diff --git a/.clang-format b/.clang-format index 48940d21..1589d5c4 100644 --- a/.clang-format +++ b/.clang-format @@ -1,6 +1,4 @@ --- -Language: Cpp -# BasedOnStyle: WebKit AccessModifierOffset: -4 AlignAfterOpenBracket: DontAlign AlignConsecutiveAssignments: false @@ -18,22 +16,11 @@ AlwaysBreakAfterDefinitionReturnType: None AlwaysBreakAfterReturnType: None AlwaysBreakBeforeMultilineStrings: true AlwaysBreakTemplateDeclarations: true +BasedOnStyle: None BinPackArguments: true BinPackParameters: true -BraceWrapping: - AfterClass: true - AfterControlStatement: true - AfterEnum: true - AfterFunction: true - AfterNamespace: true - AfterObjCDeclaration: true - AfterStruct: true - AfterUnion: true - BeforeCatch: true - BeforeElse: true - IndentBraces: false BreakBeforeBinaryOperators: All -BreakBeforeBraces: Custom +BreakBeforeBraces: Allman BreakBeforeTernaryOperators: true BreakConstructorInitializersBeforeComma: true ColumnLimit: 120 @@ -46,7 +33,8 @@ DerivePointerAlignment: false DisableFormat: false ExperimentalAutoDetectBinPacking: false ForEachMacros: [ foreach, Q_FOREACH, BOOST_FOREACH ] -IncludeCategories: +IncludeBlocks: Preserve +IncludeCategories: - Regex: '^"(llvm|llvm-c|clang|clang-c)/' Priority: 2 - Regex: '^(<|"(gtest|isl|json)/)' @@ -57,6 +45,7 @@ IndentCaseLabels: false IndentWidth: 4 IndentWrappedFunctionNames: false KeepEmptyLinesAtTheStartOfBlocks: true +Language: Cpp MacroBlockBegin: '' MacroBlockEnd: '' MaxEmptyLinesToKeep: 1 @@ -71,6 +60,7 @@ PenaltyBreakString: 1000 PenaltyExcessCharacter: 1000000 PenaltyReturnTypeOnItsOwnLine: 60 PointerAlignment: Left +PointerBindsToType: false ReflowComments: true SortIncludes: true SpaceAfterCStyleCast: true @@ -79,12 +69,11 @@ SpaceBeforeParens: ControlStatements SpaceInEmptyParentheses: false SpacesBeforeTrailingComments: 1 SpacesInAngles: false -SpacesInContainerLiterals: true SpacesInCStyleCastParentheses: false +SpacesInContainerLiterals: true SpacesInParentheses: false SpacesInSquareBrackets: false Standard: Cpp11 TabWidth: 4 UseTab: Never ... - diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index eac057d6..61c25903 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -23,7 +23,7 @@ assignees: '' **Python Version (if applicable)**: **TensorFlow Version (if applicable)**: **PyTorch Version (if applicable)**: -**Baremetal or Container (which commit + image + tag)**: +**Baremetal or Container (if container which image + tag)**: ## Relevant Files diff --git a/.gitmodules b/.gitmodules index 0ef10f1a..703a13b8 100644 --- a/.gitmodules +++ b/.gitmodules @@ -9,4 +9,4 @@ [submodule "parsers/onnx"] path = parsers/onnx url = https://github.com/onnx/onnx-tensorrt.git - branch = 7.1 + branch = 7.2.1 diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..6e06d2d6 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,22 @@ +# TensorRT OSS Release Changelog + +## [7.2.1](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-7.html#rel_7-2-1) - 2020-10-20 +### Added +- [Polygraphy](tools/Polygraphy) v0.20.13 - Deep Learning Inference Prototyping and Debugging Toolkit +- [PyTorch-Quantization Toolkit](tools/pytorch-quantization) v2.0.0 +- Updated BERT plugins for [variable sequence length inputs](demo/BERT#variable-sequence-length) + - Optimized kernels for sequence lengths of 64 and 96 added +- Added Tacotron2 + Waveglow TTS demo [#677](https://github.com/NVIDIA/TensorRT/pull/677) +- Re-enable `GridAnchorRect_TRT` plugin with rectangular feature maps [#679](https://github.com/NVIDIA/TensorRT/pull/679) +- Update batchedNMS plugin to IPluginV2DynamicExt interface [#738](https://github.com/NVIDIA/TensorRT/pull/738) +- Support 3D inputs in InstanceNormalization plugin [#745](https://github.com/NVIDIA/TensorRT/pull/745) +- Added this CHANGELOG.md + +### Changed +- ONNX GraphSurgeon - [v0.2.7](tools/onnx-graphsurgeon/CHANGELOG.md#v027-2020-09-29) with bugfixes, new examples. +- demo/BERT bugfixes for Jetson Xavier +- Updated build Dockerfile to cuda-11.1 +- Updated ClangFormat style specification according to TensorRT coding guidelines + +### Removed +- N/A diff --git a/CMakeLists.txt b/CMakeLists.txt index 4b5e148e..0abf4111 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -80,7 +80,7 @@ endif() ############################################################################################ # Dependencies -set(DEFAULT_CUDA_VERSION 11.0) +set(DEFAULT_CUDA_VERSION 11.1) set(DEFAULT_CUDNN_VERSION 8.0) set(DEFAULT_PROTOBUF_VERSION 3.0.0) set(DEFAULT_CUB_VERSION 1.8.0) diff --git a/README.md b/README.md index 501a92d8..0f2eec55 100644 --- a/README.md +++ b/README.md @@ -1,88 +1,64 @@ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Documentation](https://img.shields.io/badge/TensorRT-documentation-brightgreen.svg)](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html) - - # TensorRT Open Source Software +This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. Included are the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes. -This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. Included are the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. +* For code contributions to TensorRT-OSS, please see our [Contribution Guide](CONTRIBUTING.md) and [Coding Guidelines](CODING-GUIDELINES.md). +* For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the [Changelog](CHANGELOG.md). +# Build ## Prerequisites +To build the TensorRT-OSS components, you will first need the following software packages. -To build the TensorRT OSS components, ensure you meet the following package requirements: +**TensorRT GA build** +* [TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) v7.2.1 + - See [Downloading TensorRT Builds](#downloading-tensorrt-builds) for details **System Packages** - * [CUDA](https://developer.nvidia.com/cuda-toolkit) * Recommended versions: + * cuda-11.1 + cuDNN-8.0 * cuda-11.0 + cuDNN-8.0 * cuda-10.2 + cuDNN-8.0 +* [GNU make](https://ftp.gnu.org/gnu/make/) >= v4.1 +* [cmake](https://github.com/Kitware/CMake/releases) >= v3.13 +* [python]() >= v3.6.5 +* [pip](https://pypi.org/project/pip/#history) >= v19.0 +* Essential utilities + * [git](https://git-scm.com/downloads), [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), [wget](https://www.gnu.org/software/wget/faq.html#download), [zlib](https://zlib.net/) -* [GNU Make](https://ftp.gnu.org/gnu/make/) >= v4.1 - -* [CMake](https://github.com/Kitware/CMake/releases) >= v3.13 - -* [Python]() >= v3.6.5 - -* [PIP](https://pypi.org/project/pip/#history) >= v19.0 - * PyPI packages +**Optional Packages** +* Containerized build + * [Docker](https://docs.docker.com/install/) >= 19.03 + * [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) +* Toolchains and SDKs + * (Cross compilation for Jetson platform) [NVIDIA JetPack](https://developer.nvidia.com/embedded/jetpack) >= 4.4 + * (For Windows builds) [Visual Studio](https://visualstudio.microsoft.com/vs/older-downloads/) 2017 Community or Enterprise edition + * (Cross compilation for QNX platform) [QNX Toolchain](https://blackberry.qnx.com/en) +* PyPI packages (for demo applications/tests) * [numpy](https://pypi.org/project/numpy/) * [onnx](https://pypi.org/project/onnx/1.6.0/) 1.6.0 * [onnxruntime](https://pypi.org/project/onnxruntime/) >= 1.3.0 * [pytest](https://pypi.org/project/pytest/) - -* Essential libraries and utilities - * [Git](https://git-scm.com/downloads), [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), [Wget](https://www.gnu.org/software/wget/faq.html#download), [Zlib](https://zlib.net/) - -* Cross compilation for Jetson platforms requires JetPack's host component installation - * [JetPack](https://developer.nvidia.com/embedded/jetpack) >= 4.4 - -* Windows requires Visual Studio 2017 either Community or Enterprise Version - * [Visual Studio](https://visualstudio.microsoft.com/vs/older-downloads/) - -* Cross compilation for QNX requires the qnx developer toolchain - * [QNX](https://blackberry.qnx.com/en) - -**Optional Packages** - -* Containerized builds - * [Docker](https://docs.docker.com/install/) >= 19.03 - * [NVIDIA Docker](https://github.com/NVIDIA/nvidia-docker) >= 2.0 or `nvidia-container-toolkit` - -* Code formatting tools + * [tensorflow-gpu](https://pypi.org/project/tensorflow/1.15.4/) 1.15.4 +* Code formatting tools (for contributors) * [Clang-format](https://clang.llvm.org/docs/ClangFormat.html) * [Git-clang-format](https://github.com/llvm-mirror/clang/blob/master/tools/clang-format/git-clang-format) -* Required PyPI packages for Demos - * [Tensorflow-gpu](https://pypi.org/project/tensorflow/1.14.0/) == 1.15.0 - -**TensorRT Release** - -* [TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) v7.1 - - -NOTE: Along with the TensorRT OSS components, the following source packages will also be downloaded, and they are not required to be installed on the system. - -- [ONNX-TensorRT](https://github.com/onnx/onnx-tensorrt) v7.1 -- [CUB](http://nvlabs.github.io/cub/) v1.8.0 -- [Protobuf](https://github.com/protocolbuffers/protobuf.git) v3.0.0 - + > NOTE: [onnx-tensorrt](https://github.com/onnx/onnx-tensorrt), [cub](http://nvlabs.github.io/cub/), and [protobuf](https://github.com/protocolbuffers/protobuf.git) packages are downloaded along with TensorRT OSS, and not required to be installed. -## Downloading The TensorRT Components - -1. #### Download TensorRT OSS sources. - - **Example: Bash** +## Downloading TensorRT Build +1. #### Download TensorRT OSS + **On Linux: Bash** ```bash git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT git submodule update --init --recursive export TRT_SOURCE=`pwd` ``` - - **Example: Powershell** - + **On Windows: Powershell** ```powershell git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT @@ -90,228 +66,175 @@ NOTE: Along with the TensorRT OSS components, the following source packages will $Env:TRT_SOURCE = $(Get-Location) ``` -2. #### Download the TensorRT binary release. - - To build the TensorRT OSS, obtain the corresponding TensorRT 7.1 binary release from [NVidia Developer Zone](https://developer.nvidia.com/nvidia-tensorrt-7x-download). For a list of key features, known and fixed issues, refer to the [TensorRT 7.1 Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-7.html#rel_7-1-0). +2. #### Download TensorRT GA + To build TensorRT OSS, obtain the corresponding TensorRT GA build from [NVIDIA Developer Zone](https://developer.nvidia.com/nvidia-tensorrt-download). - **Example: Ubuntu 18.04 with cuda-11.0** + **Example: Ubuntu 18.04 on x86-64 with cuda-11.1** - Download and extract the latest *TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0* + Download and extract the latest *TensorRT 7.2.1 GA package for Ubuntu 18.04 and CUDA 11.1* ```bash cd ~/Downloads - tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz - export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 + tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.x86_64-gnu.cuda-11.1.cudnn8.0.tar.gz + export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6 ``` + **Example: Ubuntu 18.04 on PowerPC with cuda-11.0** - **Example: Ubuntu 18.04 with cuda-11.0 on PowerPC** - - Download and extract the latest *TensorRT 7.1 GA package for Ubuntu 18.04 and CUDA 11.0* - ```bash - cd ~/Downloads - # Download TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz - tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz - export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 - ``` - - **Example: CentOS/RedHat 7 with cuda-11.0** - - Download and extract the *TensorRT 7.1 GA for CentOS/RedHat 7 and CUDA 11.0 tar package* + Download and extract the latest *TensorRT 7.2.1 GA package for Ubuntu 18.04 and CUDA 11.0* ```bash cd ~/Downloads - tar -xvzf TensorRT-7.1.3.4.CentOS-8.0.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz - export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 + tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.powerpc64le-gnu.cuda-11.0.cudnn8.0.tar.gz + export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6 ``` + **Example: CentOS/RedHat 7 on x86-64 with cuda-11.0** - **Example: Ubuntu 16.04 with cuda-11.0** - - Download and extract the *TensorRT 7.1 GA for Ubuntu 16.04 and CUDA 11.0 tar package* + Download and extract the *TensorRT 7.2.1 GA for CentOS/RedHat 7 and CUDA 11.0 tar package* ```bash cd ~/Downloads - tar -xvzf TensorRT-7.1.3.4.Ubuntu-16.04.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz - export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 + tar -xvzf TensorRT-7.2.1.6.CentOS-7.6.x86_64-gnu.cuda-11.0.cudnn8.0.tar.gz + export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6 ``` + **Example: Ubuntu18.04 Cross-Compile for QNX with cuda-10.2** - **Example: Ubuntu18.04 cross compile QNX with cuda-10.2** - - Download and extract the *TensorRT 7.1 GA for QNX and CUDA 10.2 tar package* + Download and extract the *TensorRT 7.2.1 GA for QNX and CUDA 10.2 tar package* ```bash cd ~/Downloads - tar -xvzf TensorRT-7.1.3.4.Ubuntu-18.04.aarch64-qnx.cuda-10.2.cudnn7.6.tar.gz - export TRT_RELEASE=`pwd`/TensorRT-7.1.3.4 - export QNX_HOST=/path/to/qnx/toolchain/host/linux/x86_64 - export QNX_TARGET=/path/to/qnx/toolchain/target/qnx7 + tar -xvzf TensorRT-7.2.1.6.Ubuntu-18.04.aarch64-qnx.cuda-10.2.cudnn7.6.tar.gz + export TRT_RELEASE=`pwd`/TensorRT-7.2.1.6 + export QNX_HOST=//host/linux/x86_64 + export QNX_TARGET=//target/qnx7 ``` - **Example: Windows with cuda-11.0** + **Example: Windows on x86-64 with cuda-11.0** - Download and extract the *TensorRT 7.1 GA for Windows and CUDA 11.0 zip package* and add *msbuild* to *PATH* + Download and extract the *TensorRT 7.2.1 GA for Windows and CUDA 11.0 zip package* and add *msbuild* to *PATH* ```powershell cd ~\Downloads - Expand-Archive .\TensorRT-7.1.3.4.Windows10.x86_64.cuda-11.0.cudnn8.0.zip - $Env:TRT_RELEASE = '$(Get-Location)\TensorRT-7.1.3.4' + Expand-Archive .\TensorRT-7.2.1.6.Windows10.x86_64.cuda-11.0.cudnn8.0.zip + $Env:TRT_RELEASE = '$(Get-Location)\TensorRT-7.2.1.6' $Env:PATH += 'C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\' ``` -3. #### Download JetPack toolchain for cross-compilation.[OPTIONAL] - - **JetPack example** - - Using the SDK manager, download the host components of the PDK version or Jetpack specified in the name of the Dockerfile. To do this: - 1. [**SDK Manager Step 01**] Log into the SDK manager - 2. [**SDK Manager Step 02**] Select the correct platform and Target OS System (should be corresponding to the name of the Dockerfile you are building (e.g. Jetson AGX Xavier, `Linux Jetpack 4.4`), then click `Continue` - 3. [**SDK Manager Step 03**] Under `Download & Install Options` make note of or change the download folder **and Select Download now. Install later.** then agree to the license terms and click `Continue` - You should now have all expected files to build the container. Move these into the `docker/jetpack_files` folder. +3. #### (Optional) JetPack SDK for Jetson builds + Using the JetPack SDK manager, download the host components. Steps: + 1. Download and launch the SDK manager. Login with your developer account. + 2. Select the platform and target OS (example: Jetson AGX Xavier, `Linux Jetpack 4.4`), and click Continue. + 3. Under `Download & Install Options` change the download folder and select `Download now, Install later`. Agree to the license terms and click Continue. + 4. Move the extracted files into the `$TRT_SOURCE/docker/jetpack_files` folder. ## Setting Up The Build Environment -* Install the *System Packages* list of components in the *Prerequisites* section. - -* Alternatively, use the build containers as described below: - -1. #### Generate the TensorRT build container. +For native builds, install the [prerequisite](#prerequisites) *System Packages*. Alternatively (recommended for non-Windows builds), install Docker and generate a build container as described below: - The docker container can be built using the included Dockerfiles and build script. The build container is configured with the environment and packages required for building TensorRT OSS. - - **Example: Ubuntu 18.04 with cuda-11.0** +1. #### Generate the TensorRT-OSS build container. + The TensorRT-OSS build container can be generated using the Dockerfiles and build script included with TensorRT-OSS. The build container is bundled with packages and environment required for building TensorRT OSS. + **Example: Ubuntu 18.04 on x86-64 with cuda-11.1** ```bash - ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.0 + ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu --os 18.04 --cuda 11.1 ``` - - **Example: Ubuntu 16.04 with cuda-11.0** - + **Example: Ubuntu 18.04 on PowerPC with cuda-11.0** ```bash - ./docker/build.sh --file docker/ubuntu.Dockerfile --tag tensorrt-ubuntu1604 --os 16.04 --cuda 11.0 + ./docker/build.sh --file docker/ubuntu-cross-ppc64le.Dockerfile --tag tensorrt-ubuntu-ppc --os 18.04 --cuda 11.0 ``` - - **Example: CentOS/RedHat 7 with cuda-11.0** - + **Example: CentOS/RedHat 7 on x86-64 with cuda-11.0** ```bash ./docker/build.sh --file docker/centos.Dockerfile --tag tensorrt-centos --os 7 --cuda 11.0 ``` - - **Example: Cross compile for JetPack 4.4 with cuda-10.2** - ```bash - ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-ubuntu-jetpack --os 18.04 --cuda 10.2 - ``` - - **Example: Cross compile for PowerPC with cuda-11.0** + **Example: Ubuntu 18.04 Cross-Compile for Jetson (arm64) with cuda-10.2 (JetPack)** ```bash - ./docker/build.sh --file docker/ubuntu-cross-ppc64le.Dockerfile --tag tensorrt-ubuntu-ppc --os 18.04 --cuda 11.0 + ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-cross-jetpack --os 18.04 --cuda 10.2 ``` -2. #### Launch the TensorRT build container. - +2. #### Launch the TensorRT-OSS build container. + **Example: Ubuntu 18.04 build container** ```bash ./docker/launch.sh --tag tensorrt-ubuntu --gpus all --release $TRT_RELEASE --source $TRT_SOURCE ``` + > NOTE: + 1. Use the tag corresponding to the build container you generated in + 2. To run TensorRT/CUDA programs in the build container, install [NVIDIA Container Toolkit](#prerequisites). Docker versions < 19.03 require `nvidia-docker2` and `--runtime=nvidia` flag for docker run commands. On versions >= 19.03, you need the `nvidia-container-toolkit` package and `--gpus all` flag. - > NOTE: To run TensorRT/CUDA programs in the build container, install [NVIDIA Docker support](#prerequisites). Docker versions < 19.03 require `nvidia-docker2` and `--runtime=nvidia` flag for docker run commands. On versions >= 19.03, you need the `nvidia-container-toolkit` package and `--gpus all` flag. - - -## Building The TensorRT OSS Components - -* Generate Makefiles and build. - - **Example: Linux** +## Building TensorRT-OSS +* Generate Makefiles or VS project (Windows) and build. + **Example: Linux (x86-64) build with default cuda-11.1** ```bash cd $TRT_SOURCE mkdir -p build && cd build cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out make -j$(nproc) ``` - - **Example: Bare-metal build on Jetson (ARM64) with cuda-10.2** - + **Example: Native build on Jetson (arm64) with cuda-10.2** ```bash cd $TRT_SOURCE mkdir -p build && cd build cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=10.2 make -j$(nproc) ``` - - **Example: Cross compile for QNX with cuda-10.2** - + **Example: Ubuntu 18.04 Cross-Compile for Jetson (arm64) with cuda-10.2 (JetPack)** ```bash cd $TRT_SOURCE mkdir -p build && cd build - cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_SOURCE/cmake/toolchains/cmake_qnx.toolchain + cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_SOURCE/cmake/toolchains/cmake_aarch64.toolchain -DCUDA_VERSION=10.2 make -j$(nproc) ``` - - **Example: Powershell** - + **Example: Cross-Compile for QNX with cuda-10.2** + ```bash + cd $TRT_SOURCE + mkdir -p build && cd build + cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out -DCMAKE_TOOLCHAIN_FILE=$TRT_SOURCE/cmake/toolchains/cmake_qnx.toolchain -DCUDA_VERSION=10.2 + make -j$(nproc) + ``` + **Example: Windows (x86-64) build in Powershell** ```powershell cd $Env:TRT_SOURCE mkdir -p build ; cd build cmake .. -DTRT_LIB_DIR=$Env:TRT_RELEASE\lib -DTRT_OUT_DIR='$(Get-Location)\out' -DCMAKE_TOOLCHAIN_FILE=..\cmake\toolchains\cmake_x64_win.toolchain msbuild ALL_BUILD.vcxproj ``` - > NOTE: - > 1. The default CUDA version used by CMake is 11.0. To override this, for example to 10.2, append `-DCUDA_VERSION=10.2` to the cmake command. - > 2. Samples may fail to link on CentOS7. To work around this create the following symbolic link: - > `ln -s $TRT_OUT_DIR/libnvinfer_plugin.so $TRT_OUT_DIR/libnvinfer_plugin.so.7` - - The required CMake arguments are: - + 1. The default CUDA version used by CMake is 11.1. To override this, for example to 10.2, append `-DCUDA_VERSION=10.2` to the cmake command. + 2. If samples fail to link on CentOS7, create this symbolic link: `ln -s $TRT_OUT_DIR/libnvinfer_plugin.so $TRT_OUT_DIR/libnvinfer_plugin.so.7` +* Required CMake build arguments are: - `TRT_LIB_DIR`: Path to the TensorRT installation directory containing libraries. - - `TRT_OUT_DIR`: Output directory where generated build artifacts will be copied. - - The following CMake build parameters are *optional*: - +* Optional CMake build arguments: - `CMAKE_BUILD_TYPE`: Specify if binaries generated are for release or debug (contain debug symbols). Values consists of [`Release`] | `Debug` - - - `CUDA_VERISON`: The version of CUDA to target, for example [`11.0`]. - + - `CUDA_VERISON`: The version of CUDA to target, for example [`11.1`]. - `CUDNN_VERSION`: The version of cuDNN to target, for example [`8.0`]. - - `NVCR_SUFFIX`: Optional nvcr/cuda image suffix. Set to "-rc" for CUDA11 RC builds until general availability. Blank by default. - - `PROTOBUF_VERSION`: The version of Protobuf to use, for example [`3.0.0`]. Note: Changing this will not configure CMake to use a system version of Protobuf, it will configure CMake to download and try building that version. - - `CMAKE_TOOLCHAIN_FILE`: The path to a toolchain file for cross compilation. - - `BUILD_PARSERS`: Specify if the parsers should be built, for example [`ON`] | `OFF`. If turned OFF, CMake will try to find precompiled versions of the parser libraries to use in compiling samples. First in `${TRT_LIB_DIR}`, then on the system. If the build type is Debug, then it will prefer debug builds of the libraries before release versions if available. - - `BUILD_PLUGINS`: Specify if the plugins should be built, for example [`ON`] | `OFF`. If turned OFF, CMake will try to find a precompiled version of the plugin library to use in compiling samples. First in `${TRT_LIB_DIR}`, then on the system. If the build type is Debug, then it will prefer debug builds of the libraries before release versions if available. - - `BUILD_SAMPLES`: Specify if the samples should be built, for example [`ON`] | `OFF`. - - Other build options with limited applicability: - - `CUB_VERSION`: The version of CUB to use, for example [`1.8.0`]. - - `GPU_ARCHS`: GPU (SM) architectures to target. By default we generate CUDA code for all major SMs. Specific SM versions can be specified here as a quoted space-separated list to reduce compilation time and binary size. Table of compute capabilities of NVIDIA GPUs can be found [here](https://developer.nvidia.com/cuda-gpus). Examples: - NVidia A100: `-DGPU_ARCHS="80"` - Tesla T4, GeForce RTX 2080: `-DGPU_ARCHS="75"` - Titan V, Tesla V100: `-DGPU_ARCHS="70"` - Multiple SMs: `-DGPU_ARCHS="80 75"` - - `TRT_PLATFORM_ID`: Bare-metal build (unlike containerized cross-compilation) on non Linux/x86 platforms must explicitly specify the target platform. Currently supported options: `x86_64` (default), `aarch64` -### (Optional - recommended) installing the tensorrt python API bindings - -whl files for the TensorRT python API are in the `python` directory of the TensorRT release +#### (Optional) Install TensorRT python bindings -*Example* install for python3: +* The TensorRT python API bindings must be installed for running TensorRT python applications -``` -pip3 install $TRT_RELEASE/python/tensorrt-7.1.3.4-cp36-none-linux_x86_64.whl -``` + **Example: install TensorRT wheel for python 3.6** + ```bash + pip3 install $TRT_RELEASE/python/tensorrt-7.2.1.6-cp36-none-linux_x86_64.whl + ``` -## Useful Resources +# References -#### TensorRT +## TensorRT Resources * [TensorRT Homepage](https://developer.nvidia.com/tensorrt) * [TensorRT Developer Guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html) * [TensorRT Sample Support Guide](https://docs.nvidia.com/deeplearning/tensorrt/sample-support-guide/index.html) * [TensorRT Discussion Forums](https://devtalk.nvidia.com/default/board/304/tensorrt/) - +* [TensorRT Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html). ## Known Issues -#### TensorRT 7.1 -* See [Release Notes](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/tensorrt-7.html#rel_7-1-3). +#### TensorRT 7.2.1 +* None diff --git a/VERSION b/VERSION index 08c98abb..99a24a08 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -7.1.3.4 +7.2.1.6 diff --git a/cmake/modules/set_ifndef.cmake b/cmake/modules/set_ifndef.cmake index c67f3d03..2613585e 100644 --- a/cmake/modules/set_ifndef.cmake +++ b/cmake/modules/set_ifndef.cmake @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # + function (set_ifndef variable value) if(NOT DEFINED ${variable}) set(${variable} ${value} PARENT_SCOPE) diff --git a/demo/BERT/README.md b/demo/BERT/README.md index 3e30b7af..452ae370 100644 --- a/demo/BERT/README.md +++ b/demo/BERT/README.md @@ -33,6 +33,9 @@ This subfolder of the BERT TensorFlow repository, tested and maintained by NVIDI * [Inference performance: NVIDIA V100](#inference-performance-nvidia-v100-16gb) * [BERT Base](#bert-base-2) * [BERT Large](#bert-large-2) +- [Experimental](#experimental) + * [Variable sequence length](#variable-sequence-length) + * [Run command lines](#run-command-lines) ## Model overview @@ -76,7 +79,7 @@ The following software version configuration has been tested: |Software|Version| |--------|-------| |Python|3.6.9| -|TensorRT|7.1.3.4| +|TensorRT|7.2.0.13| |CUDA|11.0.171| @@ -115,7 +118,7 @@ This demo BERT application can be run within the TensorRT Open Source build cont cmake .. -DTRT_LIB_DIR=$TRT_RELEASE/lib -DTRT_OUT_DIR=`pwd`/out make -j$(nproc) - pip3 install /tensorrt/python/tensorrt-7.1*-cp36-none-linux_x86_64.whl + pip3 install /tensorrt/python/tensorrt-7.2*-cp36-none-linux_x86_64.whl ``` **Note:** While the workflow and Performance Data presented here are based on plugin library built from source, the BERT sample is also expected to work with pre-compiled libraries shipped with TensorRT releases. @@ -499,3 +502,61 @@ Our results were obtained by running the `scripts/inference_benchmark.sh --gpu V | 384 | 24 | 94.22 | 94.39 | 94.04 | 92.08 | 92.2 | 91.86 | | 384 | 32 | 148.96 | 149.11 | 148.59 | 147.7 | 147.84 | 147.23 | | 384 | 64 | 245.91 | 246.09 | 244.67 | 240.16 | 240.43 | 239.07 | + +## Experimental +### Variable sequence length +In our prior implementation, we used inputs padded to max length along with corresponding input masks to handle variable sequence length inputs in a batch. The padding results in some wasted computations which can be avoided by handling variable sequence length inputs natively. Now we have a new approach called the variable sequence length method. By concatenating each input id into a single long input id, and concatenating each input segment id into a single long segment id, TensorRT can know the exact starts and ends by providing an extra sequence length buffer that contains the start and end positions of each sequence. Now we can eliminate the wasted computation in the input paddings. + +Note this is an experimental feature because we only support Xavier+ GPUs, also there is neither FP32 support nor INT8 PTQ calibration. + +#### Run command lines + +1. Download checkpoint for BERT Large FP16 SQuAD v1.1 model with sequence length of 384: + ```bash + bash scripts/download_model.sh pyt v1_1 + ``` + +2. Build an engine: + + **FP16 engine** + ```bash + mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder_varseqlen.py -x /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx -o /workspace/TensorRT/demo/BERT/engines/bert_varseq_fp16.engine -b 1 -s 64 --fp16 -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt + ``` + + This will build and engine with a maximum batch size of 1 (`-b 1`) and sequence length of 64 (`-s 64`) using FP16 precision computation where possible (`--fp16`). + + + **INT8 engine** + ```bash + mkdir -p /workspace/TensorRT/demo/BERT/engines && python3 builder_varseqlen.py -x /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_pyt_onnx_large_qa_squad11_amp_fake_quant_v1/bert_large_v1_1_fake_quant.onnx -o /workspace/TensorRT/demo/BERT/engines/bert_varseq_int8.engine -b 1 -s 256 --int8 --fp16 -c /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2 -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt + ``` + + This will build and engine with a maximum batch size of 1 (`-b 1`) and sequence length of 256 (`-s 256`) using INT8 precision computation where possible (`--int8`). + +3. Run inference + + Evaluate the F1 score and exact match score using the squad dataset: + + ```bash + python3 inference_varseqlen.py -e /workspace/TensorRT/demo/BERT/engines/bert_varseq_int8.engine -s 256 -sq ./squad/dev-v1.1.json -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_384_v2/vocab.txt -o ./predictions.json + python3 squad/evaluate-v1.1.py squad/dev-v1.1.json ./predictions.json 90 + ``` + + Run the quesion and answer mode: + + ```bash + python3 inference_varseqlen.py -e /workspace/TensorRT/demo/BERT/engines/bert_varseq_int8.engine -p "TensorRT is a high performance deep learning inference platform that delivers low latency and high throughput for apps such as recommenders, speech and image/video on NVIDIA GPUs. It includes parsers to import models, and plugins to support novel ops and layers before applying optimizations for inference. Today NVIDIA is open-sourcing parsers and plugins in TensorRT so that the deep learning community can customize and extend these components to take advantage of powerful TensorRT optimizations for your apps." -q "What is TensorRT?" -v /workspace/TensorRT/demo/BERT/models/fine-tuned/bert_tf_v2_large_fp16_128_v2/vocab.txt -s 256 + ``` + +3. Collect performance data + + ```bash + python3 perf_varseqlen.py -e /workspace/TensorRT/demo/BERT/engines/bert_varseq_int8.engine -b 1 -s 256 + ``` + + This will collect performance data run use batch size 1 (`-b 1`) and sequence length of 256 (`-s 256`). + +4. Collect performance data with CUDA graph enabled + + We can use the same `inference_c.py` and `build/perf` to collect performance data with cuda graph enabled. The command line is the same as run without variable sequence length. + diff --git a/demo/BERT/builder.py b/demo/BERT/builder.py index 831a06dc..5dbf7bed 100644 --- a/demo/BERT/builder.py +++ b/demo/BERT/builder.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import argparse import ctypes @@ -111,6 +112,15 @@ def set_output_name(layer, prefix, name, out_idx = 0): def set_output_range(layer, maxval, out_idx = 0): layer.get_output(out_idx).set_dynamic_range(-maxval, maxval) +def get_mha_dtype(config): + dtype = trt.float32 + if config.use_fp16: + dtype = trt.float16 + # Multi-head attention doesn't use INT8 inputs and output by default unless it is specified. + if config.use_int8 and config.use_int8_multihead and not config.is_calib_mode: + dtype = trt.int8 + return int(dtype) + def attention_layer_opt(prefix, config, init_dict, network, input_tensor, imask): """ Add the attention layer @@ -141,14 +151,7 @@ def attention_layer_opt(prefix, config, init_dict, network, input_tensor, imask) has_mask = imask is not None # QKV2CTX - dtype = trt.float32 - if config.use_fp16: - dtype = trt.float16 - # Multi-head attention doesn't use INT8 inputs and output by default unless it is specified. - if config.use_int8 and config.use_int8_multihead and not config.is_calib_mode: - dtype = trt.int8 - - pf_type = trt.PluginField("type_id", np.array([int(dtype)], np.int32), trt.PluginFieldType.INT32) + pf_type = trt.PluginField("type_id", np.array([get_mha_dtype(config)], np.int32), trt.PluginFieldType.INT32) pf_hidden_size = trt.PluginField("hidden_size", np.array([hidden_size], np.int32), trt.PluginFieldType.INT32) pf_num_heads = trt.PluginField("num_heads", np.array([num_heads], np.int32), trt.PluginFieldType.INT32) pf_has_mask = trt.PluginField("has_mask", np.array([has_mask], np.int32), trt.PluginFieldType.INT32) @@ -522,28 +525,41 @@ def load_onnx_weights_and_quant(path, config): TRT_LOGGER.log(TRT_LOGGER.INFO, "Found {:} entries in weight map".format(len(weights_dict))) return weights_dict -def emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_length, batch_sizes): - if len(batch_sizes) > 1: - input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(-1, sequence_length)) - segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(-1, sequence_length)) - input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(-1, sequence_length)) +def emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_lengths, batch_sizes): + if len(batch_sizes) > 1 or len(sequence_lengths) > 1: + # int8 only support some of the sequence length, we dynamic on sequence length is not allowed. + input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(-1 if len(sequence_lengths) > 1 else sequence_lengths[0], -1 if len(batch_sizes) > 1 else batch_sizes[0])) + segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(-1 if len(sequence_lengths) > 1 else sequence_lengths[0], -1 if len(batch_sizes) > 1 else batch_sizes[0])) + input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(-1 if len(sequence_lengths) > 1 else sequence_lengths[0], -1 if len(batch_sizes) > 1 else batch_sizes[0])) # Specify profiles for the batch sizes we're interested in. # Make sure the profile also works for all sizes not covered by the previous profile. - prev_size = 0 + prev_batch_size = 0 for batch_size in sorted(batch_sizes): - profile = builder.create_optimization_profile() - min_shape = (prev_size + 1, sequence_length) - shape = (batch_size, sequence_length) - profile.set_shape("input_ids", min=min_shape, opt=shape, max=shape) - profile.set_shape("segment_ids", min=min_shape, opt=shape, max=shape) - profile.set_shape("input_mask", min=min_shape, opt=shape, max=shape) - builder_config.add_optimization_profile(profile) - prev_size = batch_size + if len(sequence_lengths) == 1: + min_shape = (sequence_lengths[0], prev_batch_size + 1) + shape = (sequence_lengths[0], batch_size) + profile.set_shape("input_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("segment_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("input_mask", min=min_shape, opt=shape, max=shape) + builder_config.add_optimization_profile(profile) + else: + prev_sequence_length = 0 + for sequence_length in sorted(sequence_lengths): + profile = builder.create_optimization_profile() + min_shape = (prev_sequence_length + 1, prev_batch_size + 1) + shape = (sequence_length, batch_size) + profile.set_shape("input_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("segment_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("input_mask", min=min_shape, opt=shape, max=shape) + builder_config.add_optimization_profile(profile) + prev_sequence_length = sequence_length + prev_batch_size = batch_size else: - input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(batch_sizes[0], sequence_length)) - segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(batch_sizes[0], sequence_length)) - input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(batch_sizes[0], sequence_length)) + input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(sequence_lengths[0], batch_sizes[0])) + segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(sequence_lengths[0], batch_sizes[0])) + input_mask = network.add_input(name="input_mask", dtype=trt.int32, shape=(sequence_lengths[0], batch_sizes[0])) + wbeta = trt.PluginField("bert_embeddings_layernorm_beta", weights_dict["bert_embeddings_layernorm_beta"].numpy(), trt.PluginFieldType.FLOAT32) wgamma = trt.PluginField("bert_embeddings_layernorm_gamma", weights_dict["bert_embeddings_layernorm_gamma"].numpy(), trt.PluginFieldType.FLOAT32) wwordemb = trt.PluginField("bert_embeddings_word_embeddings", weights_dict["bert_embeddings_word_embeddings"].numpy(), trt.PluginFieldType.FLOAT32) @@ -551,27 +567,12 @@ def emb_layernorm(builder, network, config, weights_dict, builder_config, sequen wposemb = trt.PluginField("bert_embeddings_position_embeddings", weights_dict["bert_embeddings_position_embeddings"].numpy(), trt.PluginFieldType.FLOAT32) output_fp16 = trt.PluginField("output_fp16", np.array([1 if config.use_fp16 else 0]).astype(np.int32), trt.PluginFieldType.INT32) + mha_type = trt.PluginField("mha_type_id", np.array([get_mha_dtype(config)], np.int32), trt.PluginFieldType.INT32) - use_full_mask = 0 - # use full_mask for XMMA kernels (use fp16/int8 precision and SM version >= 72) - if not config.is_calib_mode and (config.use_fp16 or config.use_int8): - cc = pycuda.autoinit.device.compute_capability() - if cc[0] * 10 + cc[1] >= 72: - use_full_mask = 1 - full_mask = trt.PluginField("full_mask", np.array([use_full_mask]).astype(np.int32), trt.PluginFieldType.INT32) - - pfc = trt.PluginFieldCollection([wbeta, wgamma, wwordemb, wtokemb, wposemb, output_fp16, full_mask]) + pfc = trt.PluginFieldCollection([wbeta, wgamma, wwordemb, wtokemb, wposemb, output_fp16, mha_type]) fn = emln_plg_creator.create_plugin("embeddings", pfc) - input_ids = network.add_shuffle(input_ids) - input_ids.second_transpose = (1, 0) - segment_ids = network.add_shuffle(segment_ids) - segment_ids.second_transpose = (1, 0) - input_mask = network.add_shuffle(input_mask) - input_mask.second_transpose = (1, 0) - inputs = [input_ids.get_output(0), - segment_ids.get_output(0), - input_mask.get_output(0)] + inputs = [input_ids, segment_ids, input_mask] emb_layer = network.add_plugin_v2(inputs, fn) if config.use_qat: @@ -579,7 +580,7 @@ def emb_layernorm(builder, network, config, weights_dict, builder_config, sequen set_output_name(emb_layer, "embeddings_", "output") return emb_layer -def build_engine(batch_sizes, workspace_size, sequence_length, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num): +def build_engine(batch_sizes, workspace_size, sequence_lengths, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num): explicit_batch_flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) with trt.Builder(TRT_LOGGER) as builder, builder.create_network(explicit_batch_flag) as network, builder.create_builder_config() as builder_config: @@ -589,14 +590,18 @@ def build_engine(batch_sizes, workspace_size, sequence_length, config, weights_d if config.use_int8: builder_config.set_flag(trt.BuilderFlag.INT8) if not config.use_qat: - calibrator = BertCalibrator(squad_json, vocab_file, calibrationCacheFile, 1, sequence_length, calib_num) + calibrator = BertCalibrator(squad_json, vocab_file, calibrationCacheFile, 1, sequence_lengths[-1], calib_num) builder_config.set_quantization_flag(trt.QuantizationFlag.CALIBRATE_BEFORE_FUSION) builder_config.int8_calibrator = calibrator if config.use_strict: builder_config.set_flag(trt.BuilderFlag.STRICT_TYPES) + # only use the largest sequence when in calibration mode + if config.is_calib_mode: + sequence_lengths = sequence_lengths[-1:] + # Create the network - emb_layer = emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_length, batch_sizes) + emb_layer = emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_lengths, batch_sizes) embeddings = emb_layer.get_output(0) mask_idx = emb_layer.get_output(1) @@ -615,7 +620,7 @@ def build_engine(batch_sizes, workspace_size, sequence_length, config, weights_d calibrator.free() return engine -def generate_calibration_cache(sequence_length, workspace_size, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num): +def generate_calibration_cache(sequence_lengths, workspace_size, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num): """ BERT demo needs a separate engine building path to generate calibration cache. This is because we need to configure SLN and MHA plugins in FP32 mode when @@ -632,7 +637,7 @@ def generate_calibration_cache(sequence_length, workspace_size, config, weights_ config.use_fp16 = False config.is_calib_mode = True - with build_engine([1], workspace_size, sequence_length, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num) as engine: + with build_engine([1], workspace_size, sequence_lengths, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num) as engine: TRT_LOGGER.log(TRT_LOGGER.INFO, "calibration cache generated in {:}".format(calibrationCacheFile)) config.use_fp16 = saved_use_fp16 @@ -645,7 +650,7 @@ def main(): parser.add_argument("-x", "--onnx", required=False, help="The ONNX model file path.") parser.add_argument("-o", "--output", required=True, default="bert_base_384.engine", help="The bert engine file, ex bert.engine") parser.add_argument("-b", "--batch-size", default=[], action="append", help="Batch size(s) to optimize for. The engine will be usable with any batch size below this, but may not be optimal for smaller sizes. Can be specified multiple times to optimize for more than one batch size.", type=int) - parser.add_argument("-s", "--sequence-length", default=128, help="Sequence length of the BERT model", type=int) + parser.add_argument("-s", "--sequence-length", default=[], action="append", help="Sequence length of the BERT model", type=int) parser.add_argument("-c", "--config-dir", required=True, help="The folder containing the bert_config.json, which can be downloaded e.g. from https://github.com/google-research/bert#pre-trained-models or by running download_models.py in dle/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google") parser.add_argument("-f", "--fp16", action="store_true", help="Indicates that inference should be run in FP16 precision", required=False) @@ -662,6 +667,13 @@ def main(): args, _ = parser.parse_known_args() args.batch_size = args.batch_size or [1] + args.sequence_length = args.sequence_length or [128] + + cc = pycuda.autoinit.device.compute_capability() + if cc[0] * 10 + cc[1] < 75 and args.force_int8_multihead: + raise RuntimeError("--force-int8-multihead option is only supported on Turing+ GPU.") + if cc[0] * 10 + cc[1] < 72 and args.force_int8_skipln: + raise RuntimeError("--force-int8-skipln option is only supported on Xavier+ GPU.") bert_config_path = os.path.join(args.config_dir, "bert_config.json") TRT_LOGGER.log(TRT_LOGGER.INFO, "Using configuration file: {:}".format(bert_config_path)) @@ -671,7 +683,7 @@ def main(): if args.calib_path != None: calib_cache = args.calib_path else: - calib_cache = "BertSquadL{}H{}A{}S{}CalibCache".format(config.num_hidden_layers, config.head_size, config.num_attention_heads, args.sequence_length) + calib_cache = "BertSquadL{}H{}A{}S{}CalibCache".format(config.num_hidden_layers, config.head_size, config.num_attention_heads, "-".join(str(len) for len in args.sequence_length)) if args.onnx != None: weights_dict = load_onnx_weights_and_quant(args.onnx, config) diff --git a/demo/BERT/builder_varseqlen.py b/demo/BERT/builder_varseqlen.py new file mode 100644 index 00000000..028afbc8 --- /dev/null +++ b/demo/BERT/builder_varseqlen.py @@ -0,0 +1,632 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import ctypes +import json +import numpy as np +import os +import os.path +import re +import sys +import time +import onnx +import pycuda.autoinit + +# TensorRT +import tensorrt as trt + +try: + from tensorflow.python import pywrap_tensorflow as pyTF +except ImportError as err: + sys.stderr.write("""Error: Failed to import tensorflow module ({})\n""".format(err)) + sys.exit() + +""" +TensorRT Initialization +""" +TRT_LOGGER = trt.Logger(trt.Logger.INFO) + +handle = ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL) +if not handle: + raise RuntimeError("Could not load plugin library. Is `libnvinfer_plugin.so` on your LD_LIBRARY_PATH?") + +trt.init_libnvinfer_plugins(TRT_LOGGER, "") +plg_registry = trt.get_plugin_registry() +emln_plg_creator2 = plg_registry.get_plugin_creator("CustomEmbLayerNormPluginDynamic", "2", "") +mha_plg_creator2 = plg_registry.get_plugin_creator("CustomQKVToContextPluginDynamic", "2", "") +skln_plg_creator2 = plg_registry.get_plugin_creator("CustomSkipLayerNormPluginDynamic", "2", "") + +mha_plg_creator3 = plg_registry.get_plugin_creator("CustomQKVToContextPluginDynamic", "3", "") +skln_plg_creator3 = plg_registry.get_plugin_creator("CustomSkipLayerNormPluginDynamic", "3", "") + +""" +Attentions Keys +""" +WQ = "self_query_kernel" +BQ = "self_query_bias" +WK = "self_key_kernel" +BK = "self_key_bias" +WV = "self_value_kernel" +BV = "self_value_bias" +WQKV = "self_qkv_kernel" +BQKV = "self_qkv_bias" + +""" +Transformer Keys +""" +W_AOUT = "attention_output_dense_kernel" +B_AOUT = "attention_output_dense_bias" +AOUT_LN_BETA = "attention_output_layernorm_beta" +AOUT_LN_GAMMA = "attention_output_layernorm_gamma" +W_MID = "intermediate_dense_kernel" +B_MID = "intermediate_dense_bias" +W_LOUT = "output_dense_kernel" +B_LOUT = "output_dense_bias" +LOUT_LN_BETA = "output_layernorm_beta" +LOUT_LN_GAMMA = "output_layernorm_gamma" + +""" +Squad Output Keys +""" +SQD_W = "squad_output_weights" +SQD_B = "squad_output_bias" + +class BertConfig: + def __init__(self, bert_config_path, use_fp16, use_int8, use_qat, interleaved): + with open(bert_config_path, "r") as f: + data = json.load(f) + self.num_attention_heads = data["num_attention_heads"] + self.hidden_size = data["hidden_size"] + self.intermediate_size = data["intermediate_size"] + self.num_hidden_layers = data["num_hidden_layers"] + self.head_size = self.hidden_size // self.num_attention_heads + self.use_fp16 = use_fp16 + self.use_int8 = use_int8 + self.use_qat = use_qat + self.interleaved = interleaved + + def get_trt_dtype(self): + dtype = trt.float32 + if self.use_fp16: + dtype = trt.float16 + if self.use_int8: + dtype = trt.int8 + return dtype + +def set_tensor_name(tensor, prefix, name): + tensor.name = prefix + name + +def set_output_name(layer, prefix, name, out_idx = 0): + set_tensor_name(layer.get_output(out_idx), prefix, name) + +def set_output_range(layer, maxval, out_idx = 0): + layer.get_output(out_idx).set_dynamic_range(-maxval, maxval) + +def attention_layer_opt(prefix, config, init_dict, network, input_tensor, mask_idx, cu_seqlens, max_seqlen): + """ + Add the attention layer + """ + hidden_size = config.hidden_size + num_heads = config.num_attention_heads + head_size = int(hidden_size / num_heads) + + Wall = init_dict[prefix + WQKV] + Ball = init_dict[prefix + BQKV] + + # FC_attention + if config.use_int8: + mult_all = network.add_convolution(input_tensor, 3 * hidden_size, (1, 1), Wall, Ball) + else: + mult_all = network.add_fully_connected(input_tensor, 3 * hidden_size, Wall, Ball) + + if config.use_qat: + dr_qkv = max( + init_dict[prefix + 'self_qv_a_input_quantizer_amax'], + init_dict[prefix + 'self_qv_b_input_quantizer_amax'], + init_dict[prefix + 'self_av_b_input_quantizer_amax'], + ) + set_output_range(mult_all, dr_qkv) + set_output_name(mult_all, prefix, "qkv_mult") + + # QKV2CTX + dtype = config.get_trt_dtype() + + pf_type = trt.PluginField("type_id", np.array([int(dtype)], np.int32), trt.PluginFieldType.INT32) + pf_hidden_size = trt.PluginField("hidden_size", np.array([hidden_size], np.int32), trt.PluginFieldType.INT32) + pf_num_heads = trt.PluginField("num_heads", np.array([num_heads], np.int32), trt.PluginFieldType.INT32) + pf_has_mask = trt.PluginField("has_mask", np.array([1], np.int32), trt.PluginFieldType.INT32) + pf_var_seqlen = trt.PluginField("var_seqlen", np.array([int(1)], np.int32), trt.PluginFieldType.FLOAT32) + + if config.use_qat: + dr_probs = init_dict[prefix + 'self_av_a_input_quantizer_amax'] + dq_probs = dr_probs / 127.0 + pf_dq_probs = trt.PluginField("dq_probs", np.array([dq_probs], np.float32), trt.PluginFieldType.FLOAT32) + fields = [pf_hidden_size, pf_num_heads, pf_dq_probs] + else: + fields = [pf_hidden_size, pf_num_heads] + + if config.use_int8 and config.interleaved: + pfc = trt.PluginFieldCollection(fields) + qkv2ctx_plug = mha_plg_creator3.create_plugin("qkv2ctx", pfc) + qkv_in = [mult_all.get_output(0), cu_seqlens, max_seqlen] + else: + fields.append(pf_has_mask) + fields.append(pf_type) + fields.append(pf_var_seqlen) + pfc = trt.PluginFieldCollection(fields) + qkv2ctx_plug = mha_plg_creator2.create_plugin("qkv2ctx", pfc) + qkv_in = [mult_all.get_output(0), mask_idx, cu_seqlens, max_seqlen] + qkv2ctx = network.add_plugin_v2(qkv_in, qkv2ctx_plug) + qkv2ctx.name = prefix + 'qkv_to_ctx' + + if config.use_qat: + dr_ctx = init_dict[prefix + 'output_dense_input_amax'] + set_output_range(qkv2ctx, dr_ctx) + set_output_name(qkv2ctx, prefix, "context_layer") + return qkv2ctx + +def skipln(prefix, config, init_dict, network, input_tensor, skip): + """ + Add the skip layer + """ + hidden_size = config.hidden_size + dtype = config.get_trt_dtype() + + pf_ld = trt.PluginField("ld", np.array([hidden_size], np.int32), trt.PluginFieldType.INT32) + wbeta = init_dict[prefix + "beta"] + pf_beta = trt.PluginField("beta", wbeta.numpy(), trt.PluginFieldType.FLOAT32) + wgamma = init_dict[prefix + "gamma"] + pf_gamma = trt.PluginField("gamma", wgamma.numpy(), trt.PluginFieldType.FLOAT32) + pf_type = trt.PluginField("type_id", np.array([int(dtype)], np.int32), trt.PluginFieldType.INT32) + + if config.use_int8 and config.interleaved: + pfc = trt.PluginFieldCollection([pf_beta, pf_gamma]) + skipln_plug = skln_plg_creator3.create_plugin("skipln", pfc) + else: + pfc = trt.PluginFieldCollection([pf_ld, pf_beta, pf_gamma, pf_type]) + skipln_plug = skln_plg_creator2.create_plugin("skipln", pfc) + + skipln_inputs = [input_tensor, skip] + layer = network.add_plugin_v2(skipln_inputs, skipln_plug) + return layer + +def transformer_layer_opt(prefix, config, init_dict, network, input_tensor, mask_idx, cu_seqlens, max_seqlen): + """ + Add the transformer layer + """ + hidden_size = config.hidden_size + + if config.use_qat: + dr_input = init_dict[prefix + 'attention_self_query_input_amax'] + assert(dr_input ==init_dict[prefix + 'attention_self_key_input_amax'] ) + assert(dr_input ==init_dict[prefix + 'attention_self_value_input_amax'] ) + input_tensor.set_dynamic_range(-dr_input, dr_input) + + context_transposed = attention_layer_opt(prefix + "attention_", config, init_dict, network, input_tensor, mask_idx, cu_seqlens, max_seqlen) + attention_heads = context_transposed.get_output(0) + + # FC0 + B_aout = init_dict[prefix + B_AOUT] + W_aout = init_dict[prefix + W_AOUT] + if config.use_int8: + attention_out_fc = network.add_convolution(attention_heads, hidden_size, (1, 1), W_aout, B_aout) + else: + attention_out_fc = network.add_fully_connected(attention_heads, hidden_size, W_aout, B_aout) + if config.use_int8 and config.use_qat: + dr_fc_aout = init_dict[prefix + 'attention_output_add_local_input_quantizer_amax'] + set_output_range(attention_out_fc, dr_fc_aout) + + skiplayer = skipln(prefix + "attention_output_layernorm_", config, init_dict, network, attention_out_fc.get_output(0), input_tensor) + attention_ln = skiplayer.get_output(0) + if config.use_qat: + dr_skln1 = init_dict[prefix + 'intermediate_dense_input_amax'] + set_output_range(skiplayer, dr_skln1) + + # FC1 + GELU + B_mid = init_dict[prefix + B_MID] + W_mid = init_dict[prefix + W_MID] + if config.use_int8: + mid_dense = network.add_convolution(attention_ln, config.intermediate_size, (1, 1), W_mid, B_mid) + else: + mid_dense = network.add_fully_connected(attention_ln, config.intermediate_size, W_mid, B_mid) + + mid_dense_out = mid_dense.get_output(0) + POW = network.add_constant((1,) * len(mid_dense_out.shape), trt.Weights(np.ascontiguousarray([3.0], dtype=np.float32))) + MULTIPLY = network.add_constant((1,) * len(mid_dense_out.shape), trt.Weights(np.ascontiguousarray([0.044715], dtype=np.float32))) + SQRT = network.add_constant((1,) * len(mid_dense_out.shape), trt.Weights((np.ascontiguousarray([0.79788456080286535587989211986876], dtype=np.float32)))) + ONE = network.add_constant((1,) * len(mid_dense_out.shape), trt.Weights((np.ascontiguousarray([1.0], dtype=np.float32)))) + HALF = network.add_constant((1,) * len(mid_dense_out.shape), trt.Weights((np.ascontiguousarray([0.5], dtype=np.float32)))) + X_pow = network.add_elementwise(mid_dense_out, POW.get_output(0), trt.ElementWiseOperation.POW) + X_pow_t = X_pow.get_output(0) + X_mul = network.add_elementwise(X_pow_t, MULTIPLY.get_output(0), trt.ElementWiseOperation.PROD) + X_add = network.add_elementwise(mid_dense_out, X_mul.get_output(0), trt.ElementWiseOperation.SUM) + X_sqrt = network.add_elementwise(X_add.get_output(0), SQRT.get_output(0), trt.ElementWiseOperation.PROD) + X_sqrt_tensor = X_sqrt.get_output(0) + X_tanh = network.add_activation(X_sqrt_tensor, trt.ActivationType.TANH) + X_tanh_tensor = X_tanh.get_output(0) + X_one = network.add_elementwise(X_tanh_tensor, ONE.get_output(0), trt.ElementWiseOperation.SUM) + CDF = network.add_elementwise(X_one.get_output(0), HALF.get_output(0), trt.ElementWiseOperation.PROD) + gelu_layer = network.add_elementwise(CDF.get_output(0), mid_dense_out, trt.ElementWiseOperation.PROD) + + intermediate_act = gelu_layer.get_output(0) + set_tensor_name(intermediate_act, prefix, "gelu") + if config.use_int8: + if config.use_qat: + dr_gelu = init_dict[prefix + 'output_dense_input_amax'] + set_output_range(gelu_layer, dr_gelu) + else: + # use gelu10 according to whitepaper http://arxiv.org/abs/2004.09602 + set_output_range(gelu_layer, 10) + + # FC2 + # Dense to hidden size + B_lout = init_dict[prefix + B_LOUT] + W_lout = init_dict[prefix + W_LOUT] + + if config.use_int8: + out_dense = network.add_convolution(intermediate_act, hidden_size, (1, 1), W_lout, B_lout) + else: + out_dense = network.add_fully_connected(intermediate_act, hidden_size, W_lout, B_lout) + if config.use_int8 and config.use_qat: + dr_fc_out = init_dict[prefix + 'output_add_local_input_quantizer_amax'] + set_output_range(out_dense, dr_fc_out) + set_output_name(out_dense, prefix + "output_", "dense") + + out_layer = skipln(prefix + "output_layernorm_", config, init_dict, network, out_dense.get_output(0), attention_ln) + set_output_name(out_layer, prefix + "output_", "reshape") + + return out_layer + +def bert_model(config, init_dict, network, input_tensor, mask_idx, cu_seqlens, max_seqlen): + """ + Create the bert model + """ + prev_input = input_tensor + for layer in range(0, config.num_hidden_layers): + ss = "l{}_".format(layer) + out_layer = transformer_layer_opt(ss, config, init_dict, network, prev_input, mask_idx, cu_seqlens, max_seqlen) + prev_input = out_layer.get_output(0) + + if config.use_qat: + dr_out = init_dict["bert_encoder_final_input_quantizer_amax"] + set_output_range(out_layer, dr_out) + return prev_input + +def squad_output(prefix, config, init_dict, network, input_tensor): + """ + Create the squad output + """ + hidden_size = config.hidden_size + + W_out = init_dict[prefix + SQD_W] + B_out = init_dict[prefix + SQD_B] + + if config.use_int8: + dense = network.add_convolution(input_tensor, 2, (1, 1), W_out, B_out) + else: + dense = network.add_fully_connected(input_tensor, 2, W_out, B_out) + + OUT = network.add_shuffle(dense.get_output(0)) + if config.use_int8 and config.interleaved: + OUT.second_transpose = (1, 2, 0, 3) + else: + OUT.second_transpose = (1, 0, 2, 3) + set_output_name(OUT, prefix, "squad_logits") + return OUT + +def load_tf_weights(inputbase, config): + """ + Load the weights from the tensorflow checkpoint + """ + weights_dict = dict() + + try: + reader = pyTF.NewCheckpointReader(inputbase) + tensor_dict = reader.get_variable_to_shape_map() + + # There might be training-related variables in the checkpoint that can be discarded + param_names = [key for key in sorted(tensor_dict) if "adam" not in key and "global_step" not in key and "pooler" not in key] + count = len(param_names) + TRT_LOGGER.log(TRT_LOGGER.INFO, "Found {:} entries in weight map".format(count)) + + for pn in param_names: + toks = pn.lower().split("/") + if "encoder" in pn: + assert ("layer" in pn) + l = (re.findall("\d+", pn))[0] + outname = "l{}_".format(l) + "_".join(toks[3:]) + else: + outname = "_".join(toks) + + tensor = reader.get_tensor(pn) + shape = tensor.shape + if pn.find("kernel") != -1: + weights_dict[outname + "_notrans"] = trt.Weights(np.ascontiguousarray(tensor).flatten()) + + TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Transposing {}\n".format(np)) + tensor = np.transpose(tensor) + + shape = tensor.shape + flat_tensor = tensor.flatten() + shape_str = "{} ".format(len(shape)) + " ".join([str(d) for d in shape]) + weights_dict[outname] = trt.Weights(flat_tensor) + + TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Original name: {:}, TensorRT name: {:}, shape: {:}".format(pn, outname, shape_str)) + + N = config.num_attention_heads + H = config.head_size + + additional_dict = dict() + for key, value in weights_dict.items(): + pos = key.find(BQ) + if pos != -1: + hidden_size = value.size + prefix = key[:pos] + + Bq_ = value + Bk_ = weights_dict[prefix + BK] + Bv_ = weights_dict[prefix + BV] + Wq_ = weights_dict[prefix + WQ] + Wk_ = weights_dict[prefix + WK] + Wv_ = weights_dict[prefix + WV] + + mat_size = hidden_size * hidden_size + wcount = 3 * mat_size + Wall = np.zeros(wcount, np.float32) + bcount = 3 * hidden_size + Ball = np.zeros(bcount, np.float32) + Wall[0:mat_size] = Wq_.numpy()[0:mat_size] + Wall[mat_size:2*mat_size] = Wk_.numpy()[0:mat_size] + Wall[2*mat_size:3*mat_size] = Wv_.numpy()[0:mat_size] + Ball[0:hidden_size] = Bq_.numpy()[0:hidden_size] + Ball[hidden_size:2*hidden_size] = Bk_.numpy()[0:hidden_size] + Ball[2*hidden_size:3*hidden_size] = Bv_.numpy()[0:hidden_size] + + if config.use_int8 and config.interleaved: + Wall = np.ascontiguousarray(Wall.reshape((3, N, H, N, H)), dtype=np.float32) + Ball = np.ascontiguousarray(Ball.reshape((3, N, H)), dtype=np.float32) + else: + Wall = np.ascontiguousarray(Wall.reshape((3, N, H, N, H)).transpose((1, 0, 2, 3, 4)), dtype=np.float32) + Ball = np.ascontiguousarray(Ball.reshape((3, N, H)).transpose((1, 0, 2)), dtype=np.float32) + + additional_dict[prefix + WQKV] = trt.Weights(Wall) + additional_dict[prefix + BQKV] = trt.Weights(Ball) + + additional_dict[prefix + WQKV + "_notrans"] = trt.Weights(Wall.T) + + except Exception as error: + TRT_LOGGER.log(TRT_LOGGER.ERROR, str(error)) + + weights_dict.update(additional_dict) + return weights_dict + +def onnx_to_trt_name(onnx_name): + """ + Converting variables in the onnx checkpoint to names corresponding to the naming convention used in the TF version, expected by the builder + """ + onnx_name = onnx_name.lower() + toks = [t.strip('_') for t in onnx_name.split('.')] + if toks[0] == 'bert': #embeddings or encoder + if toks[1] == 'encoder': #transformer + + if toks[-2] == 'layernorm': #bias->beta, weight->gamma + toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma' + elif (toks[-2] == 'dense' or toks[-2] in {'key', 'value', 'query'}) and toks[-1] == 'weight': + toks[-1] = 'kernel' + elif (toks[-3] == 'dense' or toks[-3] in {'key', 'value', 'query'}) and toks[-1] == 'amax': + if toks[-2] == 'weight_quantizer': + toks[-2] = 'kernel' + elif toks[-2] == 'input_quantizer': + toks[-2] = 'input' + + if 'final_input_quantizer' not in toks[2]: + toks = toks[3:] + toks[0] = 'l{}'.format(int(toks[0])) + else: + if toks[-2] == 'layernorm': #bias->beta, weight->gamma + toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma' + else: #embeddings: drop "_weight" suffix + if toks[-1] == 'amax': + toks[-2] = 'amax' + toks = toks[:-1] + elif 'qa' in onnx_name: + name = 'cls_squad_output_bias' if toks[-1] == 'bias' else 'cls_squad_output_weights' + return name + else: + print("Encountered unknown case:", onnx_name) + assert(False) + parsed = '_'.join(toks) + return parsed + +def load_onnx_weights_and_quant(path, config): + """ + Load the weights from the onnx checkpoint + """ + N = config.num_attention_heads + H = config.head_size + hidden_size = config.hidden_size + + model = onnx.load(path) + weights = model.graph.initializer + tensor_dict = dict([(onnx_to_trt_name(w.name), np.frombuffer(w.raw_data, np.float32).reshape(w.dims)) for w in weights]) + + weights_dict = dict() + for outname, tensor in tensor_dict.items(): + if outname.find("_amax") != -1: + weights_dict[outname] = tensor + elif outname.find(BQ) != -1: + prefix = outname[:outname.find(BQ)] + + Wqkv = np.zeros((3, hidden_size, hidden_size), np.float32) + Bqkv = np.zeros((3, hidden_size), np.float32) + + Wqkv[0,:,:] = tensor_dict[prefix + WQ] + Wqkv[1,:,:] = tensor_dict[prefix + WK] + Wqkv[2,:,:] = tensor_dict[prefix + WV] + Bqkv[0,:] = tensor + Bqkv[1,:] = tensor_dict[prefix + BK] + Bqkv[2,:] = tensor_dict[prefix + BV] + + if config.use_int8 and config.interleaved: + Wqkv = np.ascontiguousarray(Wqkv.reshape((3, N, H, N, H))) + Bqkv = np.ascontiguousarray(Bqkv.reshape((3, N, H))) + else: + Wqkv = np.ascontiguousarray(Wqkv.reshape((3, N, H, N, H)).transpose((1,0,2,3,4))) + Bqkv = np.ascontiguousarray(Bqkv.reshape((3, N, H)).transpose((1,0,2))) + + weights_dict[prefix + WQKV] = trt.Weights(Wqkv) + weights_dict[prefix + BQKV] = trt.Weights(Bqkv) + weights_dict[prefix + WQKV + "_notrans"] = trt.Weights(Wqkv.T) + + elif outname.find(BK) != -1 or outname.find(BV) != -1 or outname.find(WQ) != -1 or outname.find(WK) != -1 or outname.find(WV) != -1: + pass + else: + flat_tensor = np.ascontiguousarray(tensor).flatten() + weights_dict[outname] = trt.Weights(flat_tensor) + + if outname.find("kernel") != -1: + tensor = np.transpose(tensor) + weights_dict[outname + "_notrans"] = trt.Weights(np.ascontiguousarray(tensor).flatten()) + + + TRT_LOGGER.log(TRT_LOGGER.INFO, "Found {:} entries in weight map".format(len(weights_dict))) + return weights_dict + +def emb_layernorm(builder, network, config, weights_dict, builder_config, max_sequence_length, max_batch_size): + input_ids = network.add_input(name="input_ids", dtype=trt.int32, shape=(-1,)) + segment_ids = network.add_input(name="segment_ids", dtype=trt.int32, shape=(-1,)) + cu_seqlens = network.add_input(name="cu_seqlens", dtype=trt.int32, shape=(-1,)) + max_seqlen = network.add_input(name="max_seqlen", dtype=trt.int32, shape=(-1,)) + + # Specify profiles + profile = builder.create_optimization_profile() + min_shape = (1,) + shape = (max_sequence_length*max_batch_size,) + profile.set_shape("input_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("segment_ids", min=min_shape, opt=shape, max=shape) + profile.set_shape("cu_seqlens", min=min_shape, opt=(max_batch_size+1,), max=(max_batch_size+1,)) + profile.set_shape("max_seqlen", min=min_shape, opt=(max_sequence_length,), max=(max_sequence_length,)) + builder_config.add_optimization_profile(profile) + + wbeta = trt.PluginField("bert_embeddings_layernorm_beta", weights_dict["bert_embeddings_layernorm_beta"].numpy(), trt.PluginFieldType.FLOAT32) + wgamma = trt.PluginField("bert_embeddings_layernorm_gamma", weights_dict["bert_embeddings_layernorm_gamma"].numpy(), trt.PluginFieldType.FLOAT32) + wwordemb = trt.PluginField("bert_embeddings_word_embeddings", weights_dict["bert_embeddings_word_embeddings"].numpy(), trt.PluginFieldType.FLOAT32) + wtokemb = trt.PluginField("bert_embeddings_token_type_embeddings", weights_dict["bert_embeddings_token_type_embeddings"].numpy(), trt.PluginFieldType.FLOAT32) + wposemb = trt.PluginField("bert_embeddings_position_embeddings", weights_dict["bert_embeddings_position_embeddings"].numpy(), trt.PluginFieldType.FLOAT32) + + output_fp16 = trt.PluginField("output_fp16", np.array([1 if config.use_fp16 or config.use_int8 else 0]).astype(np.int32), trt.PluginFieldType.INT32) + + pfc = trt.PluginFieldCollection([wbeta, wgamma, wwordemb, wtokemb, wposemb, output_fp16]) + fn = emln_plg_creator2.create_plugin("embeddings", pfc) + + inputs = [input_ids, segment_ids, cu_seqlens, max_seqlen] + emb_layer = network.add_plugin_v2(inputs, fn) + + if config.use_int8 and config.use_qat: + dr_input = weights_dict['l0_attention_self_query_input_amax'] + set_output_range(emb_layer, dr_input) + set_output_name(emb_layer, "embeddings_", "output") + return emb_layer, cu_seqlens, max_seqlen + +def build_engine(batch_size, workspace_size, sequence_length, config, weights_dict, squad_json, vocab_file, calibrationCacheFile, calib_num): + explicit_batch_flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + + with trt.Builder(TRT_LOGGER) as builder, builder.create_network(explicit_batch_flag) as network, builder.create_builder_config() as builder_config: + builder_config.max_workspace_size = workspace_size * (1024 * 1024) + if config.use_fp16: + builder_config.set_flag(trt.BuilderFlag.FP16) + if config.use_int8: + builder_config.set_flag(trt.BuilderFlag.INT8) + if not config.use_qat: + raise RuntimeError("Post training calibration is not supported in variable-length BERT.") + + # Create the network + emb_layer, cu_seqlens, max_seqlen = emb_layernorm(builder, network, config, weights_dict, builder_config, sequence_length, batch_size) + embeddings = emb_layer.get_output(0) + if config.use_int8 and config.interleaved: + shuffle = network.add_shuffle(embeddings) + shuffle.second_transpose = (2, 1, 0, 3) + embeddings = shuffle.get_output(0) + mask_idx = None + else: + mask_idx = emb_layer.get_output(1) + + bert_out = bert_model(config, weights_dict, network, embeddings, mask_idx, cu_seqlens, max_seqlen) + + squad_logits = squad_output("cls_", config, weights_dict, network, bert_out) + squad_logits_out = squad_logits.get_output(0) + network.mark_output(squad_logits_out) + + build_start_time = time.time() + engine = builder.build_engine(network, builder_config) + build_time_elapsed = (time.time() - build_start_time) + TRT_LOGGER.log(TRT_LOGGER.INFO, "build engine in {:.3f} Sec".format(build_time_elapsed)) + return engine + +def main(): + parser = argparse.ArgumentParser(description="TensorRT BERT Sample", formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("-m", "--ckpt", required=False, + help="The checkpoint file basename, e.g.: basename(model.ckpt-766908.data-00000-of-00001) is model.ckpt-766908") + parser.add_argument("-x", "--onnx", required=False, help="The ONNX model file path.") + parser.add_argument("-o", "--output", required=True, default="bert_base_384.engine", help="The bert engine file, ex bert.engine") + parser.add_argument("-b", "--max-batch-size", default=1, help="Max batch size. The engine will be usable with any input with (batch-size * sequence-length) below (max-batch-size * max-sequence-length).", type=int) + parser.add_argument("-s", "--max-sequence-length", default=128, help="Max sequence length of the BERT model. The engine will be usable with any input with (batch-size * sequence-length) below (max-batch-size * max-sequence-length).", type=int) + parser.add_argument("-c", "--config-dir", required=True, + help="The folder containing the bert_config.json, which can be downloaded e.g. from https://github.com/google-research/bert#pre-trained-models or by running download_models.py in dle/TensorFlow/LanguageModeling/BERT/data/pretrained_models_google") + parser.add_argument("-f", "--fp16", action="store_true", help="Indicates that inference should be run in FP16 precision", required=False) + parser.add_argument("-i", "--int8", action="store_true", help="Indicates that inference should be run in INT8 precision", required=False) + parser.add_argument("-w", "--workspace-size", default=1000, help="Workspace size in MiB for building the BERT engine", type=int) + parser.add_argument("-j", "--squad-json", default="squad/dev-v1.1.json", help="squad json dataset used for int8 calibration", required=False) + parser.add_argument("-v", "--vocab-file", default="./pre-trained_model/uncased_L-24_H-1024_A-16/vocab.txt", help="Path to file containing entire understandable vocab", required=False) + parser.add_argument("-n", "--calib-num", default=100, help="calibration batch numbers", type=int) + parser.add_argument("-p", "--calib-path", help="calibration cache path", required=False) + parser.add_argument("-il", "--interleaved", action="store_true", help="use interleaved format, only valid in INT8 precision", required=False) + + args, _ = parser.parse_known_args() + + cc = pycuda.autoinit.device.compute_capability() + if cc[0] * 10 + cc[1] < 72: + raise RuntimeError("This variable-length BERT demo only support Xavier+ GPU.") + + bert_config_path = os.path.join(args.config_dir, "bert_config.json") + TRT_LOGGER.log(TRT_LOGGER.INFO, "Using configuration file: {:}".format(bert_config_path)) + + config = BertConfig(bert_config_path, args.fp16, args.int8, args.int8 and args.onnx != None, args.interleaved) + + if args.calib_path != None: + calib_cache = args.calib_path + else: + calib_cache = "BertSquadL{}H{}A{}S{}CalibCache".format(config.num_hidden_layers, config.head_size, config.num_attention_heads, args.max_sequence_length) + + if args.onnx != None: + weights_dict = load_onnx_weights_and_quant(args.onnx, config) + elif args.ckpt != None: + weights_dict = load_tf_weights(args.ckpt, config) + else: + raise RuntimeError("You need either specify TF checkpoint using option --ckpt or ONNX using option --onnx to build TRT BERT model.") + + with build_engine(args.max_batch_size, args.workspace_size, args.max_sequence_length, config, weights_dict, args.squad_json, args.vocab_file, calib_cache, args.calib_num) as engine: + TRT_LOGGER.log(TRT_LOGGER.VERBOSE, "Serializing Engine...") + serialized_engine = engine.serialize() + TRT_LOGGER.log(TRT_LOGGER.INFO, "Saving Engine to {:}".format(args.output)) + with open(args.output, "wb") as fout: + fout.write(serialized_engine) + TRT_LOGGER.log(TRT_LOGGER.INFO, "Done.") + +if __name__ == "__main__": + main() diff --git a/demo/BERT/helpers/calibrator.py b/demo/BERT/helpers/calibrator.py index 33d801c9..f463760a 100644 --- a/demo/BERT/helpers/calibrator.py +++ b/demo/BERT/helpers/calibrator.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import tensorrt as trt import os diff --git a/demo/BERT/helpers/data_processing.py b/demo/BERT/helpers/data_processing.py index cb7b661f..e3dce71b 100644 --- a/demo/BERT/helpers/data_processing.py +++ b/demo/BERT/helpers/data_processing.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import helpers.tokenization as tokenization import collections diff --git a/demo/BERT/infer.py b/demo/BERT/infer.py index 5dc0edde..ab50ba82 100644 --- a/demo/BERT/infer.py +++ b/demo/BERT/infer.py @@ -60,8 +60,8 @@ def main(): with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context: # Allocate buffers large enough to store the largest batch size - max_input_shape = (max(args.batch_size), args.sequence_length) - max_output_shape = (max(args.batch_size), args.sequence_length, 2, 1, 1) + max_input_shape = (args.sequence_length, max(args.batch_size)) + max_output_shape = (args.sequence_length, max(args.batch_size), 2, 1, 1) buffers = [ DeviceBuffer(max_input_shape), DeviceBuffer(max_input_shape), @@ -106,10 +106,11 @@ def question_features(tokens, question): bindings = [0] * binding_idx_offset + [buf.binding() for buf in buffers] shapes = { - "input_ids": (batch_size, args.sequence_length), - "segment_ids": (batch_size, args.sequence_length), - "input_mask": (batch_size, args.sequence_length), + "input_ids": (args.sequence_length, batch_size), + "segment_ids": (args.sequence_length, batch_size), + "input_mask": (args.sequence_length, batch_size), } + for binding, shape in shapes.items(): context.set_binding_shape(engine[binding] + binding_idx_offset, shape) assert context.all_binding_shapes_specified diff --git a/demo/BERT/infer_c/bert_infer.h b/demo/BERT/infer_c/bert_infer.h index 37c84386..86628401 100644 --- a/demo/BERT/infer_c/bert_infer.h +++ b/demo/BERT/infer_c/bert_infer.h @@ -25,6 +25,7 @@ #include #include #include +#include #include using namespace nvinfer1; @@ -80,6 +81,18 @@ struct BertInference exit(-1); } gLogInfo << "Done\n"; + + const int numBindingPerProfile = mEngine->getNbBindings() / mEngine->getNbOptimizationProfiles(); + mEnableVariableLen = numBindingPerProfile == kBERT_INPUT_NUM + 1 ? false : true; + if (mEnableVariableLen) + { + gLogInfo << "Variable length is enabled\n"; + } + else + { + gLogInfo << "Variable length is disabled\n"; + } + mContext = TrtUniquePtr(mEngine->createExecutionContext()); if (!mContext) { @@ -97,18 +110,42 @@ struct BertInference const size_t allocationSize = mSeqLength * maxBatchSize * sizeof(int32_t); // Static sizes with implicit batch size: allocation sizes known to engine - for (int i = 0; i < kBERT_INPUT_NUM; i++) + if (mEnableVariableLen) + { + const size_t allocationSizes[] = {allocationSize, allocationSize, + sizeof(int32_t) * (maxBatchSize + 1), + sizeof(int32_t) * (mSeqLength)}; + for (int i = 0; i < sizeof(allocationSizes) / sizeof(allocationSizes[0]); i++) + { + void* devBuf; + gpuErrChk(cudaMalloc(&devBuf, allocationSizes[i])); + gpuErrChk(cudaMemset(devBuf, 0, allocationSizes[i])); + mDeviceBuffers.emplace_back(devBuf); + mInputSizes.emplace_back(allocationSizes[i]); + } + } + else { - void* devBuf; - gpuErrChk(cudaMalloc(&devBuf, allocationSize)); - gpuErrChk(cudaMemset(devBuf, 0, allocationSize)); - mDeviceBuffers.emplace_back(devBuf); - mInputSizes.emplace_back(allocationSize); + for (int i = 0; i < kBERT_INPUT_NUM; i++) + { + void* devBuf; + gpuErrChk(cudaMalloc(&devBuf, allocationSize)); + gpuErrChk(cudaMemset(devBuf, 0, allocationSize)); + mDeviceBuffers.emplace_back(devBuf); + mInputSizes.emplace_back(allocationSize); + } } const size_t numOutputItems = maxBatchSize * mSeqLength * 2; mOutputSize = numOutputItems * sizeof(float); - mOutputDims = {maxBatchSize, mSeqLength, 2, 1, 1}; + if (mEnableVariableLen) + { + mOutputDims = {maxBatchSize * mSeqLength * 2}; + } + else + { + mOutputDims = {maxBatchSize, mSeqLength, 2, 1, 1}; + } void* devBuf; gpuErrChk(cudaMalloc(&devBuf, mOutputSize)); gpuErrChk(cudaMemset(devBuf, 0, mOutputSize)); @@ -126,9 +163,20 @@ struct BertInference const int bindingIdxOffset = profIdx * numBindingPerProfile; std::copy(mDeviceBuffers.begin(), mDeviceBuffers.end(), mBindings.begin() + bindingIdxOffset); - for (int i = 0; i < kBERT_INPUT_NUM; i++) + if (mEnableVariableLen) + { + const int allocationSizes[] = {mSeqLength * batchSize, mSeqLength * batchSize, batchSize + 1, mSeqLength}; + for (int i = 0; i < sizeof(allocationSizes)/sizeof(allocationSizes[0]); i++) + { + mContext->setBindingDimensions(i + bindingIdxOffset, Dims{1, {allocationSizes[i]}}); + } + } + else { - mContext->setBindingDimensions(i + bindingIdxOffset, Dims2(batchSize, mSeqLength)); + for (int i = 0; i < kBERT_INPUT_NUM; i++) + { + mContext->setBindingDimensions(i + bindingIdxOffset, Dims2(mSeqLength, batchSize)); + } } if (!mContext->allInputDimensionsSpecified()) @@ -164,13 +212,12 @@ struct BertInference gpuErrChk(cudaGraphInstantiate(&exec, graph, NULL, NULL, 0)); mExecGraph = exec; } + mCuSeqlens.resize(batchSize + 1); + std::generate(mCuSeqlens.begin(), mCuSeqlens.end(), [pos = -mSeqLength, this]() mutable{ pos += mSeqLength; return pos; }); } - void run(const void* inputIds, const void* segmentIds, const void* inputMask, int warmUps, int iterations) + void run(const void* const* inputBuffers, int warmUps, int iterations) { - - const std::vector inputBuffers = {inputIds, segmentIds, inputMask}; - for (int i = 0; i < kBERT_INPUT_NUM; i++) { gpuErrChk( @@ -226,13 +273,27 @@ struct BertInference } gpuErrChk(cudaMemcpyAsync( - mHostOutput.data(), mDeviceBuffers[kBERT_INPUT_NUM], mOutputSize, cudaMemcpyDeviceToHost, mStream)); + mHostOutput.data(), mDeviceBuffers[mEnableVariableLen ? kBERT_INPUT_NUM + 1 : kBERT_INPUT_NUM], mOutputSize, cudaMemcpyDeviceToHost, mStream)); gpuErrChk(cudaStreamSynchronize(mStream)); mTimes.push_back(times); } + void run(const void* inputIds, const void* segmentIds, const void* inputMask, int warmUps, int iterations) + { + if (mEnableVariableLen) + { + const std::vector inputBuffers = {inputIds, segmentIds, mCuSeqlens.data()}; + run(inputBuffers.data(), warmUps, iterations); + } + else + { + const std::vector inputBuffers = {inputIds, segmentIds, inputMask}; + run(inputBuffers.data(), warmUps, iterations); + } + } + void run(int profIdx, int batchSize, const void* inputIds, const void* segmentIds, const void* inputMask, int warmUps, int iterations) { @@ -279,6 +340,8 @@ struct BertInference TrtUniquePtr mEngine{nullptr}; TrtUniquePtr mContext{nullptr}; std::vector mBindings; + bool mEnableVariableLen; + std::vector mCuSeqlens; cudaStream_t mStream{NULL}; std::vector mDeviceBuffers; diff --git a/demo/BERT/inference.py b/demo/BERT/inference.py index b45eb9db..654c8ffa 100644 --- a/demo/BERT/inference.py +++ b/demo/BERT/inference.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# """ This script uses a prebuilt TensorRT BERT QA Engine to answer a question @@ -128,7 +130,7 @@ def question_features(tokens, question): num_binding_per_profile = engine.num_bindings // engine.num_optimization_profiles for idx in range(engine.num_optimization_profiles): profile_shape = engine.get_profile_shape(profile_index = idx, binding = idx * num_binding_per_profile) - if profile_shape[0][0] <= args.batch_size and profile_shape[2][0] >= args.batch_size: + if profile_shape[0][1] <= args.batch_size and profile_shape[2][1] >= args.batch_size and profile_shape[0][0] <= max_seq_length and profile_shape[2][0] >= max_seq_length: selected_profile = idx break if selected_profile == -1: @@ -139,7 +141,7 @@ def question_features(tokens, question): # Specify input shapes. These must be within the min/max bounds of the active profile # Note that input shapes can be specified on a per-inference basis, but in this case, we only have a single shape. - input_shape = (args.batch_size, max_seq_length) + input_shape = (max_seq_length, args.batch_size) input_nbytes = trt.volume(input_shape) * trt.int32.itemsize for binding in range(3): context.set_binding_shape(binding_idx_offset + binding, input_shape) @@ -166,9 +168,9 @@ def inference(features, tokens): eval_time_elapsed = 0 for feature_index, feature in enumerate(features): # Copy inputs - input_ids_batch = np.vstack([feature.input_ids] * args.batch_size) - segment_ids_batch = np.vstack([feature.segment_ids] * args.batch_size) - input_mask_batch = np.vstack([feature.input_mask] * args.batch_size) + input_ids_batch = np.dstack([feature.input_ids] * args.batch_size).squeeze() + segment_ids_batch = np.dstack([feature.segment_ids] * args.batch_size).squeeze() + input_mask_batch = np.dstack([feature.input_mask] * args.batch_size).squeeze() input_ids = cuda.register_host_memory(np.ascontiguousarray(input_ids_batch.ravel())) segment_ids = cuda.register_host_memory(np.ascontiguousarray(segment_ids_batch.ravel())) diff --git a/demo/BERT/inference_c.py b/demo/BERT/inference_c.py index d0a27fe1..661e0c19 100644 --- a/demo/BERT/inference_c.py +++ b/demo/BERT/inference_c.py @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# """ This script uses a prebuilt TensorRT BERT QA Engine to answer a question @@ -148,13 +149,21 @@ def inference(features, tokens): eval_time_elapsed += (time.time() - eval_start_time) - for index, batch in enumerate(h_output): - # Data Post-processing + # Data Post-processing + if len(h_output.shape) == 1: + S = int(h_output.shape[0] / 2) networkOutputs.append(_NetworkOutput( - start_logits = np.array(batch.squeeze()[:, 0]), - end_logits = np.array(batch.squeeze()[:, 1]), + start_logits = np.array(h_output[0:S]), + end_logits = np.array(h_output[S:S*2]), feature_index = feature_index )) + else: + for index, batch in enumerate(h_output): + networkOutputs.append(_NetworkOutput( + start_logits = np.array(batch.squeeze()[:, 0]), + end_logits = np.array(batch.squeeze()[:, 1]), + feature_index = feature_index + )) eval_time_elapsed /= len(features) diff --git a/demo/BERT/inference_varseqlen.py b/demo/BERT/inference_varseqlen.py new file mode 100644 index 00000000..524ca5c3 --- /dev/null +++ b/demo/BERT/inference_varseqlen.