains_reshape_mean.size * (self._config_dict[n] * self._step / self._total_pruning_iterations)) + # remain_mask = np.ones(gt_remains_reshape_mean_argsort.shape) + # remain_mask[0:threshold_index] = 0. + # remain_mask = remain_mask.reshape(gt_remains_reshape_mean.shape) + # remains = g.shape[0] % 4 + # remain_mask = np.concatenate([remain_mask for x in range(0, remains)], axis=1) + # if mask is not None: + # mask = np.concatenate([mask, remain_mask], axis=1) + # else: + # mask = remain_mask + + # g_shape = np.array([x for x in g.shape]) + # reshape_shape = np.concatenate([g_shape[1:], g_shape[0:1]]) + # mask = mask.reshape(reshape_shape) + # dims = len(g.shape) + # trans_dims = [dims-1] + [x for x in range(0, dims-1)] + # mask = mask.transpose(trans_dims) + + # if self._debug: + # total_weight_num += mask.size + # total_pruned_weight_num += np.sum((1 - mask)) + # sparsity = 1.0 - np.mean(mask) + # config_dict[n] = sparsity.tolist() + # self._pname_mask[n] = torch.Tensor(mask).to(g.device) + + # if self._debug: + # print("prune step:", self._step, "total prune steps:", self._total_pruning_iterations) + # print("overall prune ratio:", float(total_pruned_weight_num) / total_weight_num) + # print("pruning config:") + # for key, value in config_dict.items(): + # print(key, ":", value) diff --git a/tools/mnncompress/mnncompress/pytorch/SNIP_level_pruner.py b/tools/mnncompress/mnncompress/pytorch/SNIP_level_pruner.py new file mode 100644 index 00000000..d6ad4b78 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/SNIP_level_pruner.py @@ -0,0 +1,229 @@ +from __future__ import print_function +import torch +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid + +class SNIPLevelPruner(object): + def __init__(self, model, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99): + self._model = model + if not isinstance(model, torch.nn.Module): + raise TypeError("model should be a torch.nn.Module instance") + if sparsity < 0 or sparsity > 1: + raise ValueError("sparsity should be a float number in [0, 1]") + if total_pruning_iterations < 1: + raise ValueError("total_pruning_iterations should be an integer >= 1") + + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._current_accumulate_step = 0 + self._total_accumulate_steps = 10 + self._step = 0 + self._finetune_step = 0 + self._config_file = config_file + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._debug = debug_info + self._pname_accgrad = {} + self._pname_mask = {} + self._config_dict = {} + self._generated_config_dict = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + def _init(self): + for module_name, module in self._model.named_modules(): + if (isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear)): + for n, p in module.named_parameters(): + if 'weight' == n: + pname = module_name + "." + n + if p.grad is None: + continue + self._pname_accgrad[pname] = torch.zeros_like(p.grad) + self._pname_mask[pname] = torch.ones_like(p.data) + + def current_prune_ratios(self): + prune_ratios = {} + total_weight_num = 0 + pruned_weight_num = 0 + for n, m in self._pname_mask.items(): + ratio = 1 - np.mean(m.cpu().numpy()) + prune_ratios[n] = ratio + total_weight_num += m.numel() + pruned_weight_num += int(m.numel() * ratio) + + prune_ratios['overall_prune_ratio'] = float(pruned_weight_num) / total_weight_num + + return prune_ratios + + def do_pruning(self, result_file = "SNIP_found_prune_ratios.yml"): + if self._current_accumulate_step == 0: + self._init() + + if self._current_accumulate_step < self._total_accumulate_steps: + print("SNIPLevelPruner: accumulating weight sensitivity...") + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + self._pname_accgrad[n] += torch.abs(p.grad * p.data) + + self._current_accumulate_step += 1 + + elif self._current_accumulate_step == self._total_accumulate_steps: + # named_parameters = dict(self._model.named_parameters()) + # for n, g in self._pname_accgrad.items(): + # self._pname_accgrad[n] = torch.abs(named_parameters[n]) * g + + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + self._generate_prune_ratios(config_dict) + + f = open(result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", result_file) + + self._current_accumulate_step += 1 + + else: + if self._step < self._total_pruning_iterations: + if self._prune_finetune_iterations == 0: + self._step += 1 + else: + if self._finetune_step == 0: + self._step += 1 + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + self._generate_prune_mask() + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + p.data.mul_(self._pname_mask[n]) + + if self._step == self._total_pruning_iterations: + self._step += 1 + + def save_compress_params(self, filename, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.RANDOM + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for n, m in self._pname_mask.items(): + # layer_names.append(op_name) + weight_tensor_names.append(n) + ratio = 1 - np.mean(m.cpu().numpy()) + self._total_weight_num += m.numel() + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (m.numel() * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + print(n, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "SNIP_Level", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("pytorch", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _generate_prune_ratios(self, config_dict = {}): + all_mask_grad_flatten = [] + for n, g in self._pname_accgrad.items(): + all_mask_grad_flatten.append(g.flatten()) + all_mask_grad_flatten = torch.cat(all_mask_grad_flatten) + all_mask_grad_flatten = torch.sort(all_mask_grad_flatten).values + threshold_index = int(all_mask_grad_flatten.numel() * self._target_sparsity) + threshold = all_mask_grad_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + nm = dict(self._model.named_modules()) + for n, g in self._pname_accgrad.items(): + mask = g > threshold + total_weight_num += mask.numel() + sparsity = 1.0 - np.mean(mask.cpu().numpy()) + self._generated_config_dict[n] = sparsity.tolist() + if n in config_dict.keys(): + self._config_dict[n] = config_dict[n] + else: + self._config_dict[n] = sparsity.tolist() + + if self._config_dict[n] > self._max_prune_ratio: + self._config_dict[n] = self._max_prune_ratio + + module_name = n[:-7] + if isinstance(nm[module_name], torch.nn.Conv2d) and nm[module_name].groups > 1: + self._config_dict[n] = 0.0 + + total_pruned_weight_num += int(mask.numel() * self._config_dict[n]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _generate_prune_mask(self): + if self._step > self._total_pruning_iterations: + return + + total_weight_num = 0 + total_pruned_weight_num = 0 + config_dict = {} + + for n, g in self._pname_accgrad.items(): + g_flat = g.flatten() + g_flat_argsort = torch.argsort(g_flat) + final_threshold_index = int(g_flat.numel() * self._config_dict[n]) + threshold_index = int(g_flat.numel() * (self._config_dict[n] - self._config_dict[n] * self._step / self._total_pruning_iterations)) + mask = torch.ones(g_flat.shape) + mask[g_flat_argsort[threshold_index:final_threshold_index]] = 0. + mask = mask.reshape(g.shape) + if self._debug: + total_weight_num += mask.numel() + total_pruned_weight_num += torch.sum((1 - mask).int()).cpu().numpy() + sparsity = 1.0 - np.mean(mask.cpu().numpy()) + config_dict[n] = sparsity.tolist() + self._pname_mask[n] = mask.to(g.device) + + if self._debug: + print("prune step:", self._step, "total prune steps:", self._total_pruning_iterations) + print("overall prune ratio:", float(total_pruned_weight_num) / total_weight_num) + print("pruning config:") + for key, value in config_dict.items(): + print(key, ":", value) diff --git a/tools/mnncompress/mnncompress/pytorch/TaylorFO_channel_pruner.py b/tools/mnncompress/mnncompress/pytorch/TaylorFO_channel_pruner.py new file mode 100644 index 00000000..1e1902af --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/TaylorFO_channel_pruner.py @@ -0,0 +1,271 @@ +from __future__ import print_function +import torch +try: + import torch.fx as fx +except ImportError: + print("need torch version >= 1.8.0, try 'pip install -U torch torchvision torchaudio' to upgrade torch") +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +from .utils import find_conv_bn_module_pairs, not_safe_to_prune_weights +import uuid + +class TaylorFOChannelPruner(object): + def __init__(self, model, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, align_channels=4): + self._model = model + if not isinstance(model, torch.nn.Module): + raise TypeError("model should be a torch.nn.Module instance") + if sparsity < 0 or sparsity > 1: + raise ValueError("sparsity should be a float number in [0, 1]") + if total_pruning_iterations < 1: + raise ValueError("total_pruning_iterations should be an integer >= 1") + + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._current_accumulate_step = 0 + self._total_accumulate_steps = 10 + self._step = 0 + self._finetune_step = 0 + self._config_file = config_file + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._debug = debug_info + self._pname_accgrad = {} + self._pname_kernel_sens = {} + self._pname_mask = {} + self._pname_bias = {} + self._pname_bn = {} + self._config_dict = {} + self._generated_config_dict = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._align_channels = align_channels + self._not_safe_weights = [] + # self._not_safe_weights = not_safe_to_prune_weights(model) + + def _init(self): + conv_bn_pairs = find_conv_bn_module_pairs(self._model) + named_parameters = dict(self._model.named_parameters()) + for module_name, module in self._model.named_modules(): + if (isinstance(module, torch.nn.Conv2d) or isinstance(module, torch.nn.Linear)): + for n, p in module.named_parameters(): + if 'weight' == n: + pname = module_name + "." + n + if p.grad is None: + continue + self._pname_accgrad[pname] = torch.zeros_like(p.grad) + self._pname_mask[pname] = torch.ones_like(torch.mean(p.data, [i for i in range(1, len(p.data.shape))], True)) + + bias_name = module_name + "." + "bias" + if bias_name in named_parameters.keys(): + self._pname_bias[pname] = named_parameters[bias_name] + + if module_name in conv_bn_pairs.keys(): + self._pname_bn[pname] = conv_bn_pairs[module_name] + + def current_prune_ratios(self): + prune_ratios = {} + total_weight_num = 0 + pruned_weight_num = 0 + for n, m in self._pname_mask.items(): + ratio = 1 - np.mean(m.cpu().numpy()) + prune_ratios[n] = ratio + total_weight_num += self._pname_accgrad[n].numel() + pruned_weight_num += int(self._pname_accgrad[n].numel() * ratio) + + prune_ratios['overall_prune_ratio'] = float(pruned_weight_num) / total_weight_num + + return prune_ratios + + def do_pruning(self, result_file = "TalayFO_found_prune_ratios.yml"): + if self._current_accumulate_step == 0: + self._init() + + if self._current_accumulate_step < self._total_accumulate_steps: + print("TaylorFOChannelPruner: accumulating weight sensitivity...") + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + self._pname_accgrad[n] += torch.abs(p.grad * p.data) + + self._current_accumulate_step += 1 + + elif self._current_accumulate_step == self._total_accumulate_steps: + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + self._generate_prune_ratios(config_dict) + + f = open(result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", result_file) + + self._current_accumulate_step += 1 + + else: + if self._step < self._total_pruning_iterations: + if self._prune_finetune_iterations == 0: + self._step += 1 + else: + if self._finetune_step == 0: + self._step += 1 + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + self._generate_prune_mask() + for n, p in self._model.named_parameters(): + if n in self._pname_accgrad.keys(): + p.data.mul_(self._pname_mask[n]) + + if n in self._pname_bias.keys(): + self._pname_bias[n].data.mul_(torch.flatten(self._pname_mask[n])) + + if n in self._pname_bn.keys(): + bn_module_name = self._pname_bn[n] + bn = dict(self._model.named_modules())[bn_module_name] + if bn.weight is not None: + bn.weight.data.mul_(torch.flatten(self._pname_mask[n])) + bn.bias.data.mul_(torch.flatten(self._pname_mask[n])) + if bn.running_mean is not None: + bn.running_mean = bn.running_mean * torch.flatten(self._pname_mask[n]) + + if self._step == self._total_pruning_iterations: + self._step += 1 + + def save_compress_params(self, filename, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.FILTER + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for n, m in self._pname_mask.items(): + # layer_names.append(op_name) + weight_tensor_names.append(n) + ratio = 1 - np.mean(m.cpu().numpy()) + self._total_weight_num += self._pname_accgrad[n].numel() + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (self._pname_accgrad[n].numel() * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + print(n, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "TaylorFO_channel", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("pytorch", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _generate_prune_ratios(self, config_dict = {}): + for n, g in self._pname_accgrad.items(): + self._pname_kernel_sens[n] = torch.sum(g*g, [i for i in range(1, len(g.shape))], True) + + all_mask_grad_flatten = [] + for n, g in self._pname_kernel_sens.items(): + all_mask_grad_flatten.append(g.flatten()) + all_mask_grad_flatten = torch.cat(all_mask_grad_flatten) + all_mask_grad_flatten = torch.sort(all_mask_grad_flatten).values + threshold_index = int(all_mask_grad_flatten.numel() * self._target_sparsity) + threshold = all_mask_grad_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + nm = dict(self._model.named_modules()) + for n, g in self._pname_kernel_sens.items(): + mask = g >= threshold + total_weight_num += self._pname_accgrad[n].numel() + sparsity = 1.0 - np.mean(mask.cpu().numpy()) + self._generated_config_dict[n] = sparsity.tolist() + if n in config_dict.keys(): + self._config_dict[n] = config_dict[n] + else: + self._config_dict[n] = sparsity.tolist() + + if self._config_dict[n] > self._max_prune_ratio: + self._config_dict[n] = self._max_prune_ratio + + if n in self._not_safe_weights: + self._config_dict[n] = 0.0 + + module_name = n[:-7] + if isinstance(nm[module_name], torch.nn.Conv2d) and nm[module_name].groups > 1: + self._config_dict[n] = 0.0 + + remain_channels = int(mask.numel() * (1.0 - self._config_dict[n])) + if remain_channels != mask.numel(): + remain_channels = remain_channels - remain_channels % self._align_channels + if remain_channels <= 0: + remain_channels = min(self._align_channels, mask.numel()) + + self._config_dict[n] = (mask.numel() - remain_channels + 0.0) / mask.numel() + + total_pruned_weight_num += int(self._pname_accgrad[n].numel() * self._config_dict[n]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _generate_prune_mask(self): + if self._step > self._total_pruning_iterations: + return + + total_weight_num = 0 + total_pruned_weight_num = 0 + config_dict = {} + + for n, g in self._pname_kernel_sens.items(): + g_flat = g.flatten() + g_flat_argsort = torch.argsort(g_flat) + final_threshold_index = int(g_flat.numel() * self._config_dict[n]) + threshold_index = int(g_flat.numel() * (self._config_dict[n] - self._config_dict[n] * self._step / self._total_pruning_iterations)) + mask = torch.ones(g_flat.shape) + mask[g_flat_argsort[threshold_index:final_threshold_index]] = 0. + mask = mask.reshape(g.shape) + if self._debug: + total_weight_num += mask.numel() + total_pruned_weight_num += torch.sum((1 - mask).int()).cpu().numpy() + sparsity = 1.0 - np.mean(mask.cpu().numpy()) + config_dict[n] = sparsity.tolist() + self._pname_mask[n] = mask.to(g.device) + + if self._debug: + print("prune step:", self._step, "total prune steps:", self._total_pruning_iterations) + print("overall prune ratio:", float(total_pruned_weight_num) / total_weight_num) + print("pruning config:") + for key, value in config_dict.items(): + print(key, ":", value) diff --git a/tools/mnncompress/mnncompress/pytorch/__init__.py b/tools/mnncompress/mnncompress/pytorch/__init__.py new file mode 100644 index 00000000..448746ac --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/__init__.py @@ -0,0 +1,7 @@ +from .SNIP_level_pruner import SNIPLevelPruner +from .SIMD_OC_pruner import SIMDOCPruner +from .TaylorFO_channel_pruner import TaylorFOChannelPruner +from .LSQ_quantizer import LSQQuantizer +from .decomposition import low_rank_decompose +from .linear_over_param import LOP +from .weight_quantizer import WeightQuantizer diff --git a/tools/mnncompress/mnncompress/pytorch/compute_new_weights.py b/tools/mnncompress/mnncompress/pytorch/compute_new_weights.py new file mode 100644 index 00000000..89246ec1 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/compute_new_weights.py @@ -0,0 +1,284 @@ +""" +Functions used to contract ExpandNets back to SmallNet + +- from_expandnet_cl_to_snet: from ExpandNet-CL/ExpandNet-FC/ExpandNet-CL+FC to SmallNet +- from_expandnet_ck_to_snet: from ExpandNet-CK/ExpandNet-FC/ExpandNet-CK+FC to SmallNet + +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np + + +def compute_cl(s_1, s_2): + """ + Compute weights from s_1 and s_2 + :param s_1: 1*1 conv layer + :param s_2: 3*3 conv layer + :return: new weight and bias + """ + if isinstance(s_1, nn.Conv2d): + w_s_1 = s_1.weight # output# * input# * kernel * kernel + b_s_1 = s_1.bias + else: + w_s_1 = s_1['weight'] + b_s_1 = s_1['bias'] + if isinstance(s_2, nn.Conv2d): + w_s_2 = s_2.weight + b_s_2 = s_2.bias + else: + w_s_2 = s_2['weight'] + b_s_2 = s_2['bias'] + + w_s_1_ = w_s_1.view(w_s_1.size(0), w_s_1.size(1) * w_s_1.size(2) * w_s_1.size(3)) + w_s_2_tmp = w_s_2.view(w_s_2.size(0), w_s_2.size(1), w_s_2.size(2) * w_s_2.size(3)) + + new_weight = torch.Tensor(w_s_2.size(0), w_s_1.size(1), w_s_2.size(2)*w_s_2.size(3)).to(w_s_1.device) + for i in range(w_s_2.size(0)): + tmp = w_s_2_tmp[i, :, :].view( w_s_2.size(1), w_s_2.size(2) * w_s_2.size(3)) + new_weight[i, :, :] = torch.matmul(w_s_1_.t(), tmp) + new_weight = new_weight.view(w_s_2.size(0), w_s_1.size(1), w_s_2.size(2), w_s_2.size(3)) + + if b_s_1 is not None and b_s_2 is not None: + b_sum = torch.sum(w_s_2_tmp, dim=2) + new_bias = torch.matmul(b_sum, b_s_1) + b_s_2 # with bias + elif b_s_1 is None: + new_bias = b_s_2 #without Bias + else: + new_bias = None + + return {'weight': new_weight, 'bias': new_bias} + + +def compute_cl_2(s_1, s_2): + """ + compute weights from former computation and last 1*1 conv layer + :param s_1: 3*3 conv layer + :param s_2: 1*1 conv layer + :return: new weight and bias + """ + if isinstance(s_1, nn.Conv2d): + w_s_1 = s_1.weight # output# * input# * kernel * kernel + b_s_1 = s_1.bias + else: + w_s_1 = s_1['weight'] + b_s_1 = s_1['bias'] + if isinstance(s_2, nn.Conv2d): + w_s_2 = s_2.weight + b_s_2 = s_2.bias + else: + w_s_2 = s_2['weight'] + b_s_2 = s_2['bias'] + + w_s_1_ = w_s_1.view(w_s_1.size(0), w_s_1.size(1), w_s_1.size(2) * w_s_1.size(3)) + w_s_2_ = w_s_2.view(w_s_2.size(0), w_s_2.size(1) * w_s_2.size(2) * w_s_2.size(3)) + new_weight_ = torch.Tensor(w_s_2.size(0), w_s_1.size(1), w_s_1.size(2)*w_s_1.size(3)).to(w_s_1.device) + for i in range(w_s_1.size(1)): + tmp = w_s_1_[:, i, :].view(w_s_1.size(0), w_s_1.size(2) * w_s_1.size(3)) + new_weight_[:, i, :] = torch.matmul(w_s_2_, tmp) + new_weight = new_weight_.view(w_s_2.size(0), w_s_1.size(1), w_s_1.size(2), w_s_1.size(3)) + + if b_s_1 is not None and b_s_2 is not None: + new_bias = torch.matmul(w_s_2_, b_s_1) + b_s_2 # with bias + elif b_s_1 is None: + new_bias = b_s_2 #without Bias + else: + new_bias = None + + return {'weight': new_weight, 'bias': new_bias} + +def compute_ck(s_1, s_2): + """ + Compute weight from 2 conv layers, whose kernel size larger than 3*3 + After derivation, F.conv_transpose2d can be used to compute weight of original conv layer + :param s_1: 3*3 or larger conv layer + :param s_2: 3*3 or larger conv layer + :return: new weight and bias + """ + if isinstance(s_1, nn.Conv2d): + w_s_1 = s_1.weight # output# * input# * kernel * kernel + b_s_1 = s_1.bias + else: + w_s_1 = s_1['weight'] + b_s_1 = s_1['bias'] + if isinstance(s_2, nn.Conv2d): + w_s_2 = s_2.weight + b_s_2 = s_2.bias + else: + w_s_2 = s_2['weight'] + b_s_2 = s_2['bias'] + + w_s_2_tmp = w_s_2.view(w_s_2.size(0), w_s_2.size(1), w_s_2.size(2) * w_s_2.size(3)) + + if b_s_1 is not None and b_s_2 is not None: + b_sum = torch.sum(w_s_2_tmp, dim=2) + new_bias = torch.matmul(b_sum, b_s_1) + b_s_2 + elif b_s_1 is None: + new_bias = b_s_2 #without Bias + else: + new_bias = None + + new_weight = F.conv_transpose2d(w_s_2, w_s_1) + + return {'weight': new_weight, 'bias': new_bias} + + +def compute_fc(s_1, s_2): + """ + Compute weight from 2 fc layers + :param s_1: p * n + :param s_2: m * p + :return: new weight m*n and bias + """ + if isinstance(s_1, nn.Linear): + w_s_1 = s_1.weight + b_s_1 = s_1.bias + else: + w_s_1 = s_1['weight'] + b_s_1 = s_1['bias'] + if isinstance(s_2, nn.Linear): + w_s_2 = s_2.weight + b_s_2 = s_2.bias + else: + w_s_2 = s_2['weight'] + b_s_2 = s_2['bias'] + + if b_s_1 is not None and b_s_2 is not None: + new_bias = torch.matmul(w_s_2, b_s_1) + b_s_2 + elif b_s_1 is None: + new_bias = b_s_2 #without Bias + else: + new_bias = None + + new_weight = torch.matmul(w_s_2, w_s_1) + + return {'weight': new_weight, 'bias': new_bias} + +def from_expandnet_cl_identity_to_snet(Enet, Snet, exp_layer_names=['conv0'], identity_layer_names=[]): + Edict = Enet.state_dict() + Sdict = Snet.state_dict() + Edict = {k: v for k, v in Edict.items() if (k in Sdict and k not in exp_layer_names)} + + Sdict.update(Edict) + Snet.load_state_dict(Sdict) + if exp_layer_names is not None: + for m in exp_layer_names: + print(m) + conv1_1 = getattr(Enet, m)[0] + conv1_2 = getattr(Enet, m)[1] + conv1_3 = getattr(Enet, m)[2] + if m.find('conv') != -1 or m.find('concat_out') != -1: + tmp = compute_cl(conv1_1, conv1_2) + tmp = compute_cl_2(tmp, conv1_3) + print('passed') + else: + tmp = compute_fc(conv1_1, conv1_2) + tmp = compute_fc(tmp, conv1_3) + + if m in identity_layer_names: + tmp = fuse_identity(tmp) + + conv1 = getattr(Snet, m) + if conv1.bias is not None: + conv1.weight.data, conv1.bias.data = tmp['weight'], tmp['bias'] + else: + conv1.weight.data = tmp['weight'] + + return Snet + +def fuse_identity(conv): + kernel = conv['weight'] + + #in_channel is kernel size 1 + in_channels = kernel.size(1) + kernel_size = kernel.size(2) + #bias = conv.bias + #kernel_value = np.zeros((in_channels, in_channels, 3, 3), dtype=np.float32) + kernel_value = np.zeros((in_channels, in_channels, kernel_size, kernel_size), dtype=np.float32) + for i in range(in_channels): + kernel_value[i, i % in_channels, 1, 1] = 1 + #id_tensor = torch.from_numpy(kernel_value).to(conv.weight.device) + id_tensor = torch.from_numpy(kernel_value).to(kernel.device) + conv['weight'] = kernel + id_tensor + + return conv + +def fuse_add_conv(s_1, s_2): + if isinstance(s_1, nn.Conv2d): + w_s_1 = s_1.weight # output# * input# * kernel * kernel + b_s_1 = s_1.bias + else: + w_s_1 = s_1['weight'] + b_s_1 = s_1['bias'] + if isinstance(s_2, nn.Conv2d): + w_s_2 = s_2.weight + b_s_2 = s_2.bias + else: + w_s_2 = s_2['weight'] + b_s_2 = s_2['bias'] + + conv = dict() + conv['weight'] = w_s_1 + w_s_2 + conv['bias'] = b_s_1 + b_s_2 + + return conv + + +def from_expandnet_cl_to_snet(Enet, Snet, exp_layer_names=['conv0']): + Edict = Enet.state_dict() + Sdict = Snet.state_dict() + Edict = {k: v for k, v in Edict.items() if (k in Sdict and k not in exp_layer_names)} + + Sdict.update(Edict) + Snet.load_state_dict(Sdict) + if exp_layer_names is not None: + for m in exp_layer_names: + print(m) + conv1_1 = getattr(Enet, m)[0] + conv1_2 = getattr(Enet, m)[1] + conv1_3 = getattr(Enet, m)[2] + if m.find('conv') != -1 or m.find('concat_out') != -1: + tmp = compute_cl(conv1_1, conv1_2) + tmp = compute_cl_2(tmp, conv1_3) + print('passed') + else: + tmp = compute_fc(conv1_1, conv1_2) + tmp = compute_fc(tmp, conv1_3) + + conv1 = getattr(Snet, m) + if conv1.bias is not None: + conv1.weight.data, conv1.bias.data = tmp['weight'], tmp['bias'] + else: + conv1.weight.data = tmp['weight'] + + return Snet + + +def from_expandnet_ck_to_snet(Enet, Snet, exp_layer_names=['conv0']): + Edict = Enet.state_dict() + Sdict = Snet.state_dict() + Edict = {k: v for k, v in Edict.items() if (k in Sdict and k not in exp_layer_names)} + + Sdict.update(Edict) + Snet.load_state_dict(Sdict) + + if exp_layer_names is not None: + for m in exp_layer_names: + layers = getattr(Enet, m) + tmp = layers[0] + for i in range(len(layers)-1): + conv1_2 = layers[i+1] + if m.find('conv') != -1: + tmp = compute_ck(tmp, conv1_2) + else: + tmp = compute_fc(tmp, conv1_2) + + conv1 = getattr(Snet, m) + if conv1.bias is not None: + conv1.weight.data, conv1.bias.data = tmp['weight'], tmp['bias'] + else: + conv1.weight.data = tmp['weight'] + + return Snet diff --git a/tools/mnncompress/mnncompress/pytorch/decomposition.py b/tools/mnncompress/mnncompress/pytorch/decomposition.py new file mode 100644 index 00000000..d1d95a68 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/decomposition.py @@ -0,0 +1,160 @@ +from __future__ import print_function +import copy +import os +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import tensorly as tl +import scipy +from tensorly.decomposition import partial_tucker +from mnncompress.common import VBMF +import mnncompress.common.MNN_compression_pb2 as compress_pb +from .utils import get_module_parameter_num +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_align_channels +import uuid + + +def low_rank_decompose(model, compress_params_file, skip_layers=[""], align_channels=8, in_place=False, tucker_minimal_ratio=0.25, reserved_singular_value_ratio=0.5, append=False): + origin_params_num = get_module_parameter_num(model) + decompose_model = model + if not in_place: + decompose_model = copy.deepcopy(model) + + compress_proto = compress_pb.Pipeline() + if append: + f = open(compress_params_file, 'rb') + compress_proto.ParseFromString(f.read()) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + model_guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = model_guid + else: + model_guid = compress_proto.mnn_uuid + + f = open(compress_params_file, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + def _decompose_module(module, name=""): + for n, m in module.named_children(): + m_name = name + "." + n + if name == "": + m_name = n + if not isinstance(m, (nn.Conv2d, nn.Linear)): + _decompose_module(m, m_name) + else: + if m_name in skip_layers: + print("skip decomposition:", m_name) + continue + + if isinstance(m, nn.Conv2d) and m.groups == 1 and m.kernel_size != (1, 1): + weight = m.weight.data.detach().cpu().numpy() + + if m.in_channels <= align_channels or m.out_channels <= align_channels: + print("skip tucker for:", m_name, "weight shape:", weight.shape) + continue + + u0 = tl.base.unfold(weight, 0) + u1 = tl.base.unfold(weight, 1) + res0 = VBMF.EVBMF(u0) + res1 = VBMF.EVBMF(u1) + rank0 = get_align_channels(res0[1].shape[0], m.out_channels, align_channels, tucker_minimal_ratio) + rank1 = get_align_channels(res1[1].shape[1], m.in_channels, align_channels, tucker_minimal_ratio) + ranks = [rank0, rank1] + + core, [last, first] = partial_tucker(weight, modes=[0, 1], rank=ranks, init='svd') + print("tucker for", m_name, ":", [m.in_channels, m.out_channels], "<===>", [core.shape[1], core.shape[0]], "ranks:", ranks) + + has_bias = True + if m.bias is None: + has_bias = False + + first_layer = nn.Conv2d(in_channels=first.shape[0], \ + out_channels=first.shape[1], kernel_size=1, + stride=1, padding=0, bias=False) + + core_layer = nn.Conv2d(in_channels=core.shape[1], \ + out_channels=core.shape[0], kernel_size=m.kernel_size, + stride=m.stride, padding=m.padding, dilation=m.dilation, + bias=False) + + last_layer = nn.Conv2d(in_channels=last.shape[1], \ + out_channels=last.shape[0], kernel_size=1, stride=1, + padding=0, bias=has_bias) + + if has_bias: + last_layer.bias.data = m.bias.data + + first_layer.weight.data = torch.transpose(torch.Tensor(first.copy()), 1, 0).unsqueeze(-1).unsqueeze(-1) + last_layer.weight.data = torch.Tensor(last.copy()).unsqueeze(-1).unsqueeze(-1) + core_layer.weight.data = torch.Tensor(core.copy()) + + # first_bn = nn.BatchNorm2d(first_layer.out_channels) + # core_bn = nn.BatchNorm2d(core_layer.out_channels) + # last_bn = nn.BatchNorm2d(last_layer.out_channels) + + decomposed_layers = [first_layer, core_layer, last_layer] + module.__setattr__(n, nn.Sequential(*decomposed_layers)) + + if isinstance(m, nn.Linear) or (isinstance(m, nn.Conv2d) and m.kernel_size == (1, 1) and m.stride == (1, 1) and m.padding == (0, 0) and m.dilation == (1, 1) and m.groups == 1): + weight = m.weight.data.detach().cpu().numpy() + squeeze_shape = weight.squeeze().shape + if len(squeeze_shape) != 2: + print("skip svd for", m_name, "weight shape:", weight.shape) + continue + + if squeeze_shape[0] <= align_channels or squeeze_shape[1] <= align_channels: + print("skip svd for", m_name, "weight shape:", weight.shape) + continue + + u, s, v = scipy.linalg.svd(weight.squeeze()) + singular_value_sum = np.sum(s) + n_dim = 1 + temp_sum = 0.0 + for i in range(0, s.size): + temp_sum += s[i] + n_dim = i+1 + if temp_sum / singular_value_sum >= reserved_singular_value_ratio: + break + n_dim = get_align_channels(n_dim, s.size, align_channels) + + has_bias = True + if m.bias is None: + has_bias = False + + if isinstance(m, nn.Conv2d): + print("svd for", m_name, ":", [m.in_channels, m.out_channels], "<===>", [m.in_channels, n_dim, m.out_channels]) + fc1_weight = (np.matmul(np.diag(s[0:n_dim]), v[0:n_dim, :])).reshape((n_dim, -1, 1, 1)) + fc2_weight = u[:, 0:n_dim].reshape((-1, n_dim, 1, 1)) + fc1 = nn.Conv2d(m.in_channels, n_dim, 1, bias=False) + fc2 = nn.Conv2d(n_dim, m.out_channels, 1, bias=has_bias) + else: + print("svd for", m_name, ":", [m.in_features, m.out_features], "<===>", [m.in_features, n_dim, m.out_features]) + fc1_weight = np.matmul(np.diag(s[0:n_dim]), v[0:n_dim, :]) + fc2_weight = u[:, 0:n_dim] + fc1 = nn.Linear(m.in_features, n_dim, bias=False) + fc2 = nn.Linear(n_dim, m.out_features, bias=has_bias) + + fc1.weight.data = torch.Tensor(fc1_weight.copy()) + fc2.weight.data = torch.Tensor(fc2_weight.copy()) + + if has_bias: + fc2.bias.data = m.bias.data + + decomposed_layers = [fc1, fc2] + module.__setattr__(n, nn.Sequential(*decomposed_layers)) + + _decompose_module(decompose_model) + + decompose_model_params_num = get_module_parameter_num(decompose_model) + + detail = {"algorithm": "low_rank_decompose", "compression_rate": origin_params_num / decompose_model_params_num, \ + "ori_model_size": origin_params_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"skip_layers": skip_layers, "align_channels": align_channels, "tucker_minimal_ratio": tucker_minimal_ratio, "reserved_singular_value_ratio": reserved_singular_value_ratio}} + + mnn_logger.on_done("pytorch", model_guid, detail) + + return decompose_model diff --git a/tools/mnncompress/mnncompress/pytorch/linear_over_param.py b/tools/mnncompress/mnncompress/pytorch/linear_over_param.py new file mode 100644 index 00000000..558ce095 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/linear_over_param.py @@ -0,0 +1,136 @@ +from __future__ import print_function, with_statement +import copy +import os +from numpy import add +import torch +import torch.nn as nn +import torch.nn.functional as F +from .compute_new_weights import compute_cl, compute_cl_2, fuse_identity +import mnncompress.common.MNN_compression_pb2 as compress_pb +from .utils import get_module_parameter_num +from mnncompress.common.log import mnn_logger +import uuid + + +class _ExpandLayerWithBypass(nn.Module): + def __init__(self, conv2dm, expand_rate, add_batchnorm, add_bypass): + super().__init__() + self.first = nn.Conv2d(conv2dm.in_channels, conv2dm.in_channels*expand_rate, 1, padding=conv2dm.padding) + self.middle = nn.Conv2d(conv2dm.in_channels*expand_rate, conv2dm.out_channels*expand_rate, conv2dm.kernel_size, stride=conv2dm.stride, dilation=conv2dm.dilation) + self.last = nn.Conv2d(conv2dm.out_channels*expand_rate, conv2dm.out_channels, 1) + self.with_bypass = False + self.add_batchnorm = add_batchnorm + self.add_bypass = add_bypass + + if add_batchnorm: + self.first_bn = nn.BatchNorm2d(conv2dm.in_channels*expand_rate) + self.middle_bn = nn.BatchNorm2d(conv2dm.out_channels*expand_rate) + + def forward(self, x_input): + x = self.first(x_input) + if self.add_batchnorm: + x = self.first_bn(x) + + x = self.middle(x) + if self.add_batchnorm: + x = self.middle_bn(x) + + x = self.last(x) + + if self.add_bypass and (self.with_bypass or (x.shape == x_input.shape)): + x = x + x_input + self.with_bypass = True + + return x + +class LOP(object): + def __init__(self, model): + self._expand_model = copy.deepcopy(model) + self._merged_model = None + self._module_expand_attr = {} + self._expand_rate = 1 + self._add_bn = True + self._add_bypass = True + + def linear_merge_layers(self): + self._merged_model = copy.deepcopy(self._expand_model) + nm = dict(self._merged_model.named_modules()) + for n, m in nm.items(): + if isinstance(m, _ExpandLayerWithBypass): + conv_name = n.split(".")