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script uses a prebuilt TensorRT BERT QA Engine to answer a question +based on the provided passage. It additionally includes an interactive mode +where multiple questions can be asked. +""" + +import time +import json +import ctypes +import argparse +import collections +import numpy as np +import tensorrt as trt +import pycuda.driver as cuda +import pycuda.autoinit + +import helpers.tokenization as tokenization +import helpers.data_processing as dp + +TRT_LOGGER = trt.Logger(trt.Logger.INFO) + +def parse_args(): + """ + Parse command line arguments + """ + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('-e', '--engine', + help='Path to BERT TensorRT engine') + parser.add_argument('-p', '--passage', nargs='*', + help='Text for paragraph/passage for BERT QA', + default='') + parser.add_argument('-pf', '--passage-file', + help='File containing input passage', + default='') + parser.add_argument('-q', '--question', nargs='*', + help='Text for query/question for BERT QA', + default='') + parser.add_argument('-qf', '--question-file', + help='File containing input question', + default='') + parser.add_argument('-sq', '--squad-json', + help='SQuAD json file', + default='') + parser.add_argument('-o', '--output-prediction-file', + help='Output prediction file for SQuAD evaluation', + default='./predictions.json') + parser.add_argument('-v', '--vocab-file', + help='Path to file containing entire understandable vocab') + parser.add_argument('-s', '--sequence-length', + help='The sequence length to use. Defaults to 128', + default=128, type=int) + parser.add_argument('--max-query-length', + help='The maximum length of a query in number of tokens. Queries longer than this will be truncated', + default=64, type=int) + parser.add_argument('--max-answer-length', + help='The maximum length of an answer that can be generated', + default=30, type=int) + parser.add_argument('--n-best-size', + help='Total number of n-best predictions to generate in the nbest_predictions.json output file', + default=20, type=int) + args, _ = parser.parse_known_args() + return args + +if __name__ == '__main__': + args = parse_args() + + paragraph_text = None + squad_examples = None + output_prediction_file = None + + if not args.passage == '': + paragraph_text = ' '.join(args.passage) + elif not args.passage_file == '': + f = open(args.passage_file, 'r') + paragraph_text = f.read() + elif not args.squad_json == '': + squad_examples = dp.read_squad_json(args.squad_json) + output_prediction_file = args.output_prediction_file + else: + paragraph_text = input("Paragraph: ") + + question_text = None + if not args.question == '': + question_text = ' '.join(args.question) + elif not args.question_file == '': + f = open(args.question_file, 'r') + question_text = f.read() + + tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True) + # When splitting up a long document into chunks, how much stride to take between chunks. + doc_stride = 128 + # The maximum total input sequence length after WordPiece tokenization. + # Sequences longer than this will be truncated, and sequences shorter + max_seq_length = args.sequence_length + + def question_features(tokens, question): + # Extract features from the paragraph and question + return dp.convert_example_to_features(tokens, question, tokenizer, max_seq_length, doc_stride, args.max_query_length) + + # Import necessary plugins for BERT TensorRT + handle = ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL) + if not handle: + raise RuntimeError("Could not load plugin library. Is `libnvinfer_plugin.so` on your LD_LIBRARY_PATH?") + + # The first context created will use the 0th profile. A new context must be created + # for each additional profile needed. Here, we only use batch size 1, thus we only need the first profile. + with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, \ + runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context: + + # select engine profile + context.active_optimization_profile = 0 + + input_nbytes = max_seq_length * trt.int32.itemsize + + # Create a stream in which to copy inputs/outputs and run inference. + stream = cuda.Stream() + + # Allocate device memory for inputs. + d_inputs = [cuda.mem_alloc(input_nbytes) for binding in range(4)] + + # Allocate output buffer by querying the size from the context. This may be different for different input shapes. + h_output = cuda.pagelocked_empty((2 * max_seq_length), dtype=np.float32) + d_output = cuda.mem_alloc(h_output.nbytes) + + + def inference(features, tokens): + global h_output + + _NetworkOutput = collections.namedtuple( # pylint: disable=invalid-name + "NetworkOutput", + ["start_logits", "end_logits", "feature_index"]) + networkOutputs = [] + + eval_time_elapsed = 0 + for feature_index, feature in enumerate(features): + # Copy inputs + B = 1 + S = np.sum(feature.input_mask) + input_ids = feature.input_ids[0:S] + segment_ids = feature.segment_ids[0:S] + cu_seq_lens = np.array([0, S], dtype=np.int32); + + if context.get_binding_shape(0)[0] != S: + context.set_binding_shape(0, (S,)) + if context.get_binding_shape(1)[0] != S: + context.set_binding_shape(1, (S,)) + if context.get_binding_shape(2)[0] != 2: + context.set_binding_shape(2, (2,)) + if context.get_binding_shape(3)[0] != S: + context.set_binding_shape(3, (S,)) + + h_input_ids = cuda.register_host_memory(np.ascontiguousarray(input_ids.ravel())) + h_segment_ids = cuda.register_host_memory(np.ascontiguousarray(segment_ids.ravel())) + h_cu_seq_lens = cuda.register_host_memory(np.ascontiguousarray(cu_seq_lens.ravel())) + + eval_start_time = time.time() + cuda.memcpy_htod_async(d_inputs[0], h_input_ids, stream) + cuda.memcpy_htod_async(d_inputs[1], h_segment_ids, stream) + cuda.memcpy_htod_async(d_inputs[2], h_cu_seq_lens, stream) + + # Run inference + context.execute_async_v2(bindings=[int(d_inp) for d_inp in d_inputs] + [int(d_output)], stream_handle=stream.handle) + # Synchronize the stream + stream.synchronize() + eval_time_elapsed += (time.time() - eval_start_time) + + # Transfer predictions back from GPU + cuda.memcpy_dtoh_async(h_output, d_output, stream) + stream.synchronize() + + # Only retrieve and post-process the first batch + networkOutputs.append(_NetworkOutput( + start_logits = np.array(h_output[0:S]), + end_logits = np.array(h_output[S:S*2]), + feature_index = feature_index + )) + + eval_time_elapsed /= len(features) + + # Total number of n-best predictions to generate in the nbest_predictions.json output file + n_best_size = 20 + + # The maximum length of an answer that can be generated. This is needed + # because the start and end predictions are not conditioned on one another + max_answer_length = 30 + + prediction, nbest_json, scores_diff_json = dp.get_predictions(tokens, features, + networkOutputs, args.n_best_size, args.max_answer_length) + + return eval_time_elapsed, prediction, nbest_json + + def print_single_query(eval_time_elapsed, prediction, nbest_json): + print("------------------------") + print("Running inference in {:.3f} Sentences/Sec".format(1/eval_time_elapsed)) + print("------------------------") + + print("Answer: '{}'".format(prediction)) + print("With probability: {:.3f}".format(nbest_json[0]['probability'] * 100.0)) + + if squad_examples: + all_predictions = collections.OrderedDict() + + for example in squad_examples: + features = question_features(example.doc_tokens, example.question_text) + eval_time_elapsed, prediction, nbest_json = inference(features, example.doc_tokens) + all_predictions[example.id] = prediction + + with open(output_prediction_file, "w") as f: + f.write(json.dumps(all_predictions, indent=4)) + print("\nOutput dump to {}".format(output_prediction_file)) + else: + # Extract tokecs from the paragraph + doc_tokens = dp.convert_doc_tokens(paragraph_text) + + if question_text: + print("\nPassage: {}".format(paragraph_text)) + print("\nQuestion: {}".format(question_text)) + + features = question_features(doc_tokens, question_text) + eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens) + print_single_query(eval_time_elapsed, prediction, nbest_json) + + else: + # If no question text is provided, loop until the question is 'exit' + EXIT_CMDS = ["exit", "quit"] + question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS)) + + while question_text.strip() not in EXIT_CMDS: + features = question_features(doc_tokens, question_text) + eval_time_elapsed, prediction, nbest_json = inference(features, doc_tokens) + print_single_query(eval_time_elapsed, prediction, nbest_json) + question_text = input("Question (to exit, type one of {:}): ".format(EXIT_CMDS)) diff --git a/demo/BERT/perf.py b/demo/BERT/perf.py index 3c8b8bc4..afa5131a 100644 --- a/demo/BERT/perf.py +++ b/demo/BERT/perf.py @@ -52,8 +52,8 @@ def main(): with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context: # Allocate buffers large enough to store the largest batch size - max_input_shape = (max(args.batch_size), args.sequence_length) - max_output_shape = (max(args.batch_size), args.sequence_length, 2, 1, 1) + max_input_shape = (args.sequence_length, max(args.batch_size)) + max_output_shape = (args.sequence_length, max(args.batch_size), 2, 1, 1) buffers = [ DeviceBuffer(max_input_shape), DeviceBuffer(max_input_shape), @@ -65,9 +65,9 @@ def main(): pseudo_vocab_size = 30522 pseudo_type_vocab_size = 2 np.random.seed(args.random_seed) - test_word_ids = np.random.randint(0, pseudo_vocab_size, (max(args.batch_size), args.sequence_length), dtype=np.int32) - test_segment_ids = np.random.randint(0, pseudo_type_vocab_size, (max(args.batch_size), args.sequence_length), dtype=np.int32) - test_input_mask = np.ones((max(args.batch_size), args.sequence_length), dtype=np.int32) + test_word_ids = np.random.randint(0, pseudo_vocab_size, (args.sequence_length, max(args.batch_size)), dtype=np.int32) + test_segment_ids = np.random.randint(0, pseudo_type_vocab_size, (args.sequence_length, max(args.batch_size)), dtype=np.int32) + test_input_mask = np.ones((args.sequence_length, max(args.batch_size)), dtype=np.int32) # Copy input h2d cuda.memcpy_htod(buffers[0].buf, test_word_ids.ravel()) @@ -86,9 +86,9 @@ def main(): bindings = [0] * binding_idx_offset + [buf.binding() for buf in buffers] shapes = { - "input_ids": (batch_size, args.sequence_length), - "segment_ids": (batch_size, args.sequence_length), - "input_mask": (batch_size, args.sequence_length), + "input_ids": (args.sequence_length, batch_size), + "segment_ids": (args.sequence_length, batch_size), + "input_mask": (args.sequence_length, batch_size), } for binding, shape in shapes.items(): diff --git a/demo/BERT/perf_varseqlen.py b/demo/BERT/perf_varseqlen.py new file mode 100644 index 00000000..04d3c4a8 --- /dev/null +++ b/demo/BERT/perf_varseqlen.py @@ -0,0 +1,132 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import ctypes +import numpy as np +import tensorrt as trt +import pycuda.driver as cuda +import pycuda.autoinit + +import numpy as np + +TRT_LOGGER = trt.Logger(trt.Logger.ERROR) + +class DeviceBuffer(object): + def __init__(self, shape, dtype=trt.int32): + self.buf = cuda.mem_alloc(trt.volume(shape) * dtype.itemsize) + + def binding(self): + return int(self.buf) + + def free(self): + self.buf.free() + + +def main(): + parser = argparse.ArgumentParser(description='BERT Inference Benchmark') + parser.add_argument("-e", "--engine", help='Path to BERT TensorRT engine') + parser.add_argument('-b', '--batch-size', default=[], action="append", help='Batch size(s) to benchmark. Can be specified multiple times for more than one batch size. This script assumes that the engine has been built with one optimization profile for each batch size, and that these profiles are in order of increasing batch size.', type=int) + parser.add_argument('-s', '--sequence-length', default=128, help='Sequence length of the BERT model', type=int) + parser.add_argument('-i', '--iterations', default=200, help='Number of iterations to run when benchmarking each batch size.', type=int) + parser.add_argument('-w', '--warm-up-runs', default=10, help='Number of iterations to run prior to benchmarking.', type=int) + parser.add_argument('-r', '--random-seed', required=False, default=12345, help='Random seed.', type=int) + args, _ = parser.parse_known_args() + args.batch_size = args.batch_size or [1] + + # Import necessary plugins for BERT TensorRT + ctypes.CDLL("libnvinfer_plugin.so", mode=ctypes.RTLD_GLOBAL) + + with open(args.engine, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(f.read()) as engine, engine.create_execution_context() as context: + # Allocate buffers large enough to store the largest batch size + max_input_shape = (args.sequence_length * max(args.batch_size), ) + max_output_shape = (args.sequence_length * max(args.batch_size), 2, 1, 1) + buffers = [ + DeviceBuffer(max_input_shape), + DeviceBuffer(max_input_shape), + DeviceBuffer((max(args.batch_size) + 1, )), + DeviceBuffer((args.sequence_length, )), + DeviceBuffer(max_output_shape) + ] + + # Prepare random input + pseudo_vocab_size = 30522 + pseudo_type_vocab_size = 2 + np.random.seed(args.random_seed) + test_word_ids = np.random.randint(0, pseudo_vocab_size, (args.sequence_length * max(args.batch_size)), dtype=np.int32) + test_segment_ids = np.random.randint(0, pseudo_type_vocab_size, (args.sequence_length * max(args.batch_size)), dtype=np.int32) + test_cu_seq_lens = np.arange(0, args.sequence_length * max(args.batch_size) + 1, args.sequence_length, dtype=np.int32) + + # Copy input h2d + cuda.memcpy_htod(buffers[0].buf, test_word_ids.ravel()) + cuda.memcpy_htod(buffers[1].buf, test_segment_ids.ravel()) + cuda.memcpy_htod(buffers[2].buf, test_cu_seq_lens.ravel()) + + bench_times = {} + + for idx, batch_size in enumerate(sorted(args.batch_size)): + context.active_optimization_profile = 0 + + # Each profile has unique bindings + bindings = [buf.binding() for buf in buffers] + + shapes = { + "input_ids": (args.sequence_length * batch_size, ), + "segment_ids": (args.sequence_length * batch_size, ), + "cu_seqlens": (batch_size + 1, ), + "max_seqlen": (args.sequence_length, ), + } + + for binding, shape in shapes.items(): + context.set_binding_shape(engine[binding], shape) + assert context.all_binding_shapes_specified + + # Inference + total_time = 0 + start = cuda.Event() + end = cuda.Event() + stream = cuda.Stream() + + # Warmup + for _ in range(args.warm_up_runs): + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + stream.synchronize() + + # Timing loop + times = [] + for _ in range(args.iterations): + start.record(stream) + context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) + end.record(stream) + stream.synchronize() + times.append(end.time_since(start)) + + # Compute average time, 95th percentile time and 99th percentile time. + bench_times[batch_size] = times + + [b.free() for b in buffers] + + for batch_size, times in bench_times.items(): + total_time = sum(times) + avg_time = total_time / float(len(times)) + times.sort() + percentile95 = times[int(len(times) * 0.95)] + percentile99 = times[int(len(times) * 0.99)] + print("Running {:} iterations with Batch Size: {:}\n\tTotal Time: {:} ms \tAverage Time: {:} ms\t95th Percentile Time: {:} ms\t99th Percentile Time: {:}".format(args.iterations, batch_size, total_time, avg_time, percentile95, percentile99)) + + +if __name__ == '__main__': + main() diff --git a/demo/BERT/scripts/download_model.sh b/demo/BERT/scripts/download_model.sh index 04d15ac8..d1cd7e52 100755 --- a/demo/BERT/scripts/download_model.sh +++ b/demo/BERT/scripts/download_model.sh @@ -1,6 +1,6 @@ #!/bin/bash - -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,7 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - +# # Setup default parameters (if no command-line parameters given) VERSION='v2' diff --git a/demo/BERT/scripts/download_squad.sh b/demo/BERT/scripts/download_squad.sh index 72afc611..32b6a182 100755 --- a/demo/BERT/scripts/download_squad.sh +++ b/demo/BERT/scripts/download_squad.sh @@ -1,6 +1,6 @@ #!/bin/bash - -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# # Setup default parameters (if no command-line parameters given) VERSION='v1.1' diff --git a/demo/BERT/scripts/inference_benchmark.sh b/demo/BERT/scripts/inference_benchmark.sh index da66dad5..dfbec015 100755 --- a/demo/BERT/scripts/inference_benchmark.sh +++ b/demo/BERT/scripts/inference_benchmark.sh @@ -1,6 +1,6 @@ #!/bin/bash - -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -13,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# # Usage: run_benchmark(batch_sizes, model_variant: (base/large), precision: (int8/int8-qat/fp16/fp32), sequence_length, max_batch_size, gpu_arch) run_benchmark() { diff --git a/demo/BERT/squad/evaluate-v1.1.py b/demo/BERT/squad/evaluate-v1.1.py index 25c93a8d..142350cc 100644 --- a/demo/BERT/squad/evaluate-v1.1.py +++ b/demo/BERT/squad/evaluate-v1.1.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 # -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -14,6 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # + # Obtained from https://rajpurkar.github.io/SQuAD-explorer/ """ Official evaluation script for v1.1 of the SQuAD dataset. """ diff --git a/demo/Tacotron2/README.md b/demo/Tacotron2/README.md index 34729feb..724f584a 100644 --- a/demo/Tacotron2/README.md +++ b/demo/Tacotron2/README.md @@ -14,7 +14,7 @@ Software version configuration tested for the instructions that follow: |Python|3.6.9| |CUDA|11.0.171| |Apex|0.1| -|TensorRT|7.1.3.4| +|TensorRT|7.2.0.13| |PyTorch|1.5.1| @@ -29,7 +29,7 @@ Software version configuration tested for the instructions that follow: cd $TRT_SOURCE/demo/Tacotron2 export LD_LIBRARY_PATH=$TRT_SOURCE/build/out:/tensorrt/lib:$LD_LIBRARY_PATH - pip3 install /tensorrt/python/tensorrt-7.1*-cp36-none-linux_x86_64.whl + pip3 install /tensorrt/python/tensorrt-7.2*-cp36-none-linux_x86_64.whl bash ./scripts/install_prerequisites.sh ``` diff --git a/demo/Tacotron2/common/audio_processing.py b/demo/Tacotron2/common/audio_processing.py index 89130a67..5d109449 100644 --- a/demo/Tacotron2/common/audio_processing.py +++ b/demo/Tacotron2/common/audio_processing.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch import numpy as np diff --git a/demo/Tacotron2/common/layers.py b/demo/Tacotron2/common/layers.py index 3c6bcef6..e5e833da 100644 --- a/demo/Tacotron2/common/layers.py +++ b/demo/Tacotron2/common/layers.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch from librosa.filters import mel as librosa_mel_fn diff --git a/demo/Tacotron2/common/utils.py b/demo/Tacotron2/common/utils.py index 3312ef95..9d902ae1 100644 --- a/demo/Tacotron2/common/utils.py +++ b/demo/Tacotron2/common/utils.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import numpy as np from scipy.io.wavfile import read diff --git a/demo/Tacotron2/data_functions.py b/demo/Tacotron2/data_functions.py index a3d197a5..2c6abad6 100644 --- a/demo/Tacotron2/data_functions.py +++ b/demo/Tacotron2/data_functions.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch from tacotron2.data_function import TextMelCollate diff --git a/demo/Tacotron2/exports/export_tacotron2_onnx.py b/demo/Tacotron2/exports/export_tacotron2_onnx.py index a6bfa3ed..ff12a09c 100644 --- a/demo/Tacotron2/exports/export_tacotron2_onnx.py +++ b/demo/Tacotron2/exports/export_tacotron2_onnx.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch from torch import nn diff --git a/demo/Tacotron2/exports/export_waveglow_onnx.py b/demo/Tacotron2/exports/export_waveglow_onnx.py index 4c3ab011..ee8ab088 100644 --- a/demo/Tacotron2/exports/export_waveglow_onnx.py +++ b/demo/Tacotron2/exports/export_waveglow_onnx.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import types import torch diff --git a/demo/Tacotron2/inference.py b/demo/Tacotron2/inference.py index 17a74f9d..db7f8887 100644 --- a/demo/Tacotron2/inference.py +++ b/demo/Tacotron2/inference.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# from tacotron2.text import text_to_sequence import models diff --git a/demo/Tacotron2/inference_perf.py b/demo/Tacotron2/inference_perf.py index ca48ce10..33c4c6a5 100644 --- a/demo/Tacotron2/inference_perf.py +++ b/demo/Tacotron2/inference_perf.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import models import torch diff --git a/demo/Tacotron2/loss_functions.py b/demo/Tacotron2/loss_functions.py index a70191cb..7104f732 100644 --- a/demo/Tacotron2/loss_functions.py +++ b/demo/Tacotron2/loss_functions.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch import torch.nn as nn diff --git a/demo/Tacotron2/main.py b/demo/Tacotron2/main.py index 491153c7..8d876f59 100644 --- a/demo/Tacotron2/main.py +++ b/demo/Tacotron2/main.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import argparse from train import main as main_train diff --git a/demo/Tacotron2/models.py b/demo/Tacotron2/models.py index a7de492d..5a8161b0 100644 --- a/demo/Tacotron2/models.py +++ b/demo/Tacotron2/models.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import sys from os.path import abspath, dirname diff --git a/demo/Tacotron2/multiproc.py b/demo/Tacotron2/multiproc.py index 59332c0a..63a7726f 100644 --- a/demo/Tacotron2/multiproc.py +++ b/demo/Tacotron2/multiproc.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import sys import subprocess diff --git a/demo/Tacotron2/preprocess_audio2mel.py b/demo/Tacotron2/preprocess_audio2mel.py index 02f02b16..11a21287 100644 --- a/demo/Tacotron2/preprocess_audio2mel.py +++ b/demo/Tacotron2/preprocess_audio2mel.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import argparse import torch diff --git a/demo/Tacotron2/run_latency_tests.sh b/demo/Tacotron2/run_latency_tests.sh index 065fb39f..34ebb465 100644 --- a/demo/Tacotron2/run_latency_tests.sh +++ b/demo/Tacotron2/run_latency_tests.sh @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# unset CUDA_VISIBLE_DEVICES bash test_infer.sh -bs 1 -il 128 --fp16 --num-iters 1003 --tacotron2 ./checkpoints/tacotron2_1032590_6000_amp --waveglow ./checkpoints/waveglow_1076430_14000_amp --wn-channels 256 diff --git a/demo/Tacotron2/scripts/download_checkpoints.sh b/demo/Tacotron2/scripts/download_checkpoints.sh index c3edc885..002360b7 100755 --- a/demo/Tacotron2/scripts/download_checkpoints.sh +++ b/demo/Tacotron2/scripts/download_checkpoints.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# # Prepare the download directory mkdir -p checkpoints && cd checkpoints diff --git a/demo/Tacotron2/scripts/inference_benchmark.sh b/demo/Tacotron2/scripts/inference_benchmark.sh index a3dd86a8..804334fc 100755 --- a/demo/Tacotron2/scripts/inference_benchmark.sh +++ b/demo/Tacotron2/scripts/inference_benchmark.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# echo "TensorRT BS=1, S=128" bash test_infer.sh --test trt/test_infer_trt.py -bs 1 -il 128 --fp16 --num-iters 1003 --encoder ./output/encoder_fp16.engine --decoder ./output/decoder_iter_fp16.engine --postnet ./output/postnet_fp16.engine --waveglow ./output/waveglow_fp16.engine --wn-channels 256 diff --git a/demo/Tacotron2/scripts/install_prerequisites.sh b/demo/Tacotron2/scripts/install_prerequisites.sh index f092364b..b12349f6 100755 --- a/demo/Tacotron2/scripts/install_prerequisites.sh +++ b/demo/Tacotron2/scripts/install_prerequisites.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# pip3 install numba==0.48 torch==1.4.0 pip3 install -r requirements.txt diff --git a/demo/Tacotron2/scripts/prepare_dataset.sh b/demo/Tacotron2/scripts/prepare_dataset.sh index f315bd46..3adcd475 100755 --- a/demo/Tacotron2/scripts/prepare_dataset.sh +++ b/demo/Tacotron2/scripts/prepare_dataset.sh @@ -1,5 +1,6 @@ #!/usr/bin/env bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# set -e diff --git a/demo/Tacotron2/scripts/prepare_mels.sh b/demo/Tacotron2/scripts/prepare_mels.sh index 7873e1dd..5ffd2e35 100644 --- a/demo/Tacotron2/scripts/prepare_mels.sh +++ b/demo/Tacotron2/scripts/prepare_mels.sh @@ -1,5 +1,6 @@ #!/usr/bin/env bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# set -e diff --git a/demo/Tacotron2/tacotron2/arg_parser.py b/demo/Tacotron2/tacotron2/arg_parser.py index 8b3dc18a..b8254309 100644 --- a/demo/Tacotron2/tacotron2/arg_parser.py +++ b/demo/Tacotron2/tacotron2/arg_parser.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import argparse diff --git a/demo/Tacotron2/tacotron2/data_function.py b/demo/Tacotron2/tacotron2/data_function.py index fb54f184..05dc8293 100644 --- a/demo/Tacotron2/tacotron2/data_function.py +++ b/demo/Tacotron2/tacotron2/data_function.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import random import numpy as np diff --git a/demo/Tacotron2/tacotron2/loss_function.py b/demo/Tacotron2/tacotron2/loss_function.py index 7fcb324b..cea4d2ca 100644 --- a/demo/Tacotron2/tacotron2/loss_function.py +++ b/demo/Tacotron2/tacotron2/loss_function.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# from torch import nn diff --git a/demo/Tacotron2/tacotron2/model.py b/demo/Tacotron2/tacotron2/model.py index b42d9ed7..519e0256 100644 --- a/demo/Tacotron2/tacotron2/model.py +++ b/demo/Tacotron2/tacotron2/model.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# from math import sqrt import torch diff --git a/demo/Tacotron2/tacotron2/text/cleaners.py b/demo/Tacotron2/tacotron2/text/cleaners.py index e2e35c1a..e7419093 100644 --- a/demo/Tacotron2/tacotron2/text/cleaners.py +++ b/demo/Tacotron2/tacotron2/text/cleaners.py @@ -1,3 +1,18 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# """ from https://github.com/keithito/tacotron """ ''' diff --git a/demo/Tacotron2/tacotron2/text/cmudict.py b/demo/Tacotron2/tacotron2/text/cmudict.py index 62bfef74..e658012d 100644 --- a/demo/Tacotron2/tacotron2/text/cmudict.py +++ b/demo/Tacotron2/tacotron2/text/cmudict.py @@ -1,3 +1,18 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# """ from https://github.com/keithito/tacotron """ import re diff --git a/demo/Tacotron2/tacotron2/text/numbers.py b/demo/Tacotron2/tacotron2/text/numbers.py index 0d5f7fa8..a75e5621 100644 --- a/demo/Tacotron2/tacotron2/text/numbers.py +++ b/demo/Tacotron2/tacotron2/text/numbers.py @@ -1,3 +1,18 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# """ from https://github.com/keithito/tacotron """ import inflect diff --git a/demo/Tacotron2/tacotron2/text/symbols.py b/demo/Tacotron2/tacotron2/text/symbols.py index 175703cb..d74fffbc 100644 --- a/demo/Tacotron2/tacotron2/text/symbols.py +++ b/demo/Tacotron2/tacotron2/text/symbols.py @@ -1,3 +1,18 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# """ from https://github.com/keithito/tacotron """ ''' diff --git a/demo/Tacotron2/test_infer.py b/demo/Tacotron2/test_infer.py index c1e43edf..c50a5eac 100644 --- a/demo/Tacotron2/test_infer.py +++ b/demo/Tacotron2/test_infer.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# from tacotron2.text import text_to_sequence import models diff --git a/demo/Tacotron2/test_infer.sh b/demo/Tacotron2/test_infer.sh index 108d069b..ff2cbf07 100644 --- a/demo/Tacotron2/test_infer.sh +++ b/demo/Tacotron2/test_infer.sh @@ -1,5 +1,6 @@ #!/bin/bash -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +13,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# BATCH_SIZE=1 INPUT_LENGTH=128 diff --git a/demo/Tacotron2/train.py b/demo/Tacotron2/train.py index 7d89f030..c34e0df3 100644 --- a/demo/Tacotron2/train.py +++ b/demo/Tacotron2/train.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import os import time diff --git a/demo/Tacotron2/trt/export_onnx2trt.py b/demo/Tacotron2/trt/export_onnx2trt.py index 32937a36..c232404d 100644 --- a/demo/Tacotron2/trt/export_onnx2trt.py +++ b/demo/Tacotron2/trt/export_onnx2trt.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import pycuda.driver as cuda import pycuda.autoinit diff --git a/demo/Tacotron2/trt/inference_trt.py b/demo/Tacotron2/trt/inference_trt.py index 731059da..5e15b54e 100644 --- a/demo/Tacotron2/trt/inference_trt.py +++ b/demo/Tacotron2/trt/inference_trt.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import tensorrt as trt import numpy as np diff --git a/demo/Tacotron2/trt/run_latency_tests_trt.sh b/demo/Tacotron2/trt/run_latency_tests_trt.sh index 91005427..238c95b6 100644 --- a/demo/Tacotron2/trt/run_latency_tests_trt.sh +++ b/demo/Tacotron2/trt/run_latency_tests_trt.sh @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,5 +12,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# bash test_infer.sh --test trt/test_infer_trt.py -bs 1 -il 128 --fp16 --num-iters 1003 --encoder ./output/encoder_fp16.engine --decoder ./output/decoder_iter_fp16.engine --postnet ./output/postnet_fp16.engine --waveglow ./output/waveglow_fp16.engine --wn-channels 256 diff --git a/demo/Tacotron2/trt/test_infer_trt.py b/demo/Tacotron2/trt/test_infer_trt.py index 4bddd811..9ac1167f 100644 --- a/demo/Tacotron2/trt/test_infer_trt.py +++ b/demo/Tacotron2/trt/test_infer_trt.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import sys sys.path.append('./') diff --git a/demo/Tacotron2/trt/trt_utils.py b/demo/Tacotron2/trt/trt_utils.py index 71bd2e4c..e534d724 100644 --- a/demo/Tacotron2/trt/trt_utils.py +++ b/demo/Tacotron2/trt/trt_utils.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import tensorrt as trt diff --git a/demo/Tacotron2/waveglow/arg_parser.py b/demo/Tacotron2/waveglow/arg_parser.py index e3587610..0f374712 100644 --- a/demo/Tacotron2/waveglow/arg_parser.py +++ b/demo/Tacotron2/waveglow/arg_parser.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import argparse diff --git a/demo/Tacotron2/waveglow/data_function.py b/demo/Tacotron2/waveglow/data_function.py index f66b3df2..33d8b98f 100644 --- a/demo/Tacotron2/waveglow/data_function.py +++ b/demo/Tacotron2/waveglow/data_function.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch import random diff --git a/demo/Tacotron2/waveglow/denoiser.py b/demo/Tacotron2/waveglow/denoiser.py index ca6d9291..5356b5f6 100644 --- a/demo/Tacotron2/waveglow/denoiser.py +++ b/demo/Tacotron2/waveglow/denoiser.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import sys sys.path.append('tacotron2') diff --git a/demo/Tacotron2/waveglow/loss_function.py b/demo/Tacotron2/waveglow/loss_function.py index b243b109..a6a66723 100644 --- a/demo/Tacotron2/waveglow/loss_function.py +++ b/demo/Tacotron2/waveglow/loss_function.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch diff --git a/demo/Tacotron2/waveglow/model.py b/demo/Tacotron2/waveglow/model.py index bb915726..3bbc1fb8 100644 --- a/demo/Tacotron2/waveglow/model.py +++ b/demo/Tacotron2/waveglow/model.py @@ -1,4 +1,5 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -11,6 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +# import torch from torch.autograd import Variable diff --git a/docker/build.sh b/docker/build.sh index dbc4015f..b636f550 100755 --- a/docker/build.sh +++ b/docker/build.sh @@ -18,9 +18,8 @@ arg_dockerfile=docker/ubuntu.Dockerfile arg_imagename=tensorrt-ubuntu arg_osversion=18.04 -arg_cudaversion=11.0 +arg_cudaversion=11.1 arg_cudnnversion=8.0 -arg_trtversion=7.1.3 arg_help=0 while [[ "$#" -gt 0 ]]; do case $1 in @@ -42,12 +41,7 @@ if [ "$arg_help" -eq "1" ]; then exit; fi -extra_args="" -# Use RC builds for CUDA 11.0 -if [ "$arg_cudaversion" = "11.0" ]; then - extra_args="$extra_args --build-arg NVCR_SUFFIX=-rc" -fi -docker_args="-f $arg_dockerfile --build-arg OS_VERSION=$arg_osversion --build-arg CUDA_VERSION=$arg_cudaversion $extra_args --build-arg uid=$(id -u) --build-arg gid=$(id -g) --tag=$arg_imagename ." +docker_args="-f $arg_dockerfile --build-arg OS_VERSION=$arg_osversion --build-arg CUDA_VERSION=$arg_cudaversion --build-arg uid=$(id -u) --build-arg gid=$(id -g) --tag=$arg_imagename ." echo "Building container:" echo "> docker build $docker_args" diff --git a/docker/centos.Dockerfile b/docker/centos.Dockerfile index ea27cf3f..982408dc 100644 --- a/docker/centos.Dockerfile +++ b/docker/centos.Dockerfile @@ -12,10 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -ARG CUDA_VERSION=11.0 +ARG CUDA_VERSION=11.1 ARG OS_VERSION=7 -ARG NVCR_SUFFIX= -FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-centos${OS_VERSION}${NVCR_SUFFIX} +FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-centos${OS_VERSION} LABEL maintainer="NVIDIA CORPORATION" @@ -66,6 +65,7 @@ RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux. # Set environment and working directory ENV TRT_RELEASE /tensorrt ENV TRT_SOURCE /workspace/TensorRT +ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${TRT_SOURCE}/build/out:${TRT_RELEASE}/lib" WORKDIR /workspace USER trtuser diff --git a/docker/ubuntu-cross-aarch64.Dockerfile b/docker/ubuntu-cross-aarch64.Dockerfile index 946b18b5..fc60fb70 100644 --- a/docker/ubuntu-cross-aarch64.Dockerfile +++ b/docker/ubuntu-cross-aarch64.Dockerfile @@ -14,8 +14,7 @@ ARG CUDA_VERSION=10.2 ARG OS_VERSION=18.04 -ARG NVCR_SUFFIX= -FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX} +FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION} LABEL maintainer="NVIDIA CORPORATION" @@ -81,11 +80,19 @@ RUN dpkg -x /pdk_files/libcudnn[7-8]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/cudn && dpkg -x /pdk_files/libcudnn[7-8]-dev_*-1+cuda10.[0-9]_arm64.deb /pdk_files/cudnn \ && cd /pdk_files/cudnn/usr/include/aarch64-linux-gnu \ && cd /pdk_files/cudnn/usr/lib/aarch64-linux-gnu \ - && ln -s libcudnn.so.7 libcudnn.so \ + && ln -s libcudnn.so.[7-9] libcudnn.so \ && cd /pdk_files/cudnn \ && ln -s usr/include/aarch64-linux-gnu include \ && ln -s usr/lib/aarch64-linux-gnu lib \ + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_adv_infer_v[7-9].h /usr/include/cudnn_adv_infer.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_adv_train_v[7-9].h /usr/include/cudnn_adv_train.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_backend_v[7-9].h /usr/include/cudnn_backend.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_cnn_infer_v[7-9].h /usr/include/cudnn_cnn_infer.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_cnn_train_v[7-9].h /usr/include/cudnn_cnn_train.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_ops_infer_v[7-9].h /usr/include/cudnn_ops_infer.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_ops_train_v[7-9].h /usr/include/cudnn_ops_train.h && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_v[7-9].h /usr/include/cudnn.h + && ln -s /pdk_files/cudnn/usr/include/aarch64-linux-gnu/cudnn_version_v[7-9].h /usr/include/cudnn_version.h # Unpack libnvinfer RUN dpkg -x /pdk_files/libnvinfer[0-7]_*-1+cuda10.[0-9]_arm64.deb /pdk_files/tensorrt \ diff --git a/docker/ubuntu-cross-ppc64le.Dockerfile b/docker/ubuntu-cross-ppc64le.Dockerfile index 2299fb7a..0068d8a0 100755 --- a/docker/ubuntu-cross-ppc64le.Dockerfile +++ b/docker/ubuntu-cross-ppc64le.Dockerfile @@ -14,8 +14,7 @@ ARG CUDA_VERSION=11.0 ARG OS_VERSION=18.04 -ARG NVCR_SUFFIX= -FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX} +FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION} LABEL maintainer="NVIDIA CORPORATION" diff --git a/docker/ubuntu.Dockerfile b/docker/ubuntu.Dockerfile index 241d9a92..cdeefc71 100644 --- a/docker/ubuntu.Dockerfile +++ b/docker/ubuntu.Dockerfile @@ -12,10 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -ARG CUDA_VERSION=11.0 +ARG CUDA_VERSION=11.1 ARG OS_VERSION=18.04 -ARG NVCR_SUFFIX= -FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION}${NVCR_SUFFIX} + +FROM nvidia/cuda:${CUDA_VERSION}-cudnn8-devel-ubuntu${OS_VERSION} LABEL maintainer="NVIDIA CORPORATION" @@ -89,6 +89,7 @@ RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux. # Set environment and working directory ENV TRT_RELEASE /tensorrt ENV TRT_SOURCE /workspace/TensorRT +ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${TRT_SOURCE}/build/out:${TRT_RELEASE}/lib" WORKDIR /workspace USER trtuser diff --git a/include/NvCaffeParser.h b/include/NvCaffeParser.h index b42f5dab..24ddb2de 100644 --- a/include/NvCaffeParser.h +++ b/include/NvCaffeParser.h @@ -102,7 +102,8 @@ class IPluginFactory //! \param weights Weights used for the layer. //! \param nbWeights Number of weights. //! - virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) TRTNOEXCEPT = 0; + virtual nvinfer1::IPlugin* createPlugin( + const char* layerName, const nvinfer1::Weights* weights, int32_t nbWeights) TRTNOEXCEPT = 0; virtual ~IPluginFactory() {} }; @@ -115,7 +116,7 @@ class IPluginFactory class IPluginFactoryExt : public IPluginFactory { public: - virtual int getVersion() const TRTNOEXCEPT + virtual int32_t getVersion() const TRTNOEXCEPT { return NV_TENSORRT_VERSION; } @@ -151,7 +152,8 @@ class IPluginFactoryV2 //! \param nbWeights Number of weights. //! \param libNamespace Library Namespace associated with the plugin object //! - virtual nvinfer1::IPluginV2* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights, const char* libNamespace = "") TRTNOEXCEPT = 0; + virtual nvinfer1::IPluginV2* createPlugin(const char* layerName, const nvinfer1::Weights* weights, + int32_t nbWeights, const char* libNamespace = "") TRTNOEXCEPT = 0; virtual ~IPluginFactoryV2() {} }; diff --git a/include/NvInfer.h b/include/NvInfer.h index 4c0d2c25..27fcf245 100644 --- a/include/NvInfer.h +++ b/include/NvInfer.h @@ -67,7 +67,7 @@ class Dims2 : public Dims //! \param d0 The first element. //! \param d1 The second element. //! - Dims2(int d0, int d1) + Dims2(int32_t d0, int32_t d1) { nbDims = 2; d[0] = d0; @@ -97,7 +97,7 @@ class DimsHW : public Dims2 //! \param Height the height of the data //! \param Width the width of the data //! - DimsHW(int height, int width) + DimsHW(int32_t height, int32_t width) : Dims2(height, width) { type[0] = type[1] = DimensionType::kSPATIAL; @@ -108,28 +108,40 @@ class DimsHW : public Dims2 //! //! \return The height. //! - int& h() { return d[0]; } + int32_t& h() + { + return d[0]; + } //! //! \brief Get the height. //! //! \return The height. //! - int h() const { return d[0]; } + int32_t h() const + { + return d[0]; + } //! //! \brief Get the width. //! //! \return The width. //! - int& w() { return d[1]; } + int32_t& w() + { + return d[1]; + } //! //! \brief Get the width. //! //! \return The width. //! - int w() const { return d[1]; } + int32_t w() const + { + return d[1]; + } }; //! @@ -155,7 +167,7 @@ class Dims3 : public Dims //! \param d1 The second element. //! \param d2 The third element. //! - Dims3(int d0, int d1, int d2) + Dims3(int32_t d0, int32_t d1, int32_t d2) { nbDims = 3; d[0] = d0; @@ -168,7 +180,7 @@ class Dims3 : public Dims //! \class DimsCHW //! \brief Descriptor for data with one channel dimension and two spatial dimensions. //! -//! \deprecated DimsCHW will be removed in a future version of TensorRT, use Dims3 instead. +//! \deprecated DimsCHW will be removed in TensorRT 8.0, use Dims3 instead. //! class TRT_DEPRECATED DimsCHW : public Dims3 { @@ -190,7 +202,7 @@ class TRT_DEPRECATED DimsCHW : public Dims3 //! \param height The height of the data. //! \param width The width of the data. //! - DimsCHW(int channels, int height, int width) + DimsCHW(int32_t channels, int32_t height, int32_t width) : Dims3(channels, height, width) { type[0] = DimensionType::kCHANNEL; @@ -202,42 +214,60 @@ class TRT_DEPRECATED DimsCHW : public Dims3 //! //! \return The channel count. //! - int& c() { return d[0]; } + int32_t& c() + { + return d[0]; + } //! //! \brief Get the channel count. //! //! \return The channel count. //! - int c() const { return d[0]; } + int32_t c() const + { + return d[0]; + } //! //! \brief Get the height. //! //! \return The height. //! - int& h() { return d[1]; } + int32_t& h() + { + return d[1]; + } //! //! \brief Get the height. //! //! \return The height. //! - int h() const { return d[1]; } + int32_t h() const + { + return d[1]; + } //! //! \brief Get the width. //! //! \return The width. //! - int& w() { return d[2]; } + int32_t& w() + { + return d[2]; + } //! //! \brief Get the width. //! //! \return The width. //! - int w() const { return d[2]; } + int32_t w() const + { + return d[2]; + } }; //! @@ -264,7 +294,7 @@ class Dims4 : public Dims //! \param d2 The third element. //! \param d3 The fourth element. //! - Dims4(int d0, int d1, int d2, int d3) + Dims4(int32_t d0, int32_t d1, int32_t d2, int32_t d3) { nbDims = 4; d[0] = d0; @@ -278,7 +308,7 @@ class Dims4 : public Dims //! \class DimsNCHW //! \brief Descriptor for data with one index dimension, one channel dimension and two spatial dimensions. //! -//! \deprecated DimsNCHW will be removed in a future version of TensorRT, use Dims instead. +//! \deprecated DimsNCHW will be removed in TensorRT 8.0, use Dims4 instead. //! class TRT_DEPRECATED DimsNCHW : public Dims4 { @@ -302,7 +332,7 @@ class TRT_DEPRECATED DimsNCHW : public Dims4 //! \param height The height of the data. //! \param width The width of the data. //! - DimsNCHW(int batchSize, int channels, int height, int width) + DimsNCHW(int32_t batchSize, int32_t channels, int32_t height, int32_t width) : Dims4(batchSize, channels, height, width) { type[0] = DimensionType::kINDEX; @@ -315,56 +345,80 @@ class TRT_DEPRECATED DimsNCHW : public Dims4 //! //! \return The index count. //! - int& n() { return d[0]; } + int32_t& n() + { + return d[0]; + } //! //! \brief Get the index count. //! //! \return The index count. //! - int n() const { return d[0]; } + int32_t n() const + { + return d[0]; + } //! //! \brief Get the channel count. //! //! \return The channel count. //! - int& c() { return d[1]; } + int32_t& c() + { + return d[1]; + } //! //! \brief Get the channel count. //! //! \return The channel count. //! - int c() const { return d[1]; } + int32_t c() const + { + return d[1]; + } //! //! \brief Get the height. //! //! \return The height. //! - int& h() { return d[2]; } + int32_t& h() + { + return d[2]; + } //! //! \brief Get the height. //! //! \return The height. //! - int h() const { return d[2]; } + int32_t h() const + { + return d[2]; + } //! //! \brief Get the width. //! //! \return The width. //! - int& w() { return d[3]; } + int32_t& w() + { + return d[3]; + } //! //! \brief Get the width. //! //! \return The width. //! - int w() const { return d[3]; } + int32_t w() const + { + return d[3]; + } }; //! @@ -374,7 +428,7 @@ class TRT_DEPRECATED DimsNCHW : public Dims4 //! //! \see ILayer::getType() //! -enum class LayerType : int +enum class LayerType : int32_t { kCONVOLUTION = 0, //!< Convolution layer. kFULLY_CONNECTED = 1, //!< Fully connected layer. @@ -412,11 +466,12 @@ enum class LayerType : int kFILL = 33 //!< Fill layer }; +//! Maximum number of elements in LayerType enum. \see LayerType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 34; -} //!< Maximum number of elements in LayerType enum. \see LayerType +} //! //! \class ITensor @@ -521,7 +576,8 @@ class ITensor //! //! \return maximal absolute value of the dynamic range, -1.0f if no dynamic range is set. //! - //! \deprecated This interface is superseded by getDynamicRangeMin and getDynamicRangeMax. + //! \deprecated This interface is superseded by getDynamicRangeMin and getDynamicRangeMax and will be removed in + //! TensorRT 8.0. //! TRT_DEPRECATED virtual float getDynamicRange() const TRTNOEXCEPT = 0; @@ -620,9 +676,15 @@ class ITensor //! \brief Set allowed formats for this tensor. By default all formats are allowed. //! Shape tensors (for which isShapeTensor() returns true) may only have row major linear format. //! + //! When running network on DLA and allowGPUFallback is disabled, if DLA format(kCHW4 with Int8, kCHW4 with + //! FP16, kCHW16 with FP16, kCHW32 with Int8) is set, the input format is treated as native DLA format with + //! line stride requirement. Input/output binding with these format should have correct layout during + //! inference. + //! //! \param formats A bitmask of TensorFormat values that are supported for this tensor. //! //! \see ITensor::getAllowedFormats() + //! \see TensorFormats //! virtual void setAllowedFormats(TensorFormats formats) TRTNOEXCEPT = 0; @@ -725,7 +787,7 @@ class ILayer //! //! \brief Get the number of inputs of a layer. //! - virtual int getNbInputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbInputs() const TRTNOEXCEPT = 0; //! //! \brief Get the layer input corresponding to the given index. @@ -735,12 +797,12 @@ class ILayer //! \return The input tensor, or nullptr if the index is out of range or the tensor is optional //! (\ref ISliceLayer, \ref IRNNLayer and \ref IRNNv2Layer). //! - virtual ITensor* getInput(int index) const TRTNOEXCEPT = 0; + virtual ITensor* getInput(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief Get the number of outputs of a layer. //! - virtual int getNbOutputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0; //! //! \brief Get the layer output corresponding to the given index. @@ -748,7 +810,7 @@ class ILayer //! \return The indexed output tensor, or nullptr if the index is out of range or the tensor is optional //! (\ref IRNNLayer and \ref IRNNv2Layer). //! - virtual ITensor* getOutput(int index) const TRTNOEXCEPT = 0; + virtual ITensor* getOutput(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief Replace an input of this layer with a specific tensor @@ -756,15 +818,15 @@ class ILayer //! \param index the index of the input to modify. //! \param tensor the new input tensor // - //! Except for IShuffleLayer, ISliceLayer, IResizeLayer and ILoopOutputLayer, this method cannot change the number of inputs to a layer. - //! The index argument must be less than the value of getNbInputs(). + //! Except for IShuffleLayer, ISliceLayer, IResizeLayer and ILoopOutputLayer, this method cannot change the number + //! of inputs to a layer. The index argument must be less than the value of getNbInputs(). //! //! See overloaded setInput() comments for the layers special behavior. //! //! \param index the index of the input to modify. //! \param tensor the new input tensor //! - virtual void setInput(int index, ITensor& tensor) TRTNOEXCEPT = 0; + virtual void setInput(int32_t index, ITensor& tensor) TRTNOEXCEPT = 0; //! //! \brief Set the computational precision of this layer @@ -822,18 +884,19 @@ class ILayer //! operations must be DataType::kINT32, and all attempts to set the output type to some other data type will be //! ignored except for issuing an error message. //! - //! Note that the layer output type is generally not identical to the data type of the output tensor, as TensorRT may insert - //! implicit reformatting operations to convert the former to the latter. Calling layer->setOutputType(i, type) - //! has no effect on the data type of the i-th output tensor of layer, and users need to call layer->getOutput(i)->setType(type) - //! to change the tensor data type. This is particularly relevant if the tensor is marked as a network output, since only - //! setType() [but not setOutputType()] will affect the data representation in the corresponding output binding. + //! Note that the layer output type is generally not identical to the data type of the output tensor, as TensorRT + //! may insert implicit reformatting operations to convert the former to the latter. Calling layer->setOutputType(i, + //! type) has no effect on the data type of the i-th output tensor of layer, and users need to call + //! layer->getOutput(i)->setType(type) to change the tensor data type. This is particularly relevant if the tensor + //! is marked as a network output, since only setType() [but not setOutputType()] will affect the data + //! representation in the corresponding output binding. //! //! \param index the index of the output to set //! \param dataType the type of the output //! //! \see getOutputType() outputTypeIsSet() resetOutputType() - virtual void setOutputType(int index, DataType dataType) TRTNOEXCEPT = 0; + virtual void setOutputType(int32_t index, DataType dataType) TRTNOEXCEPT = 0; //! //! \brief get the output type of this layer @@ -844,7 +907,7 @@ class ILayer //! //! \see getOutputType() outputTypeIsSet() resetOutputType() - virtual DataType getOutputType(int index) const TRTNOEXCEPT = 0; + virtual DataType getOutputType(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief whether the output type has been set for this layer @@ -854,7 +917,7 @@ class ILayer //! //! \see setOutputType() getOutputType() resetOutputType() - virtual bool outputTypeIsSet(int index) const TRTNOEXCEPT = 0; + virtual bool outputTypeIsSet(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief reset the output type for this layer @@ -863,7 +926,7 @@ class ILayer //! //! \see setOutputType() getOutputType() outputTypeIsSet() - virtual void resetOutputType(int index) TRTNOEXCEPT = 0; + virtual void resetOutputType(int32_t index) TRTNOEXCEPT = 0; protected: virtual ~ILayer() {} @@ -1091,7 +1154,7 @@ class ILayer //! CAFFE_ROUND_UP: B >= (F + 1) is an error if (B + S) >= (F + 1) //! \endcode //! -enum class PaddingMode : int +enum class PaddingMode : int32_t { kEXPLICIT_ROUND_DOWN = 0, //!< Use explicit padding, rounding output size down. kEXPLICIT_ROUND_UP = 1, //!< Use explicit padding, rounding output size up. @@ -1101,11 +1164,12 @@ enum class PaddingMode : int kCAFFE_ROUND_UP = 5 //!< Use CAFFE padding, rounding output size up, uses prePadding value. }; +//! Maximum number of elements in PaddingMode enum. \see PaddingMode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 6; -} //!< Maximum number of elements in PaddingMode enum. \see PaddingMode +} //! //! \class IConvolutionLayer @@ -1129,7 +1193,7 @@ class IConvolutionLayer : public ILayer //! //! \see getKernelSize() //! - //! \deprecated Superseded by setKernelSizeNd. + //! \deprecated Superseded by setKernelSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setKernelSize(DimsHW kernelSize) TRTNOEXCEPT = 0; @@ -1138,7 +1202,7 @@ class IConvolutionLayer : public ILayer //! //! \see setKernelSize() //! - //! \deprecated Superseded by getKernelSizeNd. + //! \deprecated Superseded by getKernelSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getKernelSize() const TRTNOEXCEPT = 0; @@ -1149,14 +1213,14 @@ class IConvolutionLayer : public ILayer //! //! \see getNbOutputMaps() //! - virtual void setNbOutputMaps(int nbOutputMaps) TRTNOEXCEPT = 0; + virtual void setNbOutputMaps(int32_t nbOutputMaps) TRTNOEXCEPT = 0; //! //! \brief Get the number of output maps for the convolution. //! //! \see setNbOutputMaps() //! - virtual int getNbOutputMaps() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputMaps() const TRTNOEXCEPT = 0; //! //! \brief Get the stride of the convolution. @@ -1167,14 +1231,14 @@ class IConvolutionLayer : public ILayer //! //! \see getStride() //! - //! \deprecated Superseded by setStrideNd. + //! \deprecated Superseded by setStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setStride(DimsHW stride) TRTNOEXCEPT = 0; //! //! \brief Get the stride of the convolution. //! - //! \deprecated Superseded by getStrideNd. + //! \deprecated Superseded by getStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getStride() const TRTNOEXCEPT = 0; @@ -1191,7 +1255,7 @@ class IConvolutionLayer : public ILayer //! //! \see getPadding() //! - //! \deprecated Superseded by setPaddingNd + //! \deprecated Superseded by setPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setPadding(DimsHW padding) TRTNOEXCEPT = 0; @@ -1200,7 +1264,7 @@ class IConvolutionLayer : public ILayer //! //! \see setPadding() //! - //! \deprecated Superseded by getPaddingNd + //! \deprecated Superseded by getPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getPadding() const TRTNOEXCEPT = 0; @@ -1219,14 +1283,14 @@ class IConvolutionLayer : public ILayer //! //! \see getNbGroups() //! - virtual void setNbGroups(int nbGroups) TRTNOEXCEPT = 0; + virtual void setNbGroups(int32_t nbGroups) TRTNOEXCEPT = 0; //! //! \brief Get the number of groups of the convolution. //! //! \see setNbGroups() //! - virtual int getNbGroups() const TRTNOEXCEPT = 0; + virtual int32_t getNbGroups() const TRTNOEXCEPT = 0; //! //! \brief Set the kernel weights for the convolution. @@ -1274,7 +1338,7 @@ class IConvolutionLayer : public ILayer //! //! \see getDilation() //! - //! \deprecated Superseded by setDilationNd + //! \deprecated Superseded by setDilationNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setDilation(DimsHW dilation) TRTNOEXCEPT = 0; @@ -1283,7 +1347,7 @@ class IConvolutionLayer : public ILayer //! //! \see setDilation() //! - //! \deprecated Superseded by getDilationNd + //! \deprecated Superseded by getDilationNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getDilation() const TRTNOEXCEPT = 0; @@ -1292,14 +1356,14 @@ class IConvolutionLayer : public ILayer public: //! - //! \brief Set the pre-padding. + //! \brief Set the multi-dimension pre-padding of the convolution. //! - //! The start of input will be zero-padded by this number of elements in the height and width directions. + //! The start of the input will be zero-padded by this number of elements in each dimension. //! - //! Default: 0 + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be in the range [0,31], - //! and the padding must be less than the kernel size. + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,31], and the padding must be less than the kernel size. //! //! \see getPrePadding() //! @@ -1313,14 +1377,14 @@ class IConvolutionLayer : public ILayer virtual Dims getPrePadding() const TRTNOEXCEPT = 0; //! - //! \brief Set the post-padding. + //! \brief Set the multi-dimension post-padding of the convolution. //! - //! The end of the input will be zero-padded by this number of elements in the height and width directions. + //! The end of the input will be zero-padded by this number of elements in each dimension. //! - //! Default: (0,0) + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be in the range [0,31], - //! and the padding must be less than the kernel size. + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range + //! [0,31], and the padding must be less than the kernel size. //! //! \see getPostPadding() //! @@ -1438,16 +1502,17 @@ class IConvolutionLayer : public ILayer //! //! For a IConvolutionLayer, only index 0 is valid unless explicit precision mode is enabled. //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights. - //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output) - //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call. + //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer + //! with int8 input and float output) in explicit precision network. Conversely, this input tensor can be overridden + //! via appropriate set call. + //! //! The indices are as follows: //! - //! Index | Description - //! 0 | The input activation tensor. - //! 1 | The kernel weights tensor (a constant tensor). + //! - 0: The input activation tensor. + //! - 1: The kernel weights tensor (a constant tensor). //! //! If this function is called with a value greater than 0, then the function getNbInputs() changes - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; }; //! \class IFullyConnectedLayer @@ -1489,14 +1554,14 @@ class IFullyConnectedLayer : public ILayer //! //! \see getNbOutputChannels() //! - virtual void setNbOutputChannels(int nbOutputs) TRTNOEXCEPT = 0; + virtual void setNbOutputChannels(int32_t nbOutputs) TRTNOEXCEPT = 0; //! //! \brief Get the number of output channels `K` from the fully connected layer. //! //! \see setNbOutputChannels() //! - virtual int getNbOutputChannels() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputChannels() const TRTNOEXCEPT = 0; //! //! \brief Set the kernel weights, given as a `KxC` matrix in row-major order. @@ -1540,16 +1605,15 @@ class IFullyConnectedLayer : public ILayer //! //! For a IFullyConnectedLayer, only index 0 is valid unless explicit precision mode is enabled. //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights. - //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output) - //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call. - //! The indices are as follows: + //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer + //! with int8 input and float output) in explicit precision network. Conversely, this input tensor can be overridden + //! via appropriate set call. The indices are as follows: //! - //! Index | Description - //! 0 | The input activation tensor. - //! 1 | The kernel weights tensor (a constant tensor). + //! - 0: The input activation tensor. + //! - 1: The kernel weights tensor (a constant tensor). //! //! If this function is called with a value greater than 0, then the function getNbInputs() changes - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; }; //! @@ -1626,18 +1690,19 @@ class IActivationLayer : public ILayer //! //! \brief The type of pooling to perform in a pooling layer. //! -enum class PoolingType : int +enum class PoolingType : int32_t { kMAX = 0, // Maximum over elements kAVERAGE = 1, // Average over elements. If the tensor is padded, the count includes the padding kMAX_AVERAGE_BLEND = 2 // Blending between max and average pooling: (1-blendFactor)*maxPool + blendFactor*avgPool }; +//! Maximum number of elements in PoolingType enum. \see PoolingType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of elements in PoolingType enum. \see PoolingType +} //! \class IPoolingLayer //! @@ -1676,7 +1741,7 @@ class IPoolingLayer : public ILayer //! //! \see getWindowSize() //! - //! \deprecated Superseded by setWindowSizeNd. + //! \deprecated Superseded by setWindowSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setWindowSize(DimsHW windowSize) TRTNOEXCEPT = 0; @@ -1685,7 +1750,7 @@ class IPoolingLayer : public ILayer //! //! \see setWindowSize() //! - //! \deprecated Superseded by getWindowSizeNd. + //! \deprecated Superseded by getWindowSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getWindowSize() const TRTNOEXCEPT = 0; @@ -1698,7 +1763,7 @@ class IPoolingLayer : public ILayer //! //! \see getStride() //! - //! \deprecated Superseded by setStrideNd + //! \deprecated Superseded by setStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setStride(DimsHW stride) TRTNOEXCEPT = 0; @@ -1707,7 +1772,7 @@ class IPoolingLayer : public ILayer //! //! \see setStride() //! - //! \deprecated Superseded by getStrideNd + //! \deprecated Superseded by getStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getStride() const TRTNOEXCEPT = 0; @@ -1720,7 +1785,7 @@ class IPoolingLayer : public ILayer //! //! \see getPadding() //! - //! \deprecated Superseded by setPaddingNd + //! \deprecated Superseded by setPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setPadding(DimsHW padding) TRTNOEXCEPT = 0; @@ -1731,7 +1796,7 @@ class IPoolingLayer : public ILayer //! //! \see setPadding() //! - //! \deprecated Superseded by getPaddingNd + //! \deprecated Superseded by getPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getPadding() const TRTNOEXCEPT = 0; @@ -1785,15 +1850,16 @@ class IPoolingLayer : public ILayer public: //! - //! \brief Set the pre-padding. + //! \brief Set the multi-dimension pre-padding for pooling. //! - //! The start of input will be zero-padded by this number of elements in the height and width directions. + //! The start of the input will be padded by this number of elements in each dimension. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. //! - //! Default: 0 + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be in the range [0,7]. + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,7]. //! - //! \see getPadding() + //! \see getPrePadding() //! virtual void setPrePadding(Dims padding) TRTNOEXCEPT = 0; @@ -1805,15 +1871,16 @@ class IPoolingLayer : public ILayer virtual Dims getPrePadding() const TRTNOEXCEPT = 0; //! - //! \brief Set the post-padding. + //! \brief Set the multi-dimension post-padding for pooling. //! - //! The end of the input will be zero-padded by this number of elements in the height and width directions. + //! The end of the input will be padded by this number of elements in each dimension. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. //! - //! Default: (0,0) + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be in the range [0,7]. + //! If executing this layer on DLA, only support 2D padding, both height and width of padding must be in the range [0,7]. //! - //! \see getPadding() + //! \see getPostPadding() //! virtual void setPostPadding(Dims padding) TRTNOEXCEPT = 0; @@ -1879,8 +1946,9 @@ class IPoolingLayer : public ILayer //! //! \brief Set the multi-dimension padding for pooling. //! - //! The input will be zero-padded by this number of elements in each dimension. + //! The input will be padded by this number of elements in each dimension. //! Padding is symmetric. + //! Padding value depends on pooling type, -inf is used for max pooling and zero padding for average pooling. //! //! Default: (0, 0, ..., 0) //! @@ -1921,14 +1989,14 @@ class ILRNLayer : public ILayer //! //! \see setWindowStride() //! - virtual void setWindowSize(int windowSize) TRTNOEXCEPT = 0; + virtual void setWindowSize(int32_t windowSize) TRTNOEXCEPT = 0; //! //! \brief Get the LRN window size. //! //! \see getWindowStride() //! - virtual int getWindowSize() const TRTNOEXCEPT = 0; + virtual int32_t getWindowSize() const TRTNOEXCEPT = 0; //! //! \brief Set the LRN alpha value. @@ -1984,18 +2052,19 @@ class ILRNLayer : public ILayer //! //! \see IScaleLayer //! -enum class ScaleMode : int +enum class ScaleMode : int32_t { kUNIFORM = 0, //!< Identical coefficients across all elements of the tensor. kCHANNEL = 1, //!< Per-channel coefficients. kELEMENTWISE = 2 //!< Elementwise coefficients. }; +//! Maximum number of elements in ScaleMode enum. \see ScaleMode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of elements in ScaleMode enum. \see ScaleMode +} //! //! \class IScaleLayer @@ -2094,7 +2163,7 @@ class IScaleLayer : public ILayer //! //! \see addScaleNd() //! - virtual int getChannelAxis() const TRTNOEXCEPT = 0; + virtual int32_t getChannelAxis() const TRTNOEXCEPT = 0; }; //! @@ -2179,14 +2248,14 @@ class IConcatenationLayer : public ILayer //! //! \param axis The axis along which concatenation occurs. //! - virtual void setAxis(int axis) TRTNOEXCEPT = 0; + virtual void setAxis(int32_t axis) TRTNOEXCEPT = 0; //! //! \brief Get the axis along which concatenation occurs. //! //! \see setAxis() //! - virtual int getAxis() const TRTNOEXCEPT = 0; + virtual int32_t getAxis() const TRTNOEXCEPT = 0; }; //! @@ -2210,7 +2279,7 @@ class IDeconvolutionLayer : public ILayer //! //! \see getKernelSize() //! - //! \deprecated Superseded by setKernelSizeNd + //! \deprecated Superseded by setKernelSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setKernelSize(DimsHW kernelSize) TRTNOEXCEPT = 0; @@ -2219,7 +2288,7 @@ class IDeconvolutionLayer : public ILayer //! //! \see setKernelSize() //! - //! \deprecated Superseded by getKernelSizeNd + //! \deprecated Superseded by getKernelSizeNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getKernelSize() const TRTNOEXCEPT = 0; @@ -2230,14 +2299,14 @@ class IDeconvolutionLayer : public ILayer //! //! \see getNbOutputMaps() //! - virtual void setNbOutputMaps(int nbOutputMaps) TRTNOEXCEPT = 0; + virtual void setNbOutputMaps(int32_t nbOutputMaps) TRTNOEXCEPT = 0; //! //! \brief Get the number of output feature maps for the deconvolution. //! //! \see setNbOutputMaps() //! - virtual int getNbOutputMaps() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputMaps() const TRTNOEXCEPT = 0; //! //! \brief Get the stride of the deconvolution. @@ -2248,7 +2317,7 @@ class IDeconvolutionLayer : public ILayer //! //! \see setStride() //! - //! \deprecated Superseded by setStrideNd + //! \deprecated Superseded by setStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setStride(DimsHW stride) TRTNOEXCEPT = 0; @@ -2257,7 +2326,7 @@ class IDeconvolutionLayer : public ILayer //! //! Default: (1,1) //! - //! \deprecated Superseded by getStrideNd + //! \deprecated Superseded by getStrideNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getStride() const TRTNOEXCEPT = 0; @@ -2274,7 +2343,7 @@ class IDeconvolutionLayer : public ILayer //! //! \see getPadding() //! - //! \deprecated Superseded by setPaddingNd + //! \deprecated Superseded by setPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setPadding(DimsHW padding) TRTNOEXCEPT = 0; @@ -2285,7 +2354,7 @@ class IDeconvolutionLayer : public ILayer //! //! \see setPadding() //! - //! \deprecated Superseded by getPaddingNd + //! \deprecated Superseded by getPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getPadding() const TRTNOEXCEPT = 0; @@ -2304,14 +2373,14 @@ class IDeconvolutionLayer : public ILayer //! //! \see getNbGroups() //! - virtual void setNbGroups(int nbGroups) TRTNOEXCEPT = 0; + virtual void setNbGroups(int32_t nbGroups) TRTNOEXCEPT = 0; //! //! \brief Get the number of groups for a deconvolution. //! //! \see setNbGroups() //! - virtual int getNbGroups() const TRTNOEXCEPT = 0; + virtual int32_t getNbGroups() const TRTNOEXCEPT = 0; //! //! \brief Set the kernel weights for the deconvolution. @@ -2355,15 +2424,17 @@ class IDeconvolutionLayer : public ILayer public: //! - //! \brief Set the pre-padding. + //! \brief Set the multi-dimension pre-padding of the deconvolution. //! - //! The start of input will be zero-padded by this number of elements in the height and width directions. + //! The output will be trimmed by this number of elements on the start of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Negative padding is not supported. //! - //! Default: 0 + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be 0. + //! If executing this layer on DLA, padding must be 0. //! - //! \see getPadding() + //! \see getPrePadding() //! virtual void setPrePadding(Dims padding) TRTNOEXCEPT = 0; @@ -2375,15 +2446,17 @@ class IDeconvolutionLayer : public ILayer virtual Dims getPrePadding() const TRTNOEXCEPT = 0; //! - //! \brief Set the post-padding. + //! \brief Set the multi-dimension post-padding of the deconvolution. //! - //! The end of the input will be zero-padded by this number of elements in the height and width directions. + //! The output will be trimmed by this number of elements on the end of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Negative padding is not supported. //! - //! Default: (0,0) + //! Default: (0, 0, ..., 0) //! - //! If executing this layer on DLA, both height and width of padding must be 0. + //! If executing this layer on DLA, padding must be 0. //! - //! \see getPadding() + //! \see getPostPadding() //! virtual void setPostPadding(Dims padding) TRTNOEXCEPT = 0; @@ -2453,8 +2526,9 @@ class IDeconvolutionLayer : public ILayer //! //! \brief Set the multi-dimension padding of the deconvolution. //! - //! The input will be zero-padded by this number of elements in each dimension. - //! Padding is symmetric. + //! The output will be trimmed by this number of elements on both sides of every dimension. + //! In other words, it resembles the inverse of a convolution layer with this padding size. + //! Padding is symmetric, and negative padding is not supported. //! //! Default: (0, 0, ..., 0) //! @@ -2481,17 +2555,16 @@ class IDeconvolutionLayer : public ILayer //! //! For a IDeconvolutionLayer, only index 0 is valid unless explicit precision mode is enabled. //! With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights. - //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output) - //! in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call. - //! The indices are as follows: + //! Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer + //! with int8 input and float output) in explicit precision network. Conversely, this input tensor can be overridden + //! via appropriate set call. The indices are as follows: //! - //! Index | Description - //! 0 | The input activation tensor. - //! 1 | The kernel weights tensor (a constant tensor). + //! - 0: The input activation tensor. + //! - 1: The kernel weights tensor (a constant tensor). //! //! If this function is called with a value greater than 0, then the function getNbInputs() changes //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; //! \brief Set the multi-dimension dilation of the deconvolution. //! @@ -2516,29 +2589,30 @@ class IDeconvolutionLayer : public ILayer //! //! \see IElementWiseLayer //! -enum class ElementWiseOperation : int +enum class ElementWiseOperation : int32_t { - kSUM = 0, //!< Sum of the two elements. - kPROD = 1, //!< Product of the two elements. - kMAX = 2, //!< Maximum of the two elements. - kMIN = 3, //!< Minimum of the two elements. - kSUB = 4, //!< Substract the second element from the first. - kDIV = 5, //!< Divide the first element by the second. - kPOW = 6, //!< The first element to the power of the second element. - kFLOOR_DIV = 7,//!< Floor division of the first element by the second. - kAND = 8, //!< Logical AND of two elements. - kOR = 9, //!< Logical OR of two elements. - kXOR = 10, //!< Logical XOR of two elements. - kEQUAL = 11, //!< Check if two elements are equal. - kGREATER = 12, //!< Check if element in first tensor is greater than corresponding element in second tensor. - kLESS = 13 //!< Check if element in first tensor is less than corresponding element in second tensor. + kSUM = 0, //!< Sum of the two elements. + kPROD = 1, //!< Product of the two elements. + kMAX = 2, //!< Maximum of the two elements. + kMIN = 3, //!< Minimum of the two elements. + kSUB = 4, //!< Substract the second element from the first. + kDIV = 5, //!< Divide the first element by the second. + kPOW = 6, //!< The first element to the power of the second element. + kFLOOR_DIV = 7, //!< Floor division of the first element by the second. + kAND = 8, //!< Logical AND of two elements. + kOR = 9, //!< Logical OR of two elements. + kXOR = 10, //!< Logical XOR of two elements. + kEQUAL = 11, //!< Check if two elements are equal. + kGREATER = 12, //!< Check if element in first tensor is greater than corresponding element in second tensor. + kLESS = 13 //!< Check if element in first tensor is less than corresponding element in second tensor. }; +//! Maximum number of elements in ElementWiseOperation enum. \see ElementWiseOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 14; -} //!< Maximum number of elements in ElementWiseOperation enum. \see ElementWiseOperation +} //! //! \class IElementWiseLayer @@ -2593,29 +2667,30 @@ class IGatherLayer : public ILayer //! //! \see getGatherAxis() //! - virtual void setGatherAxis(int axis) TRTNOEXCEPT = 0; + virtual void setGatherAxis(int32_t axis) TRTNOEXCEPT = 0; //! //! \brief Get the axis to gather on. //! //! \see setGatherAxis() //! - virtual int getGatherAxis() const TRTNOEXCEPT = 0; + virtual int32_t getGatherAxis() const TRTNOEXCEPT = 0; //! //! \brief Set the number of leading dimensions of indices tensor to be handled elementwise. - //! k must be 0 if there is an implicit batch dimension. It can be 0 or 1 if there is not an implicit batch dimension. + //! k must be 0 if there is an implicit batch dimension. It can be 0 or 1 if there is not an implicit batch + //! dimension. //! //! \see getNbElementWiseDims() //! - virtual void setNbElementWiseDims(int k) TRTNOEXCEPT = 0; + virtual void setNbElementWiseDims(int32_t k) TRTNOEXCEPT = 0; //! //! \brief Get the number of leading dimensions of indices tensor to be handled elementwise. //! //! \see setNbElementWiseDims() //! - virtual int getNbElementWiseDims() const TRTNOEXCEPT = 0; + virtual int32_t getNbElementWiseDims() const TRTNOEXCEPT = 0; protected: virtual ~IGatherLayer() {} @@ -2700,7 +2775,7 @@ class IGatherLayer : public ILayer //! //! \see IRNNLayer, IRNNv2Layer //! -enum class RNNOperation : int +enum class RNNOperation : int32_t { kRELU = 0, //!< Single gate RNN w/ ReLU activation function. kTANH = 1, //!< Single gate RNN w/ TANH activation function. @@ -2708,11 +2783,12 @@ enum class RNNOperation : int kGRU = 3 //!< Three-gate network consisting of Gated Recurrent Units. }; +//! Maximum number of elements in RNNOperation enum. \see RNNOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 4; -} //!< Maximum number of elements in RNNOperation enum. \see RNNOperation +} //! //! \enum RNNDirection @@ -2721,17 +2797,18 @@ constexpr inline int EnumMax() //! //! \see IRNNLayer, IRNNv2Layer //! -enum class RNNDirection : int +enum class RNNDirection : int32_t { kUNIDIRECTION = 0, //!< Network iterations from first input to last input. kBIDIRECTION = 1 //!< Network iterates from first to last and vice versa and outputs concatenated. }; +//! Maximum number of elements in RNNDirection enum. \see RNNDirection template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in RNNDirection enum. \see RNNDirection +} //! //! \enum RNNInputMode @@ -2748,17 +2825,18 @@ constexpr inline int EnumMax() //! //! \see IRNNLayer, IRNNv2Layer //! -enum class RNNInputMode : int +enum class RNNInputMode : int32_t { kLINEAR = 0, //!< Perform the normal matrix multiplication in the first recurrent layer. kSKIP = 1 //!< No operation is performed on the first recurrent layer. }; +//! Maximum number of elements in RNNInputMode enum. \see RNNInputMode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in RNNInputMode enum. \see RNNInputMode +} //! //! \class IRNNLayer @@ -2769,7 +2847,7 @@ constexpr inline int EnumMax() //! that have an implicit batch dimension. For dynamic shapes and explicit batch dimension networks, //! use IRNNv2Layer. //! -//! \deprecated This interface is superseded by IRNNv2Layer. +//! \deprecated This interface is superseded by IRNNv2Layer and will be removed in TensorRT 8.0. //! //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. //! @@ -2781,7 +2859,7 @@ class TRT_DEPRECATED IRNNLayer : public ILayer //! //! \return The number of layers in the RNN. //! - virtual unsigned getLayerCount() const TRTNOEXCEPT = 0; + virtual uint32_t getLayerCount() const TRTNOEXCEPT = 0; //! //! \brief Get the size of the hidden layers. @@ -2801,7 +2879,7 @@ class TRT_DEPRECATED IRNNLayer : public ILayer //! //! \return the maximum number of time steps that can be executed by a single call RNN layer. //! - virtual int getSeqLength() const TRTNOEXCEPT = 0; + virtual int32_t getSeqLength() const TRTNOEXCEPT = 0; //! //! \brief Set the operation of the RNN layer. @@ -3040,7 +3118,7 @@ class TRT_DEPRECATED IRNNLayer : public ILayer //! //! \see setHiddenState(), setCellState() //! - virtual int getDataLength() const TRTNOEXCEPT = 0; + virtual int32_t getDataLength() const TRTNOEXCEPT = 0; //! //! \param hidden The initial hidden state of the RNN. @@ -3107,7 +3185,7 @@ class TRT_DEPRECATED IRNNLayer : public ILayer //! //! \see RNNOperation //! -enum class RNNGateType : int +enum class RNNGateType : int32_t { kINPUT = 0, //!< Input gate (i). kOUTPUT = 1, //!< Output gate (o). @@ -3118,11 +3196,12 @@ enum class RNNGateType : int kHIDDEN = 6 //!< Hidden gate (h). }; +//! Maximum number of elements in RNNGateType enum. \see RNNGateType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 7; -} //!< Maximum number of elements in RNNGateType enum. \see RNNGateType +} //! //! \class IRNNv2Layer @@ -3131,15 +3210,17 @@ constexpr inline int EnumMax() //! //! This layer supersedes IRNNLayer. //! +//! \deprecated IRNNv2Layer will be removed in TensorRT 9.0, use ILoop::addLoop instead. +//! //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. //! -class IRNNv2Layer : public ILayer +class TRT_DEPRECATED IRNNv2Layer : public ILayer { public: - virtual int32_t getLayerCount() const TRTNOEXCEPT = 0; //< Get the layer count of the RNN - virtual int32_t getHiddenSize() const TRTNOEXCEPT = 0; //< Get the hidden size of the RNN - virtual int32_t getMaxSeqLength() const TRTNOEXCEPT = 0; //< Get the maximum sequence length of the RNN - virtual int32_t getDataLength() const TRTNOEXCEPT = 0; //< Get the maximum data length of the RNN + virtual int32_t getLayerCount() const TRTNOEXCEPT = 0; //!< Get the layer count of the RNN + virtual int32_t getHiddenSize() const TRTNOEXCEPT = 0; //!< Get the hidden size of the RNN + virtual int32_t getMaxSeqLength() const TRTNOEXCEPT = 0; //!< Get the maximum sequence length of the RNN + virtual int32_t getDataLength() const TRTNOEXCEPT = 0; //!< Get the maximum data length of the RNN //! //! \brief Specify individual sequence lengths in the batch with the ITensor pointed to by @@ -3219,13 +3300,13 @@ class IRNNv2Layer : public ILayer //! in IRNNLayer::setWeights() for documentation on the expected //! dimensions of this matrix. //! - virtual void setWeightsForGate(int layerIndex, RNNGateType gate, bool isW, Weights weights) TRTNOEXCEPT = 0; + virtual void setWeightsForGate(int32_t layerIndex, RNNGateType gate, bool isW, Weights weights) TRTNOEXCEPT = 0; //! //! \brief Get the weight parameters for an individual gate in the RNN. //! \see setWeightsForGate() //! - virtual Weights getWeightsForGate(int layerIndex, RNNGateType gate, bool isW) const TRTNOEXCEPT = 0; + virtual Weights getWeightsForGate(int32_t layerIndex, RNNGateType gate, bool isW) const TRTNOEXCEPT = 0; //! //! \brief Set the bias parameters for an individual gate in the RNN. @@ -3242,13 +3323,13 @@ class IRNNv2Layer : public ILayer //! \param bias The weight structure holding the bias parameters, which should be an //! array of size getHiddenSize(). //! - virtual void setBiasForGate(int layerIndex, RNNGateType gate, bool isW, Weights bias) TRTNOEXCEPT = 0; + virtual void setBiasForGate(int32_t layerIndex, RNNGateType gate, bool isW, Weights bias) TRTNOEXCEPT = 0; //! //! \brief Get the bias parameters for an individual gate in the RNN. //! \see setBiasForGate() //! - virtual Weights getBiasForGate(int layerIndex, RNNGateType gate, bool isW) const TRTNOEXCEPT = 0; + virtual Weights getBiasForGate(int32_t layerIndex, RNNGateType gate, bool isW) const TRTNOEXCEPT = 0; //! //! \brief Set the initial hidden state of the RNN with the provided \p hidden ITensor. @@ -3301,7 +3382,7 @@ class IRNNv2Layer : public ILayer //! //! \brief Application-implemented interface to compute layer output sizes. //! -//! \deprecated IOutputDimensionsFormula has been superseded by PaddingMode. +//! \deprecated IOutputDimensionsFormula has been superseded by PaddingMode and will be removed in TensorRT 9.0. //! class TRT_DEPRECATED IOutputDimensionsFormula { @@ -3333,7 +3414,7 @@ class TRT_DEPRECATED IOutputDimensionsFormula //! //! \see IPluginExt //! -//! \deprecated This interface is superseded by IPluginV2Layer +//! \deprecated This interface is superseded by IPluginV2Layer and will be removed in TensorRT 8.0. //! //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. //! @@ -3381,7 +3462,7 @@ class IPluginV2Layer : public ILayer //! //! \see IUnaryLayer //! -enum class UnaryOperation : int +enum class UnaryOperation : int32_t { kEXP = 0, //!< Exponentiation. kLOG = 1, //!< Log (base e). @@ -3406,11 +3487,12 @@ enum class UnaryOperation : int kNOT = 20 //!< Logical NOT. }; +//! Maximum number of elements in UnaryOperation enum. \see UnaryOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 21; -} //!< Maximum number of elements in UnaryOperation enum. \see UnaryOperation +} //! //! \class IUnaryLayer @@ -3458,7 +3540,7 @@ class IUnaryLayer : public ILayer //! The current version of TensorRT usually performs reduction for kINT8 via kFLOAT or kHALF. //! The kINT8 values show the quantized representations of the floating-point values. //! -enum class ReduceOperation : int +enum class ReduceOperation : int32_t { kSUM = 0, kPROD = 1, @@ -3467,11 +3549,12 @@ enum class ReduceOperation : int kAVG = 4 }; +//! Maximum number of elements in ReduceOperation enum. \see ReduceOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 5; -} //!< Maximum number of elements in ReduceOperation enum. \see ReduceOperation +} //! //! \class IReduceLayer @@ -3549,7 +3632,7 @@ class IPaddingLayer : public ILayer //! //! \see getPrePadding //! - //! \deprecated Superseded by setPrePaddingNd + //! \deprecated Superseded by setPrePaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setPrePadding(DimsHW padding) TRTNOEXCEPT = 0; @@ -3558,7 +3641,7 @@ class IPaddingLayer : public ILayer //! //! \see setPrePadding //! - //! \deprecated Superseded by getPrePaddingNd + //! \deprecated Superseded by getPrePaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getPrePadding() const TRTNOEXCEPT = 0; @@ -3569,7 +3652,7 @@ class IPaddingLayer : public ILayer //! //! \see getPostPadding //! - //! \deprecated Superseded by setPostPaddingNd + //! \deprecated Superseded by setPostPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual void setPostPadding(DimsHW padding) TRTNOEXCEPT = 0; @@ -3578,7 +3661,7 @@ class IPaddingLayer : public ILayer //! //! \see setPostPadding //! - //! \deprecated Superseded by getPostPaddingNd + //! \deprecated Superseded by getPostPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual DimsHW getPostPadding() const TRTNOEXCEPT = 0; @@ -3635,7 +3718,7 @@ struct Permutation //! permute from CHW order to HWC order, the required permutation is [1, 2, 0], and to permute //! from HWC to CHW, the required permutation is [2, 0, 1]. //! - int order[Dims::MAX_DIMS]; + int32_t order[Dims::MAX_DIMS]; }; //! \class IShuffleLayer @@ -3718,14 +3801,13 @@ class IShuffleLayer : public ILayer //! For a dynamic shuffle layer, the values 0 and 1 are valid. //! The indices in the dynamic case are as follows: //! - //! Index | Description - //! 0 | Data or Shape tensor to be shuffled. - //! 1 | The dimensions for the reshape operation, as a 1D Int32 shape tensor. + //! - 0: Data or Shape tensor to be shuffled. + //! - 1: The dimensions for the reshape operation, as a 1D Int32 shape tensor. //! //! If this function is called with a value 1, then the function getNbInputs() changes //! from returning 1 to 2. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; //! //! \brief Set the permutation applied by the second transpose operation. @@ -3783,17 +3865,18 @@ class IShuffleLayer : public ILayer //! //! \see ISliceLayer //! -enum class SliceMode : int +enum class SliceMode : int32_t { kDEFAULT = 0, //!< Fail with error when the coordinates are out of bounds. This is the default. kWRAP = 1, //!< Coordinates wrap around periodically. }; +//! Maximum number of elements in SliceMode enum. \see SliceMode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in SliceMode enum. \see SliceMode +} //! //! \brief Slices an input tensor into an output tensor based on the offset and strides. @@ -3919,16 +4002,15 @@ class ISliceLayer : public ILayer //! dimensions, respectively. Conversely, this input tensor can be overridden via appropriate set call. //! The indices are as follows: //! - //! Index | Description - //! 0 | Data or Shape tensor to be sliced. - //! 1 | The start tensor to begin slicing, as a 1D Int32 shape tensor. - //! 2 | The size tensor of the resulting slice, as a 1D Int32 shape tensor. - //! 3 | The stride of the slicing operation, as a 1D Int32 shape tensor. + //! - 0: Data or Shape tensor to be sliced. + //! - 1: The start tensor to begin slicing, as a 1D Int32 shape tensor. + //! - 2: The size tensor of the resulting slice, as a 1D Int32 shape tensor. + //! - 3: The stride of the slicing operation, as a 1D Int32 shape tensor. //! //! If this function is called with a value greater than 0, then the function getNbInputs() changes //! from returning 1 to index + 1. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; protected: virtual ~ISliceLayer() {} @@ -3957,17 +4039,18 @@ class IShapeLayer : public ILayer //! //! \brief Enumerates the operations that may be performed by a TopK layer. //! -enum class TopKOperation : int +enum class TopKOperation : int32_t { kMAX = 0, //!< Maximum of the elements. kMIN = 1, //!< Minimum of the elements. }; +//! Maximum number of elements in TopKOperation enum. \see TopKOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in TopKOperation enum. \see TopKOperation +} //! //! \class ITopKLayer @@ -4000,14 +4083,14 @@ class ITopKLayer : public ILayer //! //! \see getK() //! - virtual void setK(int k) TRTNOEXCEPT = 0; + virtual void setK(int32_t k) TRTNOEXCEPT = 0; //! //! \brief Get the k value for the layer. //! //! \see setK() //! - virtual int getK() const TRTNOEXCEPT = 0; + virtual int32_t getK() const TRTNOEXCEPT = 0; //! //! \brief Set which axes to reduce for the layer. @@ -4033,7 +4116,7 @@ class ITopKLayer : public ILayer //! \brief Enumerates the operations that may be performed on a tensor //! by IMatrixMultiplyLayer before multiplication. //! -enum class MatrixOperation : int +enum class MatrixOperation : int32_t { //! Treat x as a matrix if it has two dimensions, or as a collection of //! matrices if x has more than two dimensions, where the last two dimensions @@ -4055,11 +4138,12 @@ enum class MatrixOperation : int kVECTOR }; +//! Maximum number of elements in MatrixOperation enum. \see DataType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of elements in MatrixOperation enum. \see DataType +} //! //! \class IMatrixMultiplyLayer @@ -4095,14 +4179,14 @@ class IMatrixMultiplyLayer : public ILayer //! \param op New operation. //! \see getTranspose() //! - virtual void setOperation(int index, MatrixOperation op) TRTNOEXCEPT = 0; + virtual void setOperation(int32_t index, MatrixOperation op) TRTNOEXCEPT = 0; //! //! \brief Get the operation for an input tensor. //! \param index Input tensor number (0 or 1). //! \see setTranspose() //! - virtual MatrixOperation getOperation(int index) const TRTNOEXCEPT = 0; + virtual MatrixOperation getOperation(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief Set the transpose flag for an input tensor. @@ -4110,18 +4194,18 @@ class IMatrixMultiplyLayer : public ILayer //! \param val New transpose flag. //! \see getTranspose() //! - //! \deprecated setTranspose is superseded by setOperation. + //! \deprecated setTranspose is superseded by setOperation and will be removed in TensorRT 8.0. //! - TRT_DEPRECATED virtual void setTranspose(int index, bool val) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual void setTranspose(int32_t index, bool val) TRTNOEXCEPT = 0; //! //! \brief Get the transpose flag for an input tensor. //! \param index Input tensor number (0 or 1). //! \see setTranspose() //! - //! \deprecated getTranspose is superseded by getOperation. + //! \deprecated getTranspose is superseded by getOperation and will be removed in TensorRT 8.0. //! - TRT_DEPRECATED virtual bool getTranspose(int index) const TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual bool getTranspose(int32_t index) const TRTNOEXCEPT = 0; protected: virtual ~IMatrixMultiplyLayer() {} @@ -4152,6 +4236,8 @@ class IRaggedSoftMaxLayer : public ILayer //! \brief A layer that represents the identity function. //! //! If tensor precision is being explicitly specified, it can be used to transform from one precision to another. +//! Other than transforming between the same precision (kFLOAT -> kFLOAT for example), the only valid +//! tranformations supported are: (kFLOAT -> kHALF), (kFLOAT -> kINT8), (kHALF -> kFLOAT) and (kINT8 -> kFLOAT). //! //! \warning Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI. //! @@ -4229,17 +4315,18 @@ class IParametricReLULayer : public ILayer //! \brief Enumerates various modes of resize in the resize layer. //! Resize mode set using setResizeMode(). //! -enum class ResizeMode : int +enum class ResizeMode : int32_t { - kNEAREST = 0, // ND (0 < N <= 8) nearest neighbor resizing. - kLINEAR = 1 // Can handle linear (1D), bilinear (2D), and trilinear (3D) resizing. + kNEAREST = 0, //!< ND (0 < N <= 8) nearest neighbor resizing. + kLINEAR = 1 //!< Can handle linear (1D), bilinear (2D), and trilinear (3D) resizing. }; +//! Maximum number of elements in ResizeMode enum. \see ResizeMode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in ResizeMode enum. \see ResizeMode +} //! \class IResizeLayer //! @@ -4306,7 +4393,7 @@ class IResizeLayer : public ILayer //! \see setOutputDimensions //! \see getScales //! - virtual void setScales(const float* scales, int nbScales) TRTNOEXCEPT = 0; + virtual void setScales(const float* scales, int32_t nbScales) TRTNOEXCEPT = 0; //! //! \brief Copies resize scales to scales[0, ..., nbScales-1], where nbScales is the number of scales that were set. @@ -4322,7 +4409,7 @@ class IResizeLayer : public ILayer //! \return The number of resize scales i.e. nbScales if scales were set. //! Return -1 in case no scales were set or resize layer is used in dynamic mode. //! - virtual int getScales(int size, float* scales) const TRTNOEXCEPT = 0; + virtual int32_t getScales(int32_t size, float* scales) const TRTNOEXCEPT = 0; //! //! \brief Set resize mode for an input tensor. @@ -4371,21 +4458,20 @@ class IResizeLayer : public ILayer //! For a dynamic resize layer, the values 0 and 1 are valid. //! The indices in the dynamic case are as follows: //! - //! Index | Description - //! 0 | Data or Shape tensor to be resized. - //! 1 | The output dimensions, as a 1D Int32 shape tensor. + //! - 0: Data or Shape tensor to be resized. + //! - 1: The output dimensions, as a 1D Int32 shape tensor. //! //! If this function is called with a value 1, then the function getNbInputs() changes //! from returning 1 to 2. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; protected: virtual ~IResizeLayer() {} }; //! Enum that describes kinds of loop outputs. -enum class LoopOutput : int +enum class LoopOutput : int32_t { //! Output value is value of tensor for last iteration. kLAST_VALUE = 0, @@ -4397,27 +4483,27 @@ enum class LoopOutput : int kREVERSE = 2 }; +//! Maximum number of elements in LoopOutput enum. \see DataType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of elements in LoopOutput enum. \see DataType +} //! Enum that describes kinds of trip limits. -enum class TripLimit : int +enum class TripLimit : int32_t { - // Tensor is scalar of type kINT32 that contains the trip count. - kCOUNT = 0, - // Tensor is a scalar of type kBOOL. Loop terminates when value is false. - kWHILE = 1 + kCOUNT = 0, //!< Tensor is scalar of type kINT32 that contains the trip count. + kWHILE = 1 //!< Tensor is a scalar of type kBOOL. Loop terminates when value is false. }; +//! Maximum number of elements in TripLimit enum. \see DataType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in TripLimit enum. \see DataType +} class ILoop; @@ -4442,14 +4528,14 @@ class IRecurrenceLayer : public ILoopBoundaryLayer //! For a recurrence layer, the values 0 and 1 are valid. //! The indices are as follows: //! - //! Index | Description - //! 0 | The initial value of the output tensor. The value must come from outside the loop. - //! 1 | The next value of the output tensor. The value usually comes from inside the loop, and must have the same dimensions as input 0. + //! - 0: The initial value of the output tensor. The value must come from outside the loop. + //! - 1: The next value of the output tensor. The value usually comes from inside the loop, and must have the same + //! dimensions as input 0. //! //! If this function is called with a value 1, then the function getNbInputs() changes //! from returning 1 to 2. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; }; //! @@ -4486,10 +4572,10 @@ class ILoopOutputLayer : public ILoopBoundaryLayer //! setAxis(3) causes the output to have dimensions [b,c,d,a]. //! Default is axis is 0. //! - virtual void setAxis(int axis) noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; //! Get axis being concatenated over. - virtual int getAxis() const noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; //! //! \brief Append or replace an input of this layer with a specific tensor @@ -4498,20 +4584,20 @@ class ILoopOutputLayer : public ILoopBoundaryLayer //! \param tensor the new input tensor // //! Sets the input tensor for the given index. The index must be 0 for a kLAST_VALUE loop output layer. - //! Loop output layer is converted to a kCONCATENATE or kREVERSE loop output layer by calling setInput with an index 1. - //! A kCONCATENATE or kREVERSE loop output layer cannot be converted back to a kLAST_VALUE loop output layer. + //! Loop output layer is converted to a kCONCATENATE or kREVERSE loop output layer by calling setInput with an + //! index 1. A kCONCATENATE or kREVERSE loop output layer cannot be converted back to a kLAST_VALUE loop output + //! layer. //! //! For a kCONCATENATE or kREVERSE loop output layer, the values 0 and 1 are valid. //! The indices in the kCONCATENATE or kREVERSE cases are as follows: //! - //! Index | Description - //! 0 | Contribution to the output tensor. The contribution must come from inside the loop. - //! 1 | The concatenation length scalar value, must come from outside the loop, as a 0D Int32 shape tensor. + //! - 0: Contribution to the output tensor. The contribution must come from inside the loop. + //! - 1: The concatenation length scalar value, must come from outside the loop, as a 0D Int32 shape tensor. //! //! If this function is called with a value 1, then the function getNbInputs() changes //! from returning 1 to 2. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; }; class ITripLimitLayer : public ILoopBoundaryLayer @@ -4524,10 +4610,10 @@ class IIteratorLayer : public ILoopBoundaryLayer { public: //! Set axis to iterate over. - virtual void setAxis(int axis) noexcept = 0; + virtual void setAxis(int32_t axis) noexcept = 0; //! Get axis being iterated over. - virtual int getAxis() const noexcept = 0; + virtual int32_t getAxis() const noexcept = 0; //! For reverse=false, the layer is equivalent to addGather(tensor, I, 0) where I is a //! scalar tensor containing the loop iteration number. @@ -4581,7 +4667,7 @@ class ILoop //! For reverse=true, this is equivalent to addGather(tensor, M-1-I, 0) where M is the trip count //! computed from TripLimits of kind kCOUNT. //! - virtual IIteratorLayer* addIterator(ITensor& tensor, int axis = 0, bool reverse = false) noexcept = 0; + virtual IIteratorLayer* addIterator(ITensor& tensor, int32_t axis = 0, bool reverse = false) noexcept = 0; //! \brief Make an output for this loop, based on the given tensor. //! @@ -4590,7 +4676,7 @@ class ILoop //! If outputKind is kCONCATENATE or kREVERSE, a second input specifying the //! concatenation dimension must be added via method ILoopOutputLayer::setInput. //! - virtual ILoopOutputLayer* addLoopOutput(ITensor& tensor, LoopOutput outputKind, int axis = 0) noexcept = 0; + virtual ILoopOutputLayer* addLoopOutput(ITensor& tensor, LoopOutput outputKind, int32_t axis = 0) noexcept = 0; //! //! \brief Set the name of the loop. @@ -4629,17 +4715,18 @@ class ISelectLayer : public ILayer //! //! \see IFillLayer //! -enum class FillOperation : int +enum class FillOperation : int32_t { - kLINSPACE = 0, //!< Generate evenly spaced numbers over a specified interval. - kRANDOM_UNIFORM = 1 //!< Generate a tensor with random values drawn from a uniform distribution. + kLINSPACE = 0, //!< Generate evenly spaced numbers over a specified interval. + kRANDOM_UNIFORM = 1 //!< Generate a tensor with random values drawn from a uniform distribution. }; +//! Maximum number of elements in FillOperation enum. \see FillOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in FillOperation enum. \see FillOperation +} //! //! \brief Generate an output tensor with specified mode. @@ -4759,15 +4846,17 @@ class IFillLayer : public ILayer //! \param index the index of the input to set. //! \param tensor the new input tensor //! - //! Index | Description for kLINSPACE - //! 0 | Shape tensor, represents the output tensor's dimensions. - //! 1 | Start, a scalar, represents the start value. - //! 2 | Delta, a 1D tensor, length equals to shape tensor's nbDims, represents the delta value for each dimension. + //! Indices for kLINSPACE are described as: + //! + //! - 0: Shape tensor, represents the output tensor's dimensions. + //! - 1: Start, a scalar, represents the start value. + //! - 2: Delta, a 1D tensor, length equals to shape tensor's nbDims, represents the delta value for each dimension. + //! + //! Indices for kRANDOM_UNIFORM are described as: //! - //! Index | Description for kRANDOM_UNIFORM - //! 0 | Shape tensor, represents the output tensor's dimensions. - //! 1 | Minimum, a scalar, represents the minimum random value. - //! 2 | Maximum, a scalar, represents the maximal random value. + //! - 0: Shape tensor, represents the output tensor's dimensions. + //! - 1: Minimum, a scalar, represents the minimum random value. + //! - 2: Maximum, a scalar, represents the maximal random value. //! //! Using the corresponding setter resets the input to null. //! @@ -4777,7 +4866,7 @@ class IFillLayer : public ILayer //! then afterwards getNbInputs() returns index + 1, and any missing intervening //! inputs are set to null. //! - void setInput(int index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; + void setInput(int32_t index, ITensor& tensor) _TENSORRT_OVERRIDE TRTNOEXCEPT = 0; protected: virtual ~IFillLayer() {} @@ -4867,9 +4956,9 @@ class INetworkDefinition //! //! \return The new convolution layer, or nullptr if it could not be created. //! - //! \deprecated Superseded by addConvolutionNd + //! \deprecated Superseded by addConvolutionNd and will be removed in TensorRT 9.0. //! - TRT_DEPRECATED virtual IConvolutionLayer* addConvolution(ITensor& input, int nbOutputMaps, DimsHW kernelSize, + TRT_DEPRECATED virtual IConvolutionLayer* addConvolution(ITensor& input, int32_t nbOutputMaps, DimsHW kernelSize, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; //! @@ -4888,7 +4977,7 @@ class INetworkDefinition //! \return The new fully connected layer, or nullptr if it could not be created. //! virtual IFullyConnectedLayer* addFullyConnected( - ITensor& input, int nbOutputs, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; + ITensor& input, int32_t nbOutputs, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; //! //! \brief Add an activation layer to the network. @@ -4918,7 +5007,7 @@ class INetworkDefinition //! //! \return The new pooling layer, or nullptr if it could not be created. //! - //! \deprecated Superseded than addPoolingNd + //! \deprecated Superseded by addPoolingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual IPoolingLayer* addPooling( ITensor& input, PoolingType type, DimsHW windowSize) TRTNOEXCEPT = 0; @@ -4937,7 +5026,7 @@ class INetworkDefinition //! //! \return The new LRN layer, or nullptr if it could not be created. //! - virtual ILRNLayer* addLRN(ITensor& input, int window, float alpha, float beta, float k) TRTNOEXCEPT = 0; + virtual ILRNLayer* addLRN(ITensor& input, int32_t window, float alpha, float beta, float k) TRTNOEXCEPT = 0; //! //! \brief Add a Scale layer to the network. @@ -4983,7 +5072,7 @@ class INetworkDefinition //! //! \warning All tensors must have the same dimensions for all dimensions except for channel. //! - virtual IConcatenationLayer* addConcatenation(ITensor* const* inputs, int nbInputs) TRTNOEXCEPT = 0; + virtual IConcatenationLayer* addConcatenation(ITensor* const* inputs, int32_t nbInputs) TRTNOEXCEPT = 0; //! //! \brief Add a deconvolution layer to the network. @@ -5001,10 +5090,10 @@ class INetworkDefinition //! //! \return The new deconvolution layer, or nullptr if it could not be created. //! - //! \deprecated Superseded by addDeconvolutionNd + //! \deprecated Superseded by addDeconvolutionNd and will be removed in TensorRT 9.0. //! - TRT_DEPRECATED virtual IDeconvolutionLayer* addDeconvolution(ITensor& input, int nbOutputMaps, DimsHW kernelSize, - Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual IDeconvolutionLayer* addDeconvolution(ITensor& input, int32_t nbOutputMaps, + DimsHW kernelSize, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; //! //! \brief Add an elementwise layer to the network. @@ -5078,7 +5167,7 @@ class INetworkDefinition //! is the concatenation of its forward hidden state and its backward hidden //! state, and its size is 2*H. //! - //! \deprecated IRNNLayer is superseded by IRNNv2Layer. Use addRNNv2() instead. + //! \deprecated Superseded by addRNNv2 and will be removed in TensorRT 8.0. //! //! \see IRNNLayer //! @@ -5087,8 +5176,9 @@ class INetworkDefinition //! //! \return The new RNN layer, or nullptr if it could not be created. //! - TRT_DEPRECATED virtual IRNNLayer* addRNN(ITensor& inputs, int layerCount, std::size_t hiddenSize, int maxSeqLen, - RNNOperation op, RNNInputMode mode, RNNDirection dir, Weights weights, Weights bias) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual IRNNLayer* addRNN(ITensor& inputs, int32_t layerCount, std::size_t hiddenSize, + int32_t maxSeqLen, RNNOperation op, RNNInputMode mode, RNNDirection dir, Weights weights, + Weights bias) TRTNOEXCEPT = 0; //! //! \brief Add a plugin layer to the network. @@ -5099,7 +5189,7 @@ class INetworkDefinition //! //! \see IPluginLayer //! - //! \deprecated IPluginLayer is superseded by IPluginV2. use addPluginV2 instead. + //! \deprecated Superseded by addPluginV2 and will be removed in TensorRT 8.0. //! //! \warning Plugin inputs do not support wildcard dimensions or explicit batch size networks. //! \warning Int32 tensors are not valid input tensors. @@ -5107,7 +5197,7 @@ class INetworkDefinition //! \return the new plugin layer, or nullptr if it could not be created. //! TRT_DEPRECATED virtual IPluginLayer* addPlugin( - ITensor* const* inputs, int nbInputs, IPlugin& plugin) TRTNOEXCEPT = 0; + ITensor* const* inputs, int32_t nbInputs, IPlugin& plugin) TRTNOEXCEPT = 0; //! //! \brief Add a unary layer to the network. @@ -5135,7 +5225,7 @@ class INetworkDefinition //! //! \return The new padding layer, or nullptr if it could not be created. //! - //! \deprecated Superseded by addPaddingNd. + //! \deprecated Superseded by addPaddingNd and will be removed in TensorRT 9.0. //! TRT_DEPRECATED virtual IPaddingLayer* addPadding( ITensor& input, DimsHW prePadding, DimsHW postPadding) TRTNOEXCEPT = 0; @@ -5154,6 +5244,8 @@ class INetworkDefinition //! //! \brief Set the pooling output dimensions formula. //! + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. + //! //! \param formula The formula from computing the pooling output dimensions. If null is passed, the default //! formula is used. //! @@ -5168,6 +5260,8 @@ class INetworkDefinition //! //! \brief Get the pooling output dimensions formula. //! + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. + //! //! \return The formula from computing the pooling output dimensions. //! //! \warning Custom output dimensions formulas are not supported with wildcard dimensions. @@ -5179,7 +5273,7 @@ class INetworkDefinition //! //! \brief Set the convolution output dimensions formula. //! - //! \deprecated This method does not currently work reliably and will be removed in a future release. + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. //! //! \param formula The formula from computing the convolution output dimensions. If null is passed, the default //! formula is used. @@ -5196,7 +5290,7 @@ class INetworkDefinition //! //! \brief Get the convolution output dimensions formula. //! - //! \deprecated This method does not currently work reliably and will be removed in a future release. + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. //! //! \return The formula from computing the convolution output dimensions. //! @@ -5209,7 +5303,7 @@ class INetworkDefinition //! //! \brief Set the deconvolution output dimensions formula. //! - //! \deprecated This method does not currently work reliably and will be removed in a future release. + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. //! //! \param formula The formula from computing the deconvolution output dimensions. If null is passed, the default! //! formula is used. @@ -5228,7 +5322,7 @@ class INetworkDefinition //! //! \return The formula from computing the deconvolution output dimensions. //! - //! \deprecated This method does not currently work reliably and will be removed in a future release. + //! \deprecated This method does not currently work reliably and will be removed in TensorRT 8.0. //! //! \warning Custom output dimensions formulas are not supported with wildcard dimensions. //! @@ -5243,7 +5337,7 @@ class INetworkDefinition //! //! \see getLayer() //! - virtual int getNbLayers() const TRTNOEXCEPT = 0; + virtual int32_t getNbLayers() const TRTNOEXCEPT = 0; //! //! \brief Get the layer specified by the given index. @@ -5254,7 +5348,7 @@ class INetworkDefinition //! //! \see getNbLayers() //! - virtual ILayer* getLayer(int index) const TRTNOEXCEPT = 0; + virtual ILayer* getLayer(int32_t index) const TRTNOEXCEPT = 0; //! //! \brief Get the number of inputs in the network. @@ -5263,7 +5357,7 @@ class INetworkDefinition //! //! \see getInput() //! - virtual int getNbInputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbInputs() const TRTNOEXCEPT = 0; //! //! \brief Get the input tensor specified by the given index. @@ -5274,7 +5368,7 @@ class INetworkDefinition //! //! \see getNbInputs() //! - virtual ITensor* getInput(int index) const TRTNOEXCEPT = 0; // adding inputs invalidates indexing here + virtual ITensor* getInput(int32_t index) const TRTNOEXCEPT = 0; // adding inputs invalidates indexing here //! //! \brief Get the number of outputs in the network. @@ -5285,7 +5379,7 @@ class INetworkDefinition //! //! \see getOutput() //! - virtual int getNbOutputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0; //! //! \brief Get the output tensor specified by the given index. @@ -5296,7 +5390,7 @@ class INetworkDefinition //! //! \see getNbOutputs() //! - virtual ITensor* getOutput(int index) const TRTNOEXCEPT = 0; // adding outputs invalidates indexing here + virtual ITensor* getOutput(int32_t index) const TRTNOEXCEPT = 0; // adding outputs invalidates indexing here //! //! \brief Destroy this INetworkDefinition object. @@ -5360,7 +5454,7 @@ class INetworkDefinition //! //! \return The new TopK layer, or nullptr if it could not be created. //! - virtual ITopKLayer* addTopK(ITensor& input, TopKOperation op, int k, uint32_t reduceAxes) TRTNOEXCEPT = 0; + virtual ITopKLayer* addTopK(ITensor& input, TopKOperation op, int32_t k, uint32_t reduceAxes) TRTNOEXCEPT = 0; //! //! \brief Add a gather layer to the network. @@ -5373,7 +5467,7 @@ class INetworkDefinition //! //! \return The new gather layer, or nullptr if it could not be created. //! - virtual IGatherLayer* addGather(ITensor& data, ITensor& indices, int axis) TRTNOEXCEPT = 0; + virtual IGatherLayer* addGather(ITensor& data, ITensor& indices, int32_t axis) TRTNOEXCEPT = 0; //! //! \brief Add a RaggedSoftMax layer to the network. @@ -5421,7 +5515,8 @@ class INetworkDefinition //! //! \warning Int32 tensors are not valid input tensors. //! - //! \deprecated This interface is superseded by the overload that replaces bool with MatrixOperation. + //! \deprecated This interface is superseded by the overload that replaces bool with MatrixOperation and will be + //! removed in TensorRT 8.0. //! TRT_DEPRECATED virtual IMatrixMultiplyLayer* addMatrixMultiply( ITensor& input0, bool transpose0, ITensor& input1, bool transpose1) TRTNOEXCEPT = 0; @@ -5504,12 +5599,14 @@ class INetworkDefinition //! //! \see IRNNv2Layer //! + //! \deprecated Superseded by ILoop::addLoop and will be removed in TensorRT 9.0. + //! //! \warning RNN inputs do not support wildcard dimensions or explicit batch size networks. //! \warning Int32 tensors are not valid input tensors, only for sequence lengths. //! //! \return The new RNN layer, or nullptr if it could not be created. //! - virtual IRNNv2Layer* addRNNv2( + TRT_DEPRECATED virtual IRNNv2Layer* addRNNv2( ITensor& input, int32_t layerCount, int32_t hiddenSize, int32_t maxSeqLen, RNNOperation op) TRTNOEXCEPT = 0; //! @@ -5521,7 +5618,7 @@ class INetworkDefinition //! //! \see IPluginLayer //! - //! \deprecated IPluginLayer is superseded by IPluginV2. use addPluginV2 instead. + //! \deprecated Superseded by addPluginV2 and will be removed in TensorRT 8.0. //! //! \warning Plugin inputs do not support wildcard dimensions or explicit batch size networks. //! \warning Int32 tensors are not valid input tensors. @@ -5529,7 +5626,7 @@ class INetworkDefinition //! \return The new plugin layer, or nullptr if it could not be created. //! TRT_DEPRECATED virtual IPluginLayer* addPluginExt( - ITensor* const* inputs, int nbInputs, IPluginExt& plugin) TRTNOEXCEPT = 0; + ITensor* const* inputs, int32_t nbInputs, IPluginExt& plugin) TRTNOEXCEPT = 0; //! //! \brief Add an identity layer. @@ -5579,7 +5676,7 @@ class INetworkDefinition //! //! \return The new plugin layer, or nullptr if it could not be created. //! - virtual IPluginV2Layer* addPluginV2(ITensor* const* inputs, int nbInputs, IPluginV2& plugin) TRTNOEXCEPT = 0; + virtual IPluginV2Layer* addPluginV2(ITensor* const* inputs, int32_t nbInputs, IPluginV2& plugin) TRTNOEXCEPT = 0; //! //! \brief Add a slice layer to the network. @@ -5714,8 +5811,8 @@ class INetworkDefinition //! //! \return The new convolution layer, or nullptr if it could not be created. //! - virtual IConvolutionLayer* addConvolutionNd( - ITensor& input, int nbOutputMaps, Dims kernelSize, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; + virtual IConvolutionLayer* addConvolutionNd(ITensor& input, int32_t nbOutputMaps, Dims kernelSize, + Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; //! //! \brief Add a multi-dimension pooling layer to the network. @@ -5750,8 +5847,8 @@ class INetworkDefinition // //! \return The new deconvolution layer, or nullptr if it could not be created. //! - virtual IDeconvolutionLayer* addDeconvolutionNd( - ITensor& input, int nbOutputMaps, Dims kernelSize, Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; + virtual IDeconvolutionLayer* addDeconvolutionNd(ITensor& input, int32_t nbOutputMaps, Dims kernelSize, + Weights kernelWeights, Weights biasWeights) TRTNOEXCEPT = 0; //! //! \brief Add a multi-dimension scale layer to the network. @@ -5779,7 +5876,8 @@ class INetworkDefinition //! //! \return The new Scale layer, or nullptr if it could not be created. //! - virtual IScaleLayer* addScaleNd(ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power, int channelAxis) TRTNOEXCEPT = 0; + virtual IScaleLayer* addScaleNd(ITensor& input, ScaleMode mode, Weights shift, Weights scale, Weights power, + int32_t channelAxis) TRTNOEXCEPT = 0; //! \brief Add a resize layer to the network. //! @@ -5858,7 +5956,7 @@ class INetworkDefinition //! //! \brief Version of calibration algorithm to use. //! -enum class CalibrationAlgoType : int +enum class CalibrationAlgoType : int32_t { kLEGACY_CALIBRATION = 0, kENTROPY_CALIBRATION = 1, @@ -5866,11 +5964,12 @@ enum class CalibrationAlgoType : int kMINMAX_CALIBRATION = 3, }; +//! Maximum number of elements in CalibrationAlgoType enum. \see DataType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 4; -} //!< Maximum number of elements in CalibrationAlgoType enum. \see DataType +} //! //! \class IInt8Calibrator @@ -5891,7 +5990,7 @@ class IInt8Calibrator //! //! \return The batch size. //! - virtual int getBatchSize() const TRTNOEXCEPT = 0; + virtual int32_t getBatchSize() const TRTNOEXCEPT = 0; //! //! \brief Get a batch of input for calibration. @@ -5906,7 +6005,7 @@ class IInt8Calibrator //! //! \see getBatchSize() //! - virtual bool getBatch(void* bindings[], const char* names[], int nbBindings) TRTNOEXCEPT = 0; + virtual bool getBatch(void* bindings[], const char* names[], int32_t nbBindings) TRTNOEXCEPT = 0; //! //! \brief Load a calibration cache. @@ -6247,11 +6346,12 @@ enum class QuantizationFlag : int32_t kCALIBRATE_BEFORE_FUSION = 0 }; +//! Maximum number of quantization flags in QuantizationFlag enum. \see QuantizationFlag template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 1; -} //!< Maximum number of quantization flags in QuantizationFlag enum. \see QuantizationFlag +} //! //! \brief Represents a collection of one or more QuantizationFlag values using binary OR @@ -6270,12 +6370,12 @@ typedef uint32_t BuilderFlags; //! enum class BuilderFlag : int32_t { - kFP16 = 0, //!< Enable FP16 layer selection, with FP32 fallback. - kINT8 = 1, //!< Enable Int8 layer selection, with FP32 fallback with FP16 fallback if kFP16 also specified. - kDEBUG = 2, //!< Enable debugging of layers via synchronizing after every layer. - kGPU_FALLBACK = 3, //!< Enable layers marked to execute on GPU if layer cannot execute on DLA. - kSTRICT_TYPES = 4, //!< Enables strict type constraints. - kREFIT = 5, //!< Enable building a refittable engine. + kFP16 = 0, //!< Enable FP16 layer selection, with FP32 fallback. + kINT8 = 1, //!< Enable Int8 layer selection, with FP32 fallback with FP16 fallback if kFP16 also specified. + kDEBUG = 2, //!< Enable debugging of layers via synchronizing after every layer. + kGPU_FALLBACK = 3, //!< Enable layers marked to execute on GPU if layer cannot execute on DLA. + kSTRICT_TYPES = 4, //!< Enables strict type constraints. + kREFIT = 5, //!< Enable building a refittable engine. kDISABLE_TIMING_CACHE = 6, //!< Disable reuse of timing information across identical layers. //! Allow (but not require) computations on tensors of type DataType::kFLOAT to use TF32. @@ -6284,11 +6384,12 @@ enum class BuilderFlag : int32_t kTF32 = 7 }; +//! Maximum number of builder flags in BuilderFlag enum. \see BuilderFlag template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 8; -} //!< Maximum number of builder flags in BuilderFlag enum. \see BuilderFlag +} //! //! \enum ProfilingVerbosity @@ -6300,16 +6401,47 @@ constexpr inline int EnumMax() //! enum class ProfilingVerbosity : int32_t { - kDEFAULT = 0, //!< Register layer names in NVTX message field. - kNONE = 1, //!< Turn off NVTX traces. - kVERBOSE = 2, //!< Register layer names in NVTX message field and register layer detail in NVTX JSON payload field. + kDEFAULT = 0, //!< Register layer names in NVTX message field. + kNONE = 1, //!< Turn off NVTX traces. + kVERBOSE = 2, //!< Register layer names in NVTX message field and register layer detail in NVTX JSON payload field. }; +//! Maximum number of profile verbosity levels in ProfilingVerbosity enum. \see ProfilingVerbosity template <> constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of profile verbosity levels in ProfilingVerbosity enum. \see ProfilingVerbosity +} + + +//! +//! \enum TacticSource +//! +//! \brief List of tactic sources for TensorRT. +//! +//! \see TacticSources, IBuilderConfig::setTacticSources(), IBuilderConfig::getTacticSources() +//! +enum class TacticSource : int32_t +{ + //! \note Disabling kCUBLAS will cause the cublas handle passed to plugins in attachToContext to be null. + kCUBLAS = 0, //!< cuBLAS tactics. + kCUBLAS_LT = 1 //!< cuBLAS LT tactics +}; + +//! Maximum number of tactic sources in TacticSource enum. \see TacticSource +template <> +constexpr inline int32_t EnumMax() +{ + return 2; +} + +//! +//! \brief Represents a collection of one or more TacticSource values +//! combine using bitwise-OR operations. +//! +//! \see IBuilderConfig::setTacticSources(), IBuilderConfig::getTacticSources() +//! +using TacticSources = uint32_t; //! //! \class IBuilderConfig @@ -6329,7 +6461,7 @@ class IBuilderConfig //! //! \see getMinTimingIterations() //! - virtual void setMinTimingIterations(int minTiming) TRTNOEXCEPT = 0; + virtual void setMinTimingIterations(int32_t minTiming) TRTNOEXCEPT = 0; //! //! \brief Query the number of minimization iterations. @@ -6338,7 +6470,7 @@ class IBuilderConfig //! //! \see setMinTimingIterations() //! - virtual int getMinTimingIterations() const TRTNOEXCEPT = 0; + virtual int32_t getMinTimingIterations() const TRTNOEXCEPT = 0; //! //! \brief Set the number of averaging iterations used when timing layers. @@ -6348,7 +6480,7 @@ class IBuilderConfig //! //! \see getAvgTimingIterations() //! - virtual void setAvgTimingIterations(int avgTiming) TRTNOEXCEPT = 0; + virtual void setAvgTimingIterations(int32_t avgTiming) TRTNOEXCEPT = 0; //! //! \brief Query the number of averaging iterations. @@ -6357,7 +6489,7 @@ class IBuilderConfig //! //! \see setAvgTimingIterations() //! - virtual int getAvgTimingIterations() const TRTNOEXCEPT = 0; + virtual int32_t getAvgTimingIterations() const TRTNOEXCEPT = 0; //! //! \brief Configure the builder to target specified EngineCapability flow. @@ -6508,7 +6640,7 @@ class IBuilderConfig //! //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused. //! - virtual void setDLACore(int dlaCore) TRTNOEXCEPT = 0; + virtual void setDLACore(int32_t dlaCore) TRTNOEXCEPT = 0; //! //! \brief Get the DLA core that the engine executes on. @@ -6516,7 +6648,7 @@ class IBuilderConfig //! //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused. //! - virtual int getDLACore() const TRTNOEXCEPT = 0; + virtual int32_t getDLACore() const TRTNOEXCEPT = 0; //! //! \brief Sets the default DeviceType to be used by the builder. It ensures that all the layers that can run on @@ -6575,7 +6707,7 @@ class IBuilderConfig //! \return The index of the optimization profile (starting from 0) if the input is valid, or -1 if the input is //! not valid. //! - virtual int addOptimizationProfile(const IOptimizationProfile* profile) noexcept = 0; + virtual int32_t addOptimizationProfile(const IOptimizationProfile* profile) noexcept = 0; //! //! \brief Get number of optimization profiles. @@ -6585,12 +6717,10 @@ class IBuilderConfig //! //! \return The number of the optimization profiles. //! - virtual int getNbOptimizationProfiles() const noexcept = 0; + virtual int32_t getNbOptimizationProfiles() const noexcept = 0; protected: - virtual ~IBuilderConfig() - { - } + virtual ~IBuilderConfig() {} public: //! @@ -6690,8 +6820,41 @@ class IBuilderConfig //! \return True if quantization flag is set, false if unset. //! virtual bool getQuantizationFlag(QuantizationFlag flag) const TRTNOEXCEPT = 0; -}; + //! + //! \brief Set tactic sources. + //! + //! This bitset controls which tactic sources TensorRT is allowed to use for tactic + //! selection. + //! + //! By default, kCUBLAS is always enabled. kCUBLAS_LT is enabled for x86 + //! platforms, as well as non-x86 platforms if CUDA >= 11.0 + //! + //! Multiple tactic sources may be combined with a bitwise OR operation. For example, + //! to enable cublas and cublasLt as tactic sources, use a value of: + //! + //! 1U << static_cast(TacticSource::kCUBLAS) | 1U << + //! static_cast(TacticSource::kCUBLAS_LT) + //! + //! \see getTacticSources + //! + //! \return true if the tactic sources in the build configuration were updated. + //! The tactic sources in the build configuration will not be updated if the provided value is invalid. + //! + virtual bool setTacticSources(TacticSources tacticSources) TRTNOEXCEPT = 0; + + //! + //! \brief Get tactic sources. + //! + //! Get the tactic sources currently set in the engine build + //! configuration. + //! + //! \see setTacticSources + //! + //! \return tactic sources + //! + virtual TacticSources getTacticSources() const TRTNOEXCEPT = 0; +}; //! \typedef NetworkDefinitionCreationFlags //! @@ -6712,7 +6875,7 @@ typedef uint32_t NetworkDefinitionCreationFlags; //! //! \see IBuilder::createNetworkV2 //! -enum class NetworkDefinitionCreationFlag : int +enum class NetworkDefinitionCreationFlag : int32_t { //! Dynamic shape support requires that the kEXPLICIT_BATCH flag is set. //! With dynamic shapes, any of the input dimensions can vary at run-time, @@ -6734,13 +6897,14 @@ enum class NetworkDefinitionCreationFlag : int //! will be performed using explicit Scale layers with input/output precision set appropriately. kEXPLICIT_PRECISION = 1, //!< Mark the network to be an explicit precision network }; + +//! Maximum number of elements in NetworkDefinitionCreationFlag enum. \see NetworkDefinitionCreationFlag template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; } - //! //! \class IBuilder //! @@ -6760,7 +6924,7 @@ class IBuilder //! //! \see INetworkDefinition, createNetworkV2 //! - //! \deprecated API will be removed in a future release, use IBuilder::createNetworkV2() instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilder::createNetworkV2 instead. //! TRT_DEPRECATED virtual nvinfer1::INetworkDefinition* createNetwork() TRTNOEXCEPT = 0; @@ -6772,7 +6936,7 @@ class IBuilder //! //! \see getMaxBatchSize() //! - virtual void setMaxBatchSize(int batchSize) TRTNOEXCEPT = 0; + virtual void setMaxBatchSize(int32_t batchSize) TRTNOEXCEPT = 0; //! //! \brief Get the maximum batch size. @@ -6782,7 +6946,7 @@ class IBuilder //! \see setMaxBatchSize() //! \see getMaxDLABatchSize() //! - virtual int getMaxBatchSize() const TRTNOEXCEPT = 0; + virtual int32_t getMaxBatchSize() const TRTNOEXCEPT = 0; //! //! \brief Set the maximum workspace size. @@ -6791,7 +6955,7 @@ class IBuilder //! //! \see getMaxWorkspaceSize() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setMaxWorkspaceSize instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setMaxWorkspaceSize instead. //! TRT_DEPRECATED virtual void setMaxWorkspaceSize(std::size_t workspaceSize) TRTNOEXCEPT = 0; @@ -6802,7 +6966,7 @@ class IBuilder //! //! \see setMaxWorkspaceSize() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getMaxWorkspaceSize instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getMaxWorkspaceSize instead. //! TRT_DEPRECATED virtual std::size_t getMaxWorkspaceSize() const TRTNOEXCEPT = 0; @@ -6816,7 +6980,7 @@ class IBuilder //! //! \see getHalf2Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setHalf2Mode(bool mode) TRTNOEXCEPT = 0; @@ -6825,7 +6989,7 @@ class IBuilder //! //! \see setHalf2Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getHalf2Mode() const TRTNOEXCEPT = 0; @@ -6835,7 +6999,7 @@ class IBuilder //! If this flag is true, the builder will synchronize after timing each layer, and report the layer name. It can //! be useful when diagnosing issues at build time. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setDebugSync(bool sync) TRTNOEXCEPT = 0; @@ -6844,7 +7008,7 @@ class IBuilder //! //! \see setDebugSync() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getDebugSync() const TRTNOEXCEPT = 0; @@ -6856,18 +7020,18 @@ class IBuilder //! //! \see getMinFindIterations() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setMinTimingIterations instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setMinTimingIterations instead. //! - TRT_DEPRECATED virtual void setMinFindIterations(int minFind) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual void setMinFindIterations(int32_t minFind) TRTNOEXCEPT = 0; //! //! \brief Query the number of minimization iterations. //! //! \see setMinFindIterations() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getMinTimingIterations instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getMinTimingIterations instead. //! - TRT_DEPRECATED virtual int getMinFindIterations() const TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual int32_t getMinFindIterations() const TRTNOEXCEPT = 0; //! //! \brief Set the number of averaging iterations used when timing layers. @@ -6877,25 +7041,25 @@ class IBuilder //! //! \see getAverageFindIterations() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setAvgTimingIterations instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setAvgTimingIterations instead. //! - TRT_DEPRECATED virtual void setAverageFindIterations(int avgFind) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual void setAverageFindIterations(int32_t avgFind) TRTNOEXCEPT = 0; //! //! \brief Query the number of averaging iterations. //! //! \see setAverageFindIterations() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getAvgTimingIterations instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getAvgTimingIterations instead. //! - TRT_DEPRECATED virtual int getAverageFindIterations() const TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual int32_t getAverageFindIterations() const TRTNOEXCEPT = 0; //! //! \brief Build a CUDA engine from a network definition. //! //! \see INetworkDefinition ICudaEngine //! - //! \deprecated API will be removed in a future release, use IBuilder::buildEngineWithConfig instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilder::buildEngineWithConfig instead. //! TRT_DEPRECATED virtual nvinfer1::ICudaEngine* buildCudaEngine( nvinfer1::INetworkDefinition& network) TRTNOEXCEPT = 0; @@ -6924,7 +7088,7 @@ class IBuilder //! //! \see getInt8Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setInt8Mode(bool mode) TRTNOEXCEPT = 0; @@ -6933,14 +7097,14 @@ class IBuilder //! //! \see setInt8Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getInt8Mode() const TRTNOEXCEPT = 0; //! //! \brief Set Int8 Calibration interface. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setInt8Calibrator instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setInt8Calibrator instead. //! TRT_DEPRECATED virtual void setInt8Calibrator(IInt8Calibrator* calibrator) TRTNOEXCEPT = 0; @@ -6954,7 +7118,7 @@ class IBuilder //! //! \see getDeviceType() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setDeviceType instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setDeviceType instead. //! TRT_DEPRECATED virtual void setDeviceType(ILayer* layer, DeviceType deviceType) TRTNOEXCEPT = 0; @@ -6962,7 +7126,7 @@ class IBuilder //! \brief Get the device that this layer executes on. //! \return Returns DeviceType of the layer. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getDeviceType instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getDeviceType instead. //! TRT_DEPRECATED virtual DeviceType getDeviceType(const ILayer* layer) const TRTNOEXCEPT = 0; @@ -6971,7 +7135,7 @@ class IBuilder //! \return whether the DeviceType has been explicitly set //! \see setDeviceType() getDeviceType() resetDeviceType() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::isDeviceTypeSet instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::isDeviceTypeSet instead. //! TRT_DEPRECATED virtual bool isDeviceTypeSet(const ILayer* layer) const TRTNOEXCEPT = 0; @@ -6980,7 +7144,7 @@ class IBuilder //! //! \see setDeviceType() getDeviceType() isDeviceTypeSet() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::resetDeviceType instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::resetDeviceType instead. //! TRT_DEPRECATED virtual void resetDeviceType(ILayer* layer) TRTNOEXCEPT = 0; @@ -6988,7 +7152,7 @@ class IBuilder //! \brief Checks if a layer can run on DLA. //! \return status true if the layer can on DLA else returns false. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::canRunOnDLA instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::canRunOnDLA instead. //! TRT_DEPRECATED virtual bool canRunOnDLA(const ILayer* layer) const TRTNOEXCEPT = 0; @@ -6997,14 +7161,14 @@ class IBuilder //! this device will run on it, unless setDeviceType is used to override the default DeviceType for a layer. //! \see getDefaultDeviceType() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setDefaultDeviceType instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setDefaultDeviceType instead. //! TRT_DEPRECATED virtual void setDefaultDeviceType(DeviceType deviceType) TRTNOEXCEPT = 0; //! //! \brief Get the default DeviceType which was set by setDefaultDeviceType. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getDefaultDeviceType instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getDefaultDeviceType instead. //! TRT_DEPRECATED virtual DeviceType getDefaultDeviceType() const TRTNOEXCEPT = 0; @@ -7015,7 +7179,7 @@ class IBuilder //! //! \warning getMaxDLABatchSize does not work with dynamic shapes. //! - virtual int getMaxDLABatchSize() const TRTNOEXCEPT = 0; + virtual int32_t getMaxDLABatchSize() const TRTNOEXCEPT = 0; //! //! \brief Sets the builder to use GPU if a layer that was supposed to run on DLA can not run on DLA. @@ -7024,14 +7188,14 @@ class IBuilder //! \note GPU fallback may only be specified for non-safety modes. \see EngineCapability //! Simultaneously enabling GPU fallback and safety-restricted modes is disallowed. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void allowGPUFallback(bool setFallBackMode) TRTNOEXCEPT = 0; //! //! \brief Return the number of DLA engines available to this builder. //! - virtual int getNbDLACores() const TRTNOEXCEPT = 0; + virtual int32_t getNbDLACores() const TRTNOEXCEPT = 0; //! //! \brief Set the DLA core that the engine must execute on. @@ -7041,22 +7205,22 @@ class IBuilder //! it will be associated with the DLA core which is configured for the runtime. //! \see IRuntime::setDLACore() getDLACore() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setDLACore instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setDLACore instead. //! - TRT_DEPRECATED virtual void setDLACore(int dlaCore) TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual void setDLACore(int32_t dlaCore) TRTNOEXCEPT = 0; //! //! \brief Get the DLA core that the engine executes on. //! \return If setDLACore is called, returns DLA core from 0 to N-1, else returns 0. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getDLACore instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getDLACore instead. //! - TRT_DEPRECATED virtual int getDLACore() const TRTNOEXCEPT = 0; + TRT_DEPRECATED virtual int32_t getDLACore() const TRTNOEXCEPT = 0; //! //! \brief Resets the builder state //! - //! \deprecated API will be removed in a future release, use IBuilder::reset() instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilder::reset instead. //! TRT_DEPRECATED virtual void reset(nvinfer1::INetworkDefinition& network) TRTNOEXCEPT = 0; @@ -7088,7 +7252,7 @@ class IBuilder //! //! \see getFp16Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setFp16Mode(bool mode) TRTNOEXCEPT = 0; @@ -7097,7 +7261,7 @@ class IBuilder //! //! \see setFp16Mode() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getFp16Mode() const TRTNOEXCEPT = 0; @@ -7117,7 +7281,7 @@ class IBuilder //! //! \see getStrictTypeConstraints() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setStrictTypeConstraints(bool mode) TRTNOEXCEPT = 0; @@ -7126,14 +7290,14 @@ class IBuilder //! //! \see setStrictTypeConstraints() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getStrictTypeConstraints() const TRTNOEXCEPT = 0; //! //! Set whether engines will be refittable. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setFlag instead. //! TRT_DEPRECATED virtual void setRefittable(bool canRefit) TRTNOEXCEPT = 0; @@ -7142,14 +7306,14 @@ class IBuilder //! //! \see getRefittable() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getFlag instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getFlag instead. //! TRT_DEPRECATED virtual bool getRefittable() const TRTNOEXCEPT = 0; //! //! \brief Configure the builder to target specified EngineCapability flow. //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::setEngineCapability instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::setEngineCapability instead. //! TRT_DEPRECATED virtual void setEngineCapability(EngineCapability capability) TRTNOEXCEPT = 0; @@ -7158,7 +7322,7 @@ class IBuilder //! //! \see setEngineCapability() //! - //! \deprecated API will be removed in a future release, use IBuilderConfig::getEngineCapability instead. + //! \deprecated API will be removed in TensorRT 8.0, use IBuilderConfig::getEngineCapability instead. //! TRT_DEPRECATED virtual EngineCapability getEngineCapability() const TRTNOEXCEPT = 0; @@ -7247,7 +7411,7 @@ class IBuilder //! Internal C entry point for creating IBuilder. //! @private //! -extern "C" TENSORRTAPI void* createInferBuilder_INTERNAL(void* logger, int version); +extern "C" TENSORRTAPI void* createInferBuilder_INTERNAL(void* logger, int32_t version); namespace nvinfer1 { diff --git a/include/NvInferPlugin.h b/include/NvInferPlugin.h index 01ba3f1b..58157ce8 100644 --- a/include/NvInferPlugin.h +++ b/include/NvInferPlugin.h @@ -27,15 +27,13 @@ extern "C" { -//! -//! \brief Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional namespace. -//! The plugin library author should ensure that this function name is unique to the library. -//! This function should be called once before accessing the Plugin Registry. -//! \param logger Logger object to print plugin registration information -//! \param libNamespace Namespace used to register all the plugins in this library -//! -TENSORRTAPI bool initLibNvInferPlugins(void* logger, const char* libNamespace); - + //! + //! \brief Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional + //! namespace. The plugin library author should ensure that this function name is unique to the library. This + //! function should be called once before accessing the Plugin Registry. \param logger Logger object to print plugin + //! registration information \param libNamespace Namespace used to register all the plugins in this library + //! + TENSORRTAPI bool initLibNvInferPlugins(void* logger, const char* libNamespace); } // extern "C" #endif // NV_INFER_PLUGIN_H diff --git a/include/NvInferPluginUtils.h b/include/NvInferPluginUtils.h index a92488cf..3d7908fb 100644 --- a/include/NvInferPluginUtils.h +++ b/include/NvInferPluginUtils.h @@ -34,7 +34,7 @@ namespace nvinfer1 //! //! \see INvPlugin::getPluginType() //! -enum class PluginType : int +enum class PluginType : int32_t { kFASTERRCNN = 0, //!< FasterRCNN fused plugin (RPN + ROI pooling). kNORMALIZE = 1, //!< Normalize plugin. @@ -48,9 +48,9 @@ enum class PluginType : int kANCHORGENERATOR = 9, //!< SSD Grid Anchor Generator. }; -//!< Maximum number of elements in PluginType enum. \see PluginType +//! Maximum number of elements in PluginType enum. \see PluginType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 10; } @@ -60,41 +60,37 @@ namespace plugin //! //! \brief The Permute plugin layer permutes the input tensor by changing the memory order of the data. -//! Quadruple defines a structure that contains an array of 4 integers. They can represent the permute orders or the strides in each dimension. +//! Quadruple defines a structure that contains an array of 4 integers. They can represent the permute orders or the +//! strides in each dimension. //! typedef struct { - int data[4]; + int32_t data[4]; } Quadruple; - //! -//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W). -//! PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. -//! It contains: -//! \param minSize Minimum box size in pixels. Can not be nullptr. -//! \param maxSize Maximum box size in pixels. Can be nullptr. -//! \param aspectRatios Aspect ratios of the boxes. Can be nullptr. -//! \param numMinSize Number of elements in minSize. Must be larger than 0. -//! \param numMaxSize Number of elements in maxSize. Can be 0 or same as numMinSize. +//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all +//! dimensions (H x W). PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. It +//! contains: \param minSize Minimum box size in pixels. Can not be nullptr. \param maxSize Maximum box size in pixels. +//! Can be nullptr. \param aspectRatios Aspect ratios of the boxes. Can be nullptr. \param numMinSize Number of elements +//! in minSize. Must be larger than 0. \param numMaxSize Number of elements in maxSize. Can be 0 or same as numMinSize. //! \param numAspectRatios Number of elements in aspectRatios. Can be 0. -//! \param flip If true, will flip each aspect ratio. For example, if there is aspect ratio "r", the aspect ratio "1.0/r" will be generated as well. -//! \param clip If true, will clip the prior so that it is within [0,1]. -//! \param variance Variance for adjusting the prior boxes. -//! \param imgH Image height. If 0, then the H dimension of the data tensor will be used. -//! \param imgW Image width. If 0, then the W dimension of the data tensor will be used. -//! \param stepH Step in H. If 0, then (float)imgH/h will be used where h is the H dimension of the 1st input tensor. -//! \param stepW Step in W. If 0, then (float)imgW/w will be used where w is the W dimension of the 1st input tensor. -//! \param offset Offset to the top left corner of each cell. +//! \param flip If true, will flip each aspect ratio. For example, if there is aspect ratio "r", the aspect ratio +//! "1.0/r" will be generated as well. \param clip If true, will clip the prior so that it is within [0,1]. \param +//! variance Variance for adjusting the prior boxes. \param imgH Image height. If 0, then the H dimension of the data +//! tensor will be used. \param imgW Image width. If 0, then the W dimension of the data tensor will be used. \param +//! stepH Step in H. If 0, then (float)imgH/h will be used where h is the H dimension of the 1st input tensor. \param +//! stepW Step in W. If 0, then (float)imgW/w will be used where w is the W dimension of the 1st input tensor. \param +//! offset Offset to the top left corner of each cell. //! struct PriorBoxParameters { float *minSize, *maxSize, *aspectRatios; - int numMinSize, numMaxSize, numAspectRatios; + int32_t numMinSize, numMaxSize, numAspectRatios; bool flip; bool clip; float variance[4]; - int imgH, imgW; + int32_t imgH, imgW; float stepH, stepW; float offset; }; @@ -104,10 +100,9 @@ struct PriorBoxParameters //! It contains: //! \param poolingH Height of the output in pixels after ROI pooling on feature map. //! \param poolingW Width of the output in pixels after ROI pooling on feature map. -//! \param featureStride Feature stride; ratio of input image size to feature map size. Assuming that max pooling layers in neural network use square filters. -//! \param preNmsTop Number of proposals to keep before applying NMS. -//! \param nmsMaxOut Number of remaining proposals after applying NMS. -//! \param anchorsRatioCount Number of anchor box ratios. +//! \param featureStride Feature stride; ratio of input image size to feature map size. Assuming that max pooling layers +//! in neural network use square filters. \param preNmsTop Number of proposals to keep before applying NMS. \param +//! nmsMaxOut Number of remaining proposals after applying NMS. \param anchorsRatioCount Number of anchor box ratios. //! \param anchorsScaleCount Number of anchor box scales. //! \param iouThreshold IoU (Intersection over Union) threshold used for the NMS step. //! \param minBoxSize Minimum allowed bounding box size before scaling, used for anchor box calculation. @@ -115,13 +110,13 @@ struct PriorBoxParameters //! struct RPROIParams { - int poolingH; - int poolingW; - int featureStride; - int preNmsTop; - int nmsMaxOut; - int anchorsRatioCount; - int anchorsScaleCount; + int32_t poolingH; + int32_t poolingW; + int32_t featureStride; + int32_t preNmsTop; + int32_t nmsMaxOut; + int32_t anchorsRatioCount; + int32_t anchorsScaleCount; float iouThreshold; float minBoxSize; float spatialScale; @@ -144,7 +139,7 @@ struct GridAnchorParameters { float minSize, maxSize; float* aspectRatios; - int numAspectRatios, H, W; + int32_t numAspectRatios, H, W; float variance[4]; }; @@ -152,7 +147,7 @@ struct GridAnchorParameters //! \enum CodeTypeSSD //! \brief The type of encoding used for decoding the bounding boxes and loc_data. //! -enum class CodeTypeSSD : int +enum class CodeTypeSSD : int32_t { CORNER = 0, //!< Use box corners. CENTER_SIZE = 1, //!< Use box centers and size. @@ -181,45 +176,49 @@ enum class CodeTypeSSD : int struct DetectionOutputParameters { bool shareLocation, varianceEncodedInTarget; - int backgroundLabelId, numClasses, topK, keepTopK; + int32_t backgroundLabelId, numClasses, topK, keepTopK; float confidenceThreshold, nmsThreshold; CodeTypeSSD codeType; - int inputOrder[3]; + int32_t inputOrder[3]; bool confSigmoid; bool isNormalized; }; - //! -//! \brief The Region plugin layer performs region proposal calculation: generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell). -//! For each box, calculating its probablities of objects detections from 80 pre-defined classifications (yolo9000 has 9418 pre-defined classifications, -//! and these 9418 items are organized as work-tree structure). +//! \brief The Region plugin layer performs region proposal calculation: generate 5 bounding boxes per cell (for +//! yolo9000, generate 3 bounding boxes per cell). +//! +//! For each box, calculating its probablities of objects detections from 80 pre-defined classifications (yolo9000 +//! has 9418 pre-defined classifications, and these 9418 items are organized as work-tree structure). //! RegionParameters defines a set of parameters for creating the Region plugin layer. +//! //! \param num Number of predicted bounding box for each grid cell. //! \param coords Number of coordinates for a bounding box. //! \param classes Number of classfications to be predicted. -//! \param softmaxTree When performing yolo9000, softmaxTree is helping to do softmax on confidence scores, for element to get the precise classfication through word-tree structured classfication definition. -//! \deprecated. This plugin is superseded by createRegionPlugin() +//! \param softmaxTree When performing yolo9000, softmaxTree is helping to do softmax on confidence scores, for element +//! to get the precise classfication through word-tree structured classfication definition. +//! +//! \deprecated. This plugin is superseded by createRegionPlugin and will be removed in TensorRT 8.0. //! TRT_DEPRECATED typedef struct { - int* leaf; - int n; - int* parent; - int* child; - int* group; + int32_t* leaf; + int32_t n; + int32_t* parent; + int32_t* child; + int32_t* group; char** name; - int groups; - int* groupSize; - int* groupOffset; + int32_t groups; + int32_t* groupSize; + int32_t* groupOffset; } softmaxTree; // softmax tree struct TRT_DEPRECATED RegionParameters { - int num; - int coords; - int classes; + int32_t num; + int32_t coords; + int32_t classes; softmaxTree* smTree; }; @@ -243,7 +242,7 @@ struct TRT_DEPRECATED RegionParameters struct NMSParameters { bool shareLocation; - int backgroundLabelId, numClasses, topK, keepTopK; + int32_t backgroundLabelId, numClasses, topK, keepTopK; float scoreThreshold, iouThreshold; bool isNormalized; }; diff --git a/include/NvInferRuntime.h b/include/NvInferRuntime.h index 52c8d15e..7ce6f465 100644 --- a/include/NvInferRuntime.h +++ b/include/NvInferRuntime.h @@ -46,18 +46,21 @@ class IPluginFactory; //!< Forward declaration of IPluginFactory for use by othe //! the resulting serialized engine can be executed using NvMediaDLA's runtime APIs. See sampleNvmedia for an //! example of integrating NvMediaDLA APIs with TensorRT APIs. //! -enum class EngineCapability : int +enum class EngineCapability : int32_t { kDEFAULT = 0, //!< Full capability, TensorRT mode without any restrictions using TensorRT nvinfer1 APIs. - kSAFE_GPU = 1, //!< Safety restricted capability, TensorRT flow that can only run on GPU devices via TensorRT nvinfer1::safe APIs. - kSAFE_DLA = 2, //!< Safety restricted capability, TensorRT flow that can only run on DLA devices via NvMediaDLA APIs. + kSAFE_GPU = 1, //!< Safety restricted capability, TensorRT flow that can only run on GPU devices via TensorRT + //!< nvinfer1::safe APIs. + kSAFE_DLA = 2, //!< Safety restricted capability, TensorRT flow that can only run on DLA devices via + //!< NvMediaDLA APIs. }; +//! Maximum number of elements in EngineCapability enum. \see EngineCapability template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Maximum number of elements in EngineCapability enum. \see EngineCapability +} //! //! \class Weights @@ -115,9 +118,10 @@ class IPlugin //! //! \return The number of outputs. //! - //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize(). + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). //! - virtual int getNbOutputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0; //! //! \brief Get the dimension of an output tensor. @@ -126,15 +130,17 @@ class IPlugin //! \param inputs The input tensors. //! \param nbInputDims The number of input tensors. //! - //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize(). + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). //! - virtual Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRTNOEXCEPT = 0; + virtual Dims getOutputDimensions(int32_t index, const Dims* inputs, int32_t nbInputDims) TRTNOEXCEPT = 0; //! //! \brief Configure the layer. //! - //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis - //! of its weights, dimensions, and maximum batch size. The type is assumed to be FP32 and format NCHW. + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, and maximum batch size. The type is assumed to be + //! FP32 and format NCHW. //! //! \param inputDims The input tensor dimensions. //! \param nbInputs The number of inputs. @@ -142,34 +148,36 @@ class IPlugin //! \param nbOutputs The number of outputs. //! \param maxBatchSize The maximum batch size. //! - //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). + //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be + //! 3-dimensional CHW dimensions). //! //! This method is not called for PluginExt classes, configureWithFormat is called instead. //! - virtual void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) TRTNOEXCEPT = 0; + virtual void configure(const Dims* inputDims, int32_t nbInputs, const Dims* outputDims, int32_t nbOutputs, + int32_t maxBatchSize) TRTNOEXCEPT = 0; //! //! \brief Initialize the layer for execution. This is called when the engine is created. //! //! \return 0 for success, else non-zero (which will cause engine termination). //! - virtual int initialize() TRTNOEXCEPT = 0; + virtual int32_t initialize() TRTNOEXCEPT = 0; //! - //! \brief Release resources acquired during plugin layer initialization. This is called when the engine is destroyed. - //! \see initialize() + //! \brief Release resources acquired during plugin layer initialization. This is called when the engine is + //! destroyed. \see initialize() //! virtual void terminate() TRTNOEXCEPT = 0; //! //! \brief Find the workspace size required by the layer. //! - //! This function is called during engine startup, after initialize(). The workspace size returned should be sufficient for any - //! batch size up to the maximum. + //! This function is called during engine startup, after initialize(). The workspace size returned should be + //! sufficient for any batch size up to the maximum. //! //! \return The workspace size. //! - virtual size_t getWorkspaceSize(int maxBatchSize) const TRTNOEXCEPT = 0; + virtual size_t getWorkspaceSize(int32_t maxBatchSize) const TRTNOEXCEPT = 0; //! //! \brief Execute the layer. @@ -182,7 +190,8 @@ class IPlugin //! //! \return 0 for success, else non-zero (which will cause engine termination). //! - virtual int enqueue(int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) TRTNOEXCEPT = 0; + virtual int32_t enqueue(int32_t batchSize, const void* const* inputs, void** outputs, void* workspace, + cudaStream_t stream) TRTNOEXCEPT = 0; //! //! \brief Find the size of the serialization buffer required. @@ -217,9 +226,10 @@ class IPluginExt : public IPlugin //! //! \brief Return the API version with which this plugin was built. //! - //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins. + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. //! - virtual int getTensorRTVersion() const TRTNOEXCEPT + virtual int32_t getTensorRTVersion() const TRTNOEXCEPT { return NV_TENSORRT_VERSION; } @@ -241,8 +251,8 @@ class IPluginExt : public IPlugin //! //! \brief Configure the layer. //! - //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis - //! of its weights, dimensions, and maximum batch size. + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, and maximum batch size. //! //! \param inputDims The input tensor dimensions. //! \param nbInputs The number of inputs. @@ -252,11 +262,13 @@ class IPluginExt : public IPlugin //! \param format The format selected for the engine. //! \param maxBatchSize The maximum batch size. //! - //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). + //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be + //! 3-dimensional CHW dimensions). //! //! \warning DataType:kBOOL not supported. //! - virtual void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) TRTNOEXCEPT = 0; + virtual void configureWithFormat(const Dims* inputDims, int32_t nbInputs, const Dims* outputDims, int32_t nbOutputs, + DataType type, PluginFormat format, int32_t maxBatchSize) TRTNOEXCEPT = 0; virtual ~IPluginExt() {} @@ -264,7 +276,10 @@ class IPluginExt : public IPlugin //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! - void configure(const Dims* /*inputDims*/, int /*nbInputs*/, const Dims* /*outputDims*/, int /*nbOutputs*/, int /*maxBatchSize*/) _TENSORRT_FINAL TRTNOEXCEPT {} + void configure(const Dims* /*inputDims*/, int32_t /*nbInputs*/, const Dims* /*outputDims*/, int32_t /*nbOutputs*/, + int32_t /*maxBatchSize*/) _TENSORRT_FINAL TRTNOEXCEPT + { + } }; //! @@ -277,7 +292,7 @@ class IPluginExt : public IPlugin //! //! \see IDimensionExpr, IExprBuilder //! -enum class DimensionOperation : int +enum class DimensionOperation : int32_t { kSUM = 0, //!< Sum of the two operands. kPROD = 1, //!< Product of the two operands. @@ -290,11 +305,12 @@ enum class DimensionOperation : int kCEIL_DIV = 8 //!< Division rounding up }; +//! Maximum number of elements in DimensionOperation enum. \see DimensionOperation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 9; -} //!< Maximum number of elements in DimensionOperation enum. \see DimensionOperation +} //! //! \class IDimensionExpr @@ -313,8 +329,8 @@ class IDimensionExpr virtual bool isConstant() const = 0; //! If isConstant(), returns value of the constant. - //! If !isConstant(), return std::numeric_limits::min(). - virtual int getConstantValue() const = 0; + //! If !isConstant(), return std::numeric_limits::min(). + virtual int32_t getConstantValue() const = 0; protected: virtual ~IDimensionExpr() {} @@ -341,7 +357,7 @@ class IExprBuilder { public: //! Return pointer to IDimensionExp for given value. - virtual const IDimensionExpr* constant(int value) = 0; + virtual const IDimensionExpr* constant(int32_t value) = 0; //! Return pointer to IDimensionExp that represents the given operation applied to first and second. //! Returns nullptr if op is not a valid DimensionOperation. @@ -359,7 +375,7 @@ class IExprBuilder class DimsExprs { public: - int nbDims; //!< The number of dimensions. + int32_t nbDims; //!< The number of dimensions. const IDimensionExpr* d[Dims::MAX_DIMS]; //!< The extent of each dimension. }; @@ -387,13 +403,12 @@ struct DynamicPluginTensorDesc //! //! Clients should override the public methods, including the following inherited methods: //! -//! virtual int getNbOutputs() const TRTNOEXCEPT = 0; -//! virtual nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRTNOEXCEPT = 0; -//! virtual size_t getSerializationSize() const TRTNOEXCEPT = 0; -//! virtual void serialize(void* buffer) const TRTNOEXCEPT = 0; -//! virtual void destroy() TRTNOEXCEPT = 0; -//! virtual void setPluginNamespace(const char* pluginNamespace) TRTNOEXCEPT = 0; -//! virtual const char* getPluginNamespace() const TRTNOEXCEPT = 0; +//! virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0; +//! virtual nvinfer1::DataType getOutputDataType(int32_t index, const nvinfer1::DataType* inputTypes, int32_t +//! nbInputs) const TRTNOEXCEPT = 0; virtual size_t getSerializationSize() const TRTNOEXCEPT = 0; virtual void +//! serialize(void* buffer) const TRTNOEXCEPT = 0; virtual void destroy() TRTNOEXCEPT = 0; virtual void +//! setPluginNamespace(const char* pluginNamespace) TRTNOEXCEPT = 0; virtual const char* getPluginNamespace() const +//! TRTNOEXCEPT = 0; //! //! For getOutputDataType, the inputTypes will always be DataType::kFLOAT or DataType::kINT32, //! and the returned type is canonicalized to DataType::kFLOAT if it is DataType::kHALF or DataType:kINT8. @@ -425,15 +440,17 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! The body of the override of getOutputDimensions can be: //! //! DimsExprs output(inputs[0]); - //! output.d[0] = exprBuilder.operation(DimensionOperation::kSUM, inputs[0].d[0], inputs[1].d[0]); + //! output.d[0] = exprBuilder.operation(DimensionOperation::kSUM, *inputs[0].d[0], *inputs[1].d[0]); //! return output; //! - virtual DimsExprs getOutputDimensions(int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) = 0; + virtual DimsExprs getOutputDimensions( + int32_t outputIndex, const DimsExprs* inputs, int32_t nbInputs, IExprBuilder& exprBuilder) + = 0; //! //! Limit on number of format combinations accepted. //! - static constexpr int kFORMAT_COMBINATION_LIMIT = 100; + static constexpr int32_t kFORMAT_COMBINATION_LIMIT = 100; //! //! \brief Return true if plugin supports the format and datatype for the input/output indexed by pos. @@ -457,7 +474,8 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! * A definition for a plugin that supports only FP16 NCHW for its two inputs, //! and FP32 NCHW for its single output: //! - //! return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ? DataType::kHALF : DataType::kFLOAT); + //! return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ? DataType::kHALF : + //! DataType::kFLOAT); //! //! * A definition for a "polymorphic" plugin with two inputs and one output that supports //! any format or type, but the inputs and output must have the same format and type: @@ -466,7 +484,8 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! Warning: TensorRT will stop asking for formats once it finds kFORMAT_COMBINATION_LIMIT on combinations. //! - virtual bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) TRTNOEXCEPT = 0; + virtual bool supportsFormatCombination( + int32_t pos, const PluginTensorDesc* inOut, int32_t nbInputs, int32_t nbOutputs) TRTNOEXCEPT = 0; //! //! \brief Configure the layer. @@ -481,7 +500,8 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! \param out The output tensors attributes that are used for configuration. //! \param nbOutputs Number of output tensors. //! - virtual void configurePlugin(const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs) TRTNOEXCEPT = 0; + virtual void configurePlugin(const DynamicPluginTensorDesc* in, int32_t nbInputs, + const DynamicPluginTensorDesc* out, int32_t nbOutputs) TRTNOEXCEPT = 0; //! //! \brief Find the workspace size required by the layer. @@ -492,7 +512,8 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! \return The workspace size. //! - virtual size_t getWorkspaceSize(const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const TRTNOEXCEPT = 0; + virtual size_t getWorkspaceSize(const PluginTensorDesc* inputs, int32_t nbInputs, const PluginTensorDesc* outputs, + int32_t nbOutputs) const TRTNOEXCEPT = 0; //! //! \brief Execute the layer. @@ -506,12 +527,13 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! \return 0 for success, else non-zero (which will cause engine termination). //! - virtual int enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) TRTNOEXCEPT = 0; + virtual int32_t enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) TRTNOEXCEPT = 0; protected: - int getTensorRTVersion() const _TENSORRT_OVERRIDE TRTNOEXCEPT + int32_t getTensorRTVersion() const _TENSORRT_OVERRIDE TRTNOEXCEPT { - return (static_cast(PluginVersion::kV2_DYNAMICEXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); + return (static_cast(PluginVersion::kV2_DYNAMICEXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); } virtual ~IPluginV2DynamicExt() {} @@ -524,8 +546,11 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! Instead, derived classes should override the overload of getOutputDimensions that returns DimsExprs. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED - Dims getOutputDimensions(int /*index*/, const Dims* /*inputs*/, int /*nbInputDims*/) _TENSORRT_FINAL TRTNOEXCEPT + Dims getOutputDimensions( + int32_t /*index*/, const Dims* /*inputs*/, int32_t /*nbInputDims*/) _TENSORRT_FINAL TRTNOEXCEPT { return Dims{-1, {}, {}}; } @@ -533,10 +558,13 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! - //! With dynamic shapes, there is no implicit batch dimension to broadcast across. + //! This method is not used because with dynamic shapes there is no implicit batch dimension to broadcast across. + //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. //! TRT_DEPRECATED - bool isOutputBroadcastAcrossBatch(int /*outputIndex*/, const bool* /*inputIsBroadcasted*/, int /*nbInputs*/) const _TENSORRT_FINAL TRTNOEXCEPT + bool isOutputBroadcastAcrossBatch(int32_t /*outputIndex*/, const bool* /*inputIsBroadcasted*/, + int32_t /*nbInputs*/) const _TENSORRT_FINAL TRTNOEXCEPT { return false; } @@ -544,10 +572,12 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! - //! With dynamic shapes, there is no implicit batch dimension to broadcast across. + //! This method is not used because with dynamic shapes there is no implicit batch dimension to broadcast across. + //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. //! TRT_DEPRECATED - bool canBroadcastInputAcrossBatch(int /*inputIndex*/) const _TENSORRT_FINAL TRTNOEXCEPT + bool canBroadcastInputAcrossBatch(int32_t /*inputIndex*/) const _TENSORRT_FINAL TRTNOEXCEPT { return true; } @@ -560,21 +590,32 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! Instead, derived classes should override supportsFormatCombination, which allows plugins //! to express mixed formats. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED - bool supportsFormat(DataType /*type*/, PluginFormat /*format*/) const _TENSORRT_FINAL TRTNOEXCEPT { return false; } + bool supportsFormat(DataType /*type*/, PluginFormat /*format*/) const _TENSORRT_FINAL TRTNOEXCEPT + { + return false; + } //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! //! This method is not used because tensors with dynamic shapes do not have an implicit batch dimension, - //! input dimensions might be variable, and outputs might have different floating-point formats.. + //! input dimensions might be variable, and outputs might have different floating-point formats. //! - //! Instead, derived classes should override the overload of configurePlugin that takes poiners to DynamicPluginTensorDesc. + //! Instead, derived classes should override the overload of configurePlugin that takes poiners to + //! DynamicPluginTensorDesc. + //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. //! TRT_DEPRECATED - void configurePlugin(const Dims* /*inputDims*/, int /*nbInputs*/, const Dims* /*outputDims*/, - int /*nbOutputs*/, const DataType* /*inputTypes*/, const DataType* /*outputTypes*/, - const bool* /*inputIsBroadcast*/, const bool* /*outputIsBroadcast*/, PluginFormat /*floatFormat*/, int /*maxBatchSize*/) _TENSORRT_FINAL TRTNOEXCEPT {} + void configurePlugin(const Dims* /*inputDims*/, int32_t /*nbInputs*/, const Dims* /*outputDims*/, + int32_t /*nbOutputs*/, const DataType* /*inputTypes*/, const DataType* /*outputTypes*/, + const bool* /*inputIsBroadcast*/, const bool* /*outputIsBroadcast*/, PluginFormat /*floatFormat*/, + int32_t /*maxBatchSize*/) _TENSORRT_FINAL TRTNOEXCEPT + { + } //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. @@ -582,21 +623,30 @@ class IPluginV2DynamicExt : public nvinfer1::IPluginV2Ext //! This method is not used because tensors with dynamic shapes do not have an implicit batch dimension, //! and the other dimensions might not be build-time constants. //! - //! Instead, derived classes should override the overload of getWorkspaceSize that takes pointers to PluginTensorDesc. - //! The arguments to that overload provide maximum bounds on all dimensions. + //! Instead, derived classes should override the overload of getWorkspaceSize that takes pointers to + //! PluginTensorDesc. The arguments to that overload provide maximum bounds on all dimensions. + //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. //! TRT_DEPRECATED - size_t getWorkspaceSize(int /*maxBatchSize*/) const _TENSORRT_FINAL TRTNOEXCEPT { return 0; } + size_t getWorkspaceSize(int32_t /*maxBatchSize*/) const _TENSORRT_FINAL TRTNOEXCEPT + { + return 0; + } //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! - //! This method is not used because tensors with dynamic shapes can have different sizes in different execution contexts. + //! This method is not used because tensors with dynamic shapes can have different sizes in different execution + //! contexts. //! //! Instead, derived classes should override the overload of enqueue that takes pointers to PluginTensorDesc. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED - int enqueue(int /*batchSize*/, const void* const* /*inputs*/, void** /*outputs*/, void* /*workspace*/, cudaStream_t /*stream*/) _TENSORRT_FINAL TRTNOEXCEPT + int32_t enqueue(int32_t /*batchSize*/, const void* const* /*inputs*/, void** /*outputs*/, void* /*workspace*/, + cudaStream_t /*stream*/) _TENSORRT_FINAL TRTNOEXCEPT { return 1; } @@ -632,7 +682,7 @@ class IProfiler //! //! The power weights of an IScaleLayer are omitted. Refitting those is not supported. //! -enum class WeightsRole : int +enum class WeightsRole : int32_t { kKERNEL = 0, //!< kernel for IConvolutionLayer, IDeconvolutionLayer, or IFullyConnectedLayer kBIAS = 1, //!< bias for IConvolutionLayer, IDeconvolutionLayer, or IFullyConnectedLayer @@ -641,28 +691,30 @@ enum class WeightsRole : int kCONSTANT = 4, //!< weights for IConstantLayer }; +//! Maximum number of elements in WeightsRole enum. \see WeightsRole template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 5; -} //!< Maximum number of elements in WeightsRole enum. \see WeightsRole +} //! //! \enum DeviceType //! \brief The device that this layer/network will execute on. //! //! -enum class DeviceType : int +enum class DeviceType : int32_t { kGPU, //!< GPU Device kDLA, //!< DLA Core }; + +//! Maximum number of elements in DeviceType enum. \see DeviceType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in DeviceType enum. \see DeviceType - +} //! //! \class IRuntime @@ -687,12 +739,12 @@ class IRuntime //! //! \brief Set the DLA core that the deserialized engine must execute on. - //! \param dlaCore The DLA core to execute the engine on (0 to N-1, where N is the maximum number of DLA's present on the device). Default value is 0. - //! \see getDLACore() + //! \param dlaCore The DLA core to execute the engine on (0 to N-1, where N is the maximum number of DLA's present + //! on the device). Default value is 0. \see getDLACore() //! //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused. //! - virtual void setDLACore(int dlaCore) noexcept = 0; + virtual void setDLACore(int32_t dlaCore) noexcept = 0; //! //! \brief Get the DLA core that the engine executes on. @@ -700,12 +752,12 @@ class IRuntime //! //! \warning Starting with TensorRT 8, the default value will be -1 if the DLA is not specified or unused. //! - virtual int getDLACore() const noexcept = 0; + virtual int32_t getDLACore() const noexcept = 0; //! //! \brief Returns number of DLA hardware cores accessible. //! - virtual int getNbDLACores() const noexcept = 0; + virtual int32_t getNbDLACores() const noexcept = 0; //! //! \brief Destroy this object. @@ -811,7 +863,7 @@ class IRefitter //! If layerNames!=nullptr, each written pointer points to a string owned by //! the engine being refitted, and becomes invalid when the engine is destroyed. //! - virtual int getMissing(int size, const char** layerNames, WeightsRole* roles) TRTNOEXCEPT = 0; + virtual int32_t getMissing(int32_t size, const char** layerNames, WeightsRole* roles) TRTNOEXCEPT = 0; //! //! \brief Get description of all weights that could be refit. @@ -825,7 +877,7 @@ class IRefitter //! If layerNames!=nullptr, each written pointer points to a string owned by //! the engine being refitted, and becomes invalid when the engine is destroyed. //! - virtual int getAll(int size, const char** layerNames, WeightsRole* roles) TRTNOEXCEPT = 0; + virtual int32_t getAll(int32_t size, const char** layerNames, WeightsRole* roles) TRTNOEXCEPT = 0; virtual void destroy() TRTNOEXCEPT = 0; @@ -876,7 +928,7 @@ class IRefitter //! If tensorNames!=nullptr, each written pointer points to a string owned by //! the engine being refitted, and becomes invalid when the engine is destroyed. //! - virtual int getTensorsWithDynamicRange(int size, const char** tensorNames) const TRTNOEXCEPT = 0; + virtual int32_t getTensorsWithDynamicRange(int32_t size, const char** tensorNames) const TRTNOEXCEPT = 0; //! //! \brief Set the ErrorRecorder for this interface @@ -943,18 +995,19 @@ class IPluginFactory //! //! \see IOptimizationProfile::setDimensions(), IOptimizationProfile::setShapeValues() //! -enum class OptProfileSelector : int +enum class OptProfileSelector : int32_t { kMIN = 0, //!< This is used to set or get the minimum permitted value for dynamic dimensions etc. kOPT = 1, //!< This is used to set or get the value that is used in the optimization (kernel selection). kMAX = 2 //!< This is used to set or get the maximum permitted value for dynamic dimensions etc. }; +//!< Number of different values of OptProfileSelector enum. \see OptProfileSelector template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 3; -} //!