[-1] + parent_module = nm[n[0:-(len(conv_name)+1)]] + expanded_layer = parent_module.__getattr__(conv_name) + first = expanded_layer.first + middle = expanded_layer.middle + last = expanded_layer.last + with_bypass = expanded_layer.with_bypass + + if self._add_bn: + first_bn = expanded_layer.first_bn + middle_bn = expanded_layer.middle_bn + + first.weight.data = first.weight * first_bn.weight.reshape((-1, 1, 1, 1)) / torch.sqrt(first_bn.running_var + first_bn.eps).reshape((-1, 1, 1, 1)) + first.bias.data = (first.bias - first_bn.running_mean) * first_bn.weight / torch.sqrt(first_bn.running_var + first_bn.eps) + first_bn.bias + middle.weight.data = middle.weight * middle_bn.weight.reshape((-1, 1, 1, 1)) / torch.sqrt(middle_bn.running_var + middle_bn.eps).reshape((-1, 1, 1, 1)) + middle.bias.data = (middle.bias - middle_bn.running_mean) * middle_bn.weight / torch.sqrt(middle_bn.running_var + middle_bn.eps) + middle_bn.bias + + merged_layer = nn.Conv2d(first.in_channels, last.out_channels, middle.kernel_size, stride=middle.stride, padding=first.padding, dilation=middle.dilation, bias=lambda: True if last.bias is not None else False) + temp = compute_cl(first, middle) + wb = compute_cl_2(temp, last) + if with_bypass: + wb = fuse_identity(wb) + merged_layer.weight.data = wb['weight'] + if merged_layer.bias is not None: + merged_layer.bias.data = wb['bias'] + + parent_module.__setattr__(conv_name, merged_layer.to(first.weight.device)) + + return self._merged_model + + def linear_expand_layers(self, expand_rate, compress_params_file, add_batchnorm=True, add_bypass=True, append=False): + self._expand_rate = expand_rate + self._add_bn = add_batchnorm + self._add_bypass = add_bypass + + compress_proto = compress_pb.Pipeline() + if append: + f = open(compress_params_file, 'rb') + compress_proto.ParseFromString(f.read()) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + model_guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = model_guid + else: + model_guid = compress_proto.mnn_uuid + + f = open(compress_params_file, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + origin_params_num = get_module_parameter_num(self._expand_model) + + def _expand_module(module, name=""): + for n, m in module.named_children(): + m_name = name + "." + n + if name == "": + m_name = n + if not isinstance(m, (nn.Conv2d, nn.Linear)): + _expand_module(m, m_name) + else: + if isinstance(m, nn.Conv2d) and m.groups == 1 and m.kernel_size != (1, 1): + expanded_layer = _ExpandLayerWithBypass(m, expand_rate, add_batchnorm, add_bypass).to(m.weight.device) + module.__setattr__(n, expanded_layer) + self._module_expand_attr[module] = n + + _expand_module(self._expand_model) + + expand_model_params_num = get_module_parameter_num(self._expand_model) + + detail = {"algorithm": "linear_over_param", "compression_rate": expand_model_params_num / origin_params_num, \ + "expand_model_size": expand_model_params_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"expand_rate": expand_rate, "add_batchnorm": add_batchnorm, "add_bypass": add_bypass}} + + mnn_logger.on_done("pytorch", model_guid, detail) + + return self._expand_model diff --git a/tools/mnncompress/mnncompress/pytorch/utils.py b/tools/mnncompress/mnncompress/pytorch/utils.py new file mode 100644 index 00000000..ae9cdcbd --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/utils.py @@ -0,0 +1,109 @@ +from __future__ import print_function +from numpy import isin +import torch +import torch.fx +import torch.nn as nn +import torch.nn.functional as F + +def find_conv_bn_module_pairs(model): + gm = torch.fx.symbolic_trace(model) + nodes = list(gm.graph.nodes) + nm = dict(model.named_modules()) + + conv_bn_pairs = {} + + for node in nodes: + if node.target in nm.keys() and isinstance(nm[node.target], torch.nn.Conv2d): + if len(node.users) == 1: + user_node = [n for n in node.users.keys()][0] + if user_node.target in nm.keys() and isinstance(nm[user_node.target], torch.nn.BatchNorm2d): + conv2d_name = node.target + bn_name = user_node.target + conv_bn_pairs[conv2d_name] = bn_name + + return conv_bn_pairs + + +def find_conv_or_linear_output_node(graph_module, conv_or_linear_node): + gm = graph_module + nm = dict(gm.named_modules()) + output_node = conv_or_linear_node + bn_name = None + + # TODO: linear's bn or relu not supported + if conv_or_linear_node.target is F.linear: + return output_node, bn_name + + if len(output_node.users) > 1: + return output_node, bn_name + + node = [n for n in output_node.users.keys()][0] + has_bn = node.target in nm.keys() and isinstance(nm[node.target], torch.nn.BatchNorm2d) + if has_bn: + bn = nm[node.target] + if bn.running_var is None: + return output_node, bn_name + + output_node = node + bn_name = node.target + if len(output_node.users) > 1: + return output_node, bn_name + + node = [n for n in output_node.users.keys()][0] + has_relu = node.target is torch.relu or node.target is F.relu + has_relu = has_relu or (node.op == "call_method" and node.target in ["relu", "relu_"]) + # TODO: mnn not support this relu6 yet + # has_relu6 = node.target is F.relu6 + has_relu6 = False + + if node.op == "call_module": + if isinstance(nm[node.target], nn.ReLU): + has_relu = True + + if isinstance(nm[node.target], nn.ReLU6): + has_relu6 = True + + if has_relu or has_relu6: + output_node = node + return output_node, bn_name + + return output_node, bn_name + + +def get_module_parameter_num(module): + nm = dict(module.named_modules()) + count_types=(nn.Conv2d, nn.Linear) + + num_params = 0 + for n, m in nm.items(): + if isinstance(m, count_types): + if m.weight is not None: + num_params += m.weight.numel() + if m.bias is not None: + num_params += m.bias.numel() + + return num_params + + +chanel_prune_safe_ops = (nn.Conv2d, nn.Dropout) + +def not_safe_to_prune_weights(model, safe_ops=chanel_prune_safe_ops): + gm = torch.fx.symbolic_trace(model) + nodes = list(gm.graph.nodes) + nm = dict(model.named_modules()) + not_safe_weights = [] + + for node in nodes: + if node.target in nm.keys() and isinstance(nm[node.target], torch.nn.Conv2d): + output_node, _ = find_conv_or_linear_output_node(gm, node) + user_nodes = [n for n in output_node.users.keys()] + for user_node in user_nodes: + print("pairs:", node.target, user_node.target, user_node.target in nm.keys() and isinstance(nm[user_node.target], safe_ops)) + if not (user_node.target in nm.keys() and isinstance(nm[user_node.target], safe_ops)): + name = node.target + ".weight" + not_safe_weights.append(name) + + if node.target in nm.keys() and isinstance(nm[node.target], torch.nn.Linear): + raise ValueError("don't support nn.Linear, please use (reshape(x, [-1, len, 1, 1]) --> 1x1 Conv2d --> ... --> flatten(x, 1)) combination instead") + + return not_safe_weights diff --git a/tools/mnncompress/mnncompress/pytorch/weight_quantizer.py b/tools/mnncompress/mnncompress/pytorch/weight_quantizer.py new file mode 100644 index 00000000..23b0c774 --- /dev/null +++ b/tools/mnncompress/mnncompress/pytorch/weight_quantizer.py @@ -0,0 +1,309 @@ +from __future__ import print_function, with_statement +import copy +import numpy as np +from onnx import numpy_helper +import torch +try: + import torch.fx as fx +except ImportError: + print("need torch version >= 1.8.0, try 'pip install -U torch torchvision torchaudio' to upgrade torch") +from torch.fx import symbolic_trace +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair +import onnx +import mnncompress.common.MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid + + +_Supported_Modules = (torch.nn.Conv2d, torch.nn.Linear) +_MNN_Quant_prefix = "mnn_quant_" +_eps = 1e-6 + + +class _Conv2dWeightFakeQuantModule(nn.Module): + def __init__(self, prune_mask, clamp_value = 127.0): + super().__init__() + self._clamp_value = clamp_value + self._prune_mask = nn.Parameter(prune_mask, requires_grad=False) + + def forward(self, w): + w.data = w.data * self._prune_mask + abs_w = torch.abs(w) + max3 = torch.max(abs_w, 3, keepdim=True) + max2 = torch.max(max3.values, 2, keepdim=True) + max1 = torch.max(max2.values, 1, keepdim=True) + scale = max1.values / self._clamp_value + _eps + + quanted = w / scale + rounded = torch.detach(torch.round(quanted + torch.sign(quanted)*_eps) - quanted) + quanted + clamped = torch.clamp(rounded, -self._clamp_value, self._clamp_value) + dequanted = clamped * scale + + return dequanted + +class _LinearWeightFakeQuantModule(nn.Module): + def __init__(self, prune_mask, clamp_value = 127.0): + super().__init__() + self._clamp_value = clamp_value + self._prune_mask = nn.Parameter(prune_mask, requires_grad=False) + + def forward(self, w): + w.data = w.data * self._prune_mask + abs_w = torch.abs(w) + max1 = torch.max(abs_w, 1, keepdim=True) + scale = max1.values / self._clamp_value + _eps + + quanted = w / scale + rounded = torch.detach(torch.round(quanted + torch.sign(quanted)*_eps) - quanted) + quanted + clamped = torch.clamp(rounded, -self._clamp_value, self._clamp_value) + dequanted = clamped * scale + + return dequanted + + +class WeightQuantizer(object): + def __init__(self, model, bits = 8, debug_info = False, mode = 'symmetric'): + self._model = copy.deepcopy(model) + self._gm = symbolic_trace(model) + + nm = dict(model.named_modules()) + supported_nm = {n:m for n, m in nm.items() if isinstance(m, _Supported_Modules)} + + # self._skip_quant_types = skip_quant_types + if bits < 2 or bits > 8: + raise ValueError("bits must be a integer in [2, 8]") + self._bits = bits + self._clamp_value = float(pow(2, bits-1) - 1) + # self._layer_weight_bits_config = layer_weight_bits_config + self._debug = debug_info + + if mode not in ['symmetric', 'asymmetric']: + raise ValueError("mode should be 'symmetric' or 'asymmetric'") + # TODO: support asymmetric + self._mode = mode + + self._pname_mask = {} + self._quant_module_nodes = [] + self._quant_func_nodes = [] + self._init_prune_ratios = {} + self._ori_prune_weights = {} + self._weight_node_ori_args = {} + self._quant_user_nodes_update_args = {} + self._wq_ops_stripped = False + self._weight_quant_info_map = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + self._init() + + def save_compress_params(self, filename, append=False): + nm = dict(self._gm.named_modules()) + named_params = dict(self._gm.named_parameters()) + + compress_proto = compress_pb.Pipeline() + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + + if not self._reported: + detail = {"algorithm": "WQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self._bits, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("pytorch", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def strip_wq_ops(self): + for node, index_weight_node in self._weight_node_ori_args.items(): + ori_args = list(node.args) + for index, ori_node in index_weight_node: + ori_args[index] = ori_node + node.args = tuple(ori_args) + + self._gm.recompile() + self._wq_ops_stripped = True + + def resume_wq_graph(self): + if not self._wq_ops_stripped: + return + + for node, new_args in self._quant_user_nodes_update_args.items(): + node.args = new_args + + self._gm.recompile() + self._wq_ops_stripped = False + + def _current_prune_ratios(self): + prune_ratios = {} + for n, m in self._ori_prune_weights.items(): + ratio = 1 - np.mean(np.abs(m.cpu().numpy()) > _eps) + prune_ratios[n] = ratio + return prune_ratios + + def _init(self): + for module_name, module in self._model.named_modules(): + if isinstance(module, _Supported_Modules): + for n, p in module.named_parameters(): + if 'weight' == n: + pname = module_name + "." + n + mask = (torch.abs(p.data) > _eps).float() + self._pname_mask[pname] = mask + self._ori_prune_weights[pname] = p.data + self._init_prune_ratios[pname] = 1 - torch.mean(mask).cpu().numpy().tolist() + + print("init pruning ratios:") + for key, value in self._init_prune_ratios.items(): + print(key, ":", value) + + nm = dict(self._gm.named_modules()) + for node in self._gm.graph.nodes: + if node.target in nm.keys() and isinstance(nm[node.target], _Supported_Modules): + self._quant_module_nodes.append(node) + + for node in self._gm.graph.nodes: + if node.target is F.conv2d or node.target is F.linear: + self._quant_func_nodes.append(node) + + self._extract() + + def _extract(self): + gm = self._gm + nm = dict(gm.named_modules()) + for n in self._quant_module_nodes: + if n.target in nm.keys() and isinstance(nm[n.target], torch.nn.Conv2d): + conv_module = nm[n.target] + with gm.graph.inserting_after(n): + w = gm.graph.get_attr(n.target+".weight") + + self._total_weight_num += dict(self._model.named_parameters())[n.target + ".weight"].data.numel() + self._remain_weight_num += (dict(self._model.named_parameters())[n.target + ".weight"].data.numel() / (32.0 / self._bits) * (1 - self._init_prune_ratios[n.target + ".weight"])) + + p = w + b = None + if conv_module.bias is not None: + with gm.graph.inserting_after(w): + b = gm.graph.get_attr(n.target+".bias") + p = b + + pt = n.args[0] + if conv_module.padding_mode != 'zeros': + with gm.graph.inserting_after(p): + pad = gm.graph.call_function(F.pad) + pad.args = (n.args[0],) + pad.kwargs = {'pad':conv_module._reversed_padding_repeated_twice, 'mode':conv_module.padding_mode} + p = pad + pt = pad + + with gm.graph.inserting_after(p): + conv_func = gm.graph.call_function(F.conv2d) + conv_func.args = (pt, w, b) + if conv_module.padding_mode != 'zeros': + conv_func.kwargs = {'stride': conv_module.stride, 'padding': _pair(0), 'dilation': conv_module.dilation, 'groups': conv_module.groups} + else: + conv_func.kwargs = {'stride': conv_module.stride, 'padding': conv_module.padding, 'dilation': conv_module.dilation, 'groups': conv_module.groups} + + n.replace_all_uses_with(conv_func) + self._quant_func_nodes.append(conv_func) + gm.graph.erase_node(n) + + if n.target in nm.keys() and isinstance(nm[n.target], torch.nn.Linear): + linear_module = nm[n.target] + with gm.graph.inserting_after(n): + w = gm.graph.get_attr(n.target+".weight") + + self._total_weight_num += dict(self._model.named_parameters())[n.target + ".weight"].data.numel() + self._remain_weight_num += (dict(self._model.named_parameters())[n.target + ".weight"].data.numel() / (32.0 / self._bits) * (1 - self._init_prune_ratios[n.target + ".weight"])) + + p = w + b = None + if linear_module.bias is not None: + with gm.graph.inserting_after(w): + b = gm.graph.get_attr(n.target+".bias") + p = b + + with gm.graph.inserting_after(p): + linear_func = gm.graph.call_function(F.linear) + linear_func.args = (n.args[0], w, b) + + n.replace_all_uses_with(linear_func) + self._quant_func_nodes.append(linear_func) + gm.graph.erase_node(n) + + gm.recompile() + + def convert(self): + for i, node in zip(range(len(self._quant_func_nodes)), self._quant_func_nodes): + if node.target is F.conv2d or node.target is F.linear: + self._quant_one_layer(node, _MNN_Quant_prefix + str(i) + "_" + node.name, node.target) + + self._gm.recompile() + return self._gm + + def _quant_one_layer(self, node, prefix, type): + input_node = node.args[0] + weight_node = node.args[1] + + weight_node_user_num = 0 + for n in self._gm.graph.nodes: + if n.op == "get_attr" and n.target == weight_node.target: + weight_node_user_num += len(n.users.keys()) + if weight_node_user_num != 1: + print("weight_node:", weight_node.target, "has", weight_node_user_num, "users, skip quant for this layer") + return + + weight_scale_name = self._fake_quant_weight(weight_node, prefix + "_weight", type) + + self._weight_quant_info_map[weight_node.target] = [weight_scale_name] + + def _fake_quant_weight(self, weight_node, name, type): + if _MNN_Quant_prefix in weight_node.name: + return weight_node.name + ".scale" + + nps = dict(self._gm.named_parameters()) + if type is F.conv2d: + p = nps[weight_node.target] + self._gm.add_module(name, _Conv2dWeightFakeQuantModule(self._pname_mask[weight_node.target], self._clamp_value)) + + if type is F.linear: + p = nps[weight_node.target] + self._gm.add_module(name, _LinearWeightFakeQuantModule(self._pname_mask[weight_node.target], self._clamp_value)) + + with self._gm.graph.inserting_after(weight_node): + quant_weight = self._gm.graph.call_module(name) + quant_weight.args = (weight_node,) + + nodes_update_args = [] + for node in weight_node.users.keys(): + if _MNN_Quant_prefix not in node.name: + new_args = tuple() + if node not in self._weight_node_ori_args.keys(): + self._weight_node_ori_args[node] = [] + for i in range(0, len(node.args)): + if node.args[i] == weight_node: + new_args += (quant_weight,) + self._weight_node_ori_args[node].append([i, weight_node]) + else: + new_args += (node.args[i],) + nodes_update_args.append((node, new_args)) + self._quant_user_nodes_update_args[node] = new_args + + for node, new_args in nodes_update_args: + node.args = new_args + + return name + ".scale" diff --git a/tools/mnncompress/mnncompress/tensorflow/EMA_quantizer.py b/tools/mnncompress/mnncompress/tensorflow/EMA_quantizer.py new file mode 100644 index 00000000..99db260c --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/EMA_quantizer.py @@ -0,0 +1,699 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_input_tensor_index, is_weight_tensor, get_variable_by_tensor +from .helpers import find_quant_output_tensor, find_bias_add_op, get_batch_norm_statistics, get_variable_by_name +from .graph_checker import check_for_grad_ops + + +_MNN_compress_scope = 'MNN_QAT_' + +_MNN_mark_start_name = 'MNN_QAT_MARK_START' +_MNN_mark_end_name = 'MNN_QAT_MARK_END' + +_MNN_variable_collection_name = 'MNN_QAT_variables' + +def MNN_QAT_variables(): + all_vars = tf.global_variables() + return [v for v in all_vars if 'MNN_QAT' in v.name] + +_Quant_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + +_Variable_types = ['Variable', 'VariableV2'] + +_MNN_QAT_DEBUG = False + + +def strip_MNN_QAT_ops(graph): + '''only invoke this when you need to save the model for inference''' + def get_MNN_QAT_scope(name, match): + name_split = name.split('/') + for i in range(len(name_split)): + if name_split[i] == match: + assert i > 0, "get MNN QAT scope error" + return name_split[i-1] + return '' + + all_ops = graph.get_operations() + start_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_start_name) and op.type in ['Abs', 'RealDiv'] and 'gradients/' not in op.name] + end_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_end_name+'/Merge') and op.type == 'Merge' and 'gradients/' not in op.name] + + for start_name in start_op_names: + start_scope = get_MNN_QAT_scope(start_name, _MNN_mark_start_name) + end_name = '' + for end in end_op_names: + if start_scope in end: + end_name = end + break + assert end_name != '', "error: end op name not found" + + start_op = graph.get_operation_by_name(start_name) + start_input_tensor = start_op.inputs[0] + end_tensor = graph.get_tensor_by_name(end_name+':0') + end_consumers = end_tensor.consumers() + + for c in end_consumers: + indices = get_input_tensor_index(c, end_tensor) + for id in indices: + c._update_input(id, start_input_tensor) + +def debug_print(*argv): + if _MNN_QAT_DEBUG: + print("debug info: ", end="") + for arg in argv: + print(arg, end=' ') + print() + +def get_qat_state_placeholder(graph=None): + if graph is None: + graph = tf.get_default_graph() + + all_tensor_names = [tensor.name for op in graph.get_operations() for tensor in op.values()] + for name in all_tensor_names: + if 'MNN_QAT_is_training:0' in name: + return graph.get_tensor_by_name(name) + + raise ValueError('Could not find QAT state placeholder') + + +class EMAQuantizer(object): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, debug_info = False, retain_sparsity=False): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + self._ops = self._graph.get_operations() + self.skip_quant_layers = skip_quant_layers + self.skip_quant_flag = skip_quant_flag + self._all_conv_and_matmul_layers = [l for l in self._ops if l.type in _Quant_Support_Ops] + self.quant_layer_names = [l.name for l in self._all_conv_and_matmul_layers] + + skip_flag_usable = False + for sf in self.skip_quant_flag: + for item in self.quant_layer_names: + if sf in item: + self.skip_quant_layers.append(item) + skip_flag_usable = True + + if skip_quant_flag != [] and skip_flag_usable == False: + raise ValueError("skip_quant_flag invalid") + + for item in self.skip_quant_layers: + if item in [l.name for l in self._all_conv_and_matmul_layers]: + self.quant_layer_names.remove(item) + else: + raise ValueError(item+" not found in graph ops, availables are: "+str([l.name for l in self._all_conv_and_matmul_layers])) + + if bits < 2 or bits > 8: + raise ValueError("bits must be a integer in [2, 8]") + self.bits = bits + self._clamp_value = float(pow(2, bits-1) - 1) + + global _MNN_QAT_DEBUG + _MNN_QAT_DEBUG = debug_info + + self._retain_sparsity = retain_sparsity + self._momentum = 0.99 + if is_training is not None: + if not isinstance(is_training, bool): + raise ValueError("is_training should be None or python bool") + if isinstance(is_training, bool): + self._is_training = tf.constant(is_training) + else: + print("is_training set as: tf.placeholder(tf.bool, name='MNN_QAT_is_training')") + self._is_training = tf.placeholder(tf.bool, name='MNN_QAT_is_training') + self._original_tensor_consumer_index_map = {} + self._quant_tensor_consumer_index_map = {} + self._feature_scale_original_tensor_dims_map = {} + self._weight_scale_original_tensor_map = {} + self._model_per_layer_scale_info = [] + self._prune_weight_ops = [] + self._mask = {} + self._all_weight_tensors = [] + self._feature_scale_init_op = {} + self._op_scale_initialized = {} + self._initialized = False + self._layer_input_clamp_value = {} + self._eps = 1e-9 + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._init_prune_ratios = {} + self._op_name_weight_tensor = {} + self._skip_quant_weight_tensors = [] + self._find_all_variable_tensors() + self._insert_quant_ops() + + @property + def is_training(self): + return self._is_training + + def init(self, sess): + self._initialized = True + + for name in self.skip_quant_layers: + if name in self._op_name_weight_tensor.keys(): + weight_tensor = self._op_name_weight_tensor[name] + self._skip_quant_weight_tensors.append(weight_tensor) + + for v in self._all_weight_tensors: + self._mask[v] = sess.run(self._mask[v]) + self._init_prune_ratios[v.name] = 1. - np.mean(self._mask[v]) + self._total_weight_num += self._mask[v].size + if v in self._skip_quant_weight_tensors: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / 4.0 + else: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / (32.0 / self.bits) + + def update(self, sess): + if not self._initialized: + raise RuntimeError("EMAQuantizer is not initialized, please call 'init(sess)' method before training loop") + if self._retain_sparsity: + sess.run(self._prune_weight_ops) + if _MNN_QAT_DEBUG: + for v in self._all_weight_tensors: + weight = sess.run(v) + prune_ratio = 1 - np.mean(np.abs(weight) > self._eps) + print(v, "prune_ratio:", prune_ratio) + + def _generate_mask_and_prune_ops(self, v): + if self._retain_sparsity: + th = self._eps + else: + th = -1.0 + + self._mask[v] = tf.cast(tf.abs(v) > th, tf.float32) + + if isinstance(v, tf.Tensor): + return + + if self._retain_sparsity: + prune_weight_op = tf.assign(v, v * self._mask[v]) + self._prune_weight_ops.append(prune_weight_op) + + def _find_all_variable_tensors(self): + for op in self._all_conv_and_matmul_layers: + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_tensor = get_variable_by_tensor(op.inputs[1]) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if op.type == 'MatMul': + find_weight = False + for i in range(len(op.inputs)): + input_tensor = op.inputs[i] + if is_weight_tensor(input_tensor): + find_weight = True + weight_tensor = get_variable_by_tensor(input_tensor) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if not find_weight: + if op.name in self.quant_layer_names: + self.quant_layer_names.remove(op.name) + print("no weight find for {}, skip".format(op.name)) + + def strip_qat_ops(self): + '''only invoke this when you need to save the model for inference''' + for tensor in self._original_tensor_consumer_index_map.keys(): + for index, consumer in self._original_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on save:", tensor.name, consumer.name, consumer.type, index) + + def save_compress_params(self, filename, sess, append=False): + def get_save_tensor_name(name): + if name.endswith(':0'): + return name[:-2] + else: + return name + + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.QUANTIZE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + quant_algorithm = compress_proto.algo.add() + for layer_info in self._model_per_layer_scale_info: + l = quant_algorithm.quant_params.layer.add() + # {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim} + weight_info = layer_info["weight_info"] + input_info = layer_info["input_info"] + output_info = layer_info["output_info"] + + if weight_info is not []: + weight_params = compress_pb.LayerQuantizeParams.WeightParams() + weight_params.name = get_save_tensor_name(get_variable_by_tensor(weight_info[0]).name) + scales = sess.run(weight_info[1]) + scales = scales.reshape((scales.size)) + bn_stat = layer_info["bn_stat"] + + gamma = np.ones_like(scales) + if bn_stat[0] is not None: + gamma = sess.run(bn_stat[0]) + gamma = gamma.reshape((gamma.size)) + rstd = np.ones_like(scales) + if bn_stat[3] is not None: + bn_var = sess.run(bn_stat[3]) + bn_var = bn_var.reshape((bn_var.size)) + rstd = 1. / np.sqrt(bn_var + bn_stat[4]) + scales = scales * gamma * rstd + + for s in scales: + weight_params.scales.append(abs(s)) + weight_params.bits = self.bits + weight_params.clamp_min = -int(self._clamp_value) + weight_params.clamp_max = int(self._clamp_value) + l.weight.append(weight_params) + + input_params = compress_pb.LayerQuantizeParams.ActivationParams() + input_params.name = get_save_tensor_name(input_info[0].name) + scale = sess.run(input_info[1]).tolist() + debug_print(input_info[1].name, scale) + # for count in range(input_info[2]): + input_params.scales.append(scale) + if len(input_info) == 4: + clamp = sess.run(input_info[3]).tolist() + input_params.clamp_min = -int(clamp) + input_params.clamp_max = int(clamp) + l.input.append(input_params) + + if len(input_info) == 6: + input_params2 = compress_pb.LayerQuantizeParams.ActivationParams() + input_params2.name = get_save_tensor_name(input_info[3].name) + scale = sess.run(input_info[4]).tolist() + debug_print(input_info[4].name, scale) + for count in range(input_info[5]): + input_params2.scales.append(scale) + l.input.append(input_params2) + + output_params = compress_pb.LayerQuantizeParams.ActivationParams() + output_params.name = get_save_tensor_name(output_info[0].name) + scale = sess.run(output_info[1]) + debug_print(output_info[1].name, scale) + # for count in range(output_info[2]): + output_params.scales.append(scale) + if len(output_info) == 4: + clamp = sess.run(output_info[3]).tolist() + output_params.clamp_min = -int(clamp) + output_params.clamp_max = int(clamp) + l.output.append(output_params) + + if not self._reported: + detail = {"algorithm": "EMAQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self.bits, "skip_quant_layers": self.skip_quant_layers, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + with tf.gfile.Open(filename, mode="wb") as f: + f.write(compress_proto.SerializeToString()) + + print("compress proto saved to:", filename) + + def _recover_train_quant_graph(self): + if self._quant_tensor_consumer_index_map == {}: + return + + for tensor in self._quant_tensor_consumer_index_map.keys(): + for index, consumer in self._quant_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on recover:", consumer.name, consumer.type, index, tensor.name) + + def _insert_quant_ops(self): + grad_ops = check_for_grad_ops(self._graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only. create quantizer before construct model optimizer\n' % grad_ops) + scope_index = -1 + for layer_name in self.quant_layer_names: + if 'gradients/' in layer_name and '_grad' in layer_name: + continue + scope_index += 1 + self._quant_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _quant_one_layer(self, layer_name, scope): + all_op_names_ops = {op.name : op for op in self._ops} + if layer_name not in all_op_names_ops.keys(): + raise ValueError("%s is not in the graph." % layer_name) + + if all_op_names_ops[layer_name].type not in _Quant_Support_Ops: + raise ValueError("op name: %s, type = %s is not supported." % (layer_name, all_op_names_ops[layer_name].type)) + + if layer_name not in self._layer_input_clamp_value.keys(): + self._layer_input_clamp_value[layer_name] = None + + if all_op_names_ops[layer_name].type == 'Conv2D' or all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + depthwise = False + if all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + depthwise = True + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_conv(all_op_names_ops[layer_name], scope, depthwise) + self._model_per_layer_scale_info.append(res) + + if all_op_names_ops[layer_name].type == 'MatMul': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_matmul(all_op_names_ops[layer_name], scope) + self._model_per_layer_scale_info.append(res) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _save_original_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._original_tensor_consumer_index_map.keys(): + self._original_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._original_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _save_quant_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._quant_tensor_consumer_index_map.keys(): + self._quant_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._quant_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _get_quant_scope(self, name, match): + name_split = name.split("/") + return_scope = '' + for i in range(len(name_split)): + cond1 = _MNN_compress_scope in name_split[i] + cond2 = match in name_split[i] + if cond1 and cond2: + for j in range(i+1): + return_scope = return_scope + name_split[j] + '/' + + return return_scope[:-1] + + return return_scope + + def _quant_conv(self, conv2d_op, scope, depthwise): + input_feature = conv2d_op.inputs[0] + weight_tensor = conv2d_op.inputs[1] + + reduce_dims = [0, 1, 2] + if depthwise: + reduce_dims = [0, 1, 3] + + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + if _MNN_compress_scope not in input_feature.name: + conv2d_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_feature, scope + '_input', conv2d_op, 'input') + input_scale_and_dim.append(input_feature) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + conv2d_op._update_input(0, conv2d_op_input) + self._save_quant_tensor_consumer_info(conv2d_op_input, 0, conv2d_op) + self._save_original_tensor_consumer_info(input_feature, 0, conv2d_op) + else: + pre_output_scope = self._get_quant_scope(input_feature.