< Number of different values of OptProfileSelector enum. \see OptProfileSelector +} //! //! \class IOptimizationProfile @@ -1039,7 +1092,8 @@ class IOptimizationProfile //! \warning If run on DLA, minimum, optimum, and maximum shape values must to be the same. //! virtual bool setShapeValues( - const char* inputName, OptProfileSelector select, const int32_t* values, int nbValues) noexcept = 0; + const char* inputName, OptProfileSelector select, const int32_t* values, int32_t nbValues) noexcept + = 0; //! //! \brief Get the number of values for an input shape tensor. @@ -1047,7 +1101,7 @@ class IOptimizationProfile //! This will return the number of shape values if setShapeValues() has been called before for this input tensor. //! Otherwise, return -1. //! - virtual int getNbShapeValues(const char* inputName) const noexcept = 0; + virtual int32_t getNbShapeValues(const char* inputName) const noexcept = 0; //! //! \brief Get the minimum / optimum / maximum values for an input shape tensor. @@ -1113,7 +1167,7 @@ class ICudaEngine //! //! \see getBindingIndex(); //! - virtual int getNbBindings() const noexcept = 0; + virtual int32_t getNbBindings() const noexcept = 0; //! //! \brief Retrieve the binding index for a named tensor. @@ -1121,7 +1175,8 @@ class ICudaEngine //! IExecutionContext::enqueue() and IExecutionContext::execute() require an array of buffers. //! //! Engine bindings map from tensor names to indices in this array. - //! Binding indices are assigned at engine build time, and take values in the range [0 ... n-1] where n is the total number of inputs and outputs. + //! Binding indices are assigned at engine build time, and take values in the range [0 ... n-1] where n is the total + //! number of inputs and outputs. //! //! To get the binding index of the name in an optimization profile with index k > 0, //! mangle the name by appending " [profile k]", as described for method getBindingName(). @@ -1131,7 +1186,7 @@ class ICudaEngine //! //! \see getNbBindings() getBindingName() //! - virtual int getBindingIndex(const char* name) const noexcept = 0; + virtual int32_t getBindingIndex(const char* name) const noexcept = 0; //! //! \brief Retrieve the name corresponding to a binding index. @@ -1148,7 +1203,7 @@ class ICudaEngine //! //! \see getBindingIndex() //! - virtual const char* getBindingName(int bindingIndex) const noexcept = 0; + virtual const char* getBindingName(int32_t bindingIndex) const noexcept = 0; //! //! \brief Determine whether a binding is an input binding. @@ -1158,7 +1213,7 @@ class ICudaEngine //! //! \see getBindingIndex() //! - virtual bool bindingIsInput(int bindingIndex) const noexcept = 0; + virtual bool bindingIsInput(int32_t bindingIndex) const noexcept = 0; //! //! \brief Get the dimensions of a binding. @@ -1180,7 +1235,7 @@ class ICudaEngine //! //! \see getBindingIndex() //! - virtual Dims getBindingDimensions(int bindingIndex) const noexcept = 0; + virtual Dims getBindingDimensions(int32_t bindingIndex) const noexcept = 0; //! //! \brief Determine the required data type for a buffer from its binding index. @@ -1190,7 +1245,7 @@ class ICudaEngine //! //! \see getBindingIndex() //! - virtual DataType getBindingDataType(int bindingIndex) const noexcept = 0; + virtual DataType getBindingDataType(int32_t bindingIndex) const noexcept = 0; //! //! \brief Get the maximum batch size which can be used for inference. @@ -1199,23 +1254,29 @@ class ICudaEngine //! //! \return The maximum batch size for this engine. //! - virtual int getMaxBatchSize() const noexcept = 0; + virtual int32_t getMaxBatchSize() const noexcept = 0; //! //! \brief Get the number of layers in the network. //! - //! The number of layers in the network is not necessarily the number in the original network definition, as layers may be combined or eliminated as the engine is - //! optimized. This value can be useful when building per-layer tables, such as when aggregating profiling data over a number of executions. + //! The number of layers in the network is not necessarily the number in the original network definition, as layers + //! may be combined or eliminated as the engine is optimized. This value can be useful when building per-layer + //! tables, such as when aggregating profiling data over a number of executions. //! //! \return The number of layers in the network. //! - virtual int getNbLayers() const noexcept = 0; + virtual int32_t getNbLayers() const noexcept = 0; //! //! \brief Get the amount of workspace the engine uses. //! - //! The workspace size will be no greater than the value provided to the builder when the engine was built, and will typically be smaller. - //! Workspace will be allocated for each execution context. + //! The workspace size will be no greater than the value provided to the builder when the engine was built, and will + //! typically be smaller. Workspace will be allocated for each execution context. + //! + //! This method is not used because getDeviceMemorySize returns the total amount of device memory required by an + //! execution context. + //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. //! TRT_DEPRECATED virtual std::size_t getWorkspaceSize() const noexcept = 0; @@ -1258,7 +1319,7 @@ class ICudaEngine //! \param bindingIndex The binding index. //! \return The location of the bound tensor with given index. //! - virtual TensorLocation getLocation(int bindingIndex) const noexcept = 0; + virtual TensorLocation getLocation(int32_t bindingIndex) const noexcept = 0; protected: virtual ~ICudaEngine() {} @@ -1295,7 +1356,7 @@ class ICudaEngine //! //! \see ICudaEngine::getBindingVectorizedDim() //! - virtual int getBindingBytesPerComponent(int bindingIndex) const noexcept = 0; + virtual int32_t getBindingBytesPerComponent(int32_t bindingIndex) const noexcept = 0; //! //! \brief Return the number of components included in one element. @@ -1306,14 +1367,14 @@ class ICudaEngine //! //! \see ICudaEngine::getBindingVectorizedDim() //! - virtual int getBindingComponentsPerElement(int bindingIndex) const noexcept = 0; + virtual int32_t getBindingComponentsPerElement(int32_t bindingIndex) const noexcept = 0; //! //! \brief Return the binding format. //! //! \param bindingIndex The binding Index. //! - virtual TensorFormat getBindingFormat(int bindingIndex) const noexcept = 0; + virtual TensorFormat getBindingFormat(int32_t bindingIndex) const noexcept = 0; //! //! \brief Return the human readable description of the tensor format. @@ -1329,7 +1390,7 @@ class ICudaEngine //! //! \param bindingIndex The binding Index. //! - virtual const char* getBindingFormatDesc(int bindingIndex) const noexcept = 0; + virtual const char* getBindingFormatDesc(int32_t bindingIndex) const noexcept = 0; //! //! \brief Return the dimension index that the buffer is vectorized. @@ -1338,7 +1399,7 @@ class ICudaEngine //! //! \param bindingIndex The binding Index. //! - virtual int getBindingVectorizedDim(int bindingIndex) const noexcept = 0; + virtual int32_t getBindingVectorizedDim(int32_t bindingIndex) const noexcept = 0; //! //! \brief Returns the name of the network associated with the engine. @@ -1358,7 +1419,7 @@ class ICudaEngine //! \return Number of optimization profiles. It is always at least 1. //! //! \see IExecutionContext::setOptimizationProfile() - virtual int getNbOptimizationProfiles() const noexcept = 0; + virtual int32_t getNbOptimizationProfiles() const noexcept = 0; //! //! \brief Get the minimum / optimum / maximum dimensions for a particular binding under an optimization profile. @@ -1382,7 +1443,9 @@ class ICudaEngine //! //! Otherwise the bindingIndex is considered invalid. //! - virtual Dims getProfileDimensions(int bindingIndex, int profileIndex, OptProfileSelector select) const noexcept = 0; + virtual Dims getProfileDimensions(int32_t bindingIndex, int32_t profileIndex, OptProfileSelector select) const + noexcept + = 0; //! //! \brief Get minimum / optimum / maximum values for an input shape binding under an optimization profile. @@ -1405,8 +1468,8 @@ class ICudaEngine //! //! \see ICudaEngine::getProfileDimensions //! - virtual const int32_t* getProfileShapeValues(int profileIndex, int inputIndex, OptProfileSelector select) const - noexcept + virtual const int32_t* getProfileShapeValues( + int32_t profileIndex, int32_t inputIndex, OptProfileSelector select) const noexcept = 0; //! @@ -1440,7 +1503,7 @@ class ICudaEngine //! //! \see isExecutionBinding() //! - virtual bool isShapeBinding(int bindingIndex) const noexcept = 0; + virtual bool isShapeBinding(int32_t bindingIndex) const noexcept = 0; //! //! \brief True if pointer to tensor data is required for execution phase, false if nullptr can be supplied. @@ -1451,14 +1514,15 @@ class ICudaEngine //! //! \see isShapeBinding() //! - virtual bool isExecutionBinding(int bindingIndex) const noexcept = 0; + virtual bool isExecutionBinding(int32_t bindingIndex) const noexcept = 0; //! //! \brief determine that execution capability this engine has. //! //! If the engine has EngineCapability::kDEFAULT, then all engine functionality is valid.. //! If the engine has EngineCapability::kSAFE_GPU, then only the functionality in safe::ICudaEngine is valid. - //! If the engine has EngineCapability::kSAFE_DLA, then only serialize, destroy, and const-accessor functions are valid. + //! If the engine has EngineCapability::kSAFE_DLA, then only serialize, destroy, and const-accessor functions are + //! valid. //! //! \return The EngineCapability flag that the engine was built for. //! @@ -1522,35 +1586,38 @@ class IExecutionContext //! //! \brief Synchronously execute inference on a batch. //! - //! This method requires an array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine::getBindingIndex() - //! \param batchSize The batch size. This is at most the value supplied when the engine was built. - //! \param bindings An array of pointers to input and output buffers for the network. + //! This method requires an array of input and output buffers. The mapping from tensor names to indices can be + //! queried using ICudaEngine::getBindingIndex() \param batchSize The batch size. This is at most the value supplied + //! when the engine was built. \param bindings An array of pointers to input and output buffers for the network. //! //! \return True if execution succeeded. //! //! \see ICudaEngine::getBindingIndex() ICudaEngine::getMaxBatchSize() //! - virtual bool execute(int batchSize, void** bindings) noexcept = 0; + virtual bool execute(int32_t batchSize, void** bindings) noexcept = 0; //! //! \brief Asynchronously execute inference on a batch. //! - //! This method requires an array of input and output buffers. The mapping from tensor names to indices can be queried using ICudaEngine::getBindingIndex() - //! \param batchSize The batch size. This is at most the value supplied when the engine was built. - //! \param bindings An array of pointers to input and output buffers for the network. + //! This method requires an array of input and output buffers. The mapping from tensor names to indices can be + //! queried using ICudaEngine::getBindingIndex() \param batchSize The batch size. This is at most the value supplied + //! when the engine was built. \param bindings An array of pointers to input and output buffers for the network. //! \param stream A cuda stream on which the inference kernels will be enqueued - //! \param inputConsumed An optional event which will be signaled when the input buffers can be refilled with new data + //! \param inputConsumed An optional event which will be signaled when the input buffers can be refilled with new + //! data //! //! \return True if the kernels were enqueued successfully. //! //! \see ICudaEngine::getBindingIndex() ICudaEngine::getMaxBatchSize() //! - virtual bool enqueue(int batchSize, void** bindings, cudaStream_t stream, cudaEvent_t* inputConsumed) noexcept = 0; + virtual bool enqueue(int32_t batchSize, void** bindings, cudaStream_t stream, cudaEvent_t* inputConsumed) noexcept + = 0; //! //! \brief Set the debug sync flag. //! - //! If this flag is set to true, the engine will log the successful execution for each kernel during execute(). It has no effect when using enqueue(). + //! If this flag is set to true, the engine will log the successful execution for each kernel during execute(). It + //! has no effect when using enqueue(). //! //! \see getDebugSync() //! @@ -1638,7 +1705,7 @@ class IExecutionContext //! //! \see getBindingComponentsPerElement //! - virtual Dims getStrides(int bindingIndex) const noexcept = 0; + virtual Dims getStrides(int32_t bindingIndex) const noexcept = 0; public: //! @@ -1649,6 +1716,10 @@ class IExecutionContext //! //! The selected profile will be used in subsequent calls to execute() or enqueue(). //! + //! When an optimization profile is switched via this API, TensorRT may + //! enqueue GPU memory copy operations required to set up the new profile during the subsequent enqueue() + //! operations. To avoid these calls during enqueue(), use setOptimizationProfileAsync() instead. + //! //! If the associated CUDA engine has dynamic inputs, this method must be called at least once //! with a unique profileIndex before calling execute or enqueue (i.e. the profile index //! may not be in use by another execution context that has not been destroyed yet). @@ -1665,8 +1736,12 @@ class IExecutionContext //! //! \return true if the call succeeded, else false (e.g. input out of range) //! - //! \see ICudaEngine::getNbOptimizationProfiles() - virtual bool setOptimizationProfile(int profileIndex) noexcept = 0; + //! \deprecated This API is superseded by setOptimizationProfileAsync and will be removed in TensorRT 9.0. + //! + //! \see ICudaEngine::getNbOptimizationProfiles() IExecutionContext::setOptimizationProfileAsync() + //! + TRT_DEPRECATED + virtual bool setOptimizationProfile(int32_t profileIndex) noexcept = 0; //! //! \brief Get the index of the currently selected optimization profile. @@ -1675,7 +1750,7 @@ class IExecutionContext //! to be created, or explicitly for all subsequent contexts), an invalid value of -1 will be returned //! and all calls to enqueue() or execute() will fail until a valid profile index has been set. //! - virtual int getOptimizationProfile() const noexcept = 0; + virtual int32_t getOptimizationProfile() const noexcept = 0; //! //! \brief Set the dynamic dimensions of a binding @@ -1698,7 +1773,7 @@ class IExecutionContext //! //! \see ICudaEngine::getBindingIndex //! - virtual bool setBindingDimensions(int bindingIndex, Dims dimensions) noexcept = 0; + virtual bool setBindingDimensions(int32_t bindingIndex, Dims dimensions) noexcept = 0; //! //! \brief Get the dynamic dimensions of a binding @@ -1725,7 +1800,7 @@ class IExecutionContext //! //! \see ICudaEngine::getProfileDimensions //! - virtual Dims getBindingDimensions(int bindingIndex) const noexcept = 0; + virtual Dims getBindingDimensions(int32_t bindingIndex) const noexcept = 0; //! //! \brief Set values of input tensor required by shape calculations. @@ -1742,10 +1817,11 @@ class IExecutionContext //! This method will fail unless a valid optimization profile is defined for the current //! execution context (getOptimizationProfile() must not be -1). //! - virtual bool setInputShapeBinding(int bindingIndex, const int32_t* data) noexcept = 0; + virtual bool setInputShapeBinding(int32_t bindingIndex, const int32_t* data) noexcept = 0; //! - //! \brief Get values of an input tensor required for shape calculations or an output tensor produced by shape calculations. + //! \brief Get values of an input tensor required for shape calculations or an output tensor produced by shape + //! calculations. //! //! \param bindingIndex index of an input or output tensor for which //! ICudaEngine::isShapeBinding(bindingIndex) is true. @@ -1760,7 +1836,7 @@ class IExecutionContext //! //! \see isShapeBinding(bindingIndex) //! - virtual bool getShapeBinding(int bindingIndex, int32_t* data) const noexcept = 0; + virtual bool getShapeBinding(int32_t bindingIndex, int32_t* data) const noexcept = 0; //! //! \brief Whether all dynamic dimensions of input tensors have been specified @@ -1845,19 +1921,62 @@ class IExecutionContext //! due to resource allocation. Please call enqueueV2() once before capturing the graph. //! virtual bool enqueueV2(void** bindings, cudaStream_t stream, cudaEvent_t* inputConsumed) noexcept = 0; -}; -} + + //! + //! \brief Select an optimization profile for the current context with async + //! semantics. + //! + //! \param profileIndex Index of the profile. It must lie between 0 and + //! getEngine().getNbOptimizationProfiles() - 1 + //! + //! \param stream A cuda stream on which the cudaMemcpyAsyncs may be + //! enqueued + //! + //! When an optimization profile is switched via this API, TensorRT may + //! require that data is copied via cudaMemcpyAsync. It is the + //! application’s responsibility to guarantee that synchronization between + //! the profile sync stream and the enqueue stream occurs. + //! + //! The selected profile will be used in subsequent calls to execute() or + //! enqueue(). + //! If the associated CUDA engine has inputs with dynamic shapes, the + //! optimization profile must be set with a unique profileIndex before + //! calling execute or enqueue. + //! For the first execution context that is created for an engine, + //! setOptimizationProfile(0) is called implicitly. + //! + //! If the associated CUDA engine does not have inputs with dynamic shapes, + //! this method need not be called, in which case the default profile index + //! of 0 will be used. + //! + //! setOptimizationProfileAsync() must be called before calling + //! setBindingDimensions() and setInputShapeBinding() for all dynamic input + //! tensors or input shape tensors, which in turn must be called before + //! either execute() or enqueue(). + //! + //! \warning Not synchronizing the stream used at enqueue with the stream + //! used to set optimization profile asynchronously using this API will + //! result in undefined behavior. + //! + //! \return true if the call succeeded, else false (e.g. input out of range) + //! + //! \see ICudaEngine::getNbOptimizationProfiles() + //! IExecutionContext::setOptimizationProfile() + virtual bool setOptimizationProfileAsync(int32_t profileIndex, cudaStream_t stream) noexcept = 0; +}; // class IExecutionContext +} // namespace nvinfer1 + //! //! Internal C entry point for creating IRuntime. //! @private //! -extern "C" TENSORRTAPI void* createInferRuntime_INTERNAL(void* logger, int version); +extern "C" TENSORRTAPI void* createInferRuntime_INTERNAL(void* logger, int32_t version); //! //! Internal C entry point for creating IRefitter. //! @private //! -extern "C" TENSORRTAPI void* createInferRefitter_INTERNAL(void* engine, void* logger, int version); +extern "C" TENSORRTAPI void* createInferRefitter_INTERNAL(void* engine, void* logger, int32_t version); namespace nvinfer1 { diff --git a/include/NvInferRuntimeCommon.h b/include/NvInferRuntimeCommon.h index fb3c139d..e6c6dd00 100644 --- a/include/NvInferRuntimeCommon.h +++ b/include/NvInferRuntimeCommon.h @@ -64,6 +64,9 @@ #else #define TENSORRTAPI #endif + +//! Defined for use with legacy APIs that have not been updated to noexcept yet. +//! Do not use with new APIs, use noexcept instead. #define TRTNOEXCEPT //! //! \file NvInferRuntimeCommon.h @@ -79,7 +82,8 @@ struct cudnnContext; typedef struct CUstream_st* cudaStream_t; //!< Forward declaration of cudaStream_t. typedef struct CUevent_st* cudaEvent_t; //!< Forward declaration of cudaEvent_t. -static const int NV_TENSORRT_VERSION = (NV_TENSORRT_MAJOR * 1000) + (NV_TENSORRT_MINOR * 100) + NV_TENSORRT_PATCH; // major, minor, patch +static const int32_t NV_TENSORRT_VERSION + = (NV_TENSORRT_MAJOR * 1000) + (NV_TENSORRT_MINOR * 100) + NV_TENSORRT_PATCH; // major, minor, patch //! //! \namespace nvinfer1 @@ -90,14 +94,18 @@ namespace nvinfer1 { class IErrorRecorder; //!< Forward declare IErrorRecorder for use in other interfaces. -class IGpuAllocator; //!< Forward declare IGpuAllocator for use in other interfaces. +class IGpuAllocator; //!< Forward declare IGpuAllocator for use in other interfaces. + +//! Maximum number of elements in an enumeration type. +template +constexpr inline int32_t EnumMax(); //! //! \enum ActivationType //! //! \brief Enumerates the types of activation to perform in an activation layer. //! -enum class ActivationType : int +enum class ActivationType : int32_t { kRELU = 0, //!< Rectified linear activation. kSIGMOID = 1, //!< Sigmoid activation. @@ -113,21 +121,19 @@ enum class ActivationType : int kTHRESHOLDED_RELU = 11 //!< Thresholded ReLU activation: x>alpha ? x : 0 }; -template -constexpr inline int EnumMax(); //!< Maximum number of elements in an enumeration type. - +//! Maximum number of elements in ActivationType enum. \see ActivationType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 12; -} //!< Maximum number of elements in ActivationType enum. \see ActivationType +} //! //! \enum DataType //! //! \brief The type of weights and tensors. //! -enum class DataType : int +enum class DataType : int32_t { //! 32-bit floating point format. kFLOAT = 0, @@ -145,17 +151,18 @@ enum class DataType : int kBOOL = 4 }; +//! Maximum number of elements in DataType enum. \see DataType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 5; -} //!< Maximum number of elements in DataType enum. \see DataType +} //! //! \enum DimensionType //! \brief The type of data encoded across this dimension. //! -enum class DimensionType : int +enum class DimensionType : int32_t { kSPATIAL = 0, //!< Elements correspond to different spatial data. kCHANNEL = 1, //!< Elements correspond to different channels. @@ -163,11 +170,12 @@ enum class DimensionType : int kSEQUENCE = 3 //!< Elements correspond to different sequence values. }; +//! Maximum number of elements in DimensionType enum. \see DimensionType template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 4; -} //!< Maximum number of elements in DimensionType enum. \see DimensionType +} //! //! \class Dims @@ -186,10 +194,11 @@ constexpr inline int EnumMax() class Dims { public: - static const int MAX_DIMS = 8; //!< The maximum number of dimensions supported for a tensor. - int nbDims; //!< The number of dimensions. - int d[MAX_DIMS]; //!< The extent of each dimension. - TRT_DEPRECATED DimensionType type[MAX_DIMS]; //!< The type of each dimension. + static const int32_t MAX_DIMS = 8; //!< The maximum number of dimensions supported for a tensor. + int32_t nbDims; //!< The number of dimensions. + int32_t d[MAX_DIMS]; //!< The extent of each dimension. + TRT_DEPRECATED DimensionType type[MAX_DIMS]; //!< The type of each dimension, provided for backwards compatibility + //!< and will be removed in TensorRT 8.0. }; //! @@ -213,17 +222,15 @@ typedef uint32_t TensorFormats; //! For more information about data formats, see the topic "Data Format Description" located in the //! TensorRT Developer Guide (https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html). //! -enum class TensorFormat : int +enum class TensorFormat : int32_t { //! Row major linear format. //! For a tensor with dimensions {N, C, H, W} or {numbers, channels, //! columns, rows}, the dimensional index corresponds to {3, 2, 1, 0} //! and thus the order is W minor. - //! - //! For DLA usage, the tensor sizes are limited to C,H,W in the range [1,8192]. - //! kLINEAR = 0, - kNCHW TRT_DEPRECATED_ENUM = kLINEAR, //!< Deprecated name of kLINEAR, provided for backwards compatibility + kNCHW TRT_DEPRECATED_ENUM = kLINEAR, //!< Deprecated name of kLINEAR, provided for backwards compatibility and will + //!< be removed in TensorRT 8.0. //! Two wide channel vectorized row major format. This format is bound to //! FP16. It is only available for dimensions >= 3. @@ -232,7 +239,8 @@ enum class TensorFormat : int //! [N][(C+1)/2][H][W][2], with the tensor coordinates (n, c, h, w) //! mapping to array subscript [n][c/2][h][w][c%2]. kCHW2 = 1, - kNC2HW2 TRT_DEPRECATED_ENUM = kCHW2, //!< Deprecated name of kCHW2, provided for backwards compatibility + kNC2HW2 TRT_DEPRECATED_ENUM = kCHW2, //!< Deprecated name of kCHW2, provided for backwards compatibility and will + //!< be removed in TensorRT 8.0. //! Eight channel format where C is padded to a multiple of 8. This format //! is bound to FP16. It is only available for dimensions >= 3. @@ -241,7 +249,8 @@ enum class TensorFormat : int //! [N][H][W][(C+7)/8*8], with the tensor coordinates (n, h, w, c) //! mapping to array subscript [n][h][w][c]. kHWC8 = 2, - kNHWC8 TRT_DEPRECATED_ENUM = kHWC8, //!< Deprecated name of kHWC8, provided for backwards compatibility + kNHWC8 TRT_DEPRECATED_ENUM = kHWC8, //!< Deprecated name of kHWC8, provided for backwards compatibility and will + //!< be removed in TensorRT 8.0. //! Four wide channel vectorized row major format. This format is bound to //! INT8 or FP16. It is only available for dimensions >= 3. @@ -251,7 +260,10 @@ enum class TensorFormat : int //! [N][(C+3)/4][H][W][4], with the tensor coordinates (n, c, h, w) //! mapping to array subscript [n][c/4][h][w][c%4]. //! If running on the DLA, this format can be used for acceleration - //! with the caveat that C must equal 4. + //! with the caveat that C must be equal or lesser than 4. + //! If used as DLA input with allowGPUFallback disable, it needs to meet + //! line stride requirement of DLA format. Column stride in bytes should + //! be multiple of 32. kCHW4 = 3, //! Sixteen wide channel vectorized row major format. This format is bound @@ -261,9 +273,8 @@ enum class TensorFormat : int //! [N][(C+15)/16][H][W][16], with the tensor coordinates (n, c, h, w) //! mapping to array subscript [n][c/16][h][w][c%16]. //! - //! For DLA usage, this format maps to the native image format for FP16, + //! For DLA usage, this format maps to the native format for FP16, //! and the tensor sizes are limited to C,H,W in the range [1,8192]. - //! kCHW16 = 4, //! Thirty-two wide channel vectorized row major format. This format is @@ -273,10 +284,29 @@ enum class TensorFormat : int //! [N][(C+31)/32][H][W][32], with the tensor coordinates (n, c, h, w) //! mapping to array subscript [n][c/32][h][w][c%32]. //! - //! For DLA usage, this format maps to the native image format for INT8, + //! For DLA usage, this format maps to the native format for INT8, //! and the tensor sizes are limited to C,H,W in the range [1,8192]. - //! - kCHW32 = 5 + kCHW32 = 5, + + //! Eight channel format where C is padded to a multiple of 8. This format + //! is bound to FP16, and it is only available for dimensions >= 4. + //! For a tensor with dimensions {N, D, H, W, C}, + //! the memory layout is equivalent to an array with dimensions + //! [N][D][H][W][(C+7)/8*8], with the tensor coordinates (n, d, h, w, c) + //! mapping to array subscript [n][d][h][w][c]. + kDHWC8 = 6, + + //! Thirty-two wide channel vectorized row major format. This format is + //! bound to FP16 and INT8 and is only available for dimensions >= 4. + //! For a tensor with dimensions {N, C, D, H, W}, + //! the memory layout is equivalent to a C array with dimensions + //! [N][(C+31)/32][D][H][W][32], with the tensor coordinates (n, c, d, h, w) + //! mapping to array subscript [n][c/32][d][h][w][c%32]. + kCDHW32 = 7, + + //! Non-vectorized channel-last format. This format is bound to FP32 + //! and is only available for dimensions >= 3. + kHWC = 8 }; //! @@ -286,11 +316,12 @@ enum class TensorFormat : int //! using PluginFormat = TensorFormat; +//! Maximum number of elements in TensorFormat enum. \see TensorFormat template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { - return 6; -} //!< Maximum number of elements in TensorFormat enum. \see TensorFormat + return 9; +} //! \struct PluginTensorDesc //! @@ -341,9 +372,10 @@ class IPluginV2 //! //! \brief Return the API version with which this plugin was built. //! - //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins. + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. //! - virtual int getTensorRTVersion() const TRTNOEXCEPT + virtual int32_t getTensorRTVersion() const TRTNOEXCEPT { return NV_TENSORRT_VERSION; } @@ -365,9 +397,10 @@ class IPluginV2 //! //! \return The number of outputs. //! - //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize(). + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). //! - virtual int getNbOutputs() const TRTNOEXCEPT = 0; + virtual int32_t getNbOutputs() const TRTNOEXCEPT = 0; //! //! \brief Get the dimension of an output tensor. @@ -376,9 +409,10 @@ class IPluginV2 //! \param inputs The input tensors. //! \param nbInputDims The number of input tensors. //! - //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called prior to any call to initialize(). + //! This function is called by the implementations of INetworkDefinition and IBuilder. In particular, it is called + //! prior to any call to initialize(). //! - virtual Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) TRTNOEXCEPT = 0; + virtual Dims getOutputDimensions(int32_t index, const Dims* inputs, int32_t nbInputDims) TRTNOEXCEPT = 0; //! //! \brief Check format support. @@ -387,8 +421,9 @@ class IPluginV2 //! \param format PluginFormat requested. //! \return true if the plugin supports the type-format combination. //! - //! This function is called by the implementations of INetworkDefinition, IBuilder, and safe::ICudaEngine/ICudaEngine. - //! In particular, it is called when creating an engine and when deserializing an engine. + //! This function is called by the implementations of INetworkDefinition, IBuilder, and + //! safe::ICudaEngine/ICudaEngine. In particular, it is called when creating an engine and when deserializing an + //! engine. //! //! \warning for the format field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt @@ -401,8 +436,8 @@ class IPluginV2 //! //! \brief Configure the layer. //! - //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis - //! of its weights, dimensions, and maximum batch size. + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, and maximum batch size. //! //! \param inputDims The input tensor dimensions. //! \param nbInputs The number of inputs. @@ -412,7 +447,8 @@ class IPluginV2 //! \param format The format selected for the engine. //! \param maxBatchSize The maximum batch size. //! - //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). + //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be + //! 3-dimensional CHW dimensions). //! //! \warning for the format field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt @@ -420,30 +456,31 @@ class IPluginV2 //! //! \warning DataType:kBOOL not supported. //! - virtual void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) TRTNOEXCEPT = 0; + virtual void configureWithFormat(const Dims* inputDims, int32_t nbInputs, const Dims* outputDims, int32_t nbOutputs, + DataType type, PluginFormat format, int32_t maxBatchSize) TRTNOEXCEPT = 0; //! //! \brief Initialize the layer for execution. This is called when the engine is created. //! //! \return 0 for success, else non-zero (which will cause engine termination). //! - virtual int initialize() TRTNOEXCEPT = 0; + virtual int32_t initialize() TRTNOEXCEPT = 0; //! - //! \brief Release resources acquired during plugin layer initialization. This is called when the engine is destroyed. - //! \see initialize() + //! \brief Release resources acquired during plugin layer initialization. This is called when the engine is + //! destroyed. \see initialize() //! virtual void terminate() TRTNOEXCEPT = 0; //! //! \brief Find the workspace size required by the layer. //! - //! This function is called during engine startup, after initialize(). The workspace size returned should be sufficient for any - //! batch size up to the maximum. + //! This function is called during engine startup, after initialize(). The workspace size returned should be + //! sufficient for any batch size up to the maximum. //! //! \return The workspace size. //! - virtual size_t getWorkspaceSize(int maxBatchSize) const TRTNOEXCEPT = 0; + virtual size_t getWorkspaceSize(int32_t maxBatchSize) const TRTNOEXCEPT = 0; //! //! \brief Execute the layer. @@ -456,7 +493,8 @@ class IPluginV2 //! //! \return 0 for success, else non-zero (which will cause engine termination). //! - virtual int enqueue(int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) TRTNOEXCEPT = 0; + virtual int32_t enqueue(int32_t batchSize, const void* const* inputs, void** outputs, void* workspace, + cudaStream_t stream) TRTNOEXCEPT = 0; //! //! \brief Find the size of the serialization buffer required. @@ -514,13 +552,13 @@ class IPluginV2Ext : public IPluginV2 public: //! //! \brief Return the DataType of the plugin output at the requested index. - //! The default behavior should be to return the type of the first input, or DataType::kFLOAT if the layer has no inputs. - //! The returned data type must have a format that is supported by the plugin. - //! \see supportsFormat() + //! The default behavior should be to return the type of the first input, or DataType::kFLOAT if the layer has no + //! inputs. The returned data type must have a format that is supported by the plugin. \see supportsFormat() //! //! \warning DataType:kBOOL not supported. //! - virtual nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const TRTNOEXCEPT = 0; + virtual nvinfer1::DataType getOutputDataType( + int32_t index, const nvinfer1::DataType* inputTypes, int32_t nbInputs) const TRTNOEXCEPT = 0; //! \brief Return true if output tensor is broadcast across a batch. //! @@ -532,7 +570,8 @@ class IPluginV2Ext : public IPluginV2 //! i.e. are unaffected by whether method canBroadcastInputAcrossBatch requests //! physical replication of the values. //! - virtual bool isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const TRTNOEXCEPT = 0; + virtual bool isOutputBroadcastAcrossBatch( + int32_t outputIndex, const bool* inputIsBroadcasted, int32_t nbInputs) const TRTNOEXCEPT = 0; //! \brief Return true if plugin can use input that is broadcast across batch without replication. //! @@ -547,13 +586,13 @@ class IPluginV2Ext : public IPluginV2 //! //! This method is called only for inputs that can be broadcast. //! - virtual bool canBroadcastInputAcrossBatch(int inputIndex) const TRTNOEXCEPT = 0; + virtual bool canBroadcastInputAcrossBatch(int32_t inputIndex) const TRTNOEXCEPT = 0; //! //! \brief Configure the layer with input and output data types. //! - //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make algorithm choices on the basis - //! of its weights, dimensions, data types and maximum batch size. + //! This function is called by the builder prior to initialize(). It provides an opportunity for the layer to make + //! algorithm choices on the basis of its weights, dimensions, data types and maximum batch size. //! //! \param inputDims The input tensor dimensions. //! \param nbInputs The number of inputs. @@ -566,19 +605,20 @@ class IPluginV2Ext : public IPluginV2 //! \param floatFormat The format selected for the engine for the floating point inputs/outputs. //! \param maxBatchSize The maximum batch size. //! - //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be 3-dimensional CHW dimensions). - //! When inputIsBroadcast or outputIsBroadcast is true, the outermost batch size for that input or output should be treated as if it is one. - //! \ref inputIsBroadcast[i] is true only if the input is semantically broadcast across the batch and \ref canBroadcastInputAcrossBatch(i) returned true. - //! \ref outputIsBroadcast[i] is true only if \ref isOutputBroadcastAcrossBatch(i) returned true. + //! The dimensions passed here do not include the outermost batch size (i.e. for 2-D image networks, they will be + //! 3-dimensional CHW dimensions). When inputIsBroadcast or outputIsBroadcast is true, the outermost batch size for + //! that input or output should be treated as if it is one. \ref inputIsBroadcast[i] is true only if the input is + //! semantically broadcast across the batch and \ref canBroadcastInputAcrossBatch(i) returned true. \ref + //! outputIsBroadcast[i] is true only if \ref isOutputBroadcastAcrossBatch(i) returned true. //! - //! \warning for the floatFormat field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and PluginFormat::kCHW32 - //! will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use PluginV2IOExt - //! or PluginV2DynamicExt for other PluginFormats. + //! \warning for the floatFormat field, the values PluginFormat::kCHW4, PluginFormat::kCHW16, and + //! PluginFormat::kCHW32 will not be passed in, this is to keep backward compatibility with TensorRT 5.x series. Use + //! PluginV2IOExt or PluginV2DynamicExt for other PluginFormats. //! - virtual void configurePlugin(const Dims* inputDims, int nbInputs, const Dims* outputDims, - int nbOutputs, const DataType* inputTypes, const DataType* outputTypes, - const bool* inputIsBroadcast, const bool* outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize) TRTNOEXCEPT = 0; + virtual void configurePlugin(const Dims* inputDims, int32_t nbInputs, const Dims* outputDims, int32_t nbOutputs, + const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast, + const bool* outputIsBroadcast, PluginFormat floatFormat, int32_t maxBatchSize) TRTNOEXCEPT = 0; virtual ~IPluginV2Ext() {} @@ -615,18 +655,22 @@ class IPluginV2Ext : public IPluginV2 //! \brief Return the API version with which this plugin was built. The //! upper byte reserved by TensorRT and is used to differentiate this from IPlguinV2. //! - //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with plugins. + //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with + //! plugins. //! - int getTensorRTVersion() const _TENSORRT_OVERRIDE TRTNOEXCEPT + int32_t getTensorRTVersion() const _TENSORRT_OVERRIDE TRTNOEXCEPT { - return (static_cast(PluginVersion::kV2_EXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); + return (static_cast(PluginVersion::kV2_EXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); } //! //! \brief Derived classes should not implement this. In a C++11 API it would be override final. //! - void configureWithFormat(const Dims* /*inputDims*/, int /*nbInputs*/, const Dims* /*outputDims*/, - int /*nbOutputs*/, DataType /*type*/, PluginFormat /*format*/, int /*maxBatchSize*/) _TENSORRT_OVERRIDE TRTNOEXCEPT {} + void configureWithFormat(const Dims* /*inputDims*/, int32_t /*nbInputs*/, const Dims* /*outputDims*/, + int32_t /*nbOutputs*/, DataType /*type*/, PluginFormat /*format*/, + int32_t /*maxBatchSize*/) _TENSORRT_OVERRIDE TRTNOEXCEPT + { + } }; //! \class IPluginV2IOExt @@ -652,7 +696,8 @@ class IPluginV2IOExt : public IPluginV2Ext //! \param out The output tensors attributes that are used for configuration. //! \param nbOutput Number of output tensors. //! - virtual void configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput) TRTNOEXCEPT = 0; + virtual void configurePlugin( + const PluginTensorDesc* in, int32_t nbInput, const PluginTensorDesc* out, int32_t nbOutput) TRTNOEXCEPT = 0; //! //! \brief Return true if plugin supports the format and datatype for the input/output indexed by pos. @@ -676,7 +721,8 @@ class IPluginV2IOExt : public IPluginV2Ext //! * A definition for a plugin that supports only FP16 NCHW for its two inputs, //! and FP32 NCHW for its single output: //! - //! return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ? DataType::kHALF : DataType::kFLOAT); + //! return inOut.format[pos] == TensorFormat::kLINEAR && (inOut.type[pos] == pos < 2 ? DataType::kHALF : + //! DataType::kFLOAT); //! //! * A definition for a "polymorphic" plugin with two inputs and one output that supports //! any format or type, but the inputs and output must have the same format and type: @@ -685,7 +731,8 @@ class IPluginV2IOExt : public IPluginV2Ext //! //! Warning: TensorRT will stop asking for formats once it finds kFORMAT_COMBINATION_LIMIT on combinations. //! - virtual bool supportsFormatCombination(int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) const TRTNOEXCEPT = 0; + virtual bool supportsFormatCombination( + int32_t pos, const PluginTensorDesc* inOut, int32_t nbInputs, int32_t nbOutputs) const TRTNOEXCEPT = 0; protected: //! @@ -695,19 +742,23 @@ class IPluginV2IOExt : public IPluginV2Ext //! Do not override this method as it is used by the TensorRT library to maintain backwards-compatibility with //! plugins. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED - int getTensorRTVersion() const _TENSORRT_OVERRIDE + int32_t getTensorRTVersion() const _TENSORRT_OVERRIDE { - return (static_cast(PluginVersion::kV2_IOEXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); + return (static_cast(PluginVersion::kV2_IOEXT) << 24 | (NV_TENSORRT_VERSION & 0xFFFFFF)); } //! //! \brief Deprecated interface inheriting from base class. Derived classes should not implement this. In a C++11 //! API it would be override final. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED void configureWithFormat( - const Dims*, int, const Dims*, int, DataType, PluginFormat, int) _TENSORRT_OVERRIDE _TENSORRT_FINAL + const Dims*, int32_t, const Dims*, int32_t, DataType, PluginFormat, int32_t) _TENSORRT_OVERRIDE _TENSORRT_FINAL { } @@ -715,9 +766,11 @@ class IPluginV2IOExt : public IPluginV2Ext //! \brief Deprecated interface inheriting from base class. Derived classes should not implement this. In a C++11 //! API it would be override final. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED - void configurePlugin(const Dims*, int, const Dims*, int, const DataType*, const DataType*, const bool*, const bool*, - PluginFormat, int) _TENSORRT_OVERRIDE _TENSORRT_FINAL + void configurePlugin(const Dims*, int32_t, const Dims*, int32_t, const DataType*, const DataType*, const bool*, + const bool*, PluginFormat, int32_t) _TENSORRT_OVERRIDE _TENSORRT_FINAL { } @@ -725,6 +778,8 @@ class IPluginV2IOExt : public IPluginV2Ext //! \brief Deprecated interface inheriting from base class. Derived classes should not implement this. In a C++11 //! API it would be override final. //! + //! \deprecated Deprecated interface will be removed in TensorRT 8.0. + //! TRT_DEPRECATED bool supportsFormat(DataType, PluginFormat) const _TENSORRT_OVERRIDE _TENSORRT_FINAL { @@ -737,7 +792,7 @@ class IPluginV2IOExt : public IPluginV2Ext //! \brief The possible field types for custom layer. //! -enum class PluginFieldType : int +enum class PluginFieldType : int32_t { kFLOAT16 = 0, //!< FP16 field type. kFLOAT32 = 1, //!< FP32 field type. @@ -789,7 +844,7 @@ class PluginField struct PluginFieldCollection { - int nbFields; //!< Number of PluginField entries + int32_t nbFields; //!< Number of PluginField entries const PluginField* fields; //!< Pointer to PluginField entries }; @@ -807,7 +862,10 @@ class IPluginCreator //! //! \brief Return the version of the API the plugin creator was compiled with. //! - virtual int getTensorRTVersion() const TRTNOEXCEPT { return NV_TENSORRT_VERSION; } + virtual int32_t getTensorRTVersion() const TRTNOEXCEPT + { + return NV_TENSORRT_VERSION; + } //! //! \brief Return the plugin name. @@ -879,7 +937,7 @@ class IPluginRegistry //! \brief Return all the registered plugin creators and the number of //! registered plugin creators. Returns nullptr if none found. //! - virtual IPluginCreator* const* getPluginCreatorList(int* numCreators) const noexcept = 0; + virtual IPluginCreator* const* getPluginCreatorList(int32_t* numCreators) const noexcept = 0; //! //! \brief Return plugin creator based on plugin type, version and @@ -919,22 +977,22 @@ class IPluginRegistry virtual IErrorRecorder* getErrorRecorder() const noexcept = 0; }; - //! //! \enum TensorLocation //! \brief The location for tensor data storage, device or host. //! -enum class TensorLocation : int +enum class TensorLocation : int32_t { kDEVICE = 0, //!< Data stored on device. kHOST = 1, //!< Data stored on host. }; +//! Maximum number of elements in TensorLocation enum. \see TensorLocation template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 2; -} //!< Maximum number of elements in TensorLocation enum. \see TensorLocation +} //! //! \class IGpuAllocator @@ -992,13 +1050,13 @@ class ILogger //! //! The severity corresponding to a log message. //! - enum class Severity : int + enum class Severity : int32_t { - kINTERNAL_ERROR = 0, //!< An internal error has occurred. Execution is unrecoverable. - kERROR = 1, //!< An application error has occurred. - kWARNING = 2, //!< An application error has been discovered, but TensorRT has recovered or fallen back to a default. - kINFO = 3, //!< Informational messages with instructional information. - kVERBOSE = 4, //!< Verbose messages with debugging information. + kINTERNAL_ERROR = 0, //!< Internal error has occurred. Execution is unrecoverable. + kERROR = 1, //!< Application error has occurred. + kWARNING = 2, //!< Application error has been discovered. TensorRT has recovered or fallen back to a default. + kINFO = 3, //!< Informational messages with instructional information. + kVERBOSE = 4, //!< Verbose messages with debugging information. }; //! @@ -1012,18 +1070,19 @@ class ILogger virtual ~ILogger() {} }; +//! Maximum number of elements in ILogger::Severity enum. \see ILogger::Severity template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 5; -} //!< Maximum number of elements in ILogger::Severity enum. \see ILogger::Severity +} //! //! \enum ErrorCode //! //! \brief Error codes that can be returned by TensorRT during execution. //! -enum class ErrorCode : int +enum class ErrorCode : int32_t { //! //! Execution completed successfully. @@ -1112,11 +1171,12 @@ enum class ErrorCode : int }; +//! Maximum number of elements in ErrorCode enum. \see ErrorCode template <> -constexpr inline int EnumMax() +constexpr inline int32_t EnumMax() { return 11; -} //!< Maximum number of elements in ErrorCode enum. \see ErrorCode +} //! //! \class IErrorRecorder @@ -1274,7 +1334,7 @@ class IErrorRecorder //! Internal C entry point for creating safe::IRuntime. //! @private //! -extern "C" TENSORRTAPI void* createSafeInferRuntime_INTERNAL(void* logger, int version); +extern "C" TENSORRTAPI void* createSafeInferRuntime_INTERNAL(void* logger, int32_t version); //! //! \brief Return the logger object. @@ -1286,7 +1346,7 @@ extern "C" TENSORRTAPI nvinfer1::ILogger* getLogger(); //! //! The format is as for TENSORRT_VERSION: (TENSORRT_MAJOR * 1000) + (TENSORRT_MINOR * 100) + TENSOR_PATCH. //! -extern "C" TENSORRTAPI int getInferLibVersion(); +extern "C" TENSORRTAPI int32_t getInferLibVersion(); //! //! \brief Return the plugin registry diff --git a/include/NvInferVersion.h b/include/NvInferVersion.h index ab02b239..caabc229 100644 --- a/include/NvInferVersion.h +++ b/include/NvInferVersion.h @@ -24,12 +24,12 @@ #define NV_INFER_VERSION_H #define NV_TENSORRT_MAJOR 7 //!< TensorRT major version. -#define NV_TENSORRT_MINOR 1 //!< TensorRT minor version. -#define NV_TENSORRT_PATCH 3 //!< TensorRT patch version. -#define NV_TENSORRT_BUILD 4 //!< TensorRT build number. +#define NV_TENSORRT_MINOR 2 //!< TensorRT minor version. +#define NV_TENSORRT_PATCH 1 //!< TensorRT patch version. +#define NV_TENSORRT_BUILD 6 //!< TensorRT build number. #define NV_TENSORRT_SONAME_MAJOR 7 //!< Shared object library major version number. -#define NV_TENSORRT_SONAME_MINOR 1 //!< Shared object library minor version number. -#define NV_TENSORRT_SONAME_PATCH 3 //!< Shared object library patch version number. +#define NV_TENSORRT_SONAME_MINOR 2 //!< Shared object library minor version number. +#define NV_TENSORRT_SONAME_PATCH 1 //!< Shared object library patch version number. #endif // NV_INFER_VERSION_H diff --git a/include/NvOnnxParser.h b/include/NvOnnxParser.h index e8b96346..ebe53fde 100644 --- a/include/NvOnnxParser.h +++ b/include/NvOnnxParser.h @@ -38,14 +38,14 @@ static const int NV_ONNX_PARSER_VERSION = ((NV_ONNX_PARSER_MAJOR * 10000) + (NV_ //! \brief The data structure containing the parsing capability of //! a set of nodes in an ONNX graph. //! -typedef std::pair, bool> SubGraph_t; +using SubGraph_t = std::pair, bool>; //! \typedef SubGraphCollection_t //! //! \brief The data structure containing all SubGraph_t partitioned //! out of an ONNX graph. //! -typedef std::vector SubGraphCollection_t; +using SubGraphCollection_t = std::vector; class onnxTensorDescriptorV1; //! @@ -57,7 +57,7 @@ namespace nvonnxparser { template -inline int EnumMax(); +inline int32_t EnumMax(); /** \enum ErrorCode * @@ -75,8 +75,9 @@ enum class ErrorCode : int kUNSUPPORTED_GRAPH = 7, kUNSUPPORTED_NODE = 8 }; + template <> -inline int EnumMax() +inline int32_t EnumMax() { return 9; } @@ -204,6 +205,23 @@ class IParser */ virtual void clearErrors() = 0; + /** \brief Get description of all ONNX weights that can be refitted. + * + * \param weightsNames Where to write the weight names to + * \param layerNames Where to write the layer names to + * \param roles Where to write the roles to + * + * \return The number of weights from the ONNX model that can be refitted + * + * If weightNames or layerNames != nullptr, each written pointer points to a string owned by + * the parser, and becomes invalid when the parser is destroyed + * + * If the same weight is used in multiple TRT layers it will be represented as a new + * entry in weightNames with name _x, with x being the number of times the weight + * has been used before the current layer + */ + virtual int getRefitMap(const char** weightNames, const char** layerNames, nvinfer1::WeightsRole* roles) = 0; + protected: virtual ~IParser() {} }; diff --git a/include/NvUffParser.h b/include/NvUffParser.h index c9b3b245..75306f22 100644 --- a/include/NvUffParser.h +++ b/include/NvUffParser.h @@ -42,11 +42,11 @@ namespace nvuffparser //! \enum UffInputOrder //! \brief The different possible supported input order. //! -enum class UffInputOrder : int +enum class UffInputOrder : int32_t { - kNCHW = 0, //!< NCHW order. - kNHWC = 1, //!< NHWC order. - kNC = 2 //!< NC order. + kNCHW = 0, //!< NCHW order. + kNHWC = 1, //!< NHWC order. + kNC = 2 //!< NC order. }; //! @@ -54,13 +54,13 @@ enum class UffInputOrder : int //! \brief The possible field types for custom layer. //! -enum class FieldType : int +enum class FieldType : int32_t { - kFLOAT = 0, //!< FP32 field type. - kINT32 = 1, //!< INT32 field type. - kCHAR = 2, //!< char field type. String for length>1. - kDIMS = 4, //!< nvinfer1::Dims field type. - kDATATYPE = 5, //!< nvinfer1::DataType field type. + kFLOAT = 0, //!< FP32 field type. + kINT32 = 1, //!< INT32 field type. + kCHAR = 2, //!< char field type. String for length>1. + kDIMS = 4, //!< nvinfer1::Dims field type. + kDATATYPE = 5, //!< nvinfer1::DataType field type. kUNKNOWN = 6 }; @@ -79,14 +79,14 @@ class TENSORRTAPI FieldMap const char* name; const void* data; FieldType type = FieldType::kUNKNOWN; - int length = 1; + int32_t length = 1; - FieldMap(const char* name, const void* data, const FieldType type, int length = 1) TRTNOEXCEPT; + FieldMap(const char* name, const void* data, const FieldType type, int32_t length = 1) TRTNOEXCEPT; }; struct FieldCollection { - int nbFields; + int32_t nbFields; const FieldMap* fields; }; @@ -115,8 +115,8 @@ class IPluginFactory //! //! \see FieldCollection //! - virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights, - const FieldCollection fc) TRTNOEXCEPT = 0; + virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int32_t nbWeights, + const FieldCollection fc) TRTNOEXCEPT = 0; virtual ~IPluginFactory() {} }; @@ -129,7 +129,7 @@ class IPluginFactory class IPluginFactoryExt : public IPluginFactory { public: - virtual int getVersion() const TRTNOEXCEPT + virtual int32_t getVersion() const TRTNOEXCEPT { return NV_TENSORRT_VERSION; } @@ -196,17 +196,17 @@ class IUffParser //! //! \brief Return Version Major of the UFF. //! - virtual int getUffRequiredVersionMajor() TRTNOEXCEPT = 0; + virtual int32_t getUffRequiredVersionMajor() TRTNOEXCEPT = 0; //! //! \brief Return Version Minor of the UFF. //! - virtual int getUffRequiredVersionMinor() TRTNOEXCEPT = 0; + virtual int32_t getUffRequiredVersionMinor() TRTNOEXCEPT = 0; //! //! \brief Return Patch Version of the UFF. //! - virtual int getUffRequiredVersionPatch() TRTNOEXCEPT = 0; + virtual int32_t getUffRequiredVersionPatch() TRTNOEXCEPT = 0; //! //! \brief Set the IPluginFactory used to create the user defined plugins. diff --git a/include/NvUtils.h b/include/NvUtils.h index dece2df9..c2681013 100644 --- a/include/NvUtils.h +++ b/include/NvUtils.h @@ -30,106 +30,107 @@ namespace nvinfer1 namespace utils { - //! - //! \param input The input weights to reshape. - //! \param shape The shape of the weights. - //! \param shapeOrder The order of the dimensions to process for the output. - //! \param data The location where the output data is placed. - //! \param nbDims The number of dimensions to process. - //! - //! \brief Reformat the input weights of the given shape based on the new - //! order of dimensions. - //! - //! Take the weights specified by \p input with the dimensions specified by - //! \p shape and re-order the weights based on the new dimensions specified - //! by \p shapeOrder. The size of each dimension and the input data is not - //! modified. The output volume pointed to by \p data must be the same as - //! he \p input volume. - //! - //! Example usage: - //! float *out = new float[N*C*H*W]; - //! Weights input{DataType::kFLOAT, {0 ... N*C*H*W-1}, N*C*H*W size}; - //! int order[4]{1, 0, 3, 2}; - //! int shape[4]{C, N, W, H}; - //! reshapeWeights(input, shape, order, out, 4); - //! Weights reshaped{input.type, out, input.count}; - //! - //! Input Matrix{3, 2, 3, 2}: - //! { 0 1}, { 2 3}, { 4 5} <-- {0, 0, *, *} - //! { 6 7}, { 8 9}, {10 11} <-- {0, 1, *, *} - //! {12 13}, {14 15}, {16 17} <-- {1, 0, *, *} - //! {18 19}, {20 21}, {22 23} <-- {1, 1, *, *} - //! {24 25}, {26 27}, {28 29} <-- {2, 0, *, *} - //! {30 31}, {32 33}, {34 35} <-- {2, 1, *, *} - //! - //! Output Matrix{2, 3, 2, 3}: - //! { 0 2 4}, { 1 3 5} <-- {0, 0, *, *} - //! {12 14 16}, {13 15 17} <-- {0, 1, *, *} - //! {24 26 28}, {25 27 29} <-- {0, 2, *, *} - //! { 6 8 10}, { 7 9 11} <-- {1, 0, *, *} - //! {18 20 22}, {19 21 23} <-- {1, 1, *, *} - //! {30 32 34}, {31 33 35} <-- {1, 2, *, *} - //! - //! \return True on success, false on failure. - //! - TENSORRTAPI bool reshapeWeights(const Weights &input, const int *shape, const int *shapeOrder, void *data, int nbDims); +//! +//! \param input The input weights to reshape. +//! \param shape The shape of the weights. +//! \param shapeOrder The order of the dimensions to process for the output. +//! \param data The location where the output data is placed. +//! \param nbDims The number of dimensions to process. +//! +//! \brief Reformat the input weights of the given shape based on the new +//! order of dimensions. +//! +//! Take the weights specified by \p input with the dimensions specified by +//! \p shape and re-order the weights based on the new dimensions specified +//! by \p shapeOrder. The size of each dimension and the input data is not +//! modified. The output volume pointed to by \p data must be the same as +//! he \p input volume. +//! +//! Example usage: +//! float *out = new float[N*C*H*W]; +//! Weights input{DataType::kFLOAT, {0 ... N*C*H*W-1}, N*C*H*W size}; +//! int32_t order[4]{1, 0, 3, 2}; +//! int32_t shape[4]{C, N, W, H}; +//! reshapeWeights(input, shape, order, out, 4); +//! Weights reshaped{input.type, out, input.count}; +//! +//! Input Matrix{3, 2, 3, 2}: +//! { 0 1}, { 2 3}, { 4 5} <-- {0, 0, *, *} +//! { 6 7}, { 8 9}, {10 11} <-- {0, 1, *, *} +//! {12 13}, {14 15}, {16 17} <-- {1, 0, *, *} +//! {18 19}, {20 21}, {22 23} <-- {1, 1, *, *} +//! {24 25}, {26 27}, {28 29} <-- {2, 0, *, *} +//! {30 31}, {32 33}, {34 35} <-- {2, 1, *, *} +//! +//! Output Matrix{2, 3, 2, 3}: +//! { 0 2 4}, { 1 3 5} <-- {0, 0, *, *} +//! {12 14 16}, {13 15 17} <-- {0, 1, *, *} +//! {24 26 28}, {25 27 29} <-- {0, 2, *, *} +//! { 6 8 10}, { 7 9 11} <-- {1, 0, *, *} +//! {18 20 22}, {19 21 23} <-- {1, 1, *, *} +//! {30 32 34}, {31 33 35} <-- {1, 2, *, *} +//! +//! \return True on success, false on failure. +//! +TENSORRTAPI bool reshapeWeights( + const Weights& input, const int32_t* shape, const int32_t* shapeOrder, void* data, int32_t nbDims); - //! - //! \param input The input data to re-order. - //! \param order The new order of the data sub-buffers. - //! \param num The number of data sub-buffers to re-order. - //! \param size The size of each data sub-buffer in bytes. - //! - //! \brief Takes an input stream and re-orders \p num chunks of the data - //! given the \p size and \p order. - //! - //! In some frameworks, the ordering of the sub-buffers within a dimension - //! is different than the way that TensorRT expects them. - //! TensorRT expects the gate/bias sub-buffers for LSTM's to be in fico order. - //! TensorFlow however formats the sub-buffers in icfo order. - //! This helper function solves this in a generic fashion. - //! - //! Example usage output of reshapeWeights above: - //! int indir[1]{1, 0} - //! int stride = W*H; - //! for (int x = 0, y = N*C; x < y; ++x) - //! reorderSubBuffers(out + x * stride, indir, H, W); - //! - //! Input Matrix{2, 3, 2, 3}: - //! { 0 2 4}, { 1 3 5} <-- {0, 0, *, *} - //! {12 14 16}, {13 15 17} <-- {0, 1, *, *} - //! {24 26 28}, {25 27 29} <-- {0, 2, *, *} - //! { 6 8 10}, { 7 9 11} <-- {1, 0, *, *} - //! {18 20 22}, {19 21 23} <-- {1, 1, *, *} - //! {30 32 34}, {31 33 35} <-- {1, 2, *, *} - //! - //! Output Matrix{2, 3, 2, 3}: - //! { 1 3 5}, { 0 2 4} <-- {0, 0, *, *} - //! {13 15 17}, {12 14 16} <-- {0, 1, *, *} - //! {25 27 29}, {24 26 28} <-- {0, 2, *, *} - //! { 7 9 11}, { 6 8 10} <-- {1, 0, *, *} - //! {19 21 23}, {18 20 22} <-- {1, 1, *, *} - //! {31 33 35}, {30 32 34} <-- {1, 2, *, *} - //! - //! \return True on success, false on failure. - //! - //! \see reshapeWeights() - //! - TENSORRTAPI bool reorderSubBuffers(void *input, const int *order, int num, int size); +//! +//! \param input The input data to re-order. +//! \param order The new order of the data sub-buffers. +//! \param num The number of data sub-buffers to re-order. +//! \param size The size of each data sub-buffer in bytes. +//! +//! \brief Takes an input stream and re-orders \p num chunks of the data +//! given the \p size and \p order. +//! +//! In some frameworks, the ordering of the sub-buffers within a dimension +//! is different than the way that TensorRT expects them. +//! TensorRT expects the gate/bias sub-buffers for LSTM's to be in fico order. +//! TensorFlow however formats the sub-buffers in icfo order. +//! This helper function solves this in a generic fashion. +//! +//! Example usage output of reshapeWeights above: +//! int32_t indir[1]{1, 0} +//! int32_t stride = W*H; +//! for (int32_t x = 0, y = N*C; x < y; ++x) +//! reorderSubBuffers(out + x * stride, indir, H, W); +//! +//! Input Matrix{2, 3, 2, 3}: +//! { 0 2 4}, { 1 3 5} <-- {0, 0, *, *} +//! {12 14 16}, {13 15 17} <-- {0, 1, *, *} +//! {24 26 28}, {25 27 29} <-- {0, 2, *, *} +//! { 6 8 10}, { 7 9 11} <-- {1, 0, *, *} +//! {18 20 22}, {19 21 23} <-- {1, 1, *, *} +//! {30 32 34}, {31 33 35} <-- {1, 2, *, *} +//! +//! Output Matrix{2, 3, 2, 3}: +//! { 1 3 5}, { 0 2 4} <-- {0, 0, *, *} +//! {13 15 17}, {12 14 16} <-- {0, 1, *, *} +//! {25 27 29}, {24 26 28} <-- {0, 2, *, *} +//! { 7 9 11}, { 6 8 10} <-- {1, 0, *, *} +//! {19 21 23}, {18 20 22} <-- {1, 1, *, *} +//! {31 33 35}, {30 32 34} <-- {1, 2, *, *} +//! +//! \return True on success, false on failure. +//! +//! \see reshapeWeights() +//! +TENSORRTAPI bool reorderSubBuffers(void* input, const int32_t* order, int32_t num, int32_t size); - //! - //! \param input The input data to transpose. - //! \param type The type of the data to transpose. - //! \param num The number of data sub-buffers to transpose. - //! \param height The size of the height dimension to transpose. - //! \param width The size of the width dimension to transpose. - //! - //! \brief Transpose \p num sub-buffers of \p height * \p width. - //! - //! \return True on success, false on failure. - //! - TENSORRTAPI bool transposeSubBuffers(void *input, DataType type, int num, int height, int width); +//! +//! \param input The input data to transpose. +//! \param type The type of the data to transpose. +//! \param num The number of data sub-buffers to transpose. +//! \param height The size of the height dimension to transpose. +//! \param width The size of the width dimension to transpose. +//! +//! \brief Transpose \p num sub-buffers of \p height * \p width. +//! +//! \return True on success, false on failure. +//! +TENSORRTAPI bool transposeSubBuffers(void* input, DataType type, int32_t num, int32_t height, int32_t width); -} // utils namespace -} // nvinfer1 namespace +} // namespace utils +} // namespace nvinfer1 #endif // NV_UTILS_H diff --git a/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp b/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp index 77a70034..fc1fd886 100644 --- a/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseBatchNorm.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -174,4 +175,4 @@ ILayer* parseBatchNormalization(INetworkDefinition& network, const trtcaffe::Lay weightFactory.convert(power); return network.addScale(*tensors[msg.bottom(0)], ScaleMode::kCHANNEL, shift, scale, power); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseClip.cpp b/parsers/caffe/caffeParser/opParsers/parseClip.cpp index b2b86e86..246fee21 100644 --- a/parsers/caffe/caffeParser/opParsers/parseClip.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseClip.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -41,4 +42,4 @@ ILayer* parseClip(INetworkDefinition& network, const trtcaffe::LayerParameter& m layer->setBeta(beta); return layer; } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseConcat.cpp b/parsers/caffe/caffeParser/opParsers/parseConcat.cpp index 7eb44056..edf1e6a1 100644 --- a/parsers/caffe/caffeParser/opParsers/parseConcat.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseConcat.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/caffe/caffeParser/opParsers/parseConv.cpp b/parsers/caffe/caffeParser/opParsers/parseConv.cpp index 67d77275..43e1fb70 100644 --- a/parsers/caffe/caffeParser/opParsers/parseConv.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseConv.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/caffe/caffeParser/opParsers/parseCrop.cpp b/parsers/caffe/caffeParser/opParsers/parseCrop.cpp index 7f3fe05d..3bc69a04 100644 --- a/parsers/caffe/caffeParser/opParsers/parseCrop.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseCrop.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -114,4 +115,4 @@ ILayer* parseCrop(INetworkDefinition& network, const trtcaffe::LayerParameter& m DimsHW postPadding = DimsHW{postPadHeight, postPadWidth}; return network.addPadding(*tensors[msg.bottom(0)], prePadding, postPadding); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseELU.cpp b/parsers/caffe/caffeParser/opParsers/parseELU.cpp index 4c14e9d7..32ce7351 100644 --- a/parsers/caffe/caffeParser/opParsers/parseELU.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseELU.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -37,4 +38,4 @@ ILayer* parseELU(INetworkDefinition& network, const trtcaffe::LayerParameter& ms newLayer->setAlpha(alpha); return newLayer; } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp b/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp index 9963fb11..5a6a4c57 100644 --- a/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseEltwise.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -38,4 +39,4 @@ ILayer* parseEltwise(INetworkDefinition& network, const trtcaffe::LayerParameter return network.addElementWise(*tensors[msg.bottom(0)], *tensors[msg.bottom(1)], op); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp b/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp index 85ae2542..d900b84a 100644 --- a/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseInnerProduct.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -34,4 +35,4 @@ ILayer* parseInnerProduct(INetworkDefinition& network, const trtcaffe::LayerPara weightFactory.convert(biasWeights); return network.addFullyConnected(*tensors[msg.bottom(0)], p.num_output(), kernelWeights, biasWeights); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseLRN.cpp b/parsers/caffe/caffeParser/opParsers/parseLRN.cpp index e7caa03f..c1694e8c 100644 --- a/parsers/caffe/caffeParser/opParsers/parseLRN.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseLRN.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -34,4 +35,4 @@ ILayer* parseLRN(INetworkDefinition& network, const trtcaffe::LayerParameter& ms return network.addLRN(*tensors[msg.bottom(0)], localSize, alpha, beta, k); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parsePermute.cpp b/parsers/caffe/caffeParser/opParsers/parsePermute.cpp index 8d3b75fa..b63ed6ee 100644 --- a/parsers/caffe/caffeParser/opParsers/parsePermute.cpp +++ b/parsers/caffe/caffeParser/opParsers/parsePermute.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -77,4 +78,4 @@ ILayer* parsePermute(INetworkDefinition& network, const trtcaffe::LayerParameter permute->setFirstTranspose(permuteOrder); return permute; } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parsePooling.cpp b/parsers/caffe/caffeParser/opParsers/parsePooling.cpp index e4ebfd45..ce723724 100644 --- a/parsers/caffe/caffeParser/opParsers/parsePooling.cpp +++ b/parsers/caffe/caffeParser/opParsers/parsePooling.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -74,4 +75,4 @@ ILayer* parsePooling(INetworkDefinition& network, const trtcaffe::LayerParameter } return layer; } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parsePower.cpp b/parsers/caffe/caffeParser/opParsers/parsePower.cpp index 898dcc15..d9f16426 100644 --- a/parsers/caffe/caffeParser/opParsers/parsePower.cpp +++ b/parsers/caffe/caffeParser/opParsers/parsePower.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -60,4 +61,4 @@ ILayer* parsePower(INetworkDefinition& network, const trtcaffe::LayerParameter& weightFactory.convert(wPower); return network.addScale(*tensors[msg.bottom(0)], ScaleMode::kUNIFORM, wShift, wScale, wPower); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseReLU.cpp b/parsers/caffe/caffeParser/opParsers/parseReLU.cpp index 1cc25c51..930a8400 100644 --- a/parsers/caffe/caffeParser/opParsers/parseReLU.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseReLU.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -36,4 +37,4 @@ ILayer* parseReLU(INetworkDefinition& network, const trtcaffe::LayerParameter& m } return network.addActivation(*tensors[msg.bottom(0)], ActivationType::kRELU); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseReduction.cpp b/parsers/caffe/caffeParser/opParsers/parseReduction.cpp index ac507cf4..cbced0ba 100644 --- a/parsers/caffe/caffeParser/opParsers/parseReduction.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseReduction.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/caffe/caffeParser/opParsers/parseReshape.cpp b/parsers/caffe/caffeParser/opParsers/parseReshape.cpp index 81d225c8..917b6e7c 100644 --- a/parsers/caffe/caffeParser/opParsers/parseReshape.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseReshape.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp b/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp index d48ce98c..a7cbada1 100644 --- a/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseSigmoid.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -28,4 +29,4 @@ ILayer* parseSigmoid(INetworkDefinition& network, const trtcaffe::LayerParameter return network.addActivation(*tensors[msg.bottom(0)], ActivationType::kSIGMOID); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp b/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp index 2d11bb77..4f1b555e 100644 --- a/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseSoftMax.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/caffe/caffeParser/opParsers/parseTanH.cpp b/parsers/caffe/caffeParser/opParsers/parseTanH.cpp index 1e20e79b..261dc68c 100644 --- a/parsers/caffe/caffeParser/opParsers/parseTanH.cpp +++ b/parsers/caffe/caffeParser/opParsers/parseTanH.cpp @@ -1,4 +1,5 @@ -/* Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -28,4 +29,4 @@ ILayer* parseTanH(INetworkDefinition& network, const trtcaffe::LayerParameter& m return network.addActivation(*tensors[msg.bottom(0)], ActivationType::kTANH); } -} //namespace nvcaffeparser1 \ No newline at end of file +} //namespace nvcaffeparser1 diff --git a/parsers/common/half.h b/parsers/common/half.h index 126ac6b1..70e7af54 100644 --- a/parsers/common/half.h +++ b/parsers/common/half.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/parsers/common/parserUtils.h b/parsers/common/parserUtils.h index 0103f2db..2681d2b0 100644 --- a/parsers/common/parserUtils.h +++ b/parsers/common/parserUtils.h @@ -1,5 +1,5 @@ -/* - * Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -13,7 +13,6 @@ * See the License for the specific language governing permissions and * limitations under the License. */ - #ifndef TRT_PARSER_UTILS_H #define TRT_PARSER_UTILS_H diff --git a/parsers/onnx b/parsers/onnx index fbebb144..eb559b6c 160000 --- a/parsers/onnx +++ b/parsers/onnx @@ -1 +1 @@ -Subproject commit fbebb144744b3be8defecd8478d74940056df305 +Subproject commit eb559b6cdd1ec2169d64c0112fab9b564d8d503b diff --git a/plugin/CMakeLists.txt b/plugin/CMakeLists.txt index 940392a8..8c9f8e58 100644 --- a/plugin/CMakeLists.txt +++ b/plugin/CMakeLists.txt @@ -30,30 +30,31 @@ set(PLUGIN_SOURCES) set(PLUGIN_CU_SOURCES) set(PLUGIN_LISTS - nmsPlugin - normalizePlugin - priorBoxPlugin - reorgPlugin - gridAnchorPlugin - regionPlugin - nvFasterRCNN batchedNMSPlugin - flattenConcat - cropAndResizePlugin - proposalPlugin batchTilePlugin + coordConvACPlugin + cropAndResizePlugin detectionLayerPlugin - proposalLayerPlugin - pyramidROIAlignPlugin + flattenConcat generateDetectionPlugin - multilevelProposeROI + gridAnchorPlugin + groupNormalizationPlugin + instanceNormalizationPlugin + leakyReluPlugin multilevelCropAndResizePlugin + multilevelProposeROI + nmsPlugin + normalizePlugin + nvFasterRCNN + priorBoxPlugin + proposalLayerPlugin + proposalPlugin + pyramidROIAlignPlugin + regionPlugin + reorgPlugin resizeNearestPlugin specialSlicePlugin - instanceNormalizationPlugin - groupNormalizationPlugin - coordConvACPlugin - leakyReluPlugin + splitPlugin ) # Add BERT sources if ${BERT_GENCODES} was populated @@ -61,10 +62,10 @@ if(BERT_GENCODES) set(BERT_CU_SOURCES) set(PLUGIN_LISTS ${PLUGIN_LISTS} + bertQKVToContextPlugin embLayerNormPlugin fcPlugin geluPlugin - bertQKVToContextPlugin skipLayerNormPlugin ) endif() diff --git a/plugin/InferPlugin.cpp b/plugin/InferPlugin.cpp index b8e9121c..190f1947 100644 --- a/plugin/InferPlugin.cpp +++ b/plugin/InferPlugin.cpp @@ -13,9 +13,11 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "NvInfer.h" #include "NvInferPlugin.h" #include "checkMacrosPlugin.h" +#include "plugin.h" #include #include #include @@ -34,6 +36,7 @@ using namespace nvinfer1::plugin; #include "flattenConcat.h" #include "generateDetectionPlugin.h" #include "gridAnchorPlugin.h" +#include "groupNormalizationPlugin.h" #include "instanceNormalizationPlugin.h" #include "lReluPlugin.h" #include "multilevelCropAndResizePlugin.h" @@ -49,6 +52,7 @@ using namespace nvinfer1::plugin; #include "reorgPlugin.h" #include "resizeNearestPlugin.h" #include "specialSlicePlugin.h" +#include "split.h" using nvinfer1::plugin::RPROIParams; @@ -154,34 +158,36 @@ void initializePlugin(void* logger, const char* libNamespace) } // namespace nvinfer1 // New Plugin APIs -extern "C" { -bool initLibNvInferPlugins(void* logger, const char* libNamespace) +extern "C" { - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - initializePlugin(logger, libNamespace); - return true; -} + bool initLibNvInferPlugins(void* logger, const char* libNamespace) + { + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + initializePlugin(logger, libNamespace); + return true; + } } // extern "C" diff --git a/plugin/README.md b/plugin/README.md index e0affab9..6e813c84 100644 --- a/plugin/README.md +++ b/plugin/README.md @@ -2,16 +2,16 @@ ## Plugins shipped in this release -| Plugin | Name | Version | +| Plugin | Name | Versions | |---|---|---| | [batchTilePlugin](batchTilePlugin) | BatchTilePlugin_TRT | 1 | | [batchedNMSPlugin](batchedNMSPlugin) | BatchedNMS_TRT | 1 | | [batchedNMSDynamicPlugin](batchedNMSPlugin) | BatchedNMSDynamic_TRT | 1 | -| [bertQKVToContextPlugin](bertQKVToContextPlugin) | CustomQKVToContextPluginDynamic | 1 | +| [bertQKVToContextPlugin](bertQKVToContextPlugin) | CustomQKVToContextPluginDynamic | 1, 2, 3 | | [coordConvACPlugin](coordConvACPlugin) | CoordConvAC | 1 | | [cropAndResizePlugin](cropAndResizePlugin) | CropAndResize | 1 | | [detectionLayerPlugin](detectionLayerPlugin) | DetectionLayer_TRT | 1 | -| [embLayerNormPlugin](embLayerNormPlugin) | CustomEmbLayerNormPluginDynamic | 1 | +| [embLayerNormPlugin](embLayerNormPlugin) | CustomEmbLayerNormPluginDynamic | 1, 2 | | [fcPlugin](fcPlugin) | CustomFCPluginDynamic | 1 | | [flattenConcat](flattenConcat) | FlattenConcat_TRT | 1 | | [geluPlugin](geluPlugin) | CustomGeluPluginDynamic | 1 | @@ -33,5 +33,6 @@ | [regionPlugin](regionPlugin) | Region_TRT | 1 | | [reorgPlugin](reorgPlugin) | Reorg_TRT | 1 | | [resizeNearestPlugin](resizeNearestPlugin) | ResizeNearest_TRT | 1 | -| [skipLayerNormPlugin](skipLayerNormPlugin) | CustomSkipLayerNormPluginDynamic | 1 | +| [skipLayerNormPlugin](skipLayerNormPlugin) | CustomSkipLayerNormPluginDynamic | 1, 2, 3 | | [specialSlicePlugin](specialSlicePlugin) | SpecialSlice_TRT | 1 | +| [splitPlugin](splitPlugin) | Split | 1 | diff --git a/plugin/batchTilePlugin/batchTilePlugin.cpp b/plugin/batchTilePlugin/batchTilePlugin.cpp index aa5deb75..f0a63bd8 100644 --- a/plugin/batchTilePlugin/batchTilePlugin.cpp +++ b/plugin/batchTilePlugin/batchTilePlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "batchTilePlugin.h" #include #include diff --git a/plugin/batchTilePlugin/batchTilePlugin.h b/plugin/batchTilePlugin/batchTilePlugin.h index abbaf274..61a27346 100644 --- a/plugin/batchTilePlugin/batchTilePlugin.h +++ b/plugin/batchTilePlugin/batchTilePlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef BATCHTILEPLUGIN_H #define BATCHTILEPLUGIN_H #include "NvInferPlugin.h" diff --git a/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp b/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp index dc561386..e2ff89dd 100644 --- a/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp +++ b/plugin/batchedNMSPlugin/batchedNMSPlugin.cpp @@ -22,11 +22,11 @@ #include using namespace nvinfer1; -using nvinfer1::plugin::BatchedNMSPlugin; -using nvinfer1::plugin::BatchedNMSDynamicPlugin; using nvinfer1::plugin::BatchedNMSBasePluginCreator; -using nvinfer1::plugin::BatchedNMSPluginCreator; +using nvinfer1::plugin::BatchedNMSDynamicPlugin; using nvinfer1::plugin::BatchedNMSDynamicPluginCreator; +using nvinfer1::plugin::BatchedNMSPlugin; +using nvinfer1::plugin::BatchedNMSPluginCreator; using nvinfer1::plugin::NMSParameters; namespace diff --git a/plugin/batchedNMSPlugin/batchedNMSPlugin.h b/plugin/batchedNMSPlugin/batchedNMSPlugin.h index 28621162..7e7b9738 100644 --- a/plugin/batchedNMSPlugin/batchedNMSPlugin.h +++ b/plugin/batchedNMSPlugin/batchedNMSPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_BATCHED_NMS_PLUGIN_H #define TRT_BATCHED_NMS_PLUGIN_H #include "gatherNMSOutputs.h" diff --git a/plugin/batchedNMSPlugin/gatherNMSOutputs.h b/plugin/batchedNMSPlugin/gatherNMSOutputs.h index f3c291ff..5bf8280a 100644 --- a/plugin/batchedNMSPlugin/gatherNMSOutputs.h +++ b/plugin/batchedNMSPlugin/gatherNMSOutputs.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_BATCHED_NMS_HELPER_H #define TRT_BATCHED_NMS_HELPER_H #include "plugin.h" diff --git a/plugin/bertQKVToContextPlugin/README.md b/plugin/bertQKVToContextPlugin/README.md index 38c5082f..f926c208 100644 --- a/plugin/bertQKVToContextPlugin/README.md +++ b/plugin/bertQKVToContextPlugin/README.md @@ -44,12 +44,14 @@ output is a tensor with shape `[S, B, E]` where `B` is the batch size. The parameters are defined below and consists of the following attributes: -| Type | Parameter | Description -|----------|-----------------------------------------|------------------------------------------------------------------- -|`int` |`type_id` |Integer encoding the DataType (0: FP32, 1: FP16) -|`int` |`hidden_size` |The hidden size, denoted by `E` above. -|`int` |`num_heads` |The number of self-attention heads. -|`bool` |`has_mask` |Whether to use the input_mask input. +| Type | Parameter | Version | Description +|----------|-----------------------------------------|-----------------------------------|------------------------------------------------------------------- +|`int` |`type_id` | 1, 2 |Integer encoding the DataType (0: FP32, 1: FP16) +|`int` |`hidden_size` | 1, 2, 3 |The hidden size, denoted by `E` above. +|`int` |`num_heads` | 1, 2, 3 |The number of self-attention heads. +|`bool` |`has_mask` | 1, 2 |Whether to use the input_mask input. +|`float` |`dq_probs` | 1, 2, 3 |inner layer scale factor when run in int8 precision, default 1.f/127.f. +|`int` |`var_seqlen` | 2 |Whether to use variable sequence length (0: disable, 1: enable), default 0. ## Additional resources @@ -66,7 +68,11 @@ documentation. ## Changelog -November 2019 +October 2020 +Add v2 plugin that supports variable sequence length. +Add v3 plugin that supports int8 interleaved variable sequence length. + +November 2019 This is the first release of this `README.md` file. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention.h b/plugin/bertQKVToContextPlugin/fused_multihead_attention.h index acf909b8..2aa3a7de 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention.h +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -17,31 +17,16 @@ #pragma once #include "cudaDriverWrapper.h" #include "cuda_runtime_api.h" +#include "fused_multihead_attention_common.h" #include #include #include +#include #include #include #include - namespace bert { - -static constexpr int32_t kSM_TURING = 75; -static constexpr int32_t kSM_AMPERE = 80; - -enum Data_type -{ - DATA_TYPE_BOOL, - DATA_TYPE_E8M10, - DATA_TYPE_E8M7, - DATA_TYPE_FP16, - DATA_TYPE_FP32, - DATA_TYPE_INT4, - DATA_TYPE_INT8, - DATA_TYPE_INT32 -}; - static inline size_t get_size_in_bytes(size_t n, Data_type dtype) { switch (dtype) @@ -101,6 +86,10 @@ struct Fused_multihead_attention_params }; //////////////////////////////////////////////////////////////////////////////////////////////////// +extern unsigned char fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o[]; +extern unsigned char fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o[]; +extern unsigned char fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o[]; +extern unsigned char fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o[]; extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o[]; extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o[]; extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm75_cu_o[]; @@ -110,6 +99,10 @@ extern unsigned char fused_multihead_attention_int8_128_64_kernel_sm80_cu_o[]; extern unsigned char fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o[]; extern unsigned char fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o[]; +extern unsigned int fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o_len; +extern unsigned int fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o_len; +extern unsigned int fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len; +extern unsigned int fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len; extern unsigned int fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len; extern unsigned int fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len; extern unsigned int fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len; @@ -119,7 +112,7 @@ extern unsigned int fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len; extern unsigned int fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len; extern unsigned int fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len; -static const struct FusedMultiHeadAttentionKernelMetaInfo +static const struct FusedMultiHeadAttentionKernelMetaInfoV1 { Data_type mDataType; unsigned int mS; @@ -132,54 +125,113 @@ static const struct FusedMultiHeadAttentionKernelMetaInfo unsigned int mThreadsPerCTA; } sMhaKernelMetaInfos[] = { // Turing - {DATA_TYPE_FP16, 128, 64, kSM_TURING, fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o, + {DATA_TYPE_FP16, 64, 64, kSM_75, fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o, + fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm75", 24576, 128}, + {DATA_TYPE_FP16, 96, 64, kSM_75, fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o, + fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm75", 24576, 128}, + {DATA_TYPE_FP16, 128, 64, kSM_75, fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o, fused_multihead_attention_fp16_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm75", 32768, 128}, - {DATA_TYPE_FP16, 384, 64, kSM_TURING, fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o, + {DATA_TYPE_FP16, 384, 64, kSM_75, fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o, fused_multihead_attention_fp16_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm75", 57344, 256}, - {DATA_TYPE_INT8, 128, 64, kSM_TURING, fused_multihead_attention_int8_128_64_kernel_sm75_cu_o, + {DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_int8_128_64_kernel_sm75_cu_o, fused_multihead_attention_int8_128_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm75", 16384, 128}, - {DATA_TYPE_INT8, 384, 64, kSM_TURING, fused_multihead_attention_int8_384_64_kernel_sm75_cu_o, + {DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_int8_384_64_kernel_sm75_cu_o, fused_multihead_attention_int8_384_64_kernel_sm75_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm75", 53284, 256}, #if CUDA_VERSION >= 11000 // Ampere - {DATA_TYPE_FP16, 128, 64, kSM_AMPERE, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o, + {DATA_TYPE_FP16, 64, 64, kSM_80, fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o, + fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128}, + {DATA_TYPE_FP16, 96, 64, kSM_80, fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o, + fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128}, + {DATA_TYPE_FP16, 128, 64, kSM_80, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm80", 49152, 128}, - {DATA_TYPE_FP16, 384, 64, kSM_AMPERE, fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o, + {DATA_TYPE_FP16, 384, 64, kSM_80, fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o, fused_multihead_attention_fp16_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_384_64_kernel_sm80", 114688, 256}, - {DATA_TYPE_INT8, 128, 64, kSM_AMPERE, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o, + {DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm80", 24576, 128}, - {DATA_TYPE_INT8, 384, 64, kSM_AMPERE, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o, + {DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm80", 57344, 256}, + + // GA10x + // Note: For GA10X keep only kernels whose sharedMemBytes < 100KiB + {DATA_TYPE_FP16, 64, 64, kSM_86, fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o, + fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128}, + {DATA_TYPE_FP16, 96, 64, kSM_86, fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o, + fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128}, + {DATA_TYPE_FP16, 128, 64, kSM_86, fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o, + fused_multihead_attention_fp16_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_fp16_128_64_kernel_sm80", + 49152, 128}, + {DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_int8_128_64_kernel_sm80_cu_o, + fused_multihead_attention_int8_128_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_128_64_kernel_sm80", + 24576, 128}, + {DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_int8_384_64_kernel_sm80_cu_o, + fused_multihead_attention_int8_384_64_kernel_sm80_cu_o_len, "fused_multihead_attention_int8_384_64_kernel_sm80", + 57344, 256}, + #endif }; -struct FusedMultiHeadAttentionXMMAKernel +template +class TFusedMultiHeadAttentionXMMAKernel { +public: + using KernelMeta = TKernelMeta; + using KernelParam = TKernelParam; inline uint64_t hashID(unsigned int s, unsigned int d) const { return (uint64_t) s << 32 | d; } + virtual uint64_t hashID(const KernelMeta& kernelMeta) const + { + return hashID(kernelMeta.mS, kernelMeta.mD); + } - FusedMultiHeadAttentionXMMAKernel(Data_type type, unsigned int sm) + TFusedMultiHeadAttentionXMMAKernel( + const TKernelMeta* pMetaStart, unsigned int nMetaCount, Data_type type, unsigned int sm) : mDataType(type) + , mKernelMeta(pMetaStart) + , mKernelMetaCount(nMetaCount) , mSM(sm) { - for (unsigned int i = 0; i < sizeof(sMhaKernelMetaInfos) / sizeof(sMhaKernelMetaInfos[0]); ++i) + } + + void loadXMMAKernels() + { + if (!mFunctions.empty()) { - const auto& kernelMeta = sMhaKernelMetaInfos[i]; - if (kernelMeta.mSM == sm && kernelMeta.mDataType == type) + return; + } + + for (unsigned int i = 0; i < mKernelMetaCount; ++i) + { + const auto& kernelMeta = mKernelMeta[i]; + if (kernelMeta.mSM == mSM && kernelMeta.mDataType == mDataType) { CUmodule hmod{0}; - cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver); - mModules.push_back(hmod); + auto findModuleIter = mModules.find(kernelMeta.mCubin); + if (findModuleIter != mModules.end()) + { + hmod = findModuleIter->second; + } + else + { + cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver); + mModules.insert(std::make_pair(kernelMeta.mCubin, hmod)); + } FusedMultiHeadAttentionKernelInfo funcInfo; funcInfo.mMetaInfoIndex = i; @@ -190,32 +242,25 @@ struct FusedMultiHeadAttentionXMMAKernel CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, kernelMeta.mSharedMemBytes), mDriver); } - mFunctions.insert(std::make_pair(hashID(kernelMeta.mS, kernelMeta.mD), funcInfo)); + mFunctions.insert(std::make_pair(hashID(kernelMeta), funcInfo)); + int s = static_cast(kernelMeta.mS); + if (mValidSequences.find(s) == mValidSequences.end()) + mValidSequences.insert(s); } } } - ~FusedMultiHeadAttentionXMMAKernel() - { - for (auto mod : mModules) - { - mDriver.cuModuleUnload(mod); - } - mFunctions.clear(); - mModules.clear(); - } - - bool isValid() const + bool isValid(int s) const { - return !mFunctions.empty(); + return (mValidSequences.find(s) != mValidSequences.end()); } - void run(Fused_multihead_attention_params& params, size_t s, size_t d, cudaStream_t ss) const + virtual void run(TKernelParam& params, cudaStream_t ss) const { - const auto findIter = mFunctions.find(hashID(s, d)); + const auto findIter = mFunctions.find(hashID(params.s, params.d)); ASSERT(findIter != mFunctions.end()); - const auto& kernelMeta = sMhaKernelMetaInfos[findIter->second.mMetaInfoIndex]; + const auto& kernelMeta = mKernelMeta[findIter->second.mMetaInfoIndex]; const CUfunction func = findIter->second.mDeviceFunction; void* kernelParams[] = {¶ms, nullptr}; @@ -224,23 +269,31 @@ struct FusedMultiHeadAttentionXMMAKernel mDriver); } + virtual ~TFusedMultiHeadAttentionXMMAKernel() = default; + +protected: nvinfer1::CUDADriverWrapper mDriver; Data_type mDataType; + const TKernelMeta* mKernelMeta; + unsigned int mKernelMetaCount; unsigned int mSM; - std::vector mModules; + std::unordered_map mModules; struct FusedMultiHeadAttentionKernelInfo { unsigned int mMetaInfoIndex; CUfunction mDeviceFunction; }; std::unordered_map mFunctions; + std::set mValidSequences; }; -class FusedMHAKernelFactory +template +class TFusedMHAKernelFactory { public: - const FusedMultiHeadAttentionXMMAKernel* getXMMAKernels(Data_type type, unsigned int sm) + const TFusedMHAKernelList* getXMMAKernels(const typename TFusedMHAKernelList::KernelMeta* pKernelList, + unsigned int nbKernels, Data_type type, unsigned int sm) { static std::mutex s_mutex; std::lock_guard lg(s_mutex); @@ -249,28 +302,39 @@ class FusedMHAKernelFactory const auto findIter = mKernels.find(id); if (findIter == mKernels.end()) { - FusedMultiHeadAttentionXMMAKernel* newKernel = new FusedMultiHeadAttentionXMMAKernel{type, sm}; - mKernels.insert(std::make_pair(id, std::unique_ptr(newKernel))); + TFusedMHAKernelList* newKernel = new TFusedMHAKernelList{pKernelList, nbKernels, type, sm}; + newKernel->loadXMMAKernels(); + mKernels.insert(std::make_pair(id, std::unique_ptr(newKernel))); return newKernel; } return findIter->second.get(); } - static FusedMHAKernelFactory& Get() + static TFusedMHAKernelFactory& Get() { - static FusedMHAKernelFactory s_factory; + static TFusedMHAKernelFactory s_factory; return s_factory; } private: - FusedMHAKernelFactory() = default; + TFusedMHAKernelFactory() = default; inline uint64_t hashID(Data_type type, unsigned int sm) const { return (uint64_t) type << 32 | sm; } - std::unordered_map> mKernels; + std::unordered_map> mKernels; }; +using FusedMultiHeadAttentionXMMAKernel + = TFusedMultiHeadAttentionXMMAKernel; +using FusedMHAKernelFactory = TFusedMHAKernelFactory; + +inline const FusedMultiHeadAttentionXMMAKernel* getXMMAKernels(Data_type type, unsigned int sm) +{ + return FusedMHAKernelFactory::Get().getXMMAKernels( + sMhaKernelMetaInfos, sizeof(sMhaKernelMetaInfos) / sizeof(sMhaKernelMetaInfos[0]), type, sm); +} + } // namespace bert diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_common.h b/plugin/bertQKVToContextPlugin/fused_multihead_attention_common.h new file mode 100644 index 00000000..862265f4 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_common.h @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +namespace bert +{ +enum Data_type +{ + DATA_TYPE_BOOL, + DATA_TYPE_E8M10, + DATA_TYPE_E8M7, + DATA_TYPE_FP16, + DATA_TYPE_FP32, + DATA_TYPE_INT4, + DATA_TYPE_INT8, + DATA_TYPE_INT32 +}; +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp index d50d95be..6a2b50f1 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm75.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp index 667cbf87..32e4c664 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_128_64_kernel.sm80.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp index 5ee20bcc..b602e06b 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm75.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp index 05c68eac..02ea2e4a 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_384_64_kernel.sm80.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_64_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_64_64_kernel.sm75.cpp new file mode 100644 index 00000000..f2f83e04 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_64_64_kernel.sm75.cpp @@ -0,0 +1,1440 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_fp16_64_64_kernel_sm75_cu_o[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x41, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 0x05, 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b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_64_64_kernel.sm80.cpp @@ -0,0 +1,1418 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xc0, 0x40, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 0x05, 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0x08, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_fp16_64_64_kernel_sm80_cu_o_len = 16744; +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_96_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_96_64_kernel.sm75.cpp new file mode 100644 index 00000000..04749f37 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_fp16_96_64_kernel.sm75.cpp @@ -0,0 +1,2410 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_fp16_96_64_kernel_sm75_cu_o[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x6f, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x80, 0x6c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 0x05, 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Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0x6c, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x80, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 0x05, 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0x00, 0x00, 0x06, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x07, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x62, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x91, 0x80, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xa9, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x80, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, + 0x40, 0x6c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xa8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xa8, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x78, 0x05, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x64, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x08, 0x64, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x80, 0x69, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_fp16_96_64_kernel_sm80_cu_o_len = 27880; +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp index ccc2aa48..432c909b 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm75.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp index 0be5356f..bdb6622b 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_128_64_kernel.sm80.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp index 898269f9..6551f57f 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm75.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp index 28a6914c..092ffed6 100644 --- a/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_int8_384_64_kernel.sm80.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2.h b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2.h new file mode 100644 index 00000000..a7153ccc --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2.h @@ -0,0 +1,528 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once +#include "fused_multihead_attention.h" +#include "fused_multihead_attention_common.h" +#include +#include + +namespace bert +{ +struct Fused_multihead_attention_params_v2 +{ + // The QKV matrices. + void* qkv_ptr; + // The mask to implement drop-out. + void* packed_mask_ptr; + // The O matrix (output). + void* o_ptr; + + // The stride between rows of the Q, K and V matrices. + int64_t qkv_stride_in_bytes; + // The stride between matrices of packed mask. + int64_t packed_mask_stride_in_bytes; + // The stride between rows of O. + int64_t o_stride_in_bytes; + +#if defined(STORE_P) + // The pointer to the P matrix (for debugging). + void* p_ptr; + // The stride between rows of the P matrix (for debugging). + int64_t p_stride_in_bytes; +#endif // defined(STORE_P) + +#if defined(STORE_S) + // The pointer to the S matrix (for debugging). + void* s_ptr; + // The stride between rows of the S matrix (for debugging). + int64_t s_stride_in_bytes; +#endif // defined(STORE_S) + + // The dimensions. + int b, h, s, d; + // The scaling factors for the kernel. + uint32_t scale_bmm1, scale_softmax, scale_bmm2; + + // Do we use Niall's trick to avoid I2F/F2I in the INT8 kernel. + // See https://confluence.nvidia.com/pages/viewpage.action?pageId=302779721 for details. + bool enable_i2f_trick; + + // array of length b+1 holding prefix sum of actual sequence lenghts + int* cu_seqlens; + + // use C/32 Format. + bool interleaved = false; + bool ignore_b1opt = false; + bool force_unroll = false; + bool use_int8_scale_max = false; + + void clear() + { + qkv_ptr = nullptr; + packed_mask_ptr = nullptr; + o_ptr = nullptr; + + qkv_stride_in_bytes = 0; + packed_mask_stride_in_bytes = 0; + o_stride_in_bytes = 0; +#if defined(STORE_P) + p_ptr = nullptr; + p_stride_in_bytes = 0 +#endif // defined(STORE_P) + +#if defined(STORE_S) + s_ptr + = nullptr; + s_stride_in_bytes = 0; +#endif // defined(STORE_S) + + b = 0; + h = 0; + s = 0; + d = 0; + // The scaling factors for the kernel. + scale_bmm1 = 0; + scale_softmax = 0; + scale_bmm2 = 0; + + enable_i2f_trick = false; + + cu_seqlens = nullptr; + interleaved = false; + ignore_b1opt = false; + force_unroll = false; + use_int8_scale_max = false; + } +}; +//////////////////////////////////////////////////////////////////////////////////////////////////// +extern unsigned char fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_128_64_kernel_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_192_64_kernel_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_256_64_kernel_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_384_64_kernel_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin[]; +extern unsigned char fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin[]; + +extern unsigned int fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_128_64_kernel_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_192_64_kernel_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_256_64_kernel_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_384_64_kernel_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin_len; +extern unsigned int fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len; + +static const struct FusedMultiHeadAttentionKernelMetaInfoV2 +{ + Data_type mDataType; + unsigned int mS; + unsigned int mD; + unsigned int mSM; + const unsigned char* mCubin; + unsigned int mCubinSize; + const char* mFuncName; + unsigned int mSharedMemBytes; + unsigned int mThreadsPerCTA; + unsigned int mUnrollStep; + bool mInterleaved; +} sMhaKernelMetaInfosV2[] = { + // Xavier + {DATA_TYPE_INT8, 128, 64, kSM_72, fused_multihead_attention_v2_int8_128_64_kernel_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm72_interleaved", 24576, 128, 0, true}, + {DATA_TYPE_INT8, 128, 64, kSM_72, fused_multihead_attention_v2_int8_128_64_kernel_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm72", 32768, 128, 0, false}, + {DATA_TYPE_INT8, 192, 64, kSM_72, fused_multihead_attention_v2_int8_192_64_kernel_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm72_interleaved", 28672, 128, 0, true}, + {DATA_TYPE_INT8, 192, 64, kSM_72, fused_multihead_attention_v2_int8_192_64_kernel_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm72", 45056, 128, 0, false}, + {DATA_TYPE_INT8, 256, 64, kSM_72, fused_multihead_attention_v2_int8_256_64_kernel_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm72_interleaved", 36864, 128, 0, true}, + {DATA_TYPE_INT8, 256, 64, kSM_72, fused_multihead_attention_v2_int8_256_64_kernel_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm72", 57344, 128, 0, false}, + {DATA_TYPE_INT8, 384, 64, kSM_72, fused_multihead_attention_v2_int8_384_64_kernel_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm72_interleaved", 51200, 128, 0, true}, + {DATA_TYPE_INT8, 384, 64, kSM_72, fused_multihead_attention_v2_int8_384_64_kernel_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm72", 77824, 128, 0, false}, + + // Turing + {DATA_TYPE_FP16, 64, 64, kSM_75, fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm75", 24576, 128, 0, false}, + {DATA_TYPE_FP16, 96, 64, kSM_75, fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm75", 24576, 128, 0, false}, + {DATA_TYPE_FP16, 128, 64, kSM_75, fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm75_noloop", 20480, 128, 32, false}, + {DATA_TYPE_FP16, 128, 64, kSM_75, fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm75", 32768, 128, 0, false}, + {DATA_TYPE_FP16, 256, 64, kSM_75, fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm75_noloop", 36864, 128, 32, false}, + {DATA_TYPE_FP16, 256, 64, kSM_75, fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm75", 36864, 128, 0, false}, + {DATA_TYPE_FP16, 384, 64, kSM_75, fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_384_64_kernel_sm75_noloop", 53248, 256, 32, false}, + {DATA_TYPE_FP16, 384, 64, kSM_75, fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_fp16_384_64_kernel_sm75", 53248, 256, 0, false}, + + {DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm75_interleaved_noloop", 18432, 128, 16, true}, + {DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm75_noloop", 18432, 128, 16, false}, + {DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm75_interleaved", 24576, 128, 0, true}, + {DATA_TYPE_INT8, 128, 64, kSM_75, fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm75", 24576, 128, 0, false}, + {DATA_TYPE_INT8, 192, 64, kSM_75, fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm75_interleaved_noloop", 28672, 128, 64, true}, + {DATA_TYPE_INT8, 192, 64, kSM_75, fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm75_noloop", 28672, 128, 64, false}, + {DATA_TYPE_INT8, 192, 64, kSM_75, fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm75_interleaved", 28672, 128, 0, true}, + {DATA_TYPE_INT8, 192, 64, kSM_75, fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm75", 28672, 128, 0, false}, + {DATA_TYPE_INT8, 256, 64, kSM_75, fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm75_interleaved_noloop", 34816, 128, 32, true}, + {DATA_TYPE_INT8, 256, 64, kSM_75, fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm75_noloop", 34816, 128, 32, false}, + {DATA_TYPE_INT8, 256, 64, kSM_75, fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm75_interleaved", 34816, 128, 0, true}, + {DATA_TYPE_INT8, 256, 64, kSM_75, fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm75", 34816, 128, 0, false}, + {DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm75_interleaved_noloop", 51200, 128, 32, true}, + {DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm75_noloop", 51200, 128, 32, false}, + {DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm75_interleaved", 51200, 128, 0, true}, + {DATA_TYPE_INT8, 384, 64, kSM_75, fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm75", 51200, 128, 0, false}, + +#if CUDA_VERSION >= 11000 + // Ampere + {DATA_TYPE_FP16, 64, 64, kSM_80, fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_FP16, 96, 64, kSM_80, fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128, 0, false}, + {DATA_TYPE_FP16, 128, 64, kSM_80, fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm80_noloop", 40960, 128, 32, false}, + {DATA_TYPE_FP16, 128, 64, kSM_80, fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm80", 65536, 128, 0, false}, + {DATA_TYPE_FP16, 256, 64, kSM_80, fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm80_noloop", 73728, 128, 32, false}, + {DATA_TYPE_FP16, 256, 64, kSM_80, fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm80", 73728, 128, 0, false}, + {DATA_TYPE_FP16, 384, 64, kSM_80, fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_384_64_kernel_sm80_noloop", 114688, 256, 48, false}, + {DATA_TYPE_FP16, 384, 64, kSM_80, fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_384_64_kernel_sm80", 114688, 256, 0, false}, + + {DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_interleaved_noloop", 20480, 128, 16, true}, + {DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_noloop", 20480, 128, 16, false}, + {DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_interleaved", 24576, 128, 0, true}, + {DATA_TYPE_INT8, 128, 64, kSM_80, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_INT8, 192, 64, kSM_80, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_interleaved_noloop", 28672, 128, 32, true}, + {DATA_TYPE_INT8, 192, 64, kSM_80, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_noloop", 28672, 128, 32, false}, + {DATA_TYPE_INT8, 192, 64, kSM_80, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_interleaved", 32768, 128, 0, true}, + {DATA_TYPE_INT8, 192, 64, kSM_80, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_INT8, 256, 64, kSM_80, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_interleaved_noloop", 36864, 128, 32, true}, + {DATA_TYPE_INT8, 256, 64, kSM_80, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_noloop", 36864, 128, 32, false}, + {DATA_TYPE_INT8, 256, 64, kSM_80, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_interleaved", 36864, 128, 0, true}, + {DATA_TYPE_INT8, 256, 64, kSM_80, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80", 36864, 128, 0, false}, + {DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_interleaved_noloop", 53248, 128, 32, true}, + {DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_noloop", 53248, 128, 32, false}, + {DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_interleaved", 51200, 128, 0, true}, + {DATA_TYPE_INT8, 384, 64, kSM_80, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80", 53248, 128, 0, false}, + + // GA10x + // Note: For GA10X keep only kernels whose sharedMemBytes < 100KiB + {DATA_TYPE_FP16, 64, 64, kSM_86, fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_64_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_FP16, 96, 64, kSM_86, fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_96_64_kernel_sm80", 49152, 128, 0, false}, + {DATA_TYPE_FP16, 128, 64, kSM_86, fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm80_noloop", 40960, 128, 32, false}, + {DATA_TYPE_FP16, 128, 64, kSM_86, fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_128_64_kernel_sm80", 65536, 128, 0, false}, + {DATA_TYPE_FP16, 256, 64, kSM_86, fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm80_noloop", 73728, 128, 32, false}, + {DATA_TYPE_FP16, 256, 64, kSM_86, fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_fp16_256_64_kernel_sm80", 73728, 128, 0, false}, + + {DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_interleaved_noloop", 20480, 128, 16, true}, + {DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_noloop", 20480, 128, 16, false}, + {DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80_interleaved", 24576, 128, 0, true}, + {DATA_TYPE_INT8, 128, 64, kSM_86, fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_128_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_INT8, 192, 64, kSM_86, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_interleaved_noloop", 28672, 128, 32, true}, + {DATA_TYPE_INT8, 192, 64, kSM_86, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_noloop", 28672, 128, 32, false}, + {DATA_TYPE_INT8, 192, 64, kSM_86, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80_interleaved", 32768, 128, 0, true}, + {DATA_TYPE_INT8, 192, 64, kSM_86, fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_192_64_kernel_sm80", 32768, 128, 0, false}, + {DATA_TYPE_INT8, 256, 64, kSM_86, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_interleaved_noloop", 36864, 128, 32, true}, + {DATA_TYPE_INT8, 256, 64, kSM_86, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_noloop", 36864, 128, 32, false}, + {DATA_TYPE_INT8, 256, 64, kSM_86, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80_interleaved", 36864, 128, 0, true}, + {DATA_TYPE_INT8, 256, 64, kSM_86, fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_256_64_kernel_sm80", 36864, 128, 0, false}, + {DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_interleaved_noloop", 53248, 128, 32, true}, + {DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_noloop", 53248, 128, 32, false}, + {DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80_interleaved", 51200, 128, 0, true}, + {DATA_TYPE_INT8, 384, 64, kSM_86, fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin, + fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len, + "fused_multihead_attention_v2_int8_384_64_kernel_sm80", 53248, 128, 0, false}, +#endif +}; + +class FusedMultiHeadAttentionXMMAKernelV2 + : public TFusedMultiHeadAttentionXMMAKernel +{ +public: + FusedMultiHeadAttentionXMMAKernelV2(const FusedMultiHeadAttentionKernelMetaInfoV2* pMetaStart, + unsigned int nMetaCount, Data_type type, unsigned int sm) + : TFusedMultiHeadAttentionXMMAKernel(pMetaStart, nMetaCount, type, sm) + { + } + + inline uint64_t hashID(unsigned int s, bool interleaved, bool unroll) const + { + return (uint64_t) s << 32 | (interleaved ? 2ull : 0ull) | (unroll ? 1ull : 0ull); + } + + virtual uint64_t hashID(const KernelMeta& kernelMeta) const + { + assert(kernelMeta.mD == 64); + return hashID(kernelMeta.mS, kernelMeta.mInterleaved, kernelMeta.mUnrollStep); + } + + virtual void run(Fused_multihead_attention_params_v2& params, cudaStream_t ss) const + { + assert(params.d == 64); + if (params.interleaved) + { + assert(mDataType == bert::DATA_TYPE_INT8); + } + + bool forceUnroll = params.force_unroll; + if (!forceUnroll && !params.ignore_b1opt && mSM >= kSM_75) + { + const struct + { + unsigned int mSM; + Data_type mDataType; + int mS; + int mMaxBatch; + } unrollList[] + = { {kSM_75, bert::DATA_TYPE_FP16, 256, 1}, + {kSM_75, bert::DATA_TYPE_FP16, 384, 1}, + {kSM_75, bert::DATA_TYPE_INT8, 128, 1}, + {kSM_75, bert::DATA_TYPE_INT8, 192, 2}, + {kSM_75, bert::DATA_TYPE_INT8, 256, 1}, + {kSM_75, bert::DATA_TYPE_INT8, 384, 1}, +#if CUDA_VERSION >= 11000 + {kSM_80, bert::DATA_TYPE_FP16, 128, 4}, + {kSM_80, bert::DATA_TYPE_FP16, 256, 4}, + {kSM_80, bert::DATA_TYPE_FP16, 384, 4}, + {kSM_80, bert::DATA_TYPE_INT8, 128, 4}, + {kSM_80, bert::DATA_TYPE_INT8, 192, 16}, + {kSM_80, bert::DATA_TYPE_INT8, 256, 8}, + {kSM_80, bert::DATA_TYPE_INT8, 384, 8}, + + {kSM_86, bert::DATA_TYPE_FP16, 128, 4}, + {kSM_86, bert::DATA_TYPE_FP16, 256, 4}, + {kSM_86, bert::DATA_TYPE_INT8, 128, 4}, + {kSM_86, bert::DATA_TYPE_INT8, 192, 16}, + {kSM_86, bert::DATA_TYPE_INT8, 256, 8}, + {kSM_86, bert::DATA_TYPE_INT8, 384, 8}, +#endif + }; + for (unsigned int i = 0u; i < sizeof(unrollList) / sizeof(unrollList[0]); ++i) + { + if (mSM == unrollList[i].mSM && mDataType == unrollList[i].mDataType && params.s == unrollList[i].mS + && params.b <= unrollList[i].mMaxBatch) + { + forceUnroll = true; + break; + } + } + } + + const auto findIter = mFunctions.find(hashID(params.s, params.interleaved, forceUnroll)); + ASSERT(findIter != mFunctions.end()); + + const auto& kernelMeta = mKernelMeta[findIter->second.mMetaInfoIndex]; + const CUfunction func = findIter->second.mDeviceFunction; + + void* kernelParams[] = {¶ms, nullptr}; + if (!forceUnroll) + { + cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, 1, kernelMeta.mThreadsPerCTA, 1, 1, + kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr), + mDriver); + } + else + { + int unroll = kernelMeta.mS / kernelMeta.mUnrollStep; + assert(kernelMeta.mS == kernelMeta.mUnrollStep * unroll); + cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, unroll, kernelMeta.mThreadsPerCTA, 1, 1, + kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr), + mDriver); + } + } +}; + +using FusedMHAKernelFactoryV2 = TFusedMHAKernelFactory; + +inline const FusedMultiHeadAttentionXMMAKernelV2* getXMMAKernelsV2(Data_type type, unsigned int sm) +{ + return FusedMHAKernelFactoryV2::Get().getXMMAKernels( + sMhaKernelMetaInfosV2, sizeof(sMhaKernelMetaInfosV2) / sizeof(sMhaKernelMetaInfosV2[0]), type, sm); +} + +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm75.cpp new file mode 100644 index 00000000..9b893ae5 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm75.cpp @@ -0,0 +1,6579 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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0x00, 0xe0, 0xd1, 0x00, 0x00, + 0xb0, 0xd2, 0x00, 0x00, 0x04, 0x1e, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_128_64_kernel_sm75_cubin_len = 78684; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm80.cpp new file mode 100644 index 00000000..7a681d96 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_128_64_kernel.sm80.cpp @@ -0,0 +1,6079 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_128_64_kernel_sm80_cubin_len = 72676; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm75.cpp new file mode 100644 index 00000000..460a465c --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm75.cpp @@ -0,0 +1,5790 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_256_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm80.cpp new file mode 100644 index 00000000..a1e7bccc --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_256_64_kernel.sm80.cpp @@ -0,0 +1,5428 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_256_64_kernel_sm80_cubin_len = 64864; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm75.cpp new file mode 100644 index 00000000..fd6ee678 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm75.cpp @@ -0,0 +1,4701 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_384_64_kernel_sm75_cubin_len = 56148; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm80.cpp new file mode 100644 index 00000000..cb36b8d1 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_384_64_kernel.sm80.cpp @@ -0,0 +1,6004 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_384_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_64_64_kernel.sm75.cpp new file mode 100644 index 00000000..22d9d86a --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_64_64_kernel.sm75.cpp @@ -0,0 +1,1621 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_64_64_kernel_sm75_cubin_len = 19184; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_64_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_64_64_kernel.sm80.cpp new file mode 100644 index 00000000..ff2d10a2 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_64_64_kernel.sm80.cpp @@ -0,0 +1,1654 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_64_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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+1,2853 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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0x00, 0x30, 0x5c, 0x00, 0x00, + 0x40, 0x5c, 0x00, 0x00, 0x04, 0x1c, 0x10, 0x00, 0xa0, 0x00, 0x00, 0x00, + 0x00, 0x79, 0x00, 0x00, 0x10, 0x7a, 0x00, 0x00, 0xe0, 0x7a, 0x00, 0x00, + 0x04, 0x1e, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_fp16_96_64_kernel_sm75_cubin_len = 33968; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_96_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_96_64_kernel.sm80.cpp new file mode 100644 index 00000000..2accc1ea --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_fp16_96_64_kernel.sm80.cpp @@ -0,0 +1,2789 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_fp16_96_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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+++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm72.cpp @@ -0,0 +1,20171 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_128_64_kernel_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x48, 0x05, 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0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_int8_128_64_kernel_cubin_len = 241780; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm75.cpp new file mode 100644 index 00000000..9b53d9c1 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm75.cpp @@ -0,0 +1,15846 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_128_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm80.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm80.cpp new file mode 100644 index 00000000..870bcf2b --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_128_64_kernel.sm80.cpp @@ -0,0 +1,14792 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_128_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm72.cpp new file mode 100644 index 00000000..db827abd --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm72.cpp @@ -0,0 +1,16000 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_192_64_kernel_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x48, 0x05, 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0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_int8_192_64_kernel_cubin_len = 191728; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm75.cpp new file mode 100644 index 00000000..8e50a86c --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm75.cpp @@ -0,0 +1,20326 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_192_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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--- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_192_64_kernel.sm80.cpp @@ -0,0 +1,15005 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_192_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_256_64_kernel_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x48, 0x05, 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0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_int8_256_64_kernel_cubin_len = 239856; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_256_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_256_64_kernel.sm75.cpp new file mode 100644 index 00000000..aec74229 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_256_64_kernel.sm75.cpp @@ -0,0 +1,13969 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_256_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_256_64_kernel.sm80.cpp new file mode 100644 index 00000000..8bea6e0d --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_256_64_kernel.sm80.cpp @@ -0,0 +1,13063 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_256_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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index 00000000..3978205c --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm72.cpp @@ -0,0 +1,19648 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_384_64_kernel_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x48, 0x05, 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0x90, 0x00, 0x00, 0x00, + 0x70, 0x05, 0x01, 0x00, 0x50, 0xcd, 0x01, 0x00, 0x04, 0x1e, 0x04, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_int8_384_64_kernel_cubin_len = 235504; +} diff --git a/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm75.cpp b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm75.cpp new file mode 100644 index 00000000..35122161 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm75.cpp @@ -0,0 +1,18694 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_384_64_kernel_sm75_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x4b, 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b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm80.cpp new file mode 100644 index 00000000..f5124e75 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/fused_multihead_attention_v2_int8_384_64_kernel.sm80.cpp @@ -0,0 +1,17245 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace bert +{ +unsigned char fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x6e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x50, 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0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x28, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x98, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int fused_multihead_attention_v2_int8_384_64_kernel_sm80_cubin_len = 206668; +} diff --git a/plugin/bertQKVToContextPlugin/qkvToContext.cu b/plugin/bertQKVToContextPlugin/qkvToContext.cu index b0dd3dd3..309c10ac 100644 --- a/plugin/bertQKVToContextPlugin/qkvToContext.cu +++ b/plugin/bertQKVToContextPlugin/qkvToContext.cu @@ -13,9 +13,6 @@ * See the License for the specific language governing permissions and * limitations under the License. */ -// Need 10.1 for cublasGemmStridedBatchedEx -#include -#if CUDA_VERSION >= 10010 #include "NvInfer.h" #include "bertCommon.h" @@ -26,10 +23,10 @@ #include #include #include -#include #include +#include -#include +#include "fused_multihead_attention_v2.h" using namespace nvinfer1; namespace bert @@ -60,6 +57,7 @@ __global__ void maskedSoftmax(const float rsqrtHeadSize, const T* input, T* outp float local[VPT]; __shared__ float rZ; + __shared__ float fMax[VPT]; const int idx = (blockOffset + threadIdx.x) * VPT; T* myshm = &tmp.shm[threadIdx.x * VPT]; @@ -70,15 +68,31 @@ __global__ void maskedSoftmax(const float rsqrtHeadSize, const T* input, T* outp #pragma unroll for (int it = 0; it < VPT; it++) { - local[it] - = (threadIdx.x < lastValid) ? myExp((rsqrtHeadSize) * float(tmp.shm[it * TPB + threadIdx.x])) : 0.f; + local[it] = (threadIdx.x < lastValid) ? float(tmp.shm[it * TPB + threadIdx.x]) : -FLT_MAX; } __syncthreads(); #pragma unroll for (int it = 0; it < VPT; it++) { + float maxElem = BlockReduce(tmp.reduce).Reduce(local[it], cub::Max()); + if (threadIdx.x == 0) + { + fMax[it] = maxElem; + } + __syncthreads(); + } + +#pragma unroll + for (int it = 0; it < VPT; it++) + { + local[it] = (threadIdx.x < lastValid) ? myExp(rsqrtHeadSize * (local[it] - fMax[it])) : 0.f; + } + __syncthreads(); +#pragma unroll + for (int it = 0; it < VPT; it++) + { const auto Z = BlockReduce(tmp.reduce).Reduce(local[it], cub::Sum()); if (threadIdx.x == 0) @@ -114,6 +128,7 @@ __global__ void softmax(const float rsqrtHeadSize, const T* input, T* output) __shared__ SMem tmp; __shared__ float rZ; + __shared__ float fMax[VPT]; const int idx = (TPB * blockIdx.x + threadIdx.x) * VPT; T* myshm = &tmp.shm[threadIdx.x * VPT]; @@ -124,7 +139,25 @@ __global__ void softmax(const float rsqrtHeadSize, const T* input, T* output) #pragma unroll for (int it = 0; it < VPT; it++) { - local[it] = myExp(rsqrtHeadSize * float(tmp.shm[it * TPB + threadIdx.x])); + local[it] = float(tmp.shm[it * TPB + threadIdx.x]); + } + __syncthreads(); + +#pragma unroll + for (int it = 0; it < VPT; it++) + { + float maxElem = BlockReduce(tmp.reduce).Reduce(local[it], cub::Max()); + if (threadIdx.x == 0) + { + fMax[it] = maxElem; + } + __syncthreads(); + } + +#pragma unroll + for (int it = 0; it < VPT; it++) + { + local[it] = myExp(rsqrtHeadSize * (local[it] - fMax[it])); } __syncthreads(); @@ -426,6 +459,7 @@ void MHARunner::deserialize(const void* data, size_t length) UnfusedMHARunner::UnfusedMHARunner(const nvinfer1::DataType type, const int numHeads, const int headSize) : MHARunner(type, numHeads, headSize) + , mIsBestAlgoFound(false) , mAlgoBatchedEx1(CUBLAS_GEMM_DEFAULT_TENSOR_OP) , mAlgoBatchedEx2(CUBLAS_GEMM_DEFAULT_TENSOR_OP) { @@ -451,6 +485,7 @@ void UnfusedMHARunner::serialize(void* buffer) const void UnfusedMHARunner::deserialize(const void* data, size_t length) { + mIsBestAlgoFound = true; deserialize_value(&data, &length, &mAlgoBatchedEx1); deserialize_value(&data, &length, &mAlgoBatchedEx2); MHARunner::deserialize(data, length); @@ -459,12 +494,13 @@ void UnfusedMHARunner::deserialize(const void* data, size_t length) void UnfusedMHARunner::setup(const int S, const int B) { MHARunner::setup(S, B); - if (mType == DataType::kHALF) + if (mType == DataType::kHALF && !mIsBestAlgoFound) { std::tie(mAlgoBatchedEx1, mAlgoBatchedEx2) = tuneBatchedGemm(B, S, mNumHeads, mHeadSize); + mIsBestAlgoFound = true; gLogVerbose << "QKV Plugin - Selected Algos for batch gemms: " << mAlgoBatchedEx1 << ", " << mAlgoBatchedEx2 - << "\n"; + << "\n"; } } @@ -473,6 +509,12 @@ size_t UnfusedMHARunner::getWorkspaceSize() const return 2UL * mWordSize * mOmatSize * mNumMats; } +void UnfusedMHARunner::run(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) +{ + this->run(inputDesc[0], outputDesc[0], inputs[0], inputs[1], outputs[0], workspace, stream); +} + void UnfusedMHARunner::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) { @@ -541,7 +583,7 @@ void UnfusedMHARunner::run(const PluginTensorDesc& inputDesc, const PluginTensor } } -bool UnfusedMHARunner::isValid() const +bool UnfusedMHARunner::isValid(int s) const { return mType != DataType::kINT8; } @@ -574,7 +616,7 @@ public: mhaImpl(FusedMHARunnerFP16* interface) : interface(interface) , sm(interface->mSm) - , xmmaKernel(FusedMHAKernelFactory::Get().getXMMAKernels(DATA_TYPE_FP16, sm)) + , xmmaKernel(getXMMAKernels(DATA_TYPE_FP16, sm)) { memset(¶ms, 0, sizeof(params)); } @@ -595,7 +637,7 @@ public: // TODO these implementation details might be better centralized into the XMMA code, since they are needed in // several places (also outside of this plugin) size_t warps_m, warps_n, warps_k = 1; - if (S == 128) + if (S == 64 || S == 96 || S == 128) { warps_m = 2; warps_n = 2; @@ -644,14 +686,14 @@ public: params.o_ptr = output; - xmmaKernel->run(params, interface->mS, 64, stream); + xmmaKernel->run(params, stream); CHECK(cudaPeekAtLastError()); } - bool isValid() const + bool isValid(int s) const { - return xmmaKernel->isValid(); + return xmmaKernel->isValid(s); } private: @@ -694,9 +736,15 @@ void FusedMHARunnerFP16::run(const PluginTensorDesc& inputDesc, const PluginTens pimpl->run(inputDesc, outputDesc, qkvPtr, maskPtr, output, workspace, stream); } -bool FusedMHARunnerFP16::isValid() const +void FusedMHARunnerFP16::run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) { - return pimpl->isValid(); + assert(false && "not implemented"); +} + +bool FusedMHARunnerFP16::isValid(int s) const +{ + return pimpl->isValid(s); } // Int8 starts here: TODO refactor the duplicate stuff @@ -708,11 +756,10 @@ public: mhaImpl(FusedMHARunnerInt8* interface) : interface(interface) , sm(interface->mSm) - , xmmaKernel(FusedMHAKernelFactory::Get().getXMMAKernels(DATA_TYPE_INT8, sm)) + , xmmaKernel(getXMMAKernels(DATA_TYPE_INT8, sm)) , mDqProbs(interface->mDqProbs) { memset(¶ms, 0, sizeof(params)); - } ~mhaImpl() {} @@ -783,18 +830,16 @@ public: params.o_ptr = output; - xmmaKernel->run(params, interface->mS, 64, stream); + xmmaKernel->run(params, stream); CHECK(cudaPeekAtLastError()); - } - bool isValid() const + bool isValid(int s) const { - return xmmaKernel->isValid(); + return xmmaKernel->isValid(s); } private: - float mDqProbs; FusedMHARunnerInt8* interface; Fused_multihead_attention_params params; @@ -833,14 +878,324 @@ void FusedMHARunnerInt8::deserialize(const void* data, size_t length) void FusedMHARunnerInt8::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) { - pimpl->run(inputDesc, outputDesc,qkvPtr, maskPtr, output, workspace, stream); + pimpl->run(inputDesc, outputDesc, qkvPtr, maskPtr, output, workspace, stream); } -bool FusedMHARunnerInt8::isValid() const +void FusedMHARunnerInt8::run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) { - return pimpl->isValid(); + assert(false && "not implemented"); } -} // namespace bert +bool FusedMHARunnerInt8::isValid(int s) const +{ + return pimpl->isValid(s); +} + +class FusedMHARunnerFP16v2::mhaImpl +{ +public: + mhaImpl(FusedMHARunnerFP16v2* interface) + : interface(interface) + , sm(interface->mSm) + , xmmaKernel(getXMMAKernelsV2(DATA_TYPE_FP16, sm)) + { + assert((sm == kSM_80 || sm == kSM_75) && "Unsupported architecture"); + params.clear(); + } + + ~mhaImpl() {} + + size_t getPackedMaskSizeInBytes() const + { + // check that we initialized + assert(xmmas_m > 0); + assert(threads_per_cta > 0); + assert(interface->mB > 0); + return interface->mB * xmmas_m * threads_per_cta * sizeof(uint32_t); + } + + void setup(const int S, const int B) + { + // TODO these implementation details might be better centralized into the XMMA code, since they are needed in + // several places (also outside of this plugin) + size_t warps_m, warps_n, warps_k = 1; + if (S == 64 || S == 96 || S == 128) + { + warps_m = 2; + warps_n = 2; + } + else if (S == 256 || S == 192) + { + warps_m = 1; + warps_n = 4; + } + else if (S == 384) + { + warps_m = 1; + warps_n = 8; + } + + else + { + assert(false && "Unsupporte seqlen"); + } + // The number of threads per CTA. + threads_per_cta = warps_m * warps_n * warps_k * 32; + // The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension. + xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m); + // The number of xmmas in the N dimension. + xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n); + + const float scale_bmm1 = interface->mRsqrtHeadSize; + const float scale_softmax = 1.f; // Seems to be only required for int8 + const float scale_bmm2 = 1.f; + + Data_type scale_type = DATA_TYPE_FP16; + set_alpha(params.scale_bmm1, scale_bmm1, scale_type); + set_alpha(params.scale_softmax, scale_softmax, scale_type); + set_alpha(params.scale_bmm2, scale_bmm2, scale_type); + + params.b = B; + params.h = interface->mNumHeads; + params.s = S; + params.d = interface->mHeadSize; + + // mLdQKV = 3 * B * mNumHeads * mHeadSize; + // mLdOut = B * mNumHeads * mHeadSize; + + params.qkv_stride_in_bytes = 3 * interface->mNumHeads * interface->mHeadSize * sizeof(half); + params.packed_mask_stride_in_bytes = xmmas_m * threads_per_cta * sizeof(uint32_t); + params.o_stride_in_bytes = interface->mNumHeads * interface->mHeadSize * sizeof(half); + } + + void run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr, + const void* maskPtr, const void* cuSeqlenPtr, void* output, void* workspace, cudaStream_t stream) + { + + params.qkv_ptr = const_cast(qkvPtr); + + params.packed_mask_ptr = const_cast(maskPtr); + + params.o_ptr = output; + + params.cu_seqlens = static_cast(const_cast(cuSeqlenPtr)); + xmmaKernel->run(params, stream); + CHECK(cudaPeekAtLastError()); + } + + bool isValid(int s) const + { + return xmmaKernel->isValid(s); + } + +private: + FusedMHARunnerFP16v2* interface; + Fused_multihead_attention_params_v2 params; + int sm; + const FusedMultiHeadAttentionXMMAKernelV2* xmmaKernel; + size_t xmmas_m; + size_t xmmas_n; + size_t threads_per_cta; +}; + +FusedMHARunnerFP16v2::FusedMHARunnerFP16v2(const int numHeads, const int headSize, const int sm) + : MHARunner(DataType::kHALF, numHeads, headSize) + , mSm(sm) + , pimpl(new mhaImpl(this)) +{ +} + +void FusedMHARunnerFP16v2::setup(const int S, const int B) +{ + MHARunner::setup(S, B); + pimpl->setup(S, B); +} + +size_t FusedMHARunnerFP16v2::getWorkspaceSize() const +{ + return 0; +} + +void FusedMHARunnerFP16v2::deserialize(const void* data, size_t length) +{ + MHARunner::deserialize(data, length); + setup(mS, mB); +} + +void FusedMHARunnerFP16v2::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, + const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) +{ + assert(false && "not implemented"); + // pimpl->run(inputDesc, outputDesc, qkvPtr, maskPtr, output, workspace, stream); +} + +void FusedMHARunnerFP16v2::run(const nvinfer1::PluginTensorDesc* inputDesc, + const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace, + cudaStream_t stream) +{ + pimpl->run(inputDesc[0], outputDesc[0], inputs[0], inputs[1], inputs[2], outputs[0], workspace, stream); +} + +bool FusedMHARunnerFP16v2::isValid(int s) const +{ + return pimpl->isValid(s); +} + +// Int8 starts here: TODO refactor the duplicate stuff + +class FusedMHARunnerInt8v2::mhaImpl +{ + +public: + mhaImpl(FusedMHARunnerInt8v2* interface) + : interface(interface) + , sm(interface->mSm) + , xmmaKernel(getXMMAKernelsV2(DATA_TYPE_INT8, sm)) + , mDqProbs(interface->mDqProbs) + { + assert((sm == kSM_80 || sm == kSM_75 || sm == kSM_72) && "Unsupported architecture"); + params.clear(); + } + + ~mhaImpl() {} + + size_t getPackedMaskSizeInBytes() const + { + assert(xmmas_m > 0); + assert(threads_per_cta > 0); + assert(interface->mB > 0); + return interface->mB * xmmas_m * threads_per_cta * sizeof(uint32_t); + } + + void setup(const int S, const int B) + { + size_t warps_m, warps_n, warps_k = 1; + if (S == 128) + { + warps_m = 2; + warps_n = 2; + } + else if (S == 256 || S == 192) + { + warps_m = 1; + warps_n = 4; + } + else if (S == 384) + { + warps_m = 1; + warps_n = 8; + } + + else + { + assert(false && "Unsupported seqlen."); + } + // The number of threads per CTA. + threads_per_cta = warps_m * warps_n * warps_k * 32; + // The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension. + xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m); + // The number of xmmas in the N dimension. + xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n); + + params.b = B; + params.h = interface->mNumHeads; + params.s = S; + params.d = interface->mHeadSize; + params.use_int8_scale_max = true; + params.packed_mask_stride_in_bytes = xmmas_m * threads_per_cta * sizeof(uint32_t); + params.qkv_stride_in_bytes = 3 * interface->mNumHeads * interface->mHeadSize * sizeof(int8_t); + params.o_stride_in_bytes = interface->mNumHeads * interface->mHeadSize * sizeof(int8_t); + } + + void run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, const void* qkvPtr, + const void* maskPtr, const void* cuSeqlenPtr, void* output, void* workspace, cudaStream_t stream) + { + float scaleQkv = inputDesc.scale; + float scaleCtx = outputDesc.scale; + + float scaleBmm1 = scaleQkv * scaleQkv * interface->mRsqrtHeadSize; + float scaleBmm2 = mDqProbs * scaleQkv / scaleCtx; + float scaleSoftmax = 1.f / mDqProbs; + + params.scale_bmm1 = reinterpret_cast(scaleBmm1); + params.scale_bmm2 = reinterpret_cast(scaleBmm2); + params.scale_softmax = reinterpret_cast(scaleSoftmax); + + params.enable_i2f_trick + = -double(1 << 22) * double(scaleBmm2) <= -128.f && double(1 << 22) * double(scaleBmm2) >= 127.f; -#endif // CUDA_VERSION >= 10010 + params.qkv_ptr = const_cast(qkvPtr); + + params.packed_mask_ptr = const_cast(maskPtr); + + params.use_int8_scale_max = true; + + params.o_ptr = output; + + params.cu_seqlens = static_cast(const_cast(cuSeqlenPtr)); + + xmmaKernel->run(params, stream); + CHECK(cudaPeekAtLastError()); + } + + bool isValid(int s) const + { + return xmmaKernel->isValid(s); + } + +private: + float mDqProbs; + FusedMHARunnerInt8v2* interface; + Fused_multihead_attention_params_v2 params; + int sm; + const FusedMultiHeadAttentionXMMAKernelV2* xmmaKernel; + size_t xmmas_m; + size_t xmmas_n; + size_t threads_per_cta; +}; + +FusedMHARunnerInt8v2::FusedMHARunnerInt8v2(const int numHeads, const int headSize, const int sm, const float dqProbs) + : MHARunner(DataType::kINT8, numHeads, headSize) + , mSm(sm) + , pimpl(new mhaImpl(this)) + , mDqProbs(dqProbs) +{ +} + +void FusedMHARunnerInt8v2::setup(const int S, const int B) +{ + MHARunner::setup(S, B); + pimpl->setup(S, B); +} + +size_t FusedMHARunnerInt8v2::getWorkspaceSize() const +{ + return 0; +} + +void FusedMHARunnerInt8v2::deserialize(const void* data, size_t length) +{ + MHARunner::deserialize(data, length); + setup(mS, mB); +} + +void FusedMHARunnerInt8v2::run(const PluginTensorDesc& inputDesc, const PluginTensorDesc& outputDesc, + const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) +{ + assert(false && "Not implemented"); +} + +void FusedMHARunnerInt8v2::run(const nvinfer1::PluginTensorDesc* inputDesc, + const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace, + cudaStream_t stream) +{ + pimpl->run(inputDesc[0], outputDesc[0], inputs[0], inputs[1], inputs[2], outputs[0], workspace, stream); +} + +bool FusedMHARunnerInt8v2::isValid(int s) const +{ + return pimpl->isValid(s); +} + +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.cpp b/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.cpp new file mode 100644 index 00000000..841728e0 --- /dev/null +++ b/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.cpp @@ -0,0 +1,362 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "NvInfer.h" +#include "bertCommon.h" +#include "qkvToContextInt8InterleavedPlugin.h" +#include "serialize.hpp" + +#include +#include +#include +#include +#include +#include + +#include + +using namespace nvinfer1; +// using namespace fused_multihead_attention; + +namespace bert +{ + +namespace +{ +static const char* QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_VERSION{"3"}; +static const char* QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_NAME{"CustomQKVToContextPluginDynamic"}; +} // namespace + +// Static class fields initialization +PluginFieldCollection QKVToContextInterleavedPluginCreator::mFC{}; +std::vector QKVToContextInterleavedPluginCreator::mPluginAttributes; + +REGISTER_TENSORRT_PLUGIN(QKVToContextInterleavedPluginCreator); + +constexpr uint32_t IIDX = 0; // index of the input tensor + +QKVToContextInterleavedPlugin::QKVToContextInterleavedPlugin( + const std::string name, const int hiddenSize, const int numHeads, const float dqProbs) + : mLayerName(name) + , mS(0) + , mB(0) + , mHeadSize(hiddenSize / numHeads) + , mHiddenSize(hiddenSize) + , mNumHeads(numHeads) + , mDqProbs(dqProbs) + +{ + mSM = getSMVersion(); + // variable sequence length is only supported with the fused MHA kernels + // we should not override mS! + assert((mSM == kSM_AMPERE || mSM == kSM_TURING || mSM == kSM_XAVIER) + && "requesting maxSeqlen not compatible with GPU arch"); + // the layout changes: SxB will be a combined \sum_i s_i and hdim will be the 2nd dimension instead of the third + mXmmaKernel = getXMMAKernelsV2(DATA_TYPE_INT8, mSM); +} + +QKVToContextInterleavedPlugin::QKVToContextInterleavedPlugin(const std::string name, const void* data, size_t length) + : mLayerName(name) +{ + deserialize_value(&data, &length, &mNumHeads); + deserialize_value(&data, &length, &mHeadSize); + deserialize_value(&data, &length, &mHiddenSize); + deserialize_value(&data, &length, &mSM); + deserialize_value(&data, &length, &mS); + deserialize_value(&data, &length, &mB); + deserialize_value(&data, &length, &mDqProbs); +} + +int QKVToContextInterleavedPlugin::getSMVersion() const +{ + int device{-1}; + CHECK(cudaGetDevice(&device)); + cudaDeviceProp props; + CHECK(cudaGetDeviceProperties(&props, device)); + return props.major * 10 + props.minor; +} + +// IPluginV2DynamicExt Methods +nvinfer1::IPluginV2DynamicExt* QKVToContextInterleavedPlugin::clone() const +{ + QKVToContextInterleavedPlugin* ret + = new QKVToContextInterleavedPlugin(mLayerName, mHiddenSize, mNumHeads, mDqProbs); + + ret->setPluginNamespace(mNamespace.c_str()); + return ret; +} + +DimsExprs QKVToContextInterleavedPlugin::getOutputDimensions( + int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) +{ + // Input SHAPE is 1x(3*N*H)xTotalx1 (NCHW) + // Output SHAPE is 1x(N*H)xTotalx1 + // In SupportsFormatCombination, we force the layout to be CHW, i.e. + // Input: 3xNx(H/32)xsumSx32, Output: 1xNx(H/32)xsumSx32 + assert(outputIndex == 0); + // Copy over everything + DimsExprs output(inputs[IIDX]); + // output.d[0] = exprBuilder.constant(1); + // Divide last dim by three + auto three = exprBuilder.constant(3); + output.d[1] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[1], *three); + return output; +} +bool QKVToContextInterleavedPlugin::supportsFormatCombination( + int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) +{ + assert(nbInputs == 3); + assert(nbOutputs == 1); + // 3 inputs: + // 0: qkv + // 1: cu_seqlens + // 2: dummy + // 1 output + if (pos == 0 || pos == nbInputs) + { + return (inOut[pos].type == DataType::kINT8) && (inOut[pos].format == TensorFormat::kCHW32); + } + + if (pos == 1) + { + // cuSeqlens is a int array of size B+1 + const auto* seqlens = &inOut[pos]; + return (seqlens->type == DataType::kINT32) && (seqlens->format == TensorFormat::kLINEAR); + } + if (pos == 2) + { + // this is the dummy input + return inOut[pos].dims.nbDims == 1; + } + return false; +} + +void QKVToContextInterleavedPlugin::configurePlugin( + const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs) +{ +} + +size_t QKVToContextInterleavedPlugin::getWorkspaceSize( + const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const +{ + return 0; +} + +// IPluginV2Ext Methods +DataType QKVToContextInterleavedPlugin::getOutputDataType( + int index, const nvinfer1::DataType* inputTypes, int nbInputs) const +{ + assert(index == 0); + return DataType::kINT8; +} + +// IPluginV2 Methods +const char* QKVToContextInterleavedPlugin::getPluginType() const +{ + return QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_NAME; +} + +const char* QKVToContextInterleavedPlugin::getPluginVersion() const +{ + return QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_VERSION; +} + +int QKVToContextInterleavedPlugin::getNbOutputs() const +{ + return 1; +} + +int QKVToContextInterleavedPlugin::initialize() +{ + return 0; +} + +void QKVToContextInterleavedPlugin::terminate() {} + +size_t QKVToContextInterleavedPlugin::getSerializationSize() const +{ + return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(mHiddenSize) + sizeof(mSM) + sizeof(mS) + sizeof(mB) + + sizeof(mDqProbs); +} + +void QKVToContextInterleavedPlugin::serialize(void* buffer) const +{ + serialize_value(&buffer, mNumHeads); + serialize_value(&buffer, mHeadSize); + serialize_value(&buffer, mHiddenSize); + serialize_value(&buffer, mSM); + serialize_value(&buffer, mS); + serialize_value(&buffer, mB); + serialize_value(&buffer, mDqProbs); +} + +void QKVToContextInterleavedPlugin::destroy() +{ + delete this; +} + +void QKVToContextInterleavedPlugin::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* QKVToContextInterleavedPlugin::getPluginNamespace() const +{ + return mNamespace.c_str(); +} + +int QKVToContextInterleavedPlugin::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) +{ + + const int total = inputDesc[0].dims.d[2]; + const int B = inputDesc[1].dims.d[0] - 1; + const int maxS = inputDesc[2].dims.d[0]; + int S = 384; + if (maxS <= 128) + { + S = 128; + } + else if (maxS <= 192) + { + S = 192; + } + else if (maxS <= 256) + { + S = 256; + } + Fused_multihead_attention_params_v2 params{}; + params.b = B; + params.s = S; + params.h = mNumHeads; + params.d = mHeadSize; + + params.interleaved = true; + + params.o_ptr = outputs[0]; + params.qkv_ptr = const_cast(inputs[0]); + params.cu_seqlens = static_cast(const_cast(inputs[1])); + + float scaleQkv = inputDesc[0].scale; + float scaleCtx = outputDesc[0].scale; + + float scaleBmm1 = scaleQkv * scaleQkv * 0.125; // 1 / sqrt(64) + float scaleBmm2 = mDqProbs * scaleQkv / scaleCtx; + float scaleSoftmax = 1.f / mDqProbs; + + params.scale_bmm1 = reinterpret_cast(scaleBmm1); + params.scale_bmm2 = reinterpret_cast(scaleBmm2); + params.scale_softmax = reinterpret_cast(scaleSoftmax); + + params.qkv_stride_in_bytes = total; + params.o_stride_in_bytes = total; + + params.use_int8_scale_max = true; + params.enable_i2f_trick + = -double(1 << 22) * double(scaleBmm2) <= -128.f && double(1 << 22) * double(scaleBmm2) >= 127.f; + + mXmmaKernel->run(params, stream); + CHECK(cudaPeekAtLastError()); + return 0; +} + +QKVToContextInterleavedPluginCreator::QKVToContextInterleavedPluginCreator() +{ + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); +} + +const char* QKVToContextInterleavedPluginCreator::getPluginName() const +{ + return QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_NAME; +} + +const char* QKVToContextInterleavedPluginCreator::getPluginVersion() const +{ + return QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_VERSION; +} + +const PluginFieldCollection* QKVToContextInterleavedPluginCreator::getFieldNames() +{ + return &mFC; +} + +IPluginV2* QKVToContextInterleavedPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) +{ + int hiddenSize = 0; + int numHeads = 0; + + float dqProbs = -1; + + for (int i = 0; i < fc->nbFields; i++) + { + std::string field_name(fc->fields[i].name); + + if (field_name.compare("hidden_size") == 0) + { + hiddenSize = *static_cast(fc->fields[i].data); + gLogVerbose << "Building hiddenSize: " << hiddenSize << std::endl; + } + if (field_name.compare("num_heads") == 0) + { + numHeads = *static_cast(fc->fields[i].data); + gLogVerbose << "Building numHeads: " << numHeads << std::endl; + } + if (field_name.compare("dq_probs") == 0) + { + dqProbs = *static_cast(fc->fields[i].data); + gLogVerbose << "Building dqProbs: " << dqProbs << std::endl; + } + } + + if (hiddenSize <= 0) + { + gLogError << "QKV: Invalid hiddenSize " << hiddenSize << std::endl; + } + + if (numHeads <= 0) + { + gLogError << "QKV: Invalid numHeads " << numHeads << std::endl; + } + + if (dqProbs < 0) + { + gLogInfo << "Using default scale factor\n"; + dqProbs = 1.f / 127.f; + } + + QKVToContextInterleavedPlugin* p = new QKVToContextInterleavedPlugin(name, hiddenSize, numHeads, dqProbs); + return p; +} + +IPluginV2* QKVToContextInterleavedPluginCreator::deserializePlugin( + const char* name, const void* serialData, size_t serialLength) +{ + // This object will be deleted when the network is destroyed, which will + // call QKVToContextInterleavedPlugin::destroy() + return new QKVToContextInterleavedPlugin(name, serialData, serialLength); +} + +void QKVToContextInterleavedPluginCreator::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* QKVToContextInterleavedPluginCreator::getPluginNamespace() const +{ + return mNamespace.c_str(); +} +} // namespace bert diff --git a/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.h b/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.h new file mode 100644 index 00000000..693f671e --- /dev/null +++ b/plugin/bertQKVToContextPlugin/qkvToContextInt8InterleavedPlugin.h @@ -0,0 +1,129 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_H +#define TRT_QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_H + +#include "NvInferPlugin.h" +#include "cublas_v2.h" +#include "fused_multihead_attention_v2.h" +#include +#include +#include + +namespace bert +{ +static constexpr int32_t kSM_XAVIER = 72; +static constexpr int32_t kSM_TURING = 75; +static constexpr int32_t kSM_AMPERE = 80; + +class QKVToContextInterleavedPlugin : public nvinfer1::IPluginV2DynamicExt +{ +public: + QKVToContextInterleavedPlugin( + const std::string name, const int hiddenSize, const int numHeads, const float dqProbs); + + QKVToContextInterleavedPlugin(const std::string name, const void* data, size_t length); + + // It doesn't make sense to make QKVToContextInterleavedPlugin without arguments, so we + // delete default constructor. + QKVToContextInterleavedPlugin() = delete; + + // IPluginV2DynamicExt Methods + nvinfer1::IPluginV2DynamicExt* clone() const override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override; + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + // IPluginV2Ext Methods + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + // IPluginV2 Methods + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + int initialize() override; + void terminate() override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void destroy() override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; + +protected: + void createMHARunner(); + int getSMVersion() const; + +private: + const std::string mLayerName; + std::string mNamespace; + + int mS; + int mB; + int mSM; + int mHeadSize; + int mHiddenSize; + int mNumHeads; + + const FusedMultiHeadAttentionXMMAKernelV2* mXmmaKernel; + + float mDqProbs; + +protected: + // To prevent compiler warnings. + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::enqueue; + using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::supportsFormat; +}; + +class QKVToContextInterleavedPluginCreator : public nvinfer1::IPluginCreator +{ +public: + QKVToContextInterleavedPluginCreator(); + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + +private: + static nvinfer1::PluginFieldCollection mFC; + static std::vector mPluginAttributes; + std::string mNamespace; +}; + +} // namespace bert +#endif // TRT_QKV_TO_CONTEXT_INTERLEAVED_PLUGIN_H diff --git a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp index 49285b84..05cbd444 100644 --- a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp +++ b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -21,6 +21,7 @@ #include "NvInfer.h" #include "bertCommon.h" #include "fused_multihead_attention.h" +#include "fused_multihead_attention_v2.h" #include "qkvToContextPlugin.h" #include "serialize.hpp" @@ -38,6 +39,7 @@ namespace bert namespace { static const char* QKV_TO_CONTEXT_PLUGIN_VERSION{"1"}; +static const char* QKV_TO_CONTEXT_VAR_SEQLEN_PLUGIN_VERSION{"2"}; static const char* QKV_TO_CONTEXT_PLUGIN_NAME{"CustomQKVToContextPluginDynamic"}; } // namespace @@ -47,10 +49,15 @@ std::vector QKVToContextPluginDynamicCreator::mPluginAttributes; REGISTER_TENSORRT_PLUGIN(QKVToContextPluginDynamicCreator); -constexpr size_t kAlignment = 256; constexpr uint32_t IIDX = 0; // index of the input tensor constexpr uint32_t MIDX = 1; // index of the mask +// Static class fields initialization +PluginFieldCollection QKVToContextVarSeqlenPluginCreator::mFC{}; +std::vector QKVToContextVarSeqlenPluginCreator::mPluginAttributes; + +REGISTER_TENSORRT_PLUGIN(QKVToContextVarSeqlenPluginCreator); + QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, const DataType type, const int hiddenSize, const int numHeads, const float dqProbs, bool hasImask) : mLayerName(name) @@ -83,35 +90,35 @@ QKVToContextPluginDynamic::QKVToContextPluginDynamic(const std::string name, con deserialize_value(&data, &length, &mDqProbs); createMHARunner(); - dispatcher->deserialize(data, length); - gLogVerbose << "QKV Deser done" << std::endl; -} + int hasUnfusedRunner = 0; + deserialize_value(&data, &length, &hasUnfusedRunner); + if (hasUnfusedRunner) + { + ASSERT(unfusedDispatcher.get()); + unfusedDispatcher->deserialize(data, length); + } -int QKVToContextPluginDynamic::getSMVersion() const -{ - int device{-1}; - CHECK(cudaGetDevice(&device)); - cudaDeviceProp props; - CHECK(cudaGetDeviceProperties(&props, device)); - return props.major * 10 + props.minor; + gLogVerbose << "QKV Deser done" << std::endl; } void QKVToContextPluginDynamic::createMHARunner() { - assert(getSMVersion() == mSM); - - if (mType == DataType::kHALF) - { - dispatcher.reset(new FusedMHARunnerFP16(mNumHeads, mHeadSize, mSM)); - } - else if (mType == DataType::kINT8) + if (!fusedDispatcher.get()) { - dispatcher.reset(new FusedMHARunnerInt8(mNumHeads, mHeadSize, mSM, mDqProbs)); + if (mType == DataType::kHALF) + { + fusedDispatcher.reset(new FusedMHARunnerFP16(mNumHeads, mHeadSize, mSM)); + } + else if (mType == DataType::kINT8) + { + fusedDispatcher.reset(new FusedMHARunnerInt8(mNumHeads, mHeadSize, mSM, mDqProbs)); + } } - if (!dispatcher || !dispatcher->isValid()) + + if (!unfusedDispatcher.get()) { - dispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mHeadSize)); + unfusedDispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mHeadSize)); } } @@ -121,7 +128,8 @@ nvinfer1::IPluginV2DynamicExt* QKVToContextPluginDynamic::clone() const gLogVerbose << "QKV Clone" << std::endl; QKVToContextPluginDynamic* ret = nullptr; - if (dispatcher.get()) + // the workspacesize is 0 if we have not call setup the dispatcher yet. + if (unfusedDispatcher.get() && unfusedDispatcher->getWorkspaceSize()) { std::vector buff; buff.resize(getSerializationSize()); @@ -154,21 +162,34 @@ DimsExprs QKVToContextPluginDynamic::getOutputDimensions( bool QKVToContextPluginDynamic::supportsFormatCombination( int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) { - - // TODO cleanup - // TODO add CHW32 assert(pos >= 0); assert(pos < 2 + mHasImask); assert(nbInputs == 1 + mHasImask); const auto* in = inOut; const auto* out = inOut + nbInputs; + int packedSize = getMHAMaskPackedSize(mSM, mType, in->dims.d[SDIM]); - // we only support int8 IO in fused mha runner, and we only support fused mha runner on Turing and Ampere - if (mType == DataType::kINT8 && mSM != bert::kSM_AMPERE && mSM != bert::kSM_TURING) + // we only support int8 IO in fused mha runner, and we only support fused mha runner on Xavier, Turing and Ampere + if (mType == DataType::kINT8) { - gLogVerbose << "INT8 IO is only supported on Turing and Ampere for plugin " << QKV_TO_CONTEXT_PLUGIN_NAME - << std::endl; - return false; + if (mSM != kSM_75 && mSM != kSM_80 && mSM != kSM_86) + { + gLogError << "INT8 IO is only supported on Turing and Ampere for plugin " << QKV_TO_CONTEXT_PLUGIN_NAME + << std::endl; + return false; + } + if (in->dims.d[SDIM] == -1) + { + gLogError << "INT8 IO not support dynamic shape in sequence dimension for plugin " + << QKV_TO_CONTEXT_PLUGIN_NAME << std::endl; + return false; + } + if (packedSize == unfusedMaskSize) + { + gLogError << "INT8 IO only support sequence length 128,384 for plugin " << QKV_TO_CONTEXT_PLUGIN_NAME + << std::endl; + return false; + } } if (pos == 0) @@ -200,22 +221,18 @@ bool QKVToContextPluginDynamic::supportsFormatCombination( if ((mHasImask && pos == 1)) // pos 1 is the mask { const auto* inMask = &inOut[1]; - // detect full mask and check that it was produced - const bool useFullMask = (mType == DataType::kHALF || mType == DataType::kINT8) - && (in->dims.d[SDIM] == 128 || in->dims.d[SDIM] == 384) && (mSM == kSM_TURING || mSM == kSM_AMPERE); - - if (useFullMask) + if (inMask->dims.d[1] != -1 && inMask->dims.d[1] != packedSize) { - return (inMask->type == DataType::kHALF) && // precision - (inMask->format == TensorFormat::kLINEAR) && // format - (inMask->dims.nbDims == 2) && // Bx2*maskSize - ((inMask->dims.d[0]) == in->dims.d[BDIM]); + gLogError << "CustomEmbLayerNormPluginDynamic returned mask with pack size " << inMask->dims.d[1] + << ", but " << QKV_TO_CONTEXT_PLUGIN_NAME << " expects mask pack size " << packedSize + << std::endl; } - return (inMask->type == DataType::kINT32) && // precision + + // detect full mask and check that it was produced + return (inMask->type == DataType::kFLOAT) && // precision (inMask->format == TensorFormat::kLINEAR) && // format - (inMask->dims.nbDims == 1) && // num dims - ((inMask->dims.d[0]) == in->dims.d[BDIM]) // check B - ; + (inMask->dims.nbDims == 2) && // Bx2*maskSize + (inMask->dims.d[0] == in->dims.d[BDIM]); } if (!mHasImask || pos == 2) // output pos @@ -267,13 +284,47 @@ void QKVToContextPluginDynamic::configurePlugin( assert(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]); } - const int S = inDesc.dims.d[SDIM] <= 0 ? in->max.d[SDIM] : inDesc.dims.d[SDIM]; + createMHARunner(); + + const int S = inDesc.dims.d[SDIM]; const int B = inDesc.dims.d[BDIM] <= 0 ? in->max.d[BDIM] : inDesc.dims.d[BDIM]; + if (S <= 0) + { + // in dynamic shape build stage, we setup with max sequence that cannot fused + const int Smin = in->min.d[SDIM]; + const int Smax = in->max.d[SDIM]; - if (S != mS || B != mB) + if (fusedDispatcher.get()) + { + for (int i = Smax; i >= Smin; --i) + { + if (!fusedDispatcher->isValid(i)) + { + unfusedDispatcher->setup(i, B); + mS = i; + mB = B; + break; + } + } + } + else + { + unfusedDispatcher->setup(Smax, B); + mS = Smax; + mB = B; + } + } + else { - createMHARunner(); - this->dispatcher->setup(S, B); + // in inference stage or in static shape build stage + if (fusedDispatcher.get() && fusedDispatcher->isValid(S)) + { + fusedDispatcher->setup(S, B); + } + else + { + unfusedDispatcher->setup(S, B); + } mS = S; mB = B; } @@ -282,7 +333,11 @@ void QKVToContextPluginDynamic::configurePlugin( size_t QKVToContextPluginDynamic::getWorkspaceSize( const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const { - return this->dispatcher->getWorkspaceSize(); + // only unfused kernel need workspace, and we need larger workspace for larger sequence length + // we have already setup unfusedDispatcher with max sequence in configurePlugin + // if unfusedDispatcher is not initialized in configurePlugin + ASSERT(unfusedDispatcher.get()); + return unfusedDispatcher->getWorkspaceSize(); } // IPluginV2Ext Methods @@ -319,8 +374,10 @@ void QKVToContextPluginDynamic::terminate() {} size_t QKVToContextPluginDynamic::getSerializationSize() const { + ASSERT(unfusedDispatcher.get()); return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mHasImask) + sizeof(mHiddenSize) - + sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + dispatcher->getSerializationSize(); + + sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + sizeof(int) + + unfusedDispatcher->getSerializationSize(); } void QKVToContextPluginDynamic::serialize(void* buffer) const @@ -335,7 +392,17 @@ void QKVToContextPluginDynamic::serialize(void* buffer) const serialize_value(&buffer, mB); serialize_value(&buffer, mDqProbs); - dispatcher->serialize(buffer); + if (unfusedDispatcher.get() && unfusedDispatcher->getWorkspaceSize()) + { + int hasUnfusedRunner = 1; + serialize_value(&buffer, hasUnfusedRunner); + unfusedDispatcher->serialize(buffer); + } + else + { + int hasUnfusedRunner = 0; + serialize_value(&buffer, hasUnfusedRunner); + } } void QKVToContextPluginDynamic::destroy() @@ -360,7 +427,15 @@ int QKVToContextPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const assert(mB == inputDesc->dims.d[BDIM]); const void* maskPtr = mHasImask ? inputs[1] : nullptr; - this->dispatcher->run(inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream); + if (fusedDispatcher.get() && fusedDispatcher->isValid(inputDesc->dims.d[SDIM])) + { + fusedDispatcher->run(inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream); + } + else + { + ASSERT(unfusedDispatcher.get()); + unfusedDispatcher->run(inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream); + } return 0; } @@ -450,7 +525,8 @@ IPluginV2* QKVToContextPluginDynamicCreator::createPlugin(const char* name, cons dqProbs = 1.f / 127.f; } - QKVToContextPluginDynamic* p = new QKVToContextPluginDynamic(name, type, hiddenSize, numHeads, dqProbs, hasMask); + QKVToContextPluginDynamic* p + = new QKVToContextPluginDynamic(name, type, hiddenSize, numHeads, dqProbs, hasMask); return p; } @@ -471,6 +547,504 @@ const char* QKVToContextPluginDynamicCreator::getPluginNamespace() const { return mNamespace.c_str(); } + +QKVToContextVarSeqlenPlugin::QKVToContextVarSeqlenPlugin(const std::string name, const DataType type, + const int hiddenSize, const int numHeads, const float dqProbs, bool hasImask, bool varSeqlen) + : mLayerName(name) + , mS(0) + , mB(0) + , mHeadSize(hiddenSize / numHeads) + , mHiddenSize(hiddenSize) + , mNumHeads(numHeads) + , mHasImask(hasImask) + , mType(type) + , mDqProbs(dqProbs) + , mHdim(HDIM) + , mUseVarSeqlen(varSeqlen) + +{ + mSM = getSMVersion(); + + if (varSeqlen) + { + // variable sequence length is only supported with the fused MHA kernels + // we should not override mS! + assert((mSM == kSM_86 || mSM == kSM_80 || mSM == kSM_75 || mSM == kSM_72) && (type == DataType::kINT8 || type == DataType::kHALF) + && "requesting maxSeqlen not compatible with GPU arch"); + // the layout changes: SxB will be a combined \sum_i s_i and hdim will be the 2nd dimension instead of the third + mHdim = 1; + } +} + +QKVToContextVarSeqlenPlugin::QKVToContextVarSeqlenPlugin(const std::string name, const void* data, size_t length) + : mLayerName(name) +{ + gLogVerbose << "QKV Deser Start" << std::endl; + deserialize_value(&data, &length, &mType); + deserialize_value(&data, &length, &mNumHeads); + deserialize_value(&data, &length, &mHeadSize); + deserialize_value(&data, &length, &mHasImask); + deserialize_value(&data, &length, &mHiddenSize); + deserialize_value(&data, &length, &mSM); + deserialize_value(&data, &length, &mS); + deserialize_value(&data, &length, &mB); + + deserialize_value(&data, &length, &mDqProbs); + + deserialize_value(&data, &length, &mUseVarSeqlen); + deserialize_value(&data, &length, &mHdim); + + createMHARunner(); + dispatcher->deserialize(data, length); + + gLogVerbose << "QKV Deser done" << std::endl; +} + +void QKVToContextVarSeqlenPlugin::createMHARunner() +{ + if (dispatcher.get()) + { + return; + } + + if (mSM == kSM_86 || mSM == kSM_80 || mSM == kSM_75 || mSM == kSM_72) + { + if (mType == DataType::kHALF) + { + dispatcher.reset(new FusedMHARunnerFP16v2(mNumHeads, mHeadSize, mSM)); + } + else if (mType == DataType::kINT8) + { + dispatcher.reset(new FusedMHARunnerInt8v2(mNumHeads, mHeadSize, mSM, mDqProbs)); + } + } + else + { + assert(!mUseVarSeqlen); + dispatcher.reset(new UnfusedMHARunner(mType, mNumHeads, mHeadSize)); + } +} + +// IPluginV2DynamicExt Methods +nvinfer1::IPluginV2DynamicExt* QKVToContextVarSeqlenPlugin::clone() const +{ + gLogVerbose << "QKV Clone" << std::endl; + + QKVToContextVarSeqlenPlugin* ret = nullptr; + if (dispatcher.get()) + { + std::vector buff; + buff.resize(getSerializationSize()); + serialize(buff.data()); + + ret = new QKVToContextVarSeqlenPlugin(mLayerName, buff.data(), buff.size()); + } + else + { + ret = new QKVToContextVarSeqlenPlugin( + mLayerName, mType, mHiddenSize, mNumHeads, mDqProbs, mHasImask, mUseVarSeqlen); + } + + ret->setPluginNamespace(mNamespace.c_str()); + gLogVerbose << "QKV Clone done" << std::endl; + return ret; +} + +DimsExprs QKVToContextVarSeqlenPlugin::getOutputDimensions( + int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) +{ + // Input is BxSx3*N*H, output should be BxSxN*H + assert(outputIndex == 0); + // Copy over everything + DimsExprs output(inputs[IIDX]); + // Divide last dim by three + auto three = exprBuilder.constant(3); + output.d[mHdim] = exprBuilder.operation(DimensionOperation::kFLOOR_DIV, *inputs[IIDX].d[mHdim], *three); + return output; +} + +bool QKVToContextVarSeqlenPlugin::supportsFormatCombination( + int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) +{ + // we only support int8 IO in fused mha runner, and we only support fused mha runner on Turing and Ampere + if (mType == DataType::kINT8 && mSM != kSM_86 && mSM != kSM_80 && mSM != kSM_75 && mSM != kSM_72) + { + gLogVerbose << "INT8 IO is only supported on Xavier, Turing and Ampere for plugin " << QKV_TO_CONTEXT_PLUGIN_NAME + << std::endl; + return false; + } + + assert(pos >= 0); + assert(pos < 2 + mHasImask + 2 * mUseVarSeqlen); + assert(nbInputs == 1 + mHasImask + 2 * mUseVarSeqlen); + assert(nbOutputs == 1); + const auto* in = inOut; + const auto* out = inOut + nbInputs; + if (mUseVarSeqlen) + { + assert((mType == DataType::kHALF || mType == DataType::kINT8) + && "Conditions for variable seqlen support not fulfilled"); + // qkv, mask, cu_seqlens, dummy + assert(nbInputs == 4 && "for varseqlen, expected 4 inputs"); + } + + const auto inDims = in->dims; + // const auto inType = in->type; + // const auto inFmt = in->format; + // const auto outType = out->type; + const auto outDims = out->dims; + // const auto outFmt = out->format; + + auto supportedFormat = TensorFormat::kLINEAR; + if (mType == DataType::kINT8) + { + supportedFormat = (inDims.d[mHdim] % 32 == 0) ? TensorFormat::kCHW32 : TensorFormat::kCHW4; + } + + int supportedNbDims = 5; + if (mUseVarSeqlen) + { + supportedNbDims = 4; + } + + bool supportedHdim = (pos == 0) ? (inDims.d[mHdim] % 3 == 0) : (inDims.d[mHdim] / 3 == outDims.d[mHdim]); + + if (pos == 0 || pos == nbInputs) + { // check input and output + const auto& desc = inOut[pos]; + return (desc.type == mType) && // check type + (desc.format == supportedFormat) && // check format + (desc.dims.nbDims == supportedNbDims) && // check dims: + (supportedHdim) && // - hidden dims multiple of 3 for qkv + (desc.dims.d[mHdim + 1] == 1) && // - dummy 1 or h + (desc.dims.d[mHdim + 2] == 1) // - dummy 1 or w + ; + } + + assert(mHasImask); + if (pos == 1) + { // must be input mask + const auto* mask = &inOut[pos]; + const auto maskType = mask->type; + const auto maskFmt = mask->format; + const auto maskDims = mask->dims; + if (maskFmt != TensorFormat::kLINEAR) + return false; + + if (mUseVarSeqlen) // use full mask for fused MHA of shape B x 2*MHAmaskSize + { + return (maskType == DataType::kHALF) && // precision + (maskDims.nbDims == 2) // Bx2*maskSize + ; + } + return (mask->type == DataType::kINT32) && // precision + (mask->dims.nbDims == 1) // num dims + ; + } + assert(mUseVarSeqlen); + if (pos == 2) + { // must be cuSeqlens + // cuSeqlens is a int array of size B+1 + const auto* seqlens = &inOut[pos]; + return (seqlens->type == DataType::kINT32) && (seqlens->format == TensorFormat::kLINEAR); + } + if (pos == 3) + { + // this is the dummy input + return inOut[pos].dims.nbDims == 1; + } + return false; +} + +void QKVToContextVarSeqlenPlugin::configurePlugin( + const DynamicPluginTensorDesc* in, int nbInputs, const DynamicPluginTensorDesc* out, int nbOutputs) +{ + assert(nbInputs == 1 + mHasImask + 2 * mUseVarSeqlen); + assert(nbOutputs == 1); + const PluginTensorDesc& inDesc = in[IIDX].desc; + TRT_UNUSED inDesc; + const PluginTensorDesc& outDesc = out->desc; + TRT_UNUSED outDesc; + assert(mType == inDesc.type); + assert(mType == outDesc.type); + if (!mUseVarSeqlen) + { + assert(inDesc.dims.d[BDIM] == outDesc.dims.d[BDIM]); + assert(inDesc.dims.d[SDIM] == outDesc.dims.d[SDIM]); + assert(inDesc.dims.d[mHdim] == 3 * outDesc.dims.d[mHdim]); + if (mHasImask) + { + const PluginTensorDesc& maskDesc = in[MIDX].desc; + TRT_UNUSED maskDesc; + assert(maskDesc.dims.d[0] == inDesc.dims.d[BDIM]); + } + + const int S = inDesc.dims.d[SDIM] <= 0 ? in->max.d[SDIM] : inDesc.dims.d[SDIM]; + const int B = inDesc.dims.d[BDIM] <= 0 ? in->max.d[BDIM] : inDesc.dims.d[BDIM]; + + if (S != mS || B != mB) + { + // gLogVerbose << "setting up MHA runner for single sequence length" << std::endl; + createMHARunner(); + this->dispatcher->setup(S, B); + mS = S; + mB = B; + } + } + else + { + // gLogVerbose << "setting up MHA runner for variable sequence length" << std::endl; + createMHARunner(); + // need to initialize S and B with somewhat useful values, they will be reset at enqueue for the actual + // batchsize + this->dispatcher->setup(256, 1); + } +} + +size_t QKVToContextVarSeqlenPlugin::getWorkspaceSize( + const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const +{ + return this->dispatcher->getWorkspaceSize(); +} + +// IPluginV2Ext Methods +DataType QKVToContextVarSeqlenPlugin::getOutputDataType( + int index, const nvinfer1::DataType* inputTypes, int nbInputs) const +{ + assert(index == 0); + assert(inputTypes[0] == DataType::kFLOAT || inputTypes[0] == DataType::kHALF || inputTypes[0] == DataType::kINT8); + return inputTypes[0]; +} + +// IPluginV2 Methods +const char* QKVToContextVarSeqlenPlugin::getPluginType() const +{ + return QKV_TO_CONTEXT_PLUGIN_NAME; +} + +const char* QKVToContextVarSeqlenPlugin::getPluginVersion() const +{ + return QKV_TO_CONTEXT_VAR_SEQLEN_PLUGIN_VERSION; +} + +int QKVToContextVarSeqlenPlugin::getNbOutputs() const +{ + return 1; +} + +int QKVToContextVarSeqlenPlugin::initialize() +{ + return 0; +} + +void QKVToContextVarSeqlenPlugin::terminate() {} + +size_t QKVToContextVarSeqlenPlugin::getSerializationSize() const +{ + return sizeof(mNumHeads) + sizeof(mHeadSize) + sizeof(DataType) + sizeof(mHasImask) + sizeof(mHiddenSize) + + sizeof(mSM) + sizeof(mS) + sizeof(mB) + sizeof(mDqProbs) + dispatcher->getSerializationSize() + + sizeof(mUseVarSeqlen) + sizeof(mHdim); +} + +void QKVToContextVarSeqlenPlugin::serialize(void* buffer) const +{ + serialize_value(&buffer, mType); + serialize_value(&buffer, mNumHeads); + serialize_value(&buffer, mHeadSize); + serialize_value(&buffer, mHasImask); + serialize_value(&buffer, mHiddenSize); + serialize_value(&buffer, mSM); + serialize_value(&buffer, mS); + serialize_value(&buffer, mB); + + serialize_value(&buffer, mDqProbs); + serialize_value(&buffer, mUseVarSeqlen); + serialize_value(&buffer, mHdim); + dispatcher->serialize(buffer); +} + +void QKVToContextVarSeqlenPlugin::destroy() +{ + delete this; +} + +void QKVToContextVarSeqlenPlugin::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* QKVToContextVarSeqlenPlugin::getPluginNamespace() const +{ + return mNamespace.c_str(); +} + +int QKVToContextVarSeqlenPlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, + const nvinfer1::PluginTensorDesc* outputDesc, const void* const* inputs, void* const* outputs, void* workspace, + cudaStream_t stream) +{ + + if (mUseVarSeqlen) + { + const int B = inputDesc[1].dims.d[0]; + const int maxS = inputDesc[3].dims.d[0]; + int S = 384; + if (DataType::kHALF == mType && maxS <= 64) + { + S = 64; + } + else if (DataType::kHALF == mType && maxS <= 96) + { + S = 96; + } + else if (maxS <= 128) + { + S = 128; + } + else if (maxS <= 192) + { + S = 192; + if(mType == DataType::kHALF) + { + S = 256; + } + + } + else if (maxS <= 256) + { + S = 256; + } + + this->dispatcher->setup(S, B); + this->dispatcher->run(inputDesc, outputDesc, inputs, outputs, workspace, stream); + } + else + { + assert(mS == inputDesc->dims.d[SDIM]); + assert(mB == inputDesc->dims.d[BDIM]); + + const void* maskPtr = mHasImask ? inputs[1] : nullptr; + this->dispatcher->run(inputDesc[0], outputDesc[0], inputs[0], maskPtr, outputs[0], workspace, stream); + return 0; + } + + return 0; +} + +QKVToContextVarSeqlenPluginCreator::QKVToContextVarSeqlenPluginCreator() +{ + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); +} + +const char* QKVToContextVarSeqlenPluginCreator::getPluginName() const +{ + return QKV_TO_CONTEXT_PLUGIN_NAME; +} + +const char* QKVToContextVarSeqlenPluginCreator::getPluginVersion() const +{ + return QKV_TO_CONTEXT_VAR_SEQLEN_PLUGIN_VERSION; +} + +const PluginFieldCollection* QKVToContextVarSeqlenPluginCreator::getFieldNames() +{ + return &mFC; +} + +IPluginV2* QKVToContextVarSeqlenPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) +{ + gLogVerbose << "Creating QKV2ContextPlugin...\n"; + + int hiddenSize = 0; + int numHeads = 0; + bool hasMask = false; + int typeId = -1; + + int varSeqlen = 0; + + float dqProbs = -1; + + for (int i = 0; i < fc->nbFields; i++) + { + std::string field_name(fc->fields[i].name); + + if (field_name.compare("type_id") == 0) + { + typeId = *static_cast(fc->fields[i].data); + gLogVerbose << "Building typeId: " << typeId << std::endl; + } + if (field_name.compare("hidden_size") == 0) + { + hiddenSize = *static_cast(fc->fields[i].data); + gLogVerbose << "Building hiddenSize: " << hiddenSize << std::endl; + } + if (field_name.compare("num_heads") == 0) + { + numHeads = *static_cast(fc->fields[i].data); + gLogVerbose << "Building numHeads: " << numHeads << std::endl; + } + if (field_name.compare("has_mask") == 0) + { + hasMask = *static_cast(fc->fields[i].data); + gLogVerbose << "Building hasMask: " << hasMask << std::endl; + } + + if (field_name.compare("dq_probs") == 0) + { + dqProbs = *static_cast(fc->fields[i].data); + gLogVerbose << "Building dqProbs: " << dqProbs << std::endl; + } + if (field_name.compare("var_seqlen") == 0) + { + varSeqlen = *static_cast(fc->fields[i].data); + gLogVerbose << "Building var_seqlen: " << varSeqlen << std::endl; + } + } + if (typeId < 0 || typeId > 3) + { + gLogError << "QKV: Invalid TypeId " << typeId << std::endl; + } + + if (hiddenSize <= 0) + { + gLogError << "QKV: Invalid hiddenSize " << hiddenSize << std::endl; + } + + if (numHeads <= 0) + { + gLogError << "QKV: Invalid numHeads " << numHeads << std::endl; + } + + gLogVerbose << "Building the Plugin...\n"; + DataType type = static_cast(typeId); + if (type == DataType::kINT8 && dqProbs < 0) + { + gLogInfo << "Using default scale factor\n"; + dqProbs = 1.f / 127.f; + } + + QKVToContextVarSeqlenPlugin* p + = new QKVToContextVarSeqlenPlugin(name, type, hiddenSize, numHeads, dqProbs, hasMask, varSeqlen); + return p; +} + +IPluginV2* QKVToContextVarSeqlenPluginCreator::deserializePlugin( + const char* name, const void* serialData, size_t serialLength) +{ + // This object will be deleted when the network is destroyed, which will + // call QKVToContextVarSeqlenPlugin::destroy() + return new QKVToContextVarSeqlenPlugin(name, serialData, serialLength); +} + +void QKVToContextVarSeqlenPluginCreator::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* QKVToContextVarSeqlenPluginCreator::getPluginNamespace() const +{ + return mNamespace.c_str(); +} } // namespace bert #endif // CUDA_VERSION >= 10010 diff --git a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h index 870b6194..c9192be0 100644 --- a/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h +++ b/plugin/bertQKVToContextPlugin/qkvToContextPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -35,9 +35,17 @@ class MHARunner public: MHARunner(const nvinfer1::DataType type, const int numHeads, const int headSize) : mType(type) + , mS(0) + , mB(0) + , mOmatSize(0) + , mNumMats(0) , mNumHeads(numHeads) , mHeadSize(headSize) , mWordSize(getElementSize(type)) + , mLdQKV(0) + , mStrideQKV(0) + , mLdOut(0) + , mStrideOut(0) , mRsqrtHeadSize(1.f / sqrtf(headSize)) { } @@ -64,13 +72,17 @@ class MHARunner const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) = 0; + virtual void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) + = 0; + virtual size_t getSerializationSize() const; virtual void serialize(void* buffer) const; virtual void deserialize(const void* data, size_t length); virtual size_t getWorkspaceSize() const = 0; - virtual bool isValid() const = 0; + virtual bool isValid(int s) const = 0; protected: nvinfer1::DataType mType; @@ -146,13 +158,16 @@ class QKVToContextPluginDynamic : public nvinfer1::IPluginV2DynamicExt protected: void createMHARunner(); - int getSMVersion() const; private: const std::string mLayerName; std::string mNamespace; - std::unique_ptr dispatcher; + // used for sequence len 128, 384, precision int8 + // used for sequence len 64, 96, 128, 384, precision fp16 + std::unique_ptr fusedDispatcher; + // used for other sequence, precision fp32 and fp16 + std::unique_ptr unfusedDispatcher; int mS; int mB; @@ -201,6 +216,105 @@ class QKVToContextPluginDynamicCreator : public nvinfer1::IPluginCreator std::string mNamespace; }; +class QKVToContextVarSeqlenPlugin : public nvinfer1::IPluginV2DynamicExt +{ +public: + QKVToContextVarSeqlenPlugin(const std::string name, const nvinfer1::DataType type, const int hiddenSize, + const int numHeads, const float dqProbs, bool hasImask = false, bool varSeqlen = false); + + QKVToContextVarSeqlenPlugin(const std::string name, const void* data, size_t length); + + // It doesn't make sense to make QKVToContextVarSeqlenPlugin without arguments, so we + // delete default constructor. + QKVToContextVarSeqlenPlugin() = delete; + + // IPluginV2DynamicExt Methods + nvinfer1::IPluginV2DynamicExt* clone() const override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override; + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + // IPluginV2Ext Methods + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + // IPluginV2 Methods + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + int initialize() override; + void terminate() override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void destroy() override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; + +protected: + void createMHARunner(); + +private: + const std::string mLayerName; + std::string mNamespace; + + std::unique_ptr dispatcher; + + int mS; + int mB; + int mSM; + int mHeadSize; + int mHiddenSize; + int mNumHeads; + bool mHasImask; + nvinfer1::DataType mType; + + float mDqProbs; + + int mHdim; + bool mUseVarSeqlen; + +protected: + // To prevent compiler warnings. + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::enqueue; + using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::supportsFormat; +}; + +class QKVToContextVarSeqlenPluginCreator : public nvinfer1::IPluginCreator +{ +public: + QKVToContextVarSeqlenPluginCreator(); + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + +private: + static nvinfer1::PluginFieldCollection mFC; + static std::vector mPluginAttributes; + std::string mNamespace; +}; + class UnfusedMHARunner : public MHARunner { public: @@ -212,14 +326,18 @@ class UnfusedMHARunner : public MHARunner void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc, const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override; + void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + size_t getWorkspaceSize() const override; size_t getSerializationSize() const override; void serialize(void* buffer) const override; void deserialize(const void* data, size_t length) override; - bool isValid() const override; + bool isValid(int s) const override; private: + bool mIsBestAlgoFound; int mAlgoBatchedEx1; int mAlgoBatchedEx2; cublasHandle_t mCublas; @@ -236,11 +354,14 @@ class FusedMHARunnerFP16 : public MHARunner void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc, const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override; + void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + size_t getWorkspaceSize() const override; void deserialize(const void* data, size_t length) override; - bool isValid() const override; + bool isValid(int s) const override; private: int mSm; @@ -259,11 +380,67 @@ class FusedMHARunnerInt8 : public MHARunner void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc, const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override; + void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + size_t getWorkspaceSize() const override; + + void deserialize(const void* data, size_t length) override; + + bool isValid(int s) const override; + +private: + float mDqProbs; + int mSm; + class mhaImpl; + std::unique_ptr pimpl; +}; + +class FusedMHARunnerFP16v2 : public MHARunner +{ +public: + FusedMHARunnerFP16v2(const int numHeads, const int headSize, const int sm); + ~FusedMHARunnerFP16v2() = default; // for pimpl + + virtual void setup(const int S, const int B) override; + + void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc, + const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override; + + void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + size_t getWorkspaceSize() const override; + + void deserialize(const void* data, size_t length) override; + + bool isValid(int s) const override; + +private: + int mSm; + class mhaImpl; + std::unique_ptr pimpl; +}; + +class FusedMHARunnerInt8v2 : public MHARunner +{ +public: + FusedMHARunnerInt8v2(const int numHeads, const int headSize, const int sm, const float dqProbs); + ~FusedMHARunnerInt8v2() = default; // for pimpl + + virtual void setup(const int S, const int B) override; + + void run(const nvinfer1::PluginTensorDesc& inputDesc, const nvinfer1::PluginTensorDesc& outputDesc, + const void* qkvPtr, const void* maskPtr, void* output, void* workspace, cudaStream_t stream) override; + + void run(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + size_t getWorkspaceSize() const override; void deserialize(const void* data, size_t length) override; - bool isValid() const override; + bool isValid(int s) const override; private: float mDqProbs; diff --git a/plugin/common/bboxUtils.h b/plugin/common/bboxUtils.h index 09f0d7d1..b9a76b40 100644 --- a/plugin/common/bboxUtils.h +++ b/plugin/common/bboxUtils.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_BBOX_UTILS_H #define TRT_BBOX_UTILS_H diff --git a/plugin/common/bertCommon.h b/plugin/common/bertCommon.h index 423e25d9..dac3ca69 100644 --- a/plugin/common/bertCommon.h +++ b/plugin/common/bertCommon.