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_feature.name, "feature already quant") + + # if _MNN_compress_scope not in weight_tensor.name: + new_weight_tensor, weight_scales = self._fake_quant_weight(weight_tensor, reduce_dims, scope + '_weight') + weight_tensor_scale = [weight_tensor, weight_scales] + conv2d_op._update_input(1, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, 1, conv2d_op) + self._save_original_tensor_consumer_info(weight_tensor, 1, conv2d_op) + + bias_add_op = find_bias_add_op(conv2d_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(conv2d_op) + output_consumers = output_tensor.consumers() + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', conv2d_op, 'output') + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + input_scale_and_dim.append(tf.constant(self._clamp_value)) + output_scale_and_dim.append(tf.constant(self._clamp_value)) + + bn_stat = get_batch_norm_statistics(conv2d_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _quant_matmul(self, matmul_op, scope): + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + for index in range(len(matmul_op.inputs)): + input_tensor = matmul_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print(input_tensor.name, "is weight tensor") + # if _MNN_compress_scope not in input_tensor.name: + reduce_dims = None + trans_a = matmul_op.get_attr("transpose_a") + trans_b = matmul_op.get_attr("transpose_b") + if index == 0: + if not trans_a: + reduce_dims = [1] + else: + reduce_dims = [0] + if index == 1: + if not trans_b: + reduce_dims = [0] + else: + reduce_dims = [1] + + new_weight_tensor, weight_scales = self._fake_quant_weight(input_tensor, reduce_dims, scope + '_weight_' + str(index)) + weight_tensor_scale = [input_tensor, weight_scales] + matmul_op._update_input(index, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("weight quant updated") + else: + debug_print(input_tensor.name, "is input tensor") + if _MNN_compress_scope not in input_tensor.name: + new_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_tensor, scope + '_input_' + str(index), matmul_op, 'input') + input_scale_and_dim.append(input_tensor) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + matmul_op._update_input(index, new_op_input) + self._save_quant_tensor_consumer_info(new_op_input, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("feature quant updated") + else: + pre_output_scope = self._get_quant_scope(input_tensor.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_tensor.name, "feature already quant") + + bias_add_op = find_bias_add_op(matmul_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(matmul_op) + output_consumers = output_tensor.consumers() + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', matmul_op, 'output') + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + input_scale_and_dim.append(tf.constant(self._clamp_value)) + output_scale_and_dim.append(tf.constant(self._clamp_value)) + + bn_stat = get_batch_norm_statistics(matmul_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _fake_quant_bias(self, bias_tensor, input_scale, weight_scales, scope, init_moving_scale_op): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + with tf.control_dependencies([init_moving_scale_op]): + weight_scales_reshape = tf.reshape(weight_scales, bias_tensor.shape) + bias_scale = input_scale * weight_scales_reshape + bias_scale = bias_scale + tf.cast(tf.less(tf.abs(bias_scale), 1e-6), tf.float32) * self._eps + before_round = tf.divide(bias_tensor, bias_scale, name=_MNN_mark_start_name) + before_round = before_round + 1e-3 * tf.sign(before_round) + quant_bias = tf.round(before_round) + new_bias = quant_bias * bias_scale + new_bias_tensor = tf.cond(tf.constant(True), lambda: new_bias, lambda: bias_tensor, name=_MNN_mark_end_name) + + return new_bias_tensor + + def _fake_quant_weight(self, weight_tensor, reduce_dims, scope, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + scales = tf.reduce_max(tf.abs(weight_tensor, name=_MNN_mark_start_name), axis=reduce_dims, keep_dims=True) / clamp_value + self._eps + weight_scales = tf.get_variable(name='weight_scales', initializer=tf.zeros_like(scales), trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, weight_scales) + scales_assign = tf.assign(weight_scales, scales) + with tf.control_dependencies([scales_assign]): + quant_w = tf.clip_by_value(tf.round(weight_tensor / scales), -clamp_value, clamp_value) + fake_quant_w = quant_w * scales + + if _MNN_QAT_DEBUG: + print_op = tf.print(scope, "weight clamp value:", clamp_value) + with tf.control_dependencies([print_op]): + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + else: + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + + self._weight_scale_original_tensor_map[weight_scales] = weight_tensor + + return new_weight_tensor, weight_scales + + def _fake_quant_feature(self, feature, scope, op, type, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + feature_abs_max = tf.reduce_max(tf.abs(feature, name=_MNN_mark_start_name)) + if _MNN_QAT_DEBUG: + print_op = tf.print("op:", op.name, type, "feature_abs_max, clamp_value:", feature_abs_max, clamp_value) + with tf.control_dependencies([print_op]): + instance_scale = feature_abs_max / clamp_value + else: + instance_scale = feature_abs_max / clamp_value + moving_average_scale = tf.get_variable(name='moving_average_scale', initializer=0., trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, moving_average_scale) + + def init(): + init_assign = tf.assign(moving_average_scale, instance_scale) + initialize_op = tf.assign(self._op_scale_initialized[op][type], 1) + if _MNN_QAT_DEBUG: + print_op = tf.print("init", moving_average_scale.name, init_assign) + with tf.control_dependencies([print_op, initialize_op]): + return tf.identity(init_assign) + else: + with tf.control_dependencies([initialize_op]): + return tf.identity(init_assign) + + def do_nothing(): + return moving_average_scale + + init_moving_scale = tf.cond(tf.equal(self._op_scale_initialized[op][type], 0), init, do_nothing) + self._feature_scale_init_op[moving_average_scale] = init_moving_scale + + def fake_quant(): + def moving_average_scale_update(): + if _MNN_QAT_DEBUG: + with tf.control_dependencies([init_moving_scale]): + assign_scale = tf.assign(moving_average_scale, moving_average_scale * self._momentum + instance_scale * (1 - self._momentum)) + with tf.control_dependencies([assign_scale]): + print_op = tf.print("train:", scope + "_scale:", instance_scale, moving_average_scale) + with tf.control_dependencies([assign_scale, print_op]): + return tf.identity(instance_scale) + else: + with tf.control_dependencies([init_moving_scale]): + assign_scale = tf.assign(moving_average_scale, moving_average_scale * self._momentum + instance_scale * (1 - self._momentum)) + with tf.control_dependencies([assign_scale]): + return tf.identity(instance_scale) + + def return_average_scale(): + if _MNN_QAT_DEBUG: + print_op = tf.print("test:", moving_average_scale.name, moving_average_scale) + with tf.control_dependencies([print_op]): + return tf.identity(moving_average_scale) + else: + return tf.identity(moving_average_scale) + + scale = tf.cond(self._is_training, moving_average_scale_update, return_average_scale) + quant_x = tf.clip_by_value(tf.round(feature / scale), -clamp_value, clamp_value) + fake_quant_x = quant_x * scale + + return fake_quant_x + + new_feature = tf.cond(tf.constant(True), fake_quant, lambda:feature, name=_MNN_mark_end_name) + + self._feature_scale_original_tensor_dims_map[moving_average_scale] = [feature, feature.shape.as_list()[-1]] + + return new_feature, moving_average_scale, feature.shape.as_list()[-1], init_moving_scale diff --git a/tools/mnncompress/mnncompress/tensorflow/LSQ_quantizer.py b/tools/mnncompress/mnncompress/tensorflow/LSQ_quantizer.py new file mode 100644 index 00000000..0e962e8b --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/LSQ_quantizer.py @@ -0,0 +1,678 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_input_tensor_index, is_weight_tensor, custom_scale_grad, get_variable_by_tensor +from .helpers import find_quant_output_tensor, find_bias_add_op, get_batch_norm_statistics, get_variable_by_name +from .graph_checker import check_for_grad_ops + + +_MNN_compress_scope = 'MNN_QAT_' + +_MNN_mark_start_name = 'MNN_QAT_MARK_START' +_MNN_mark_end_name = 'MNN_QAT_MARK_END' + +_MNN_variable_collection_name = 'MNN_QAT_variables' + +def MNN_QAT_variables(): + all_vars = tf.global_variables() + return [v for v in all_vars if 'MNN_QAT' in v.name] + +_Quant_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + +_Variable_types = ['Variable', 'VariableV2'] + +_MNN_QAT_DEBUG = False + + +def strip_MNN_QAT_ops(graph): + '''only invoke this when you need to save the model for inference''' + def get_MNN_QAT_scope(name, match): + name_split = name.split('/') + for i in range(len(name_split)): + if name_split[i] == match: + assert i > 0, "get MNN QAT scope error" + return name_split[i-1] + return '' + + all_ops = graph.get_operations() + start_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_start_name) and op.type in ['Abs', 'RealDiv'] and 'gradients/' not in op.name] + end_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_end_name+'/Merge') and op.type == 'Merge' and 'gradients/' not in op.name] + + for start_name in start_op_names: + start_scope = get_MNN_QAT_scope(start_name, _MNN_mark_start_name) + end_name = '' + for end in end_op_names: + if start_scope in end: + end_name = end + break + assert end_name != '', "error: end op name not found" + + start_op = graph.get_operation_by_name(start_name) + start_input_tensor = start_op.inputs[0] + end_tensor = graph.get_tensor_by_name(end_name+':0') + end_consumers = end_tensor.consumers() + + for c in end_consumers: + indices = get_input_tensor_index(c, end_tensor) + for id in indices: + c._update_input(id, start_input_tensor) + +def debug_print(*argv): + if _MNN_QAT_DEBUG: + print("debug info: ", end="") + for arg in argv: + print(arg, end=' ') + print() + +def get_qat_state_placeholder(graph=None): + if graph is None: + graph = tf.get_default_graph() + + all_tensor_names = [tensor.name for op in graph.get_operations() for tensor in op.values()] + for name in all_tensor_names: + if 'MNN_QAT_is_training:0' in name: + return graph.get_tensor_by_name(name) + + raise ValueError('Could not find QAT state placeholder') + + +class LSQQuantizer(object): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, debug_info = False, retain_sparsity=False): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + self._ops = self._graph.get_operations() + self.skip_quant_layers = skip_quant_layers + self.skip_quant_flag = skip_quant_flag + self._all_conv_and_matmul_layers = [l for l in self._ops if l.type in _Quant_Support_Ops] + self.quant_layer_names = [l.name for l in self._all_conv_and_matmul_layers] + + skip_flag_usable = False + for sf in self.skip_quant_flag: + for item in self.quant_layer_names: + if sf in item: + self.skip_quant_layers.append(item) + skip_flag_usable = True + + if skip_quant_flag != [] and skip_flag_usable == False: + raise ValueError("skip_quant_flag invalid") + + for item in self.skip_quant_layers: + if item in [l.name for l in self._all_conv_and_matmul_layers]: + self.quant_layer_names.remove(item) + else: + raise ValueError(item+" not found in graph ops, availables are: "+str([l.name for l in self._all_conv_and_matmul_layers])) + + if bits < 2 or bits > 8: + raise ValueError("bits must be a integer in [2, 8]") + self.bits = bits + self._clamp_value = float(pow(2, bits-1) - 1) + + global _MNN_QAT_DEBUG + _MNN_QAT_DEBUG = debug_info + + self._retain_sparsity = retain_sparsity + self._momentum = 0.99 + if is_training is not None: + if not isinstance(is_training, bool): + raise ValueError("is_training should be None or python bool") + if isinstance(is_training, bool): + self._is_training = tf.constant(is_training) + else: + print("is_training set as: tf.placeholder(tf.bool, name='MNN_QAT_is_training')") + self._is_training = tf.placeholder(tf.bool, name='MNN_QAT_is_training') + self._original_tensor_consumer_index_map = {} + self._quant_tensor_consumer_index_map = {} + self._feature_scale_original_tensor_dims_map = {} + self._weight_scale_original_tensor_map = {} + self._model_per_layer_scale_info = [] + self._prune_weight_ops = [] + self._mask = {} + self._all_weight_tensors = [] + self._feature_scale_init_op = {} + self._op_scale_initialized = {} + self._initialized = False + self._layer_input_clamp_value = {} + self._eps = 1e-9 + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._init_prune_ratios = {} + self._op_name_weight_tensor = {} + self._skip_quant_weight_tensors = [] + self._find_all_variable_tensors() + self._insert_quant_ops() + + @property + def is_training(self): + return self._is_training + + def init(self, sess): + self._initialized = True + + for name in self.skip_quant_layers: + if name in self._op_name_weight_tensor.keys(): + weight_tensor = self._op_name_weight_tensor[name] + self._skip_quant_weight_tensors.append(weight_tensor) + + for v in self._all_weight_tensors: + self._mask[v] = sess.run(self._mask[v]) + self._init_prune_ratios[v.name] = 1. - np.mean(self._mask[v]) + self._total_weight_num += self._mask[v].size + if v in self._skip_quant_weight_tensors: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / 4.0 + else: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / (32.0 / self.bits) + + def update(self, sess): + if not self._initialized: + raise RuntimeError("LSQQuantizer is not initialized, please call 'init(sess)' method before training loop") + if self._retain_sparsity: + sess.run(self._prune_weight_ops) + if _MNN_QAT_DEBUG: + for v in self._all_weight_tensors: + weight = sess.run(v) + prune_ratio = 1 - np.mean(np.abs(weight) > self._eps) + print(v, "prune_ratio:", prune_ratio) + + def _generate_mask_and_prune_ops(self, v): + if self._retain_sparsity: + th = self._eps + else: + th = -1.0 + + self._mask[v] = tf.cast(tf.abs(v) > th, tf.float32) + + if isinstance(v, tf.Tensor): + return + + if self._retain_sparsity: + prune_weight_op = tf.assign(v, v * self._mask[v]) + self._prune_weight_ops.append(prune_weight_op) + + def _find_all_variable_tensors(self): + for op in self._all_conv_and_matmul_layers: + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_tensor = get_variable_by_tensor(op.inputs[1]) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if op.type == 'MatMul': + find_weight = False + for i in range(len(op.inputs)): + input_tensor = op.inputs[i] + if is_weight_tensor(input_tensor): + find_weight = True + weight_tensor = get_variable_by_tensor(input_tensor) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if not find_weight: + if op.name in self.quant_layer_names: + self.quant_layer_names.remove(op.name) + print("no weight find for {}, skip".format(op.name)) + + def strip_qat_ops(self): + '''only invoke this when you need to save the model for inference''' + for tensor in self._original_tensor_consumer_index_map.keys(): + for index, consumer in self._original_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on save:", tensor.name, consumer.name, consumer.type, index) + + def save_compress_params(self, filename, sess, append=False): + def get_save_tensor_name(name): + if name.endswith(':0'): + return name[:-2] + else: + return name + + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.QUANTIZE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + quant_algorithm = compress_proto.algo.add() + for layer_info in self._model_per_layer_scale_info: + l = quant_algorithm.quant_params.layer.add() + # {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim} + weight_info = layer_info["weight_info"] + input_info = layer_info["input_info"] + output_info = layer_info["output_info"] + + if weight_info is not []: + weight_params = compress_pb.LayerQuantizeParams.WeightParams() + weight_params.name = get_save_tensor_name(get_variable_by_tensor(weight_info[0]).name) + scales = sess.run(weight_info[1]) + scales = scales.reshape((scales.size)) + bn_stat = layer_info["bn_stat"] + + gamma = np.ones_like(scales) + if bn_stat[0] is not None: + gamma = sess.run(bn_stat[0]) + gamma = gamma.reshape((gamma.size)) + rstd = np.ones_like(scales) + if bn_stat[3] is not None: + bn_var = sess.run(bn_stat[3]) + bn_var = bn_var.reshape((bn_var.size)) + rstd = 1. / np.sqrt(bn_var + bn_stat[4]) + scales = scales * gamma * rstd + + for s in scales: + weight_params.scales.append(abs(s)) + weight_params.bits = self.bits + weight_params.clamp_min = -int(self._clamp_value) + weight_params.clamp_max = int(self._clamp_value) + l.weight.append(weight_params) + + input_params = compress_pb.LayerQuantizeParams.ActivationParams() + input_params.name = get_save_tensor_name(input_info[0].name) + scale = sess.run(input_info[1]).tolist() + debug_print(input_info[1].name, scale) + # for count in range(input_info[2]): + input_params.scales.append(scale) + if len(input_info) == 4: + clamp = sess.run(input_info[3]).tolist() + input_params.clamp_min = -int(clamp) + input_params.clamp_max = int(clamp) + l.input.append(input_params) + + if len(input_info) == 6: + input_params2 = compress_pb.LayerQuantizeParams.ActivationParams() + input_params2.name = get_save_tensor_name(input_info[3].name) + scale = sess.run(input_info[4]).tolist() + debug_print(input_info[4].name, scale) + for count in range(input_info[5]): + input_params2.scales.append(scale) + l.input.append(input_params2) + + output_params = compress_pb.LayerQuantizeParams.ActivationParams() + output_params.name = get_save_tensor_name(output_info[0].name) + scale = sess.run(output_info[1]) + debug_print(output_info[1].name, scale) + # for count in range(output_info[2]): + output_params.scales.append(scale) + if len(output_info) == 4: + clamp = sess.run(output_info[3]).tolist() + output_params.clamp_min = -int(clamp) + output_params.clamp_max = int(clamp) + l.output.append(output_params) + + if not self._reported: + detail = {"algorithm": "LSQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self.bits, "skip_quant_layers": self.skip_quant_layers, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + with tf.gfile.Open(filename, mode="wb") as f: + f.write(compress_proto.SerializeToString()) + + print("compress proto saved to:", filename) + + def _recover_train_quant_graph(self): + if self._quant_tensor_consumer_index_map == {}: + return + + for tensor in self._quant_tensor_consumer_index_map.keys(): + for index, consumer in self._quant_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on recover:", consumer.name, consumer.type, index, tensor.name) + + def _insert_quant_ops(self): + grad_ops = check_for_grad_ops(self._graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only. create quantizer before construct model optimizer\n' % grad_ops) + scope_index = -1 + for layer_name in self.quant_layer_names: + if 'gradients/' in layer_name and '_grad' in layer_name: + continue + scope_index += 1 + self._quant_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _quant_one_layer(self, layer_name, scope): + all_op_names_ops = {op.name : op for op in self._ops} + if layer_name not in all_op_names_ops.keys(): + raise ValueError("%s is not in the graph." % layer_name) + + if all_op_names_ops[layer_name].type not in _Quant_Support_Ops: + raise ValueError("op name: %s, type = %s is not supported." % (layer_name, all_op_names_ops[layer_name].type)) + + if layer_name not in self._layer_input_clamp_value.keys(): + self._layer_input_clamp_value[layer_name] = None + + if all_op_names_ops[layer_name].type == 'Conv2D' or all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + depthwise = False + if all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + depthwise = True + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_conv(all_op_names_ops[layer_name], scope, depthwise) + self._model_per_layer_scale_info.append(res) + + if all_op_names_ops[layer_name].type == 'MatMul': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_matmul(all_op_names_ops[layer_name], scope) + self._model_per_layer_scale_info.append(res) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _save_original_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._original_tensor_consumer_index_map.keys(): + self._original_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._original_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _save_quant_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._quant_tensor_consumer_index_map.keys(): + self._quant_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._quant_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _get_quant_scope(self, name, match): + name_split = name.split("/") + return_scope = '' + for i in range(len(name_split)): + cond1 = _MNN_compress_scope in name_split[i] + cond2 = match in name_split[i] + if cond1 and cond2: + for j in range(i+1): + return_scope = return_scope + name_split[j] + '/' + + return return_scope[:-1] + + return return_scope + + def _quant_conv(self, conv2d_op, scope, depthwise): + input_feature = conv2d_op.inputs[0] + weight_tensor = conv2d_op.inputs[1] + + reduce_dims = [0, 1, 2] + if depthwise: + reduce_dims = [0, 1, 3] + + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + if _MNN_compress_scope not in input_feature.name: + conv2d_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_feature, scope + '_input', conv2d_op, 'input') + input_scale_and_dim.append(input_feature) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + conv2d_op._update_input(0, conv2d_op_input) + self._save_quant_tensor_consumer_info(conv2d_op_input, 0, conv2d_op) + self._save_original_tensor_consumer_info(input_feature, 0, conv2d_op) + else: + pre_output_scope = self._get_quant_scope(input_feature.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_feature.name, "feature already quant") + + new_weight_tensor, weight_scales = self._fake_quant_weight(weight_tensor, reduce_dims, scope + '_weight') + weight_tensor_scale = [weight_tensor, weight_scales] + conv2d_op._update_input(1, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, 1, conv2d_op) + self._save_original_tensor_consumer_info(weight_tensor, 1, conv2d_op) + + bias_add_op = find_bias_add_op(conv2d_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(conv2d_op) + output_consumers = output_tensor.consumers() + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', conv2d_op, 'output') + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + input_scale_and_dim.append(tf.constant(self._clamp_value)) + output_scale_and_dim.append(tf.constant(self._clamp_value)) + + bn_stat = get_batch_norm_statistics(conv2d_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _quant_matmul(self, matmul_op, scope): + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + for index in range(len(matmul_op.inputs)): + input_tensor = matmul_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print(input_tensor.name, "is weight tensor") + # if _MNN_compress_scope not in input_tensor.name: + reduce_dims = None + trans_a = matmul_op.get_attr("transpose_a") + trans_b = matmul_op.get_attr("transpose_b") + if index == 0: + if not trans_a: + reduce_dims = [1] + else: + reduce_dims = [0] + if index == 1: + if not trans_b: + reduce_dims = [0] + else: + reduce_dims = [1] + + new_weight_tensor, weight_scales = self._fake_quant_weight(input_tensor, reduce_dims, scope + '_weight_' + str(index)) + weight_tensor_scale = [input_tensor, weight_scales] + matmul_op._update_input(index, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("weight quant updated") + else: + debug_print(input_tensor.name, "is input tensor") + if _MNN_compress_scope not in input_tensor.name: + new_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_tensor, scope + '_input_' + str(index), matmul_op, 'input') + input_scale_and_dim.append(input_tensor) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + matmul_op._update_input(index, new_op_input) + self._save_quant_tensor_consumer_info(new_op_input, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("feature quant updated") + else: + pre_output_scope = self._get_quant_scope(input_tensor.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_tensor.name, "feature already quant") + + bias_add_op = find_bias_add_op(matmul_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(matmul_op) + output_consumers = output_tensor.consumers() + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', matmul_op, 'output') + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + input_scale_and_dim.append(tf.constant(self._clamp_value)) + output_scale_and_dim.append(tf.constant(self._clamp_value)) + + bn_stat = get_batch_norm_statistics(matmul_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _fake_quant_bias(self, bias_tensor, input_scale, weight_scales, scope, init_moving_scale_op): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + with tf.control_dependencies([init_moving_scale_op]): + weight_scales_reshape = tf.reshape(weight_scales, bias_tensor.shape) + bias_scale = input_scale * weight_scales_reshape + bias_scale = bias_scale + tf.cast(tf.less(tf.abs(bias_scale), 1e-6), tf.float32) * self._eps + before_round = tf.divide(bias_tensor, bias_scale, name=_MNN_mark_start_name) + before_round = before_round + 1e-3 * tf.sign(before_round) + quant_bias = tf.round(before_round) + new_bias = quant_bias * bias_scale + new_bias_tensor = tf.cond(tf.constant(True), lambda: new_bias, lambda: bias_tensor, name=_MNN_mark_end_name) + + return new_bias_tensor + + def _fake_quant_weight(self, weight_tensor, reduce_dims, scope, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + scales = tf.reduce_max(tf.abs(weight_tensor, name=_MNN_mark_start_name), axis=reduce_dims, keep_dims=True) / clamp_value + self._eps + weight_scales = tf.get_variable(name='weight_scales', initializer=scales, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, weight_scales) + s_scale = custom_scale_grad(weight_scales, tf.size(weight_tensor), clamp_value) + # scale_factor = 1. / tf.math.sqrt(tf.cast(tf.size(weight_tensor), tf.float32) * clamp_value + 0.) + # grad_scale = scale_factor * weight_scales + # s_scale = grad_scale + tf.stop_gradient(weight_scales - grad_scale) + quant_w = tf.clip_by_value(tf.round(weight_tensor / s_scale), -clamp_value, clamp_value) + fake_quant_w = quant_w * s_scale + + if _MNN_QAT_DEBUG: + print_op = tf.print(scope, "weight clamp value:", clamp_value) + with tf.control_dependencies([print_op]): + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + else: + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + + self._weight_scale_original_tensor_map[weight_scales] = weight_tensor + + return new_weight_tensor, weight_scales + + def _fake_quant_feature(self, feature, scope, op, type, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + feature_abs_max = tf.reduce_max(tf.abs(feature, name=_MNN_mark_start_name)) + if _MNN_QAT_DEBUG: + print_op = tf.print("op:", op.name, type, "feature_abs_max, clamp_value:", feature_abs_max, clamp_value) + with tf.control_dependencies([print_op]): + instance_scale = feature_abs_max / clamp_value + else: + instance_scale = feature_abs_max / clamp_value + moving_average_scale = tf.get_variable(name='moving_average_scale', initializer=0., trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, moving_average_scale) + + def init(): + init_assign = tf.assign(moving_average_scale, instance_scale) + initialize_op = tf.assign(self._op_scale_initialized[op][type], 1) + if _MNN_QAT_DEBUG: + print_op = tf.print("init", moving_average_scale.name, init_assign) + with tf.control_dependencies([print_op, initialize_op]): + return tf.identity(init_assign) + else: + with tf.control_dependencies([initialize_op]): + return tf.identity(init_assign) + + def do_nothing(): + return moving_average_scale + + init_moving_scale = tf.cond(tf.equal(self._op_scale_initialized[op][type], 0), init, do_nothing) + self._feature_scale_init_op[moving_average_scale] = init_moving_scale + + def fake_quant(): + with tf.control_dependencies([init_moving_scale]): + s_scale = custom_scale_grad(moving_average_scale, tf.size(feature), clamp_value) + quant_x = tf.clip_by_value(tf.round(feature / s_scale), -clamp_value, clamp_value) + fake_quant_x = quant_x * s_scale + return fake_quant_x + + new_feature = tf.cond(tf.constant(True), fake_quant, lambda:feature, name=_MNN_mark_end_name) + + self._feature_scale_original_tensor_dims_map[moving_average_scale] = [feature, feature.shape.as_list()[-1]] + + return new_feature, moving_average_scale, feature.shape.as_list()[-1], init_moving_scale diff --git a/tools/mnncompress/mnncompress/tensorflow/OAQ_quantizer.py b/tools/mnncompress/mnncompress/tensorflow/OAQ_quantizer.py new file mode 100644 index 00000000..c6d7e2b2 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/OAQ_quantizer.py @@ -0,0 +1,823 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_input_tensor_index, is_weight_tensor, custom_scale_grad, get_variable_by_tensor +from .helpers import find_quant_output_tensor, find_bias_add_op, get_batch_norm_statistics, get_variable_by_name +from .graph_checker import check_for_grad_ops +from mnncompress.tensorflow import overflow_custom_ops + + +_MNN_compress_scope = 'MNN_QAT_' + +_MNN_mark_start_name = 'MNN_QAT_MARK_START' +_MNN_mark_end_name = 'MNN_QAT_MARK_END' + +_MNN_variable_collection_name = 'MNN_QAT_variables' + +def MNN_QAT_variables(): + all_vars = tf.global_variables() + return [v for v in all_vars if 'MNN_QAT' in v.name] + +_Quant_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative'] # TODO: support 'MatMul' + +_Variable_types = ['Variable', 'VariableV2'] + +_MNN_QAT_DEBUG = False + + +def strip_MNN_QAT_ops(graph): + '''only invoke this when you need to save the model for inference''' + def get_MNN_QAT_scope(name, match): + name_split = name.split('/') + for i in range(len(name_split)): + if name_split[i] == match: + assert i > 0, "get MNN QAT scope error" + return name_split[i-1] + return '' + + all_ops = graph.get_operations() + start_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_start_name) and op.type in ['Abs', 'RealDiv'] and 'gradients/' not in op.name] + end_op_names = [op.name for op in all_ops if op.name.endswith(_MNN_mark_end_name+'/Merge') and op.type == 'Merge' and 'gradients/' not in op.name] + + for start_name in start_op_names: + start_scope = get_MNN_QAT_scope(start_name, _MNN_mark_start_name) + end_name = '' + for end in end_op_names: + if start_scope in end: + end_name = end + break + assert end_name != '', "error: end op name not found" + + start_op = graph.get_operation_by_name(start_name) + start_input_tensor = start_op.inputs[0] + end_tensor = graph.get_tensor_by_name(end_name+':0') + end_consumers = end_tensor.consumers() + + for c in end_consumers: + indices = get_input_tensor_index(c, end_tensor) + for id in indices: + c._update_input(id, start_input_tensor) + +def debug_print(*argv): + if _MNN_QAT_DEBUG: + print("debug info: ", end="") + for arg in argv: + print(arg, end=' ') + print() + +def get_qat_state_placeholder(graph=None): + if graph is None: + graph = tf.get_default_graph() + + all_tensor_names = [tensor.name for op in graph.get_operations() for tensor in op.values()] + for name in all_tensor_names: + if 'MNN_QAT_is_training:0' in name: + return graph.get_tensor_by_name(name) + + raise ValueError('Could not find QAT state placeholder') + + +class OAQQuantizer(object): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, min_clamp_value = 0, debug_info = False, retain_sparsity=False): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + self._ops = self._graph.get_operations() + self.skip_quant_layers = skip_quant_layers + self.skip_quant_flag = skip_quant_flag + self._all_conv_and_matmul_layers = [l for l in self._ops if l.type in _Quant_Support_Ops] + self.quant_layer_names = [l.name for l in self._all_conv_and_matmul_layers] + + skip_flag_usable = False + for sf in self.skip_quant_flag: + for item in self.quant_layer_names: + if sf in item: + self.skip_quant_layers.append(item) + skip_flag_usable = True + + if skip_quant_flag != [] and skip_flag_usable == False: + raise ValueError("skip_quant_flag invalid") + + for item in self.skip_quant_layers: + if item in [l.name for l in self._all_conv_and_matmul_layers]: + self.quant_layer_names.remove(item) + else: + raise ValueError(item+" not found in graph ops, availables are: "+str([l.name for l in self._all_conv_and_matmul_layers])) + + if bits < 2 or bits > 8: + raise ValueError("bits must be a integer in [2, 8]") + self.bits = bits + self._clamp_value = float(pow(2, bits-1) - 1) + + global _MNN_QAT_DEBUG + _MNN_QAT_DEBUG = debug_info + + self._retain_sparsity = retain_sparsity + self._momentum = 0.99 + if is_training is not None: + if not isinstance(is_training, bool): + raise ValueError("is_training should be None or python bool") + if isinstance(is_training, bool): + self._is_training = tf.constant(is_training) + else: + print("is_training set as: tf.placeholder(tf.bool, name='MNN_QAT_is_training')") + self._is_training = tf.placeholder(tf.bool, name='MNN_QAT_is_training') + self._original_tensor_consumer_index_map = {} + self._quant_tensor_consumer_index_map = {} + self._feature_scale_original_tensor_dims_map = {} + self._weight_scale_original_tensor_map = {} + self._model_per_layer_scale_info = [] + self._prune_weight_ops = [] + self._mask = {} + self._all_weight_tensors = [] + self._feature_scale_init_op = {} + self._op_scale_initialized = {} + self._initialized = False + self._layer_input_clamp_value = {} + self._min_clamp_value = min_clamp_value + self._conv_op_bn_statistics = {} + self._eps = 1e-9 + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._init_prune_ratios = {} + self._op_name_weight_tensor = {} + self._skip_quant_weight_tensors = [] + self._find_all_variable_tensors() + self._insert_quant_ops() + + @property + def is_training(self): + return self._is_training + + def init(self, sess): + self._initialized = True + + for name in self.skip_quant_layers: + if name in self._op_name_weight_tensor.keys(): + weight_tensor = self._op_name_weight_tensor[name] + self._skip_quant_weight_tensors.append(weight_tensor) + + for v in self._all_weight_tensors: + self._mask[v] = sess.run(self._mask[v]) + self._init_prune_ratios[v.name] = 1. - np.mean(self._mask[v]) + self._total_weight_num += self._mask[v].size + if v in self._skip_quant_weight_tensors: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / 4.0 + else: + self._remain_weight_num += self._mask[v].size * (1 - self._init_prune_ratios[v.name]) / (32.0 / self.bits) + + def update(self, sess): + if not self._initialized: + raise RuntimeError("OAQQuantizer is not initialized, please call 'init(sess)' method before training loop") + if self._retain_sparsity: + sess.run(self._prune_weight_ops) + if _MNN_QAT_DEBUG: + for v in self._all_weight_tensors: + weight = sess.run(v) + prune_ratio = 1 - np.mean(np.abs(weight) > self._eps) + print(v, "prune_ratio:", prune_ratio) + + def _generate_mask_and_prune_ops(self, v): + if self._retain_sparsity: + th = self._eps + else: + th = -1.0 + + self._mask[v] = tf.cast(tf.abs(v) > th, tf.float32) + + if isinstance(v, tf.Tensor): + return + + if self._retain_sparsity: + prune_weight_op = tf.assign(v, v * self._mask[v]) + self._prune_weight_ops.append(prune_weight_op) + + def _find_all_variable_tensors(self): + for op in self._all_conv_and_matmul_layers: + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_tensor = get_variable_by_tensor(op.inputs[1]) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if op.type == 'MatMul': + find_weight = False + for i in range(len(op.inputs)): + input_tensor = op.inputs[i] + if is_weight_tensor(input_tensor): + find_weight = True + weight_tensor = get_variable_by_tensor(input_tensor) + self._all_weight_tensors.append(weight_tensor) + self._op_name_weight_tensor[op.name] = weight_tensor + self._generate_mask_and_prune_ops(weight_tensor) + + if not find_weight: + if op.name in self.quant_layer_names: + self.quant_layer_names.remove(op.name) + print("no weight find for {}, skip".format(op.name)) + + def strip_qat_ops(self): + '''only invoke this when you need to save the model for inference''' + for tensor in self._original_tensor_consumer_index_map.keys(): + for index, consumer in self._original_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on save:", tensor.name, consumer.name, consumer.type, index) + + def save_compress_params(self, filename, sess, append=False): + def get_save_tensor_name(name): + if name.endswith(':0'): + return name[:-2] + else: + return name + + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.QUANTIZE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + quant_algorithm = compress_proto.algo.add() + for layer_info in self._model_per_layer_scale_info: + l = quant_algorithm.quant_params.layer.add() + # {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim} + weight_info = layer_info["weight_info"] + input_info = layer_info["input_info"] + output_info = layer_info["output_info"] + + if weight_info is not []: + weight_params = compress_pb.LayerQuantizeParams.WeightParams() + weight_params.name = get_save_tensor_name(get_variable_by_tensor(weight_info[0]).name) + scales = sess.run(weight_info[1]) + scales = scales.reshape((scales.size)) + bn_stat = layer_info["bn_stat"] + + gamma = np.ones_like(scales) + if bn_stat[0] is not None: + gamma = sess.run(bn_stat[0]) + gamma = gamma.reshape((gamma.size)) + rstd = np.ones_like(scales) + if bn_stat[3] is not None: + bn_var = sess.run(bn_stat[3]) + bn_var = bn_var.reshape((bn_var.size)) + rstd = 1. / np.sqrt(bn_var + bn_stat[4]) + scales = scales * gamma * rstd + + for s in scales: + weight_params.scales.append(abs(s)) + weight_params.bits = self.bits + clamp = sess.run(input_info[3]).tolist() + weight_params.clamp_min = -int(clamp) + weight_params.clamp_max = int(clamp) + l.weight.append(weight_params) + + input_params = compress_pb.LayerQuantizeParams.ActivationParams() + input_params.name = get_save_tensor_name(input_info[0].name) + scale = sess.run(input_info[1]).tolist() + debug_print(input_info[1].name, scale) + # for count in range(input_info[2]): + input_params.scales.append(scale) + if len(input_info) == 5: + clamp = sess.run(input_info[3]).tolist() + input_params.clamp_min = -int(clamp) + input_params.clamp_max = int(clamp) + overflow_or_not = sess.run(input_info[4]).tolist() + l.method = compress_pb.LayerQuantizeParams.QuantMethod.QAT + if overflow_or_not: + l.method = compress_pb.LayerQuantizeParams.QuantMethod.OverflowAware + l.input.append(input_params) + + if len(input_info) == 6: + input_params2 = compress_pb.LayerQuantizeParams.ActivationParams() + input_params2.name = get_save_tensor_name(input_info[3].name) + scale = sess.run(input_info[4]).tolist() + debug_print(input_info[4].name, scale) + for count in range(input_info[5]): + input_params2.scales.append(scale) + l.input.append(input_params2) + + output_params = compress_pb.LayerQuantizeParams.ActivationParams() + output_params.name = get_save_tensor_name(output_info[0].name) + scale = sess.run(output_info[1]) + debug_print(output_info[1].name, scale) + # for count in range(output_info[2]): + output_params.scales.append(scale) + if len(output_info) == 4: + clamp = sess.run(output_info[3]).tolist() + output_params.clamp_min = -int(clamp) + output_params.clamp_max = int(clamp) + l.output.append(output_params) + + if not self._reported: + detail = {"algorithm": "OAQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self.bits, "skip_quant_layers": self.skip_quant_layers, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + with tf.gfile.Open(filename, mode="wb") as f: + f.write(compress_proto.SerializeToString()) + + print("compress proto saved to:", filename) + + def _recover_train_quant_graph(self): + if self._quant_tensor_consumer_index_map == {}: + return + + for tensor in self._quant_tensor_consumer_index_map.keys(): + for index, consumer in self._quant_tensor_consumer_index_map[tensor]: + consumer._update_input(index, tensor) + debug_print("on recover:", consumer.name, consumer.type, index, tensor.name) + + def _insert_quant_ops(self): + grad_ops = check_for_grad_ops(self._graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only. create quantizer before construct model optimizer\n' % grad_ops) + scope_index = -1 + for layer_name in self.quant_layer_names: + if 'gradients/' in layer_name and '_grad' in layer_name: + continue + scope_index += 1 + self._quant_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _quant_one_layer(self, layer_name, scope): + all_op_names_ops = {op.name : op for op in self._ops} + if layer_name not in all_op_names_ops.keys(): + raise ValueError("%s is not in the graph." % layer_name) + + if all_op_names_ops[layer_name].type not in _Quant_Support_Ops: + raise ValueError("op name: %s, type = %s is not supported." % (layer_name, all_op_names_ops[layer_name].type)) + + if layer_name not in self._layer_input_clamp_value.keys(): + self._layer_input_clamp_value[layer_name] = None + + if all_op_names_ops[layer_name].type == 'Conv2D' or all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + depthwise = False + if all_op_names_ops[layer_name].type == 'DepthwiseConv2dNative': + depthwise = True + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_conv(all_op_names_ops[layer_name], scope, depthwise) + self._model_per_layer_scale_info.append(res) + + if all_op_names_ops[layer_name].type == 'MatMul': + print("fake quant", layer_name, all_op_names_ops[layer_name].type) + self._op_scale_initialized[all_op_names_ops[layer_name]] = {} + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + self._op_scale_initialized[all_op_names_ops[layer_name]]['input'] = tf.get_variable(name=layer_name+'_input_scale_initialized', initializer=0, trainable=False) + self._op_scale_initialized[all_op_names_ops[layer_name]]['output'] = tf.get_variable(name=layer_name+'_output_scale_initialized', initializer=0, trainable=False) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['input']) + tf.add_to_collection(_MNN_variable_collection_name, self._op_scale_initialized[all_op_names_ops[layer_name]]['output']) + res = self._quant_matmul(all_op_names_ops[layer_name], scope) + self._model_per_layer_scale_info.append(res) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _save_original_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._original_tensor_consumer_index_map.keys(): + self._original_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._original_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _save_quant_tensor_consumer_info(self, input_tensor, index, consumer_op): + if input_tensor in self._quant_tensor_consumer_index_map.keys(): + self._quant_tensor_consumer_index_map[input_tensor].append([index, consumer_op]) + else: + self._quant_tensor_consumer_index_map[input_tensor] = [[index, consumer_op]] + + def _get_quant_scope(self, name, match): + name_split = name.split("/") + return_scope = '' + for i in range(len(name_split)): + cond1 = _MNN_compress_scope in name_split[i] + cond2 = match in name_split[i] + if cond1 and cond2: + for j in range(i+1): + return_scope = return_scope + name_split[j] + '/' + + return return_scope[:-1] + + return return_scope + + # def _get_batch_norm_statistics_op(self, conv2d_op): + # bn_statistics = self._get_batch_norm_statistics(conv2d_op) + # self._conv_op_bn_statistics[conv2d_op] = bn_statistics + + def _get_conv_clamp_value(self, input_feature, weight_tensor, conv2d_op, depthwise, reduce_dims, scope): + with tf.variable_scope(scope + "_clamp", reuse=tf.AUTO_REUSE): + clamp_value = tf.get_variable(name='clamp_value', initializer=self._clamp_value, trainable=False) + if conv2d_op.type == 'DepthwiseConv2dNative': + # MNN overflow-aware quant does not support depthwise conv now + overflow_or_not = tf.get_variable(name='overflow_or_not', dtype=tf.bool, initializer=tf.constant(False), trainable=False) + else: + overflow_or_not = tf.get_variable(name='overflow_or_not', dtype=tf.bool, initializer=tf.constant(True), trainable=False) + + tf.add_to_collection(_MNN_variable_collection_name, clamp_value) + tf.add_to_collection(_MNN_variable_collection_name, overflow_or_not) + if _MNN_QAT_DEBUG: + print(conv2d_op.name, " clamp_value_name:", clamp_value.name) + + def cond(clamp_value, is_training): + def overflow_or_not_is_True(): + feature_abs_max = tf.reduce_max(tf.abs(input_feature)) + feature_scale = feature_abs_max / clamp_value + feature_quant = tf.clip_by_value(tf.round(input_feature / feature_scale), -clamp_value, clamp_value) + + # [gamma, beta, moving_mean, moving_variance, epsilon] = self._conv_op_bn_statistics[conv2d_op] + # if gamma is not None: + # alpha = 1.0 / tf.sqrt(moving_variance + epsilon) * gamma + # elif moving_mean is not None and epsilon is not None: + # alpha = 1.0 / tf.sqrt(moving_variance + epsilon) + # else: + # alpha = 1.0 + + alpha = 1.0 + bn_fused_weight_tensor = weight_tensor * alpha + weight_scales = tf.reduce_max(tf.abs(bn_fused_weight_tensor), axis=reduce_dims, keep_dims=True) / clamp_value + weight_quant = tf.clip_by_value(tf.round(bn_fused_weight_tensor / weight_scales), -clamp_value, clamp_value) + + ori_strides = conv2d_op.get_attr('strides') + ori_padding = conv2d_op.get_attr('padding') + # ori_use_cudnn_on_gpu = conv2d_op.get_attr('use_cudnn_on_gpu') + # ori_data_format = conv2d_op.get_attr('data_format') + ori_dilations = conv2d_op.get_attr('dilations') + + # if depthwise: + # OAQ_conv_result = tf.nn.depthwise_conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, use_cudnn_on_gpu=ori_use_cudnn_on_gpu, \ + # dilations=ori_dilations, name='OAQ_conv') + + # overflow_count2 = tf.reduce_sum(tf.cast(tf.abs(OAQ_conv_result) > 32766, tf.int32)) + # else: + # OAQ_conv_result = tf.nn.conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, use_cudnn_on_gpu=ori_use_cudnn_on_gpu, \ + # dilations=ori_dilations, name='OAQ_conv') + + # overflow_count2 = tf.reduce_sum(tf.cast(tf.abs(OAQ_conv_result) > 32766, tf.int32)) + + if depthwise: + conv_int16 = overflow_custom_ops.depthwise_conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, dtype=tf.int16) + conv_int32 = overflow_custom_ops.depthwise_conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, dtype=tf.int32) + else: + conv_int16 = overflow_custom_ops.conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, dtype=tf.int16) + conv_int32 = overflow_custom_ops.conv2d(feature_quant, weight_quant, strides=ori_strides, padding=ori_padding, dtype=tf.int32) + + overflow_count = tf.reduce_sum(tf.cast(tf.not_equal(tf.cast(conv_int16, tf.int32), conv_int32), tf.int32)) + + return_cond = tf.logical_and(tf.greater(overflow_count, 0), is_training) + return_cond = tf.logical_and(return_cond, overflow_or_not) + + if _MNN_QAT_DEBUG: + print_op = tf.print("op:", conv2d_op.name, scope + "_clamp", "overflow_count:", overflow_count, "overflow_or_not:", overflow_or_not) + with tf.control_dependencies([print_op]): + return tf.identity(return_cond) + else: + return tf.identity(return_cond) + + return tf.cond(overflow_or_not, overflow_or_not_is_True, lambda: tf.constant(False)) + + def body(clamp_value, is_training): + if _MNN_QAT_DEBUG: + print_op = tf.print("clamp_value:", clamp_value.name, clamp_value) + with tf.control_dependencies([print_op]): + clamp_value -= 1.0 + else: + clamp_value -= 1.0 + + def fall_back_to_normal_quant(): + update_overflow_or_not = tf.assign(overflow_or_not, False) + with tf.control_dependencies([update_overflow_or_not]): + return tf.constant(self._clamp_value) + + clamp_value = tf.cond(clamp_value < self._min_clamp_value, fall_back_to_normal_quant, lambda: clamp_value) + return [clamp_value, is_training] + + def update(): + new_clamp_value, _ = tf.while_loop(cond, body, [clamp_value, self._is_training], back_prop=False) + update_clamp_value = tf.assign(clamp_value, new_clamp_value) + return tf.identity(update_clamp_value) + + final_clamp_value = tf.cond(self._is_training, update, lambda: clamp_value) + return final_clamp_value + + def _quant_conv(self, conv2d_op, scope, depthwise): + input_feature = conv2d_op.inputs[0] + weight_tensor = conv2d_op.inputs[1] + + reduce_dims = [0, 1, 2] + if depthwise: + reduce_dims = [0, 1, 3] + + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + # self._get_batch_norm_statistics_op(conv2d_op) + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + if _MNN_compress_scope not in input_feature.name: + clamp_value = self._layer_input_clamp_value[conv2d_op.name] + if clamp_value is None: + clamp_value = self._get_conv_clamp_value(input_feature, weight_tensor, conv2d_op, depthwise, reduce_dims, scope + '_input') + self._layer_input_clamp_value[conv2d_op.name] = clamp_value + + conv2d_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_feature, scope + '_input', conv2d_op, 'input', clamp_value) + input_scale_and_dim.append(input_feature) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + input_clamp_variable = get_variable_by_name(scope + '_input' + '_clamp' + '/clamp_value') + input_scale_and_dim.append(input_clamp_variable) + overflow_or_not = get_variable_by_name(scope + '_input' + '_clamp' + '/overflow_or_not') + input_scale_and_dim.append(overflow_or_not) + conv2d_op._update_input(0, conv2d_op_input) + self._save_quant_tensor_consumer_info(conv2d_op_input, 0, conv2d_op) + self._save_original_tensor_consumer_info(input_feature, 0, conv2d_op) + else: + pre_output_scope = self._get_quant_scope(input_feature.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + input_clamp_variable = get_variable_by_name(pre_output_scope + '_clamp' + '/clamp_value') + input_scale_and_dim.append(input_clamp_variable) + overflow_or_not = get_variable_by_name(pre_output_scope + '_clamp' + '/overflow_or_not') + input_scale_and_dim.append(overflow_or_not) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_feature.name, "feature already quant") + + # if _MNN_compress_scope not in weight_tensor.name: + clamp_value = self._layer_input_clamp_value[conv2d_op.name] + new_weight_tensor, weight_scales = self._fake_quant_weight(weight_tensor, reduce_dims, scope + '_weight', clamp_value) + weight_tensor_scale = [weight_tensor, weight_scales] + conv2d_op._update_input(1, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, 1, conv2d_op) + self._save_original_tensor_consumer_info(weight_tensor, 1, conv2d_op) + + bias_add_op = find_bias_add_op(conv2d_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(conv2d_op) + output_consumers = output_tensor.consumers() + + if _MNN_QAT_DEBUG: + print(conv2d_op.name, "latter ops:", end=' ') + for c in output_consumers: + print(c.name, end=' ') + print('\n') + + c_clamp_value = None + for c in output_consumers: + if c.type in _Quant_Support_Ops and c.name in self.quant_layer_names: + c_depthwise = c.type == 'DepthwiseConv2dNative' + c_reduce_dims = [0, 1, 2] + if c_depthwise: + c_reduce_dims = [0, 1, 3] + # self._get_batch_norm_statistics_op(c) + c_clamp_value = self._get_conv_clamp_value(output_tensor, c.inputs[1], c, c_depthwise, c_reduce_dims, scope + '_output') + self._layer_input_clamp_value[c.name] = c_clamp_value + + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', conv2d_op, 'output', c_clamp_value) + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + output_clamp_variable = get_variable_by_name(scope + '_output' + '_clamp' + '/clamp_value') + if output_clamp_variable is None: + output_clamp_variable = tf.constant(self._clamp_value) + output_scale_and_dim.append(output_clamp_variable) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + bn_stat = get_batch_norm_statistics(conv2d_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _quant_matmul(self, matmul_op, scope): + weight_tensor_scale = [] + input_scale_and_dim = [] + output_scale_and_dim = [] + + input_scale = None + weight_scales = None + output_scale = None + init_moving_scale_op = None + + for index in range(len(matmul_op.inputs)): + input_tensor = matmul_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print(input_tensor.name, "is weight tensor") + # if _MNN_compress_scope not in input_tensor.name: + reduce_dims = None + trans_a = matmul_op.get_attr("transpose_a") + trans_b = matmul_op.get_attr("transpose_b") + if index == 0: + if not trans_a: + reduce_dims = [1] + else: + reduce_dims = [0] + if index == 1: + if not trans_b: + reduce_dims = [0] + else: + reduce_dims = [1] + + new_weight_tensor, weight_scales = self._fake_quant_weight(input_tensor, reduce_dims, scope + '_weight_' + str(index)) + weight_tensor_scale = [input_tensor, weight_scales] + matmul_op._update_input(index, new_weight_tensor) + self._save_quant_tensor_consumer_info(new_weight_tensor, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("weight quant updated") + else: + debug_print(input_tensor.name, "is input tensor") + if _MNN_compress_scope not in input_tensor.name: + new_op_input, input_scale, dims, init_moving_scale_op = self._fake_quant_feature(input_tensor, scope + '_input_' + str(index), matmul_op, 'input') + input_scale_and_dim.append(input_tensor) + input_scale_and_dim.append(input_scale) + input_scale_and_dim.append(dims) + matmul_op._update_input(index, new_op_input) + self._save_quant_tensor_consumer_info(new_op_input, index, matmul_op) + self._save_original_tensor_consumer_info(input_tensor, index, matmul_op) + debug_print("feature quant updated") + else: + pre_output_scope = self._get_quant_scope(input_tensor.name, '_output') + pre_output_scale_name = pre_output_scope + '/moving_average_scale' + pre_output_scale = get_variable_by_name(pre_output_scale_name) + input_scale = pre_output_scale + init_moving_scale_op = self._feature_scale_init_op[pre_output_scale] + assert pre_output_scale in self._feature_scale_original_tensor_dims_map.keys() + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][0]) + input_scale_and_dim.append(pre_output_scale) + input_scale_and_dim.append(self._feature_scale_original_tensor_dims_map[pre_output_scale][1]) + + debug_print("pre_output_scale:", pre_output_scale, pre_output_scale.name) + debug_print(pre_output_scale in self._feature_scale_original_tensor_dims_map) + debug_print(self._feature_scale_original_tensor_dims_map[pre_output_scale]) + debug_print(input_tensor.name, "feature already quant") + + bias_add_op = find_bias_add_op(matmul_op) + if bias_add_op is not None: + for index in range(len(bias_add_op.inputs)): + input_tensor = bias_add_op.inputs[index] + if is_weight_tensor(input_tensor): + debug_print("found bias:", input_tensor.name, "index:", index) + new_bias = self._fake_quant_bias(input_tensor, input_scale, weight_scales, scope + '_bias_' + str(index), init_moving_scale_op) + bias_add_op._update_input(index, new_bias) + self._save_quant_tensor_consumer_info(new_bias, index, bias_add_op) + self._save_original_tensor_consumer_info(input_tensor, index, bias_add_op) + + output_tensor = find_quant_output_tensor(matmul_op) + output_consumers = output_tensor.consumers() + # if _MNN_compress_scope not in output_consumers[0].name: + new_output_tensor, output_scale, dims, init_moving_scale_op = self._fake_quant_feature(output_tensor, scope + '_output', matmul_op, 'output') + output_scale_and_dim.append(output_tensor) + output_scale_and_dim.append(output_scale) + output_scale_and_dim.append(dims) + for c in output_consumers: + index = get_input_tensor_index(c, output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + self._save_quant_tensor_consumer_info(new_output_tensor, id, c) + self._save_original_tensor_consumer_info(output_tensor, id, c) + + bn_stat = get_batch_norm_statistics(matmul_op) + + return {"weight_info": weight_tensor_scale, "input_info": input_scale_and_dim, "output_info": output_scale_and_dim, "bn_stat": bn_stat} + + def _fake_quant_bias(self, bias_tensor, input_scale, weight_scales, scope, init_moving_scale_op): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + with tf.control_dependencies([init_moving_scale_op]): + weight_scales_reshape = tf.reshape(weight_scales, bias_tensor.shape) + bias_scale = input_scale * weight_scales_reshape + bias_scale = bias_scale + tf.cast(tf.less(tf.abs(bias_scale), 1e-6), tf.float32) * self._eps + before_round = tf.divide(bias_tensor, bias_scale, name=_MNN_mark_start_name) + before_round = before_round + 1e-3 * tf.sign(before_round) + quant_bias = tf.round(before_round) + new_bias = quant_bias * bias_scale + new_bias_tensor = tf.cond(tf.constant(True), lambda: new_bias, lambda: bias_tensor, name=_MNN_mark_end_name) + + return new_bias_tensor + + def _fake_quant_weight(self, weight_tensor, reduce_dims, scope, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + scales = tf.reduce_max(tf.abs(weight_tensor, name=_MNN_mark_start_name), axis=reduce_dims, keep_dims=True) / clamp_value + self._eps + weight_scales = tf.get_variable(name='weight_scales', initializer=tf.zeros_like(scales), trainable=True) + weight_scales_initialized = tf.get_variable(name='weight_scales_initialized', initializer=False, trainable=False) + def init(): + assign_scales = tf.assign(weight_scales, scales) + with tf.control_dependencies([assign_scales]): + return tf.identity(weight_scales) + initialized_weight_scales = tf.cond(weight_scales_initialized, lambda:weight_scales, init) + tf.add_to_collection(_MNN_variable_collection_name, weight_scales) + s_scale = custom_scale_grad(initialized_weight_scales, tf.size(weight_tensor), clamp_value) + # scale_factor = 1. / tf.math.sqrt(tf.cast(tf.size(weight_tensor), tf.float32) * clamp_value + 0.) + # grad_scale = scale_factor * weight_scales + # s_scale = grad_scale + tf.stop_gradient(weight_scales - grad_scale) + quant_w = tf.clip_by_value(tf.round(weight_tensor / s_scale), -clamp_value, clamp_value) + fake_quant_w = quant_w * s_scale + + if _MNN_QAT_DEBUG: + print_op = tf.print(scope, "weight clamp value:", clamp_value) + with tf.control_dependencies([print_op]): + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + else: + new_weight_tensor = tf.cond(tf.constant(True), lambda: fake_quant_w, lambda: weight_tensor, name=_MNN_mark_end_name) + + self._weight_scale_original_tensor_map[weight_scales] = weight_tensor + + return new_weight_tensor, weight_scales + + def _fake_quant_feature(self, feature, scope, op, type, clamp_value=None): + if clamp_value is None: + clamp_value = self._clamp_value + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + feature_abs_max = tf.reduce_max(tf.abs(feature, name=_MNN_mark_start_name)) + if _MNN_QAT_DEBUG: + print_op = tf.print("op:", op.name, type, "feature_abs_max, clamp_value:", feature_abs_max, clamp_value) + with tf.control_dependencies([print_op]): + instance_scale = feature_abs_max / clamp_value + else: + instance_scale = feature_abs_max / clamp_value + moving_average_scale = tf.get_variable(name='moving_average_scale', initializer=0., trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, moving_average_scale) + + def init(): + init_assign = tf.assign(moving_average_scale, instance_scale) + initialize_op = tf.assign(self._op_scale_initialized[op][type], 1) + if _MNN_QAT_DEBUG: + print_op = tf.print("init", moving_average_scale.name, init_assign) + with tf.control_dependencies([print_op, initialize_op]): + return tf.identity(init_assign) + else: + with tf.control_dependencies([initialize_op]): + return tf.identity(init_assign) + + def do_nothing(): + return moving_average_scale + + init_moving_scale = tf.cond(tf.equal(self._op_scale_initialized[op][type], 0), init, do_nothing) + self._feature_scale_init_op[moving_average_scale] = init_moving_scale + + def fake_quant(): + with tf.control_dependencies([init_moving_scale]): + s_scale = custom_scale_grad(moving_average_scale, tf.size(feature), clamp_value) + quant_x = tf.clip_by_value(tf.round(feature / s_scale), -clamp_value, clamp_value) + fake_quant_x = quant_x * s_scale + return fake_quant_x + + new_feature = tf.cond(tf.constant(True), fake_quant, lambda:feature, name=_MNN_mark_end_name) + + self._feature_scale_original_tensor_dims_map[moving_average_scale] = [feature, feature.shape.as_list()[-1]] + + return new_feature, moving_average_scale, feature.shape.as_list()[-1], init_moving_scale diff --git a/tools/mnncompress/mnncompress/tensorflow/SA_prune_ratio_searcher.py b/tools/mnncompress/mnncompress/tensorflow/SA_prune_ratio_searcher.py new file mode 100644 index 00000000..0382da91 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/SA_prune_ratio_searcher.py @@ -0,0 +1,281 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np +import math +import yaml +from mnncompress.tensorflow.helpers import get_op_name_of_type, is_weight_tensor, get_variable_by_tensor + +_supported_prune_algrithms = ['level'] +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class SAPruneRatioSearcher(object): + def __init__(self, sess, performance_evaluate_func, \ + sparsity = 0.5, cool_down_rate=0.95, perturbation_magnitude=0.5, start_temperature=100., stop_temperature=10., \ + measurement_mode = 'maximize', prune_algorithm = 'level', debug_info = False): + print("\n\nsparsity, start_temperature, stop_temperature, cool_down_rate, perturbation_magnitude:", sparsity, start_temperature, stop_temperature, cool_down_rate, perturbation_magnitude) + self._sess = sess + self._graph = self._sess.graph + + if sparsity >= 1.0: + raise ValueError("sparsity must be a number in (0, 1)") + self._target_sparsity = sparsity + + if stop_temperature < 0.1 or stop_temperature > start_temperature: + raise ValueError("temperature settings should be: start_temperature > stop_temperature >= 0.1") + self._start_temperature = float(start_temperature) + self._current_temperature = tf.get_variable(name='current_temperature', initializer=self._start_temperature, trainable=False) + self._sess.run(self._current_temperature.initializer) + self._stop_temperature = float(stop_temperature) + + if cool_down_rate <= 0 or cool_down_rate >= 1: + raise ValueError("cool_down_rate shoulde be between 0 and 1") + self._cool_down_rate = cool_down_rate + self._current_performance = -np.inf + self._best_performance = -np.inf + self._best_performance_iter = 1 + self._best_config_dict = {} + + if perturbation_magnitude <= 0 or perturbation_magnitude >= 1: + raise ValueError("perturbation_magnitude should be between 0 and 1") + self._perturbation_magnitude = perturbation_magnitude + + if prune_algorithm not in _supported_prune_algrithms: + raise ValueError() + self._prune_algorithm = prune_algorithm + + self._debug = debug_info + if debug_info: + tf.logging.set_verbosity(tf.logging.DEBUG) + + self._performance_evaluate_func = performance_evaluate_func + + if measurement_mode not in ['maximize', 'minimize']: + raise ValueError("measurement_mode must be either 'maximize' or 'minimize', which indicates the optimization direction of 'test_performance_node'") + self._measurement_mode = measurement_mode + + self._prune_op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._all_weight_variable_op_pairs = self._get_all_weight_variable_op_pairs(self._prune_op_names) + self._all_weight_variable_sorted = self._get_all_weight_variables_sorted() + + self._sorted_weight_variable_sizes = [] + for v in self._all_weight_variable_sorted: + self._sorted_weight_variable_sizes.append(self._sess.run(v).size) + self._sorted_weight_variable_sizes = np.array(self._sorted_weight_variable_sizes).astype(np.float32) + + self._sparsities = self._init_sparsity() + self._perturbed_sparsities = self._generate_perturbed_sparsities() + + if self._debug: + print_op = tf.print("perturbed_sparsities:", self._perturbed_sparsities) + with tf.control_dependencies([print_op]): + self._perturbed_sparsities = tf.identity(self._perturbed_sparsities) + + self._current_sparsities = self._get_current_sparsities() + print("initial sparsities:", sess.run(self._current_sparsities)) + + if self._debug: + print_op = tf.print("current sparsities:", self._current_sparsities) + with tf.control_dependencies([print_op]): + self._current_sparsities = tf.identity(self._current_sparsities) + + self._update_sparsities = tf.assign(self._sparsities, self._current_sparsities) + self._restore_backup_weights = self._get_restore_backup_weights_ops() + self._apply_masks = self._generate_masks_according_to_sparsities(self._prune_algorithm) + self._update_temperature = tf.assign(self._current_temperature, self._current_temperature * self._cool_down_rate) + self._restart_temperature = tf.assign(self._current_temperature, self._start_temperature) + self._prune_iteration = 1 + + def search(self, result_file = "SA_found_prune_ratios.yml"): + for t in range(2): + self._sess.run(self._restart_temperature) + self._search() + + print("\n\n\n----------search complete--------------") + best_performance = self._best_performance + if self._measurement_mode == 'minimize': + best_performance = -best_performance + print("best performance:", best_performance, ", found at iteration:", self._best_performance_iter) + print("pruner config dict found:", self._best_config_dict) + + f = open(result_file, "w") + yaml.dump(self._best_config_dict, f) + f.close() + print("config_dict saved to file:", result_file) + + return self._best_config_dict + + def _search(self): + + while self._sess.run(self._current_temperature) > self._stop_temperature: + print('\n\nsearch iteration:', self._prune_iteration) + print('current temperature, stop temperature:', self._sess.run(self._current_temperature), self._stop_temperature) + + while True: + self._sess.run(self._apply_masks) + evaluation_result = self._performance_evaluate_func() + + config_dict = self._generate_config_dict() + print("current config dict:", config_dict) + + if self._measurement_mode == "minimize": + evaluation_result *= -1 + + # if better evaluation result, then accept the perturbation + if evaluation_result > self._current_performance: + print("better performance") + self._current_performance = evaluation_result + self._sess.run(self._update_sparsities) + self._sess.run(self._restore_backup_weights) + + # save best performance and best params + if evaluation_result > self._best_performance: + print("update best model") + self._best_performance = evaluation_result + self._best_performance_iter = self._prune_iteration + self._best_config_dict = config_dict + break + # if not, accept with probability e^(-deltaE/current_temperature) + else: + delta_E = np.abs(evaluation_result - self._current_performance) + probability = math.exp(-1 * delta_E / self._sess.run(self._current_temperature)) + random_num = np.random.uniform(0, 1) + if random_num < probability: + print("accept with probability", random_num, probability) + self._current_performance = evaluation_result + self._sess.run(self._update_sparsities) + self._sess.run(self._restore_backup_weights) + break + + self._sess.run(self._restore_backup_weights) + + print("current best config_dict: ", self._best_config_dict) + + # cool down + self._sess.run(self._update_temperature) + self._prune_iteration += 1 + + def _get_current_sparsities(self): + current_sparsities = [] + for i in range(len(self._all_weight_variable_sorted)): + weight = self._all_weight_variable_sorted[i] + weight_abs = tf.abs(weight) + sparsity = tf.reduce_mean(tf.cast(weight_abs < 1e-8, tf.float32)) * tf.constant([1.]) + current_sparsities.append(sparsity) + current_sparsities = tf.concat(current_sparsities, axis=0) + + return current_sparsities + + def _generate_config_dict(self): + config_dict = {} + current_sparsities = self._sess.run(self._current_sparsities).tolist() + for i in range(len(self._all_weight_variable_sorted)): + weight = self._all_weight_variable_sorted[i] + op = self._all_weight_variable_op_pairs[weight] + sparsity = current_sparsities[i] + config_dict[op.name] = sparsity + + return config_dict + + def _generate_level_pruner_masks(self): + apply_masks = [] + for i in range(len(self._all_weight_variable_sorted)): + v = self._all_weight_variable_sorted[i] + size = self._sorted_weight_variable_sizes[i] + sparsity = self._perturbed_sparsities[i] + + sparse_num = size * sparsity + sparse_num = tf.cast(sparse_num, tf.int64) + weight_abs = tf.abs(v) + weight_abs_sorted = tf.contrib.framework.sort(tf.reshape(weight_abs, [-1])) + threshold = weight_abs_sorted[sparse_num] + mask = tf.cast(weight_abs >= threshold, tf.float32) + if self._debug: + print_op = tf.print("mask sparsity:", 1-tf.reduce_mean(mask), sparsity) + with tf.control_dependencies([print_op]): + mask = tf.identity(mask) + apply_mask = tf.assign(v, v * mask) + apply_masks.append(apply_mask) + + return apply_masks + + def _generate_masks_according_to_sparsities(self, prune_algorithm): + if prune_algorithm == "level": + apply_masks = self._generate_level_pruner_masks() + return apply_masks + else: + raise NotImplementedError("prune algorithm: " + prune_algorithm + " not supported now") + + def _get_restore_backup_weights_ops(self): + restore_weights_ops = [] + for v in self._all_weight_variable_sorted: + restore_weights_ops.append(tf.assign(v, self._sess.run(v))) + return restore_weights_ops + + def _generate_perturbed_sparsities(self): + magnitude = self._current_temperature / self._start_temperature * self._perturbation_magnitude + perturbation = tf.random.uniform([len(self._all_weight_variable_sorted)], -magnitude, magnitude) + if self._debug: + print_op = tf.print("perturbation, sparsities, sparsities + perturbation:", perturbation, self._sparsities, self._sparsities + perturbation) + with tf.control_dependencies([print_op]): + perturbation = tf.identity(perturbation) + sparsities = tf.clip_by_value(self._sparsities + perturbation, 0, tf.reduce_max(self._sparsities + perturbation)) + sparsities = tf.contrib.framework.sort(sparsities) + + sparse_num = tf.reduce_sum(sparsities * self._sorted_weight_variable_sizes) + overall_sparsity = sparse_num / tf.reduce_sum(self._sorted_weight_variable_sizes) + scale_factor = self._target_sparsity / overall_sparsity + perturbed_sparsities = scale_factor * sparsities + + if self._debug: + print_op = tf.print("after sparsity rescale:", perturbed_sparsities) + with tf.control_dependencies([print_op]): + perturbed_sparsities = tf.identity(perturbed_sparsities) + + perturbed_sparsities_mask = tf.cast(perturbed_sparsities < 1.0, tf.float32) + perturbed_sparsities = perturbed_sparsities * perturbed_sparsities_mask + (1.0 - perturbed_sparsities_mask) * self._target_sparsity + + if self._debug: + print_op = tf.print("before, after perturbation:", self._sparsities, perturbed_sparsities) + with tf.control_dependencies([print_op]): + return tf.identity(perturbed_sparsities) + else: + return perturbed_sparsities + + def _init_sparsity(self): + while True: + sparsities = np.sort(np.random.uniform(0, 1, len(self._all_weight_variable_sorted))) + sparse_num = np.sum(sparsities * self._sorted_weight_variable_sizes) + overall_sparsity = sparse_num / np.sum(self._sorted_weight_variable_sizes) + scale_factor = self._target_sparsity / overall_sparsity + sparsities = (scale_factor * sparsities).astype(np.float32) + if sparsities[0] >= 0 and sparsities[-1] < 1: + sparsities = tf.get_variable(name="mnn_sa_sparsities", initializer=tf.constant(sparsities), trainable=False) + self._sess.run(sparsities.initializer) + return sparsities + + def _get_all_weight_variables_sorted(self): + all_weight_variables = [v for v in self._all_weight_variable_op_pairs.keys()] + sorted_weight_variables = sorted(all_weight_variables, key=lambda v: self._sess.run(v).size) + return sorted_weight_variables + + def _get_all_weight_variable_op_pairs(self, op_names): + prune_ops = [self._graph.get_operation_by_name(op_name) for op_name in op_names] + all_weight_variables = {} + for op in prune_ops: + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_variable = get_variable_by_tensor(op.inputs[1]) + if not self._sess.run(tf.is_variable_initialized(weight_variable)): + raise ValueError("weight variable not initialized, please do prune ratio search from a checkpoint") + all_weight_variables[weight_variable] = op + if op.type == "MatMul": + if "gradients" in op.name and "_grad" in op.name: + continue + + for t in op.inputs: + if is_weight_tensor(t): + weight_variable = get_variable_by_tensor(t) + if not self._sess.run(tf.is_variable_initialized(weight_variable)): + raise ValueError("weight variable not initialized, please do prune ratio search from a checkpoint") + all_weight_variables[weight_variable] = op + return all_weight_variables diff --git a/tools/mnncompress/mnncompress/tensorflow/SIMD_OC_pruner.py b/tools/mnncompress/mnncompress/tensorflow/SIMD_OC_pruner.py new file mode 100644 index 00000000..4cf648d0 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/SIMD_OC_pruner.py @@ -0,0 +1,401 @@ +from __future__ import print_function +import tensorflow as tf +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_op_name_of_type, get_input_tensor_index, is_weight_tensor, get_variable_by_tensor + +_MNN_compress_scope = 'MNN_pruner_' +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class SIMDOCPruner(object): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="SIMDOC_found_prune_ratios.yml"): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + + self._sensitivities = np.load(sensitivity_npy_file, allow_pickle=True).item() + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._prune_ratio_result_file = prune_ratio_result_file + + self._op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._config_dict = {} + self._generated_config_dict = {} + + self._debug_info = debug_info + + self._ops = self._graph.get_operations() + self._name_to_ops = {op.name : op for op in self._ops} + self._prune_weight_variable_op_names = {} + self._mask_dict = {} + self._config_file = config_file + + if (total_pruning_iterations < 0) and (not isinstance(total_pruning_iterations, int)): + raise ValueError("total_pruning_iterations must be a integer >= 0") + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._finetune_step = 0 + + self._step = tf.get_variable(name='MNN_pruner_internal_step', initializer=0, trainable=False) + self._step_init_op = tf.variables_initializer([self._step]) + self._control_prune_op = self._step < self._total_pruning_iterations + + self._update_step_op = tf.assign(self._step, self._step + 1) + tf.add_to_collection("update_internal_step", self._update_step_op) + + self._op_name_weight_tensor_name = {} + self._initialized = False + self._prune_var_mask_abs_grads = {} + self._all_means_flatten = [] + self._pname_gt_reshape_mean = {} + self._pname_gt_remains_reshape_mean = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + self._get_prune_weight_variable_op_names() + self._generate_prune_ratios() + self._generate_all_constants() + self._insert_pruning_ops() + self._init_mask_op = tf.variables_initializer(tf.get_collection("init_masks")) + self._update_pruning_ops = tf.get_collection("update_pruning_masks") + self._op_prune_ratio_dict = {} + for op_name in self._mask_dict.keys(): + ratio = 1.0 - tf.reduce_mean(self._mask_dict[op_name]) + self._op_prune_ratio_dict[op_name] = ratio + + def do_pruning(self, sess): + if not self._initialized: + sess.run(self._step_init_op) + sess.run(self._init_mask_op) + self._initialized = True + + if sess.run(self._control_prune_op): + if self._prune_finetune_iterations == 0: + sess.run(self._update_step_op) + else: + if self._finetune_step == 0: + sess.run(self._update_step_op) + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + sess.run(self._update_pruning_ops) + + def save_compress_params(self, filename, sess, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.SIMD_OC + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + oc_blocks = algorithm.prune_params.simd_oc_pruner_params.oc_blocks + + for op_name in self._mask_dict.keys(): + tensor_name = self._op_name_weight_tensor_name[op_name] + layer_names.append(op_name) + weight_tensor_names.append(tensor_name) + ratio = sess.run(self._op_prune_ratio_dict[op_name]) + self._total_weight_num += sess.run(tensor_name).size + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (sess.run(tensor_name).size * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + oc_blocks.append(4) + print(op_name, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "SIMD_OC", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _get_prune_weight_variable_op_names(self): + for op_name in self._op_names: + op = self._name_to_ops[op_name] + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_variable = get_variable_by_tensor(op.inputs[1]) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + if op.type == "MatMul": + if "gradients" in op.name and "_grad" in op.name: + continue + + for t in op.inputs: + if is_weight_tensor(t): + weight_variable = get_variable_by_tensor(t) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + def _generate_prune_ratios(self): + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + self._generate_prune_ratios_per_layer(config_dict) + + f = open(self._prune_ratio_result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", self._prune_ratio_result_file) + + def _generate_prune_ratios_per_layer(self, config_dict = {}): + for v, gn in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + op = self._name_to_ops[op_name] + if op.type == 'DepthwiseConv2dNative': + gn = gn.transpose((0, 1, 3, 2)) + + gn_shape = gn.shape + oc_blocks = gn_shape[-1] // 4 + remains = gn_shape[-1] % 4 + + block_total_rows = 0 + if oc_blocks != 0: + block_total_rows = oc_blocks * 4 + gt_reshape = gn[..., 0:block_total_rows].reshape((-1, 4)) + gt_reshape_mean = gt_reshape.mean(axis=1, keepdims=True) + self._pname_gt_reshape_mean[v] = gt_reshape_mean + self._all_means_flatten.append(gt_reshape_mean.flatten()) + + if remains > 0: + gt_remains_reshape = gn[..., block_total_rows:block_total_rows+remains].reshape((-1, remains)) + gt_remains_reshape_mean = gt_remains_reshape.mean(axis=1, keepdims=True) + self._pname_gt_remains_reshape_mean[v] = gt_remains_reshape_mean + self._all_means_flatten.append(gt_remains_reshape_mean.flatten()) + + all_means_flatten = np.concatenate(self._all_means_flatten) + all_means_flatten = np.sort(all_means_flatten) + threshold_index = int(all_means_flatten.size * self._target_sparsity) + threshold = all_means_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + pruned_num = 0 + for v, m in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + op = self._name_to_ops[op_name] + if op.type == 'DepthwiseConv2dNative': + m = m.transpose((0, 1, 3, 2)) + + if v in self._pname_gt_reshape_mean.keys(): + gt_reshape_mean = self._pname_gt_reshape_mean[v] + mask = gt_reshape_mean > threshold + pruned_num = np.sum((mask == False)) * 4 + + if v in self._pname_gt_remains_reshape_mean.keys(): + gt_remains_reshape_mean = self._pname_gt_remains_reshape_mean[v] + mask = gt_remains_reshape_mean > threshold + pruned_num += (np.sum((mask == False)) * (m.shape[-1] % 4)) + + sparsity = float(pruned_num) / m.size + total_weight_num += m.size + op_name = self._prune_weight_variable_op_names[v] + self._generated_config_dict[op_name] = sparsity + if op_name in config_dict.keys(): + self._config_dict[op_name] = config_dict[op_name] + else: + self._config_dict[op_name] = sparsity + + if self._config_dict[op_name] > self._max_prune_ratio: + self._config_dict[op_name] = self._max_prune_ratio + + if self._config_dict[op_name] < 0.15: + self._config_dict[op_name] = 0.0 + + if self._config_dict[op_name] >= 0.15 and self._config_dict[op_name] < 0.35: + self._config_dict[op_name] = 0.35 + + if op.type == 'DepthwiseConv2dNative': + self._config_dict[op_name] = 0.0 + + total_pruned_weight_num += (m.size * self._config_dict[op_name]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _insert_pruning_ops(self): + scope_index = -1 + for layer_name in self._prune_weight_variable_op_names.values(): + scope_index += 1 + self._prune_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _prune_one_layer(self, layer_name, scope): + if self._name_to_ops[layer_name].type == 'Conv2D' or self._name_to_ops[layer_name].type == 'DepthwiseConv2dNative': + self._prune_conv(self._name_to_ops[layer_name], scope) + + if self._name_to_ops[layer_name].type == 'MatMul': + self._prune_matmul(self._name_to_ops[layer_name], scope) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _get_variable_by_name(self, name): + return [v for v in tf.global_variables() if v.op.name == name][0] + + def _prune_conv(self, conv_op, scope): + weight_tensor = conv_op.inputs[1] + self._op_name_weight_tensor_name[conv_op.name] = weight_tensor.name + + if self._config_dict[conv_op.name] == 0.0: + self._mask_dict[conv_op.name] = tf.ones_like(weight_tensor) + elif conv_op.type == 'Conv2D': + new_weight_tensor = self._prune_weight(conv_op, weight_tensor, scope) + + def _prune_matmul(self, matmul_op, scope): + if "gradients" in matmul_op.name and "_grad" in matmul_op.name: + return + + input_1 = matmul_op.inputs[0] + input_2 = matmul_op.inputs[1] + + for input_tensor in [input_1, input_2]: + index = get_input_tensor_index(matmul_op, input_tensor)[0] + if is_weight_tensor(input_tensor): + self._op_name_weight_tensor_name[matmul_op.name] = input_tensor.name + if self._config_dict[matmul_op.name] == 0.0: + self._mask_dict[matmul_op.name] = tf.ones_like(input_tensor) + else: + new_weight_tensor = self._prune_weight(matmul_op, input_tensor, scope) + + def _generate_all_constants(self): + self._pname_gt_reshape_mean_constants = {} + self._pname_gt_reshape_mean_flatten_constants = {} + self._pname_gt_remains_reshape_mean_constants = {} + self._pname_gt_remains_reshape_mean_flatten_constants = {} + self._config_dict_constants = {} + + self._all_means_flatten_constant = tf.constant(np.concatenate(self._all_means_flatten).flatten()) + + for v, _ in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + + self._config_dict_constants[op_name] = tf.constant(self._config_dict[op_name]) + + if v in self._pname_gt_reshape_mean.keys(): + self._pname_gt_reshape_mean_constants[v] = tf.constant(self._pname_gt_reshape_mean[v]) + self._pname_gt_reshape_mean_flatten_constants[v] = tf.constant(self._pname_gt_reshape_mean[v].flatten()) + + if v in self._pname_gt_remains_reshape_mean.keys(): + self._pname_gt_remains_reshape_mean_constants[v] = tf.constant(self._pname_gt_remains_reshape_mean[v]) + self._pname_gt_remains_reshape_mean_flatten_constants[v] = tf.constant(self._pname_gt_remains_reshape_mean[v].flatten()) + + def _prune_weight(self, op, weight_tensor, scope): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + weight_variable = get_variable_by_tensor(weight_tensor) + m = self._prune_var_mask_abs_grads[weight_variable] + + if op.type == 'DepthwiseConv2dNative': + m = m.transpose((0, 1, 3, 2)) + + mask = None + + if weight_variable in self._pname_gt_reshape_mean_constants.keys(): + gt_reshape_mean_const = self._pname_gt_reshape_mean_constants[weight_variable] + gt_reshape_mean_flatten_const = self._pname_gt_reshape_mean_flatten_constants[weight_variable] + + argsorted = tf.contrib.framework.argsort(gt_reshape_mean_flatten_const) + local_threshold_index = tf.cast(tf.size(gt_reshape_mean_const), tf.float32) * self._config_dict[op.name] * tf.cast(self._step, tf.float32) / self._total_pruning_iterations + local_threshold_index = tf.cast(local_threshold_index, tf.int64) + ones = lambda: tf.ones_like(gt_reshape_mean_flatten_const) + mask = tf.get_variable(name='mask', initializer=ones, trainable=False) + tf.add_to_collection("init_masks", mask) + # mask = tf.ones_like(gt_reshape_mean.flatten()) + # mask[argsorted[0:local_threshold_index]] = 0. + update_indices = argsorted[0:local_threshold_index] + update_values = tf.cast(tf.zeros_like(update_indices), tf.float32) + mask = tf.scatter_nd_update(mask, tf.reshape(update_indices, [local_threshold_index, 1]), update_values) + mask = tf.reshape(mask, gt_reshape_mean_const.shape) + + mask = tf.concat([mask, mask, mask, mask], axis=1) + oc_blocks = m.shape[-1] // 4 + block_total_rows = oc_blocks * 4 + mask = tf.reshape(mask, [-1, block_total_rows]) + + if weight_variable in self._pname_gt_remains_reshape_mean_constants.keys(): + gt_remains_reshape_mean_const = self._pname_gt_remains_reshape_mean_constants[weight_variable] + gt_remains_reshape_mean_flatten_const = self._pname_gt_remains_reshape_mean_flatten_constants[weight_variable] + + argsorted = tf.contrib.framework.argsort(gt_remains_reshape_mean_flatten_const) + local_threshold_index = tf.cast(tf.size(gt_remains_reshape_mean_const), tf.float32) * self._config_dict_constants[op.name] * tf.cast(self._step, tf.float32) / self._total_pruning_iterations + local_threshold_index = tf.cast(local_threshold_index, tf.int64) + ones = lambda: tf.ones_like(gt_remains_reshape_mean_flatten_const) + remain_mask = tf.get_variable(name='remain_mask', initializer=ones, trainable=False) + tf.add_to_collection("init_masks", remain_mask) + # remain_mask = tf.ones_like(tf.layers.flatten(gt_remains_reshape_mean_const)) + # remain_mask[argsorted[0:local_threshold_index]] = 0. + update_indices = argsorted[0:local_threshold_index] + update_values = tf.cast(tf.zeros_like(update_indices), tf.float32) + remain_mask = tf.scatter_nd_update(remain_mask, tf.reshape(update_indices, [local_threshold_index, 1]), update_values) + remain_mask = tf.reshape(remain_mask, gt_remains_reshape_mean_const.shape) + + remains = m.shape[-1] % 4 + remain_mask = tf.concat([remain_mask for x in range(0, remains)], axis=1) + + if mask is not None: + mask = tf.concat([mask, remain_mask], axis=1) + else: + mask = remain_mask + + mask = tf.reshape(mask, m.shape) + + if op.type == 'DepthwiseConv2dNative': + mask = tf.transpose(mask, (0, 1, 3, 2)) + + self._mask_dict[op.name] = mask + + if self._debug_info: + pruning_ratio = 1.0 - tf.reduce_mean(mask) + print_op = tf.print("pruning iter:", self._step, "/", self._total_pruning_iterations, op.name, "pruning_ratio:", pruning_ratio) + + with tf.control_dependencies([print_op]): + pruned_weight_tensor = weight_variable * mask + else: + pruned_weight_tensor = weight_variable * mask + + new_weight_tensor_assign = tf.assign(weight_variable, pruned_weight_tensor) + tf.add_to_collection("update_pruning_masks", new_weight_tensor_assign) + + return new_weight_tensor_assign diff --git a/tools/mnncompress/mnncompress/tensorflow/SNIP_level_pruner.py b/tools/mnncompress/mnncompress/tensorflow/SNIP_level_pruner.py new file mode 100644 index 00000000..1c1aab78 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/SNIP_level_pruner.py @@ -0,0 +1,282 @@ +from __future__ import print_function +import tensorflow as tf +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_op_name_of_type, get_input_tensor_index, is_weight_tensor, get_variable_by_tensor + +_MNN_compress_scope = 'MNN_pruner_' +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class SNIPLevelPruner(object): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="SNIPLevel_found_prune_ratios.yml"): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + + self._sensitivities = np.load(sensitivity_npy_file, allow_pickle=True).item() + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._prune_ratio_result_file = prune_ratio_result_file + + self._op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._config_dict = {} + + self._debug_info = debug_info + + self._ops = self._graph.get_operations() + self._name_to_ops = {op.name : op for op in self._ops} + self._prune_weight_variable_op_names = {} + self._mask_dict = {} + self._config_file = config_file + self._generated_config_dict = {} + + if (total_pruning_iterations < 0) and (not isinstance(total_pruning_iterations, int)): + raise ValueError("total_pruning_iterations must be a integer >= 0") + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._finetune_step = 0 + + self._step = tf.get_variable(name='MNN_pruner_internal_step', initializer=0, trainable=False) + self._step_init_op = tf.variables_initializer([self._step]) + self._control_prune_op = self._step < self._total_pruning_iterations + + self._update_step_op = tf.assign(self._step, self._step + 1) + tf.add_to_collection("update_internal_step", self._update_step_op) + + self._op_name_weight_tensor_name = {} + self._current_accumulate_step = 0 + self._total_accumulate_steps = 10 + self._prune_var_mask_abs_grads = {} + self._mask_abs_grads = [] + self._initialized = False + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + self._get_prune_weight_variable_op_names() + self._generate_prune_ratios() + self._generate_all_constants() + self._insert_pruning_ops() + self._update_pruning_ops = tf.get_collection("update_pruning_masks") + self._op_prune_ratio_dict = {} + for op_name in self._mask_dict.keys(): + ratio = 1.0 - tf.reduce_mean(self._mask_dict[op_name]) + self._op_prune_ratio_dict[op_name] = ratio + + def do_pruning(self, sess): + if not self._initialized: + sess.run(self._step_init_op) + self._initialized = True + + if sess.run(self._control_prune_op): + if self._prune_finetune_iterations == 0: + sess.run(self._update_step_op) + else: + if self._finetune_step == 0: + sess.run(self._update_step_op) + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + sess.run(self._update_pruning_ops) + + def save_compress_params(self, filename, sess, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.RANDOM + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for op_name in self._mask_dict.keys(): + tensor_name = self._op_name_weight_tensor_name[op_name] + layer_names.append(op_name) + weight_tensor_names.append(tensor_name) + ratio = sess.run(self._op_prune_ratio_dict[op_name]) + self._total_weight_num += sess.run(tensor_name).size + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (sess.run(tensor_name).size * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + print(op_name, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "SNIP_Level", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _get_prune_weight_variable_op_names(self): + for op_name in self._op_names: + op = self._name_to_ops[op_name] + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_variable = get_variable_by_tensor(op.inputs[1]) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + if op.type == "MatMul": + if "gradients" in op.name and "_grad" in op.name: + continue + + for t in op.inputs: + if is_weight_tensor(t): + weight_variable = get_variable_by_tensor(t) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + def _generate_prune_ratios(self): + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + for v, m in self._prune_var_mask_abs_grads.items(): + self._mask_abs_grads.append(m) + self._generate_prune_ratios_per_layer(config_dict) + + f = open(self._prune_ratio_result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", self._prune_ratio_result_file) + + def _generate_prune_ratios_per_layer(self, config_dict = {}): + all_weights_flatten = [] + for m in self._mask_abs_grads: + all_weights_flatten = np.concatenate([all_weights_flatten, m.flatten()]) + all_weights_flatten = np.sort(all_weights_flatten) + threshold_index = int(all_weights_flatten.size * self._target_sparsity) + threshold = all_weights_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + for v, m in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + op = self._name_to_ops[op_name] + mask = m >= threshold + total_weight_num += mask.size + sparsity = 1.0 - np.mean(mask) + op_name = self._prune_weight_variable_op_names[v] + self._generated_config_dict[op_name] = sparsity.tolist() + if op_name in config_dict.keys(): + self._config_dict[op_name] = config_dict[op_name] + else: + self._config_dict[op_name] = sparsity.tolist() + + if self._config_dict[op_name] > self._max_prune_ratio: + self._config_dict[op_name] = self._max_prune_ratio + + if op.type == 'DepthwiseConv2dNative': + self._config_dict[op_name] = 0.0 + + total_pruned_weight_num += int(mask.size * self._config_dict[op_name]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _insert_pruning_ops(self): + scope_index = -1 + for layer_name in self._config_dict.keys(): + scope_index += 1 + self._prune_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _prune_one_layer(self, layer_name, scope): + if self._name_to_ops[layer_name].type == 'Conv2D' or self._name_to_ops[layer_name].type == 'DepthwiseConv2dNative': + self._prune_conv(self._name_to_ops[layer_name], scope) + + if self._name_to_ops[layer_name].type == 'MatMul': + self._prune_matmul(self._name_to_ops[layer_name], scope) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _get_variable_by_name(self, name): + return [v for v in tf.global_variables() if v.op.name == name][0] + + def _prune_conv(self, conv_op, scope): + weight_tensor = conv_op.inputs[1] + self._op_name_weight_tensor_name[conv_op.name] = weight_tensor.name + new_weight_tensor = self._prune_weight(conv_op, weight_tensor, scope) + + def _prune_matmul(self, matmul_op, scope): + if "gradients" in matmul_op.name and "_grad" in matmul_op.name: + return + + input_1 = matmul_op.inputs[0] + input_2 = matmul_op.inputs[1] + + for input_tensor in [input_1, input_2]: + index = get_input_tensor_index(matmul_op, input_tensor)[0] + if is_weight_tensor(input_tensor): + self._op_name_weight_tensor_name[matmul_op.name] = input_tensor.name + new_weight_tensor = self._prune_weight(matmul_op, input_tensor, scope) + + def _generate_all_constants(self): + self._pname_sens_constants = {} + for v, m in self._prune_var_mask_abs_grads.items(): + self._pname_sens_constants[v] = tf.constant(m) + + def _prune_weight(self, op, weight_tensor, scope): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + weight_variable = get_variable_by_tensor(weight_tensor) + m = self._prune_var_mask_abs_grads[weight_variable] + m_const = self._pname_sens_constants[weight_variable] + m_sorted = tf.contrib.framework.sort(tf.reshape(m_const, [-1])) + num = m_sorted.shape.num_elements() * self._config_dict[op.name] / self._total_pruning_iterations * tf.cast(self._step, tf.float32) + num = tf.cast(num, tf.int64) + threshold = m_sorted[num] + mask = tf.cast(m_const >= threshold, tf.float32) + self._mask_dict[op.name] = mask + + if self._debug_info: + pruning_ratio = 1.0 - tf.reduce_mean(mask) + print_op = tf.print("pruning iter:", self._step, "/", self._total_pruning_iterations, op.name, "pruning_ratio:", pruning_ratio) + + with tf.control_dependencies([print_op]): + pruned_weight_tensor = weight_variable * mask + else: + pruned_weight_tensor = weight_variable * mask + + new_weight_tensor_assign = tf.assign(weight_variable, pruned_weight_tensor) + tf.add_to_collection("update_pruning_masks", new_weight_tensor_assign) + + return new_weight_tensor_assign diff --git a/tools/mnncompress/mnncompress/tensorflow/TaylorFO_channel_pruner.py b/tools/mnncompress/mnncompress/tensorflow/TaylorFO_channel_pruner.py new file mode 100644 index 00000000..adf3df5a --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/TaylorFO_channel_pruner.py @@ -0,0 +1,456 @@ +from __future__ import print_function +import tensorflow as tf +import yaml +import numpy as np + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_op_name_of_type, get_input_tensor_index, is_weight_tensor, get_variable_by_tensor + +_MNN_compress_scope = 'MNN_pruner_' +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class TaylorFOChannelPruner(object): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="TaylorFOChannel_found_prune_ratios.yml", align_channels=4): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + + self._sensitivities = np.load(sensitivity_npy_file, allow_pickle=True).item() + self._target_sparsity = sparsity + self._max_prune_ratio = max_prune_ratio + self._prune_ratio_result_file = prune_ratio_result_file + + self._op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._config_dict = {} + self._generated_config_dict = {} + + self._debug_info = debug_info + + self._ops = self._graph.get_operations() + self._name_to_ops = {op.name : op for op in self._ops} + self._prune_weight_variable_op_names = {} + self._mask_dict = {} + self._config_file = config_file + + if (total_pruning_iterations < 0) and (not isinstance(total_pruning_iterations, int)): + raise ValueError("total_pruning_iterations must be a integer >= 0") + self._total_pruning_iterations = total_pruning_iterations + self._prune_finetune_iterations = prune_finetune_iterations + self._finetune_step = 0 + + self._step = tf.get_variable(name='MNN_pruner_internal_step', initializer=0, trainable=False) + self._step_init_op = tf.variables_initializer([self._step]) + self._control_prune_op = self._step < self._total_pruning_iterations + + self._update_step_op = tf.assign(self._step, self._step + 1) + tf.add_to_collection("update_internal_step", self._update_step_op) + + self._op_name_weight_tensor_name = {} + self._initialized = False + self._prune_var_mask_abs_grads = {} + self._all_means_flatten = [] + self._pname_sens = {} + self._pname_bias = {} + self._pname_bn = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._align_channels = align_channels + + self._get_prune_weight_variable_op_names() + self._generate_prune_ratios() + self._generate_all_constants() + self._insert_pruning_ops() + self._init_mask_op = tf.variables_initializer(tf.get_collection("init_masks")) + self._update_pruning_ops = tf.get_collection("update_pruning_masks") + self._op_prune_ratio_dict = {} + for op_name in self._mask_dict.keys(): + ratio = 1.0 - tf.reduce_mean(self._mask_dict[op_name]) + self._op_prune_ratio_dict[op_name] = ratio + + def do_pruning(self, sess): + if not self._initialized: + sess.run(self._step_init_op) + sess.run(self._init_mask_op) + self._initialized = True + + if sess.run(self._control_prune_op): + if self._prune_finetune_iterations == 0: + sess.run(self._update_step_op) + else: + if self._finetune_step == 0: + sess.run(self._update_step_op) + self._finetune_step += 1 + else: + self._finetune_step = (self._finetune_step + 1) % self._prune_finetune_iterations + + sess.run(self._update_pruning_ops) + + def save_compress_params(self, filename, sess, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.FILTER + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for op_name in self._mask_dict.keys(): + tensor_name = self._op_name_weight_tensor_name[op_name] + layer_names.append(op_name) + weight_tensor_names.append(tensor_name) + ratio = sess.run(self._op_prune_ratio_dict[op_name]) + self._total_weight_num += sess.run(tensor_name).size + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (sess.run(tensor_name).size * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + print(op_name, "pruning_ratio:", ratio) + + if not self._reported: + detail = {"algorithm": "TaylorFOChannel", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _get_prune_weight_variable_op_names(self): + for op_name in self._op_names: + op = self._name_to_ops[op_name] + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_variable = get_variable_by_tensor(op.inputs[1]) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + if op.type == "MatMul": + if "gradients" in op.name and "_grad" in op.name: + continue + + for t in op.inputs: + if is_weight_tensor(t): + weight_variable = get_variable_by_tensor(t) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable] = self._sensitivities[weight_variable.name].astype(np.float32) + + def _generate_prune_ratios(self): + config_dict = {} + if self._config_file is not None: + f = open(self._config_file, 'r') + config_dict = yaml.safe_load(f) + + print("generating pruning ratios...") + self._generate_prune_ratios_per_layer(config_dict) + + f = open(self._prune_ratio_result_file, "w") + yaml.dump(self._config_dict, f) + f.close() + print("config_dict saved to file:", self._prune_ratio_result_file) + + def _generate_prune_ratios_per_layer(self, config_dict = {}): + for v, gn in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + op = self._name_to_ops[op_name] + if op.type == 'DepthwiseConv2dNative': + gn = gn.transpose((0, 1, 3, 2)) + + self._pname_sens[v] = np.sum(gn*gn, tuple([i for i in range(0, len(gn.shape)-1)]), keepdims=True) + self._all_means_flatten.append(self._pname_sens[v].flatten()) + + all_means_flatten = np.concatenate(self._all_means_flatten) + all_means_flatten = np.sort(all_means_flatten) + threshold_index = int(all_means_flatten.size * self._target_sparsity) + threshold = all_means_flatten[threshold_index] + + total_weight_num = 0 + total_pruned_weight_num = 0 + for v, m in self._prune_var_mask_abs_grads.items(): + op_name = self._prune_weight_variable_op_names[v] + op = self._name_to_ops[op_name] + if op.type == 'DepthwiseConv2dNative': + m = m.transpose((0, 1, 3, 2)) + + sens = self._pname_sens[v] + mask = sens > threshold + + sparsity = 1 - np.mean(mask) + total_weight_num += m.size + op_name = self._prune_weight_variable_op_names[v] + self._generated_config_dict[op_name] = sparsity + if op_name in config_dict.keys(): + self._config_dict[op_name] = config_dict[op_name] + else: + self._config_dict[op_name] = sparsity + + if self._config_dict[op_name] > self._max_prune_ratio: + self._config_dict[op_name] = self._max_prune_ratio + + if op.type == 'DepthwiseConv2dNative': + self._config_dict[op_name] = 0.0 + + remain_channels = int(mask.size * (1.0 - self._config_dict[op_name])) + if remain_channels != mask.size: + remain_channels = remain_channels - remain_channels % self._align_channels + if remain_channels <= 0: + remain_channels = min(self._align_channels, mask.size) + + self._config_dict[op_name] = (mask.size - remain_channels + 0.0) / mask.size + + total_pruned_weight_num += (m.size * self._config_dict[op_name]) + + self._target_sparsity = float(total_pruned_weight_num) / total_weight_num + print("overall prune ratio:", self._target_sparsity) + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + def _insert_pruning_ops(self): + scope_index = -1 + for layer_name in self._prune_weight_variable_op_names.values(): + scope_index += 1 + self._prune_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _prune_one_layer(self, layer_name, scope): + if self._name_to_ops[layer_name].type == 'Conv2D' or self._name_to_ops[layer_name].type == 'DepthwiseConv2dNative': + self._prune_conv(self._name_to_ops[layer_name], scope) + + if self._name_to_ops[layer_name].type == 'MatMul': + self._prune_matmul(self._name_to_ops[layer_name], scope) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _get_variable_by_name(self, name): + return [v for v in tf.global_variables() if v.op.name == name][0] + + def _prune_conv(self, conv_op, scope): + weight_tensor = conv_op.inputs[1] + self._op_name_weight_tensor_name[conv_op.name] = weight_tensor.name + + if self._config_dict[conv_op.name] == 0.0: + self._mask_dict[conv_op.name] = tf.ones_like(weight_tensor) + elif conv_op.type == 'Conv2D': + self._get_bias_and_batch_norm_statistics(conv_op) + new_weight_tensor = self._prune_weight(conv_op, weight_tensor, scope) + + def _prune_matmul(self, matmul_op, scope): + if "gradients" in matmul_op.name and "_grad" in matmul_op.name: + return + + input_1 = matmul_op.inputs[0] + input_2 = matmul_op.inputs[1] + + for input_tensor in [input_1, input_2]: + index = get_input_tensor_index(matmul_op, input_tensor)[0] + if is_weight_tensor(input_tensor): + self._op_name_weight_tensor_name[matmul_op.name] = input_tensor.name + if self._config_dict[matmul_op.name] == 0.0: + self._mask_dict[matmul_op.name] = tf.ones_like(input_tensor) + else: + new_weight_tensor = self._prune_weight(matmul_op, input_tensor, scope) + + def _generate_all_constants(self): + self._pname_sens_constants = {} + for v, _ in self._prune_var_mask_abs_grads.items(): + self._pname_sens_constants[v] = tf.constant(self._pname_sens[v]) + + def _prune_weight(self, op, weight_tensor, scope): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + weight_variable = get_variable_by_tensor(weight_tensor) + m = self._prune_var_mask_abs_grads[weight_variable] + + if op.type == 'DepthwiseConv2dNative': + m = m.transpose((0, 1, 3, 2)) + + sens_constant = self._pname_sens_constants[weight_variable] + m_sorted = tf.contrib.framework.sort(tf.reshape(sens_constant, [-1])) + num = m_sorted.shape.num_elements() * self._config_dict[op.name] / self._total_pruning_iterations * tf.cast(self._step, tf.float32) + num = tf.cast(num, tf.int64) + threshold = m_sorted[num] + mask = tf.cast(sens_constant >= threshold, tf.float32) + + if op.type == 'DepthwiseConv2dNative': + mask = tf.transpose(mask, (0, 1, 3, 2)) + + self._mask_dict[op.name] = mask + + if self._debug_info: + pruning_ratio = 1.0 - tf.reduce_mean(mask) + print_op = tf.print("pruning iter:", self._step, "/", self._total_pruning_iterations, op.name, "pruning_ratio:", pruning_ratio) + + with tf.control_dependencies([print_op]): + pruned_weight_tensor = weight_variable * mask + else: + pruned_weight_tensor = weight_variable * mask + + new_weight_tensor_assign = tf.assign(weight_variable, pruned_weight_tensor) + tf.add_to_collection("update_pruning_masks", new_weight_tensor_assign) + + if weight_variable in self._pname_bias.keys(): + bias_variable = self._pname_bias[weight_variable] + pruned_bias_tensor = bias_variable * tf.reshape(mask, bias_variable.shape) + new_bias_assign = tf.assign(bias_variable, pruned_bias_tensor) + tf.add_to_collection("update_pruning_masks", new_bias_assign) + + if weight_variable in self._pname_bn.keys(): + for variable in self._pname_bn[weight_variable]: + pruned_variable_tensor = variable * tf.reshape(mask, variable.shape) + new_variable_tensor = variable * pruned_variable_tensor + new_variable_assign = tf.assign(variable, new_variable_tensor) + tf.add_to_collection("update_pruning_masks", new_variable_assign) + + return new_weight_tensor_assign + + def _check_return_cond(self, output_tensor): + output_no_grad_consumers = [c for c in output_tensor.consumers() if 'gradients/' not in c.name] + if len(output_no_grad_consumers) != 1: + if len(output_no_grad_consumers) != 2: + return True + else: + c1 = output_tensor.consumers()[0] + c2 = output_tensor.consumers()[1] + if (c1.type != 'Switch') or (c2.type != 'Switch'): + return True + return False + + def _skip_identity(self, tensor): + while (len(tensor.consumers()) == 1) and (tensor.consumers()[0].type == 'Identity'): + tensor = tensor.consumers()[0].outputs[0] + return tensor + + def _get_bias_and_batch_norm_statistics(self, conv2d_op): + weight_variable = get_variable_by_tensor(conv2d_op.inputs[1]) + + output_tensor = conv2d_op.outputs[0] + if self._check_return_cond(output_tensor): + return + + output_tensor = self._skip_identity(output_tensor) + if self._check_return_cond(output_tensor): + return + + # bias add + if output_tensor.consumers()[0].type in ['Add', 'AddV2', 'BiasAdd']: + bias_add_op = output_tensor.consumers()[0] + bias_variable = get_variable_by_tensor(bias_add_op.inputs[1]) + self._pname_bias[weight_variable] = bias_variable + output_tensor = bias_add_op.outputs[0] + + output_tensor = self._skip_identity(output_tensor) + if self._check_return_cond(output_tensor): + return + + def _get_switch_index(switch_op): + for i in range(len(switch_op.outputs)): + if len(switch_op.outputs[i].consumers()) > 0: + return i + raise ValueError("switch op's outputs don't have consumers") + + # batch norm with tf.cond + output_no_grad_consumers = [c for c in output_tensor.consumers() if 'gradients/' not in c.name] + + if (len(output_no_grad_consumers) == 2) and (output_tensor.consumers()[0].type == 'Switch') and (output_tensor.consumers()[1].type == 'Switch'): + switch_op1 = output_tensor.consumers()[0] + switch_op2 = output_tensor.consumers()[1] + + index = _get_switch_index(switch_op1) + no_grad_consumers = [c for c in switch_op1.outputs[index].consumers() if 'gradients/' not in c.name] + cond1 = (len(no_grad_consumers) == 1) + c1 = switch_op1.outputs[index].consumers()[0] + cond2 = ('FusedBatchNorm' in c1.type) + merge_op = c1.outputs[0].consumers()[0] + cond3 = (merge_op.type == 'Merge') + + index = _get_switch_index(switch_op2) + no_grad_consumers = [c for c in switch_op2.outputs[index].consumers() if 'gradients/' not in c.name] + cond4 = (len(no_grad_consumers) == 1) + c2 = switch_op2.outputs[index].consumers()[0] + cond5 = ('FusedBatchNorm' in c2.type) + cond6 = (len(merge_op.inputs) == 2) + + sub_cond1 = ((merge_op.inputs[0] == c1.outputs[0]) and (merge_op.inputs[1] == c2.outputs[0])) + sub_cond2 = ((merge_op.inputs[0] == c2.outputs[0]) and (merge_op.inputs[1] == c1.outputs[0])) + + cond7 = (sub_cond1 or sub_cond2) + + final_cond = (cond1 and cond2 and cond3 and cond4 and cond5 and cond6 and cond7) + + if final_cond is True: + bn_scope = merge_op.name[:-10] + gamma_op = self._get_variable_by_name(bn_scope + "gamma") + beta_op = self._get_variable_by_name(bn_scope + "beta") + moving_mean_op = self._get_variable_by_name(bn_scope + "moving_mean") + moving_variance_op = self._get_variable_by_name(bn_scope + "moving_variance") + + statistics = [] + statistics.append(gamma_op) + statistics.append(beta_op) + statistics.append(moving_mean_op) + # statistics.append(moving_variance_op) + # statistics.append(c1.get_attr("epsilon")) + + self._pname_bn[weight_variable] = statistics + + return + + if 'FusedBatchNorm' in output_tensor.consumers()[0].type: + c = output_tensor.consumers()[0] + gamma_op = None + beta_op = None + bn_scope = None + input_names = [t.op.name for t in c.inputs] + for name in input_names: + if name.endswith("gamma/read"): + gamma_op = self._graph.get_operation_by_name(name) + if name.endswith("beta/read"): + beta_op = self._graph.get_operation_by_name(name) + bn_scope = name.rsplit("beta/read", 1)[0] + + assert bn_scope is not None + moving_mean_op = self._graph.get_operation_by_name(bn_scope + "moving_mean/read") + moving_variance_op = self._graph.get_operation_by_name(bn_scope + "moving_variance/read") + + statistics = [] + if gamma_op is not None: + statistics.append(self._get_variable_by_name(gamma_op.inputs[0].name[:-2])) + # else: + # statistics.append(gamma_op) + statistics.append(self._get_variable_by_name(beta_op.inputs[0].name[:-2])) + statistics.append(self._get_variable_by_name(moving_mean_op.inputs[0].name[:-2])) + # statistics.append(moving_variance_op.inputs[0]) + # statistics.append(c.get_attr("epsilon")) + + self._pname_bn[weight_variable] = statistics + + return diff --git a/tools/mnncompress/mnncompress/tensorflow/__init__.py b/tools/mnncompress/mnncompress/tensorflow/__init__.py new file mode 100644 index 00000000..b40c5d5b --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/__init__.py @@ -0,0 +1,9 @@ +from .sensitivity_analyzer import SensitivityAnalyzer +from .SNIP_level_pruner import SNIPLevelPruner +from .SIMD_OC_pruner import SIMDOCPruner +from .TaylorFO_channel_pruner import TaylorFOChannelPruner +from .EMA_quantizer import EMAQuantizer +from .LSQ_quantizer import LSQQuantizer +from .OAQ_quantizer import OAQQuantizer +from .weight_quantizer import WeightQuantizer +from .decomposition import get_op_weight_values, low_rank_decompose diff --git a/tools/mnncompress/mnncompress/tensorflow/admm_pruner.py b/tools/mnncompress/mnncompress/tensorflow/admm_pruner.py new file mode 100644 index 00000000..c03a32a5 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/admm_pruner.py @@ -0,0 +1,727 @@ +from __future__ import print_function +import yaml +import tensorflow as tf +import numpy as np + +from .helpers import kronecker_tf, is_weight_tensor +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +import uuid + +class ADMMPruner: + def __init__(self, session, config_file, admm_epoch=15, lr=0.01, lr_decay=0.1, sparsity_type='random', rho=0.001): + """ + default constructor + :param session: contains the graph, model definition + :param config_file: path to the *.yaml file, which contains detailed prune ratios for the model weights + :param rho: parameter of ADMM + """ + self.ADMM_U = {} + self.ADMM_Z = {} + self.rho = rho + self.rhos = {} + self.sess = session + self.weights = tf.trainable_variables() + self.masks = {} + self.prune_ops = [] + self.get_config(config_file) + self.admm_epoch = admm_epoch + self.lr = lr + self.lr_decay = lr_decay + self.sparsity_type = sparsity_type + self.prune_method = self.get_prune_method(sparsity_type) + + def save_compress_params(self, filename, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + method_dict = { + 'random': compress_pb.PruneParams.RANDOM, + 'SIMD_row': compress_pb.PruneParams.SIMD_ROW, + 'column': compress_pb.PruneParams.COLUMN, + 'filter': compress_pb.PruneParams.FILTER, + } + algorithm.prune_params.type = method_dict[self.sparsity_type] + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for tensor_name in self.masks.keys(): + weight_tensor_names.append(tensor_name) + ratio = 1.0 - np.mean(self.masks[tensor_name]).tolist() + prune_ratios.append(ratio) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def get_prune_method(self, sparsity_type='random'): + if sparsity_type == 'random': + # self.prune_method = self.random_prune + prune_method = self.random_prune_npy + elif sparsity_type == 'SIMD_row': + # self.prune_method = self.SIMD_row_prune + prune_method = self.SIMD_row_prune_npy + elif sparsity_type == 'column': + # self.prune_method = self.column_prune + prune_method = self.column_prune_npy + elif sparsity_type == 'filter': + # self.prune_method = self.filter_prune + prune_method = self.filter_prune_npy + else: + raise NotImplementedError + return prune_method + + def get_config(self, config): + """ + called by ADMM constructor. config should be a .yaml file + :param config: configuration file that has settings for prune ratios, rhos + + """ + if not isinstance(config, str): + raise Exception("filename must be a str") + with open(config, "r") as stream: + try: + raw_dict = yaml.load(stream) + prs = raw_dict['prune_ratios'] + self.prune_ratios = {} + for k, v in prs.items(): + op = self.sess.graph.get_operation_by_name(k) + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_tensor = op.inputs[1] + self.prune_ratios[weight_tensor.name] = v + elif op.type == 'MatMul': + input1 = op.inputs[0] + input2 = op.inputs[1] + if is_weight_tensor(input1): + self.prune_ratios[input1.name] = v + elif is_weight_tensor(input2): + self.prune_ratios[input2.name] = v + else: + print("WARNING:", k, "of type:", op.type, "has no weight, skip") + else: + raise ValueError(k + " of type: " + op.type + " is not supported") + + for k, v in self.prune_ratios.items(): + self.rhos[k] = self.rho + + variables = self.weights + np_values = self.sess.run(variables) + for var, value in zip(variables, np_values): + if var.name not in self.prune_ratios: + continue + self.ADMM_U[var.name] = tf.Variable(np.zeros(shape=var.shape), dtype=float, trainable=False, + name='admm_U') + self.ADMM_Z[var.name] = tf.Variable(value, trainable=False, name='admm_Z') + self.sess.run(tf.initialize_variables(self.ADMM_U.values(), name='init_U')) + self.sess.run(tf.initialize_variables(self.ADMM_Z.values(), name='init_Z')) + print("Prune ratios from .yaml") + for k, v in self.prune_ratios.items(): + print(" {} : {}".format(k, v)) + + except yaml.YAMLError as exc: + print(exc) + + def Z_initialize(self): + """ + initialize the Z variables for each weight + """ + variables = self.weights + np_values = self.sess.run(variables) + update_ops = [] + # for var in variables: + for var, value in zip(variables, np_values): + if var.name in self.prune_ratios: + # Z(k+1) = W(k+1)+U(k) U(k) is zeros here + # _, updated_Z = self.prune_method(var, self.prune_ratios[var.name], group=4, norm='l2') + _, updated_Z = self.prune_method(value, self.prune_ratios[var.name], group=4, norm='l2') + update_ops.append(tf.assign(self.ADMM_Z[var.name], updated_Z)) + self.sess.run(update_ops) + + def Z_U_update(self, epoch, batch_idx, verbose=False, train_writer=None): + """ + update Z and U, according to ADMM optimization with the SIMD pruning method + :param device: + :param train_loader: + :param optimizer: + :param epoch: number of epochs in the main loop + :param data: + :param batch_idx: index of the batch size + :param writer: to print some information + :return: + """ + + if epoch > 0 and epoch % self.admm_epoch == 0 and batch_idx == 0: + print('--- ADMM iteration begin ---') + variables = self.weights + np_values = self.sess.run(variables) + update_ops = [] + for var, value in zip(variables, np_values): + if var.name not in self.prune_ratios: + continue + Z_value, U_value = self.sess.run([self.ADMM_Z[var.name], self.ADMM_U[var.name]]) + + Z_prev = Z_value + # Z(k+1) = W(k+1) + U[k] + Z_value = value + U_value + + # returns the mask and pruned weight + # in mask, orginal values above threshold are 1, below are 0 + # pruned weight has percentile% channels set to 0 + mask_Z, updated_Z = self.prune_method(Z_value, self.prune_ratios[var.name], group=4, norm='l2') + update_ops.append(tf.assign(self.ADMM_Z[var.name], updated_Z)) + + if verbose: + if train_writer: + train_writer.add_scalar('layer:{} W(k+1)-Z(k+1)'.format(var.name), + np.sqrt(np.sum((value - updated_Z) ** 2)), epoch) + train_writer.add_scalar('layer:{} Z(k+1)-Z(k)'.format(var.name), + np.sqrt(np.sum((updated_Z - Z_prev) ** 2)), epoch) + + # U(k+1) = W(k+1) - Z(k+1) +U(k) + U_value = value - updated_Z + U_value + update_ops.append(tf.assign(self.ADMM_U[var.name], U_value)) + + self.sess.run(update_ops) + print('--- ADMM iteartion end ---') + + def compute_loss(self): + """ + :param ce_loss: the cross-entropy loss + + :return: + ce_loss: original cross-entropy loss + admm_loss: a dict to save loss for each layer + mixed_loss: overall cross-entropy loss + admm loss + total_U_norm: sum of norm(U, 2) + """ + variables = self.weights + admm_loss = tf.norm(0.) + admm_U_norm = tf.norm(0.) + for var in variables: + if var.name not in self.prune_ratios: + continue + admm_loss += 0.5 * self.rhos[var.name] * tf.norm(var - self.ADMM_Z[var.name] + self.ADMM_U[var.name]) ** 2 + admm_U_norm += tf.norm(self.ADMM_U[var.name]) + + return admm_loss, admm_U_norm + + def adjust_learning_rate(self, epoch): + """ + re-adjust the learning rate in the ADMM optimization. + Learning rate change is periodic. When epoch is dividable by admm_epoch, the learning rate is reset + to the original value, and decay every 3 epoch + :param epoch: number of epochs in the main loop + :return: + """ + lr = None + if epoch % self.admm_epoch == 0: + lr = self.lr + else: + offset = epoch % self.admm_epoch + admm_step = self.admm_epoch / 3 # roughly every 1/3 admm_epoch + lr = self.lr * (self.lr_decay ** (offset // admm_step)) + + return lr + + def multi_rho_scheduler(self, name): + """ + It works better to make rho monotonically increasing + we increase it by 1.9x every admm epoch + After 10 admm updates, the rho will be 0.91 + """ + + self.rhos[name] *= 2 + + def get_prune_ops(self, weights=None, masks=None): + """ + hard prune on each weight of the model + :return: + prune ops + """ + print('--- hard prune all weights ---') + if not weights: + variables = self.weights + else: + variables = weights + + if not masks: + masks = self.masks + + self.prune_ops = [] + for var in variables: + if var.name in self.prune_ratios: + # print("name: {},\tshape: {}".format(var.name, var.shape)) + # prune expected weights by using masks + self.prune_ops.append(tf.assign(var, tf.multiply(var, masks[var.name]))) + + return self.prune_ops + + def hard_prune_weight(self, weights=None): + """ + hard prune on each weight of the model + :return: + masks={name:value}, a dict of masks after pruning + """ + print('--- hard prune all weights ---') + if not weights: + variables = self.weights + else: + variables = weights + np_values = self.sess.run(variables) + prune_ops = [] + for var, value in zip(variables, np_values): + if var.name in self.prune_ratios: + # print("name: {},\tshape: {}".format(var.name, var.shape)) + # SIMD prune on each weight + mask, pruned_weight = self.prune_method(value, self.prune_ratios[var.name], group=4, norm='l2') + # assign pruned weight back to the model + prune_ops.append(tf.assign(var, pruned_weight)) + self.masks[var.name] = mask + self.sess.run(prune_ops) + return self.masks + + def SIMD_row_prune(self, weight, prune_ratio, group=4, norm='l1'): + """ + the SIMD pruning method in the row direction + :param weight: tensor of the weight from the model: (kernel, kernel, in-channel, out-channel) + :param prune_ratio: float value, 0.00 - 1.00 + :param group: the number of elements in one group + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + if len(weight.shape) == 4: + weight = tf.transpose(weight, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = tf.transpose(weight, (1, 0)) + + shape = weight.shape + print("SIMD row pruning {}: {}, length: {}".format(norm, weight.shape, group)) + weight2d = tf.reshape(weight, shape=(shape[0], -1)) + shape2d = weight2d.shape + # deal with the last group if column is not divisable + num_pad = (group - (weight2d.shape[1] % group)) % group + rescale_factor = float(group) / int(group - num_pad) + # padding zero to the last column + weight2d_pad = tf.pad(weight2d, ((0, 0), (0, num_pad)), 'constant', constant_values=0) + shape2d_pad = weight2d_pad.shape + if norm == 'l1': + weight_norm = tf.abs(weight2d_pad) + elif norm == 'l2': + weight_norm = tf.square(weight2d_pad) + + group_vals_matrix = None + for i in range(shape2d_pad[1] // group): + # sum weights of each group in row direction + vector = tf.reduce_sum(weight_norm[:, i * group: (i + 1) * group], axis=1) # l2 norm + if i == (shape2d_pad[1] // group) - 1: + # rescale the value of last group + vector *= rescale_factor + if i == 0: + group_vals_matrix = tf.reshape(vector, (-1, 1)) + else: + group_vals_matrix = tf.concat([group_vals_matrix, tf.reshape(vector, (-1, 1))], 1) + group_vals_matrix = tf.sqrt(group_vals_matrix) + + # find the threshold + group_vals_vec = tf.reshape(group_vals_matrix, (-1, 1)) + threshold = tf.gather(tf.contrib.framework.sort(group_vals_vec, axis=0), int(int(group_vals_vec.shape[0]) * prune_ratio)) + + # set those > threshold as 1, those < threshold as 0 + zeros = tf.zeros(shape=group_vals_matrix.shape, dtype='float32') + ones = tf.ones(shape=group_vals_matrix.shape, dtype='float32') + group_vals_matrix = tf.where(tf.less_equal(group_vals_matrix, threshold), zeros, ones) + # restore orignal shape by repeating 1/0 group_len times + kron_matrix = tf.ones(shape=[1, group], dtype='float32') + mask_padded = kronecker_tf(group_vals_matrix, kron_matrix) + # delete paddings to restore original shape + mask_final = mask_padded[:, : shape2d[1]] + weight_masked = tf.multiply(weight2d, mask_final) + weight_masked = tf.reshape(weight_masked, shape) + mask_final = tf.reshape(mask_final, shape) + + if len(mask_final.shape) == 4: + mask_final = tf.transpose(mask_final, (3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = tf.transpose(mask_final, (1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = tf.transpose(weight_masked, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight_masked = tf.transpose(weight_masked, (1, 0)) + + return mask_final, weight_masked + + def SIMD_row_prune_npy(self, weight, prune_ratio, group=4, norm='l1'): + """ + the SIMD pruning method in the row direction + :param weight: numpy.array of the weight from the model + :param prune_ratio: float value, 0.00 - 1.00 + :param group: the number of elements in one group + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + percent = prune_ratio * 100 + + if len(weight.shape) == 4: + weight = weight.transpose((3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = weight.transpose((1, 0)) + + shape = weight.shape + print("SIMD row pruning {}: {}, length: {}".format(norm, weight.shape, group)) + weight2d = weight.reshape(shape[0], -1) + shape2d = weight2d.shape + # deal with the last group if column is not divisable + num_pad = (group - (weight2d.shape[1] % group)) % group + rescale_factor = float(group) / int(group - num_pad) + # padding zero to the last column + weight2d_pad = np.pad(weight2d, ((0, 0), (0, num_pad)), 'constant', constant_values=0) + shape2d_pad = weight2d_pad.shape + if norm == 'l1': + weight_norm = np.abs(weight2d_pad) + elif norm == 'l2': + weight_norm = np.square(weight2d_pad) + + group_vals_matrix = np.zeros(shape=(shape2d_pad[0], shape2d_pad[1] // group), dtype=np.float32) + for i in range(shape2d_pad[1] // group): + # sum weights of each group in row direction + vector = np.sum(weight_norm[:, i * group: (i + 1) * group], axis=1) # l2 norm + if i == (shape2d_pad[1] // group) - 1: + # rescale the value of last group + vector *= rescale_factor + group_vals_matrix[:, i] = np.squeeze(vector) + group_vals_matrix = np.sqrt(group_vals_matrix) + + # find the threshold + threshold = np.percentile(group_vals_matrix, percent) + # set those > threshold as 1, those < threshold as 0 + group_vals_matrix = np.where(group_vals_matrix <= threshold, 0, 1) + # restore orignal shape by repeating 1/0 group_len times + kron_matrix = np.ones(group) + mask_padded = np.kron(group_vals_matrix, kron_matrix) + # delete paddings to restore original shape + mask_final = mask_padded[:, : shape2d[1]] + weight_masked = weight2d * mask_final + weight_masked = weight_masked.reshape(shape) + mask_final = mask_final.reshape(shape) + + if len(mask_final.shape) == 4: + mask_final = mask_final.transpose((3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = mask_final.transpose((1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = weight_masked.transpose((3, 2, 1, 0)) + elif len(weight_masked.shape) == 2: + weight_masked = weight_masked.transpose((1, 0)) + + return mask_final, weight_masked + + def random_prune(self, weight, prune_ratio, norm='l1'): + """ + the random pruning method + :param weight: tensor of the weight from the model: (kernel, kernel, in-channel, out-channel) + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + if len(weight.shape) == 4: + weight = tf.transpose(weight, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = tf.transpose(weight, (1, 0)) + + shape = weight.shape + print("random pruning {}: {}".format(norm, weight.shape)) + weight2d = tf.reshape(weight, shape=(shape[0], -1)) + if norm == 'l1': + weight_norm = tf.abs(weight2d) + elif norm == 'l2': + weight_norm = tf.square(weight2d) + + # find the threshold + weight_vec = tf.reshape(weight_norm, (-1, 1)) + threshold = tf.gather(tf.contrib.framework.sort(weight_vec, axis=0), int(int(weight_vec.shape[0]) * prune_ratio)) + + # set those > threshold as 1, those < threshold as 0 + zeros = tf.zeros(shape=weight_norm.shape, dtype='float32') + ones = tf.ones(shape=weight_norm.shape, dtype='float32') + mask_final = tf.where(tf.less_equal(weight_norm, threshold), zeros, ones) + weight_masked = tf.multiply(weight2d, mask_final) + weight_masked = tf.reshape(weight_masked, shape) + mask_final = tf.reshape(mask_final, shape) + + if len(mask_final.shape) == 4: + mask_final = tf.transpose(mask_final, (3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = tf.transpose(mask_final, (1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = tf.transpose(weight_masked, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight_masked = tf.transpose(weight_masked, (1, 0)) + + return mask_final, weight_masked + + def random_prune_npy(self, weight, prune_ratio, norm='l1'): + """ + the random pruning method + :param weight: numpy.array of the weight from the model + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + percent = prune_ratio * 100 + + if len(weight.shape) == 4: + weight = weight.transpose((3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = weight.transpose((1, 0)) + + shape = weight.shape + print("random pruning {}: {}".format(norm, weight.shape)) + weight2d = weight.reshape(shape[0], -1) + if norm == 'l1': + weight_norm = np.abs(weight2d) + elif norm == 'l2': + weight_norm = np.square(weight2d) + + # find the threshold + threshold = np.percentile(weight_norm, percent) + # set those > threshold as 1, those < threshold as 0 + mask_final = np.where(weight_norm <= threshold, 0, 1) + weight_masked = weight2d * mask_final + weight_masked = weight_masked.reshape(shape) + mask_final = mask_final.reshape(shape) + + if len(mask_final.shape) == 4: + mask_final = mask_final.transpose((3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = mask_final.transpose((1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = weight_masked.transpose((3, 2, 1, 0)) + elif len(weight_masked.shape) == 2: + weight_masked = weight_masked.transpose((1, 0)) + + return mask_final, weight_masked + + def column_prune(self, weight, prune_ratio, norm='l1'): + """ + the column pruning method + :param weight: tensor of the weight from the model: (kernel, kernel, in-channel, out-channel) + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + if len(weight.shape) == 4: + weight = tf.transpose(weight, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = tf.transpose(weight, (1, 0)) + + shape = weight.shape + print("column pruning {}: {}".format(norm, weight.shape)) + weight2d = tf.reshape(weight, shape=(shape[0], -1)) + if norm == 'l1': + weight_norm = tf.abs(weight2d) + elif norm == 'l2': + weight_norm = tf.square(weight2d) + + # find the threshold + weight_vec = tf.reduce_sum(weight_norm, axis=0) + threshold = tf.gather(tf.contrib.framework.sort(weight_vec, axis=0), int(int(weight_vec.shape[0]) * prune_ratio)) + + # set those > threshold as 1, those < threshold as 0 + zeros = tf.zeros(shape=weight_vec.shape, dtype='float32') + ones = tf.ones(shape=weight_vec.shape, dtype='float32') + mask_vec = tf.where(tf.less_equal(weight_vec, threshold), zeros, ones) + mask_final = tf.ones([weight2d.shape[0], 1], dtype='float32') * mask_vec + weight_masked = tf.multiply(weight2d, mask_final) + weight_masked = tf.reshape(weight_masked, shape) + mask_final = tf.reshape(mask_final, shape) + + if len(mask_final.shape) == 4: + mask_final = tf.transpose(mask_final, (3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = tf.transpose(mask_final, (1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = tf.transpose(weight_masked, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight_masked = tf.transpose(weight_masked, (1, 0)) + + return mask_final, weight_masked + + def column_prune_npy(self, weight, prune_ratio, norm='l1'): + """ + the column pruning method + :param weight: numpy.array of the weight from the model + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + percent = prune_ratio * 100 + + if len(weight.shape) == 4: + weight = weight.transpose((3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = weight.transpose((1, 0)) + + shape = weight.shape + print("column pruning {}: {}".format(norm, weight.shape)) + weight2d = weight.reshape(shape[0], -1) + if norm == 'l1': + weight_norm = np.abs(weight2d) + elif norm == 'l2': + weight_norm = np.square(weight2d) + + vec_norm = np.sum(weight_norm, axis=0) + threshold = np.percentile(vec_norm, percent) + under_threshold = vec_norm <= threshold + above_threshold = vec_norm > threshold + weight2d[:, under_threshold] = 0 + weight_masked = weight2d.reshape(shape) + # mask + mask = np.zeros(weight2d.shape, dtype=np.float32) + mask[:, above_threshold] = 1 + mask_final = mask.reshape(shape) + + if len(mask_final.shape) == 4: + mask_final = mask_final.transpose((3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = mask_final.transpose((1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = weight_masked.transpose((3, 2, 1, 0)) + elif len(weight_masked.shape) == 2: + weight_masked = weight_masked.transpose((1, 0)) + + return mask_final, weight_masked + + def filter_prune(self, weight, prune_ratio, norm='l1'): + """ + the filter pruning method + :param weight: tensor of the weight from the model: (kernel, kernel, in-channel, out-channel) + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + if len(weight.shape) == 4: + weight = tf.transpose(weight, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = tf.transpose(weight, (1, 0)) + + shape = weight.shape + print("column pruning {}: {}".format(norm, weight.shape)) + weight2d = tf.reshape(weight, shape=(shape[0], -1)) + if norm == 'l1': + weight_norm = tf.abs(weight2d) + elif norm == 'l2': + weight_norm = tf.square(weight2d) + + # find the threshold + weight_vec = tf.reduce_sum(weight_norm, axis=1) + threshold = tf.gather(tf.contrib.framework.sort(weight_vec, axis=0), int(int(weight_vec.shape[0]) * prune_ratio)) + + # set those > threshold as 1, those < threshold as 0 + zeros = tf.zeros(shape=weight_vec.shape, dtype='float32') + ones = tf.ones(shape=weight_vec.shape, dtype='float32') + mask_vec = tf.where(tf.less_equal(weight_vec, threshold), zeros, ones) + mask_final = mask_vec * tf.ones([1, weight2d.shape[1]], dtype='float32') + weight_masked = tf.multiply(weight2d, mask_final) + weight_masked = tf.reshape(weight_masked, shape) + mask_final = tf.reshape(mask_final, shape) + + if len(mask_final.shape) == 4: + mask_final = tf.transpose(mask_final, (3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = tf.transpose(mask_final, (1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = tf.transpose(weight_masked, (3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight_masked = tf.transpose(weight_masked, (1, 0)) + + return mask_final, weight_masked + + def filter_prune_npy(self, weight, prune_ratio, norm='l1'): + """ + the filter pruning method + :param weight: numpy.array of the weight from the model + :param prune_ratio: float value, 0.00 - 1.00 + :param norm: the normalization way + :return: + mask_final: the mask of the weight, 1 for save, 0 for prune + weight_masked: masked weight, 0 for no value + """ + percent = prune_ratio * 100 + + if len(weight.shape) == 4: + weight = weight.transpose((3, 2, 1, 0)) + elif len(weight.shape) == 2: + weight = weight.transpose((1, 0)) + + shape = weight.shape + print("filter pruning {}: {}".format(norm, weight.shape)) + weight2d = weight.reshape(shape[0], -1) + if norm == 'l1': + weight_norm = np.abs(weight2d) + elif norm == 'l2': + weight_norm = np.square(weight2d) + + vec_norm = np.sum(weight_norm, axis=1) + threshold = np.percentile(vec_norm, percent) + under_threshold = vec_norm <= threshold + above_threshold = vec_norm > threshold + weight2d[under_threshold, :] = 0 + weight_masked = weight2d.reshape(shape) + # mask + mask = np.zeros(weight2d.shape, dtype=np.float32) + mask[above_threshold, :] = 1 + mask_final = mask.reshape(shape) + + if len(mask_final.shape) == 4: + mask_final = mask_final.transpose((3, 2, 1, 0)) + elif len(mask_final.shape) == 2: + mask_final = mask_final.transpose((1, 0)) + + if len(weight_masked.shape) == 4: + weight_masked = weight_masked.transpose((3, 2, 1, 0)) + elif len(weight_masked.shape) == 2: + weight_masked = weight_masked.transpose((1, 0)) + + return mask_final, weight_masked diff --git a/tools/mnncompress/mnncompress/tensorflow/decomposition.py b/tools/mnncompress/mnncompress/tensorflow/decomposition.py new file mode 100644 index 00000000..c4fcfe4d --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/decomposition.py @@ -0,0 +1,318 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np +import tensorly as tl +import scipy +from tensorly.decomposition import partial_tucker + +from mnncompress.common import VBMF +from ..common import MNN_compression_pb2 as compress_pb +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_align_channels +import uuid +from .helpers import is_weight_tensor, update_consumer_inputs +from .graph_checker import check_for_grad_ops + +_Support_Ops = ['Conv2D', 'MatMul'] +_MNN_variable_collection_name = "MNN_decompose_variables" + +origin_params_num = decompose_model_params_num = 0 + +def _decompose_conv_1x1(weights_info, op, align_channels, reserved_singular_value_ratio, scope, ori_args): + assert op.type == "Conv2D" + feature = op.inputs[0] + weight = weights_info[op.name] + kernel_shape = [weight.shape[0], weight.shape[1]] + assert kernel_shape == [1, 1] + + global decompose_model_params_num + + if len(weight.squeeze().shape) != 2: + decompose_model_params_num += weight.size + print("skip svd for", op.name, "weight shape:", weight.shape) + return + + weight_2d = weight.squeeze().transpose() + u, s, v = scipy.linalg.svd(weight_2d) + singular_value_sum = np.sum(s) + n_dim = 1 + temp_sum = 0.0 + for i in range(0, s.size): + temp_sum += s[i] + n_dim = i+1 + if temp_sum / singular_value_sum >= reserved_singular_value_ratio: + break + n_dim = get_align_channels(n_dim, s.size, align_channels) + print("svd for", op.name, "weight shape:", weight.shape, "n_dim:", n_dim) + + conv1_weight = np.matmul(np.diag(s[0:n_dim]), v[0:n_dim, :]).transpose().reshape((1, 1, -1, n_dim)) + conv2_weight = u[:, 0:n_dim].transpose().reshape((1, 1, n_dim, -1)) + + decompose_model_params_num += (conv1_weight.size + conv2_weight.size) + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + conv1_weight_variable = tf.get_variable(name='svd_conv_w1', initializer=conv1_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, conv1_weight_variable) + conv2_weight_variable = tf.get_variable(name='svd_conv_w2', initializer=conv2_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, conv2_weight_variable) + + conv1 = tf.nn.conv2d(feature, conv1_weight_variable, strides=ori_args["strides"], padding=ori_args["padding"], dilations=ori_args["dilations"]) + conv2 = tf.nn.conv2d(conv1, conv2_weight_variable, padding="VALID") + update_consumer_inputs(op.outputs[0], conv2) + + +def _decompose_conv(weights_info, op, align_channels, tucker_minimal_ratio, reserved_singular_value_ratio, scope): + assert op.type == "Conv2D" + feature = op.inputs[0] + weight = weights_info[op.name] + kernel_shape = [weight.shape[0], weight.shape[1]] + in_out_channels = [weight.shape[2], weight.shape[3]] + + global decompose_model_params_num + + if in_out_channels[0] <= align_channels or in_out_channels[1] <= align_channels: + decompose_model_params_num += weight.size + print("skip decompose for conv:", op.name, "in_out_channels:", in_out_channels) + return + + ori_args = {} + ori_args["strides"] = op.get_attr("strides") + ori_args["padding"] = op.get_attr("padding") + ori_args["dilations"] = op.get_attr("dilations") + + if kernel_shape == [1, 1]: + _decompose_conv_1x1(weights_info, op, align_channels, reserved_singular_value_ratio, scope, ori_args) + else: + weight_nchw = weight.transpose((3, 2, 0, 1)) + u0 = tl.base.unfold(weight_nchw, 0) + u1 = tl.base.unfold(weight_nchw, 1) + res0 = VBMF.EVBMF(u0) + res1 = VBMF.EVBMF(u1) + rank0 = get_align_channels(res0[1].shape[0], in_out_channels[1], align_channels, tucker_minimal_ratio) + rank1 = get_align_channels(res1[1].shape[1], in_out_channels[0], align_channels, tucker_minimal_ratio) + ranks = [rank0, rank1] + + core, [last, first] = partial_tucker(weight_nchw, modes=[0, 1], rank=ranks, init='svd') + print("tucker for", op.name, ":", in_out_channels, "<===>", [core.shape[1], core.shape[0]], "ranks:", ranks) + + conv1_weight = np.expand_dims(np.expand_dims(first.transpose(), -1), -1).transpose((2, 3, 1, 0)) + conv2_weight = core.transpose((2, 3, 1, 0)) + conv3_weight = np.expand_dims(np.expand_dims(last, -1), -1).transpose((2, 3, 1, 0)) + + decompose_model_params_num += (conv1_weight.size + conv2_weight.size + conv3_weight.size) + + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + conv1_weight_variable = tf.get_variable(name='tucker_conv_w1', initializer=conv1_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, conv1_weight_variable) + conv2_weight_variable = tf.get_variable(name='tucker_conv_w2', initializer=conv2_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, conv2_weight_variable) + conv3_weight_variable = tf.get_variable(name='tucker_conv_w3', initializer=conv3_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, conv3_weight_variable) + + conv1 = tf.nn.conv2d(feature, conv1_weight_variable, padding="VALID") + conv2 = tf.nn.conv2d(conv1, conv2_weight_variable, strides=ori_args["strides"], padding=ori_args["padding"], dilations=ori_args["dilations"]) + conv3 = tf.nn.conv2d(conv2, conv3_weight_variable, padding="VALID") + update_consumer_inputs(op.outputs[0], conv3) + + +def _decompose_matmul(weights_info, op, align_channels, reserved_singular_value_ratio, scope): + assert op.type == "MatMul" + if is_weight_tensor(op.inputs[0]) and is_weight_tensor(op.inputs[1]): + return + if (not is_weight_tensor(op.inputs[0])) and (not is_weight_tensor(op.inputs[1])): + return + + global decompose_model_params_num + + for index in range(len(op.inputs)): + input_tensor = op.inputs[index] + if not is_weight_tensor(input_tensor): + continue + + weight = weights_info[op.name] + if len(weight.squeeze().shape) != 2: + decompose_model_params_num += weight.size + print("skip svd for", op.name, "weight shape:", weight.shape) + continue + + if weight.shape[0] <= align_channels or weight.shape[1] <= align_channels: + decompose_model_params_num += weight.size + print("skip svd for", op.name, "weight shape:", weight.shape) + continue + + u, s, v = scipy.linalg.svd(weight) + singular_value_sum = np.sum(s) + n_dim = 1 + temp_sum = 0.0 + for i in range(0, s.size): + temp_sum += s[i] + n_dim = i+1 + if temp_sum / singular_value_sum >= reserved_singular_value_ratio: + break + n_dim = get_align_channels(n_dim, s.size, align_channels) + print("svd for", op.name, "weight shape:", weight.shape, "n_dim:", n_dim) + + fc1_weight = np.matmul(np.diag(s[0:n_dim]), v[0:n_dim, :]) + fc2_weight = u[:, 0:n_dim] + + decompose_model_params_num += (fc1_weight.size + fc2_weight.size) + + if index == 0: + feature = op.inputs[1] + ori_output_tensor = op.outputs[0] + weight_trans = op.get_attr("transpose_a") + feature_trans = op.get_attr("transpose_b") + + if not weight_trans: + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + fc1_weight_variable = tf.get_variable(name='svd_fc_w1', initializer=fc1_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc1_weight_variable) + fc2_weight_variable = tf.get_variable(name='svd_fc_w2', initializer=fc2_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc2_weight_variable) + + fc1 = tf.matmul(fc1_weight_variable, feature, transpose_b=feature_trans) + fc2 = tf.matmul(fc2_weight_variable, fc1) + update_consumer_inputs(ori_output_tensor, fc2) + else: + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + fc1_weight_variable = tf.get_variable(name='svd_fc_w1', initializer=fc1_weight.transpose(), trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc1_weight_variable) + fc2_weight_variable = tf.get_variable(name='svd_fc_w2', initializer=fc2_weight.transpose(), trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc2_weight_variable) + + fc1 = tf.matmul(fc2_weight_variable, feature, transpose_b=feature_trans) + fc2 = tf.matmul(fc1_weight_variable, fc1) + update_consumer_inputs(ori_output_tensor, fc2) + + if index == 1: + feature = op.inputs[0] + ori_output_tensor = op.outputs[0] + weight_trans = op.get_attr("transpose_b") + feature_trans = op.get_attr("transpose_a") + + if not weight_trans: + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + fc1_weight_variable = tf.get_variable(name='svd_fc_w1', initializer=fc1_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc1_weight_variable) + fc2_weight_variable = tf.get_variable(name='svd_fc_w2', initializer=fc2_weight, trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc2_weight_variable) + + fc1 = tf.matmul(feature, fc2_weight_variable, transpose_a=feature_trans) + fc2 = tf.matmul(fc1, fc1_weight_variable) + update_consumer_inputs(ori_output_tensor, fc2) + else: + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + fc1_weight_variable = tf.get_variable(name='svd_fc_w1', initializer=fc1_weight.transpose(), trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc1_weight_variable) + fc2_weight_variable = tf.get_variable(name='svd_fc_w2', initializer=fc2_weight.transpose(), trainable=True) + tf.add_to_collection(_MNN_variable_collection_name, fc2_weight_variable) + + fc1 = tf.matmul(feature, fc1_weight_variable, transpose_a=feature_trans) + fc2 = tf.matmul(fc1, fc2_weight_variable) + update_consumer_inputs(ori_output_tensor, fc2) + + +def low_rank_decompose(graph, weight_npy_file, compress_params_file, skip_layers=[""], align_channels=8, tucker_minimal_ratio=0.25, reserved_singular_value_ratio=0.5, append=False): + weights_info = np.load(weight_npy_file, allow_pickle=True).item() + + grad_ops = check_for_grad_ops(graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only, before construct model optimizer\n' % grad_ops) + + global origin_params_num, decompose_model_params_num + + all_ops = graph.get_operations() + decompose_layers = [] + for op in all_ops: + if 'gradients/' in op.name and '_grad' in op.name: + continue + + if op.name in skip_layers: + if op.type == "Conv2D" and len(op.outputs[0].consumers()) > 0: + origin_params_num += op.inputs[1].shape.num_elements() + decompose_model_params_num += op.inputs[1].shape.num_elements() + if op.type == "MatMul" and len(op.outputs[0].consumers()) > 0: + for inp in op.inputs: + if is_weight_tensor(inp): + origin_params_num += inp.shape.num_elements() + decompose_model_params_num += inp.shape.num_elements() + + if (op.type in _Support_Ops) and (op.name not in skip_layers): + if len(op.outputs[0].consumers()) > 0: + decompose_layers.append(op) + + if op.type == "Conv2D": + origin_params_num += op.inputs[1].shape.num_elements() + if op.type == "MatMul": + for inp in op.inputs: + if is_weight_tensor(inp): + origin_params_num += inp.shape.num_elements() + + for i in range(len(decompose_layers)): + op = decompose_layers[i] + scope = "MNN_decompose_" + str(i) + if op.type == "Conv2D": + print("decompose conv:", op.name) + _decompose_conv(weights_info, op, align_channels, tucker_minimal_ratio, reserved_singular_value_ratio, scope) + + if op.type == "MatMul": + print("decompose matmul:", op.name) + _decompose_matmul(weights_info, op, align_channels, reserved_singular_value_ratio, scope) + + compress_proto = compress_pb.Pipeline() + if append: + f = open(compress_params_file, 'rb') + compress_proto.ParseFromString(f.read()) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + model_guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = model_guid + else: + model_guid = compress_proto.mnn_uuid + + f = open(compress_params_file, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + detail = {"algorithm": "low_rank_decompose", "compression_rate": origin_params_num / decompose_model_params_num, \ + "ori_model_size": origin_params_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"skip_layers": skip_layers, "align_channels": align_channels, "tucker_minimal_ratio": tucker_minimal_ratio, "reserved_singular_value_ratio": reserved_singular_value_ratio}} + + mnn_logger.on_done("pytorch", model_guid, detail) + + +def get_op_weight_values(sess, npy_file_name): + graph = sess.graph + + grad_ops = check_for_grad_ops(graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only, before construct model optimizer\n' % grad_ops) + + all_ops = graph.get_operations() + op_name_weight_values = {} + + for op in all_ops: + if 'gradients/' in op.name and '_grad' in op.name: + continue + + if op.type == "Conv2D": + weight = sess.run(op.inputs[1]) + op_name_weight_values[op.name] = weight + + if op.type == "MatMul": + if is_weight_tensor(op.inputs[0]) and is_weight_tensor(op.inputs[1]): + continue + if (not is_weight_tensor(op.inputs[0])) and (not is_weight_tensor(op.inputs[1])): + continue + + if is_weight_tensor(op.inputs[0]): + weight = sess.run(op.inputs[0]) + op_name_weight_values[op.name] = weight + + if is_weight_tensor(op.inputs[1]): + weight = sess.run(op.inputs[1]) + op_name_weight_values[op.name] = weight + + np.save(npy_file_name, op_name_weight_values) + print("op weights saved to npy file:", npy_file_name) diff --git a/tools/mnncompress/mnncompress/tensorflow/fix_quant_params.py b/tools/mnncompress/mnncompress/tensorflow/fix_quant_params.py new file mode 100644 index 00000000..51591aa7 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/fix_quant_params.py @@ -0,0 +1,51 @@ +import argparse +import os +import numpy as np +import mnncompress.common.MNN_compression_pb2 as compression + +parser = argparse.ArgumentParser( + description="fix asr QAT quant table.") +parser.add_argument("--qat_quant_table", required=True, type=str, + help="qat_quant_table binary filename.") +parser.add_argument("--quant_weight_io_map_npy", required=True, type=str, + help="quant_weight_io_map_npy filename.") + +args = parser.parse_args() + + +quant_weight_io_map = np.load(args.quant_weight_io_map_npy, allow_pickle=True).item() + +qat_pipeline = compression.Pipeline() +with open(args.qat_quant_table, "rb") as f: + qat_pipeline.ParseFromString(f.read()) + + +for algo in qat_pipeline.algo: + if compression.CompressionAlgo.CompressionType.Name(algo.type) == 'QUANTIZE': + for layer in algo.quant_params.layer: + qat_weight_name = layer.weight[0].name + qat_input_name = layer.input[0].name + qat_output_name = layer.output[0].name + + if qat_weight_name not in quant_weight_io_map.keys(): + raise ValueError(qat_weight_name+" not found in quant_weight_io_map_npy file") + + fixed_input_name = quant_weight_io_map[qat_weight_name][0] + fixed_output_name = quant_weight_io_map[qat_weight_name][1] + op_name = quant_weight_io_map[qat_weight_name][2] + + if qat_input_name == fixed_input_name and qat_output_name == fixed_output_name: + print("no need to fix:", op_name, qat_weight_name, qat_input_name, qat_output_name) + continue + + layer.input[0].name = fixed_input_name + layer.output[0].name = fixed_output_name + print("fixed:", op_name, qat_weight_name, qat_input_name, qat_output_name) + + break + + +with open("qat_quant_table_fixed.bin", "wb") as f: + f.write(qat_pipeline.SerializeToString()) + +print("fixed quant params saved to: qat_quant_table_fixed.bin") diff --git a/tools/mnncompress/mnncompress/tensorflow/graph_checker.py b/tools/mnncompress/mnncompress/tensorflow/graph_checker.py new file mode 100644 index 00000000..b0b644b2 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/graph_checker.py @@ -0,0 +1,94 @@ +def check_for_training_ops(g): + """Check if training ops are present in the graph. + + Args: + g: The tf.Graph on which the check for training ops needs to be + performed. + + Raises: + ValueError: If a training op is seen in the graph; + """ + + # The list here is obtained + # from https://www.tensorflow.org/api_docs/cc/group/training-ops + + training_ops = frozenset([ + 'ApplyAdadelta', + 'ApplyAdagrad', + 'ApplyAdagradDA', + 'ApplyAdam', + 'ApplyAddSign', + 'ApplyCenteredRMSProp', + 'ApplyFtrl', + 'ApplyFtrlV2', + 'ApplyGradientDescent', + 'ApplyMomentum', + 'ApplyPowerSign ', + 'ApplyProximalAdagrad', + 'ApplyProximalGradientDescent', + 'ApplyRMSProp', + 'ResourceApplyAdadelta', + 'ResourceApplyAdagrad', + 'ResourceApplyAdagradDA', + 'ResourceApplyAdam', + 'ResourceApplyAdamWithAmsgrad', + 'ResourceApplyAddSign', + 'ResourceApplyCenteredRMSProp', + 'ResourceApplyFtrl', + 'ResourceApplyFtrlV2', + 'ResourceApplyGradientDescent', + 'ResourceApplyKerasMomentum', + 'ResourceApplyMomentum', + 'ResourceApplyPowerSign', + 'ResourceApplyProximalAdagrad', + 'ResourceApplyProximalGradientDescent', + 'ResourceApplyRMSProp', + 'ResourceSparseApplyAdadelta', + 'ResourceSparseApplyAdagrad', + 'ResourceSparseApplyAdagradDA', + 'ResourceSparseApplyCenteredRMSProp', + 'ResourceSparseApplyFtrl', + 'ResourceSparseApplyFtrlV2', + 'ResourceSparseApplyKerasMomentum', + 'ResourceSparseApplyMomentum', + 'ResourceSparseApplyProximalAdagrad', + 'ResourceSparseApplyProximalGradientDescent', + 'ResourceSparseApplyRMSProp', + 'SparseApplyAdadelta', + 'SparseApplyAdagrad', + 'SparseApplyAdagradDA', + 'SparseApplyCenteredRMSProp', + 'SparseApplyFtrl', + 'SparseApplyFtrlV2', + 'SparseApplyMomentum', + 'SparseApplyProximalAdagrad', + 'SparseApplyProximalGradientDescent', + 'SparseApplyRMSProp', + ]) + + op_types = set([op.type for op in g.get_operations()]) + train_op_list = op_types.intersection(training_ops) + + return train_op_list + +def check_for_grad_ops(g): + grad_ops = [ + 'Conv2DBackpropInput', + 'Conv2DBackpropFilter', + 'Grad', # if there is 'Grad' in op type + ] + + allow_ops = [ + "StopGradient", + ] + + op_types = [op.type for op in g.get_operations()] + grad_op_list = [] + for op_type in op_types: + if op_type in allow_ops: + continue + for grad_op in grad_ops: + if grad_op in op_type: + grad_op_list.append(op_type) + + return grad_op_list diff --git a/tools/mnncompress/mnncompress/tensorflow/helpers.py b/tools/mnncompress/mnncompress/tensorflow/helpers.py new file mode 100644 index 00000000..2dc8da1d --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/helpers.py @@ -0,0 +1,421 @@ +from __future__ import print_function +import tensorflow as tf +import yaml +import numpy as np +from .graph_checker import check_for_grad_ops + +def get_op_name_of_type(graph, type_list): + ops = graph.get_operations() + names = [] + for one_op in ops: + if one_op.type in type_list: + names.append(one_op.name) + + return names + + +def get_input_tensor_index(op, input_tensor): + return_list = [] + for i in range(len(op.inputs)): + if input_tensor == op.inputs[i]: + return_list.append(i) + return return_list + + +_Variable_types = ['Variable', 'VariableV2', 'Const'] + +def is_weight_tensor(tensor): + cond1 = len(tensor.op.inputs) == 1 + if cond1 == False: + return False + + if tensor.op.type == 'Identity': + if tensor.op.inputs[0].op.type in _Variable_types: + return True + else: + # if tensor.op.type == 'Enter': + if tensor.op.inputs[0].op.type == "Identity": + if tensor.op.inputs[0].op.inputs[0].op.type in _Variable_types: + return True + + return False + +def get_trainable_weights(session): + variables = tf.trainable_variables() + np_values = session.run(variables) + return variables, np_values + +def kronecker_tf(A, B): + A_shape = A.get_shape() + B_shape = B.get_shape() + + for i in range(A_shape[0]): + for j in range(A_shape[1]): + if j == 0: + # temp = tf.squeeze(A[i, j] * B) + temp = A[i, j] * B + else: + # temp = tf.concat([temp, tf.squeeze(A[i, j] * B)], 1) + temp = tf.concat([temp, A[i, j] * B], 1) + if i == 0: + result = temp + else: + result = tf.concat([result, temp], 0) + return result + +def generate_prune_config_yaml(graph, filename, default_prune_ratio=0.5, generate_template=False): + f = open(filename, 'w') + config = {} + prune_ratio_config = {} + if generate_template: + conv2d_op_names = get_op_name_of_type(graph, ['Conv2D']) + for op_name in conv2d_op_names: + prune_ratio_config[op_name] = '-.--' + depthwise_conv_op_names = get_op_name_of_type(graph, ['DepthwiseConv2dNative']) + for op_name in depthwise_conv_op_names: + prune_ratio_config[op_name] = ',.,,' + matmul_op_names = get_op_name_of_type(graph, ['MatMul']) + for op_name in matmul_op_names: + prune_ratio_config[op_name] = '--fc--' + else: + op_names = get_op_name_of_type(graph, ['Conv2D', 'DepthwiseConv2dNative', 'MatMul']) + for op_name in op_names: + prune_ratio_config[op_name] = default_prune_ratio + + config['prune_ratios'] = prune_ratio_config + f.write(yaml.dump(config)) + f.close() + +def get_weight_variable_name(tensor): + return tensor.op.inputs[0].op.name + +def get_variable_by_name(name, graph=None): + if len(tf.global_variables()) == 0: + name = name + ":0" + return graph.get_tensor_by_name(name) + else: + res = None + all_variable_names = [v.op.name for v in tf.global_variables()] + if name in all_variable_names: + res = [v for v in tf.global_variables() if v.op.name == name][0] + return res + +def get_variable_by_tensor(tensor): + if tensor.op.type == "ExpandDims": + tensor = tensor.op.inputs[0] + if tensor.op.type == "Switch": + tensor = tensor.op.inputs[0].op.inputs[0] + variable_name = get_weight_variable_name(tensor) + if variable_name.endswith("/read"): + variable_name = variable_name[:-5] + variable = get_variable_by_name(variable_name, tensor.graph) + return variable + +@tf.custom_gradient +def custom_scale_grad(x, num, clamp_value): + def grad(dy): + gs = 1. / tf.math.sqrt(tf.cast(num, tf.float32) * clamp_value + 0.0) + return dy * gs, None, None + + return tf.identity(x), grad + +def update_consumer_inputs(ori_output_tensor, new_output_tensor): + output_consumers = ori_output_tensor.consumers() + for c in output_consumers: + index = get_input_tensor_index(c, ori_output_tensor) + for id in index: + c._update_input(id, new_output_tensor) + +def check_return_cond(output_tensor): + output_no_grad_consumers = [c for c in output_tensor.consumers() if 'gradients/' not in c.name] + if len(output_no_grad_consumers) != 1: + if len(output_no_grad_consumers) != 2: + return True + else: + c1 = output_tensor.consumers()[0] + c2 = output_tensor.consumers()[1] + if (c1.type != 'Switch') or (c2.type != 'Switch'): + return True + return False + +def skip_identity(tensor): + while (len(tensor.consumers()) == 1) and (tensor.consumers()[0].type == 'Identity'): + tensor = tensor.consumers()[0].outputs[0] + return tensor + +def find_batchnorm_output_tensor(output_tensor): + def _get_switch_index(switch_op): + for i in range(len(switch_op.outputs)): + if len(switch_op.outputs[i].consumers()) > 0: + return i + raise ValueError("switch op's outputs don't have consumers") + + # batch norm with tf.cond + output_no_grad_consumers = [c for c in output_tensor.consumers() if 'gradients/' not in c.name] + + if (len(output_no_grad_consumers) == 2) and (output_tensor.consumers()[0].type == 'Switch') and (output_tensor.consumers()[1].type == 'Switch'): + switch_op1 = output_tensor.consumers()[0] + switch_op2 = output_tensor.consumers()[1] + + index = _get_switch_index(switch_op1) + no_grad_consumers = [c for c in switch_op1.outputs[index].consumers() if 'gradients/' not in c.name] + cond1 = (len(no_grad_consumers) == 1) + c1 = switch_op1.outputs[index].consumers()[0] + cond2 = ('FusedBatchNorm' in c1.type) + merge_op = c1.outputs[0].consumers()[0] + cond3 = (merge_op.type == 'Merge') + + index = _get_switch_index(switch_op2) + no_grad_consumers = [c for c in switch_op2.outputs[index].consumers() if 'gradients/' not in c.name] + cond4 = (len(no_grad_consumers) == 1) + c2 = switch_op2.outputs[index].consumers()[0] + cond5 = ('FusedBatchNorm' in c2.type) + cond6 = (len(merge_op.inputs) == 2) + + sub_cond1 = ((merge_op.inputs[0] == c1.outputs[0]) and (merge_op.inputs[1] == c2.outputs[0])) + sub_cond2 = ((merge_op.inputs[0] == c2.outputs[0]) and (merge_op.inputs[1] == c1.outputs[0])) + + cond7 = (sub_cond1 or sub_cond2) + + final_cond = (cond1 and cond2 and cond3 and cond4 and cond5 and cond6 and cond7) + + if final_cond is True: + output_tensor = merge_op.outputs[0] + return output_tensor + + if 'FusedBatchNorm' in output_tensor.consumers()[0].type: + c = output_tensor.consumers()[0] + output_tensor = c.outputs[0] + return output_tensor + + return output_tensor + +def find_quant_output_tensor(conv2d_op): + output_tensor = conv2d_op.outputs[0] + op_output = output_tensor + if check_return_cond(output_tensor): + return output_tensor + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + return op_output + + # conv1d squeeze + if output_tensor.consumers()[0].type == "Squeeze": + squeeze_op = output_tensor.consumers()[0] + output_tensor = squeeze_op.outputs[0] + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + # if conv-squeeze, but no bias or relu + return op_output + + # bias add + if output_tensor.consumers()[0].type in ['Add', 'AddV2', 'BiasAdd']: + bias_add_op = output_tensor.consumers()[0] + output_tensor = bias_add_op.outputs[0] + op_output = output_tensor + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + return op_output + + # TODO: MNN converter will turn matmul to conv2d, bias is fused, but bn relu is not, so bn relu is not fused here. + if conv2d_op.type == "MatMul": + return op_output + + # batch norm + output_tensor = find_batchnorm_output_tensor(output_tensor) + op_output = output_tensor + output_tensor = skip_identity(output_tensor) + + if len(output_tensor.consumers()) != 1: + return op_output + + # relu or relu6 + if output_tensor.consumers()[0].type in ['Relu', 'Relu6']: + relu_op = output_tensor.consumers()[0] + output_tensor = relu_op.outputs[0] + return output_tensor + + return output_tensor + +def find_bias_add_op(conv2d_op): + output_tensor = conv2d_op.outputs[0] + if check_return_cond(output_tensor): + return None + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + return None + + # bias add + if output_tensor.consumers()[0].type in ['Add', 'AddV2', 'BiasAdd']: + bias_add_op = output_tensor.consumers()[0] + return bias_add_op + + return None + +def get_batch_norm_statistics(conv2d_op): + statistics = [None, None, None, None, None] # gamma, beta, moving_mean, moving_variance, epsilon + + output_tensor = conv2d_op.outputs[0] + if check_return_cond(output_tensor): + return statistics + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + return statistics + + # bias add + if output_tensor.consumers()[0].type in ['Add', 'AddV2', 'BiasAdd']: + bias_add_op = output_tensor.consumers()[0] + output_tensor = bias_add_op.outputs[0] + + output_tensor = skip_identity(output_tensor) + if check_return_cond(output_tensor): + return statistics + + def _get_switch_index(switch_op): + for i in range(len(switch_op.outputs)): + if len(switch_op.outputs[i].consumers()) > 0: + return i + raise ValueError("switch op's outputs don't have consumers") + + # batch norm with tf.cond + output_no_grad_consumers = [c for c in output_tensor.consumers() if 'gradients/' not in c.name] + + if (len(output_no_grad_consumers) == 2) and (output_tensor.consumers()[0].type == 'Switch') and (output_tensor.consumers()[1].type == 'Switch'): + switch_op1 = output_tensor.consumers()[0] + switch_op2 = output_tensor.consumers()[1] + + index = _get_switch_index(switch_op1) + no_grad_consumers = [c for c in switch_op1.outputs[index].consumers() if 'gradients/' not in c.name] + cond1 = (len(no_grad_consumers) == 1) + c1 = switch_op1.outputs[index].consumers()[0] + cond2 = ('FusedBatchNorm' in c1.type) + merge_op = c1.outputs[0].consumers()[0] + cond3 = (merge_op.type == 'Merge') + + index = _get_switch_index(switch_op2) + no_grad_consumers = [c for c in switch_op2.outputs[index].consumers() if 'gradients/' not in c.name] + cond4 = (len(no_grad_consumers) == 1) + c2 = switch_op2.outputs[index].consumers()[0] + cond5 = ('FusedBatchNorm' in c2.type) + cond6 = (len(merge_op.inputs) == 2) + + sub_cond1 = ((merge_op.inputs[0] == c1.outputs[0]) and (merge_op.inputs[1] == c2.outputs[0])) + sub_cond2 = ((merge_op.inputs[0] == c2.outputs[0]) and (merge_op.inputs[1] == c1.outputs[0])) + + cond7 = (sub_cond1 or sub_cond2) + + final_cond = (cond1 and cond2 and cond3 and cond4 and cond5 and cond6 and cond7) + + if final_cond is True: + bn_scope = merge_op.name.strip('cond/Merge') + "/" + gamma_op = get_variable_by_name(bn_scope + "gamma") + beta_op = get_variable_by_name(bn_scope + "beta") + moving_mean_op = get_variable_by_name(bn_scope + "moving_mean") + moving_variance_op = get_variable_by_name(bn_scope + "moving_variance") + + statistics = [] + statistics.append(tf.identity(gamma_op)) + statistics.append(tf.identity(beta_op)) + statistics.append(tf.identity(moving_mean_op)) + statistics.append(tf.identity(moving_variance_op)) + statistics.append(c1.get_attr("epsilon")) + + return statistics + + if 'FusedBatchNorm' in output_tensor.consumers()[0].type: + c = output_tensor.consumers()[0] + gamma_op = None + beta_op = None + bn_scope = None + input_names = [t.op.name for t in c.inputs] + for name in input_names: + if name.endswith("gamma/read"): + gamma_op = conv2d_op.graph.get_operation_by_name(name) + if name.endswith("beta/read"): + beta_op = conv2d_op.graph.get_operation_by_name(name) + bn_scope = name.rsplit("beta/read", 1)[0] + + assert bn_scope is not None + moving_mean_op = conv2d_op.graph.get_operation_by_name(bn_scope + "moving_mean/read") + moving_variance_op = conv2d_op.graph.get_operation_by_name(bn_scope + "moving_variance/read") + + statistics = [] + if gamma_op is not None: + statistics.append(gamma_op.outputs[0]) + else: + statistics.append(gamma_op) + statistics.append(beta_op.outputs[0]) + statistics.append(moving_mean_op.outputs[0]) + statistics.append(moving_variance_op.outputs[0]) + statistics.append(c.get_attr("epsilon")) + + return statistics + + return statistics + +def get_save_tensor_name(name): + if name.endswith(':0'): + return name[:-2] + else: + return name + +def get_quant_weight_io_map(graph, file_name="quant_weight_io_map.npy", remove_name_str=["while/",]): + grad_ops = check_for_grad_ops(graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only. create quantizer before construct model optimizer\n' % grad_ops) + + supported_types = ["Conv2D", "DepthwiseConv2dNative", "MatMul"] + all_ops = graph.get_operations() + quant_ops = [op for op in all_ops if op.type in supported_types] + + weight_io_map = {} + + for op in quant_ops: + op_name = op.name + if 'gradients/' in op_name and '_grad' in op_name: + continue + + if op.type in ["Conv2D", "DepthwiseConv2dNative"]: + weight_name = get_save_tensor_name(get_variable_by_tensor(op.inputs[1]).name) + input_name = get_save_tensor_name(op.inputs[0].name) + output_name = get_save_tensor_name(find_quant_output_tensor(op).name) + + for remove_str in remove_name_str: + weight_name = weight_name.replace(remove_str, "") + input_name = input_name.replace(remove_str, "") + output_name = output_name.replace(remove_str, "") + + weight_io_map[weight_name] = (input_name, output_name, op_name) + + if op.type == "MatMul": + if is_weight_tensor(op.inputs[0]) and is_weight_tensor(op.inputs[1]): + continue + if (not is_weight_tensor(op.inputs[0])) and (not is_weight_tensor(op.inputs[1])): + continue + + weight_name, input_name, output_name = None, None, None + + for input_tensor in op.inputs: + if is_weight_tensor(input_tensor): + weight_name = get_save_tensor_name(get_variable_by_tensor(input_tensor).name) + else: + input_name = get_save_tensor_name(input_tensor.name) + + assert (weight_name != None) and (input_name != None) + + output_name = get_save_tensor_name(find_quant_output_tensor(op).name) + + for remove_str in remove_name_str: + weight_name = weight_name.replace(remove_str, "") + input_name = input_name.replace(remove_str, "") + output_name = output_name.replace(remove_str, "") + + weight_io_map[weight_name] = (input_name, output_name, op_name) + + np.save(file_name, weight_io_map) + print("quant weight io map saved to:", file_name) diff --git a/tools/mnncompress/mnncompress/tensorflow/hooks.py b/tools/mnncompress/mnncompress/tensorflow/hooks.py new file mode 100644 index 00000000..61d02c88 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/hooks.py @@ -0,0 +1,73 @@ +from __future__ import print_function +import tensorflow as tf +from .SNIP_level_pruner import SNIPLevelPruner +from .SIMD_OC_pruner import SIMDOCPruner +from .TaylorFO_channel_pruner import TaylorFOChannelPruner +from .decomposition import low_rank_decompose +from .EMA_quantizer import EMAQuantizer +from .OAQ_quantizer import OAQQuantizer +from .LSQ_quantizer import LSQQuantizer + + +class SNIPLevelPrunerHook(tf.train.SessionRunHook): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="SNIPLevel_found_prune_ratios.yml"): + self._pruner = SNIPLevelPruner(sensitivity_npy_file, graph, sparsity, total_pruning_iterations, config_file, debug_info, prune_finetune_iterations, max_prune_ratio, prune_ratio_result_file) + + def after_run(self, run_context, run_values): + sess = run_context.session + self._pruner.do_pruning(sess) + + +class SIMDOCPrunerHook(tf.train.SessionRunHook): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="SIMDOC_found_prune_ratios.yml"): + self._pruner = SIMDOCPruner(sensitivity_npy_file, graph, sparsity, total_pruning_iterations, config_file, debug_info, prune_finetune_iterations, max_prune_ratio, prune_ratio_result_file) + + def after_run(self, run_context, run_values): + sess = run_context.session + self._pruner.do_pruning(sess) + + +class TaylorFOChannelPrunerHook(tf.train.SessionRunHook): + def __init__(self, sensitivity_npy_file, graph=None, sparsity=0.5, total_pruning_iterations=1, config_file=None, debug_info= False, prune_finetune_iterations=0, max_prune_ratio=0.99, prune_ratio_result_file="TaylorFOChannel_found_prune_ratios.yml", align_channels=4): + self._pruner = TaylorFOChannelPruner(sensitivity_npy_file, graph, sparsity, total_pruning_iterations, config_file, debug_info, prune_finetune_iterations, max_prune_ratio, prune_ratio_result_file, align_channels) + + def after_run(self, run_context, run_values): + sess = run_context.session + self._pruner.do_pruning(sess) + +class LowRankDecompositionHook(tf.train.SessionHook): + def __init__(self, graph, weight_npy_file, compress_params_file, skip_layers=[""], align_channels=8, tucker_minimal_ratio=0.25, reserved_singular_value_ratio=0.5, append=False): + low_rank_decompose(graph, weight_npy_file, compress_params_file, skip_layers, align_channels, tucker_minimal_ratio, reserved_singular_value_ratio, append) + +class EMAQuantizerHook(tf.train.SessionRunHook): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, debug_info = False, retain_sparsity=False): + self.quantizer = EMAQuantizer(graph, is_training, skip_quant_layers, skip_quant_flag, bits, debug_info, retain_sparsity) + + def after_create_session(self, session, coord): + self.quantizer.init(session) + + def after_run(self, run_context, run_values): + sess = run_context.session + self.quantizer.update(sess) + +class OAQQuantizerHook(tf.train.SessionRunHook): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, min_clamp_value = 0, debug_info = False, retain_sparsity=False): + self.quantizer = OAQQuantizer(graph, is_training, skip_quant_layers, skip_quant_flag, bits, min_clamp_value, debug_info, retain_sparsity) + + def after_create_session(self, session, coord): + self.quantizer.init(session) + + def after_run(self, run_context, run_values): + sess = run_context.session + self.quantizer.update(sess) + +class LSQQuantizerHook(tf.train.SessionRunHook): + def __init__(self, graph=None, is_training = None, skip_quant_layers = [], skip_quant_flag = [], bits = 8, debug_info = False, retain_sparsity=False): + self.quantizer = LSQQuantizer(graph, is_training, skip_quant_layers, skip_quant_flag, bits, debug_info, retain_sparsity) + + def after_create_session(self, session, coord): + self.quantizer.init(session) + + def after_run(self, run_context, run_values): + sess = run_context.session + self.quantizer.update(sess) diff --git a/tools/mnncompress/mnncompress/tensorflow/level_pruner.py b/tools/mnncompress/mnncompress/tensorflow/level_pruner.py new file mode 100644 index 00000000..88fd73d6 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/level_pruner.py @@ -0,0 +1,214 @@ +from __future__ import print_function +import tensorflow as tf +import yaml + +from ..common import MNN_compression_pb2 as compress_pb +import mnncompress +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods +import uuid +from .helpers import get_op_name_of_type, get_input_tensor_index, is_weight_tensor +from .graph_checker import check_for_grad_ops + +_MNN_compress_scope = 'MNN_pruner_' +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class LevelPruner(object): + def __init__(self, graph=None, config_dict={}, total_pruning_iterations=50000, skip_prune_layers = [], default_prune_ratio=0.0, debug_info= False): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + + op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._config_dict = {} + self._skip_prune_layers = skip_prune_layers + for op_name in op_names: + if op_name not in skip_prune_layers: + self._config_dict[op_name] = default_prune_ratio + + if isinstance(config_dict, str): + f = open(config_dict, "r") + config_dict = yaml.load(f) + f.close() + + for key, value in config_dict.items(): + if key not in op_names: + raise ValueError("op name:", key, "not found in the model, ") + if key not in skip_prune_layers: + self._config_dict[key] = value + + skip_names = [] + for key, value in self._config_dict.items(): + if value < 1e-8: + skip_names.append(key) + self._skip_prune_layers.append(key) + + for name in skip_names: + self._config_dict.pop(name) + + self._debug_info = debug_info + + if debug_info: + print("pruning config:") + for key, value in self._config_dict.items(): + print(key, ":", value) + + print("skip pruning layers:") + for layer in self._skip_prune_layers: + if layer in op_names: + print(layer) + + self._ops = self._graph.get_operations() + self._name_to_ops = {op.name : op for op in self._ops} + self._mask_dict = {} + self._reported = False + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + + if (total_pruning_iterations < 0) and (not isinstance(total_pruning_iterations, int)): + raise ValueError("total_pruning_iterations must be a integer >= 0") + self._total_pruning_iterations = total_pruning_iterations + + self._step = tf.get_variable(name='MNN_pruner_internal_step', initializer=0, trainable=False) + + self._update_step_op = tf.assign(self._step, self._step + 1) + tf.add_to_collection("update_internal_step", self._update_step_op) + + self._op_name_weight_tensor_name = {} + self._insert_pruning_ops() + self._update_pruning_ops = tf.get_collection("update_pruning_masks") + self._control_prune_op = self._step < self._total_pruning_iterations + self._initialized = False + + self._op_prune_ratio_dict = {} + for op_name in self._mask_dict.keys(): + ratio = 1.0 - tf.reduce_mean(self._mask_dict[op_name]) + self._op_prune_ratio_dict[op_name] = ratio + + def do_pruning(self, sess): + if not self._initialized: + sess.run(tf.variables_initializer([self._step])) + self._initialized = True + + if sess.run(self._control_prune_op): + sess.run(self._update_step_op) + sess.run(self._update_pruning_ops) + + def save_compress_params(self, filename, sess, append=False): + compress_proto = compress_pb.Pipeline() + + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + pop_index = [] + for i in range(len(compress_proto.algo)): + if compress_proto.algo[i].type == compress_pb.CompressionAlgo.CompressionType.PRUNE: + pop_index.append(i) + for i in pop_index: + compress_proto.algo.pop(i) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + algorithm = compress_proto.algo.add() + algorithm.type = compress_pb.CompressionAlgo.CompressionType.PRUNE + algorithm.prune_params.type = compress_pb.PruneParams.RANDOM + weight_tensor_names = algorithm.prune_params.level_pruner_params.weight_tensor_names + layer_names = algorithm.prune_params.level_pruner_params.layer_names + prune_ratios = algorithm.prune_params.level_pruner_params.prune_ratios + + for op_name in self._mask_dict.keys(): + tensor_name = self._op_name_weight_tensor_name[op_name] + layer_names.append(op_name) + weight_tensor_names.append(tensor_name) + ratio = sess.run(self._op_prune_ratio_dict[op_name]) + self._total_weight_num += sess.run(tensor_name).size + # / 4.0, for we always expect using weight quant or full quant after prune + self._remain_weight_num += (sess.run(tensor_name).size * (1 - ratio) / 4.0) + prune_ratios.append(ratio) + print(op_name, "pruning_ratio:", ratio) + + self._target_sparsity = 1.0 - self._remain_weight_num / self._total_weight_num + + if not self._reported: + detail = {"algorithm": "Level", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"overall_sparsity": self._target_sparsity, "total_pruning_iterations": self._total_pruning_iterations, "prune_ratios": self._config_dict, \ + "skip_prune_layers": self._skip_prune_layers}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + f = open(filename, 'wb') + f.write(compress_proto.SerializeToString()) + f.close() + + print("compress proto saved to:", filename) + + def _insert_pruning_ops(self): + scope_index = -1 + for layer_name in self._config_dict.keys(): + scope_index += 1 + self._prune_one_layer(layer_name, _MNN_compress_scope + str(scope_index)) + + def _prune_one_layer(self, layer_name, scope): + if self._name_to_ops[layer_name].type == 'Conv2D' or self._name_to_ops[layer_name].type == 'DepthwiseConv2dNative': + self._prune_conv(self._name_to_ops[layer_name], scope) + + if self._name_to_ops[layer_name].type == 'MatMul': + self._prune_matmul(self._name_to_ops[layer_name], scope) + + def _get_weight_variable_name(self, tensor): + return tensor.op.inputs[0].op.name + + def _get_variable_by_name(self, name): + return [v for v in tf.global_variables() if v.op.name == name][0] + + def _prune_conv(self, conv_op, scope): + weight_tensor = conv_op.inputs[1] + self._op_name_weight_tensor_name[conv_op.name] = weight_tensor.name + new_weight_tensor = self._prune_weight(conv_op, weight_tensor, scope) + + def _prune_matmul(self, matmul_op, scope): + if "gradients" in matmul_op.name and "_grad" in matmul_op.name: + return + + input_1 = matmul_op.inputs[0] + input_2 = matmul_op.inputs[1] + + for input_tensor in [input_1, input_2]: + index = get_input_tensor_index(matmul_op, input_tensor)[0] + if is_weight_tensor(input_tensor): + self._op_name_weight_tensor_name[matmul_op.name] = input_tensor.name + new_weight_tensor = self._prune_weight(matmul_op, input_tensor, scope) + + def _prune_weight(self, op, weight_tensor, scope): + with tf.variable_scope(scope, reuse=tf.AUTO_REUSE): + weight_abs_tensor = tf.abs(weight_tensor) + weight_sorted_abs_tensor = tf.contrib.framework.sort(tf.reshape(weight_abs_tensor, [-1])) + num = weight_sorted_abs_tensor.shape.num_elements() * self._config_dict[op.name] / self._total_pruning_iterations * tf.cast(self._step, tf.float32) + num = tf.cast(num, tf.int64) + threshold = weight_sorted_abs_tensor[num] + mask = tf.cast(weight_abs_tensor > threshold, tf.float32) + self._mask_dict[op.name] = mask + + if self._debug_info: + pruning_ratio = 1.0 - tf.reduce_mean(mask) + print_op = tf.print("pruning iter:", self._step, "/", self._total_pruning_iterations, op.name, "pruning_ratio:", pruning_ratio) + + with tf.control_dependencies([print_op]): + pruned_weight_tensor = weight_tensor * mask + else: + pruned_weight_tensor = weight_tensor * mask + + weight_variable_name = self._get_weight_variable_name(weight_tensor) + weight_variable = self._get_variable_by_name(weight_variable_name) + new_weight_tensor_assign = tf.assign(weight_variable, pruned_weight_tensor) + + tf.add_to_collection("update_pruning_masks", new_weight_tensor_assign) + + return new_weight_tensor_assign diff --git a/tools/mnncompress/mnncompress/tensorflow/overflow_custom_ops.py b/tools/mnncompress/mnncompress/tensorflow/overflow_custom_ops.py new file mode 100644 index 00000000..c3b81650 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/overflow_custom_ops.py @@ -0,0 +1,231 @@ +# ============================================================================== +# Copyright 2019 The Alibaba Authors. All Rights Reserved. +# @author Shuofan, Yizhi, Mingyang +# @email hongwei.xhw@alibaba-inc.com +# @company AI Labs, Alibaba Group +# @cite Overflow Aware Quantization: More Efficient Inference on Ubiquitous Embedded Hardware +# @modified 2019/11/25 +# @file overflow_custom_ops.py +# @desc int16-mac convolution and matmul. +# +# ============================================================================== +import tensorflow as tf +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import variables +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import constant_op + + +def matmul2d(A, B, dtype=None): + """ + custom matmul2d + :param A: + :param B: + :param dtype: + :return: + """ + with variable_scope.variable_scope('matmul2d'): + if dtype is not None: + A = math_ops.cast(A, dtype) + B = math_ops.cast(B, dtype) + + a_shape = A.shape + b_shape = B.shape + + assert len(a_shape) == 2 and len(b_shape) == 2 , 'Expect A and B are 2D inputs' + assert a_shape[1] == b_shape[0], 'Expect M*N and N*K' + + A_tile = array_ops.tile(A, [1, b_shape[1]]) # copy itself + A_tile = array_ops.reshape(A_tile, [a_shape[0], b_shape[1], + a_shape[1]]) # reshape for multiply + + B_transpose = array_ops.transpose(B, [1, 0]) + multi_result = math_ops.multiply(A_tile, B_transpose) + ret = math_ops.reduce_sum(multi_result, axis=-1) + return ret + + +def conv2d(input_tensor, weights, strides, padding='SAME', dtype=None): + """ + custom conv2d + :param input_tensor: + :param weights: + :param strides: + :param padding: + :param dtype: + :return: + """ + with variable_scope.variable_scope('MyConv2d'): + # filter shape: [filter_height, filter_width, in_channels, out_channels] + # flatten filters + if dtype is not None: + input_tensor = math_ops.cast(input_tensor, dtype) + weights = math_ops.cast(weights, dtype) + filter_height = int(weights.shape[0]) + filter_width = int(weights.shape[1]) + in_channels = int(weights.shape[2]) + out_channels = int(weights.shape[3]) + + flat_w = array_ops.reshape(weights, + [filter_height * filter_width * in_channels, + out_channels]) + patches = array_ops.extract_image_patches( + input_tensor, + ksizes=[1, filter_height, filter_width, 1], + strides=strides, + rates=[1, 1, 1, 1], + padding=padding + ) + + patches_reshape = patches.get_shape().as_list() + features = math_ops.reduce_sum(math_ops.multiply(flat_w[:, 0], patches), + axis=3, keepdims=True) + features = array_ops.expand_dims(features, axis=0) + + a = constant_op.constant(1, name='ii') + n = constant_op.constant(out_channels, name='nn') + + def cond(a, n, features): + return a < n + + def body(a, n, features): + tmp = array_ops.expand_dims( + math_ops.reduce_sum(math_ops.multiply(flat_w[:, a], patches), axis=3, + keepdims=True), axis=0) + features = array_ops.concat([features, tmp], axis=0) + a = a + 1 + return a, n, features + + a, n, features = control_flow_ops.while_loop(cond, body, [a, n, features], + shape_invariants=[ + a.get_shape(), + n.get_shape(), + tensor_shape.TensorShape( + [None, + patches_reshape[0], + patches_reshape[1], + patches_reshape[2], + 1] + ) + ]) + temp_patches_reshape = patches_reshape + for i in range(len(temp_patches_reshape)): + if temp_patches_reshape[i] is None: + temp_patches_reshape[i] = -1 + + features = array_ops.reshape(features, [out_channels, temp_patches_reshape[0], + temp_patches_reshape[1], + temp_patches_reshape[2], 1]) + features = array_ops.unstack(features, axis=0) + features = array_ops.concat(features, axis=-1) + + return features + + +def depthwise_conv2d(input_tensor, weights, strides, padding='SAME', dtype=None): + """ + custom depthwise_conv2d + :param input_tensor: + :param weights: + :param strides: + :param padding: + :param dtype: + :return: + """ + with variable_scope.variable_scope('MyDepthwiseConv2d'): + # filter shape: [filter_height, filter_width, in_channels, out_channels] + # flatten filters + if dtype is not None: + input_tensor = math_ops.cast(input_tensor, dtype) + weights = math_ops.cast(weights, dtype) + filter_height = int(weights.shape[0]) + filter_width = int(weights.shape[1]) + in_channels = int(weights.shape[2]) + out_channels = int(weights.shape[3]) + assert out_channels == 1, 'Expect output channel is 1.' + + flat_w = array_ops.reshape(weights, + [filter_height * filter_width, in_channels]) + + patches = array_ops.extract_image_patches( + input_tensor, + ksizes=[1, filter_height, filter_width, 1], + strides=strides, + rates=[1, 1, 1, 1], + padding=padding + ) + + patches_reshape = patches.shape.as_list() + for i in range(len(patches_reshape)): + if patches_reshape[i] is None: + patches_reshape[i] = -1 + patches_depthwise = array_ops.reshape(patches, + [patches_reshape[0], + patches_reshape[1], + patches_reshape[2], + filter_height * filter_width, + in_channels]) + flat_w_t = array_ops.transpose(flat_w, [1, 0]) + patches_depthwise_t = array_ops.transpose(patches_depthwise, + [0, 1, 2, 4, 3]) + tmp = math_ops.multiply(patches_depthwise_t, flat_w_t) + features = array_ops.squeeze( + math_ops.reduce_sum(tmp, axis=-1, keepdims=True), axis=-1) + + return features + + +def add(A, B, dtype=None): + """ + custom add + :param A: + :param B: + :param dtype: + :return: + """ + if dtype is not None: + A = math_ops.cast(A, dtype) + B = math_ops.cast(B, dtype) + return math_ops.add(A, B) + + +def matmul(A, B, dtype=None): + """ + custom matmul + :param A: + :param B: + :param dtype: + :return: + """ + if dtype is not None: + A = math_ops.cast(A, dtype) + B = math_ops.cast(B, dtype) + + dtype = A.dtype + if dtype in [dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32, + dtypes.complex64, dtypes.complex128]: + return math_ops.matmul(A, B) + + a_shape = A.shape + b_shape = B.shape + + assert len(a_shape) == 3 and len(b_shape) == 3, 'Expect A and B are 3D inputs' + assert a_shape[-1] == b_shape[1] ,'Expect M*N*K and M*K*D' + + A_tile = array_ops.tile(A, [1, b_shape[-1], 1]) # copy itself + A_tile = array_ops.reshape(A_tile, [a_shape[0], b_shape[-1], a_shape[1], + a_shape[2]]) # reshape for multiply + A_tile_transpose = array_ops.transpose(A_tile, + [0, 2, 1, 3]) # reshap for multiply + B_transpose = array_ops.transpose(B, [0, 2, 1]) + B_transpose_tile = array_ops.tile(B_transpose, [1, a_shape[1], 1]) + B_transpose_tile = array_ops.reshape(B_transpose_tile, + [b_shape[0], a_shape[1], b_shape[2], + b_shape[1]]) + ret = math_ops.reduce_sum( + math_ops.multiply(A_tile_transpose, B_transpose_tile), axis=-1) + return ret diff --git a/tools/mnncompress/mnncompress/tensorflow/sensitivity_analyzer.py b/tools/mnncompress/mnncompress/tensorflow/sensitivity_analyzer.py new file mode 100644 index 00000000..f84c5847 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/sensitivity_analyzer.py @@ -0,0 +1,63 @@ +from __future__ import print_function +import numpy as np +import tensorflow as tf +from .helpers import get_op_name_of_type, is_weight_tensor, get_variable_by_tensor + +_MNN_compress_scope = 'MNN_pruner_' +_Prune_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] + + +class SensitivityAnalyzer(object): + def __init__(self, gradient_var_ops, graph=None): + self._gradient_var_ops = gradient_var_ops + self._graph = graph + if graph is None: + self._graph = tf.get_default_graph() + + self._op_names = get_op_name_of_type(self._graph, _Prune_Support_Ops) + self._ops = self._graph.get_operations() + self._name_to_ops = {op.name : op for op in self._ops} + + self._prune_weight_variable_op_names = {} + self._prune_var_mask_abs_grads = {} + self.analyze_steps = 10 + + self._get_prune_weight_variable_op_names() + + def analyze(self, gradient_var_numpy): + print("analyzing weight sensitivity...") + for i in range(len(gradient_var_numpy)): + (g, v) = self._gradient_var_ops[i] + if g is None: + continue + + (gn, vn) = gradient_var_numpy[i] + if v in self._prune_weight_variable_op_names.keys(): + mask_abs_grad = np.abs(vn * gn) + self._prune_var_mask_abs_grads[v.name] = mask_abs_grad + self._prune_var_mask_abs_grads[v.name] + + def save_sensitivity_npy(self, filename): + np.save(filename, self._prune_var_mask_abs_grads) + print("sensitivity info saved to npy file:", filename) + + def _get_prune_weight_variable_op_names(self): + for op_name in self._op_names: + op = self._name_to_ops[op_name] + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_variable = get_variable_by_tensor(op.inputs[1]) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable.name] = np.zeros(weight_variable.shape.as_list()) + + if op.type == "MatMul": + if "gradients" in op.name and "_grad" in op.name: + continue + + for t in op.inputs: + if is_weight_tensor(t): + weight_variable = get_variable_by_tensor(t) + if len(op.outputs[0].consumers()) == 0: + continue + self._prune_weight_variable_op_names[weight_variable] = op.name + self._prune_var_mask_abs_grads[weight_variable.name] = np.zeros(weight_variable.shape.as_list()) diff --git a/tools/mnncompress/mnncompress/tensorflow/weight_quantizer.py b/tools/mnncompress/mnncompress/tensorflow/weight_quantizer.py new file mode 100644 index 00000000..2c4a2049 --- /dev/null +++ b/tools/mnncompress/mnncompress/tensorflow/weight_quantizer.py @@ -0,0 +1,172 @@ +from __future__ import print_function +import tensorflow as tf +import numpy as np + +from .helpers import is_weight_tensor +from .graph_checker import check_for_grad_ops +import mnncompress.common.MNN_compression_pb2 as compress_pb +import uuid +from mnncompress.common.log import mnn_logger +from mnncompress.common.helper import get_pipeline_methods + +_Weight_Quant_Support_Ops = ['Conv2D', 'DepthwiseConv2dNative', 'MatMul'] +_WQ_Mark_name = "_ori_weight_MNN_wq_" + +def strip_wq_ops(): + graph = tf.get_default_graph() + all_ops = graph.get_operations() + for op in all_ops: + if _WQ_Mark_name in op.name: + ori_op_name = op.name.split(_WQ_Mark_name)[0] + ori_index = int(op.name.split(_WQ_Mark_name)[1]) + ori_op = graph.get_operation_by_name(ori_op_name) + ori_op._update_input(ori_index, op.outputs[0]) + +class WeightQuantizer(object): + def __init__(self, graph=None, bits=8, debug_info=False): + if graph is None: + self._graph = tf.get_default_graph() + else: + self._graph = graph + self._ops = self._graph.get_operations() + + if bits < 2 or bits > 8: + raise ValueError("bits must be in [2, 8]") + self._bits = bits + self._clamp_value = pow(2.0, bits-1) - 1.0 + + self._all_conv_and_matmul_layers = [l for l in self._ops if l.type in _Weight_Quant_Support_Ops] + self._debug_info = debug_info + self._prune_weight_ops = [] + self._mask = {} + self._all_weight_tensors = [] + self._all_weight_tensor_op_map = {} + self._fake_quant_weight_ops = [] + self._initialized = False + self._eps = 1e-9 + self._total_weight_num = 0.0 + self._remain_weight_num = 0.0 + self._init_prune_ratios = {} + self._find_all_variable_tensors() + self._insert_wq_ops() + + def save_compress_params(self, filename, append=False): + compress_proto = compress_pb.Pipeline() + if append: + f = open(filename, 'rb') + compress_proto.ParseFromString(f.read()) + + compress_proto.version = "0.0.0" + if compress_proto.mnn_uuid == '': + self._guid = str(uuid.uuid4()) + compress_proto.mnn_uuid = self._guid + else: + self._guid = compress_proto.mnn_uuid + + if not self._reported: + detail = {"algorithm": "WQ", "pipeline": get_pipeline_methods(compress_proto), "compression_rate": self._total_weight_num / self._remain_weight_num, \ + "ori_model_size": self._total_weight_num * 4.0 / 1024.0 / 1024.0, \ + "config": {"bits": self._bits, "init_prune_ratios": self._init_prune_ratios}} + self._reported = mnn_logger.on_done("tensorflow", self._guid, detail) + + with tf.gfile.Open(filename, mode="wb") as f: + f.write(compress_proto.SerializeToString()) + + print("compress proto saved to:", filename) + + def _strip_wq_ops(self): + for ori_weight_tensor, op_index in self._all_weight_tensor_op_map.items(): + op = op_index[0] + index = op_index[1] + op._update_input(index, ori_weight_tensor) + + def init(self, sess): + if self._initialized is True: + raise RuntimeError("you should only initialize weight quantizer once") + + for v in self._all_weight_tensors: + mask = tf.cast(tf.abs(v) > 1e-9, tf.float32) + prune_ratio = 1.0 - tf.reduce_mean(mask) + self._mask[v] = sess.run(mask) + prune_weight_op = tf.assign(v, v * self._mask[v]) + self._prune_weight_ops.append(prune_weight_op) + + prune_ratio = 1 - np.mean(sess.run(mask)) + self._init_prune_ratios[v.name] = prune_ratio + self._remain_weight_num += self._mask[v].size * (1 - prune_ratio) / (32.0 / self._bits) + print(v, "initial prune ratio:", prune_ratio) + + self._initialized = True + + def update(self, sess): + if self._initialized is False: + raise RuntimeError("please initialize the quantizer by 'weight_quantizer.init(sess)', before training the model") + + sess.run(self._prune_weight_ops) + if self._debug_info: + for v in self._all_weight_tensors: + weight = sess.run(v) + prune_ratio = 1 - np.mean(np.abs(weight) > 1e-9) + print(v, "prune_ratio:", prune_ratio, "bits:", self._bits, "clamp_value:", self._clamp_value) + + def _insert_wq_ops(self): + grad_ops = check_for_grad_ops(self._graph) + if grad_ops: + raise ValueError('gradient op found in graph, exiting %s\nplease invoke with inference graph only. create quantizer before construct model optimizer\n' % grad_ops) + + for v in self._all_weight_tensors: + op = self._all_weight_tensor_op_map[v][0] + index = self._all_weight_tensor_op_map[v][1] + + reduce_dims = [] + if op.type == "Conv2D": + reduce_dims = [0, 1, 2] + if op.type == "DepthwiseConv2dNative": + reduce_dims = [0, 1, 3] + if op.type == "MatMul": + reduce_dims = None + trans_a = op.get_attr("transpose_a") + trans_b = op.get_attr("transpose_b") + if index == 0: + if not trans_a: + reduce_dims = [1] + else: + reduce_dims = [0] + if index == 1: + if not trans_b: + reduce_dims = [0] + else: + reduce_dims = [1] + reduce_dims = tuple(reduce_dims) + + with tf.variable_scope(op.name + "_weight_quant", reuse=tf.AUTO_REUSE): + with self._graph.gradient_override_map({'Round': 'Identity'}): + scales = tf.reduce_max(tf.abs(v), axis=reduce_dims, keep_dims=True) / self._clamp_value + self._eps + quant_w = tf.clip_by_value(tf.round(v / scales), -self._clamp_value, self._clamp_value) + fake_quant_w = quant_w * scales + + op._update_input(index, fake_quant_w) + + id = tf.identity(v, name=op.name+_WQ_Mark_name+str(index)) + + def _find_all_variable_tensors(self): + for op in self._all_conv_and_matmul_layers: + if op.type in _Weight_Quant_Support_Ops: + if op.type in ['Conv2D', 'DepthwiseConv2dNative']: + weight_tensor = op.inputs[1].op.inputs[0] + self._total_weight_num += weight_tensor.shape.num_elements() + self._all_weight_tensors.append(weight_tensor) + self._all_weight_tensor_op_map[weight_tensor] = [op, 1] + if self._debug_info: + print("found weight tensor:", weight_tensor) + + if op.type == 'MatMul': + for i in range(len(op.inputs)): + input_tensor = op.inputs[i] + if is_weight_tensor(input_tensor): + weight_tensor = input_tensor.op.inputs[0] + self._total_weight_num += weight_tensor.shape.num_elements() + self._all_weight_tensors.append(weight_tensor) + self._all_weight_tensor_op_map[weight_tensor] = [op, i] + if self._debug_info: + print("found weight tensor:", weight_tensor) diff --git a/tools/mnncompress/mnncompress/version.py b/tools/mnncompress/mnncompress/version.py new file mode 100644 index 00000000..493f7415 --- /dev/null +++ b/tools/mnncompress/mnncompress/version.py @@ -0,0 +1 @@ +__version__ = "0.3.0" diff --git a/tools/mnncompress/setup.py b/tools/mnncompress/setup.py new file mode 100644 index 00000000..5b4e69ce --- /dev/null +++ b/tools/mnncompress/setup.py @@ -0,0 +1,24 @@ + +from setuptools import setup, find_packages + +import os +res = os.system("cd mnncompress/common;protoc --python_out=./ MNN_compression.proto") +assert res == 0, "protobuffer not generated, please make sure you have the protoc compiler installed." + +import mnncompress + + +setup( + name = "mnncompress", + version = mnncompress.__version__, + description = "model comprssion tools for MNN", + author = "Yang Yafeng", + author_email = "nickyoung.yyf@alibaba-inc.com", + url = "https://github.com/alibaba/MNN", + packages = find_packages(), + zip_safe = False, + platforms = "any", + install_requires = [ + "aliyun-log-python-sdk", + ], +) diff --git a/tools/script/register.py b/tools/script/register.py index 94f575db..711ac9fb 100644 --- a/tools/script/register.py +++ b/tools/script/register.py @@ -117,7 +117,44 @@ def generateGeoFile(rootDir): f.write(l+'();\n') f.write("}\n}\n") +def generateCoreMLFile(rootDir): + coremlDir = os.path.join(rootDir, "source", "backend", "coreml") + coremlExeDir = os.path.join(coremlDir, "execution") + coremlRegFile = os.path.join(coremlDir, "backend", "CoreMLOPRegister.cpp") + fileNames = os.listdir(coremlExeDir) + print(fileNames) + if len(fileNames) <= 1: + # Error dirs + return + funcNames = [] + for fi in fileNames: + f = os.path.join(coremlExeDir, fi) + if os.path.isdir(f): + continue + with open(f) as fileC: + c = fileC.read().split('\n') + c = list(filter(lambda l:l.find('REGISTER_COREML_OP_CREATOR')>=0, c)) + c = list(filter(lambda l:l.find('OpType')>=0, c)) + for l in c: + l = l.split('(')[1] + l = l.split(')')[0] + l = l.replace(' ', '') + l = l.split(',') + funcName = '___' + l[0] + '__' + l[1] + '__' + funcNames.append(funcName) + with open(coremlRegFile, 'w') as f: + f.write('// This file is generated by Shell for ops register\n') + f.write('namespace MNN {\n') + for l in funcNames: + f.write("extern void " + l + '();\n') + f.write('\n') + f.write('void registerCoreMLOps() {\n') + for l in funcNames: + f.write(l+'();\n') + f.write("}\n}\n") + import sys generateShape(sys.argv[1]) generateCPUFile(sys.argv[1]) generateGeoFile(sys.argv[1]) +generateCoreMLFile(sys.argv[1])