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -22,12 +22,13 @@ #include "NvInfer.h" #include "NvInferRuntimeCommon.h" +#include "checkMacrosPlugin.h" #include "cublas_v2.h" #include "cuda_fp16.h" #include "plugin.h" -#include "pluginLogger.h" #include +#include #include #include #include @@ -37,11 +38,74 @@ #define TRT_UNUSED (void) using half = __half; +using namespace nvinfer1::plugin; constexpr uint32_t BDIM = 1; // batch dimension constexpr uint32_t SDIM = 0; // seq len dimension constexpr uint32_t HDIM = 2; // hidden dimension +constexpr int32_t kSM_72 = 72; +constexpr int32_t kSM_75 = 75; +constexpr int32_t kSM_80 = 80; +constexpr int32_t kSM_86 = 86; + +// For full mask mode, we must produce the compressed mask format expected by the fused attention path. Currently, only +// two sequence lengths are supported. We hard code the sizes here. +// The number of threads per CTA: warps_m * warps_n * warps_k * 32; +constexpr size_t threadsPerCta128 = 2 * 2 * 32; +constexpr size_t threadsPerCta384 = 1 * 8 * 32; + +// The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension: (s + 16*warps_m - 1) +// / (16*warps_m); +constexpr size_t xmmasM128 = 4; +constexpr size_t xmmasM384 = 24; + +// Packed mask size per batch. Layout is XMMAS_M * THREADS_PER_CTA. +constexpr size_t unfusedMaskSize = 1; +constexpr size_t packedMaskSize64 = xmmasM128 * threadsPerCta128; +constexpr size_t packedMaskSize96 = xmmasM128 * threadsPerCta128; +constexpr size_t packedMaskSize128 = xmmasM128 * threadsPerCta128; +constexpr size_t packedMaskSize384 = xmmasM384 * threadsPerCta384; + +namespace bert +{ + +inline int getSMVersion() +{ + int device{-1}; + CHECK(cudaGetDevice(&device)); + cudaDeviceProp props; + CHECK(cudaGetDeviceProperties(&props, device)); + return props.major * 10 + props.minor; +} + +inline int getMHAMaskPackedSize(int smVersion, nvinfer1::DataType dataType, int sequenceLength) +{ + // this code must match EmbLayerNormPluginDynamic::getOutputDimensions in embLayerNormPlugin.cpp + int packedSize = unfusedMaskSize; + if ((smVersion == kSM_75 || smVersion == kSM_80 || smVersion == kSM_86) + && (dataType == nvinfer1::DataType::kINT8 || dataType == nvinfer1::DataType::kHALF)) + { + if (sequenceLength == 64) + { + packedSize = (dataType == nvinfer1::DataType::kHALF ? packedMaskSize64 : packedSize); + } + else if (sequenceLength == 96) + { + packedSize = (dataType == nvinfer1::DataType::kHALF ? packedMaskSize96 : packedSize); + } + else if (sequenceLength == 128) + { + packedSize = packedMaskSize128; + } + else if (sequenceLength == 384) + { + packedSize = packedMaskSize384; + } + } + return packedSize; +} + inline unsigned int getElementSize(nvinfer1::DataType t) { switch (t) @@ -66,9 +130,6 @@ inline int64_t volume(const nvinfer1::Dims& d) return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies()); } -namespace bert -{ - template constexpr IntType ceildiv(IntType a, IntType b) { @@ -401,25 +462,6 @@ inline nvinfer1::DataType fieldTypeToDataType(const nvinfer1::PluginFieldType ft } } -inline unsigned int getElementSize(nvinfer1::DataType t) -{ - switch (t) - { - case nvinfer1::DataType::kINT32: return 4; - case nvinfer1::DataType::kFLOAT: return 4; - case nvinfer1::DataType::kHALF: return 2; - case nvinfer1::DataType::kBOOL: - case nvinfer1::DataType::kINT8: return 1; - } - throw std::runtime_error("Invalid DataType."); - return 0; -} - -inline int64_t volume(const nvinfer1::Dims& d) -{ - return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies()); -} - } // namespace bert #endif // BERT_COMMON_H diff --git a/plugin/common/checkMacrosPlugin.cpp b/plugin/common/checkMacrosPlugin.cpp index 17dddbe7..0565f16e 100644 --- a/plugin/common/checkMacrosPlugin.cpp +++ b/plugin/common/checkMacrosPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "checkMacrosPlugin.h" #include #include @@ -23,6 +24,7 @@ namespace nvinfer1 namespace plugin { +// This will be populated by the logger supplied by the user to initLibNvInferPlugins() ILogger* gLogger{}; template @@ -41,9 +43,12 @@ int LogStream::Buf::sync() return 0; } +// These use gLogger, and therefore require initLibNvInferPlugins() to be called with a logger +// (otherwise, it will not log) LogStream gLogError; LogStream gLogWarning; LogStream gLogInfo; +LogStream gLogVerbose; // break-pointable void throwCudaError(const char* file, const char* function, int line, int status, const char* msg) diff --git a/plugin/common/checkMacrosPlugin.h b/plugin/common/checkMacrosPlugin.h index 48eca1a0..f10bc39d 100644 --- a/plugin/common/checkMacrosPlugin.h +++ b/plugin/common/checkMacrosPlugin.h @@ -13,12 +13,16 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef CHECK_MACROS_PLUGIN_H #define CHECK_MACROS_PLUGIN_H #include "NvInfer.h" #include +#ifndef TRT_CHECK_MACROS_H +#ifndef TRT_TUT_HELPERS_H + #ifdef _MSC_VER #define FN_NAME __FUNCTION__ #else @@ -32,6 +36,9 @@ #define NORETURN [[noreturn]] #endif +#endif // TRT_TUT_HELPERS_H +#endif // TRT_CHECK_MACROS_H + namespace nvinfer1 { namespace plugin @@ -55,6 +62,7 @@ class LogStream : public std::ostream extern LogStream gLogError; extern LogStream gLogWarning; extern LogStream gLogInfo; +extern LogStream gLogVerbose; void reportAssertion(const char* msg, const char* file, int line); void logError(const char* msg, const char* file, const char* fn, int line); @@ -121,6 +129,9 @@ class CublasError : public TRTException } // namespace nvinfer1 +#ifndef TRT_CHECK_MACROS_H +#ifndef TRT_TUT_HELPERS_H + #define API_CHECK(condition) \ { \ if ((condition) == false) \ @@ -167,7 +178,6 @@ class CublasError : public TRTException #define API_CHECK_ENUM_RANGE_RETVAL(Type, val, retval) \ API_CHECK_RETVAL(int(val) >= 0 && int(val) < EnumMax(), retval) -#ifndef TRT_PLUGIN_H #define CUBLASASSERTMSG(status_, msg) \ { \ auto s_ = status_; \ @@ -244,5 +254,7 @@ class CublasError : public TRTException nvinfer1::plugin::logError(#status_ " failure.", __FILE__, FN_NAME, __LINE__); \ } -#endif // TRT_PLUGIN_H +#endif // TRT_TUT_HELPERS_H +#endif // TRT_CHECK_MACROS_H + #endif /*CHECK_MACROS_PLUGIN_H*/ diff --git a/plugin/common/common.cuh b/plugin/common/common.cuh index 89dbc64d..ec22061f 100644 --- a/plugin/common/common.cuh +++ b/plugin/common/common.cuh @@ -277,18 +277,34 @@ __device__ inline void scaledSoftmaxSmall( __shared__ typename BlockReduce::TempStorage tmpStorage; __shared__ float rZ; + __shared__ float fMax; const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld; const float w(rsqrtHeadSize); cub::Sum sum; - float threadData(0); + float threadData(-FLT_MAX); const int idx = offset + threadIdx.x; if (threadIdx.x < lastValid) { - const float val = input[idx]; - threadData = exp(val * w); + threadData = input[idx]; + } + + const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, cub::Max()); + if (threadIdx.x == 0) + { + fMax = maxElem; + } + __syncthreads(); + + if (threadIdx.x < lastValid) + { + threadData = exp((threadData - fMax) * w); + } + else + { + threadData = 0; } const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum); @@ -315,18 +331,41 @@ __device__ inline void scaledSoftmax( __shared__ typename BlockReduce::TempStorage tmpStorage; __shared__ float rZ; + __shared__ float fMax; const int offset = (blockIdx.y * gridDim.x + blockIdx.x) * ld; const float w(rsqrtHeadSize); cub::Sum sum; - float threadData(0); + float threadData(-FLT_MAX); + if (lastValid >= blockDim.x) + { + threadData = 0; + } for (int i = threadIdx.x; i < lastValid; i += TPB) { const int idx = offset + i; - const float val = input[idx]; - threadData += exp(val * w); + threadData = input[idx]; + } + + const float maxElem = BlockReduce(tmpStorage).Reduce(threadData, cub::Max()); + if (threadIdx.x == 0) + { + fMax = maxElem; + } + __syncthreads(); + + if (lastValid < blockDim.x) + { + if (threadIdx.x >= lastValid) + { + threadData = 0; + } + } + for (int i = threadIdx.x; i < lastValid; i += TPB) + { + threadData += exp((threadData - fMax) * w); } const auto Z = BlockReduce(tmpStorage).Reduce(threadData, sum); diff --git a/plugin/common/cub_helper.h b/plugin/common/cub_helper.h index 2be4ca57..fd8a9dce 100644 --- a/plugin/common/cub_helper.h +++ b/plugin/common/cub_helper.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "kernel.h" template size_t cubSortPairsWorkspaceSize(int num_items, int num_segments) diff --git a/plugin/common/cudaDriverWrapper.cpp b/plugin/common/cudaDriverWrapper.cpp index 947b825d..653f7a64 100644 --- a/plugin/common/cudaDriverWrapper.cpp +++ b/plugin/common/cudaDriverWrapper.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #define CUDA_LIB_NAME "cuda" #if defined(_WIN32) diff --git a/plugin/common/kernels/common.cu b/plugin/common/kernels/common.cu index ddf19f93..2ee411fa 100755 --- a/plugin/common/kernels/common.cu +++ b/plugin/common/kernels/common.cu @@ -13,6 +13,8 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + +#include "cuda.h" #include "cublas_v2.h" #include #include diff --git a/plugin/common/kernels/gridAnchorLayer.cu b/plugin/common/kernels/gridAnchorLayer.cu index 8685d8b4..d3854dd6 100644 --- a/plugin/common/kernels/gridAnchorLayer.cu +++ b/plugin/common/kernels/gridAnchorLayer.cu @@ -19,40 +19,27 @@ using nvinfer1::plugin::reduced_divisor; template -__launch_bounds__(nthdsPerCTA) -__global__ void gridAnchorKernel( - const GridAnchorParameters param, - const int numAspectRatios, - reduced_divisor divObj, - const float* widths, - const float* heights, - float* outputData - ) +__launch_bounds__(nthdsPerCTA) __global__ void gridAnchorKernel(const GridAnchorParameters param, + const int numAspectRatios, reduced_divisor divObj, const float* widths, const float* heights, float* outputData) { // output dims: (H, W, param.numMinSize, (1+haveMaxSize+numAR-1), 4) const int dim = param.H * param.W * numAspectRatios; - /* * Parameters used to calculate the bounding box coordinates back to input image scale * Normally we calculate the anchorStride = image_input_size (in pixel) / feature_map_size * Here we do not use image_input_size for the moment * Instead we use 1.0 * The coordinates calculated are scaled by the input image size. - * Most of the coordinates will be in a range of [0, 1], except for the bounding box coordinates going outside of the image - * Every coordinate will go back to the pixel coordinates in the input image if being multiplied by image_input_size - * Here we implicitly assumes the image input and feature map are square + * Most of the coordinates will be in a range of [0, 1], except for the bounding box coordinates going outside of + * the image Every coordinate will go back to the pixel coordinates in the input image if being multiplied by + * image_input_size Here we implicitly assumes the image input and feature map are square */ - float anchorStrideH = (1.0 / param.H); - float anchorStrideW = (1.0 / param.W); - float anchorOffsetH = 0.5 * anchorStrideH; - float anchorOffsetW = 0.5 * anchorStrideW; + float anchorStride = (1.0 / param.H); + float anchorOffset = 0.5 * anchorStride; int tid = blockIdx.x * blockDim.x + threadIdx.x; if (tid >= dim) - { return; - } - int arId, currIndex; divObj.divmod(tid, currIndex, arId); @@ -60,8 +47,8 @@ __global__ void gridAnchorKernel( const int h = currIndex / param.W; // Center coordinates - float yC = h * anchorStrideH + anchorOffsetH; - float xC = w * anchorStrideW + anchorOffsetW; + float yC = h * anchorStride + anchorOffset; + float xC = w * anchorStride + anchorOffset; // x_min, y_min float xMin = xC - 0.5 * widths[arId]; @@ -86,14 +73,8 @@ __global__ void gridAnchorKernel( output[tid * 4 + 3] = param.variance[3]; } -pluginStatus_t anchorGridInference( - cudaStream_t stream, - const GridAnchorParameters param, - const int numAspectRatios, - const void* widths, - const void* heights, - void* outputData - ) +pluginStatus_t anchorGridInference(cudaStream_t stream, const GridAnchorParameters param, const int numAspectRatios, + const void* widths, const void* heights, void* outputData) { const int dim = param.H * param.W * numAspectRatios; reduced_divisor divObj(numAspectRatios); @@ -101,20 +82,46 @@ pluginStatus_t anchorGridInference( { const int BS = 128; const int GS = (dim + BS - 1) / BS; - gridAnchorKernel<<>>(param, numAspectRatios, divObj, - (const float*) widths, (const float*) heights, - (float*) outputData); + gridAnchorKernel<<>>( + param, numAspectRatios, divObj, (const float*) widths, (const float*) heights, (float*) outputData); + } + else + { + const int BS = 32; + const int GS = (dim + BS - 1) / BS; + gridAnchorKernel<<>>( + param, numAspectRatios, divObj, (const float*) widths, (const float*) heights, (float*) outputData); + } + CSC(cudaGetLastError(), STATUS_FAILURE); + return STATUS_SUCCESS; +} +namespace nvinfer1 +{ +namespace plugin +{ +pluginStatus_t anchorGridInference(cudaStream_t stream, const GridAnchorParameters param, const int numAspectRatios, + const void* widths, const void* heights, void* outputData) +{ + const int dim = param.H * param.W * numAspectRatios; + reduced_divisor divObj(numAspectRatios); + if (dim > 5120) + { + const int BS = 128; + const int GS = (dim + BS - 1) / BS; + gridAnchorKernel<<>>( + param, numAspectRatios, divObj, (const float*) widths, (const float*) heights, (float*) outputData); } else { const int BS = 32; const int GS = (dim + BS - 1) / BS; - gridAnchorKernel<<>>(param, numAspectRatios, divObj, - (const float*) widths, (const float*) heights, - (float*) outputData); + gridAnchorKernel<<>>( + param, numAspectRatios, divObj, (const float*) widths, (const float*) heights, (float*) outputData); } CSC(cudaGetLastError(), STATUS_FAILURE); return STATUS_SUCCESS; } +} // namespace plugin +} // namespace nvinfer1 diff --git a/plugin/common/kernels/kernel.cpp b/plugin/common/kernels/kernel.cpp index 142a9b9e..49e0d848 100644 --- a/plugin/common/kernels/kernel.cpp +++ b/plugin/common/kernels/kernel.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "kernel.h" #include "plugin.h" diff --git a/plugin/common/kernels/kernel.h b/plugin/common/kernels/kernel.h index b0839105..90a4770c 100644 --- a/plugin/common/kernels/kernel.h +++ b/plugin/common/kernels/kernel.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_KERNEL_H #define TRT_KERNEL_H @@ -95,8 +96,7 @@ pluginStatus_t normalizeInference(cudaStream_t stream, cublasHandle_t handle, bo pluginStatus_t priorBoxInference(cudaStream_t stream, PriorBoxParameters param, int H, int W, int numPriors, int numAspectRatios, const void* minSize, const void* maxSize, const void* aspectRatios, void* outputData); -pluginStatus_t lReLUInference( - cudaStream_t stream, int n, float negativeSlope, const void* input, void* output); +pluginStatus_t lReLUInference(cudaStream_t stream, int n, float negativeSlope, const void* input, void* output); pluginStatus_t reorgInference( cudaStream_t stream, int batch, int C, int H, int W, int stride, const void* input, void* output); diff --git a/plugin/common/kernels/lReLU.cu b/plugin/common/kernels/lReLU.cu index 3efab821..5eb59797 100644 --- a/plugin/common/kernels/lReLU.cu +++ b/plugin/common/kernels/lReLU.cu @@ -17,12 +17,8 @@ #include "kernel.h" template -__launch_bounds__(nthdsPerCTA) - __global__ void pReLUKernel( - const int n, - const float negativeSlope, - const float* input, - float* output) +__launch_bounds__(nthdsPerCTA) __global__ + void pReLUKernel(const int n, const float negativeSlope, const float* input, float* output) { for (int i = blockIdx.x * nthdsPerCTA + threadIdx.x; i < n; i += gridDim.x * nthdsPerCTA) { @@ -30,12 +26,7 @@ __launch_bounds__(nthdsPerCTA) } } -pluginStatus_t lReLUGPU( - cudaStream_t stream, - const int n, - const float negativeSlope, - const void* input, - void* output) +pluginStatus_t lReLUGPU(cudaStream_t stream, const int n, const float negativeSlope, const void* input, void* output) { const int BS = 512; const int GS = (n + BS - 1) / BS; @@ -46,11 +37,7 @@ pluginStatus_t lReLUGPU( } pluginStatus_t lReLUInference( - cudaStream_t stream, - const int n, - const float negativeSlope, - const void* input, - void* output) + cudaStream_t stream, const int n, const float negativeSlope, const void* input, void* output) { return lReLUGPU(stream, n, negativeSlope, (const float*) input, (float*) output); } diff --git a/plugin/common/kernels/maskRCNNKernels.cu b/plugin/common/kernels/maskRCNNKernels.cu index 385d892a..0d0177b9 100644 --- a/plugin/common/kernels/maskRCNNKernels.cu +++ b/plugin/common/kernels/maskRCNNKernels.cu @@ -95,8 +95,8 @@ __global__ void resetMemValue_kernel(void* outPtr, int samples, float val) // outLabel : int[ N * sample * 1 ] // outBbox : int[ N * sample * 4 ] template -__global__ void argMaxGroup_kernel(int samples, int start_class_id, int NClass, const void* inScorePtr, const void* inBboxPtr, - const void* validSampleCountPtr, void* outScorePtr, void* outLabelPtr, void* outBboxPtr) +__global__ void argMaxGroup_kernel(int samples, int start_class_id, int NClass, const void* inScorePtr, + const void* inBboxPtr, const void* validSampleCountPtr, void* outScorePtr, void* outLabelPtr, void* outBboxPtr) { const DType* inScore = static_cast(inScorePtr); const BoxType* inBbox = static_cast(inBboxPtr); @@ -876,8 +876,8 @@ ProposalWorkSpace::ProposalWorkSpace(const int batchSize, const int inputCnt, co totalSize = sumSize; } -MultilevelProposeROIWorkSpace::MultilevelProposeROIWorkSpace(const int batchSize, const int inputCnt, const int sampleCount, - const RefineNMSParameters& param, const nvinfer1::DataType inType) +MultilevelProposeROIWorkSpace::MultilevelProposeROIWorkSpace(const int batchSize, const int inputCnt, + const int sampleCount, const RefineNMSParameters& param, const nvinfer1::DataType inType) : preRefineSortedScoreDims(inputCnt, 1) , preRefineBboxDims(inputCnt, 4) , argMaxScoreDims(sampleCount, 1) @@ -885,7 +885,7 @@ MultilevelProposeROIWorkSpace::MultilevelProposeROIWorkSpace(const int batchSize , argMaxLabelDims(sampleCount, 1) , sortClassScoreDims(sampleCount, 1) , sortClassLabelDims(sampleCount, 1) - , sortClassSampleIdxDims(sampleCount+1, 1) + , sortClassSampleIdxDims(sampleCount + 1, 1) , sortClassPosDims(param.numClasses + 1, 1) , sortNMSMarkDims(sampleCount, 1) { @@ -945,11 +945,12 @@ MultilevelProposeROIWorkSpace::MultilevelProposeROIWorkSpace(const int batchSize totalSize = sumSize; } -ConcatTopKWorkSpace::ConcatTopKWorkSpace(const int batchSize, const int concatCnt, const int topK,const nvinfer1::DataType inType) - : concatedScoreDims(concatCnt*topK, 1) - , concatedBBoxDims(concatCnt*topK, 4) - , sortedScoreDims(concatCnt*topK, 1) - , sortedBBoxDims(concatCnt*topK, 4) +ConcatTopKWorkSpace::ConcatTopKWorkSpace( + const int batchSize, const int concatCnt, const int topK, const nvinfer1::DataType inType) + : concatedScoreDims(concatCnt * topK, 1) + , concatedBBoxDims(concatCnt * topK, 4) + , sortedScoreDims(concatCnt * topK, 1) + , sortedBBoxDims(concatCnt * topK, 4) { size_t sumSize = 0; @@ -960,7 +961,7 @@ ConcatTopKWorkSpace::ConcatTopKWorkSpace(const int batchSize, const int concatCn tempStorageOffset = sumSize; sumSize += (1 << 23) * batchSize; - // concatedScoreOffset: [N, concatCnt*topK, 1] + // concatedScoreOffset: [N, concatCnt*topK, 1] concatedScoreOffset = sumSize; sumSize += AlignMem(dimVolume(concatedScoreDims) * typeSize(type) * batchSize); @@ -1114,8 +1115,7 @@ __global__ void TopKGatherBoxScore_kernel(int samples, int keepTopK, const void* typedef cub::BlockRadixSort BlockRadixSort6; typedef cub::BlockRadixSort BlockRadixSort7; typedef cub::BlockRadixSort BlockRadixSort8; - __shared__ union - { + __shared__ union { typename BlockRadixSort8::TempStorage sort8; typename BlockRadixSort7::TempStorage sort7; typename BlockRadixSort6::TempStorage sort6; @@ -1144,7 +1144,7 @@ __global__ void TopKGatherBoxScore_kernel(int samples, int keepTopK, const void* int idx[MaxItemsPerThreads]; DType score[MaxItemsPerThreads]; int totalItems = (validSamples + (blockDim.x - 1)) / blockDim.x; - + for (int ite = 0; ite < totalItems; ++ite) { int curIdx = ite * blockDim.x + threadIdx.x; @@ -1160,7 +1160,6 @@ __global__ void TopKGatherBoxScore_kernel(int samples, int keepTopK, const void* } } - switch (totalItems) { case 0: break; @@ -1226,8 +1225,8 @@ cudaError_t KeepTopKGatherBoxScore(cudaStream_t stream, int N, nvinfer1::DataTyp if (proposal) { TopKGatherBoxScore_kernel<<>>(samples, keepTopK, - validSampleCountPtr, inScorePtr, inLabelPtr, inBboxPtr, inBboxRefIdxPtr, inFlagSamplesPtr, - outScores, outDetections); + validSampleCountPtr, inScorePtr, inLabelPtr, inBboxPtr, inBboxRefIdxPtr, inFlagSamplesPtr, outScores, + outDetections); } else { @@ -1332,10 +1331,10 @@ cudaError_t RefineBatchClassNMS(cudaStream_t stream, int N, int samples, nvinfer return status; } -cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const float* regWeight, - const float inputHeight, const float inputWidth, nvinfer1::DataType dtype, - const RefineNMSParameters& param, const RefineDetectionWorkSpace& refineOffset, void* workspace, - const void* inScores, const void* inDelta, const void* inCountValid, const void* inROI, void* outDetections) +cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const float* regWeight, + const float inputHeight, const float inputWidth, nvinfer1::DataType dtype, const RefineNMSParameters& param, + const RefineDetectionWorkSpace& refineOffset, void* workspace, const void* inScores, const void* inDelta, + const void* inCountValid, const void* inROI, void* outDetections) { int NClass = param.numClasses; int8_t* wsPtr = static_cast(workspace); @@ -1352,15 +1351,15 @@ cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const cudaError_t status = cudaSuccess; CUASSERT(cudaMemsetAsync(argMaxScorePtr, 0, N * samples * sizeof(float), stream)); - CUASSERT(cudaMemsetAsync(argMaxBBoxPtr, 0, N * samples * 4* sizeof(float), stream)); + CUASSERT(cudaMemsetAsync(argMaxBBoxPtr, 0, N * samples * 4 * sizeof(float), stream)); CUASSERT(cudaMemsetAsync(sortClassValidCountPtr, 0, N * sizeof(int), stream)); - CUASSERT(cudaMemsetAsync(sortClassPosPtr, 0, N * (NClass+1) * sizeof(int), stream)); + CUASSERT(cudaMemsetAsync(sortClassPosPtr, 0, N * (NClass + 1) * sizeof(int), stream)); CUASSERT(cudaMemsetAsync(sortClassSampleIdxPtr, 0, N * (samples + 1) * sizeof(int), stream)); if (NClass > 1) { // multiple classes - status = argMaxWOBackground<32>(stream, N, dtype, samples, NClass, inScores, inDelta, inCountValid, argMaxScorePtr, - argMaxLabelPtr, argMaxBBoxPtr); // argMaxBBoxPtr means delta of bboxes + status = argMaxWOBackground<32>(stream, N, dtype, samples, NClass, inScores, inDelta, inCountValid, + argMaxScorePtr, argMaxLabelPtr, argMaxBBoxPtr); // argMaxBBoxPtr means delta of bboxes assert(status == cudaSuccess); CUASSERT(status); } @@ -1384,10 +1383,9 @@ cudaError_t DetectionPostProcess(cudaStream_t stream, int N, int samples, const } } - status = DecodeBBoxes(stream, N, samples, regWeight, inputHeight, inputWidth, - inROI, argMaxBBoxPtr, argMaxBBoxPtr); + status = DecodeBBoxes(stream, N, samples, regWeight, inputHeight, inputWidth, inROI, argMaxBBoxPtr, argMaxBBoxPtr); assert(status == cudaSuccess); - + if (samples <= 1024) { status = sortPerClass<256, 4>(stream, N, dtype, samples, NClass, param.backgroundLabelId, param.scoreThreshold, @@ -1538,7 +1536,7 @@ cudaError_t proposalRefineBatchClassNMS(cudaStream_t stream, int N, int inputCnt (float*) preRefineSortedScorePtr, (BBoxT*) inDelta, (BBoxT*) preRefineBboxPtr, N * inputCnt, N, offsets, offsets + 1, 0, 8 * sizeof(float), stream); - assert((1 << 23) * (size_t)N > temp_storage_bytes); + assert((1 << 23) * (size_t) N > temp_storage_bytes); cub::DeviceSegmentedRadixSort::SortPairsDescending(tempStoragePtr, temp_storage_bytes, (float*) preRefineScorePtr, (float*) preRefineSortedScorePtr, (BBoxT*) inDelta, (BBoxT*) preRefineBboxPtr, N * inputCnt, N, @@ -1561,8 +1559,7 @@ cudaError_t proposalRefineBatchClassNMS(cudaStream_t stream, int N, int inputCnt resetMemValue_kernel<<>>(argMaxLabelPtr, N * samples, 0); break; } - case nvinfer1::DataType::kHALF: { - break; + case nvinfer1::DataType::kHALF: { break; } default: assert(false); } @@ -1605,15 +1602,14 @@ cudaError_t proposalRefineBatchClassNMS(cudaStream_t stream, int N, int inputCnt return status; } -cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samples, const float* regWeight, - const float inputHeight, const float inputWidth, nvinfer1::DataType dtype, const RefineNMSParameters& param, +cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samples, const float* regWeight, + const float inputHeight, const float inputWidth, nvinfer1::DataType dtype, const RefineNMSParameters& param, const MultilevelProposeROIWorkSpace& proposalOffset, void* workspace, const void* inScore, //[N, inputcnt, 1] - const void* inDelta, //[N, inputcnt, 4] + const void* inDelta, //[N, inputcnt, 4] void* inCountValid, const void* inAnchors, //[N, inputcnt, 4] - void* outScore, - void* outBbox) + void* outScore, void* outBbox) { int8_t* wsPtr = static_cast(workspace); void* tempStoragePtr = wsPtr + proposalOffset.tempStorageOffset; @@ -1635,16 +1631,16 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp int NClass = param.numClasses; assert(NClass == 1); CUASSERT(cudaMemsetAsync(argMaxScorePtr, 0, N * samples * sizeof(float), stream)); - CUASSERT(cudaMemsetAsync(argMaxBBoxPtr, 0, N * samples * 4* sizeof(float), stream)); + CUASSERT(cudaMemsetAsync(argMaxBBoxPtr, 0, N * samples * 4 * sizeof(float), stream)); CUASSERT(cudaMemsetAsync(sortClassValidCountPtr, 0, N * sizeof(int), stream)); - CUASSERT(cudaMemsetAsync(sortClassPosPtr, 0, N * (NClass+1) * sizeof(int), stream)); + CUASSERT(cudaMemsetAsync(sortClassPosPtr, 0, N * (NClass + 1) * sizeof(int), stream)); CUASSERT(cudaMemsetAsync(sortClassSampleIdxPtr, 0, N * (samples + 1) * sizeof(int), stream)); CUASSERT(cudaGetLastError()); // Here, inDelta are converted to normalize coordinates based on anchors - status = DecodeBBoxes(stream, N, inputCnt, regWeight, inputHeight, inputWidth, - inAnchors, inDelta, const_cast(inDelta)); + status = DecodeBBoxes( + stream, N, inputCnt, regWeight, inputHeight, inputWidth, inAnchors, inDelta, const_cast(inDelta)); CUASSERT(cudaGetLastError()); // sort the score @@ -1655,7 +1651,7 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp // num_items: inputCnt*N // num_segments: N // offsets: [0, inputCnt, inputCnt*2, ..., ] - + int* offsets = static_cast(tempStoragePtr); set_offset_kernel<<<1, 1024, 0, stream>>>(inputCnt, N + 1, offsets); CUASSERT(cudaGetLastError()); @@ -1667,7 +1663,7 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp offsets, offsets + 1, 0, 8 * sizeof(float), stream); CUASSERT(cudaGetLastError()); - assert((1 << 23) * N > temp_storage_bytes); + assert((1 << 23) * N > (int) temp_storage_bytes); cub::DeviceSegmentedRadixSort::SortPairsDescending(tempStoragePtr, temp_storage_bytes, (float*) inScore, (float*) preRefineSortedScorePtr, (BBoxT*) inDelta, (BBoxT*) preRefineBboxPtr, N * inputCnt, N, @@ -1684,7 +1680,7 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp int threads = 512; int blocks = (N * samples + threads - 1) / threads; blocks = dMIN(blocks, 8); - + switch (dtype) { case nvinfer1::DataType::kFLOAT: @@ -1693,13 +1689,11 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp CUASSERT(cudaGetLastError()); break; } - case nvinfer1::DataType::kHALF: { - break; + case nvinfer1::DataType::kHALF: { break; } default: assert(false); } } - if (samples <= 1024) { @@ -1732,8 +1726,9 @@ cudaError_t MultilevelPropose(cudaStream_t stream, int N, int inputCnt, int samp CUASSERT(cudaGetLastError()); - status = KeepTopKGatherBoxScore<512>(stream, N, dtype, samples, param.keepTopK, sortClassValidCountPtr, sortClassScorePtr, - sortClassLabelPtr, argMaxBBoxPtr, sortClassSampleIdxPtr, sortNMSMarkPtr, outScore, outBbox, 1); + status = KeepTopKGatherBoxScore<512>(stream, N, dtype, samples, param.keepTopK, sortClassValidCountPtr, + sortClassScorePtr, sortClassLabelPtr, argMaxBBoxPtr, sortClassSampleIdxPtr, sortNMSMarkPtr, outScore, outBbox, + 1); CUASSERT(cudaGetLastError()); @@ -1750,9 +1745,8 @@ struct DELTA float dy, dx, logdh, logdw; }; -__global__ void decode_bboxes_kernel(int samples, const void* anchors, const void* delta, - const float* regWeight, const float inputHeight, const float inputWidth, - void* outputBbox, float bboxClipThresh) +__global__ void decode_bboxes_kernel(int samples, const void* anchors, const void* delta, const float* regWeight, + const float inputHeight, const float inputWidth, void* outputBbox, float bboxClipThresh) { const BBOX* anchors_in = static_cast(anchors); @@ -1769,61 +1763,59 @@ __global__ void decode_bboxes_kernel(int samples, const void* anchors, const voi if (cur_id < samples) { - BBOX cur_anchor_yxyx = anchors_in[blockOffset + cur_id]; - // convert yxyx -> cyxhw - // cy, cx, h, w - /*BBOX cur_anchor_cyxhw;*/ - - float cur_anchor_h = (cur_anchor_yxyx.y2 - cur_anchor_yxyx.y1 + 1.0); - float cur_anchor_w = (cur_anchor_yxyx.x2 - cur_anchor_yxyx.x1 + 1.0); //w - float cur_anchor_yc = cur_anchor_yxyx.y1 + cur_anchor_h * 0.5; //cy - float cur_anchor_xc = cur_anchor_yxyx.x1 + cur_anchor_w * 0.5; //cx - - DELTA cur_delta = delta_in[blockOffset + cur_id]; - - // divided by regWeight - cur_delta.dy /= regWeight[0]; - cur_delta.dx /= regWeight[1]; - cur_delta.logdh /= regWeight[2]; - cur_delta.logdw /= regWeight[3]; - - cur_delta.logdh = dMIN(cur_delta.logdh, bboxClipThresh); - cur_delta.logdw = dMIN(cur_delta.logdw, bboxClipThresh); - - // apply delta - float decoded_box_yc = cur_anchor_yc + cur_delta.dy * cur_anchor_h; - float decoded_box_xc = cur_anchor_xc + cur_delta.dx * cur_anchor_w; - float decoded_box_h = expf(cur_delta.logdh) * cur_anchor_h; - float decoded_box_w = expf(cur_delta.logdw) * cur_anchor_w; - - float decoded_box_ymin = decoded_box_yc - 0.5 * decoded_box_h; - float decoded_box_xmin = decoded_box_xc - 0.5 * decoded_box_w; - float decoded_box_ymax = decoded_box_ymin + decoded_box_h - 1.0; - float decoded_box_xmax = decoded_box_xmin + decoded_box_w - 1.0; - - // clip bbox: a more precision clip method based on real window could be implemented - decoded_box_ymin = dMAX(dMIN(decoded_box_ymin, inputHeight - 1.0), 0.0); - decoded_box_xmin = dMAX(dMIN(decoded_box_xmin, inputWidth - 1.0), 0.0); - decoded_box_ymax = dMAX(dMIN(decoded_box_ymax, inputHeight - 1.0), 0.0); - decoded_box_xmax = dMAX(dMIN(decoded_box_xmax, inputWidth - 1.0), 0.0); - - bbox_out[blockOffset + cur_id].y1 = decoded_box_ymin; - bbox_out[blockOffset + cur_id].x1 = decoded_box_xmin; - bbox_out[blockOffset + cur_id].y2 = decoded_box_ymax; - bbox_out[blockOffset + cur_id].x2 = decoded_box_xmax; + BBOX cur_anchor_yxyx = anchors_in[blockOffset + cur_id]; + // convert yxyx -> cyxhw + // cy, cx, h, w + /*BBOX cur_anchor_cyxhw;*/ + + float cur_anchor_h = (cur_anchor_yxyx.y2 - cur_anchor_yxyx.y1 + 1.0); + float cur_anchor_w = (cur_anchor_yxyx.x2 - cur_anchor_yxyx.x1 + 1.0); // w + float cur_anchor_yc = cur_anchor_yxyx.y1 + cur_anchor_h * 0.5; // cy + float cur_anchor_xc = cur_anchor_yxyx.x1 + cur_anchor_w * 0.5; // cx + + DELTA cur_delta = delta_in[blockOffset + cur_id]; + + // divided by regWeight + cur_delta.dy /= regWeight[0]; + cur_delta.dx /= regWeight[1]; + cur_delta.logdh /= regWeight[2]; + cur_delta.logdw /= regWeight[3]; + + cur_delta.logdh = dMIN(cur_delta.logdh, bboxClipThresh); + cur_delta.logdw = dMIN(cur_delta.logdw, bboxClipThresh); + + // apply delta + float decoded_box_yc = cur_anchor_yc + cur_delta.dy * cur_anchor_h; + float decoded_box_xc = cur_anchor_xc + cur_delta.dx * cur_anchor_w; + float decoded_box_h = expf(cur_delta.logdh) * cur_anchor_h; + float decoded_box_w = expf(cur_delta.logdw) * cur_anchor_w; + + float decoded_box_ymin = decoded_box_yc - 0.5 * decoded_box_h; + float decoded_box_xmin = decoded_box_xc - 0.5 * decoded_box_w; + float decoded_box_ymax = decoded_box_ymin + decoded_box_h - 1.0; + float decoded_box_xmax = decoded_box_xmin + decoded_box_w - 1.0; + + // clip bbox: a more precision clip method based on real window could be implemented + decoded_box_ymin = dMAX(dMIN(decoded_box_ymin, inputHeight - 1.0), 0.0); + decoded_box_xmin = dMAX(dMIN(decoded_box_xmin, inputWidth - 1.0), 0.0); + decoded_box_ymax = dMAX(dMIN(decoded_box_ymax, inputHeight - 1.0), 0.0); + decoded_box_xmax = dMAX(dMIN(decoded_box_xmax, inputWidth - 1.0), 0.0); + + bbox_out[blockOffset + cur_id].y1 = decoded_box_ymin; + bbox_out[blockOffset + cur_id].x1 = decoded_box_xmin; + bbox_out[blockOffset + cur_id].y2 = decoded_box_ymax; + bbox_out[blockOffset + cur_id].x2 = decoded_box_xmax; } } } cudaError_t DecodeBBoxes(cudaStream_t stream, int N, - int samples, // number of anchors per image - const float* regWeight, - const float inputHeight, - const float inputWidth, + int samples, // number of anchors per image + const float* regWeight, const float inputHeight, const float inputWidth, const void* anchors, // [N, anchors, (y1, x1, y2, x2)] const void* delta, //[N, anchors, (dy, dx, log(dh), log(dw)]) void* outputBbox //[N, anchors, (y1, x1, y2, x2)] - ) +) { int blocks = N; @@ -1836,10 +1828,10 @@ cudaError_t DecodeBBoxes(cudaStream_t stream, int N, // h = exp(dh)*anchor_h // w = exp(dw)*anchor_w // clip the bbox in absolute coordinates - float bboxClipThresh = log(1000.0f/16.0f); + float bboxClipThresh = log(1000.0f / 16.0f); - decode_bboxes_kernel<<>>(samples, anchors, - delta, regWeight, inputHeight, inputWidth, outputBbox, bboxClipThresh); + decode_bboxes_kernel<<>>( + samples, anchors, delta, regWeight, inputHeight, inputWidth, outputBbox, bboxClipThresh); return cudaGetLastError(); } @@ -2066,14 +2058,13 @@ cudaError_t roiAlign(cudaStream_t stream, int batchSize, int featureCount, int r return cudaGetLastError(); } - template __global__ void roiAlignHalfCenter_kernel(int featureCount, int roiCount, float threshold, int inputHeight, int inputWidth, const Trois* rois, const Tfeat* P2, const xy_t P2dims, const Tfeat* P3, const xy_t P3dims, const Tfeat* P4, const xy_t P4dims, - const Tfeat* P5, const xy_t P5dims, const Tfeat* P6, const xy_t P6dims, + const Tfeat* P5, const xy_t P5dims, const Tfeat* P6, const xy_t P6dims, Tfeat* pooled, const xy_t poolDims) { @@ -2089,8 +2080,8 @@ __global__ void roiAlignHalfCenter_kernel(int featureCount, int roiCount, const float y2 = roi[2]; const float x2 = roi[3]; - if (!(0 <= y1 && y1 <= inputHeight && 0 <= x1 && x1 <= inputWidth && 0 <= y2 && y2 <= inputHeight && 0 <= x2 && x2 <= inputWidth && y1 < y2 - && x1 < x2)) + if (!(0 <= y1 && y1 <= inputHeight && 0 <= x1 && x1 <= inputWidth && 0 <= y2 && y2 <= inputHeight && 0 <= x2 + && x2 <= inputWidth && y1 < y2 && x1 < x2)) { continue; @@ -2133,9 +2124,8 @@ __global__ void roiAlignHalfCenter_kernel(int featureCount, int roiCount, { src = P6; srcDims = P6dims; - ++iP; + ++iP; } - src += srcDims.x * srcDims.y * (batch * featureCount + feature); @@ -2143,9 +2133,9 @@ __global__ void roiAlignHalfCenter_kernel(int featureCount, int roiCount, = pooled + poolDims.x * poolDims.y * (batch * roiCount * featureCount + roiIdx * featureCount + feature); float scale_to_level = 1.0f; - for(int i = 0; i < iP; i++) + for (int i = 0; i < iP; i++) { - scale_to_level *= 2.0f; + scale_to_level *= 2.0f; } const float yStart = y1 / scale_to_level; @@ -2183,11 +2173,10 @@ cudaError_t roiAlignHalfCenter(cudaStream_t stream, int batchSize, int featureCo const dim3 blocks(batchSize, featureCount); const int threads(256); - roiAlignHalfCenter_kernel<<>>(featureCount, roiCount, firstThreshold, inputHeight, inputWidth, - static_cast(rois), - static_cast(layers[0]), layerDims[0], static_cast(layers[1]), layerDims[1], - static_cast(layers[2]), layerDims[2], static_cast(layers[3]), layerDims[3], - static_cast(layers[4]), layerDims[4], + roiAlignHalfCenter_kernel<<>>(featureCount, roiCount, firstThreshold, inputHeight, + inputWidth, static_cast(rois), static_cast(layers[0]), layerDims[0], + static_cast(layers[1]), layerDims[1], static_cast(layers[2]), layerDims[2], + static_cast(layers[3]), layerDims[3], static_cast(layers[4]), layerDims[4], static_cast(pooled), poolDims); return cudaGetLastError(); @@ -2263,28 +2252,28 @@ void specialSlice(cudaStream_t stream, int batch_size, int boxes_cnt, const void } template -__global__ void concatenate(int featureCnt, int sampleCnt, const void* const* inScores, const void* const* inBBox, - void* outScore, void* outBBox) +__global__ void concatenate(int featureCnt, int sampleCnt, const void* const* inScores, const void* const* inBBox, + void* outScore, void* outBBox) { int N = blockIdx.x; - int outBlockOffset = N * sampleCnt * featureCnt; - int inBlockOffset = N * sampleCnt; + int outBlockOffset = N * sampleCnt * featureCnt; + int inBlockOffset = N * sampleCnt; int itemsPerThread = (sampleCnt + blockDim.x - 1) / blockDim.x; Dtype* outScorePtr = static_cast(outScore); - BOX* outBBoxPtr = static_cast(outBBox); + BOX* outBBoxPtr = static_cast(outBBox); - for(int fId = 0; fId < featureCnt; fId++) + for (int fId = 0; fId < featureCnt; fId++) { const Dtype* fInScorePtr = static_cast(inScores[fId]); const BOX* fInBBoxPtr = static_cast(inBBox[fId]); int featureOffset = fId * sampleCnt; - for(int i = 0; i < itemsPerThread; i++) + for (int i = 0; i < itemsPerThread; i++) { int curId = i * blockDim.x + threadIdx.x; if (curId < sampleCnt) { - outScorePtr[outBlockOffset + featureOffset + curId] = fInScorePtr[inBlockOffset + curId]; - outBBoxPtr[outBlockOffset + featureOffset + curId] = fInBBoxPtr[inBlockOffset + curId]; + outScorePtr[outBlockOffset + featureOffset + curId] = fInScorePtr[inBlockOffset + curId]; + outBBoxPtr[outBlockOffset + featureOffset + curId] = fInBBoxPtr[inBlockOffset + curId]; } } } @@ -2305,23 +2294,15 @@ __global__ void resampleBBox_kernel(int orig_size, int sample_size, const void* int cur_id = i * blockDim.x + threadIdx.x; if (cur_id < sample_size) { - out_bbox[blockOffset_out + cur_id] = in_bbox[blockOffset_in + cur_id]; + out_bbox[blockOffset_out + cur_id] = in_bbox[blockOffset_in + cur_id]; } } } -cudaError_t ConcatTopK(cudaStream_t stream, - int N, - int featureCnt, - int topK, - nvinfer1::DataType dtype, - void* workspace, - const ConcatTopKWorkSpace& spaceOffset, - void** inScores, - void** inBBox, - void* outProposals) +cudaError_t ConcatTopK(cudaStream_t stream, int N, int featureCnt, int topK, nvinfer1::DataType dtype, void* workspace, + const ConcatTopKWorkSpace& spaceOffset, void** inScores, void** inBBox, void* outProposals) { - //Prepare Offset + // Prepare Offset int8_t* wsPtr = static_cast(workspace); void* tempStoragePtr = wsPtr + spaceOffset.tempStorageOffset; void* concatedScorePtr = wsPtr + spaceOffset.concatedScoreOffset; @@ -2329,14 +2310,14 @@ cudaError_t ConcatTopK(cudaStream_t stream, void* sortedScorePtr = wsPtr + spaceOffset.sortedScoreOffset; void* sortedBBoxPtr = wsPtr + spaceOffset.sortedBBoxOffset; - int blocks = N; //batch_size + int blocks = N; // batch_size int threads = dMIN(topK, 2048); - //Concat Scores and inBBox + // Concat Scores and inBBox switch (dtype) { case nvinfer1::DataType::kFLOAT: - concatenate<<>>(featureCnt, topK, inScores, inBBox, - concatedScorePtr, concatedBBoxPtr); + concatenate + <<>>(featureCnt, topK, inScores, inBBox, concatedScorePtr, concatedBBoxPtr); CUASSERT(cudaGetLastError()); break; @@ -2344,21 +2325,20 @@ cudaError_t ConcatTopK(cudaStream_t stream, default: assert(false); } - - //Sort and sample topK + // Sort and sample topK int itemCnt = topK * featureCnt; int* offsets = static_cast(tempStoragePtr); set_offset_kernel<<<1, 1024, 0, stream>>>(itemCnt, N + 1, offsets); assert(cudaGetLastError() == cudaSuccess); tempStoragePtr = static_cast(static_cast(tempStoragePtr) + (N + 1)); - //Sort + // Sort size_t temp_storage_bytes = 0; cub::DeviceSegmentedRadixSort::SortPairsDescending(NULL, temp_storage_bytes, (float*) concatedScorePtr, (float*) sortedScorePtr, (BBoxT*) concatedBBoxPtr, (BBoxT*) sortedBBoxPtr, N * itemCnt, N, offsets, offsets + 1, 0, 8 * sizeof(float), stream); - assert((1 << 23) * N > temp_storage_bytes); + assert((1 << 23) * N > (int) temp_storage_bytes); cub::DeviceSegmentedRadixSort::SortPairsDescending(tempStoragePtr, temp_storage_bytes, (float*) concatedScorePtr, (float*) sortedScorePtr, (BBoxT*) concatedBBoxPtr, (BBoxT*) sortedBBoxPtr, N * itemCnt, N, @@ -2366,9 +2346,9 @@ cudaError_t ConcatTopK(cudaStream_t stream, assert(cudaGetLastError() == cudaSuccess); - //Sample - resampleBBox_kernel<<>>(itemCnt, topK, sortedBBoxPtr, outProposals); - + // Sample + resampleBBox_kernel<<>>(itemCnt, topK, sortedBBoxPtr, outProposals); + assert(cudaGetLastError() == cudaSuccess); return cudaGetLastError(); -} +} diff --git a/plugin/common/kernels/proposalKernel.cu b/plugin/common/kernels/proposalKernel.cu index 399d8b4d..95221a65 100755 --- a/plugin/common/kernels/proposalKernel.cu +++ b/plugin/common/kernels/proposalKernel.cu @@ -31,7 +31,8 @@ // CUB's bug workaround: // To work properly for large batch size CUB segmented sort needs ridiculous // workspace alignment. -const uintptr_t ALIGNMENT = 1 << 20; +constexpr uintptr_t ALIGNMENT = 1 << 20; + template struct Bbox { @@ -470,18 +471,18 @@ __global__ void _inverse_transform_gpu(const float* RPN_prob, const float* RPN_r int as = a / anc_ratio_num; float anchor_w = ANCHOR_SIZES[as] * ANCHOR_RATIOS[ar]; float anchor_h = ANCHOR_SIZES[as] / ANCHOR_RATIOS[ar]; - float anchor_cx = (w + 0.5f) * RPN_STRIDE; - float anchor_cy = (h + 0.5f) * RPN_STRIDE; - float cx1 = anchor_cx + anchor_w * tx; - float cy1 = anchor_cy + anchor_h * ty; - float w1 = __expf(tw) * anchor_w; - float h1 = __expf(th) * anchor_h; - tx = cx1 - w1 / 2.0f; - ty = cy1 - h1 / 2.0f; - tw = w1; - th = h1; - tw += tx; - th += ty; + float anchor_cx = (w + 0.5f) * RPN_STRIDE; + float anchor_cy = (h + 0.5f) * RPN_STRIDE; + float cx1 = anchor_cx + anchor_w * tx; + float cy1 = anchor_cy + anchor_h * ty; + float w1 = __expf(tw) * anchor_w; + float h1 = __expf(th) * anchor_h; + tx = cx1 - w1 / 2.0f; + ty = cy1 - h1 / 2.0f; + tw = w1; + th = h1; + tw += tx; + th += ty; // clip to min tx = (tx >= 0.0f) ? tx : 0.0f; ty = (ty >= 0.0f) ? ty : 0.0f; @@ -704,4 +705,3 @@ int proposalInference_gpu( stream); return 0; } - diff --git a/plugin/common/kernels/reducedMathPlugin.h b/plugin/common/kernels/reducedMathPlugin.h index 4a98386e..cec920d1 100644 --- a/plugin/common/kernels/reducedMathPlugin.h +++ b/plugin/common/kernels/reducedMathPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef _REDUCED_MATH_PLUGIN_H #define _REDUCED_MATH_PLUGIN_H // Dynamically strength-reduced div and mod diff --git a/plugin/common/nmsHelper.cpp b/plugin/common/nmsHelper.cpp index 44dac0c7..37a92414 100644 --- a/plugin/common/nmsHelper.cpp +++ b/plugin/common/nmsHelper.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "plugin.h" #include diff --git a/plugin/common/nmsUtils.h b/plugin/common/nmsUtils.h index 5e38179a..42e22c4a 100644 --- a/plugin/common/nmsUtils.h +++ b/plugin/common/nmsUtils.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_NMS_UTILS_H #define TRT_NMS_UTILS_H diff --git a/plugin/common/plugin.h b/plugin/common/plugin.h index d766bef9..7e7031ce 100644 --- a/plugin/common/plugin.h +++ b/plugin/common/plugin.h @@ -13,8 +13,11 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_PLUGIN_H #define TRT_PLUGIN_H +#include "checkMacrosPlugin.h" + #include "NvInferPlugin.h" #include #include @@ -22,6 +25,7 @@ #include #include +#ifndef TRT_LEGACY_PLUGIN_H // Enumerator for status typedef enum { @@ -40,9 +44,15 @@ namespace plugin class BasePlugin : public IPluginV2 { protected: - void setPluginNamespace(const char* libNamespace) override { mNamespace = libNamespace; } + void setPluginNamespace(const char* libNamespace) override + { + mNamespace = libNamespace; + } - const char* getPluginNamespace() const override { return mNamespace.c_str(); } + const char* getPluginNamespace() const override + { + return mNamespace.c_str(); + } std::string mNamespace; }; @@ -85,42 +95,9 @@ T read(const char*& buffer) } // namespace nvinfer1 #ifndef DEBUG -#ifndef CHECK_MACROS_PLUGIN_H -#define ASSERT(assertion) \ - { \ - if (!(assertion)) \ - { \ - std::cerr << "#assertion" << __FILE__ << "," << __LINE__ << std::endl; \ - abort(); \ - } \ - } -#define FAIL(msg) \ - { \ - fprintf(stderr, "Failure - " #msg ", %s:%d\n", __FILE__, __LINE__); \ - abort(); \ - } -#define CUASSERT(status_) \ - { \ - auto s_ = status_; \ - if (s_ != cudaSuccess) \ - { \ - std::cerr << __FILE__ << ", " << __LINE__ << ", " << s_ << ", " << cudaGetErrorString(s_) << std::endl; \ - } \ - } -#define CUBLASASSERT(status_) \ - { \ - auto s_ = status_; \ - if (s_ != CUBLAS_STATUS_SUCCESS) \ - { \ - std::cerr << __FILE__ << ", " << __LINE__ << ", " << s_ << std::endl; \ - } \ - } -#define CUERRORMSG(status_) \ - { \ - auto s_ = status_; \ - if (s_ != 0) \ - std::cerr << __FILE__ << ", " << __LINE__ << ", " << s_ << std::endl; \ - } +#ifndef TRT_CHECK_MACROS_H +#ifndef TRT_TUT_HELPERS_H + #define CHECK(status) \ do \ { \ @@ -205,7 +182,9 @@ T read(const char*& buffer) printf(__VA_ARGS__); \ } while (0) -#endif // CHECK_MACROS_H +#endif // TRT_TUT_HELPERS_H +#endif // TRT_CHECK_MACROS_H +#endif // TRT_LEGACY_PLUGIN_H #endif #endif // TRT_PLUGIN_H diff --git a/plugin/common/pluginLogging.h b/plugin/common/pluginLogging.h index aae37de6..b3b9bca2 100644 --- a/plugin/common/pluginLogging.h +++ b/plugin/common/pluginLogging.h @@ -22,6 +22,7 @@ #include #include #include +#include #include #include #include @@ -112,6 +113,7 @@ class LogStreamConsumerBase } protected: + std::mutex mLogMutex; LogStreamConsumerBuffer mBuffer; }; @@ -151,6 +153,11 @@ class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream mBuffer.setShouldLog(mShouldLog); } + std::mutex& getMutex() + { + return mLogMutex; + } + private: static std::ostream& severityOstream(Severity severity) { @@ -174,6 +181,25 @@ class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream Severity mSeverity; }; +// Use mutex to protect multi-stream write to buffer +template +LogStreamConsumer& operator<<(LogStreamConsumer& logger, const T& obj) +{ + std::lock_guard guard(logger.getMutex()); + auto& os = static_cast(logger); + os << obj; + return logger; +} + +// Special handling std::endl +inline LogStreamConsumer& operator<<(LogStreamConsumer& logger, std::ostream& (*f)(std::ostream&) ) +{ + std::lock_guard guard(logger.getMutex()); + auto& os = static_cast(logger); + os << f; + return logger; +} + //! \class Logger //! //! \brief Class which manages logging of TensorRT tools and samples diff --git a/plugin/common/reducedMathPlugin.cpp b/plugin/common/reducedMathPlugin.cpp index 878ca0cf..e2ff0113 100644 --- a/plugin/common/reducedMathPlugin.cpp +++ b/plugin/common/reducedMathPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + namespace nvinfer1 { namespace plugin diff --git a/plugin/common/serialize.hpp b/plugin/common/serialize.hpp index f3b3335c..ce1d9743 100644 --- a/plugin/common/serialize.hpp +++ b/plugin/common/serialize.hpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #pragma once #include diff --git a/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp b/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp index d19fde4c..79d3379d 100644 --- a/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp +++ b/plugin/detectionLayerPlugin/detectionLayerPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "detectionLayerPlugin.h" #include "plugin.h" #include diff --git a/plugin/detectionLayerPlugin/detectionLayerPlugin.h b/plugin/detectionLayerPlugin/detectionLayerPlugin.h index 5aee1480..cfc43991 100644 --- a/plugin/detectionLayerPlugin/detectionLayerPlugin.h +++ b/plugin/detectionLayerPlugin/detectionLayerPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_DETECTION_LAYER_PLUGIN_H #define TRT_DETECTION_LAYER_PLUGIN_H #include diff --git a/plugin/embLayerNormPlugin/README.md b/plugin/embLayerNormPlugin/README.md index 312a2817..d7c83ae7 100644 --- a/plugin/embLayerNormPlugin/README.md +++ b/plugin/embLayerNormPlugin/README.md @@ -60,14 +60,16 @@ The maskIdx is a more compact representation of the input mask, consisting of th The parameters are defined below and consists of the following attributes: -| Type | Parameter | Description -|----------|-----------------------------------------|-------------------------------------------------------- -|`int` |`output_fp16` |Integer encoding the DataType (0: FP32, 1: FP16) -|`Weights` |`bert_embeddings_layernorm_beta` |Beta parameter for layer norm. Shape: `[E,]` where `E` is hidden size -|`Weights` |`bert_embeddings_layernorm_gamma` |Gamma parameter for layer norm. Shape: `[E,]` where `E` is hidden size -|`Weights` |`bert_embeddings_word_embeddings` |Token embedding matrix. Shape: `[word_vocab_size, E]` where `E` is hidden size -|`Weights` |`bert_embeddings_token_type_embeddings` |Token type embedding matrix. Shape: `[type_vocab_size, E]` where `E` is hidden size -|`Weights` |`bert_embeddings_position_embeddings` |Positional embedding matrix. Shape: `[S, E]` where `S` is the maximum sequence length and `E` is hidden size +| Type | Parameter | Version | Description +|----------|----------------------------------------|----------|-------------------------------------------------------- +|`int` |`output_fp16` | 1, 2 |Integer encoding the DataType (0: FP32, 1: FP16) +|`int` |`full_mask` | 1 |Whether to output the full mask that works with the specialized multi-head-attention plugin kernels (this is deprecated, please use mha_type_id) +|`int` |`mha_type_id` | 1 |Integer encoding the multi-head-attention plugin DataType (0: FP32, 1: FP16, 2: INT8) +|`Weights` |`bert_embeddings_layernorm_beta` | 1, 2 |Beta parameter for layer norm. Shape: `[E,]` where `E` is hidden size +|`Weights` |`bert_embeddings_layernorm_gamma` | 1, 2 |Gamma parameter for layer norm. Shape: `[E,]` where `E` is hidden size +|`Weights` |`bert_embeddings_word_embeddings` | 1, 2 |Token embedding matrix. Shape: `[word_vocab_size, E]` where `E` is hidden size +|`Weights` |`bert_embeddings_token_type_embeddings` | 1, 2 |Token type embedding matrix. Shape: `[type_vocab_size, E]` where `E` is hidden size +|`Weights` |`bert_embeddings_position_embeddings` | 1, 2 |Positional embedding matrix. Shape: `[S, E]` where `S` is the maximum sequence length and `E` is hidden size ## Additional resources @@ -86,7 +88,10 @@ documentation. ## Changelog -November 2019 +October 2020 +Add V2 plugin that supports variable sequence length. + +November 2019 This is the first release of this `README.md` file. diff --git a/plugin/embLayerNormPlugin/embLayerNormKernel.cu b/plugin/embLayerNormPlugin/embLayerNormKernel.cu index c0ffab8e..e7095211 100644 --- a/plugin/embLayerNormPlugin/embLayerNormKernel.cu +++ b/plugin/embLayerNormPlugin/embLayerNormKernel.cu @@ -48,7 +48,6 @@ __global__ void fillSBSMaskKernel( const uint32_t tidx = threadIdx.x; const size_t warp = tidx / 32; - const size_t warp_m = warp % warps_m; const size_t warp_n = warp / warps_m; const size_t lane = tidx % 32; const size_t col = warp_n * 16 + lane % 4 * 2; diff --git a/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp b/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp index 8b36965f..3afc1115 100644 --- a/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp +++ b/plugin/embLayerNormPlugin/embLayerNormPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -29,19 +29,6 @@ using namespace nvinfer1; namespace bert { -// For full mask mode, we must produce the compressed mask format expected by the fused attention path. Currently, only -// two sequence lengths are supported. We hard code the sizes here. -// The number of threads per CTA: warps_m * warps_n * warps_k * 32; -constexpr size_t threadsPerCta128 = 2 * 2 * 32; -constexpr size_t threadsPerCta384 = 1 * 8 * 32; -// The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension: (s + 16*warps_m - 1) -// / (16*warps_m); -constexpr size_t xmmasM128 = 4; -constexpr size_t xmmasM384 = 24; -// Packed mask size per batch. Layout is XMMAS_M * THREADS_PER_CTA. -constexpr size_t packedMaskSize128 = xmmasM128 * threadsPerCta128; -constexpr size_t packedMaskSize384 = xmmasM384 * threadsPerCta384; - namespace { static const char* EMB_LAYER_NORM_VERSION{"1"}; @@ -54,12 +41,14 @@ std::vector EmbLayerNormPluginDynamicCreator::mPluginAttributes; REGISTER_TENSORRT_PLUGIN(EmbLayerNormPluginDynamicCreator); -EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, const DataType type, const Weights& beta, - const Weights& gamma, const Weights& wordEmb, const Weights& posEmb, const Weights& tokEmb, const bool useFullMask) +EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, const DataType type, + const DataType mhaType, const Weights& beta, const Weights& gamma, const Weights& wordEmb, const Weights& posEmb, + const Weights& tokEmb, const bool useFullMask) : mLayerName(name) , mLd(beta.count) , mType(type) , mUseFullMask(useFullMask) + , mMhaType(mhaType) { // Assuming Weights.count is the number of elements and not bytes assert(beta.count == gamma.count); @@ -69,6 +58,7 @@ EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, co mWordVocabSize = wordEmb.count / mLd; mPosVocabSize = posEmb.count / mLd; mTokVocabSize = tokEmb.count / mLd; + mSM = getSMVersion(); // mS is set during configure mBeta.convertAndCopy(beta, nvinfer1::DataType::kFLOAT); @@ -96,12 +86,14 @@ EmbLayerNormPluginDynamic::EmbLayerNormPluginDynamic(const std::string& name, co // Deserialize in the same order as serialization deserialize_value(&data, &length, &mType); + deserialize_value(&data, &length, &mMhaType); deserialize_value(&data, &length, &mLd); deserialize_value(&data, &length, &mS); deserialize_value(&data, &length, &mWordVocabSize); deserialize_value(&data, &length, &mPosVocabSize); deserialize_value(&data, &length, &mTokVocabSize); deserialize_value(&data, &length, &mUseFullMask); + deserialize_value(&data, &length, &mSM); const char* d = static_cast(data); mBeta.convertAndCopy(d, mLd, nvinfer1::DataType::kFLOAT); @@ -122,7 +114,7 @@ IPluginV2DynamicExt* EmbLayerNormPluginDynamic::clone() const { gLogVerbose << "EmbLayerNormPluginDynamic clone\n"; - auto p = new EmbLayerNormPluginDynamic(mLayerName, mType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb, mUseFullMask); + auto p = new EmbLayerNormPluginDynamic(mLayerName, mType, mMhaType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb, mUseFullMask); p->mS = mS; p->setPluginNamespace(mNamespace.c_str()); @@ -154,30 +146,52 @@ DimsExprs EmbLayerNormPluginDynamic::getOutputDimensions( return ret; } - if (mUseFullMask) + DimsExprs ret; + ret.nbDims = 2; + ret.d[0] = inputs[0].d[BDIM]; + auto cms0 = exprBuilder.constant(unfusedMaskSize); + + // this code must match getMHAMaskPackedSize in bertCommon.h + if (mUseFullMask || ((mSM == kSM_75 || mSM == kSM_80 || mSM == kSM_86) + && (mMhaType == nvinfer1::DataType::kHALF || mMhaType == nvinfer1::DataType::kINT8))) { + // support 128, 384 in both int8 and fp16 auto cms128 = exprBuilder.constant(packedMaskSize128); auto cms384 = exprBuilder.constant(packedMaskSize384); auto c128 = exprBuilder.constant(128); auto c384 = exprBuilder.constant(384); - auto is128 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c128); auto is384 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c384); auto sel128 = exprBuilder.operation(DimensionOperation::kPROD, *is128, *cms128); auto sel384 = exprBuilder.operation(DimensionOperation::kPROD, *is384, *cms384); auto maskSize = exprBuilder.operation(DimensionOperation::kSUM, *sel384, *sel128); - auto fp16maskSize = exprBuilder.operation(DimensionOperation::kPROD, *maskSize, *exprBuilder.constant(2)); - DimsExprs ret; - ret.nbDims = 2; - ret.d[0] = inputs[0].d[BDIM]; - ret.d[1] = fp16maskSize; - return ret; + if (mMhaType == nvinfer1::DataType::kHALF) + { + // support 64, 96 only in fp16 + auto cms64 = exprBuilder.constant(packedMaskSize64); + auto cms96 = exprBuilder.constant(packedMaskSize96); + auto c64 = exprBuilder.constant(64); + auto c96 = exprBuilder.constant(96); + + auto is64 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c64); + auto is96 = exprBuilder.operation(DimensionOperation::kEQUAL, *inputs[0].d[SDIM], *c96); + auto sel64 = exprBuilder.operation(DimensionOperation::kPROD, *is64, *cms64); + auto sel96 = exprBuilder.operation(DimensionOperation::kPROD, *is96, *cms96); + auto maskSize2 = exprBuilder.operation(DimensionOperation::kSUM, *sel64, *sel96); + maskSize = exprBuilder.operation(DimensionOperation::kSUM, *maskSize, *maskSize2); + } + + auto is0 = exprBuilder.operation(DimensionOperation::kEQUAL, *maskSize, *exprBuilder.constant(0)); + auto sel0 = exprBuilder.operation(DimensionOperation::kPROD, *is0, *cms0); + auto combinedMaskSize = exprBuilder.operation(DimensionOperation::kSUM, *maskSize, *sel0); + ret.d[1] = combinedMaskSize; + } + else + { + ret.d[1] = cms0; } - DimsExprs ret; - ret.nbDims = 1; - ret.d[0] = inputs[0].d[BDIM]; return ret; } @@ -212,11 +226,7 @@ bool EmbLayerNormPluginDynamic::supportsFormatCombination( && desc.dims.d[SDIM] == prev.dims.d[SDIM] && desc.dims.d[3] == 1 && desc.dims.d[4] == 1; } // mask - if (mUseFullMask) - { - return desc.type == DataType::kHALF; - } - return desc.type == DataType::kINT32; + return desc.type == DataType::kFLOAT; } void EmbLayerNormPluginDynamic::configurePlugin( @@ -229,7 +239,8 @@ void EmbLayerNormPluginDynamic::configurePlugin( assert(nbInputs == 3); assert(inputs[0].desc.dims.nbDims == 2); - mS = inputs[0].desc.dims.d[SDIM]; + const int S = inputs[0].desc.dims.d[SDIM]; + mS = S; const int B = inputs[0].desc.dims.d[BDIM]; TRT_UNUSED B; assert(mS == static_cast(inputs[1].desc.dims.d[SDIM])); @@ -246,33 +257,33 @@ void EmbLayerNormPluginDynamic::configurePlugin( if (mUseFullMask) { + // user force full_mask assert(outputs[1].desc.dims.nbDims == 2); assert(outputs[1].desc.dims.d[0] == B); - assert((outputs[1].desc.dims.d[1] == 2 * packedMaskSize384) - || (outputs[1].desc.dims.d[1] == 2 * packedMaskSize128)); + assert((outputs[1].desc.dims.d[1] == -1) || (outputs[1].desc.dims.d[1] == packedMaskSize384) + || (outputs[1].desc.dims.d[1] == packedMaskSize128)); } else { - - assert(outputs[1].desc.dims.nbDims == 1); - assert(outputs[1].desc.dims.d[0] == B); + // auto detect using mhatype + if (S != -1 && B != -1) + { + assert(outputs[1].desc.dims.nbDims == 2); + assert(outputs[1].desc.dims.d[0] == B); + int packedSize = getMHAMaskPackedSize(mSM, mMhaType, S); + TRT_UNUSED packedSize; + assert(outputs[1].desc.dims.d[1] == -1 || outputs[1].desc.dims.d[1] == packedSize); + } } assert(inputs[0].desc.type == DataType::kINT32); assert(inputs[1].desc.type == DataType::kINT32); assert(inputs[2].desc.type == DataType::kINT32); assert(outputs[0].desc.type == mType); - if (mUseFullMask) - { - assert(outputs[1].desc.type == DataType::kHALF); - } - else - { - - assert(outputs[1].desc.type == DataType::kINT32); - } + assert(outputs[1].desc.type == DataType::kFLOAT); } + size_t EmbLayerNormPluginDynamic::getWorkspaceSize( const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const { @@ -320,11 +331,11 @@ int EmbLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const CHECK(cudaPeekAtLastError()); - if (mUseFullMask) + // check mha use fused kernel + if (mUseFullMask || unfusedMaskSize != getMHAMaskPackedSize(mSM, mMhaType, S)) { - assert((S == 128 || S == 384) && "Invalid sequence length for mask type"); size_t warps_m = 0, warps_n = 0, warps_k = 1; - if (S == 128) + if (S == 64 || S == 96 || S == 128) { warps_m = 2; warps_n = 2; @@ -340,14 +351,8 @@ int EmbLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const } else { - int* maskIdx = static_cast(outputs[1]); computeMaskIdx(stream, S, batchSize, inputMask, maskIdx); - // std::vector maskIdx_h(batchSize); - // cudaMemcpy(maskIdx_h.data(), maskIdx, batchSize*sizeof(int), cudaMemcpyDeviceToHost); - // for(auto x :maskIdx_h ){ - // printf("%d\n", x); - //} } return status; @@ -363,11 +368,7 @@ DataType EmbLayerNormPluginDynamic::getOutputDataType(int index, const DataType* assert(mType == DataType::kHALF || mType == DataType::kFLOAT); return mType; } - if (mUseFullMask) - { - return DataType::kHALF; - } - return DataType::kINT32; + return DataType::kFLOAT; } // IPluginV2 Methods @@ -399,24 +400,29 @@ void EmbLayerNormPluginDynamic::terminate() size_t EmbLayerNormPluginDynamic::getSerializationSize() const { const size_t wordSize = getElementSize(mType); - return 2 * sizeof(float) * mLd // beta + gamma - + sizeof(mType) + sizeof(mLd) * 5 // mLd, mS, m*VocabSize + return sizeof(mType) // type + + sizeof(mMhaType) // mha plugin datatype + + sizeof(mLd) * 5 // mLd, mS, m*VocabSize + + sizeof(mUseFullMask) // mask type + + sizeof(mSM) // smversion + + 2 * sizeof(float) * mLd // beta + gamma + wordSize * mLd * mWordVocabSize // word emb + wordSize * mLd * mPosVocabSize // pos emb + wordSize * mLd * mTokVocabSize // tok emb - + sizeof(mUseFullMask) // mask type ; } void EmbLayerNormPluginDynamic::serialize(void* buffer) const { serialize_value(&buffer, mType); + serialize_value(&buffer, mMhaType); serialize_value(&buffer, mLd); serialize_value(&buffer, mS); serialize_value(&buffer, mWordVocabSize); serialize_value(&buffer, mPosVocabSize); serialize_value(&buffer, mTokVocabSize); serialize_value(&buffer, mUseFullMask); + serialize_value(&buffer, mSM); char* d = static_cast(buffer); serFromDev(d, mBetaDev.get(), mLd); @@ -483,6 +489,7 @@ IPluginV2* EmbLayerNormPluginDynamicCreator::createPlugin(const char* name, cons Weights word_emb; Weights pos_emb; Weights tok_emb; + int mhaTypeId = 0; for (int i = 0; i < fc->nbFields; i++) { std::string field_name(fc->fields[i].name); @@ -537,11 +544,18 @@ IPluginV2* EmbLayerNormPluginDynamicCreator::createPlugin(const char* name, cons assert(fc->fields[i].type == PluginFieldType::kINT32); useFullMask = static_cast(fc->fields[i].data)[0] != 0; } + if (field_name.compare("mha_type_id") == 0) + { + mhaTypeId = *static_cast(fc->fields[i].data); + ASSERT(mhaTypeId >= 0 && mhaTypeId <= 3); + gLogVerbose << "Building mha typeId: " << mhaTypeId << std::endl; + } } gLogVerbose << "Building the Plugin...\n"; + DataType mhaType = static_cast(mhaTypeId); EmbLayerNormPluginDynamic* p = new EmbLayerNormPluginDynamic( - name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, beta, gamma, word_emb, pos_emb, tok_emb, useFullMask); + name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, mhaType, beta, gamma, word_emb, pos_emb, tok_emb, useFullMask); return p; } diff --git a/plugin/embLayerNormPlugin/embLayerNormPlugin.h b/plugin/embLayerNormPlugin/embLayerNormPlugin.h index 8f376483..03ad435b 100644 --- a/plugin/embLayerNormPlugin/embLayerNormPlugin.h +++ b/plugin/embLayerNormPlugin/embLayerNormPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -38,12 +38,13 @@ int embSkipLayerNorm(cudaStream_t stream, int ld, int B, int S, const int* input void convertMask(const uint32_t S, const uint32_t B, const uint32_t warps_m, const uint32_t warps_n, const uint32_t warps_k, const int* inputMaskSB, uint32_t* inputMaskX, cudaStream_t stream); + class EmbLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt { public: - EmbLayerNormPluginDynamic(const std::string& name, const nvinfer1::DataType type, const nvinfer1::Weights& beta, - const nvinfer1::Weights& gamma, const nvinfer1::Weights& word_emb, const nvinfer1::Weights& pos_emb, - const nvinfer1::Weights& tok_emb, const bool useFullMask); + EmbLayerNormPluginDynamic(const std::string& name, const nvinfer1::DataType type, const nvinfer1::DataType mhaType, + const nvinfer1::Weights& beta, const nvinfer1::Weights& gamma, const nvinfer1::Weights& word_emb, + const nvinfer1::Weights& pos_emb, const nvinfer1::Weights& tok_emb, const bool useFullMask); EmbLayerNormPluginDynamic(const std::string& name, const void* data, size_t length); @@ -89,7 +90,6 @@ class EmbLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt bert::cuda_unique_ptr mTokEmbDev; bert::cuda_unique_ptr mPosEmbDev; size_t mLd; // leading dim = hidden size - size_t mB; // batch size size_t mS; // sequence length size_t mWordVocabSize; size_t mPosVocabSize; @@ -101,16 +101,18 @@ class EmbLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt bert::WeightsWithOwnership mPosEmb; nvinfer1::DataType mType; bool mUseFullMask; + nvinfer1::DataType mMhaType; + int mSM; protected: // To prevent compiler warnings. - using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; - using nvinfer1::IPluginV2DynamicExt::configurePlugin; - using nvinfer1::IPluginV2DynamicExt::enqueue; using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; - using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; using nvinfer1::IPluginV2DynamicExt::supportsFormat; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::enqueue; }; class EmbLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator diff --git a/plugin/embLayerNormPlugin/embLayerNormVarSeqlenKernel.cu b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenKernel.cu new file mode 100644 index 00000000..39b1ebfe --- /dev/null +++ b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenKernel.cu @@ -0,0 +1,313 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include + +#include "NvInfer.h" +#include "bertCommon.h" +#include "common.cuh" +#include "embLayerNormPlugin.h" +#include "serialize.hpp" + +using namespace nvinfer1; + +namespace bert +{ + +__global__ void cuSeqlensToPackedMaskKernel( + const uint32_t warps_m, const uint32_t warps_n, const uint32_t S, const int* cuSeqlens, uint32_t* inputMaskX) +{ + + extern __shared__ int shm_mask[]; // S mask elements of this batch + + const size_t xmmas_n = (S + 16 * warps_n - 1) / (16 * warps_n); + const uint32_t threads_per_cta = blockDim.x; + const uint32_t xmmas_m = gridDim.x; + + const uint32_t mi = blockIdx.x; + const uint32_t bi = blockIdx.y; + const uint32_t tidx = threadIdx.x; + + const uint32_t sum_s = cuSeqlens[bi]; + const uint32_t s_b = cuSeqlens[bi + 1] - sum_s; + + const size_t warp = tidx / 32; + const size_t warp_n = warp / warps_m; + const size_t lane = tidx % 32; + const size_t col = warp_n * 16 + lane % 4 * 2; + + // TODO get rid of shared mem roundtrip + // load the mask corresponding to one batch + for (uint32_t si = tidx; si < S; si += threads_per_cta) + { + shm_mask[si] = si < s_b; + } + __syncthreads(); + + uint32_t mask = 0u; + + for (size_t ni = 0; ni < xmmas_n; ++ni) + { + const int offset = ni * 16 * warps_n + col; + mask |= (shm_mask[offset + 0] == 1 ? 1u : 0u) << (8 * ni + 0); + mask |= (shm_mask[offset + 1] == 1 ? 1u : 0u) << (8 * ni + 1); + mask |= (shm_mask[offset + 0] == 1 ? 1u : 0u) << (8 * ni + 2); + mask |= (shm_mask[offset + 1] == 1 ? 1u : 0u) << (8 * ni + 3); + mask |= (shm_mask[offset + 8] == 1 ? 1u : 0u) << (8 * ni + 4); + mask |= (shm_mask[offset + 9] == 1 ? 1u : 0u) << (8 * ni + 5); + mask |= (shm_mask[offset + 8] == 1 ? 1u : 0u) << (8 * ni + 6); + mask |= (shm_mask[offset + 9] == 1 ? 1u : 0u) << (8 * ni + 7); + } + + inputMaskX[(bi * xmmas_m + mi) * threads_per_cta + tidx] = mask; +} + +void cuSeqlensToPackedMask(const uint32_t S, const uint32_t B, const uint32_t warps_m, const uint32_t warps_n, + const uint32_t warps_k, const int* cuSeqlens, uint32_t* inputMaskX, cudaStream_t stream) +{ + const size_t xmmas_m = (S + 16 * warps_m - 1) / (16 * warps_m); + + const size_t threads_per_cta = warps_m * warps_n * warps_k * 32; + dim3 grid(xmmas_m, B); + cuSeqlensToPackedMaskKernel<<>>( + warps_m, warps_n, S, cuSeqlens, inputMaskX); + CHECK(cudaPeekAtLastError()); +} + +template +__global__ void embLayerNormKernelVarSeqlen(int ld, const uint32_t* cuSeqlens, const int* inputIds, + const int* segmentIds, const T* beta, const T* gamma, const T* tokEmb, const T* posEmb, const T* segEmb, T* output) +{ + + using BlockReduce = cub::BlockReduce, TPB>; + __shared__ typename BlockReduce::TempStorage temp_storage; + + const int b = blockIdx.x; + const int s = blockIdx.y; + + const int sum_s = cuSeqlens[b]; + const int s_b = cuSeqlens[b + 1] - sum_s; + + // either the whole CTA has work or not + if (s >= s_b) + return; + + const int inOffset = (sum_s + s); + const int outOffset = (sum_s + s) * ld; + + // 1. lookup word and token of the block + // blockIdx.x = position in the sequence + // blockIdx.y = batch + // gridDim.x = S + // gridDim.y = B + __shared__ int inputId; + __shared__ int segmentId; + + if (threadIdx.x == 0) + { + inputId = inputIds[inOffset]; + segmentId = segmentIds[inOffset]; + } + __syncthreads(); + + // 2. load pos/tok/word embeddings and add them toghether + // offset into embeddings is given by wordId * hidden_size + const int poffset = s * ld; + const int ioffset = inputId * ld; + const int soffset = segmentId * ld; + + // 16B per thread: 8 elements. there should be ld / VPT threads per CTA + // 1024: 128 threads + // 768: 96 threads + const int toffset = threadIdx.x * VPT; + // 4 * 1024 * 4 * 2 Bytes = 16KB per block + T i_local[VPT]; + T s_local[VPT]; + T p_local[VPT]; + + // read embeddings + copy(&tokEmb[ioffset + toffset], i_local); + copy(&segEmb[soffset + toffset], s_local); + copy(&posEmb[poffset + toffset], p_local); + T local = 0.f; + T local2 = 0.f; + + const T rld = T(1) / T(ld); +#pragma unroll + for (int it = 0; it < VPT; it++) + { + i_local[it] += s_local[it] + p_local[it]; + const T tmp = rld * i_local[it]; + local += tmp; + local2 += tmp * i_local[it]; + } + + // load params + copy(&beta[toffset], p_local); + copy(&gamma[toffset], s_local); + + __shared__ T mu; // mean + __shared__ T rsigma; // 1 / std.dev. + + const auto sumKV = BlockReduce(temp_storage).Reduce(kvp(local, local2), cub::Sum()); + + if (threadIdx.x == 0) + { + mu = sumKV.key; + rsigma = rsqrt(sumKV.value - mu * mu); + } + __syncthreads(); + ///* +#pragma unroll + for (int it = 0; it < VPT; it++) + { + i_local[it] = s_local[it] * (i_local[it] - mu) * rsigma + p_local[it]; + } + /* */ + + copy(i_local, &output[outOffset + toffset]); +} + +template +int embSkipLayerNormVarSeqlen(cudaStream_t stream, int ld, int B, int S, const uint32_t* cuSeqlens, const int* inputIds, + const int* token_ids, const T* beta, const T* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output) +{ + + const dim3 grid(B, S, 1); + + if (ld == 1024) + { + constexpr int VPT = 16 / sizeof(T); + constexpr int TPB = 1024 / VPT; + const dim3 block(TPB, 1, 1); + embLayerNormKernelVarSeqlen<<>>( + ld, cuSeqlens, inputIds, token_ids, beta, gamma, wordEmb, posEmb, tokEmb, output); + } + else if (ld == 768) + { + constexpr int VPT = 16 / sizeof(T); + constexpr int TPB = 768 / VPT; + const dim3 block(TPB, 1, 1); + embLayerNormKernelVarSeqlen<<>>( + ld, cuSeqlens, inputIds, token_ids, beta, gamma, wordEmb, posEmb, tokEmb, output); + } + else + { + assert(false && "Unsupported hidden dimension"); + } + + CHECK(cudaPeekAtLastError()); + + return 0; +} + +template int embSkipLayerNormVarSeqlen(cudaStream_t, int, int, int, const uint32_t*, const int*, const int*, + const float*, const float*, const float*, const float*, const float*, float*); + +template int embSkipLayerNormVarSeqlen(cudaStream_t, int, int, int, const uint32_t*, const int*, const int*, + const half*, const half*, const half*, const half*, const half*, half*); + +/// REDO BASED ON OLD KERNEL TO REPRODUCE EXACT RESULTS + +template +__global__ void embLayerNormKernel2(int ld, const int* inputIds, const int* tokenIds, const int* cuSeqlens, + const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output) +{ + // this code currently assumes the input shape is SxB, row-major => seqPos = s * B + b + // instead we want BxS, row-major => seqPos = b * S + s + + cub::Sum pairSum; + // 1. lookup word and token of the block + // blockIdx.x = position in the sequence + // blockIdx.y = batch + // gridDim.x = S + // gridDim.y = B + const int s = blockIdx.x; + const int b = blockIdx.y; + + const int sumS = cuSeqlens[b]; + const int s_b = cuSeqlens[b + 1] - sumS; + if (s >= s_b) + return; // This CTA has nothing to do + __shared__ int wordId; + __shared__ int tokenId; + + const T rld = T(1.f) / T(ld); + // seqPos = b + s * B + // const int seqPos = blockIdx.y + blockIdx.x * gridDim.y; + + // const int seqPos = s * B + s; + const int seqPos = sumS + s; + if (threadIdx.x == 0) + { + wordId = inputIds[seqPos]; + tokenId = tokenIds[seqPos]; + } + __syncthreads(); + + // 2. load pos/tok/word embeddings and add them toghether + // offset into embeddings is given by wordId * hidden_size + const int poffset = blockIdx.x * ld; + const int woffset = wordId * ld; + const int toffset = tokenId * ld; + // the output offset is given by b * (S*hidden_size) + s * hidden_size + const int outOffset = seqPos * ld; + + kvp threadData(0, 0); + + for (int it = threadIdx.x; it < ld; it += TPB) + { + const T w(wordEmb[woffset + it]); + const T t(tokEmb[toffset + it]); + const T p(posEmb[poffset + it]); + const T val = w + t + p; + + output[outOffset + it] = val; + const T rldval = rld * val; + threadData = pairSum(threadData, kvp(rldval, rldval * val)); + } + + // 3. layer norm on the sum + layerNorm(threadData, ld, outOffset, beta, gamma, output); +} + +template +int embSkipLayerNorm2(cudaStream_t stream, int ld, int B, int S, const int* inputIds, const int* tokenIds, + const int* cuSeqlens, const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, + T* output) +{ + + constexpr int tpb = 256; + const dim3 grid(S, B, 1); + const dim3 block(tpb, 1, 1); + + embLayerNormKernel2 + <<>>(ld, inputIds, tokenIds, cuSeqlens, beta, gamma, wordEmb, posEmb, tokEmb, output); + CHECK(cudaPeekAtLastError()); + + return 0; +} + +template int embSkipLayerNorm2(cudaStream_t, int, int, int, const int*, const int*, const int*, const float*, + const float*, const float*, const float*, const float*, float*); + +template int embSkipLayerNorm2(cudaStream_t, int, int, int, const int*, const int*, const int*, const float*, + const float*, const half*, const half*, const half*, half*); + +} // namespace bert diff --git a/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.cpp b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.cpp new file mode 100644 index 00000000..6f90cac8 --- /dev/null +++ b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.cpp @@ -0,0 +1,523 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include + +#include "NvInfer.h" +#include "embLayerNormVarSeqlenPlugin.h" +#include "serialize.hpp" + +using namespace nvinfer1; + +namespace bert +{ +// For full mask mode, we must produce the compressed mask format expected by the fused attention path. Currently, only +// two sequence lengths are supported. We hard code the sizes here. +// The number of threads per CTA: warps_m * warps_n * warps_k * 32; +constexpr size_t threadsPerCta128 = 2 * 2 * 32; +constexpr size_t threadsPerCta256 = 1 * 4 * 32; +constexpr size_t threadsPerCta384 = 1 * 8 * 32; +// The number of xmmas in the M dimension. We use one uint32_t per XMMA in the M dimension: (s + 16*warps_m - 1) +// / (16*warps_m); +constexpr size_t xmmasM128 = 4; +constexpr size_t xmmasM256 = 16; +constexpr size_t xmmasM384 = 24; +// Packed mask size per batch. Layout is XMMAS_M * THREADS_PER_CTA. +constexpr size_t packedMaskSize128 = xmmasM128 * threadsPerCta128; +constexpr size_t packedMaskSize256 = xmmasM256 * threadsPerCta256; +constexpr size_t packedMaskSize384 = xmmasM384 * threadsPerCta384; + +namespace +{ +static const char* EMB_LAYER_NORM_VAR_SEQLEN_VERSION{"2"}; +static const char* EMB_LAYER_NORM_VAR_SEQLEN_NAME{"CustomEmbLayerNormPluginDynamic"}; +} // namespace + +// Static class fields initialization +PluginFieldCollection EmbLayerNormVarSeqlenPluginCreator::mFC{}; +std::vector EmbLayerNormVarSeqlenPluginCreator::mPluginAttributes; + +REGISTER_TENSORRT_PLUGIN(EmbLayerNormVarSeqlenPluginCreator); + +EmbLayerNormVarSeqlenPlugin::EmbLayerNormVarSeqlenPlugin(const std::string& name, const DataType type, + const Weights& beta, const Weights& gamma, const Weights& wordEmb, const Weights& posEmb, const Weights& tokEmb) + : mLayerName(name) + , mLd(beta.count) + , mType(type) +{ + // Assuming Weights.count is the number of elements and not bytes + ASSERT(beta.count == gamma.count); + ASSERT(wordEmb.count % mLd == 0); + ASSERT(posEmb.count % mLd == 0); + ASSERT(tokEmb.count % mLd == 0); + mWordVocabSize = wordEmb.count / mLd; + mPosVocabSize = posEmb.count / mLd; + mTokVocabSize = tokEmb.count / mLd; + + mBeta.convertAndCopy(beta, nvinfer1::DataType::kFLOAT); + mGamma.convertAndCopy(gamma, nvinfer1::DataType::kFLOAT); + + mWordEmb.convertAndCopy(wordEmb, mType); + mTokEmb.convertAndCopy(tokEmb, mType); + mPosEmb.convertAndCopy(posEmb, mType); + + copyToDevice(mGamma, sizeof(float) * mGamma.count, mGammaDev); + copyToDevice(mBeta, sizeof(float) * mBeta.count, mBetaDev); + + copyToDevice(mWordEmb, getWeightsSize(mWordEmb, mType), mWordEmbDev); + copyToDevice(mPosEmb, getWeightsSize(mPosEmb, mType), mPosEmbDev); + copyToDevice(mTokEmb, getWeightsSize(mTokEmb, mType), mTokEmbDev); +} + +EmbLayerNormVarSeqlenPlugin::EmbLayerNormVarSeqlenPlugin(const std::string& name, const void* data, size_t length) + : mLayerName(name) + , mGammaDev(nullptr) + , mBetaDev(nullptr) + , mWordEmbDev(nullptr) + , mTokEmbDev(nullptr) + , mPosEmbDev(nullptr) +{ + gLogVerbose << "EmbLayerNormVarSeqlenPlugin deserialize\n"; + + // Deserialize in the same order as serialization + deserialize_value(&data, &length, &mType); + deserialize_value(&data, &length, &mLd); + deserialize_value(&data, &length, &mWordVocabSize); + deserialize_value(&data, &length, &mPosVocabSize); + deserialize_value(&data, &length, &mTokVocabSize); + + const char* d = static_cast(data); + mBeta.convertAndCopy(d, mLd, nvinfer1::DataType::kFLOAT); + mGamma.convertAndCopy(d, mLd, nvinfer1::DataType::kFLOAT); + + mWordEmb.convertAndCopy(d, mLd * mWordVocabSize, mType); + mPosEmb.convertAndCopy(d, mLd * mPosVocabSize, mType); + mTokEmb.convertAndCopy(d, mLd * mTokVocabSize, mType); + + copyToDevice(mGamma, sizeof(float) * mGamma.count, mGammaDev); + copyToDevice(mBeta, sizeof(float) * mBeta.count, mBetaDev); + + copyToDevice(mWordEmb, getWeightsSize(mWordEmb, mType), mWordEmbDev); + copyToDevice(mPosEmb, getWeightsSize(mPosEmb, mType), mPosEmbDev); + copyToDevice(mTokEmb, getWeightsSize(mTokEmb, mType), mTokEmbDev); +} + +// IPluginV2DynamicExt Methods +IPluginV2DynamicExt* EmbLayerNormVarSeqlenPlugin::clone() const +{ + gLogVerbose << "EmbLayerNormVarSeqlenPlugin clone\n"; + + auto p = new EmbLayerNormVarSeqlenPlugin(mLayerName, mType, mBeta, mGamma, mWordEmb, mPosEmb, mTokEmb); + p->setPluginNamespace(mNamespace.c_str()); + + return p; +} + +DimsExprs EmbLayerNormVarSeqlenPlugin::getOutputDimensions( + int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) +{ + // Input should be input ids and token ids and cumulative seqlens + // Output should be the embeddings tensor and mask indices + ASSERT(nbInputs == 4); + + ASSERT(inputs[0].nbDims == 1); // sum of all s + ASSERT(inputs[0].nbDims == inputs[1].nbDims); + + ASSERT(inputs[2].nbDims == 1); // B+1 + + ASSERT(outputIndex == 0 || outputIndex == 1); + + if (outputIndex == 0) + { + DimsExprs ret; + ret.nbDims = 4; + ret.d[0] = inputs[0].d[0]; + ret.d[1] = exprBuilder.constant(mLd); + ret.d[2] = exprBuilder.constant(1); + ret.d[3] = exprBuilder.constant(1); + return ret; + } + + // This is a hack: we just report some mask size and rely the plugins to play nicely together. + // At runtime, depending on the actual maxSeqlen, the size might be different. + int maskSize_ = packedMaskSize384; + + auto maskSize = exprBuilder.constant(maskSize_); + auto fp16maskSize = exprBuilder.operation(DimensionOperation::kPROD, *maskSize, *exprBuilder.constant(2)); + + auto Bplus1 = inputs[2].d[0]; + auto one = exprBuilder.constant(1); + auto B = exprBuilder.operation(DimensionOperation::kSUB, *Bplus1, *one); + + DimsExprs ret; + ret.nbDims = 2; + ret.d[0] = B; + ret.d[1] = fp16maskSize; + return ret; +} + +bool EmbLayerNormVarSeqlenPlugin::supportsFormatCombination( + int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) +{ + // The four inputs to this plugin input_ids, segment_ids, cu_seqlens and a dummy input with the + // size of the max seq length in that order + ASSERT(nbInputs == 4); + // The two outputs of the plugin are embedding and the mask + ASSERT(nbOutputs == 2); + + const PluginTensorDesc& desc = inOut[pos]; + if (desc.format != TensorFormat::kLINEAR) + { + return false; + } + if (pos == 0 || pos == 2) // input_ids and cu_seqlens + { + return desc.type == DataType::kINT32 && desc.dims.nbDims == 1; + } + + const PluginTensorDesc& prev = inOut[pos - 1]; + if (pos == 1) // segment ids: check it's the same as input_ids + { + return desc.type == DataType::kINT32 && desc.dims.nbDims == 1 && desc.dims.d[0] == prev.dims.d[0]; + } + + if (pos == 3) + { + return desc.dims.nbDims == 1; + } + + // embedded sequence + if (pos == nbInputs) + { + return desc.type == mType && desc.dims.nbDims == 4 && desc.dims.d[0] == inOut[0].dims.d[0] + && desc.dims.d[2] == 1 && desc.dims.d[3] == 1; + } + // mask + return desc.type == DataType::kHALF; +} + +void EmbLayerNormVarSeqlenPlugin::configurePlugin( + const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs) +{ + gLogVerbose << "EmbLayerNormVarSeqlenPlugin configurePlugin\n"; + + // Validate input arguments + ASSERT(nbInputs == 4); + ASSERT(nbOutputs == 2); + + ASSERT(inputs[0].desc.dims.nbDims == 1); + ASSERT(inputs[1].desc.dims.nbDims == 1); + + ASSERT(inputs[1].desc.dims.d[0] == inputs[0].desc.dims.d[0]); + + ASSERT(inputs[2].desc.dims.nbDims == 1); + + ASSERT(outputs[0].desc.dims.nbDims == 4); + ASSERT(static_cast(outputs[0].desc.dims.d[0]) == static_cast(inputs[0].desc.dims.d[0])); + ASSERT(static_cast(outputs[0].desc.dims.d[1]) == static_cast(mLd)); + ASSERT(outputs[0].desc.dims.d[2] == 1); + ASSERT(outputs[0].desc.dims.d[3] == 1); + + const int B = inputs[2].desc.dims.d[0] - 1; + + // check mask + ASSERT(outputs[1].desc.dims.nbDims == 2); + if (B > 0) + { + ASSERT(outputs[1].desc.dims.d[0] == B); + } + ASSERT((outputs[1].desc.dims.d[1] == 2 * packedMaskSize384) || (outputs[1].desc.dims.d[1] == 2 * packedMaskSize128) + || (outputs[1].desc.dims.d[1] == 2 * packedMaskSize256)); + + ASSERT(inputs[0].desc.type == DataType::kINT32); + ASSERT(inputs[1].desc.type == DataType::kINT32); + ASSERT(inputs[2].desc.type == DataType::kINT32); + ASSERT(outputs[0].desc.type == mType); + ASSERT(outputs[1].desc.type == DataType::kHALF); +} + +size_t EmbLayerNormVarSeqlenPlugin::getWorkspaceSize( + const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const +{ + return 0; +} + +int EmbLayerNormVarSeqlenPlugin::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) +{ + int status = -1; + const int batchSize = inputDesc[2].dims.d[0] - 1; + // read out the maximum sequence length from the dummy input + const int maxSeqlen = inputDesc[3].dims.d[0]; + + // There are four versions of the kernel which are optimized for sequence lengths 384, 256, 192 and 128. + // Find the closest sequence length bigger than the max seq length in this batch. + int S = 384; + if (maxSeqlen <= 128) + { + S = 128; + } + else if (maxSeqlen <= 192) + { + S = 192; + } + else if (maxSeqlen <= 256) + { + S = 256; + } + + // Our plugin outputs only one tensor + const auto inputIds = static_cast(inputs[0]); + const auto segmentIds = static_cast(inputs[1]); + const int* cuSeqlens = static_cast(inputs[2]); + + const float* beta = mBetaDev.get(); + const float* gamma = mGammaDev.get(); + if (mType == DataType::kFLOAT) + { + auto output = static_cast(outputs[0]); + const auto wordEmb = static_cast(mWordEmbDev.get()); + const auto tokEmb = static_cast(mTokEmbDev.get()); + const auto posEmb = static_cast(mPosEmbDev.get()); + + embSkipLayerNorm2(stream, static_cast(mLd), batchSize, S, inputIds, segmentIds, cuSeqlens, beta, + gamma, wordEmb, posEmb, tokEmb, output); + } + else if (mType == DataType::kHALF) + { + auto output = static_cast(outputs[0]); + const auto wordEmb = static_cast(mWordEmbDev.get()); + const auto tokEmb = static_cast(mTokEmbDev.get()); + const auto posEmb = static_cast(mPosEmbDev.get()); + + embSkipLayerNorm2(stream, static_cast(mLd), batchSize, S, inputIds, segmentIds, cuSeqlens, beta, + gamma, wordEmb, posEmb, tokEmb, output); + } + else + { + gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast(mType) + << std::endl; + ASSERT(false); + } + + CHECK(cudaPeekAtLastError()); + + return status; +} + +// IPluginV2Ext Methods +DataType EmbLayerNormVarSeqlenPlugin::getOutputDataType(int index, const DataType* inputTypes, int nbInputs) const +{ + + ASSERT(index == 0 || index == 1); + if (index == 0) + { + ASSERT(mType == DataType::kHALF || mType == DataType::kFLOAT); + return mType; + } + return DataType::kHALF; +} + +// IPluginV2 Methods +const char* EmbLayerNormVarSeqlenPlugin::getPluginType() const +{ + return EMB_LAYER_NORM_VAR_SEQLEN_NAME; +} + +const char* EmbLayerNormVarSeqlenPlugin::getPluginVersion() const +{ + return EMB_LAYER_NORM_VAR_SEQLEN_VERSION; +} + +int EmbLayerNormVarSeqlenPlugin::getNbOutputs() const +{ + return 2; +} + +int EmbLayerNormVarSeqlenPlugin::initialize() +{ + return 0; +} + +void EmbLayerNormVarSeqlenPlugin::terminate() +{ + gLogVerbose << "EmbLayerNormVarSeqlenPlugin terminate\n"; +} + +size_t EmbLayerNormVarSeqlenPlugin::getSerializationSize() const +{ + const size_t wordSize = getElementSize(mType); + return 2 * sizeof(float) * mLd // beta + gamma + + sizeof(mType) // + + sizeof(mLd) // + + sizeof(mWordVocabSize) // + + sizeof(mPosVocabSize) // + + sizeof(mTokVocabSize) // + + wordSize * mLd * mWordVocabSize // word emb + + wordSize * mLd * mPosVocabSize // pos emb + + wordSize * mLd * mTokVocabSize // tok emb + ; +} + +void EmbLayerNormVarSeqlenPlugin::serialize(void* buffer) const +{ + serialize_value(&buffer, mType); + serialize_value(&buffer, mLd); + serialize_value(&buffer, mWordVocabSize); + serialize_value(&buffer, mPosVocabSize); + serialize_value(&buffer, mTokVocabSize); + + char* d = static_cast(buffer); + const size_t wordSize = getElementSize(mType); + + serFromDev(d, mBetaDev.get(), mLd); + serFromDev(d, mGammaDev.get(), mLd); + serFromDev(d, static_cast(mWordEmbDev.get()), mLd * mWordVocabSize * wordSize); + serFromDev(d, static_cast(mPosEmbDev.get()), mLd * mPosVocabSize * wordSize); + serFromDev(d, static_cast(mTokEmbDev.get()), mLd * mTokVocabSize * wordSize); +} + +void EmbLayerNormVarSeqlenPlugin::destroy() +{ + gLogVerbose << "EmbLayerNormVarSeqlenPlugin destroy\n"; + // This gets called when the network containing plugin is destroyed + mGammaDev.release(); + mBetaDev.release(); + mWordEmbDev.release(); + mPosEmbDev.release(); + mTokEmbDev.release(); + delete this; +} + +void EmbLayerNormVarSeqlenPlugin::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* EmbLayerNormVarSeqlenPlugin::getPluginNamespace() const +{ + return mNamespace.c_str(); +} + +/////////////////////// + +EmbLayerNormVarSeqlenPluginCreator::EmbLayerNormVarSeqlenPluginCreator() +{ + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); +} + +const char* EmbLayerNormVarSeqlenPluginCreator::getPluginName() const +{ + return EMB_LAYER_NORM_VAR_SEQLEN_NAME; +} + +const char* EmbLayerNormVarSeqlenPluginCreator::getPluginVersion() const +{ + return EMB_LAYER_NORM_VAR_SEQLEN_VERSION; +} + +const PluginFieldCollection* EmbLayerNormVarSeqlenPluginCreator::getFieldNames() +{ + return &mFC; +} + +IPluginV2* EmbLayerNormVarSeqlenPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) +{ + gLogVerbose << "EmbLayerNormVarSeqlen createPlugin\n"; + + bool output_fp16 = false; + Weights beta; + Weights gamma; + Weights word_emb; + Weights pos_emb; + Weights tok_emb; + for (int i = 0; i < fc->nbFields; i++) + { + std::string field_name(fc->fields[i].name); + if (field_name.compare("bert_embeddings_layernorm_beta") == 0) + { + gLogVerbose << "Building bert_embeddings_layernorm_beta...\n"; + beta.values = fc->fields[i].data; + beta.count = fc->fields[i].length; + beta.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("bert_embeddings_layernorm_gamma") == 0) + { + gLogVerbose << "Building bert_embeddings_layernorm_gamma...\n"; + gamma.values = fc->fields[i].data; + gamma.count = fc->fields[i].length; + gamma.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("bert_embeddings_word_embeddings") == 0) + { + gLogVerbose << "Building bert_embeddings_word_embeddings...\n"; + word_emb.values = fc->fields[i].data; + word_emb.count = fc->fields[i].length; + word_emb.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("bert_embeddings_token_type_embeddings") == 0) + { + gLogVerbose << "Building bert_embeddings_token_type_embeddings...\n"; + tok_emb.values = fc->fields[i].data; + tok_emb.count = fc->fields[i].length; + tok_emb.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("bert_embeddings_position_embeddings") == 0) + { + gLogVerbose << "Building bert_embeddings_position_embeddings...\n"; + pos_emb.values = fc->fields[i].data; + pos_emb.count = fc->fields[i].length; + pos_emb.type = fieldTypeToDataType(fc->fields[i].type); + } + if (field_name.compare("output_fp16") == 0) + { + gLogVerbose << "Building output_fp16...\n"; + ASSERT(fc->fields[i].type == PluginFieldType::kINT32); + output_fp16 = static_cast(fc->fields[i].data)[0] != 0; + } + } + + gLogVerbose << "Building the Plugin...\n"; + EmbLayerNormVarSeqlenPlugin* p = new EmbLayerNormVarSeqlenPlugin( + name, output_fp16 ? DataType::kHALF : DataType::kFLOAT, beta, gamma, word_emb, pos_emb, tok_emb); + return p; +} + +IPluginV2* EmbLayerNormVarSeqlenPluginCreator::deserializePlugin( + const char* name, const void* serialData, size_t serialLength) +{ + // This object will be deleted when the network is destroyed, which will + // call EmbLayerNormVarSeqlen::destroy() + return new EmbLayerNormVarSeqlenPlugin(name, serialData, serialLength); +} + +void EmbLayerNormVarSeqlenPluginCreator::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* EmbLayerNormVarSeqlenPluginCreator::getPluginNamespace() const +{ + return mNamespace.c_str(); +} +} // namespace bert diff --git a/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.h b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.h new file mode 100755 index 00000000..50abf70e --- /dev/null +++ b/plugin/embLayerNormPlugin/embLayerNormVarSeqlenPlugin.h @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_EMB_LAYER_NORM_PLUGIN_H +#define TRT_EMB_LAYER_NORM_PLUGIN_H + +#include + +#include "NvInferPlugin.h" +#include "NvInferRuntime.h" + +#include "bertCommon.h" +#include +#include +namespace bert +{ + +template +int embSkipLayerNormVarSeqlen(cudaStream_t stream, int ld, int B, int S, const uint32_t* cuSeqlens, const int* inputIds, + const int* token_ids, const T* beta, const T* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, T* output); + +template +int embSkipLayerNorm2(cudaStream_t stream, int ld, int B, int S, const int* inputIds, const int* tokenIds, + const int* cuSeqlens, const float* beta, const float* gamma, const T* wordEmb, const T* posEmb, const T* tokEmb, + T* output); + +void cuSeqlensToPackedMask(const uint32_t S, const uint32_t B, const uint32_t warps_m, const uint32_t warps_n, + const uint32_t warps_k, const int* cuSeqlens, uint32_t* inputMaskX, cudaStream_t stream); + +class EmbLayerNormVarSeqlenPlugin : public nvinfer1::IPluginV2DynamicExt +{ +public: + EmbLayerNormVarSeqlenPlugin(const std::string& name, const nvinfer1::DataType type, const nvinfer1::Weights& beta, + const nvinfer1::Weights& gamma, const nvinfer1::Weights& word_emb, const nvinfer1::Weights& pos_emb, + const nvinfer1::Weights& tok_emb); + + EmbLayerNormVarSeqlenPlugin(const std::string& name, const void* data, size_t length); + + // It doesn't make sense to make EmbLayerNormVarSeqlenPlugin without arguments, so we + // delete default constructor. + EmbLayerNormVarSeqlenPlugin() = delete; + + // IPluginV2DynamicExt Methods + nvinfer1::IPluginV2DynamicExt* clone() const override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override; + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + // IPluginV2Ext Methods + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + // IPluginV2 Methods + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + int initialize() override; + void terminate() override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void destroy() override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; + +private: + const std::string mLayerName; + std::string mNamespace; + + bert::cuda_unique_ptr mGammaDev; + bert::cuda_unique_ptr mBetaDev; + bert::cuda_unique_ptr mWordEmbDev; + bert::cuda_unique_ptr mTokEmbDev; + bert::cuda_unique_ptr mPosEmbDev; + size_t mLd; // leading dim = hidden size + size_t mWordVocabSize; + size_t mPosVocabSize; + size_t mTokVocabSize; + bert::WeightsWithOwnership mBeta; + bert::WeightsWithOwnership mGamma; + bert::WeightsWithOwnership mWordEmb; + bert::WeightsWithOwnership mTokEmb; + bert::WeightsWithOwnership mPosEmb; + nvinfer1::DataType mType; + +protected: + // To prevent compiler warnings. + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::enqueue; + using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::supportsFormat; +}; + +class EmbLayerNormVarSeqlenPluginCreator : public nvinfer1::IPluginCreator +{ +public: + EmbLayerNormVarSeqlenPluginCreator(); + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + +private: + static nvinfer1::PluginFieldCollection mFC; + static std::vector mPluginAttributes; + std::string mNamespace; +}; +} // namespace bert +#endif // TRT_EMB_LAYER_NORM_PLUGIN_H diff --git a/plugin/fcPlugin/fcPlugin.cpp b/plugin/fcPlugin/fcPlugin.cpp index 73ab5070..252a392d 100644 --- a/plugin/fcPlugin/fcPlugin.cpp +++ b/plugin/fcPlugin/fcPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -59,11 +59,7 @@ static void printPerfStructure(const customMatmulPerf_t& perf, int const& m, int double timeAvg = (perf.time * 1e-3) / kernelRepeats; // Convert to seconds, then divide by loops double gflop = (2 * static_cast(m * n) * k) * 1e-9; // Real - gLogVerbose << "Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK - << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption - << " Stat=" << perf.status << " Time=" << perf.time << " WSbytes=" << perf.workspaceSize - << " math=" << p.mathMode << " waves=" << perf.wavesCount << "GFlops=" << (gflop / timeAvg) - << std::endl; + gLogVerbose << "Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " Stat=" << perf.status << " Time=" << perf.time << " WSbytes=" << perf.workspaceSize << " math=" << p.mathMode << " waves=" << perf.wavesCount << "GFlops=" << (gflop / timeAvg) << std::endl; } static inline bool time_compare(const customMatmulPerf_t& perf_a, const customMatmulPerf_t& perf_b) @@ -193,7 +189,7 @@ void LtGemmSearch(cublasLtHandle_t ltHandle, cublasOperation_t transa, cublasOpe CUBLASASSERT(cublasLtMatmulAlgoGetIds( ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIds, algoIdA, &nbAlgoIds)); - gLogVerbose << "Number of algos" << nbAlgoIds << std::endl; + gLogVerbose << "Number of algos" << nbAlgoIds << std::endl; // Create CUDA event to time the execution time of each algo CHECK(cudaEventCreate(&startEvent, cudaEventBlockingSync)); @@ -474,15 +470,14 @@ void FCPluginDynamic::configurePlugin( } else { - gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast(mType) - << std::endl; + gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast(mType) << std::endl; assert(false); } gLogVerbose << "FCPluginDynamic configurePlugin m=" << mOutDim << ", n=" << mNmax << ", k=" << mK << std::endl; size_t actualWorkspace = 0; - if (mAlgo.data[0] == 0 && memcmp(mAlgo.data, mAlgo.data + 1, sizeof(mAlgo.data) - sizeof(mAlgo.data[0])) == 0) + if (mAlgo.data[0] == 0 && memcmp(mAlgo.data, mAlgo.data+1, sizeof(mAlgo.data)-sizeof(mAlgo.data[0])) == 0) { gLogVerbose << "FCPluginDynamic gemmSearch\n"; if (mType == DataType::kFLOAT) @@ -508,10 +503,7 @@ void FCPluginDynamic::configurePlugin( gLogWarning << "TensorCore support was not selected" << std::endl; } - gLogVerbose << "FCPluginDynamic configuration Algo=" << p.algoId << " Tile=" << p.tile << " (" - << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme - << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " mathMode=" << p.mathMode - << " ws=" << actualWorkspace << std::endl; + gLogVerbose << "FCPluginDynamic configuration Algo=" << p.algoId << " Tile=" << p.tile << " (" << matmulTileName[p.tile] << ") K=" << p.numSplitsK << " Red.Sch.=" << p.reductionScheme << " Swiz=" << p.swizzle << " Cust=" << p.customOption << " mathMode=" << p.mathMode << " ws=" << actualWorkspace << std::endl; } size_t FCPluginDynamic::getWorkspaceSize( @@ -559,8 +551,7 @@ int FCPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const PluginTens } else { - gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast(mType) - << std::endl; + gLogError << "Unsupported type error, expected [kHALF,kFLOAT], but received " << static_cast(mType) << std::endl; assert(false); } @@ -700,6 +691,7 @@ IPluginV2* FCPluginDynamicCreator::createPlugin(const char* name, const PluginFi W.type = fieldTypeToDataType(fc->fields[i].type); gLogVerbose << "Is W float32: " << (W.type == DataType::kFLOAT) << std::endl; } + } if (outDims <= 0) diff --git a/plugin/fcPlugin/fcPlugin.h b/plugin/fcPlugin/fcPlugin.h index 133892bd..b84a51cf 100644 --- a/plugin/fcPlugin/fcPlugin.h +++ b/plugin/fcPlugin/fcPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. diff --git a/plugin/flattenConcat/flattenConcat.cpp b/plugin/flattenConcat/flattenConcat.cpp index d6178c3e..4a39853c 100644 --- a/plugin/flattenConcat/flattenConcat.cpp +++ b/plugin/flattenConcat/flattenConcat.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "flattenConcat.h" #include #include @@ -23,15 +24,6 @@ #include #include -#define ASSERT(assertion) \ - { \ - if (!(assertion)) \ - { \ - std::cerr << "#assertion" << __FILE__ << "," << __LINE__ << std::endl; \ - abort(); \ - } \ - } - using namespace nvinfer1; using nvinfer1::plugin::FlattenConcat; using nvinfer1::plugin::FlattenConcatPluginCreator; diff --git a/plugin/flattenConcat/flattenConcat.h b/plugin/flattenConcat/flattenConcat.h index 0692b8f2..a6b29efd 100644 --- a/plugin/flattenConcat/flattenConcat.h +++ b/plugin/flattenConcat/flattenConcat.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_FLATTENCONCAT_PLUGIN_H #define TRT_FLATTENCONCAT_PLUGIN_H diff --git a/plugin/geluPlugin/geluPlugin.cpp b/plugin/geluPlugin/geluPlugin.cpp index 63892cd5..4bab2af2 100644 --- a/plugin/geluPlugin/geluPlugin.cpp +++ b/plugin/geluPlugin/geluPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -43,6 +43,7 @@ std::vector GeluPluginDynamicCreator::mPluginAttributes; REGISTER_TENSORRT_PLUGIN(GeluPluginDynamicCreator); + GeluPluginDynamic::GeluPluginDynamic(const std::string name, const DataType type, const Weights& bias) : mLayerName(name) , mType(type) @@ -56,6 +57,7 @@ GeluPluginDynamic::GeluPluginDynamic(const std::string name, const DataType type CHECK(cudaMemcpy(cudaMem, bias.values, getWeightsSize(bias, mType), cudaMemcpyHostToDevice)); make_cuda_shared(mBiasDev, cudaMem); } + } GeluPluginDynamic::GeluPluginDynamic(const std::string name, const void* data, size_t length) @@ -68,10 +70,11 @@ GeluPluginDynamic::GeluPluginDynamic(const std::string name, const void* data, s if (mHasBias) { - assert(mLd > 0); + assert (mLd > 0); const char* d = static_cast(data); make_cuda_shared(mBiasDev, deserToDev(d, mLd * getElementSize(mType))); } + } // IPluginV2DynamicExt Methods nvinfer1::IPluginV2DynamicExt* GeluPluginDynamic::clone() const diff --git a/plugin/geluPlugin/geluPlugin.h b/plugin/geluPlugin/geluPlugin.h index 2f0365db..98981a0c 100644 --- a/plugin/geluPlugin/geluPlugin.h +++ b/plugin/geluPlugin/geluPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -20,8 +20,8 @@ #ifndef TRT_GELU_PLUGIN_H #define TRT_GELU_PLUGIN_H -#include "NvInferPlugin.h" #include "bertCommon.h" +#include "NvInferPlugin.h" #include #include diff --git a/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp b/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp index 3f43bd44..e164bb09 100644 --- a/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp +++ b/plugin/generateDetectionPlugin/generateDetectionPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "generateDetectionPlugin.h" #include "plugin.h" #include diff --git a/plugin/generateDetectionPlugin/generateDetectionPlugin.h b/plugin/generateDetectionPlugin/generateDetectionPlugin.h index eeab618a..cf330412 100644 --- a/plugin/generateDetectionPlugin/generateDetectionPlugin.h +++ b/plugin/generateDetectionPlugin/generateDetectionPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_GENERATE_DETECTION_PLUGIN_H #define TRT_GENERATE_DETECTION_PLUGIN_H #include diff --git a/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp b/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp index f21532da..5d6065cf 100644 --- a/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp +++ b/plugin/gridAnchorPlugin/gridAnchorPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "gridAnchorPlugin.h" #include #include diff --git a/plugin/gridAnchorPlugin/gridAnchorPlugin.h b/plugin/gridAnchorPlugin/gridAnchorPlugin.h index 38153317..eb780a96 100644 --- a/plugin/gridAnchorPlugin/gridAnchorPlugin.h +++ b/plugin/gridAnchorPlugin/gridAnchorPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_GRID_ANCHOR_PLUGIN_H #define TRT_GRID_ANCHOR_PLUGIN_H #include "cudnn.h" diff --git a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp index 379e2540..bdfc5534 100644 --- a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp +++ b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -10,7 +10,7 @@ * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions andcreate + * See the License for the specific language governing permissions and * limitations under the License. */ diff --git a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h index 87891863..4ea1933a 100644 --- a/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h +++ b/plugin/groupNormalizationPlugin/groupNormalizationPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -107,13 +107,13 @@ class GroupNormalizationPlugin final : public nvinfer1::IPluginV2DynamicExt protected: // To prevent compiler warnings. - using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; - using nvinfer1::IPluginV2DynamicExt::configurePlugin; - using nvinfer1::IPluginV2DynamicExt::enqueue; using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; - using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; using nvinfer1::IPluginV2DynamicExt::supportsFormat; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::enqueue; }; class GroupNormalizationPluginCreator : public IPluginCreator diff --git a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp index 306ed9f2..7e6931cc 100644 --- a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp +++ b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "instanceNormalizationPlugin.h" #include #include diff --git a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h index 9df8c0c4..953f7522 100644 --- a/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h +++ b/plugin/instanceNormalizationPlugin/instanceNormalizationPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_INSTANCE_NORMALIZATION_PLUGIN_H #define TRT_INSTANCE_NORMALIZATION_PLUGIN_H #include "plugin.h" diff --git a/plugin/leakyReluPlugin/lReluPlugin.cpp b/plugin/leakyReluPlugin/lReluPlugin.cpp index 3c7dbd0d..ffd8f547 100644 --- a/plugin/leakyReluPlugin/lReluPlugin.cpp +++ b/plugin/leakyReluPlugin/lReluPlugin.cpp @@ -13,14 +13,15 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "lReluPlugin.h" #include "checkMacrosPlugin.h" #include "kernel.h" using namespace nvinfer1; using nvinfer1::PluginType; -using nvinfer1::plugin::LReLU; using nvinfer1::plugin::LReluPluginCreator; +using nvinfer1::plugin::LReLU; static const char* LRELU_PLUGIN_VERSION{"1"}; static const char* LRELU_PLUGIN_NAME{"LReLU_TRT"}; diff --git a/plugin/leakyReluPlugin/lReluPlugin.h b/plugin/leakyReluPlugin/lReluPlugin.h index 76b11e98..6b87f566 100644 --- a/plugin/leakyReluPlugin/lReluPlugin.h +++ b/plugin/leakyReluPlugin/lReluPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_L_RELU_PLUGIN_H #define TRT_L_RELU_PLUGIN_H #include "NvInfer.h" @@ -32,25 +33,39 @@ class LReLU : public BasePlugin { public: LReLU(float negSlope); + LReLU(const void* buffer, size_t length); + ~LReLU() override = default; - // IPluginV2 Methods int getNbOutputs() const override; + Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override; + int initialize() override; + void terminate() override; + size_t getWorkspaceSize(int maxBatchSize) const override; + int enqueue( int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void configureWithFormat(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, DataType type, PluginFormat format, int maxBatchSize) override; + bool supportsFormat(DataType type, PluginFormat format) const override; + const char* getPluginType() const override; + const char* getPluginVersion() const override; + void destroy() override; + IPluginV2* clone() const override; private: diff --git a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp index f4673ac2..913dab6f 100644 --- a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp +++ b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "multilevelCropAndResizePlugin.h" #include "plugin.h" #include diff --git a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h index 1c3dfc26..ce4bf7d9 100644 --- a/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h +++ b/plugin/multilevelCropAndResizePlugin/multilevelCropAndResizePlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_MULTILEVEL_CROP_AND_RESIZE_PLUGIN_H #define TRT_MULTILEVEL_CROP_AND_RESIZE_PLUGIN_H diff --git a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp index e1072c1d..922bdf5e 100644 --- a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp +++ b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "multilevelProposeROIPlugin.h" #include "plugin.h" #include "tlt_mrcnn_config.h" diff --git a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h index 3212d89b..5a7062cc 100644 --- a/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h +++ b/plugin/multilevelProposeROI/multilevelProposeROIPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_MULTILEVEL_PROPOSE_ROI_PLUGIN_H #define TRT_MULTILEVEL_PROPOSE_ROI_PLUGIN_H #include diff --git a/plugin/nmsPlugin/nmsPlugin.cpp b/plugin/nmsPlugin/nmsPlugin.cpp index 19e0b754..cb98b12c 100644 --- a/plugin/nmsPlugin/nmsPlugin.cpp +++ b/plugin/nmsPlugin/nmsPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "nmsPlugin.h" #include #include @@ -215,10 +216,7 @@ void DetectionOutput::configurePlugin(const Dims* inputDims, int nbInputs, const const DataType* inputTypes, const DataType* outputTypes, const bool* inputIsBroadcast, const bool* outputIsBroadcast, PluginFormat floatFormat, int maxBatchSize) { - // Number of input dimension should be 3 ASSERT(nbInputs == 3); - - // Number of output dimension wil be 2 ASSERT(nbOutputs == 2); // Verify all the input dimensions diff --git a/plugin/nmsPlugin/nmsPlugin.h b/plugin/nmsPlugin/nmsPlugin.h index 29d0e21e..73832b41 100644 --- a/plugin/nmsPlugin/nmsPlugin.h +++ b/plugin/nmsPlugin/nmsPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_NMS_PLUGIN_H #define TRT_NMS_PLUGIN_H #include "kernel.h" @@ -114,6 +115,7 @@ class NMSPluginCreator : public BaseCreator DetectionOutputParameters params; static std::vector mPluginAttributes; }; + } // namespace plugin } // namespace nvinfer1 diff --git a/plugin/normalizePlugin/normalizePlugin.cpp b/plugin/normalizePlugin/normalizePlugin.cpp index a0d936ff..7efd32d4 100644 --- a/plugin/normalizePlugin/normalizePlugin.cpp +++ b/plugin/normalizePlugin/normalizePlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "normalizePlugin.h" #include "half.h" #include diff --git a/plugin/normalizePlugin/normalizePlugin.h b/plugin/normalizePlugin/normalizePlugin.h index 3479ad20..45a0e71b 100644 --- a/plugin/normalizePlugin/normalizePlugin.h +++ b/plugin/normalizePlugin/normalizePlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_NORMALIZE_PLUGIN_H #define TRT_NORMALIZE_PLUGIN_H #include "cudnn.h" diff --git a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp index b91ccc88..a68bcecd 100644 --- a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp +++ b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "nvFasterRCNNPlugin.h" #include #include diff --git a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h index f38873d5..91c0a4b0 100644 --- a/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h +++ b/plugin/nvFasterRCNN/nvFasterRCNNPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_NV_PLUGIN_FASTER_RCNN_H #define TRT_NV_PLUGIN_FASTER_RCNN_H diff --git a/plugin/priorBoxPlugin/priorBoxPlugin.cpp b/plugin/priorBoxPlugin/priorBoxPlugin.cpp index 23d25035..02d0a748 100644 --- a/plugin/priorBoxPlugin/priorBoxPlugin.cpp +++ b/plugin/priorBoxPlugin/priorBoxPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "priorBoxPlugin.h" #include #include @@ -36,9 +37,10 @@ PluginFieldCollection PriorBoxPluginCreator::mFC{}; std::vector PriorBoxPluginCreator::mPluginAttributes; // Constructor -PriorBox::PriorBox(PriorBoxParameters param) +PriorBox::PriorBox(PriorBoxParameters param, int H, int W) : mParam(param) - , mOwnsParamMemory(true) + , mH(H) + , mW(W) { // minSize is required and needs to be non-negative ASSERT(param.numMinSize > 0 && param.minSize != nullptr); @@ -80,11 +82,11 @@ PriorBox::PriorBox(PriorBoxParameters param) aspectRatios = copyToDevice(&tmpAR[0], tmpAR.size()); // Number of prior boxes per grid cell on the feature map // tmpAR already included an aspect ratio of 1.0 - numPriors = tmpAR.size() * param.numMinSize; + mNumPriors = tmpAR.size() * param.numMinSize; /* * If we have maxSizes, as long as all the maxSizes meets assertion requirement, we add one bounding box per maxSize * The final number of prior boxes per grid cell on feature map - * numPriors = + * mNumPriors = * tmpAR.size() * param.numMinSize If numMaxSize == 0 * (tmpAR.size() + 1) * param.numMinSize If param.numMinSize == param.numMaxSize */ @@ -95,29 +97,13 @@ PriorBox::PriorBox(PriorBoxParameters param) { // maxSize should be greater than minSize ASSERT(param.maxSize[i] > param.minSize[i] && "maxSize must be greater than minSize"); - numPriors++; + mNumPriors++; } maxSize = copyToDevice(param.maxSize, param.numMaxSize); } } -// Constructor used in `clone()`. -// This constructor does not modify parameters, unlike the previous one. -PriorBox::PriorBox( - PriorBoxParameters param, int numPriors, int H, int W, Weights minSize, Weights maxSize, Weights aspectRatios) - : mParam(param) - , mOwnsParamMemory(false) - , numPriors(numPriors) - , H(H) - , W(W) - , minSize(minSize) - , maxSize(maxSize) - , aspectRatios(aspectRatios) -{ -} - PriorBox::PriorBox(const void* data, size_t length) - : mOwnsParamMemory(true) { const char *d = reinterpret_cast(data), *a = d; mParam = read(d); @@ -125,9 +111,9 @@ PriorBox::PriorBox(const void* data, size_t length) mParam.maxSize = new float[mParam.numMaxSize]; mParam.aspectRatios = new float[mParam.numAspectRatios]; - numPriors = read(d); - H = read(d); - W = read(d); + mNumPriors = read(d); + mH = read(d); + mW = read(d); for (auto i = 0; i < mParam.numMinSize; i++) { mParam.minSize[i] = reinterpret_cast(d)[i]; @@ -167,11 +153,11 @@ Dims PriorBox::getOutputDimensions(int index, const Dims* inputs, int nbInputDim // Only one output from the plugin layer ASSERT(index == 0); // Particularity of the PriorBox layer: no batchSize dimension needed - H = inputs[0].d[1], W = inputs[0].d[2]; + mH = inputs[0].d[1], mW = inputs[0].d[2]; // workaround for TRT // The first channel is for prior box coordinates. // The second channel is for prior box scaling factors, which is simply a copy of the variance provided. - return DimsCHW(2, H * W * numPriors * 4, 1); + return DimsCHW(2, mH * mW * mNumPriors * 4, 1); } int PriorBox::initialize() @@ -179,25 +165,6 @@ int PriorBox::initialize() return STATUS_SUCCESS; } -void PriorBox::terminate() -{ - if (mOwnsParamMemory) - { - CUASSERT(cudaFree(const_cast(minSize.values))); - if (mParam.numMaxSize > 0) - { - CUASSERT(cudaFree(const_cast(maxSize.values))); - } - if (mParam.numAspectRatios > 0) - { - CUASSERT(cudaFree(const_cast(aspectRatios.values))); - } - delete[] mParam.minSize; - delete[] mParam.maxSize; - delete[] mParam.aspectRatios; - } -} - size_t PriorBox::getWorkspaceSize(int /*maxBatchSize*/) const { return 0; @@ -207,7 +174,7 @@ int PriorBox::enqueue( int /*batchSize*/, const void* const* /*inputs*/, void** outputs, void* /*workspace*/, cudaStream_t stream) { void* outputData = outputs[0]; - pluginStatus_t status = priorBoxInference(stream, mParam, H, W, numPriors, aspectRatios.count, minSize.values, + pluginStatus_t status = priorBoxInference(stream, mParam, mH, mW, mNumPriors, aspectRatios.count, minSize.values, maxSize.values, aspectRatios.values, outputData); ASSERT(status == STATUS_SUCCESS); @@ -217,7 +184,7 @@ int PriorBox::enqueue( // Returns the size of serialized parameters size_t PriorBox::getSerializationSize() const { - // PriorBoxParameters, numPriors,H,W, minSize, maxSize, numAspectRatios, aspectRatios + // PriorBoxParameters, mNumPriors,mH,mW, minSize, maxSize, numAspectRatios, aspectRatios return sizeof(PriorBoxParameters) + sizeof(int) * 3 + sizeof(float) * (mParam.numMinSize + mParam.numMaxSize) + sizeof(int) + sizeof(float) * aspectRatios.count; } @@ -226,9 +193,9 @@ void PriorBox::serialize(void* buffer) const { char *d = reinterpret_cast(buffer), *a = d; write(d, mParam); - write(d, numPriors); - write(d, H); - write(d, W); + write(d, mNumPriors); + write(d, mH); + write(d, mW); serializeFromDevice(d, minSize); if (mParam.numMaxSize > 0) { @@ -276,14 +243,40 @@ const char* PriorBox::getPluginVersion() const void PriorBox::destroy() { + CUASSERT(cudaFree(const_cast(minSize.values))); + if (mParam.numMaxSize > 0) + { + CUASSERT(cudaFree(const_cast(maxSize.values))); + } + if (mParam.numAspectRatios > 0) + { + CUASSERT(cudaFree(const_cast(aspectRatios.values))); + } + delete[] mParam.minSize; + delete[] mParam.maxSize; + delete[] mParam.aspectRatios; + delete this; } IPluginV2Ext* PriorBox::clone() const { - IPluginV2Ext* plugin = new PriorBox(mParam, numPriors, H, W, minSize, maxSize, aspectRatios); - plugin->setPluginNamespace(mPluginNamespace.c_str()); - return plugin; + // each obj should manage its copy of param + PriorBoxParameters params = mParam; + auto copyParamData = [](float*& dest, const float* src, const size_t size) { + if (size > 0) + { + dest = new float[size]; + memcpy(dest, src, size * sizeof(float)); + } + }; + copyParamData(params.minSize, mParam.minSize, mParam.numMinSize); + copyParamData(params.maxSize, mParam.maxSize, mParam.numMaxSize); + copyParamData(params.aspectRatios, mParam.aspectRatios, mParam.numAspectRatios); + + PriorBox* obj = new PriorBox(params, mH, mW); + obj->setPluginNamespace(mPluginNamespace.c_str()); + return obj; } // Set plugin namespace @@ -328,8 +321,8 @@ void PriorBox::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* ASSERT(inputDims[0].nbDims == 3); ASSERT(inputDims[1].nbDims == 3); ASSERT(outputDims[0].nbDims == 3); - H = inputDims[0].d[1]; - W = inputDims[0].d[2]; + mH = inputDims[0].d[1]; + mW = inputDims[0].d[2]; // prepare for the inference function if (mParam.imgH == 0 || mParam.imgW == 0) { @@ -338,8 +331,8 @@ void PriorBox::configurePlugin(const Dims* inputDims, int nbInputs, const Dims* } if (mParam.stepH == 0 || mParam.stepW == 0) { - mParam.stepH = static_cast(mParam.imgH) / H; - mParam.stepW = static_cast(mParam.imgW) / W; + mParam.stepH = static_cast(mParam.imgH) / mH; + mParam.stepW = static_cast(mParam.imgW) / mW; } } diff --git a/plugin/priorBoxPlugin/priorBoxPlugin.h b/plugin/priorBoxPlugin/priorBoxPlugin.h index e02cbf7c..136f0588 100644 --- a/plugin/priorBoxPlugin/priorBoxPlugin.h +++ b/plugin/priorBoxPlugin/priorBoxPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_PRIOR_BOX_PLUGIN_H #define TRT_PRIOR_BOX_PLUGIN_H #include "cudnn.h" @@ -31,10 +32,7 @@ namespace plugin class PriorBox : public IPluginV2Ext { public: - PriorBox(PriorBoxParameters param); - - PriorBox( - PriorBoxParameters param, int numPriors, int H, int W, Weights minSize, Weights maxSize, Weights aspectRatios); + PriorBox(PriorBoxParameters param, int H = 0, int W = 0); PriorBox(const void* buffer, size_t length); @@ -46,7 +44,7 @@ class PriorBox : public IPluginV2Ext int initialize() override; - void terminate() override; + void terminate() override{}; size_t getWorkspaceSize(int maxBatchSize) const override; @@ -92,8 +90,7 @@ class PriorBox : public IPluginV2Ext Weights deserializeToDevice(const char*& hostBuffer, size_t count); PriorBoxParameters mParam; - bool mOwnsParamMemory; - int numPriors, H, W; + int mNumPriors, mH, mW; Weights minSize, maxSize, aspectRatios; // not learnable weights std::string mPluginNamespace; }; diff --git a/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp b/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp index 57db68c8..0d5f547a 100644 --- a/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp +++ b/plugin/proposalLayerPlugin/proposalLayerPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "proposalLayerPlugin.h" #include "mrcnn_config.h" #include "plugin.h" diff --git a/plugin/proposalLayerPlugin/proposalLayerPlugin.h b/plugin/proposalLayerPlugin/proposalLayerPlugin.h index df0faaed..5cc99b82 100644 --- a/plugin/proposalLayerPlugin/proposalLayerPlugin.h +++ b/plugin/proposalLayerPlugin/proposalLayerPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_PROPOSAL_LAYER_PLUGIN_H #define TRT_PROPOSAL_LAYER_PLUGIN_H #include @@ -134,7 +135,6 @@ class ProposalLayerPluginCreator : public BaseCreator static PluginFieldCollection mFC; int mPreNMSTopK; int mKeepTopK; - float mScoreThreshold; float mIOUThreshold; static std::vector mPluginAttributes; }; diff --git a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp index 544581c6..a11b052b 100644 --- a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp +++ b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "pyramidROIAlignPlugin.h" #include "plugin.h" #include diff --git a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h index 8034ffda..e774d055 100644 --- a/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h +++ b/plugin/pyramidROIAlignPlugin/pyramidROIAlignPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_PYRAMID_ROIALIGN_PLUGIN_H #define TRT_PYRAMID_ROIALIGN_PLUGIN_H diff --git a/plugin/regionPlugin/regionPlugin.cpp b/plugin/regionPlugin/regionPlugin.cpp index 62817c5c..5f65b98e 100644 --- a/plugin/regionPlugin/regionPlugin.cpp +++ b/plugin/regionPlugin/regionPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "regionPlugin.h" #include diff --git a/plugin/regionPlugin/regionPlugin.h b/plugin/regionPlugin/regionPlugin.h index 91790640..2906b4da 100644 --- a/plugin/regionPlugin/regionPlugin.h +++ b/plugin/regionPlugin/regionPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_REGION_PLUGIN_H #define TRT_REGION_PLUGIN_H #include "kernel.h" diff --git a/plugin/reorgPlugin/reorgPlugin.cpp b/plugin/reorgPlugin/reorgPlugin.cpp index 3951824c..090fc7d1 100644 --- a/plugin/reorgPlugin/reorgPlugin.cpp +++ b/plugin/reorgPlugin/reorgPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "reorgPlugin.h" using namespace nvinfer1; diff --git a/plugin/reorgPlugin/reorgPlugin.h b/plugin/reorgPlugin/reorgPlugin.h index 5d5c2c5b..1b5eeaad 100644 --- a/plugin/reorgPlugin/reorgPlugin.h +++ b/plugin/reorgPlugin/reorgPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_REORG_PLUGIN_H #define TRT_REORG_PLUGIN_H #include "kernel.h" diff --git a/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp b/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp index c1295ead..e92ac4b0 100644 --- a/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp +++ b/plugin/resizeNearestPlugin/resizeNearestPlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "resizeNearestPlugin.h" #include "plugin.h" #include diff --git a/plugin/resizeNearestPlugin/resizeNearestPlugin.h b/plugin/resizeNearestPlugin/resizeNearestPlugin.h index a34bf7d9..5a05ad79 100644 --- a/plugin/resizeNearestPlugin/resizeNearestPlugin.h +++ b/plugin/resizeNearestPlugin/resizeNearestPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_RESIZENEAREST_PLUGIN_H #define TRT_RESIZENEAREST_PLUGIN_H diff --git a/plugin/skipLayerNormPlugin/README.md b/plugin/skipLayerNormPlugin/README.md index dca99d8f..54b02938 100644 --- a/plugin/skipLayerNormPlugin/README.md +++ b/plugin/skipLayerNormPlugin/README.md @@ -40,13 +40,13 @@ output is a tensor with shape `[S, B, E]` where `B` is the batch size. The parameters are defined below and consists of the following attributes: -| Type | Parameter | Description -|----------|-----------------------------------------|------------------------------------------------------------------- -|`int` |`type_id` |Integer encoding the DataType (0: FP32, 1: FP16) -|`int` |`ld` |The leading dimension of the input tensor, corresponding to the hidden size, denoted by `E` above. -|`Weights` |`beta` |The mean to normalize to. Shape: `[1, 1, E]` -|`Weights` |`gamma` |The standard deviation to normalize to. Shape: `[1, 1, E]` -|`Weights` |`bias` |An optional bias vector to add before normalization. Shape: `[1, 1, E]` +| Type | Parameter | Version | Description +|----------|-----------------------------------------|------------|------------------------------------------------------------------- +|`int` |`type_id` | 1, 2 |Integer encoding the DataType (0: FP32, 1: FP16) +|`int` |`ld` | 2, |The leading dimension of the input tensor, corresponding to the hidden size, denoted by `E` above. +|`Weights` |`beta` | 1, 2, 3 |The mean to normalize to. Shape: `[1, 1, E]` +|`Weights` |`gamma` | 1, 2, 3 |The standard deviation to normalize to. Shape: `[1, 1, E]` +|`Weights` |`bias` | 1, 2 |An optional bias vector to add before normalization. Shape: `[1, 1, E]` ## Additional resources @@ -62,9 +62,12 @@ documentation. ## Changelog -November 2019 -This is the first release of this `README.md` file. +October 2020 +Add V2 plugin that supports variable sequence length. +Add v3 plugin that supports int8 interleaved variable sequence length. +November 2019 +This is the first release of this `README.md` file. ## Known issues diff --git a/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedKernel.cu b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedKernel.cu new file mode 100644 index 00000000..7e0a5a62 --- /dev/null +++ b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedKernel.cu @@ -0,0 +1,372 @@ + +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include "NvInfer.h" +#include "bertCommon.h" +#include +#include "common.cuh" +#include +#include +#include + + +using namespace nvinfer1; + +namespace bert +{ + + +inline __device__ char quantize(const float x, const float qScale) +{ + int tmpq = __float2int_rn(qScale * x); // scale and round + char tmpq8 = min(127, max(-127, tmpq)); // clip and cast + return tmpq8; +} + +inline __device__ void ldg(const int8_t* input, uint4& data) +{ + data = *reinterpret_cast(input); +} + +inline __device__ void stg(int8_t* output, uint4& data) +{ + *reinterpret_cast(output) = data; +} + +inline __device__ void res_add( + float (&hdata)[4], const uint32_t idata, const uint32_t ires, const float dqData, const float dqRes) +{ + char4 ires4 = reinterpret_cast(ires); + char4 idata4 = reinterpret_cast(idata); + hdata[0] = float(idata4.x) * dqData + float(ires4.x) * dqRes; + hdata[1] = float(idata4.y) * dqData + float(ires4.y) * dqRes; + hdata[2] = float(idata4.z) * dqData + float(ires4.z) * dqRes; + hdata[3] = float(idata4.w) * dqData + float(ires4.w) * dqRes; +} + +inline __device__ uint32_t pack4(const float (&hdata)[4], const float qScale) +{ + char4 ret; + ret.x = quantize(hdata[0], qScale); + ret.y = quantize(hdata[1], qScale); + ret.z = quantize(hdata[2], qScale); + ret.w = quantize(hdata[3], qScale); + return reinterpret_cast(ret); +} + +template +__global__ void skipln_vec32(const int8_t* input, const int8_t* skip, int8_t* output, const half* beta, + const half* gamma, const float dqScaleIn, const float dqScaleSkip, const float qScale, const int total) +{ + + // clang-format off + enum { HEAD_SIZE = 64 }; + enum { BYTES_PER_LDG = 16 }; + enum { THREADS_PER_CTA = WARPS * 32 }; + enum { ROWS_PER_LDG = THREADS_PER_CTA / THREADS_PER_ROW }; + enum { VECS_PER_CTA = THREADS_PER_ROW / 2 }; + enum { PARAM_BYTES = HEADS * HEAD_SIZE * 2 }; + enum { PARAM_LDGS = PARAM_BYTES / (THREADS_PER_CTA * BYTES_PER_LDG) }; + enum { LDGS = HEADS * 2 / ROWS_PER_LDG }; + // clang-format on + static_assert(VECS_PER_CTA == 4, ""); + static_assert(PARAM_LDGS == 1, ""); + static_assert(ROWS_PER_LDG == HEADS , ""); + static_assert(LDGS == 2, ""); + static_assert(LDGS * ROWS_PER_LDG == HEADS * 2, ""); + static_assert(THREADS_PER_CTA * BYTES_PER_LDG == PARAM_BYTES, ""); + static_assert(PARAM_LDGS == 1, ""); + + extern __shared__ char smem_[]; + + // space for CTA-wide reduction + __shared__ half2 smem_red[VECS_PER_CTA][WARPS]; + + constexpr float rld = 1.f / (float(HEADS) * float(HEAD_SIZE)); + const int bidx = blockIdx.x; + const int tidx = threadIdx.x; + const int row = tidx / THREADS_PER_ROW; + const int col = tidx % THREADS_PER_ROW; + const int lane = tidx % 32; + const int warp = tidx / 32; + + const bool is_warp_lead = (lane < THREADS_PER_ROW) && ((lane & 1) == 0); + const bool is_cta_lead = (tidx < THREADS_PER_ROW) && ((tidx & 1) == 0); + + // token position: every two threads load together the 32B at one token + // position + const int pos = col / 2; + + const int pos_offset = bidx * VECS_PER_CTA + pos; // for token positions per block, disabling 2 threads per pos + const bool my_pred = pos_offset < total; + const int row_stride_bytes = total * 32; + + uint4 in_data[LDGS]; + uint4 in_skip[LDGS]; + float hdata[LDGS * 4][4]; + const int gmem_offset = row * row_stride_bytes + (bidx * THREADS_PER_ROW + col) * BYTES_PER_LDG; +#pragma unroll + for (int ii = 0; ii < LDGS; ii++) + { + in_data[ii] = {0, 0, 0, 0}; + in_skip[ii] = {0, 0, 0, 0}; + if (my_pred) + { + ldg(input + gmem_offset + ii * ROWS_PER_LDG * row_stride_bytes, in_data[ii]); + ldg(skip + gmem_offset + ii * ROWS_PER_LDG * row_stride_bytes, in_skip[ii]); + } + } + + uint4* smem_b = reinterpret_cast(&smem_[0]) + tidx; + uint4* smem_g = reinterpret_cast(&smem_[PARAM_BYTES]) + tidx; + + const int8_t* beta_ptr = reinterpret_cast(beta) + tidx * BYTES_PER_LDG; + const int8_t* gamma_ptr = reinterpret_cast(gamma) + tidx * BYTES_PER_LDG; + ldg(beta_ptr, *smem_b); + ldg(gamma_ptr, *smem_g); + + half* b = reinterpret_cast(&smem_[0]); + half* g = reinterpret_cast(&smem_[PARAM_BYTES]); +#pragma unroll + for (int ii = 0; ii < LDGS; ii++) + { + res_add(hdata[ii * 4 + 0], in_data[ii].x, in_skip[ii].x, dqScaleIn, dqScaleSkip); + res_add(hdata[ii * 4 + 1], in_data[ii].y, in_skip[ii].y, dqScaleIn, dqScaleSkip); + res_add(hdata[ii * 4 + 2], in_data[ii].z, in_skip[ii].z, dqScaleIn, dqScaleSkip); + res_add(hdata[ii * 4 + 3], in_data[ii].w, in_skip[ii].w, dqScaleIn, dqScaleSkip); + } + + half2 stats_local = {0, 0}; + +#pragma unroll + for (int ii = 0; ii < LDGS * 4; ii++) + { +#pragma unroll + for (int jj = 0; jj < 4; jj++) + { + const float tmp = hdata[ii][jj] * (rld); + stats_local = stats_local + __floats2half2_rn(tmp, tmp * hdata[ii][jj]); + } + } + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 1); __syncwarp(); + + if (VECS_PER_CTA == 1) + { + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 2); __syncwarp(); + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 4); __syncwarp(); + } + else if (VECS_PER_CTA == 2) + { + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 4); __syncwarp(); + } + + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 8); __syncwarp(); + stats_local = stats_local + __shfl_xor_sync(uint32_t(-1), stats_local, 16); __syncwarp(); + + if (is_warp_lead) + { + smem_red[pos][warp] = stats_local; + } + + __syncthreads(); + + if (is_cta_lead) + { + for (int ii = 1; ii < WARPS; ii++) + { + stats_local = stats_local + smem_red[pos][ii]; + } + + float mu = __low2float(stats_local); + float sos = __high2float(stats_local); + float rsigma = rsqrtf(sos - mu * mu); + + smem_red[pos][0] = __floats2half2_rn(mu, rsigma); + } + __syncthreads(); + // load params into smem: 2x Headsx32x2x2B + const float2 statsf = __half22float2(smem_red[pos][0]); + +#pragma unroll + for (int ii = 0; ii < LDGS; ii++) + { +#pragma unroll + for (int jj = 0; jj < 4; jj++) + { +#pragma unroll + for (int kk = 0; kk < 4; kk++) + { + const int param_idx = (ii * ROWS_PER_LDG + row) * 32 + (jj * 4 + kk) + (tidx & 1) * 16; + const float bb = b[param_idx]; + const float gg = g[param_idx]; + hdata[ii * 4 + jj][kk] = gg * statsf.y * (hdata[ii * 4 + jj][kk] - statsf.x) + bb; + } + } + } + + +#pragma unroll + for (int ii = 0; ii < LDGS; ii++) + { + in_data[ii].x = pack4(hdata[ii * 4 + 0], qScale); + in_data[ii].y = pack4(hdata[ii * 4 + 1], qScale); + in_data[ii].z = pack4(hdata[ii * 4 + 2], qScale); + in_data[ii].w = pack4(hdata[ii * 4 + 3], qScale); + } + +#pragma unroll + for (int ii = 0; ii < LDGS; ii++) + { + if (my_pred) + { + stg(output + gmem_offset + ii * ROWS_PER_LDG * row_stride_bytes, in_data[ii]); + } + } + // store +} + +void launch_large(cudaStream_t stream, const int ld, const int total, const int8_t* input, const int8_t* skip, + const half* beta, const half* gamma, int8_t* output, const float dqScaleIn, const float dqScaleSkip, + const float qScale) +{ + if (ld == 1024) + { + constexpr int WARPS = 4; + constexpr int THREADS_PER_ROW = 8; + constexpr int HEADS = 16; + constexpr int PARAM_BYTES = HEADS * 64 * 2 * sizeof(half); + constexpr int VECS_PER_CTA = THREADS_PER_ROW / 2; + const int blocks = (total + VECS_PER_CTA - 1) / VECS_PER_CTA; + + skipln_vec32<<>>( + input, skip, output, beta, gamma, dqScaleIn, dqScaleSkip, qScale, total); + } + else if (ld == 768) + { + constexpr int WARPS = 3; + constexpr int THREADS_PER_ROW = 8; + constexpr int HEADS = 12; + constexpr int PARAM_BYTES = HEADS * 64 * 2 * sizeof(half); + constexpr int VECS_PER_CTA = THREADS_PER_ROW / 2; + const int blocks = (total + VECS_PER_CTA - 1) / VECS_PER_CTA; + + skipln_vec32<<>>( + input, skip, output, beta, gamma, dqScaleIn, dqScaleSkip, qScale, total); + } + else + { + ASSERT(false); + } +} + +// naive kernel that only changes the addressing seems to be faster for small problem sizes +template +__global__ void skiplnDQQ_vec(const int ld, const int8_t* input, const int8_t* skip, int8_t* output, const half* beta, + const half* gamma, const float dqScaleIn, const float dqScaleSkip, const float qScale, const int total) +{ + const int hinner = threadIdx.x % 4; + const int houter = threadIdx.x / 4; + + const int tidx = threadIdx.x; + const int bidx = blockIdx.x; + const int idx = houter * total * 32 + bidx * 32 + hinner * VPT; + // 4 * 1024 * 4 * 2 Bytes = 16KB per block + int8_t in_local[VPT]; + int8_t skip_local[VPT]; + + half in_local_dq[VPT]; // dequantized input + skip + half beta_local[VPT]; + half gamma_local[VPT]; + + // load input tensors + copy(&input[idx], in_local); + copy(&skip[idx], skip_local); + + // load parameters + copy(&beta[tidx * VPT], beta_local); + copy(&gamma[tidx * VPT], gamma_local); + + half2 stats_local = __floats2half2_rn(0.f, 0.f); // accumulator + + const half rld = half(1.f) / half(ld); +#pragma unroll + for (int it = 0; it < VPT; it++) + { + // DQ input and skip + const float tmp_in = in_local[it]; + const float tmp_skip = skip_local[it]; + in_local_dq[it] = dqScaleIn * tmp_in + dqScaleSkip * tmp_skip; + + const half tmp = rld * in_local_dq[it]; + const half2 tmp2 = __halves2half2(tmp, tmp * in_local_dq[it]); + stats_local = stats_local + tmp2; + } + + using BlockReduce = cub::BlockReduce; + __shared__ typename BlockReduce::TempStorage temp_storage; + __shared__ half mu; // mean + __shared__ half rsigma; // 1 / std.dev. + + const half2 sum2 = BlockReduce(temp_storage).Reduce(stats_local, cub::Sum()); + + if (tidx == 0) + { + mu = __low2half(sum2); + rsigma = rsqrtf(__high2half(sum2) - mu * mu); + } + + __syncthreads(); + +#pragma unroll + for (int it = 0; it < VPT; it++) + { + const float tmp = gamma_local[it] * (in_local_dq[it] - mu) * rsigma + beta_local[it]; + in_local[it] = quantize(tmp, qScale); + } + + copy(in_local, &output[idx]); +} + +void launch_small(cudaStream_t stream, const int ld, const int total, const int8_t* input, const int8_t* skip, + const half* beta, const half* gamma, int8_t* output, const float dqScaleIn, const float dqScaleSkip, + const float qScale) +{ + const int gridSize = total; + // we align reads with the number of parameters, i.e. 8-wide instead of 16 + constexpr int VPT = 16 / sizeof(half); // 8 + if (ld == 768) + { + constexpr int TPB = 768 / VPT; + skiplnDQQ_vec + <<>>(ld, input, skip, output, beta, gamma, dqScaleIn, dqScaleSkip, qScale, total); + } + else if (ld == 1024) + { + constexpr int TPB = 1024 / VPT; // 128 + skiplnDQQ_vec + <<>>(ld, input, skip, output, beta, gamma, dqScaleIn, dqScaleSkip, qScale, total); + } + else + { + std::cout << "SkipLayerNormDQQ - FATAL: unsupported hidden layer size: " << ld << std::endl; + exit(0); + } + CHECK(cudaPeekAtLastError()); +} + +} // namespace bert + diff --git a/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.cpp b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.cpp new file mode 100644 index 00000000..d6fa8af7 --- /dev/null +++ b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.cpp @@ -0,0 +1,349 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include "NvInfer.h" +#include "serialize.hpp" +#include "skipLayerNormInt8InterleavedPlugin.h" + +#include +#include + +using namespace nvinfer1; + +namespace bert +{ + +void launch_small(cudaStream_t stream, const int ld, const int total, const int8_t* input, const int8_t* skip, + const half* beta, const half* gamma, int8_t* output, const float dqScaleIn, const float dqScaleSkip, + const float qScale); + +void launch_large(cudaStream_t stream, const int ld, const int total, const int8_t* input, const int8_t* skip, + const half* beta, const half* gamma, int8_t* output, const float dqScaleIn, const float dqScaleSkip, + const float qScale); + +// Clip plugin specific constants +namespace +{ +static const char* SKIP_LAYER_NORM_INTERLEAVED_VERSION{"3"}; +static const char* SKIP_LAYER_NORM_INTERLEAVED_NAME{"CustomSkipLayerNormPluginDynamic"}; +} // namespace + +// Static class fields initialization +PluginFieldCollection SkipLayerNormInterleavedPluginCreator::mFC{}; +std::vector SkipLayerNormInterleavedPluginCreator::mPluginAttributes; + +REGISTER_TENSORRT_PLUGIN(SkipLayerNormInterleavedPluginCreator); + +constexpr auto param_type = DataType::kHALF; + +static inline DataType getParamWordType(DataType cfgType) +{ + if (cfgType == DataType::kINT8) + { + return DataType::kHALF; + } + + return cfgType; +} + +SkipLayerNormInterleavedPlugin::SkipLayerNormInterleavedPlugin( + const std::string name, const Weights& beta, const Weights& gamma) + : mLayerName(name) + , mGammaDev(nullptr) + , mBetaDev(nullptr) + , mLd(beta.count) + , mParamsOnDevice(false) +{ + ASSERT(mLd > 0); + ASSERT(beta.count == gamma.count); + // dataType for beta, gamma weights is always fp16 + + mParamWordsize = getElementSize(param_type); + + mBeta.convertAndCopy(beta, param_type); + mGamma.convertAndCopy(gamma, param_type); +} + +SkipLayerNormInterleavedPlugin::SkipLayerNormInterleavedPlugin(const std::string name, const void* data, size_t length) + : mLayerName(name) + , mGammaDev(nullptr) + , mBetaDev(nullptr) + , mParamsOnDevice(false) +{ + gLogVerbose << "SkipLayerNormInterleavedPlugin deserialize\n"; + + // Deserialize in the same order as serialization + deserialize_value(&data, &length, &mLd); + + mParamWordsize = getElementSize(param_type); + + const char* d = static_cast(data); + mBeta.convertAndCopy(d, mLd, param_type); + mGamma.convertAndCopy(d, mLd, param_type); +} + +// IPluginV2DynamicExt Methods +IPluginV2DynamicExt* SkipLayerNormInterleavedPlugin::clone() const +{ + gLogVerbose << "SkipLayerNormInterleavedPlugin clone\n"; + + auto p = new SkipLayerNormInterleavedPlugin(mLayerName, mBeta, mGamma); + p->initialize(); + p->setPluginNamespace(mNamespace.c_str()); + return p; +} + +DimsExprs SkipLayerNormInterleavedPlugin::getOutputDimensions( + int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) +{ + ASSERT(nbInputs == 2); + ASSERT(outputIndex == 0); + ASSERT(inputs[0].nbDims == inputs[1].nbDims); + return inputs[0]; +} + +bool SkipLayerNormInterleavedPlugin::supportsFormatCombination( + int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) +{ + ASSERT(nbInputs == 2); + ASSERT(nbOutputs == 1); + + const PluginTensorDesc& desc = inOut[pos]; + return desc.type == DataType::kINT8 && desc.format == TensorFormat::kCHW32; +} + +void SkipLayerNormInterleavedPlugin::configurePlugin( + const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs) +{ + // Validate input arguments + ASSERT(nbOutputs == 1); + ASSERT(nbInputs == 2); + ASSERT(DataType::kINT8 == inputs[0].desc.type); + ASSERT(DataType::kINT8 == inputs[1].desc.type); + + const auto& inDims0 = inputs[0].desc.dims; + const auto& inDims1 = inputs[1].desc.dims; + TRT_UNUSED inDims1; + + ASSERT(inDims0.nbDims == inDims1.nbDims); + ASSERT(std::equal(inDims0.d, inDims0.d + inDims0.nbDims, inDims1.d)); + + mParamWordsize = getElementSize(param_type); + + if (!mParamsOnDevice) + { + copyToDevice(mGamma, getWeightsSize(mGamma, param_type), mGammaDev); + copyToDevice(mBeta, getWeightsSize(mBeta, param_type), mBetaDev); + mParamsOnDevice = true; + } +} + +size_t SkipLayerNormInterleavedPlugin::getWorkspaceSize( + const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const +{ + return 0; +} + +int SkipLayerNormInterleavedPlugin::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) +{ + //Input shape: 1x(hxd)xtotalx1 + const auto iDesc = inputDesc[0]; + const auto sDesc = inputDesc[1]; + const auto oDesc = outputDesc[0]; + ASSERT(iDesc.dims.nbDims == 4); + ASSERT(iDesc.dims.nbDims == sDesc.dims.nbDims); + ASSERT(std::equal(iDesc.dims.d, iDesc.dims.d + iDesc.dims.nbDims, sDesc.dims.d)); + ASSERT(std::equal(iDesc.dims.d, iDesc.dims.d + iDesc.dims.nbDims, oDesc.dims.d)); + ASSERT(iDesc.dims.d[0] == 1); + ASSERT(iDesc.dims.d[3] == 1); + ASSERT(iDesc.format == TensorFormat::kCHW32); + ASSERT(iDesc.type == DataType::kINT8); + ASSERT(iDesc.format == sDesc.format); + ASSERT(iDesc.format == oDesc.format); + ASSERT(iDesc.type == sDesc.type); + ASSERT(iDesc.type == oDesc.type); + const int ld = iDesc.dims.d[1]; + const int total = iDesc.dims.d[2]; + const float dqScaleIn = iDesc.scale; + const float dqScaleSkip = sDesc.scale; + const float qScale = 1.f / oDesc.scale; + const int8_t* input = static_cast(inputs[0]); + const int8_t* skip = static_cast(inputs[1]); + int8_t* output = static_cast(outputs[0]); + const half* gamma = static_cast(mGammaDev.get()); + const half* beta = static_cast(mBetaDev.get()); + + if(total < 4096){ + launch_small(stream, ld, total, input, skip, beta, gamma, output, dqScaleIn, dqScaleSkip, qScale); + }else{ + launch_large(stream, ld, total, input, skip, beta, gamma, output, dqScaleIn, dqScaleSkip, qScale); + } + + return 0; +} + +// IPluginV2Ext Methods +DataType SkipLayerNormInterleavedPlugin::getOutputDataType(int index, const DataType* inputTypes, int nbInputs) const +{ + ASSERT(index == 0); + ASSERT(nbInputs == 2); + return inputTypes[0]; +} + +// IPluginV2 Methods +const char* SkipLayerNormInterleavedPlugin::getPluginType() const +{ + return SKIP_LAYER_NORM_INTERLEAVED_NAME; +} + +const char* SkipLayerNormInterleavedPlugin::getPluginVersion() const +{ + return SKIP_LAYER_NORM_INTERLEAVED_VERSION; +} + +int SkipLayerNormInterleavedPlugin::getNbOutputs() const +{ + return 1; +} +int SkipLayerNormInterleavedPlugin::initialize() +{ + gLogVerbose << "SkipLayerNormInterleavedPlugin initialize\n"; + return 0; +} + +void SkipLayerNormInterleavedPlugin::terminate() +{ + gLogVerbose << "SkipLayerNormInterleavedPlugin terminate\n"; +} + +size_t SkipLayerNormInterleavedPlugin::getSerializationSize() const +{ + return 2 * mParamWordsize * mLd + sizeof(mLd); +} + +void SkipLayerNormInterleavedPlugin::serialize(void* buffer) const +{ + serialize_value(&buffer, mLd); + + char* d = static_cast(buffer); + serFromDev(d, static_cast(mBetaDev.get()), mLd * mParamWordsize); + serFromDev(d, static_cast(mGammaDev.get()), mLd * mParamWordsize); +} + +void SkipLayerNormInterleavedPlugin::destroy() +{ + gLogVerbose << "SkipLayerNormInterleavedPlugin destroy\n"; + // This gets called when the network containing plugin is destroyed + mGammaDev.release(); + mBetaDev.release(); + delete this; +} + +void SkipLayerNormInterleavedPlugin::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* SkipLayerNormInterleavedPlugin::getPluginNamespace() const +{ + return mNamespace.c_str(); +} + +///////////////////////////////////////////////////////// + +SkipLayerNormInterleavedPluginCreator::SkipLayerNormInterleavedPluginCreator() +{ + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); +} + +const char* SkipLayerNormInterleavedPluginCreator::getPluginName() const +{ + return SKIP_LAYER_NORM_INTERLEAVED_NAME; +} + +const char* SkipLayerNormInterleavedPluginCreator::getPluginVersion() const +{ + return SKIP_LAYER_NORM_INTERLEAVED_VERSION; +} + +const PluginFieldCollection* SkipLayerNormInterleavedPluginCreator::getFieldNames() +{ + return &mFC; +} + +IPluginV2* SkipLayerNormInterleavedPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) +{ + gLogVerbose << "SkipLayerNormInterleavedPluginCreator createPlugin\n"; + + Weights beta{DataType::kFLOAT, nullptr, 0}; + Weights gamma{DataType::kFLOAT, nullptr, 0}; + + for (int i = 0; i < fc->nbFields; i++) + { + std::string field_name(fc->fields[i].name); + + if (field_name.compare("beta") == 0) + { + gLogVerbose << "Building beta...\n"; + beta.values = fc->fields[i].data; + beta.count = fc->fields[i].length; + beta.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("gamma") == 0) + { + gLogVerbose << "Building gamma...\n"; + gamma.values = fc->fields[i].data; + gamma.count = fc->fields[i].length; + gamma.type = fieldTypeToDataType(fc->fields[i].type); + } + } + + if (beta.count <= 0 || beta.values == nullptr) + { + gLogError << "SkipLayerNorm: invalid beta" << std::endl; + } + + if (gamma.count <= 0 || gamma.values == nullptr) + { + gLogError << "SkipLayerNorm: invalid gamma" << std::endl; + } + + return new SkipLayerNormInterleavedPlugin(name, beta, gamma); +} + +IPluginV2* SkipLayerNormInterleavedPluginCreator::deserializePlugin( + const char* name, const void* serialData, size_t serialLength) +{ + // This object will be deleted when the network is destroyed, which will + // call SkipLayerNormInterleavedPlugin::destroy() + return new SkipLayerNormInterleavedPlugin(name, serialData, serialLength); +} + +void SkipLayerNormInterleavedPluginCreator::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* SkipLayerNormInterleavedPluginCreator::getPluginNamespace() const +{ + return mNamespace.c_str(); +} +} // namespace bert + diff --git a/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.h b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.h new file mode 100644 index 00000000..4a723a49 --- /dev/null +++ b/plugin/skipLayerNormPlugin/skipLayerNormInt8InterleavedPlugin.h @@ -0,0 +1,119 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_SKIP_LAYER_NORM_INTERLEAVED_PLUGIN_H +#define TRT_SKIP_LAYER_NORM_INTERLEAVED_PLUGIN_H +#include +#include "NvInferPlugin.h" + +#include "bertCommon.h" +#include +#include +#include + +namespace bert +{ + +class SkipLayerNormInterleavedPlugin : public nvinfer1::IPluginV2DynamicExt +{ +public: + SkipLayerNormInterleavedPlugin( + const std::string name, const nvinfer1::Weights& beta, const nvinfer1::Weights& gamma); + + SkipLayerNormInterleavedPlugin(const std::string name, const void* data, size_t length); + + // It doesn't make sense to make SkipLayerNormInterleavedPlugin without arguments, so we + // delete default constructor. + SkipLayerNormInterleavedPlugin() = delete; + + // IPluginV2DynamicExt Methods + nvinfer1::IPluginV2DynamicExt* clone() const override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override; + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + // IPluginV2Ext Methods + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + // IPluginV2 Methods + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + int initialize() override; + void terminate() override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void destroy() override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; + +private: + const std::string mLayerName; + std::string mNamespace; + + bert::cuda_unique_ptr mGammaDev; + bert::cuda_unique_ptr mBetaDev; + size_t mLd; // leading dim + bert::WeightsWithOwnership mGamma; + bert::WeightsWithOwnership mBeta; + + size_t mParamWordsize; + bool mParamsOnDevice; + +protected: + // To prevent compiler warnings. + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::enqueue; + using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::supportsFormat; +}; + +class SkipLayerNormInterleavedPluginCreator : public nvinfer1::IPluginCreator +{ +public: + SkipLayerNormInterleavedPluginCreator(); + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + +private: + static nvinfer1::PluginFieldCollection mFC; + static std::vector mPluginAttributes; + std::string mNamespace; +}; +} // namespace bert +#endif // TRT_SKIP_LAYER_NORM_INTERLEAVED_PLUGIN_H diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp index ca1c430e..510554a0 100644 --- a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp +++ b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -35,13 +35,19 @@ namespace { static const char* SKIP_LAYER_NORM_VERSION{"1"}; static const char* SKIP_LAYER_NORM_NAME{"CustomSkipLayerNormPluginDynamic"}; +static const char* SKIP_LAYER_NORM_VAR_SEQLEN_VERSION{"2"}; } // namespace // Static class fields initialization PluginFieldCollection SkipLayerNormPluginDynamicCreator::mFC{}; std::vector SkipLayerNormPluginDynamicCreator::mPluginAttributes; +PluginFieldCollection SkipLayerNormVarSeqlenPluginCreator::mFC{}; +std::vector SkipLayerNormVarSeqlenPluginCreator::mPluginAttributes; + REGISTER_TENSORRT_PLUGIN(SkipLayerNormPluginDynamicCreator); +REGISTER_TENSORRT_PLUGIN(SkipLayerNormVarSeqlenPluginCreator); + static inline DataType getParamWordType(DataType cfgType) { @@ -62,11 +68,10 @@ SkipLayerNormPluginDynamic::SkipLayerNormPluginDynamic(const std::string name, c , mType(type) , mBiasDev(nullptr) { - assert(mType == nvinfer1::DataType::kFLOAT || mType == nvinfer1::DataType::kHALF - || mType == nvinfer1::DataType::kINT8); + assert(mType == nvinfer1::DataType::kFLOAT || mType == nvinfer1::DataType::kHALF || mType == nvinfer1::DataType::kINT8); // mCfgType is the dataType for beta, gamma bias weights, always fp16 or fp32 // mType is the plugin IO datatype, can be int8 - mCfgType = mType == DataType::kINT8 ? DataType::kHALF : mType; + mCfgType = mType == DataType::kINT8 ? DataType::kHALF : mType; mParamWordsize = getElementSize(mCfgType); mBeta.convertAndCopy(beta, mCfgType); @@ -228,13 +233,13 @@ int SkipLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const const auto gamma = static_cast(mGammaDev.get()); if (mHasBias) { - status = computeSkipLayerNorm( - stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); } else { - status = computeSkipLayerNorm( - stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); } } else if (iType == DataType::kHALF) @@ -247,13 +252,12 @@ int SkipLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const const auto gamma = static_cast(mGammaDev.get()); if (mHasBias) { - status = computeSkipLayerNorm( - stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + status = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); } else { - status = computeSkipLayerNorm( - stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); } } else if (iType == DataType::kINT8) @@ -269,19 +273,18 @@ int SkipLayerNormPluginDynamic::enqueue(const PluginTensorDesc* inputDesc, const const auto gamma = static_cast(mGammaDev.get()); if (mHasBias) { - status = computeSkipLayerNormDQQ(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, - output, bias, dqScaleIn, dqScaleSkip, qScale); + status = computeSkipLayerNormDQQ( + stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias, dqScaleIn, dqScaleSkip, qScale); } else { - status = computeSkipLayerNormDQQ(stream, static_cast(mLd), inputVolume, input, skip, beta, - gamma, output, bias, dqScaleIn, dqScaleSkip, qScale); + status = computeSkipLayerNormDQQ( + stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias, dqScaleIn, dqScaleSkip, qScale); } } else { - gLogError << "Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " << static_cast(iType) - << "." << std::endl; + gLogError << "Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " << static_cast(iType) << "." << std::endl; assert(false); } return status; @@ -292,7 +295,6 @@ DataType SkipLayerNormPluginDynamic::getOutputDataType(int index, const DataType { assert(index == 0); assert(nbInputs == 2); - assert(inputTypes[0] == inputTypes[1]); return inputTypes[0]; } @@ -473,6 +475,431 @@ const char* SkipLayerNormPluginDynamicCreator::getPluginNamespace() const { return mNamespace.c_str(); } + +SkipLayerNormVarSeqlenPlugin::SkipLayerNormVarSeqlenPlugin( + const std::string name, const DataType type, const Weights& beta, const Weights& gamma, const Weights& bias) + : mLayerName(name) + , mGammaDev(nullptr) + , mBetaDev(nullptr) + , mLd(beta.count) + , mType(type) + , mBiasDev(nullptr) + , mParamsOnDevice(false) +{ + assert(mLd > 0); + assert(beta.count == gamma.count); + assert(mType == nvinfer1::DataType::kFLOAT || mType == nvinfer1::DataType::kHALF || mType == nvinfer1::DataType::kINT8); + // mCfgType is the dataType for beta, gamma bias weights, always fp16 or fp32 + // mType is the plugin IO datatype, can be int8 + mCfgType = mType == DataType::kINT8 ? DataType::kHALF : mType; + mParamWordsize = getElementSize(mCfgType); + + mBeta.convertAndCopy(beta, mCfgType); + mGamma.convertAndCopy(gamma, mCfgType); + + mHasBias = (bias.values != nullptr); + if (mHasBias) + { + mBias.convertAndCopy(bias, mCfgType); + } + +} + +SkipLayerNormVarSeqlenPlugin::SkipLayerNormVarSeqlenPlugin(const std::string name, const void* data, size_t length) + : mLayerName(name) + , mGammaDev(nullptr) + , mBetaDev(nullptr) + , mBiasDev(nullptr) + , mParamsOnDevice(false) +{ + gLogVerbose << "SkipLayerNormVarSeqlenPlugin deserialize\n"; + + // Deserialize in the same order as serialization + deserialize_value(&data, &length, &mType); + deserialize_value(&data, &length, &mCfgType); + deserialize_value(&data, &length, &mLd); + deserialize_value(&data, &length, &mHasBias); + + assert(mCfgType == nvinfer1::DataType::kFLOAT || mCfgType == nvinfer1::DataType::kHALF); + mParamWordsize = getElementSize(mCfgType); + + const char* d = static_cast(data); + mBeta.convertAndCopy(d, mLd, mCfgType); + mGamma.convertAndCopy(d, mLd, mCfgType); + if (mHasBias) + { + mBias.convertAndCopy(d, mLd, mCfgType); + } +} + +// IPluginV2DynamicExt Methods +IPluginV2DynamicExt* SkipLayerNormVarSeqlenPlugin::clone() const +{ + gLogVerbose << "SkipLayerNormVarSeqlenPlugin clone\n"; + + auto p = new SkipLayerNormVarSeqlenPlugin(mLayerName, mType, mBeta, mGamma, mBias); + p->initialize(); + p->setPluginNamespace(mNamespace.c_str()); + return p; +} + +DimsExprs SkipLayerNormVarSeqlenPlugin::getOutputDimensions( + int outputIndex, const DimsExprs* inputs, int nbInputs, IExprBuilder& exprBuilder) +{ + assert(nbInputs == 2); + assert(outputIndex == 0); + assert(inputs[0].nbDims == inputs[1].nbDims); + return inputs[0]; +} + +bool SkipLayerNormVarSeqlenPlugin::supportsFormatCombination( + int pos, const PluginTensorDesc* inOut, int nbInputs, int nbOutputs) +{ + assert(nbInputs == 2); + assert(nbOutputs == 1); + + const PluginTensorDesc& in = inOut[pos]; + + if(mType != in.type) return false; + if (pos == 0) + { + // Since H = W = 1, we can report CHWx for any x + if (mType == DataType::kINT8) + { + // won't work for hiddensize too small! + TensorFormat myFmt = TensorFormat::kCHW32; + if (mLd < 32) + { + myFmt = TensorFormat::kCHW4; + gLogVerbose << "SkipLayerNormDQQ: TensorFormat CHW4" + << " for LD=" << mLd << std::endl; + } + else + { + gLogVerbose << "SkipLayerNormDQQ: TensorFormat CHW32" + << " for LD=" << mLd << std::endl; + } + // TODO do we need to check if the vectorization divides mLd? + return in.format == myFmt; + } + return in.format == TensorFormat::kLINEAR; + } + const PluginTensorDesc& prev = inOut[pos - 1]; + + return in.format == prev.format; +} + +void SkipLayerNormVarSeqlenPlugin::copyParamToDevice() +{ + if (!mParamsOnDevice) + { + const auto paramType = getParamWordType(mCfgType); + copyToDevice(mGamma, getWeightsSize(mGamma, paramType), mGammaDev); + copyToDevice(mBeta, getWeightsSize(mBeta, paramType), mBetaDev); + if (mHasBias) + { + copyToDevice(mBias, getWeightsSize(mBias, paramType), mBiasDev); + } + mParamsOnDevice = true; + } +} + +void SkipLayerNormVarSeqlenPlugin::configurePlugin( + const DynamicPluginTensorDesc* inputs, int nbInputs, const DynamicPluginTensorDesc* outputs, int nbOutputs) +{ + // Validate input arguments + assert(nbOutputs == 1); + assert(nbInputs == 2); + if (mType == DataType::kFLOAT || mType == DataType::kHALF) + { + assert(mType == inputs[0].desc.type); + assert(mType == inputs[1].desc.type); + } + else + { + assert(mType == inputs[0].desc.type || DataType::kFLOAT == inputs[0].desc.type); + assert(mType == inputs[1].desc.type || DataType::kFLOAT == inputs[1].desc.type); + } + const auto& inDims0 = inputs[0].desc.dims; + const auto& inDims1 = inputs[1].desc.dims; + TRT_UNUSED inDims1; + assert(inDims0.nbDims == inDims1.nbDims); + + assert(std::equal(inDims0.d, inDims0.d + inDims0.nbDims, inDims1.d)); + + mCfgType = inputs[0].desc.type == DataType::kINT8 ? DataType::kHALF : inputs[0].desc.type; + + const auto paramType = getParamWordType(mCfgType); + mParamWordsize = getElementSize(paramType); + + copyParamToDevice(); +} + +size_t SkipLayerNormVarSeqlenPlugin::getWorkspaceSize( + const PluginTensorDesc* inputs, int nbInputs, const PluginTensorDesc* outputs, int nbOutputs) const +{ + return 0; +} + +int SkipLayerNormVarSeqlenPlugin::enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) +{ + const int inputVolume = volume(inputDesc[0].dims); + assert(inputVolume % mLd == 0 && "inconsistent dimensions"); + int status = -1; + DataType iType = inputDesc->type; + + // WAR to work with TRT6.0 + copyParamToDevice(); + + // Our plugin outputs only one tensor + // Launch CUDA kernel wrapper and save its return value + if (iType == DataType::kFLOAT) + { + const auto input = static_cast(inputs[0]); + const auto skip = static_cast(inputs[1]); + auto output = static_cast(outputs[0]); + const auto bias = static_cast(mBiasDev.get()); + const auto beta = static_cast(mBetaDev.get()); + const auto gamma = static_cast(mGammaDev.get()); + if (mHasBias) + { + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + } + else + { + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + } + } + else if (iType == DataType::kHALF) + { + const auto input = static_cast(inputs[0]); + const auto skip = static_cast(inputs[1]); + auto output = static_cast(outputs[0]); + const auto bias = static_cast(mBiasDev.get()); + const auto beta = static_cast(mBetaDev.get()); + const auto gamma = static_cast(mGammaDev.get()); + if (mHasBias) + { + status = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + } + else + { + status + = computeSkipLayerNorm(stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias); + } + } + else if (iType == DataType::kINT8) + { + const float dqScaleIn = inputDesc[0].scale; + const float dqScaleSkip = inputDesc[1].scale; + const float qScale = 1.f / outputDesc[0].scale; + const auto input = static_cast(inputs[0]); + const auto skip = static_cast(inputs[1]); + auto output = static_cast(outputs[0]); + const auto bias = static_cast(mBiasDev.get()); + const auto beta = static_cast(mBetaDev.get()); + const auto gamma = static_cast(mGammaDev.get()); + if (mHasBias) + { + status = computeSkipLayerNormDQQ( + stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias, dqScaleIn, dqScaleSkip, qScale); + } + else + { + status = computeSkipLayerNormDQQ( + stream, static_cast(mLd), inputVolume, input, skip, beta, gamma, output, bias, dqScaleIn, dqScaleSkip, qScale); + } + } + else + { + gLogError << "Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " << static_cast(iType) << "." << std::endl; + assert(false); + } + return status; +} + +// IPluginV2Ext Methods +DataType SkipLayerNormVarSeqlenPlugin::getOutputDataType(int index, const DataType* inputTypes, int nbInputs) const +{ + assert(index == 0); + assert(nbInputs == 2); + return inputTypes[0]; +} + +// IPluginV2 Methods +const char* SkipLayerNormVarSeqlenPlugin::getPluginType() const +{ + return SKIP_LAYER_NORM_NAME; +} + +const char* SkipLayerNormVarSeqlenPlugin::getPluginVersion() const +{ + return SKIP_LAYER_NORM_VAR_SEQLEN_VERSION; +} + +int SkipLayerNormVarSeqlenPlugin::getNbOutputs() const +{ + return 1; +} +int SkipLayerNormVarSeqlenPlugin::initialize() +{ + gLogVerbose << "SkipLayerNormVarSeqlenPlugin initialize\n"; + return 0; +} + +void SkipLayerNormVarSeqlenPlugin::terminate() +{ + gLogVerbose << "SkipLayerNormVarSeqlenPlugin terminate\n"; +} + +size_t SkipLayerNormVarSeqlenPlugin::getSerializationSize() const +{ + const size_t biasSize = mHasBias ? (mLd * mParamWordsize) : 0; + return 2 * mParamWordsize * mLd + 2 * sizeof(DataType) + sizeof(mLd) + biasSize + sizeof(mHasBias); +} + +void SkipLayerNormVarSeqlenPlugin::serialize(void* buffer) const +{ + serialize_value(&buffer, mType); + serialize_value(&buffer, mCfgType); + serialize_value(&buffer, mLd); + serialize_value(&buffer, mHasBias); + + char* d = static_cast(buffer); + serFromDev(d, static_cast(mBetaDev.get()), mLd * mParamWordsize); + serFromDev(d, static_cast(mGammaDev.get()), mLd * mParamWordsize); + if (mHasBias) + { + serFromDev(d, static_cast(mBiasDev.get()), mLd * mParamWordsize); + } +} + +void SkipLayerNormVarSeqlenPlugin::destroy() +{ + gLogVerbose << "SkipLayerNormVarSeqlenPlugin destroy\n"; + // This gets called when the network containing plugin is destroyed + mGammaDev.release(); + mBetaDev.release(); + mBiasDev.release(); + delete this; +} + +void SkipLayerNormVarSeqlenPlugin::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* SkipLayerNormVarSeqlenPlugin::getPluginNamespace() const +{ + return mNamespace.c_str(); +} + +///////////////////////////////////////////////////////// + +SkipLayerNormVarSeqlenPluginCreator::SkipLayerNormVarSeqlenPluginCreator() +{ + mFC.nbFields = mPluginAttributes.size(); + mFC.fields = mPluginAttributes.data(); +} + +const char* SkipLayerNormVarSeqlenPluginCreator::getPluginName() const +{ + return SKIP_LAYER_NORM_NAME; +} + +const char* SkipLayerNormVarSeqlenPluginCreator::getPluginVersion() const +{ + return SKIP_LAYER_NORM_VAR_SEQLEN_VERSION; +} + +const PluginFieldCollection* SkipLayerNormVarSeqlenPluginCreator::getFieldNames() +{ + return &mFC; +} + +IPluginV2* SkipLayerNormVarSeqlenPluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc) +{ + gLogVerbose << "SkipLayerNormVarSeqlenPluginCreator createPlugin\n"; + + Weights beta{DataType::kFLOAT, nullptr, 0}; + Weights gamma{DataType::kFLOAT, nullptr, 0}; + Weights bias{DataType::kFLOAT, nullptr, 0}; + int typeId = -1; + + for (int i = 0; i < fc->nbFields; i++) + { + std::string field_name(fc->fields[i].name); + + if (field_name.compare("type_id") == 0) + { + typeId = *static_cast(fc->fields[i].data); + gLogVerbose << "Building typeId: " << typeId << std::endl; + } + + if (field_name.compare("beta") == 0) + { + gLogVerbose << "Building beta...\n"; + beta.values = fc->fields[i].data; + beta.count = fc->fields[i].length; + beta.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("gamma") == 0) + { + gLogVerbose << "Building gamma...\n"; + gamma.values = fc->fields[i].data; + gamma.count = fc->fields[i].length; + gamma.type = fieldTypeToDataType(fc->fields[i].type); + } + + if (field_name.compare("bias") == 0) + { + gLogVerbose << "Building bias...\n"; + bias.values = fc->fields[i].data; + bias.count = fc->fields[i].length; + bias.type = fieldTypeToDataType(fc->fields[i].type); + } + } + gLogVerbose << "Type " << typeId << std::endl; + + if (typeId < 0 || typeId > 3) + { + gLogError << "SkipLayerNorm: Invalid type ID: " << typeId << std::endl; + } + + if (beta.count <= 0 || beta.values == nullptr) + { + gLogError << "SkipLayerNorm: invalid beta" << std::endl; + } + + if (gamma.count <= 0 || gamma.values == nullptr) + { + gLogError << "SkipLayerNorm: invalid gamma" << std::endl; + } + + return new SkipLayerNormVarSeqlenPlugin(name, static_cast(typeId), beta, gamma, bias); +} + +IPluginV2* SkipLayerNormVarSeqlenPluginCreator::deserializePlugin( + const char* name, const void* serialData, size_t serialLength) +{ + // This object will be deleted when the network is destroyed, which will + // call SkipLayerNormVarSeqlenPlugin::destroy() + return new SkipLayerNormVarSeqlenPlugin(name, serialData, serialLength); +} + +void SkipLayerNormVarSeqlenPluginCreator::setPluginNamespace(const char* libNamespace) +{ + mNamespace = libNamespace; +} + +const char* SkipLayerNormVarSeqlenPluginCreator::getPluginNamespace() const +{ + return mNamespace.c_str(); +} } // namespace bert #endif // CUDA_VERSION >= 10010 diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h index 1b5604a4..dd87454c 100644 --- a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h +++ b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -38,11 +38,12 @@ template int computeSkipLayerNorm(cudaStream_t stream, const int ld, const int n, const T* input, const T* skip, const T* beta, const T* gamma, T* output, const T* bias); + class SkipLayerNormPluginDynamic : public nvinfer1::IPluginV2DynamicExt { public: - SkipLayerNormPluginDynamic(const std::string name, const nvinfer1::DataType type, const int ld, - const nvinfer1::Weights& beta, const nvinfer1::Weights& gamma, const nvinfer1::Weights& bias); + SkipLayerNormPluginDynamic(const std::string name, const nvinfer1::DataType type, const int ld, const nvinfer1::Weights& beta, + const nvinfer1::Weights& gamma, const nvinfer1::Weights& bias); SkipLayerNormPluginDynamic(const std::string name, const void* data, size_t length); @@ -131,6 +132,105 @@ class SkipLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator static std::vector mPluginAttributes; std::string mNamespace; }; + +class SkipLayerNormVarSeqlenPlugin : public nvinfer1::IPluginV2DynamicExt +{ +public: + SkipLayerNormVarSeqlenPlugin(const std::string name, const nvinfer1::DataType type, const nvinfer1::Weights& beta, + const nvinfer1::Weights& gamma, const nvinfer1::Weights& bias); + + SkipLayerNormVarSeqlenPlugin(const std::string name, const void* data, size_t length); + + // It doesn't make sense to make SkipLayerNormVarSeqlenPlugin without arguments, so we + // delete default constructor. + SkipLayerNormVarSeqlenPlugin() = delete; + + // IPluginV2DynamicExt Methods + nvinfer1::IPluginV2DynamicExt* clone() const override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, int nbOutputs) const override; + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) override; + + // IPluginV2Ext Methods + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + + // IPluginV2 Methods + const char* getPluginType() const override; + const char* getPluginVersion() const override; + int getNbOutputs() const override; + int initialize() override; + void terminate() override; + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + void destroy() override; + void setPluginNamespace(const char* pluginNamespace) override; + const char* getPluginNamespace() const override; + +protected: + void copyParamToDevice(); + +private: + const std::string mLayerName; + std::string mNamespace; + + bert::cuda_unique_ptr mGammaDev; + bert::cuda_unique_ptr mBetaDev; + size_t mLd; // leading dim + bert::WeightsWithOwnership mGamma; + bert::WeightsWithOwnership mBeta; + nvinfer1::DataType mType; + nvinfer1::DataType mCfgType; + + bool mHasBias; + bert::cuda_unique_ptr mBiasDev; + bert::WeightsWithOwnership mBias; + + size_t mParamWordsize; + bool mParamsOnDevice; + +protected: + // To prevent compiler warnings. + using nvinfer1::IPluginV2DynamicExt::canBroadcastInputAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::configurePlugin; + using nvinfer1::IPluginV2DynamicExt::enqueue; + using nvinfer1::IPluginV2DynamicExt::getOutputDimensions; + using nvinfer1::IPluginV2DynamicExt::getWorkspaceSize; + using nvinfer1::IPluginV2DynamicExt::isOutputBroadcastAcrossBatch; + using nvinfer1::IPluginV2DynamicExt::supportsFormat; +}; + +class SkipLayerNormVarSeqlenPluginCreator : public nvinfer1::IPluginCreator +{ +public: + SkipLayerNormVarSeqlenPluginCreator(); + + const char* getPluginName() const override; + + const char* getPluginVersion() const override; + + const nvinfer1::PluginFieldCollection* getFieldNames() override; + + nvinfer1::IPluginV2* createPlugin(const char* name, const nvinfer1::PluginFieldCollection* fc) override; + + nvinfer1::IPluginV2* deserializePlugin(const char* name, const void* serialData, size_t serialLength) override; + + void setPluginNamespace(const char* pluginNamespace) override; + + const char* getPluginNamespace() const override; + +private: + static nvinfer1::PluginFieldCollection mFC; + static std::vector mPluginAttributes; + std::string mNamespace; +}; + } // namespace bert #endif // TRT_SKIP_LAYER_NORM_PLUGIN_H diff --git a/plugin/specialSlicePlugin/specialSlicePlugin.cpp b/plugin/specialSlicePlugin/specialSlicePlugin.cpp index 9656531c..f7f7d606 100644 --- a/plugin/specialSlicePlugin/specialSlicePlugin.cpp +++ b/plugin/specialSlicePlugin/specialSlicePlugin.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "specialSlicePlugin.h" #include "maskRCNNKernels.h" #include diff --git a/plugin/specialSlicePlugin/specialSlicePlugin.h b/plugin/specialSlicePlugin/specialSlicePlugin.h index 630b8d79..1fd406ee 100644 --- a/plugin/specialSlicePlugin/specialSlicePlugin.h +++ b/plugin/specialSlicePlugin/specialSlicePlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TRT_SPECIAL_SLICE_PLUGIN_H #define TRT_SPECIAL_SLICE_PLUGIN_H diff --git a/plugin/splitPlugin/CMakeLists.txt b/plugin/splitPlugin/CMakeLists.txt new file mode 100644 index 00000000..e759083f --- /dev/null +++ b/plugin/splitPlugin/CMakeLists.txt @@ -0,0 +1,18 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +file(GLOB SRCS *.cu) +set(PLUGIN_SOURCES ${PLUGIN_SOURCES} ${SRCS}) +set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE) diff --git a/plugin/splitPlugin/split.cu b/plugin/splitPlugin/split.cu new file mode 100644 index 00000000..eb027fba --- /dev/null +++ b/plugin/splitPlugin/split.cu @@ -0,0 +1,175 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include "split.h" + +using namespace nvinfer1; +using nvinfer1::plugin::SplitPlugin; + +template +__device__ +int upper_bound(T const* vals, int n, T const& key) +{ + int i = 0; + while( n > 0 ) + { + int m = n / 2; + int j = i + m; + if( !(key < vals[j]) ) + { + i = j + 1; + n -= m + 1; + } + else + { + n = m; + } + } + return i; +} + +template +__global__ +void split_kernel(int nsegment, + int const* __restrict__ segment_offsets, + T const* __restrict__ idata, + T* const* odatas, + int nx, + int src_ny, + int nz) +{ + int x0 = threadIdx.x + blockIdx.x * blockDim.x; + int src_y0 = threadIdx.y + blockIdx.y * blockDim.y; + int z0 = threadIdx.z + blockIdx.z * blockDim.z; + for( int z=z0; z 0); + return inputTypes[0]; +} + +int SplitPlugin::initialize() +{ + return 0; +} + +void SplitPlugin::terminate() +{ + +} + +void SplitPlugin::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) +{ + std::vector segment_offsets(1, 0); + for( int i = 0; i < nbOutputs; ++i ) + { + segment_offsets.push_back(segment_offsets.back() + _output_lengths[i]); + } + _d_segment_offsets = segment_offsets; + + for (int i = 0; i < nbInputs; i++) + { + for (int j = 0; j < in[0].desc.dims.nbDims; j++) + { + // Do not support dynamic dimensions + ASSERT(in[0].desc.dims.d[j] != -1); + } + } + + nvinfer1::Dims dims = in[0].desc.dims; + _nx = 1; + for( int i = dims.nbDims-1; i > _axis; --i ) + { + _nx *= dims.d[i]; + } + _ny = dims.d[_axis]; + _nz = 1; + for( int i = _axis-1; i >= 0; --i ) + { + _nz *= dims.d[i]; + } + _d_output_ptrs.resize(nbOutputs, nullptr); +} + +int SplitPlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, + void* workspace, + cudaStream_t stream) +{ + int const* d_segment_offsets_ptr = + thrust::raw_pointer_cast(&_d_segment_offsets[0]); + float const* idata = reinterpret_cast(inputs[0]); + float* const* h_odatas = reinterpret_cast(outputs); + float** odatas = thrust::raw_pointer_cast(&_d_output_ptrs[0]); + cudaError_t cuda_status = + cudaMemcpyAsync(odatas, h_odatas, + _d_output_ptrs.size() * sizeof(float*), + cudaMemcpyHostToDevice, stream); + if( cuda_status != cudaSuccess ) + { + return 1; + } + int nz = _nz * inputDesc[0].dims.d[0]; + dim3 block(32, 16); + dim3 grid(std::min((_nx - 1) / block.x + 1, 65535u), + std::min((_ny - 1) / block.y + 1, 65535u), + std::min((_nz - 1) / block.z + 1, 65535u)); + if (inputDesc[0].type==nvinfer1::DataType::kFLOAT) + { + split_kernel<<>> + (_d_segment_offsets.size(), d_segment_offsets_ptr, idata, odatas, + _nx, _ny, nz); + } + else + { + split_kernel<<>> + (_d_segment_offsets.size(), d_segment_offsets_ptr, (__half const*)idata, (__half**)odatas, + _nx, _ny, nz); + } + return cudaGetLastError() != cudaSuccess; +} + +nvinfer1::DimsExprs SplitPlugin::getOutputDimensions(int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) +{ + nvinfer1::DimsExprs output(inputs[0]); + output.d[_axis] = exprBuilder.constant(_output_lengths[outputIndex]); + return output; +} diff --git a/plugin/splitPlugin/split.h b/plugin/splitPlugin/split.h new file mode 100644 index 00000000..55e5c2bb --- /dev/null +++ b/plugin/splitPlugin/split.h @@ -0,0 +1,185 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_SPLIT_PLUGIN_H +#define TRT_SPLIT_PLUGIN_H +#include + +#include "checkMacrosPlugin.h" +#include "serialize.hpp" + +#include +#include +#include + +namespace +{ +constexpr const char* SPLIT_PLUGIN_VERSION{"1"}; +constexpr const char* SPLIT_PLUGIN_NAME{"Split"}; +} // namespace + +namespace nvinfer1 +{ +namespace plugin +{ +class SplitPlugin final : public nvinfer1::IPluginV2DynamicExt +{ + int _axis; + std::vector _output_lengths; + int _nx, _ny, _nz; + int _x_stride, _y_stride, _z_stride; + thrust::device_vector _d_segment_offsets; + thrust::device_vector _d_output_ptrs; + +protected: + // Supress warnings about hiding function names due to overloads and overrides of virtuals. + using IPluginV2DynamicExt::enqueue; + using IPluginV2DynamicExt::getOutputDimensions; + using IPluginV2DynamicExt::getWorkspaceSize; + using IPluginV2DynamicExt::configurePlugin; + void deserialize(void const* serialData, size_t serialLength) + { + deserialize_value(&serialData, &serialLength, &_axis); + deserialize_value(&serialData, &serialLength, &_output_lengths); + } + size_t getSerializationSize() const override + { + return serialized_size(_axis) + serialized_size(_output_lengths); + } + void serialize(void* buffer) const override + { + serialize_value(&buffer, _axis); + serialize_value(&buffer, _output_lengths); + } + +public: + SplitPlugin(int axis, int* const& output_lengths, int noutput) + : _axis(axis) + , _output_lengths(std::vector(output_lengths, output_lengths + noutput)) + { + assert(axis <= nvinfer1::Dims::MAX_DIMS); + } + SplitPlugin(int axis, std::vector output_lengths) + : _axis(axis) + , _output_lengths(output_lengths) + { + assert(axis <= nvinfer1::Dims::MAX_DIMS); + } + SplitPlugin(void const* serialData, size_t serialLength) + { + this->deserialize(serialData, serialLength); + } + + bool supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) override; + nvinfer1::DataType getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const override; + int initialize() override; + void terminate() override; + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) override; + int enqueue(const PluginTensorDesc* inputDesc, const PluginTensorDesc* outputDesc, const void* const* inputs, + void* const* outputs, void* workspace, cudaStream_t stream) TRTNOEXCEPT override; + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) override; + + nvinfer1::IPluginV2DynamicExt* clone() const override + { + return new SplitPlugin{_axis, _output_lengths}; + } + void destroy() override + { + delete this; + } + const char* getPluginVersion() const override + { + return SPLIT_PLUGIN_VERSION; + } + const char* getPluginType() const override + { + return SPLIT_PLUGIN_NAME; + } + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* /*inputs*/, int /*nbInputs*/, + const nvinfer1::PluginTensorDesc* /*outputs*/, int /*nbOutputs*/) const TRTNOEXCEPT override + { + return 0; + } + void setPluginNamespace(const char* /*pluginNamespace*/) override {} + const char* getPluginNamespace() const override + { + return ""; + } + int getNbOutputs() const override + { + return _output_lengths.size(); + } + void attachToContext( + cudnnContext* /*cudnn*/, cublasContext* /*cublas*/, nvinfer1::IGpuAllocator* /*allocator*/) override + { + } + void detachFromContext() override {} +}; + +class SplitPluginCreator : public nvinfer1::IPluginCreator +{ +public: + SplitPluginCreator() {} + + ~SplitPluginCreator() {} + + const char* getPluginName() const + { + return SPLIT_PLUGIN_NAME; + } + + const char* getPluginVersion() const + { + return SPLIT_PLUGIN_VERSION; + } + + const nvinfer1::PluginFieldCollection* getFieldNames() + { + std::cerr << "Function not implemented" << std::endl; + return nullptr; + } + + nvinfer1::IPluginV2DynamicExt* createPlugin(const char* /*name*/, const nvinfer1::PluginFieldCollection* /*fc*/) + { + std::cerr << "Function not implemented" << std::endl; + return nullptr; + } + + nvinfer1::IPluginV2DynamicExt* deserializePlugin(const char* /*name*/, const void* serialData, size_t serialLength) + { + return new SplitPlugin{serialData, serialLength}; + } + + void setPluginNamespace(const char* libNamespace) + { + mNamespace = libNamespace; + } + + const char* getPluginNamespace() const + { + return mNamespace.c_str(); + } + +private: + std::string mNamespace; +}; + +} // namespace plugin +} // namespace nvinfer1 +#endif // TRT_SPLIT_PLUGIN_H diff --git a/samples/common/BatchStream.h b/samples/common/BatchStream.h index 0fa75785..8906ed5c 100644 --- a/samples/common/BatchStream.h +++ b/samples/common/BatchStream.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef BATCH_STREAM_H #define BATCH_STREAM_H @@ -43,7 +44,7 @@ class MNISTBatchStream : public IBatchStream const std::vector& directories) : mBatchSize{batchSize} , mMaxBatches{maxBatches} - , mDims{3, 1, 28, 28} //!< We already know the dimensions of MNIST images. + , mDims{3, {1, 28, 28}} //!< We already know the dimensions of MNIST images. { readDataFile(locateFile(dataFile, directories)); readLabelsFile(locateFile(labelsFile, directories)); diff --git a/samples/common/argsParser.h b/samples/common/argsParser.h index 49defa12..a51d8bbb 100644 --- a/samples/common/argsParser.h +++ b/samples/common/argsParser.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TENSORRT_ARGS_PARSER_H #define TENSORRT_ARGS_PARSER_H @@ -34,8 +35,8 @@ namespace samplesCommon //! struct SampleParams { - int batchSize{1}; //!< Number of inputs in a batch - int dlaCore{-1}; //!< Specify the DLA core to run network on. + int32_t batchSize{1}; //!< Number of inputs in a batch + int32_t dlaCore{-1}; //!< Specify the DLA core to run network on. bool int8{false}; //!< Allow runnning the network in Int8 mode. bool fp16{false}; //!< Allow running the network in FP16 mode. std::vector dataDirs; //!< Directory paths where sample data files are stored @@ -80,8 +81,8 @@ struct Args bool runInInt8{false}; bool runInFp16{false}; bool help{false}; - int useDLACore{-1}; - int batch{1}; + int32_t useDLACore{-1}; + int32_t batch{1}; std::vector dataDirs; std::string saveEngine; std::string loadEngine; @@ -95,16 +96,16 @@ struct Args //! //! \return boolean If return value is true, execution can continue, otherwise program should exit //! -inline bool parseArgs(Args& args, int argc, char* argv[]) +inline bool parseArgs(Args& args, int32_t argc, char* argv[]) { while (1) { - int arg; + int32_t arg; static struct option long_options[] = {{"help", no_argument, 0, 'h'}, {"datadir", required_argument, 0, 'd'}, {"int8", no_argument, 0, 'i'}, {"fp16", no_argument, 0, 'f'}, {"useILoop", no_argument, 0, 'l'}, {"saveEngine", required_argument, 0, 's'}, {"loadEngine", no_argument, 0, 'o'}, {"useDLACore", required_argument, 0, 'u'}, {"batch", required_argument, 0, 'b'}, {nullptr, 0, nullptr, 0}}; - int option_index = 0; + int32_t option_index = 0; arg = getopt_long(argc, argv, "hd:iu", long_options, &option_index); if (arg == -1) { diff --git a/samples/common/buffers.h b/samples/common/buffers.h index f1a9385f..01e3f491 100644 --- a/samples/common/buffers.h +++ b/samples/common/buffers.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef TENSORRT_BUFFERS_H #define TENSORRT_BUFFERS_H diff --git a/samples/common/common.h b/samples/common/common.h index 6fc247a9..8c33c93e 100644 --- a/samples/common/common.h +++ b/samples/common/common.h @@ -222,8 +222,8 @@ struct SimpleProfiler : public nvinfer1::IProfiler std::map mProfile; }; -// Locate path to file, given its filename or filepath suffix and possible dirs it might lie in -// Function will also walk back MAX_DEPTH dirs from CWD to check for such a file path +//! Locate path to file, given its filename or filepath suffix and possible dirs it might lie in. +//! Function will also walk back MAX_DEPTH dirs from CWD to check for such a file path. inline std::string locateFile(const std::string& filepathSuffix, const std::vector& directories) { const int MAX_DEPTH{10}; @@ -241,14 +241,19 @@ inline std::string locateFile(const std::string& filepathSuffix, const std::vect #endif } else + { filepath = dir + filepathSuffix; + } for (int i = 0; i < MAX_DEPTH && !found; i++) { - std::ifstream checkFile(filepath); + const std::ifstream checkFile(filepath); found = checkFile.is_open(); if (found) + { break; + } + filepath = "../" + filepath; // Try again in parent dir } @@ -260,14 +265,16 @@ inline std::string locateFile(const std::string& filepathSuffix, const std::vect filepath.clear(); } + // Could not find the file if (filepath.empty()) { - std::string directoryList = std::accumulate(directories.begin() + 1, directories.end(), directories.front(), + const std::string dirList = std::accumulate(directories.begin() + 1, directories.end(), directories.front(), [](const std::string& a, const std::string& b) { return a + "\n\t" + b; }); - std::cout << "Could not find " << filepathSuffix << " in data directories:\n\t" << directoryList << std::endl; + std::cout << "Could not find " << filepathSuffix << " in data directories:\n\t" << dirList << std::endl; std::cout << "&&&& FAILED" << std::endl; exit(EXIT_FAILURE); } + return filepath; } @@ -378,7 +385,7 @@ struct InferDeleter }; template -inline std::shared_ptr infer_object(T* obj) +std::shared_ptr infer_object(T* obj) { if (!obj) { @@ -387,20 +394,15 @@ inline std::shared_ptr infer_object(T* obj) return std::shared_ptr(obj, InferDeleter()); } +//! Return vector of indices that puts magnitudes of sequence in descending order. template -inline std::vector argsort(Iter begin, Iter end, bool reverse = false) +std::vector argMagnitudeSort(Iter begin, Iter end) { - std::vector inds(end - begin); - std::iota(inds.begin(), inds.end(), 0); - if (reverse) - { - std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) { return begin[i2] < begin[i1]; }); - } - else - { - std::sort(inds.begin(), inds.end(), [&begin](size_t i1, size_t i2) { return begin[i1] < begin[i2]; }); - } - return inds; + std::vector indices(end - begin); + std::iota(indices.begin(), indices.end(), 0); + std::sort(indices.begin(), indices.end(), + [&begin](size_t i, size_t j) { return std::abs(begin[j]) < std::abs(begin[i]); }); + return indices; } inline bool readReferenceFile(const std::string& fileName, std::vector& refVector) @@ -422,12 +424,13 @@ inline bool readReferenceFile(const std::string& fileName, std::vector -inline std::vector classify( - const std::vector& refVector, const result_vector_t& output, const size_t topK) +template +std::vector classify( + const std::vector& refVector, const std::vector& output, const size_t topK) { - auto inds = samplesCommon::argsort(output.cbegin(), output.cend(), true); + const auto inds = samplesCommon::argMagnitudeSort(output.cbegin(), output.cend()); std::vector result; + result.reserve(topK); for (size_t k = 0; k < topK; ++k) { result.push_back(refVector[inds[k]]); @@ -435,18 +438,17 @@ inline std::vector classify( return result; } -// Returns top K indices, not values. +// Returns indices of highest K magnitudes in v. template -inline std::vector topK(const std::vector inp, const size_t k) +std::vector topKMagnitudes(const std::vector& v, const size_t k) { - std::vector result; - std::vector inds = samplesCommon::argsort(inp.cbegin(), inp.cend(), true); - result.assign(inds.begin(), inds.begin() + k); - return result; + std::vector indices = samplesCommon::argMagnitudeSort(v.cbegin(), v.cend()); + indices.resize(k); + return indices; } template -inline bool readASCIIFile(const std::string& fileName, const size_t size, std::vector& out) +bool readASCIIFile(const std::string& fileName, const size_t size, std::vector& out) { std::ifstream infile(fileName); if (!infile.is_open()) @@ -462,7 +464,7 @@ inline bool readASCIIFile(const std::string& fileName, const size_t size, std::v } template -inline bool writeASCIIFile(const std::string& fileName, const std::vector& in) +bool writeASCIIFile(const std::string& fileName, const std::vector& in) { std::ofstream outfile(fileName); if (!outfile.is_open()) @@ -668,7 +670,7 @@ struct BBox }; template -inline void readPPMFile(const std::string& filename, samplesCommon::PPM& ppm) +void readPPMFile(const std::string& filename, samplesCommon::PPM& ppm) { ppm.fileName = filename; std::ifstream infile(filename, std::ifstream::binary); @@ -694,7 +696,7 @@ inline void readPPMFile(const std::string& filename, vPPM& ppm, std::vector -inline void writePPMFileWithBBox(const std::string& filename, PPM& ppm, const BBox& bbox) +void writePPMFileWithBBox(const std::string& filename, PPM& ppm, const BBox& bbox) { std::ofstream outfile("./" + filename, std::ofstream::binary); assert(!outfile.fail()); @@ -702,11 +704,13 @@ inline void writePPMFileWithBBox(const std::string& filename, PPM& ppm, << "\n" << ppm.w << " " << ppm.h << "\n" << ppm.max << "\n"; + auto round = [](float x) -> int { return int(std::floor(x + 0.5f)); }; const int x1 = std::min(std::max(0, round(int(bbox.x1))), W - 1); const int x2 = std::min(std::max(0, round(int(bbox.x2))), W - 1); const int y1 = std::min(std::max(0, round(int(bbox.y1))), H - 1); const int y2 = std::min(std::max(0, round(int(bbox.y2))), H - 1); + for (int x = x1; x <= x2; ++x) { // bbox top border @@ -718,6 +722,7 @@ inline void writePPMFileWithBBox(const std::string& filename, PPM& ppm, ppm.buffer[(y2 * ppm.w + x) * 3 + 1] = 0; ppm.buffer[(y2 * ppm.w + x) * 3 + 2] = 0; } + for (int y = y1; y <= y2; ++y) { // bbox left border @@ -729,6 +734,7 @@ inline void writePPMFileWithBBox(const std::string& filename, PPM& ppm, ppm.buffer[(y * ppm.w + x2) * 3 + 1] = 0; ppm.buffer[(y * ppm.w + x2) * 3 + 2] = 0; } + outfile.write(reinterpret_cast(ppm.buffer), ppm.w * ppm.h * 3); } diff --git a/samples/common/logging.h b/samples/common/logging.h index 0fe4db18..6ecd26cb 100644 --- a/samples/common/logging.h +++ b/samples/common/logging.h @@ -494,7 +494,7 @@ inline LogStreamConsumer LOG_ERROR(const Logger& logger) //! //! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR -// ("fatal" severity) +//! ("fatal" severity) //! //! Example usage: //! diff --git a/samples/common/sampleDevice.h b/samples/common/sampleDevice.h index 333f048a..fa8dd00b 100644 --- a/samples/common/sampleDevice.h +++ b/samples/common/sampleDevice.h @@ -186,7 +186,7 @@ class TrtCudaGraph void beginCapture(TrtCudaStream& stream) { cudaCheck(cudaGraphCreate(&mGraph, 0)); - cudaCheck(cudaStreamBeginCapture(stream.get(), cudaStreamCaptureModeGlobal)); + cudaCheck(cudaStreamBeginCapture(stream.get(), cudaStreamCaptureModeThreadLocal)); } void launch(TrtCudaStream& stream) @@ -351,6 +351,27 @@ class MirroredBuffer TrtDeviceBuffer mDeviceBuffer; }; +inline void setCudaDevice(int device, std::ostream& os) +{ + cudaCheck(cudaSetDevice(device)); + + cudaDeviceProp properties; + cudaCheck(cudaGetDeviceProperties(&properties, device)); + + // clang-format off + os << "=== Device Information ===" << std::endl; + os << "Selected Device: " << properties.name << std::endl; + os << "Compute Capability: " << properties.major << "." << properties.minor << std::endl; + os << "SMs: " << properties.multiProcessorCount << std::endl; + os << "Compute Clock Rate: " << properties.clockRate / 1000000.0F << " GHz" << std::endl; + os << "Device Global Memory: " << (properties.totalGlobalMem >> 20) << " MiB" << std::endl; + os << "Shared Memory per SM: " << (properties.sharedMemPerMultiprocessor >> 10) << " KiB" << std::endl; + os << "Memory Bus Width: " << properties.memoryBusWidth << " bits" + << " (ECC " << (properties.ECCEnabled != 0 ? "enabled" : "disabled") << ")" << std::endl; + os << "Memory Clock Rate: " << properties.memoryClockRate / 1000000.0F << " GHz" << std::endl; + // clang-format on +} + } // namespace sample #endif // TRT_SAMPLE_DEVICE_H diff --git a/samples/common/sampleEngines.cpp b/samples/common/sampleEngines.cpp index a6311002..a78beb15 100644 --- a/samples/common/sampleEngines.cpp +++ b/samples/common/sampleEngines.cpp @@ -314,18 +314,18 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys } bool hasDynamicShapes{false}; - if (!build.inputFormats.empty() && build.inputFormats.size() != static_cast(network.getNbInputs())) - { - throw std::invalid_argument("The number of inputIOFormats must match network's inputs."); - } + + bool broadcastInputFormats = broadcastIOFormats(build.inputFormats, network.getNbInputs()); + for (unsigned int i = 0, n = network.getNbInputs(); i < n; i++) { // Set formats and data types of inputs auto input = network.getInput(i); if (!build.inputFormats.empty()) { - input->setType(build.inputFormats[i].first); - input->setAllowedFormats(build.inputFormats[i].second); + int inputFormatIndex = broadcastInputFormats ? 0 : i; + input->setType(build.inputFormats[inputFormatIndex].first); + input->setAllowedFormats(build.inputFormats[inputFormatIndex].second); } else { @@ -378,7 +378,7 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys { staticDims.resize(dims.nbDims); std::transform(dims.d, dims.d + dims.nbDims, staticDims.begin(), - [&DEFAULT_DIMENSION](int dim) { return dim > 0 ? dim : DEFAULT_DIMENSION; }); + [&](int dimension) { return dimension > 0 ? dimension : DEFAULT_DIMENSION; }); } sample::gLogWarning << "Dynamic dimensions required for input: " << input->getName() << ", but no shapes were provided. Automatically overriding shape to: " @@ -432,18 +432,17 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys config->addOptimizationProfile(profile) != -1, "Error in add optimization profile", nullptr, err); } - if (!build.outputFormats.empty() && build.outputFormats.size() != static_cast(network.getNbOutputs())) - { - throw std::invalid_argument("The number of outputIOFormats must match network's outputs."); - } + bool broadcastOutputFormats = broadcastIOFormats(build.outputFormats, network.getNbOutputs(), false); + for (unsigned int i = 0, n = network.getNbOutputs(); i < n; i++) { // Set formats and data types of outputs auto output = network.getOutput(i); if (!build.outputFormats.empty()) { - output->setType(build.outputFormats[i].first); - output->setAllowedFormats(build.outputFormats[i].second); + int outputFormatIndex = broadcastOutputFormats ? 0 : i; + output->setType(build.outputFormats[outputFormatIndex].first); + output->setAllowedFormats(build.outputFormats[outputFormatIndex].second); } else { @@ -477,6 +476,11 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys config->setFlag(BuilderFlag::kINT8); } + if (build.refittable) + { + config->setFlag(BuilderFlag::kREFIT); + } + if (build.int8 && !build.fp16) { sample::gLogInfo @@ -572,6 +576,14 @@ ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOptions& sys } } + if (build.enabledTactics || build.disabledTactics) + { + TacticSources tacticSources = config->getTacticSources(); + tacticSources |= build.enabledTactics; + tacticSources &= ~build.disabledTactics; + config->setTacticSources(tacticSources); + } + return builder.buildEngineWithConfig(network, *config); } @@ -584,10 +596,8 @@ ICudaEngine* modelToEngine( err << "Builder creation failed" << std::endl; return nullptr; } - const bool isOnnxModel = model.baseModel.format == ModelFormat::kONNX; - auto batchFlag = (build.maxBatch && !isOnnxModel) - ? 0U - : 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); + auto batchFlag + = (build.maxBatch) ? 0U : 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH); TrtUniquePtr network{builder->createNetworkV2(batchFlag)}; if (!network) { @@ -604,6 +614,21 @@ ICudaEngine* modelToEngine( return networkToEngine(build, sys, *builder, *network, err); } +void dumpRefittable(nvinfer1::ICudaEngine& engine) +{ + TrtUniquePtr refitter{createInferRefitter(engine, sample::gLogger.getTRTLogger())}; + // Get number of refittable items. + const int nbAll = refitter->getAll(0, nullptr, nullptr); + std::vector layerNames(nbAll); + // Allocate buffers for the items and get them. + std::vector weightsRoles(nbAll); + refitter->getAll(nbAll, layerNames.data(), weightsRoles.data()); + for (int i = 0; i < nbAll; ++i) + { + sample::gLogInfo << layerNames[i] << " " << weightsRoles[i] << std::endl; + } +} + ICudaEngine* loadEngine(const std::string& engine, int DLACore, std::ostream& err) { std::ifstream engineFile(engine, std::ios::binary); diff --git a/samples/common/sampleEngines.h b/samples/common/sampleEngines.h index 152c8248..c000b905 100644 --- a/samples/common/sampleEngines.h +++ b/samples/common/sampleEngines.h @@ -68,6 +68,11 @@ nvinfer1::ICudaEngine* networkToEngine(const BuildOptions& build, const SystemOp nvinfer1::ICudaEngine* modelToEngine( const ModelOptions& model, const BuildOptions& build, const SystemOptions& sys, std::ostream& err); +//! +//! \brief Log refittable layers and weights of a refittable engine +//! +void dumpRefittable(nvinfer1::ICudaEngine& engine); + //! //! \brief Load a serialized engine //! diff --git a/samples/common/sampleInference.cpp b/samples/common/sampleInference.cpp index 1d276674..074c18c3 100644 --- a/samples/common/sampleInference.cpp +++ b/samples/common/sampleInference.cpp @@ -94,7 +94,7 @@ bool setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inferenc { staticDims.resize(dims.nbDims); std::transform(dims.d, dims.d + dims.nbDims, staticDims.begin(), - [&](int dim) { return dim >= 0 ? dim : DEFAULT_DIMENSION; }); + [&](int dimension) { return dimension >= 0 ? dimension : DEFAULT_DIMENSION; }); } sample::gLogWarning << "Dynamic dimensions required for input: " << iEnv.engine->getBindingName(b) << ", but no shapes were provided. Automatically overriding shape to: " @@ -132,7 +132,9 @@ bool setUpInference(InferenceEnvironment& iEnv, const InferenceOptions& inferenc const auto vecDim = iEnv.engine->getBindingVectorizedDim(b); const auto comps = iEnv.engine->getBindingComponentsPerElement(b); const auto dataType = iEnv.engine->getBindingDataType(b); - const auto vol = volume(dims, vecDim, comps, inference.batch); + const auto strides = iEnv.context.front()->getStrides(b); + const int batch = iEnv.engine->hasImplicitBatchDimension() ? inference.batch : 1; + const auto vol = volume(dims, strides, vecDim, comps, batch); const auto name = iEnv.engine->getBindingName(b); const auto isInput = iEnv.engine->bindingIsInput(b); for (auto& bindings : iEnv.bindings) @@ -298,50 +300,65 @@ class Iteration createEnqueueFunction(inference, context, bindings); } - void query() + void query(bool skipTransfers) { if (mActive[mNext]) { return; } - record(EventType::kINPUT_S, StreamType::kINPUT); - mBindings.transferInputToDevice(getStream(StreamType::kINPUT)); - record(EventType::kINPUT_E, StreamType::kINPUT); + if (!skipTransfers) + { + record(EventType::kINPUT_S, StreamType::kINPUT); + mBindings.transferInputToDevice(getStream(StreamType::kINPUT)); + record(EventType::kINPUT_E, StreamType::kINPUT); + wait(EventType::kINPUT_E, StreamType::kCOMPUTE); // Wait for input DMA before compute + } - wait(EventType::kINPUT_E, StreamType::kCOMPUTE); // Wait for input DMA before compute record(EventType::kCOMPUTE_S, StreamType::kCOMPUTE); recordEnqueueTime(); mEnqueue(getStream(StreamType::kCOMPUTE)); recordEnqueueTime(); record(EventType::kCOMPUTE_E, StreamType::kCOMPUTE); - wait(EventType::kCOMPUTE_E, StreamType::kOUTPUT); // Wait for compute before output DMA - record(EventType::kOUTPUT_S, StreamType::kOUTPUT); - mBindings.transferOutputToHost(getStream(StreamType::kOUTPUT)); - record(EventType::kOUTPUT_E, StreamType::kOUTPUT); + if (!skipTransfers) + { + wait(EventType::kCOMPUTE_E, StreamType::kOUTPUT); // Wait for compute before output DMA + record(EventType::kOUTPUT_S, StreamType::kOUTPUT); + mBindings.transferOutputToHost(getStream(StreamType::kOUTPUT)); + record(EventType::kOUTPUT_E, StreamType::kOUTPUT); + } mActive[mNext] = true; moveNext(); } - float sync(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector& trace) + float sync( + const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector& trace, bool skipTransfers) { if (mActive[mNext]) { - getEvent(EventType::kOUTPUT_E).synchronize(); - trace.emplace_back(getTrace(cpuStart, gpuStart)); + if (skipTransfers) + { + getEvent(EventType::kCOMPUTE_E).synchronize(); + } + else + { + getEvent(EventType::kOUTPUT_E).synchronize(); + } + trace.emplace_back(getTrace(cpuStart, gpuStart, skipTransfers)); mActive[mNext] = false; return getEvent(EventType::kCOMPUTE_S) - gpuStart; } return 0; } - void syncAll(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector& trace) + void syncAll( + const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, std::vector& trace, bool skipTransfers) { for (int d = 0; d < mDepth; ++d) { - sync(cpuStart, gpuStart, trace); + sync(cpuStart, gpuStart, trace, skipTransfers); moveNext(); } } @@ -351,6 +368,16 @@ class Iteration getStream(StreamType::kINPUT).wait(gpuStart); } + void setInputData() + { + mBindings.transferInputToDevice(getStream(StreamType::kINPUT)); + } + + void fetchOutputData() + { + mBindings.transferOutputToHost(getStream(StreamType::kOUTPUT)); + } + private: void moveNext() { @@ -388,14 +415,20 @@ class Iteration getStream(s).wait(getEvent(e)); } - InferenceTrace getTrace(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart) + InferenceTrace getTrace(const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, bool skipTransfers) { + float is + = skipTransfers ? getEvent(EventType::kCOMPUTE_S) - gpuStart : getEvent(EventType::kINPUT_S) - gpuStart; + float ie + = skipTransfers ? getEvent(EventType::kCOMPUTE_S) - gpuStart : getEvent(EventType::kINPUT_E) - gpuStart; + float os + = skipTransfers ? getEvent(EventType::kCOMPUTE_E) - gpuStart : getEvent(EventType::kOUTPUT_S) - gpuStart; + float oe + = skipTransfers ? getEvent(EventType::kCOMPUTE_E) - gpuStart : getEvent(EventType::kOUTPUT_E) - gpuStart; return InferenceTrace(mStreamId, std::chrono::duration(getEnqueueTime(true) - cpuStart).count(), - std::chrono::duration(getEnqueueTime(false) - cpuStart).count(), - getEvent(EventType::kINPUT_S) - gpuStart, getEvent(EventType::kINPUT_E) - gpuStart, - getEvent(EventType::kCOMPUTE_S) - gpuStart, getEvent(EventType::kCOMPUTE_E) - gpuStart, - getEvent(EventType::kOUTPUT_S) - gpuStart, getEvent(EventType::kOUTPUT_E) - gpuStart); + std::chrono::duration(getEnqueueTime(false) - cpuStart).count(), is, ie, + getEvent(EventType::kCOMPUTE_S) - gpuStart, getEvent(EventType::kCOMPUTE_E) - gpuStart, os, oe); } void createEnqueueFunction( @@ -441,7 +474,7 @@ class Iteration using IterationStreams = std::vector>; void inferenceLoop(IterationStreams& iStreams, const TimePoint& cpuStart, const TrtCudaEvent& gpuStart, int iterations, - float maxDurationMs, float warmupMs, std::vector& trace) + float maxDurationMs, float warmupMs, std::vector& trace, bool skipTransfers) { float durationMs = 0; int skip = 0; @@ -450,11 +483,11 @@ void inferenceLoop(IterationStreams& iStreams, const TimePoint& cpuStart, const { for (auto& s : iStreams) { - s->query(); + s->query(skipTransfers); } for (auto& s : iStreams) { - durationMs = std::max(durationMs, s->sync(cpuStart, gpuStart, trace)); + durationMs = std::max(durationMs, s->sync(cpuStart, gpuStart, trace, skipTransfers)); } if (durationMs < warmupMs) // Warming up { @@ -467,7 +500,7 @@ void inferenceLoop(IterationStreams& iStreams, const TimePoint& cpuStart, const } for (auto& s : iStreams) { - s->syncAll(cpuStart, gpuStart, trace); + s->syncAll(cpuStart, gpuStart, trace, skipTransfers); } } @@ -482,7 +515,12 @@ void inferenceExecution(const InferenceOptions& inference, InferenceEnvironment& IterationStreams iStreams; for (int s = 0; s < streams; ++s) { - iStreams.emplace_back(new Iteration(offset + s, inference, *iEnv.context[offset], *iEnv.bindings[offset])); + Iteration* iteration = new Iteration(offset + s, inference, *iEnv.context[offset], *iEnv.bindings[offset]); + if (inference.skipTransfers) + { + iteration->setInputData(); + } + iStreams.emplace_back(iteration); } for (auto& s : iStreams) @@ -491,7 +529,16 @@ void inferenceExecution(const InferenceOptions& inference, InferenceEnvironment& } std::vector localTrace; - inferenceLoop(iStreams, sync.cpuStart, sync.gpuStart, inference.iterations, durationMs, warmupMs, localTrace); + inferenceLoop(iStreams, sync.cpuStart, sync.gpuStart, inference.iterations, durationMs, warmupMs, localTrace, + inference.skipTransfers); + + if (inference.skipTransfers) + { + for (auto& s : iStreams) + { + s->fetchOutputData(); + } + } sync.mutex.lock(); trace.insert(trace.end(), localTrace.begin(), localTrace.end()); diff --git a/samples/common/sampleOptions.cpp b/samples/common/sampleOptions.cpp index acf49a06..d5e49098 100644 --- a/samples/common/sampleOptions.cpp +++ b/samples/common/sampleOptions.cpp @@ -93,7 +93,7 @@ nvinfer1::DataType stringToValue(const std::string& option) const std::unordered_map strToDT{{"fp32", nvinfer1::DataType::kFLOAT}, {"fp16", nvinfer1::DataType::kHALF}, {"int8", nvinfer1::DataType::kINT8}, {"int32", nvinfer1::DataType::kINT32}}; - auto dt = strToDT.find(option); + const auto dt = strToDT.find(option); if (dt == strToDT.end()) { throw std::invalid_argument("Invalid DataType " + option); @@ -108,11 +108,12 @@ nvinfer1::TensorFormats stringToValue(const std::string const std::unordered_map strToFmt{{"chw", nvinfer1::TensorFormat::kLINEAR}, {"chw2", nvinfer1::TensorFormat::kCHW2}, {"chw4", nvinfer1::TensorFormat::kCHW4}, {"hwc8", nvinfer1::TensorFormat::kHWC8}, {"chw16", nvinfer1::TensorFormat::kCHW16}, - {"chw32", nvinfer1::TensorFormat::kCHW32}}; + {"chw32", nvinfer1::TensorFormat::kCHW32}, {"dhwc8", nvinfer1::TensorFormat::kDHWC8}, + {"hwc", nvinfer1::TensorFormat::kHWC}}; nvinfer1::TensorFormats formats{}; for (auto f : optionStrings) { - auto tf = strToFmt.find(f); + const auto tf = strToFmt.find(f); if (tf == strToFmt.end()) { throw std::invalid_argument(std::string("Invalid TensorFormat ") + f); @@ -127,12 +128,13 @@ template <> IOFormat stringToValue(const std::string& option) { IOFormat ioFormat{}; - size_t colon = option.find(':'); + const size_t colon = option.find(':'); if (colon == std::string::npos) { throw std::invalid_argument(std::string("Invalid IOFormat ") + option); } + ioFormat.first = stringToValue(option.substr(0, colon)); ioFormat.second = stringToValue(option.substr(colon + 1)); @@ -335,6 +337,38 @@ std::ostream& printBatch(std::ostream& os, int maxBatch) return os; } +std::ostream& printTacticSources( + std::ostream& os, nvinfer1::TacticSources enabledSources, nvinfer1::TacticSources disabledSources) +{ + if (!enabledSources && !disabledSources) + { + os << "Using default tactic sources"; + } + else + { + uint32_t cublas = 1U << static_cast(nvinfer1::TacticSource::kCUBLAS); + uint32_t cublasLt = 1U << static_cast(nvinfer1::TacticSource::kCUBLAS_LT); + + if (enabledSources & cublas) + { + os << " +cublas"; + } + if (disabledSources & cublas) + { + os << " -cublas"; + } + if (enabledSources & cublasLt) + { + os << " +cublasLt"; + } + if (disabledSources & cublasLt) + { + os << " -cublasLt"; + } + } + return os; +} + std::ostream& printPrecision(std::ostream& os, const BuildOptions& options) { os << "FP32"; @@ -462,7 +496,7 @@ void BuildOptions::parse(Arguments& arguments) auto getFormats = [&arguments](std::vector& formatsVector, const char* argument) { std::string list; checkEraseOption(arguments, argument, list); - std::vector formats{splitToStringVec(list, ',')}; + const std::vector formats{splitToStringVec(list, ',')}; for (const auto& f : formats) { formatsVector.push_back(stringToValue(f)); @@ -519,6 +553,7 @@ void BuildOptions::parse(Arguments& arguments) fp16 = true; } + checkEraseOption(arguments, "--refit", refittable); checkEraseNegativeOption(arguments, "--noTF32", tf32); checkEraseOption(arguments, "--fp16", fp16); checkEraseOption(arguments, "--int8", int8); @@ -561,6 +596,55 @@ void BuildOptions::parse(Arguments& arguments) { throw std::invalid_argument("Incompatible load and save engine options selected"); } + + std::string tacticSourceArgs; + if (checkEraseOption(arguments, "--tacticSources", tacticSourceArgs)) + { + std::vector tacticList = splitToStringVec(tacticSourceArgs, ','); + for (auto& t : tacticList) + { + bool enable{false}; + if (t.front() == '+') + { + enable = true; + } + else if (t.front() != '-') + { + throw std::invalid_argument("Tactic conditional (+|-) is missing"); + } + t.erase(0, 1); + + nvinfer1::TacticSource source{}; + if (t == "cublas") + { + source = nvinfer1::TacticSource::kCUBLAS; + } + else if (t == "cublasLt") + { + source = nvinfer1::TacticSource::kCUBLAS_LT; + } + else + { + throw std::invalid_argument(std::string("Unknown tactic source: ") + t); + } + + uint32_t sourceBit = 1U << static_cast(source); + + if (enable) + { + enabledTactics |= sourceBit; + } + else + { + disabledTactics |= sourceBit; + } + + if (enabledTactics & disabledTactics) + { + throw std::invalid_argument(std::string("Cannot enable and disable ") + t); + } + } + } } void SystemOptions::parse(Arguments& arguments) @@ -587,9 +671,11 @@ void InferenceOptions::parse(Arguments& arguments) { overlap = !exposeDMA; } + checkEraseOption(arguments, "--noDataTransfers", skipTransfers); checkEraseOption(arguments, "--useSpinWait", spin); checkEraseOption(arguments, "--threads", threads); checkEraseOption(arguments, "--useCudaGraph", graph); + checkEraseOption(arguments, "--separateProfileRun", rerun); checkEraseOption(arguments, "--buildOnly", skip); std::string list; @@ -624,6 +710,7 @@ void ReportingOptions::parse(Arguments& arguments) checkEraseOption(arguments, "--percentile", percentile); checkEraseOption(arguments, "--avgRuns", avgs); checkEraseOption(arguments, "--verbose", verbose); + checkEraseOption(arguments, "--dumpRefit", refit); checkEraseOption(arguments, "--dumpOutput", output); checkEraseOption(arguments, "--dumpProfile", profile); checkEraseOption(arguments, "--exportTimes", exportTimes); @@ -651,6 +738,11 @@ void AllOptions::parse(Arguments& arguments) system.parse(arguments); inference.parse(arguments); + if (model.baseModel.format == ModelFormat::kONNX) + { + build.maxBatch = 0; // ONNX only supports explicit batch mode. + } + if ((!build.maxBatch && inference.batch && inference.batch != defaultBatch && !build.shapes.empty()) || (build.maxBatch && build.maxBatch != defaultMaxBatch && !inference.batch)) { @@ -881,6 +973,21 @@ std::ostream& operator<<(std::ostream& os, const IOFormat& format) os << "chw32"; break; } + case nvinfer1::TensorFormat::kDHWC8: + { + os << "dhwc8"; + break; + } + case nvinfer1::TensorFormat::kCDHW32: + { + os << "cdhw32"; + break; + } + case nvinfer1::TensorFormat::kHWC: + { + os << "hwc"; + break; + } } } } @@ -908,16 +1015,18 @@ std::ostream& operator<<(std::ostream& os, const BuildOptions& options) os << "=== Build Options ===" << std::endl << "Max batch: "; printBatch(os, options.maxBatch) << std::endl << - "Workspace: " << options.workspace << " MB" << std::endl << + "Workspace: " << options.workspace << " MiB" << std::endl << "minTiming: " << options.minTiming << std::endl << "avgTiming: " << options.avgTiming << std::endl << "Precision: "; printPrecision(os, options) << std::endl << "Calibration: " << (options.int8 && options.calibration.empty() ? "Dynamic" : options.calibration.c_str()) << std::endl << + "Refit: " << boolToEnabled(options.refittable) << std::endl << "Safe mode: " << boolToEnabled(options.safe) << std::endl << "Save engine: " << (options.save ? options.engine : "") << std::endl << "Load engine: " << (options.load ? options.engine : "") << std::endl << "Builder Cache: " << boolToEnabled(options.builderCache) << std::endl << - "NVTX verbosity: " << static_cast(options.nvtxMode) << std::endl; + "NVTX verbosity: " << static_cast(options.nvtxMode) << std::endl << + "Tactic sources: "; printTacticSources(os, options.enabledTactics, options.disabledTactics) << std::endl; // clang-format on auto printIOFormats = [](std::ostream& os, const char* direction, const std::vector formats) { @@ -934,8 +1043,8 @@ std::ostream& operator<<(std::ostream& os, const BuildOptions& options) } }; - printIOFormats(os, "Input", options.inputFormats); - printIOFormats(os, "Output", options.outputFormats); + printIOFormats(os, "Input(s)", options.inputFormats); + printIOFormats(os, "Output(s)", options.outputFormats); printShapes(os, "build", options.shapes); printShapes(os, "calibration", options.shapesCalib); @@ -964,28 +1073,30 @@ std::ostream& operator<<(std::ostream& os, const SystemOptions& options) std::ostream& operator<<(std::ostream& os, const InferenceOptions& options) { // clang-format off - os << "=== Inference Options ===" << std::endl << + os << "=== Inference Options ===" << std::endl << "Batch: "; if (options.batch && options.shapes.empty()) { - os << options.batch << std::endl; + os << options.batch << std::endl; } else { - os << "Explicit" << std::endl; + os << "Explicit" << std::endl; } printShapes(os, "inference", options.shapes); - os << "Iterations: " << options.iterations << std::endl << - "Duration: " << options.duration << "s (+ " - << options.warmup << "ms warm up)" << std::endl << - "Sleep time: " << options.sleep << "ms" << std::endl << - "Streams: " << options.streams << std::endl << - "ExposeDMA: " << boolToEnabled(!options.overlap) << std::endl << - "Spin-wait: " << boolToEnabled(options.spin) << std::endl << - "Multithreading: " << boolToEnabled(options.threads) << std::endl << - "CUDA Graph: " << boolToEnabled(options.graph) << std::endl << - "Skip inference: " << boolToEnabled(options.skip) << std::endl; + os << "Iterations: " << options.iterations << std::endl << + "Duration: " << options.duration << "s (+ " + << options.warmup << "ms warm up)" << std::endl << + "Sleep time: " << options.sleep << "ms" << std::endl << + "Streams: " << options.streams << std::endl << + "ExposeDMA: " << boolToEnabled(!options.overlap) << std::endl << + "Data transfers: " << boolToEnabled(!options.skipTransfers) << std::endl << + "Spin-wait: " << boolToEnabled(options.spin) << std::endl << + "Multithreading: " << boolToEnabled(options.threads) << std::endl << + "CUDA Graph: " << boolToEnabled(options.graph) << std::endl << + "Separate profiling: " << boolToEnabled(options.rerun) << std::endl << + "Skip inference: " << boolToEnabled(options.skip) << std::endl; // clang-format on os << "Inputs:" << std::endl; @@ -1000,16 +1111,17 @@ std::ostream& operator<<(std::ostream& os, const InferenceOptions& options) std::ostream& operator<<(std::ostream& os, const ReportingOptions& options) { // clang-format off - os << "=== Reporting Options ===" << std::endl << - - "Verbose: " << boolToEnabled(options.verbose) << std::endl << - "Averages: " << options.avgs << " inferences" << std::endl << - "Percentile: " << options.percentile << std::endl << - "Dump output: " << boolToEnabled(options.output) << std::endl << - "Profile: " << boolToEnabled(options.profile) << std::endl << - "Export timing to JSON file: " << options.exportTimes << std::endl << - "Export output to JSON file: " << options.exportOutput << std::endl << - "Export profile to JSON file: " << options.exportProfile << std::endl; + os << "=== Reporting Options ===" << std::endl << + + "Verbose: " << boolToEnabled(options.verbose) << std::endl << + "Averages: " << options.avgs << " inferences" << std::endl << + "Percentile: " << options.percentile << std::endl << + "Dump refittable layers:" << boolToEnabled(options.refit) << std::endl << + "Dump output: " << boolToEnabled(options.output) << std::endl << + "Profile: " << boolToEnabled(options.profile) << std::endl << + "Export timing to JSON file: " << options.exportTimes << std::endl << + "Export output to JSON file: " << options.exportOutput << std::endl << + "Export profile to JSON file: " << options.exportProfile << std::endl; // clang-format on return os; @@ -1055,51 +1167,62 @@ void ModelOptions::help(std::ostream& os) void BuildOptions::help(std::ostream& os) { // clang-format off - os << "=== Build Options ===" << std::endl << - - " --maxBatch Set max batch size and build an implicit batch engine (default = " << defaultMaxBatch << ")" << std::endl << - " --explicitBatch Use explicit batch sizes when building the engine (default = implicit)" << std::endl << - " --minShapes=spec Build with dynamic shapes using a profile with the min shapes provided" << std::endl << - " --optShapes=spec Build with dynamic shapes using a profile with the opt shapes provided" << std::endl << - " --maxShapes=spec Build with dynamic shapes using a profile with the max shapes provided" << std::endl << - " --minShapesCalib=spec Calibrate with dynamic shapes using a profile with the min shapes provided" << std::endl << - " --optShapesCalib=spec Calibrate with dynamic shapes using a profile with the opt shapes provided" << std::endl << - " --maxShapesCalib=spec Calibrate with dynamic shapes using a profile with the max shapes provided" << std::endl << - " Note: All three of min, opt and max shapes must be supplied." << std::endl << - " However, if only opt shapes is supplied then it will be expanded so" << std::endl << - " that min shapes and max shapes are set to the same values as opt shapes." << std::endl << - " In addition, use of dynamic shapes implies explicit batch." << std::endl << - " Input names can be wrapped with escaped single quotes (ex: \\\'Input:0\\\')." << std::endl << - " Example input shapes spec: input0:1x3x256x256,input1:1x3x128x128" << std::endl << - " Each input shape is supplied as a key-value pair where key is the input name and" << std::endl << - " value is the dimensions (including the batch dimension) to be used for that input." << std::endl << - " Each key-value pair has the key and value separated using a colon (:)." << std::endl << - " Multiple input shapes can be provided via comma-separated key-value pairs." << std::endl << - " --inputIOFormats=spec Type and formats of the input tensors (default = all inputs in fp32:chw)" << std::endl << - " Note: If this option is specified, please make sure that all inputs are in the same order " << std::endl << - " as network inputs ID." << std::endl << - " --outputIOFormats=spec Type and formats of the output tensors (default = all outputs in fp32:chw)" << std::endl << - " Note: If this option is specified, please make sure that all outputs are in the same order " << std::endl << - " as network outputs ID." << std::endl << - " IO Formats: spec ::= IOfmt[\",\"spec]" << std::endl << - " IOfmt ::= type:fmt" << std::endl << - " type ::= \"fp32\"|\"fp16\"|\"int32\"|\"int8\"" << std::endl << - " fmt ::= (\"chw\"|\"chw2\"|\"chw4\"|\"hwc8\"|\"chw16\"|\"chw32\")[\"+\"fmt]" << std::endl << - " --workspace=N Set workspace size in megabytes (default = " << defaultWorkspace << ")" << std::endl << - " --noBuilderCache Disable timing cache in builder (default is to enable timing cache)" << std::endl << - " --nvtxMode=[default|verbose|none] Specify NVTX annotation verbosity" << std::endl << + os << "=== Build Options ===" << std::endl << + + " --maxBatch Set max batch size and build an implicit batch engine (default = " << defaultMaxBatch << ")" << std::endl << + " --explicitBatch Use explicit batch sizes when building the engine (default = implicit)" << std::endl << + " --minShapes=spec Build with dynamic shapes using a profile with the min shapes provided" << std::endl << + " --optShapes=spec Build with dynamic shapes using a profile with the opt shapes provided" << std::endl << + " --maxShapes=spec Build with dynamic shapes using a profile with the max shapes provided" << std::endl << + " --minShapesCalib=spec Calibrate with dynamic shapes using a profile with the min shapes provided" << std::endl << + " --optShapesCalib=spec Calibrate with dynamic shapes using a profile with the opt shapes provided" << std::endl << + " --maxShapesCalib=spec Calibrate with dynamic shapes using a profile with the max shapes provided" << std::endl << + " Note: All three of min, opt and max shapes must be supplied." << std::endl << + " However, if only opt shapes is supplied then it will be expanded so" << std::endl << + " that min shapes and max shapes are set to the same values as opt shapes." << std::endl << + " In addition, use of dynamic shapes implies explicit batch." << std::endl << + " Input names can be wrapped with escaped single quotes (ex: \\\'Input:0\\\')." << std::endl << + " Example input shapes spec: input0:1x3x256x256,input1:1x3x128x128" << std::endl << + " Each input shape is supplied as a key-value pair where key is the input name and" << std::endl << + " value is the dimensions (including the batch dimension) to be used for that input." << std::endl << + " Each key-value pair has the key and value separated using a colon (:)." << std::endl << + " Multiple input shapes can be provided via comma-separated key-value pairs." << std::endl << + " --inputIOFormats=spec Type and format of each of the input tensors (default = all inputs in fp32:chw)" << std::endl << + " See --outputIOFormats help for the grammar of type and format list." << std::endl << + " Note: If this option is specified, please set comma-separated types and formats for all" << std::endl << + " inputs following the same order as network inputs ID (even if only one input" << std::endl << + " needs specifying IO format) or set the type and format once for broadcasting." << std::endl << + " --outputIOFormats=spec Type and format of each of the output tensors (default = all outputs in fp32:chw)" << std::endl << + " Note: If this option is specified, please set comma-separated types and formats for all" << std::endl << + " outputs following the same order as network outputs ID (even if only one output" << std::endl << + " needs specifying IO format) or set the type and format once for broadcasting." << std::endl << + " IO Formats: spec ::= IOfmt[\",\"spec]" << std::endl << + " IOfmt ::= type:fmt" << std::endl << + " type ::= \"fp32\"|\"fp16\"|\"int32\"|\"int8\"" << std::endl << + " fmt ::= (\"chw\"|\"chw2\"|\"chw4\"|\"hwc8\"|\"chw16\"|\"chw32\"|\"dhwc8\")[\"+\"fmt]" << std::endl << + " --workspace=N Set workspace size in megabytes (default = " << defaultWorkspace << ")" << std::endl << + " --noBuilderCache Disable timing cache in builder (default is to enable timing cache)" << std::endl << + " --nvtxMode=mode Specify NVTX annotation verbosity. mode ::= default|verbose|none" << std::endl << " --minTiming=M Set the minimum number of iterations used in kernel selection (default = " - << defaultMinTiming << ")" << std::endl << + << defaultMinTiming << ")" << std::endl << " --avgTiming=M Set the number of times averaged in each iteration for kernel selection (default = " - << defaultAvgTiming << ")" << std::endl << - " --noTF32 Disable tf32 precision (default is to enable tf32, in addition to fp32)" << std::endl << - " --fp16 Enable fp16 precision, in addition to fp32 (default = disabled)" << std::endl << - " --int8 Enable int8 precision, in addition to fp32 (default = disabled)" << std::endl << - " --best Enable all precisions to achieve the best performance (default = disabled)" << std::endl << - " --calib= Read INT8 calibration cache file" << std::endl << - " --safe Only test the functionality available in safety restricted flows" << std::endl << - " --saveEngine= Save the serialized engine" << std::endl << - " --loadEngine= Load a serialized engine" << std::endl; + << defaultAvgTiming << ")" << std::endl << + " --noTF32 Disable tf32 precision (default is to enable tf32, in addition to fp32)" << std::endl << + " --refit Mark the engine as refittable. This will allow the inspection of refittable layers " << std::endl << + " and weights within the engine." << std::endl << + " --fp16 Enable fp16 precision, in addition to fp32 (default = disabled)" << std::endl << + " --int8 Enable int8 precision, in addition to fp32 (default = disabled)" << std::endl << + " --best Enable all precisions to achieve the best performance (default = disabled)" << std::endl << + " --calib= Read INT8 calibration cache file" << std::endl << + " --safe Only test the functionality available in safety restricted flows" << std::endl << + " --saveEngine= Save the serialized engine" << std::endl << + " --loadEngine= Load a serialized engine" << std::endl << + " --tacticSources=tactics Specify the tactics to be used by adding (+) or removing (-) tactics from the default " << std::endl << + " tactic sources (default = all available tactics)." << std::endl << + " Note: Currently only cuBLAS and cuBLAS LT are listed as optional tactics." << std::endl << + " Tactic Sources: tactics ::= [\",\"tactic]" << std::endl << + " tactic ::= (+|-)lib" << std::endl << + " lib ::= \"cublas\"|\"cublasLt\"" << std::endl; // clang-format on } @@ -1141,10 +1264,13 @@ void InferenceOptions::help(std::ostream& os) "(default = " << defaultSleep << ")" << std::endl << " --streams=N Instantiate N engines to use concurrently (default = " << defaultStreams << ")" << std::endl << " --exposeDMA Serialize DMA transfers to and from device. (default = disabled)" << std::endl << + " --noDataTransfers Do not transfer data to and from the device during inference. (default = disabled)" << std::endl << " --useSpinWait Actively synchronize on GPU events. This option may decrease synchronization time but " "increase CPU usage and power (default = disabled)" << std::endl << " --threads Enable multithreading to drive engines with independent threads (default = disabled)" << std::endl << " --useCudaGraph Use cuda graph to capture engine execution and then launch inference (default = disabled)" << std::endl << + " --separateProfileRun Do not attach the profiler in the benchmark run; if profiling is enabled, a second " + "profile run will be executed (default = disabled)" << std::endl << " --buildOnly Skip inference perf measurement (default = disabled)" << std::endl; // clang-format on } @@ -1159,6 +1285,8 @@ void ReportingOptions::help(std::ostream& os) " --percentile=P Report performance for the P percentage (0<=P<=100, 0 " "representing max perf, and 100 representing min perf; (default" " = " << defaultPercentile << "%)" << std::endl << + " --dumpRefit Print the refittable layers and weights from a refittable " + "engine" << std::endl << " --dumpOutput Print the output tensor(s) of the last inference iteration " "(default = disabled)" << std::endl << " --dumpProfile Print profile information per layer (default = disabled)" << std::endl << diff --git a/samples/common/sampleOptions.h b/samples/common/sampleOptions.h index dc3fdae0..7c313dbc 100644 --- a/samples/common/sampleOptions.h +++ b/samples/common/sampleOptions.h @@ -110,6 +110,7 @@ struct BuildOptions : public Options int minTiming{defaultMinTiming}; int avgTiming{defaultAvgTiming}; bool tf32{true}; + bool refittable{false}; bool fp16{false}; bool int8{false}; bool safe{false}; @@ -123,7 +124,8 @@ struct BuildOptions : public Options std::unordered_map shapesCalib; std::vector inputFormats; std::vector outputFormats; - + nvinfer1::TacticSources enabledTactics{0}; + nvinfer1::TacticSources disabledTactics{0}; void parse(Arguments& arguments) override; static void help(std::ostream& out); @@ -150,10 +152,12 @@ struct InferenceOptions : public Options int sleep{defaultSleep}; int streams{defaultStreams}; bool overlap{true}; + bool skipTransfers{false}; bool spin{false}; bool threads{false}; bool graph{false}; bool skip{false}; + bool rerun{false}; std::unordered_map inputs; std::unordered_map> shapes; @@ -167,6 +171,7 @@ struct ReportingOptions : public Options bool verbose{false}; int avgs{defaultAvgRuns}; float percentile{defaultPercentile}; + bool refit{false}; bool output{false}; bool profile{false}; std::string exportTimes; diff --git a/samples/common/sampleReporting.cpp b/samples/common/sampleReporting.cpp index 9d8fb749..57d57046 100644 --- a/samples/common/sampleReporting.cpp +++ b/samples/common/sampleReporting.cpp @@ -205,6 +205,11 @@ void printPerformanceReport(const std::vector& trace, const Repo std::transform(noWarmup, trace.end(), timings.begin(), traceToTiming); printTiming(timings, reporting.avgs, os); printEpilog(timings, benchTime, reporting.percentile, queries, os); + + if (!reporting.exportTimes.empty()) + { + exportJSONTrace(trace, reporting.exportTimes); + } } //! Printed format: @@ -281,13 +286,13 @@ void Profiler::print(std::ostream& os) const { // clang off os << std::setw(nameLength) << p.name << std::setw(timeLength) << std::fixed << std::setprecision(2) << p.timeMs - << std::setw(avgLength) << std::fixed << std::setprecision(2) << p.timeMs / mUpdatesCount + << std::setw(avgLength) << std::fixed << std::setprecision(4) << p.timeMs / mUpdatesCount << std::setw(percentageLength) << std::fixed << std::setprecision(1) << p.timeMs / totalTimeMs * 100 << std::endl; } { os << std::setw(nameLength) << "Total" << std::setw(timeLength) << std::fixed << std::setprecision(2) - << totalTimeMs << std::setw(avgLength) << std::fixed << std::setprecision(2) << totalTimeMs / mUpdatesCount + << totalTimeMs << std::setw(avgLength) << std::fixed << std::setprecision(4) << totalTimeMs / mUpdatesCount << std::setw(percentageLength) << std::fixed << std::setprecision(1) << 100.0 << std::endl; // clang on } diff --git a/samples/common/sampleUtils.h b/samples/common/sampleUtils.h index a6b11e43..2f0c3d28 100644 --- a/samples/common/sampleUtils.h +++ b/samples/common/sampleUtils.h @@ -34,12 +34,27 @@ #include "NvInfer.h" +#include "common.h" #include "logger.h" #include "sampleDevice.h" +#include "sampleOptions.h" namespace sample { +inline int dataTypeSize(nvinfer1::DataType dataType) +{ + switch (dataType) + { + case nvinfer1::DataType::kINT32: + case nvinfer1::DataType::kFLOAT: return 4; + case nvinfer1::DataType::kHALF: return 2; + case nvinfer1::DataType::kBOOL: + case nvinfer1::DataType::kINT8: return 1; + } + return 0; +} + template inline T roundUp(T m, T n) { @@ -51,6 +66,27 @@ inline int volume(const nvinfer1::Dims& d) return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies()); } +inline int volume(const nvinfer1::Dims& dims, const nvinfer1::Dims& strides, int vecDim, int comps, int batch) +{ + int maxNbElems = 1; + for (int i = 0; i < dims.nbDims; ++i) + { + // Get effective length of axis. + int d = dims.d[i]; + // Any dimension is 0, it is an empty tensor. + if (d == 0) + { + return 0; + } + if (i == vecDim) + { + d = samplesCommon::divUp(d, comps); + } + maxNbElems = std::max(maxNbElems, d * strides.d[i]); + } + return maxNbElems * batch * (vecDim < 0 ? 1 : comps); +} + inline int volume(nvinfer1::Dims dims, int vecDim, int comps, int batch) { if (vecDim != -1) @@ -78,6 +114,40 @@ inline std::ostream& operator<<(std::ostream& os, const std::vector& vec) return os; } +inline std::ostream& operator<<(std::ostream& os, const nvinfer1::WeightsRole role) +{ + switch (role) + { + case nvinfer1::WeightsRole::kKERNEL: + { + os << "Kernel"; + break; + } + case nvinfer1::WeightsRole::kBIAS: + { + os << "Bias"; + break; + } + case nvinfer1::WeightsRole::kSHIFT: + { + os << "Shift"; + break; + } + case nvinfer1::WeightsRole::kSCALE: + { + os << "Scale"; + break; + } + case nvinfer1::WeightsRole::kCONSTANT: + { + os << "Constant"; + break; + } + } + + return os; +} + inline nvinfer1::Dims toDims(const std::vector& vec) { int limit = static_cast(nvinfer1::Dims::MAX_DIMS); @@ -91,19 +161,6 @@ inline nvinfer1::Dims toDims(const std::vector& vec) return dims; } -inline int dataTypeSize(nvinfer1::DataType dataType) -{ - switch (dataType) - { - case nvinfer1::DataType::kINT32: - case nvinfer1::DataType::kFLOAT: return 4; - case nvinfer1::DataType::kHALF: return 2; - case nvinfer1::DataType::kBOOL: - case nvinfer1::DataType::kINT8: return 1; - } - return 0; -} - template inline void fillBuffer(void* buffer, int volume, T min, T max) { @@ -408,6 +465,26 @@ struct TrtDestroyer template using TrtUniquePtr = std::unique_ptr>; +inline bool broadcastIOFormats(const std::vector& formats, size_t nbBindings, bool isInput = true) +{ + bool broadcast = formats.size() == 1; + bool validFormatsCount = broadcast || (formats.size() == nbBindings); + if (!formats.empty() && !validFormatsCount) + { + if (isInput) + { + throw std::invalid_argument( + "The number of inputIOFormats must match network's inputs or be one for broadcasting."); + } + else + { + throw std::invalid_argument( + "The number of outputIOFormats must match network's outputs or be one for broadcasting."); + } + } + return broadcast; +} + } // namespace sample #endif // TRT_SAMPLE_UTILS_H diff --git a/samples/common/windows/getopt.h b/samples/common/windows/getopt.h index 9fc4e24a..2fa652e1 100644 --- a/samples/common/windows/getopt.h +++ b/samples/common/windows/getopt.h @@ -26,17 +26,18 @@ #endif #ifdef __cplusplus -extern "C" { +extern "C" +{ #endif -WINGETOPT_API extern int optind; /* index of first non-option in argv */ -WINGETOPT_API extern int optopt; /* single option character, as parsed */ -WINGETOPT_API extern int opterr; /* flag to enable built-in diagnostics... */ -/* (user may set to zero, to suppress) */ + WINGETOPT_API extern int optind; /* index of first non-option in argv */ + WINGETOPT_API extern int optopt; /* single option character, as parsed */ + WINGETOPT_API extern int opterr; /* flag to enable built-in diagnostics... */ + /* (user may set to zero, to suppress) */ -WINGETOPT_API extern char* optarg; /* pointer to argument of current option */ + WINGETOPT_API extern char* optarg; /* pointer to argument of current option */ -extern int getopt(int nargc, char* const* nargv, const char* options); + extern int getopt(int nargc, char* const* nargv, const char* options); #ifdef _BSD_SOURCE /* @@ -46,7 +47,7 @@ extern int getopt(int nargc, char* const* nargv, const char* options); * to maintain portability, developers are advised to avoid it. */ #define optreset __mingw_optreset -extern int optreset; + extern int optreset; #endif #ifdef __cplusplus } @@ -66,27 +67,29 @@ extern int optreset; #define __GETOPT_LONG_H__ #ifdef __cplusplus -extern "C" { +extern "C" +{ #endif -struct option /* specification for a long form option... */ -{ - const char* name; /* option name, without leading hyphens */ - int has_arg; /* does it take an argument? */ - int* flag; /* where to save its status, or NULL */ - int val; /* its associated status value */ -}; + struct option /* specification for a long form option... */ + { + const char* name; /* option name, without leading hyphens */ + int has_arg; /* does it take an argument? */ + int* flag; /* where to save its status, or NULL */ + int val; /* its associated status value */ + }; -enum /* permitted values for its `has_arg' field... */ -{ - no_argument = 0, /* option never takes an argument */ - required_argument, /* option always requires an argument */ - optional_argument /* option may take an argument */ -}; + enum /* permitted values for its `has_arg' field... */ + { + no_argument = 0, /* option never takes an argument */ + required_argument, /* option always requires an argument */ + optional_argument /* option may take an argument */ + }; -extern int getopt_long(int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); -extern int getopt_long_only( - int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); + extern int getopt_long( + int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); + extern int getopt_long_only( + int nargc, char* const* nargv, const char* options, const struct option* long_options, int* idx); /* * Previous MinGW implementation had... */ diff --git a/samples/opensource/sampleCharRNN/sampleCharRNN.cpp b/samples/opensource/sampleCharRNN/sampleCharRNN.cpp index 9d75f2d5..939504c5 100644 --- a/samples/opensource/sampleCharRNN/sampleCharRNN.cpp +++ b/samples/opensource/sampleCharRNN/sampleCharRNN.cpp @@ -149,6 +149,7 @@ class SampleCharRNNBase : mParams(params) { } + virtual ~SampleCharRNNBase() = default; //! //! \brief Builds the network engine @@ -182,6 +183,7 @@ class SampleCharRNNBase nvinfer1::Weights convertRNNBias(nvinfer1::Weights input); std::map mWeightMap; + std::vector> weightsMemory; SampleCharRNNParams mParams; nvinfer1::ITensor* addReshape( @@ -399,7 +401,10 @@ std::map SampleCharRNNBase::loadWeights(const st // Read weight values input.seekg(input.tellg() + static_cast(1)); // skip space char - char* wtVals = new char[numOfBytes]; + // We do not really care about the setup of DataType here. Use char here to avoid additional conversion + auto mem = new samplesCommon::TypedHostMemory(numOfBytes); + weightsMemory.emplace_back(mem); + auto wtVals = mem->raw(); input.read(wtVals, numOfBytes); input.seekg(input.tellg() + static_cast(1)); // skip new-line char wt.values = wtVals; @@ -436,7 +441,9 @@ std::map SampleCharRNNBase::loadWeights(const st nvinfer1::Weights SampleCharRNNBase::convertRNNWeights(nvinfer1::Weights orig, int dataSize) { nvinfer1::Weights input{orig.type, orig.values, (dataSize + mParams.hiddenSize) * 4 * mParams.hiddenSize}; - float* ptr = new float[input.count]; + auto mem = new samplesCommon::FloatMemory(input.count); + weightsMemory.emplace_back(mem); + auto ptr = mem->raw(); const float* data = static_cast(input.values); int dimsW[2]{dataSize, 4 * mParams.hiddenSize}; int dimsR[2]{mParams.hiddenSize, 4 * mParams.hiddenSize}; @@ -463,7 +470,9 @@ nvinfer1::Weights SampleCharRNNBase::convertRNNWeights(nvinfer1::Weights orig, i //! we double the size and set all of U to zero. nvinfer1::Weights SampleCharRNNBase::convertRNNBias(nvinfer1::Weights input) { - float* ptr = new float[input.count * 2]; + auto mem = new samplesCommon::FloatMemory(input.count * 2); + weightsMemory.emplace_back(mem); + auto ptr = mem->raw(); const float* iptr = static_cast(input.values); int64_t count = 4 * mParams.hiddenSize; ASSERT(input.count == count); @@ -938,12 +947,6 @@ void SampleCharRNNBase::copyRNNOutputsToInputs(samplesCommon::BufferManager& buf //! bool SampleCharRNNBase::teardown() { - // Clean up runtime resources - for (auto& mem : mWeightMap) - { - delete[] static_cast(mem.second.values); - } - return true; } diff --git a/samples/opensource/sampleINT8/int8_caffe.patch b/samples/opensource/sampleINT8/int8_caffe.patch deleted file mode 100644 index db949941..00000000 --- a/samples/opensource/sampleINT8/int8_caffe.patch +++ /dev/null @@ -1,31 +0,0 @@ -diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp -index 66e6301..da615e5 100644 ---- a/src/caffe/layers/data_layer.cpp -+++ b/src/caffe/layers/data_layer.cpp -@@ -99,6 +99,26 @@ void DataLayer::load_batch(Batch* batch) { - } - timer.Stop(); - batch_timer.Stop(); -+#define LOG_BATCHES_FOR_INT8_TESTING 1 -+#if LOG_BATCHES_FOR_INT8_TESTING -+ static int sBatchId = 0; -+ char* batch_dump_dir = getenv("TENSORRT_INT8_BATCH_DIRECTORY"); -+ if(batch_dump_dir != 0) -+ { -+ char buffer[1000]; -+ sprintf(buffer, "batches/batch%d", sBatchId++); -+ FILE* file = fopen(buffer, "w"); -+ if(file==0) -+ abort(); -+ -+ int s[4] = { top_shape[0], top_shape[1], top_shape[2], top_shape[3] }; -+ fwrite(s, sizeof(int), 4, file); -+ fwrite(top_data, sizeof(float), top_shape[0]*top_shape[1]*top_shape[2]*top_shape[3], file); -+ fwrite(&top_label[0], sizeof(int), top_shape[0], file); -+ fclose(file); -+ } -+#endif -+ - DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms."; - DLOG(INFO) << " Read time: " << read_time / 1000 << " ms."; - DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms."; | fromdos diff --git a/samples/opensource/sampleINT8API/README.md b/samples/opensource/sampleINT8API/README.md index 0d27e3ac..784e28ac 100644 --- a/samples/opensource/sampleINT8API/README.md +++ b/samples/opensource/sampleINT8API/README.md @@ -33,7 +33,7 @@ Specifically, this sample demonstrates how to: In order to perform INT8 inference, you need to provide TensorRT with the dynamic range for each network tensor, including network input and output tensor. One way to choose the dynamic range is to use the TensorRT INT8 calibrator. But if you don't want to go that route (for example, let’s say you used quantization-aware training or you just want to use the min and max tensor values seen during training), you can skip the INT8 calibration and set custom per-network tensor dynamic ranges. This sample implements INT8 inference for the ONNX ResNet-50 model using per-network tensor dynamic ranges specified in an input file. -This sample uses the [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/resnet50). +This sample uses the [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/model). Specifically, this sample performs the following steps: - [Configuring the builder to use INT8 without the INT8 calibrator](#configuring-the-builder-to-use-int8-without-the-int8-calibrator) @@ -160,7 +160,7 @@ The ResNet-50 per tensor dynamic ranges file. `airliner.ppm` The image to be inferred. -1. Download the ONNX ResNet-50 model. +1. Download the [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/model). `wget https://s3.amazonaws.com/download.onnx/models/opset_9/resnet50.tar.gz` 2. Unpackage the model file. @@ -283,7 +283,7 @@ The following resources provide a deeper understanding how to perform inference - [8-bit Inference with TensorRT](http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf) **Models:** -- [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/resnet50) +- [ONNX ResNet-50 model](https://github.com/onnx/models/tree/master/vision/classification/resnet/model) - [Image Classification Model Files](https://github.com/onnx/models/tree/master/vision/classification) **Blogs:** diff --git a/samples/opensource/sampleINT8API/sampleINT8API.cpp b/samples/opensource/sampleINT8API/sampleINT8API.cpp index 2350654e..4eef64d5 100644 --- a/samples/opensource/sampleINT8API/sampleINT8API.cpp +++ b/samples/opensource/sampleINT8API/sampleINT8API.cpp @@ -392,31 +392,6 @@ bool SampleINT8API::setDynamicRange(SampleUniquePtrgetNbLayers(); ++i) - { - for (int j = 0; j < network->getLayer(i)->getNbOutputs(); ++j) - { - std::string tName = network->getLayer(i)->getOutput(j)->getName(); - if (mPerTensorDynamicRangeMap.find(tName) != mPerTensorDynamicRangeMap.end()) - { - // Calibrator generated dynamic range for network tensor can be overriden or set using below API - if (!network->getLayer(i)->getOutput(j)->setDynamicRange( - -mPerTensorDynamicRangeMap.at(tName), mPerTensorDynamicRangeMap.at(tName))) - { - return false; - } - } - else - { - if (mParams.verbose) - { - sample::gLogWarning << "Missing dynamic range for tensor: " << tName << std::endl; - } - } - } - } - if (mParams.verbose) { sample::gLogInfo << "Per Tensor Dynamic Range Values for the Network:" << std::endl; @@ -445,9 +420,6 @@ bool SampleINT8API::prepareInput(const samplesCommon::BufferManager& buffers) std::string magic{""}; std::vector fileData(channels * height * width); - // Prepardde PPM Buffer to read the input image - // samplesCommon::PPM ppm; - // samplesCommon::readPPMFile(mParams.imageFileName, ppm); std::ifstream infile(mParams.imageFileName, std::ifstream::binary); assert(infile.is_open() && "Attempting to read from a file that is not open."); @@ -486,7 +458,7 @@ bool SampleINT8API::verifyOutput(const samplesCommon::BufferManager& buffers) co const float* probPtr = static_cast(buffers.getHostBuffer(mInOut.at("output"))); std::vector output(probPtr, probPtr + mOutputDims.d[1]); - auto inds = samplesCommon::argsort(output.cbegin(), output.cend(), true); + auto inds = samplesCommon::argMagnitudeSort(output.cbegin(), output.cend()); // read reference lables to generate prediction lables std::vector referenceVector; @@ -578,8 +550,6 @@ sample::Logger::TestResult SampleINT8API::build() config->setInt8Calibrator(nullptr); // force layer to execute with required precision - // NVBUG: http://nvbugs/2725746 - // config->setFlag(BuilderFlag::kSTRICT_TYPES); setLayerPrecision(network); // set INT8 Per Tensor Dynamic range diff --git a/samples/opensource/sampleMLP/README.md b/samples/opensource/sampleMLP/README.md index 92a2bd5e..ee3cab19 100644 --- a/samples/opensource/sampleMLP/README.md +++ b/samples/opensource/sampleMLP/README.md @@ -173,4 +173,4 @@ This `README.md` file was recreated, updated and reviewed. # Known issues -There are no known issues in this sample. +- Fake INT8 dynamic ranges are used in this sample. So there might be an accuracy loss when running the sample under INT8 mode, which would consequently lead a wrong classification result. diff --git a/samples/opensource/sampleMLP/sampleMLP.cpp b/samples/opensource/sampleMLP/sampleMLP.cpp index cb310f37..5893e854 100644 --- a/samples/opensource/sampleMLP/sampleMLP.cpp +++ b/samples/opensource/sampleMLP/sampleMLP.cpp @@ -91,6 +91,8 @@ class SampleMLP std::shared_ptr mEngine; //!< The TensorRT engine used to run the network + std::vector> weightsMemory; //!< Host weights memory holder + //! //! \brief Uses the API to create the MLP Network //! @@ -346,15 +348,6 @@ bool SampleMLP::verifyOutput(const samplesCommon::BufferManager& buffers) //! bool SampleMLP::teardown() { - // Release weights host memory - for (auto& mem : mWeightMap) - { - auto weight = mem.second.second; - { - delete[] static_cast(weight.values); - } - } - return true; } @@ -385,8 +378,9 @@ std::map> SampleMLP::l wt.second.count = std::accumulate(wt.first.d, wt.first.d + wt.first.nbDims, 1, std::multiplies()); assert(wt.second.type == nvinfer1::DataType::kFLOAT); - float* value = new float[wt.second.count]; - input.read(reinterpret_cast(value), wt.second.count * sizeof(float)); + weightsMemory.emplace_back(new samplesCommon::FloatMemory(wt.second.count)); + auto value = weightsMemory.back()->data(); + input.read(static_cast(value), wt.second.count * sizeof(float)); assert(input.peek() == '\n'); // Consume the newline at the end of the data blob. input.get(); diff --git a/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp b/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp index ec3edca9..54271b1f 100644 --- a/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp +++ b/samples/opensource/sampleMNISTAPI/sampleMNISTAPI.cpp @@ -90,6 +90,8 @@ class SampleMNISTAPI std::map mWeightMap; //!< The weight name to weight value map + std::vector> weightsMemory; //!< Host weights memory holder + std::shared_ptr mEngine; //!< The TensorRT engine used to run the network //! @@ -375,20 +377,6 @@ bool SampleMNISTAPI::verifyOutput(const samplesCommon::BufferManager& buffers) //! bool SampleMNISTAPI::teardown() { - // Release weights host memory - for (auto& mem : mWeightMap) - { - auto weight = mem.second; - if (weight.type == DataType::kFLOAT) - { - delete[] static_cast(weight.values); - } - else - { - delete[] static_cast(weight.values); - } - } - return true; } @@ -426,7 +414,10 @@ std::map SampleMNISTAPI::loadWeights(const std:: // Load blob if (wt.type == DataType::kFLOAT) { - uint32_t* val = new uint32_t[size]; + // Use uint32_t to create host memory to avoid additional conversion. + auto mem = new samplesCommon::TypedHostMemory(size); + weightsMemory.emplace_back(mem); + uint32_t* val = mem->raw(); for (uint32_t x = 0; x < size; ++x) { input >> std::hex >> val[x]; @@ -435,7 +426,10 @@ std::map SampleMNISTAPI::loadWeights(const std:: } else if (wt.type == DataType::kHALF) { - uint16_t* val = new uint16_t[size]; + // HalfMemory's raw type is uint16_t + auto mem = new samplesCommon::HalfMemory(size); + weightsMemory.emplace_back(mem); + auto val = mem->raw(); for (uint32_t x = 0; x < size; ++x) { input >> std::hex >> val[x]; diff --git a/samples/opensource/sampleMovieLens/preprocess.py b/samples/opensource/sampleMovieLens/preprocess.py index 1d13423d..d16cfe99 100644 --- a/samples/opensource/sampleMovieLens/preprocess.py +++ b/samples/opensource/sampleMovieLens/preprocess.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # + import graphsurgeon as gs import tensorflow as tf diff --git a/samples/opensource/sampleNMT/cudaError.h b/samples/opensource/sampleNMT/cudaError.h index fc90e8e5..47802a24 100644 --- a/samples/opensource/sampleNMT/cudaError.h +++ b/samples/opensource/sampleNMT/cudaError.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_CUDA_ERROR_ #define SAMPLE_NMT_CUDA_ERROR_ diff --git a/samples/opensource/sampleNMT/data/benchmarkWriter.h b/samples/opensource/sampleNMT/data/benchmarkWriter.h index 4b18e562..7a1aebf3 100644 --- a/samples/opensource/sampleNMT/data/benchmarkWriter.h +++ b/samples/opensource/sampleNMT/data/benchmarkWriter.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_BENCHMARK_WRITER_ #define SAMPLE_NMT_BENCHMARK_WRITER_ diff --git a/samples/opensource/sampleNMT/data/dataWriter.h b/samples/opensource/sampleNMT/data/dataWriter.h index a5226798..f95b4945 100644 --- a/samples/opensource/sampleNMT/data/dataWriter.h +++ b/samples/opensource/sampleNMT/data/dataWriter.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_DATA_WRITER_ #define SAMPLE_NMT_DATA_WRITER_ diff --git a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp index a8924997..b776227f 100644 --- a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp +++ b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "limitedSamplesDataReader.h" #include diff --git a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h index a9444d47..8cd430bb 100644 --- a/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h +++ b/samples/opensource/sampleNMT/data/limitedSamplesDataReader.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_LIMITED_SAMPLES_DATA_READER_ #define SAMPLE_NMT_LIMITED_SAMPLES_DATA_READER_ diff --git a/samples/opensource/sampleNMT/data/textReader.h b/samples/opensource/sampleNMT/data/textReader.h index 4d01bb57..2d080376 100644 --- a/samples/opensource/sampleNMT/data/textReader.h +++ b/samples/opensource/sampleNMT/data/textReader.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_TEXT_READER_ #define SAMPLE_NMT_TEXT_READER_ diff --git a/samples/opensource/sampleNMT/model/componentWeights.cpp b/samples/opensource/sampleNMT/model/componentWeights.cpp index af8f5722..5463c0df 100644 --- a/samples/opensource/sampleNMT/model/componentWeights.cpp +++ b/samples/opensource/sampleNMT/model/componentWeights.cpp @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include "componentWeights.h" #include #include diff --git a/samples/opensource/sampleNMT/model/componentWeights.h b/samples/opensource/sampleNMT/model/componentWeights.h index 6b20d56b..02adf414 100644 --- a/samples/opensource/sampleNMT/model/componentWeights.h +++ b/samples/opensource/sampleNMT/model/componentWeights.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_COMPONENT_WEIGHTS_ #define SAMPLE_NMT_COMPONENT_WEIGHTS_ diff --git a/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h b/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h index 85e6fdf6..bcf6c08e 100644 --- a/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h +++ b/samples/opensource/sampleNMT/model/likelihoodCombinationOperator.h @@ -26,7 +26,7 @@ class LikelihoodCombinationOperator public: typedef std::shared_ptr ptr; - // The return value should be less or equal to rayLikelihood + // The return value should be less or equal to rayLikelihood virtual float combine(float rayLikelihood, float optionLikelihood) const = 0; virtual float init() const = 0; diff --git a/samples/opensource/sampleNMT/pinnedHostBuffer.h b/samples/opensource/sampleNMT/pinnedHostBuffer.h index bfd79669..d6b64562 100644 --- a/samples/opensource/sampleNMT/pinnedHostBuffer.h +++ b/samples/opensource/sampleNMT/pinnedHostBuffer.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_PINNED_HOST_BUFFER_ #define SAMPLE_NMT_PINNED_HOST_BUFFER_ diff --git a/samples/opensource/sampleNMT/trtUtil.h b/samples/opensource/sampleNMT/trtUtil.h index 4a76aad4..fd024c15 100644 --- a/samples/opensource/sampleNMT/trtUtil.h +++ b/samples/opensource/sampleNMT/trtUtil.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #ifndef SAMPLE_NMT_TRT_UTIL_ #define SAMPLE_NMT_TRT_UTIL_ diff --git a/samples/opensource/samplePlugin/fcPlugin.h b/samples/opensource/samplePlugin/fcPlugin.h index 17641838..e55059ac 100644 --- a/samples/opensource/samplePlugin/fcPlugin.h +++ b/samples/opensource/samplePlugin/fcPlugin.h @@ -13,6 +13,7 @@ * See the License for the specific language governing permissions and * limitations under the License. */ + #include #include #include diff --git a/samples/opensource/sampleUffFasterRCNN/config.py b/samples/opensource/sampleUffFasterRCNN/config.py index 257de1a4..7a781fec 100644 --- a/samples/opensource/sampleUffFasterRCNN/config.py +++ b/samples/opensource/sampleUffFasterRCNN/config.py @@ -1,3 +1,4 @@ +# # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -12,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # + import tensorflow as tf import graphsurgeon as gs diff --git a/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp b/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp index f05e8442..5c6a40c3 100644 --- a/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp +++ b/samples/opensource/sampleUffFasterRCNN/sampleUffFasterRCNN.cpp @@ -560,7 +560,6 @@ void SampleUffFasterRcnn::batch_inverse_transform_classifier(const float* roi_af // inverse transform float tx, ty, tw, th; - //(i, 20, 4) tx = classifier_regr[n * roi_num_per_img * mParams.outputBboxSize + i * mParams.outputBboxSize + max_idx * 4] / mParams.classifierRegressorStd[0]; diff --git a/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp b/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp index 941775b8..6d13a08a 100644 --- a/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp +++ b/samples/opensource/sampleUffMaskRCNN/sampleUffMaskRCNN.cpp @@ -400,7 +400,7 @@ bool SampleMaskRCNN::constructNetwork(SampleUniquePtr& build } builder->setMaxBatchSize(mParams.batchSize); - builder->setMaxWorkspaceSize(2_GiB); + builder->setMaxWorkspaceSize(1_GiB); builder->setFp16Mode(mParams.fp16); // Only for speed test diff --git a/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp b/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp index c717e3b4..1b40a2af 100755 --- a/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp +++ b/samples/opensource/sampleUffPluginV2Ext/sampleUffPluginV2Ext.cpp @@ -595,36 +595,36 @@ class UffPoolPluginV2 : public IPluginV2IOExt { assert(mDataType == DataType::kINT8); size_t inCount = getC(mInputDims) * getH(mInputDims) * getW(mInputDims); - std::unique_ptr inputTmp{new char[inCount * elementSize(mDataType)]}; - CHECK(cudaMemcpy(inputTmp.get(), src, inCount * elementSize(mDataType), cudaMemcpyDeviceToHost)); - std::unique_ptr inputFP32{new float[inCount]}; - transform(inputTmp.get(), inputFP32.get(), inCount); + std::vector inputTmp(inCount * elementSize(mDataType)); + CHECK(cudaMemcpy(inputTmp.data(), src, inCount * elementSize(mDataType), cudaMemcpyDeviceToHost)); + std::vector inputFP32(inCount); + transform(inputTmp.data(), inputFP32.data(), inCount); // int8 scale int hw = mInputDims.d[1] * mInputDims.d[2]; for (int j = 0; j < mInputDims.d[0]; ++j) { - std::transform(inputFP32.get() + hw * j, inputFP32.get() + hw * (j + 1), inputFP32.get() + hw * j, + std::transform(inputFP32.data() + hw * j, inputFP32.data() + hw * (j + 1), inputFP32.data() + hw * j, [&](float in) -> float { return in * mInHostScale; }); } CHECK(cudaMalloc(&dst, inCount * elementSize(DataType::kFLOAT))); - CHECK(cudaMemcpy(dst, inputFP32.get(), inCount * elementSize(DataType::kFLOAT), cudaMemcpyHostToDevice)); + CHECK(cudaMemcpy(dst, inputFP32.data(), inCount * elementSize(DataType::kFLOAT), cudaMemcpyHostToDevice)); } void copyDeviceToInt8Output(const void* src, void* dst) { size_t outCount = getC(mOutputDims) * getH(mOutputDims) * getW(mOutputDims); - std::unique_ptr outTmp{new float[outCount]}; - CHECK(cudaMemcpy(outTmp.get(), src, outCount * elementSize(DataType::kFLOAT), cudaMemcpyDeviceToHost)); - std::unique_ptr outInt8{new char[outCount * elementSize(DataType::kINT8)]}; + std::vector outTmp(outCount); + CHECK(cudaMemcpy(outTmp.data(), src, outCount * elementSize(DataType::kFLOAT), cudaMemcpyDeviceToHost)); + std::vector outInt8(outCount * elementSize(DataType::kINT8)); // int8 + scale int hw = mOutputDims.d[1] * mOutputDims.d[2]; for (int j = 0; j < mInputDims.d[0]; ++j) { - std::transform(outTmp.get() + hw * j, outTmp.get() + hw * (j + 1), outTmp.get() + hw * j, + std::transform(outTmp.data() + hw * j, outTmp.data() + hw * (j + 1), outTmp.data() + hw * j, [&](float in) -> float { return in / mOutHostScale; }); } - transform(outTmp.get(), outInt8.get(), outCount); - CHECK(cudaMemcpy(dst, outInt8.get(), outCount, cudaMemcpyHostToDevice)); + transform(outTmp.data(), outInt8.data(), outCount); + CHECK(cudaMemcpy(dst, outInt8.data(), outCount, cudaMemcpyHostToDevice)); } private: diff --git a/samples/opensource/sampleUffSSD/README.md b/samples/opensource/sampleUffSSD/README.md index b773805e..dffe738f 100644 --- a/samples/opensource/sampleUffSSD/README.md +++ b/samples/opensource/sampleUffSSD/README.md @@ -217,7 +217,7 @@ The Shuffle layer implements a reshape and transpose operator for tensors. 2. Run the following command for the conversion. `convert-to-uff frozen_inference_graph.pb -O NMS -p config.py` - This saves the converted `.uff` file in the same directory as the input with the name `frozen_inference_graph.uff`. + This saves the converted `.uff` file in the same directory as the input with the name `frozen_inference_graph.pb.uff`. The `config.py` script specifies the preprocessing operations necessary for the SSD TensorFlow graph. The plugin nodes and plugin parameters used in the `config.py` script should match the registered plugins in TensorRT. diff --git a/samples/opensource/sampleUffSSD/sampleUffSSD.cpp b/samples/opensource/sampleUffSSD/sampleUffSSD.cpp index 0a07d332..99a06fd9 100644 --- a/samples/opensource/sampleUffSSD/sampleUffSSD.cpp +++ b/samples/opensource/sampleUffSSD/sampleUffSSD.cpp @@ -39,6 +39,7 @@ #include const std::string gSampleName = "TensorRT.sample_uff_ssd"; +const std::vector gImgFnames = {"dog.ppm", "bus.ppm"}; //! //! \brief The SampleUffSSDParams structure groups the additional parameters required by @@ -48,10 +49,10 @@ struct SampleUffSSDParams : public samplesCommon::SampleParams { std::string uffFileName; //!< The file name of the UFF model to use std::string labelsFileName; //!< The file namefo the class labels - int outputClsSize; //!< The number of output classes - int calBatchSize; //!< The size of calibration batch - int nbCalBatches; //!< The number of batches for calibration - int keepTopK; //!< The maximum number of detection post-NMS + int32_t outputClsSize; //!< The number of output classes + int32_t calBatchSize; //!< The size of calibration batch + int32_t nbCalBatches; //!< The number of batches for calibration + int32_t keepTopK; //!< The maximum number of detection post-NMS float visualThreshold; //!< The minimum score threshold to consider a detection }; @@ -199,9 +200,9 @@ bool SampleUffSSD::constructNetwork(SampleUniquePtr& builder { sample::gLogInfo << "Using Entropy Calibrator 2" << std::endl; const std::string listFileName = "list.txt"; - const int imageC = 3; - const int imageH = 300; - const int imageW = 300; + const int32_t imageC = 3; + const int32_t imageH = 300; + const int32_t imageW = 300; nvinfer1::DimsNCHW imageDims{}; imageDims = nvinfer1::DimsNCHW{mParams.calBatchSize, imageC, imageH, imageW}; BatchStream calibrationStream( @@ -226,7 +227,7 @@ bool SampleUffSSD::constructNetwork(SampleUniquePtr& builder //! \brief Runs the TensorRT inference engine for this sample //! //! \details This function is the main execution function of the sample. It allocates the buffer, -//! sets inputs and executes the engine. +//! sets inputs, executes the engine and verifies the detection outputs. //! bool SampleUffSSD::infer() { @@ -249,7 +250,7 @@ bool SampleUffSSD::infer() // Memcpy from host input buffers to device input buffers buffers.copyInputToDevice(); - bool status = context->execute(mParams.batchSize, buffers.getDeviceBindings().data()); + const bool status = context->execute(mParams.batchSize, buffers.getDeviceBindings().data()); if (!status) { return false; @@ -284,28 +285,26 @@ bool SampleUffSSD::teardown() //! bool SampleUffSSD::processInput(const samplesCommon::BufferManager& buffers) { - const int inputC = mInputDims.d[0]; - const int inputH = mInputDims.d[1]; - const int inputW = mInputDims.d[2]; - const int batchSize = mParams.batchSize; + const int32_t inputC = mInputDims.d[0]; + const int32_t inputH = mInputDims.d[1]; + const int32_t inputW = mInputDims.d[2]; + const int32_t batchSize = mParams.batchSize; - // Available images - std::vector imageList = {"dog.ppm", "bus.ppm"}; mPPMs.resize(batchSize); - assert(mPPMs.size() <= imageList.size()); - for (int i = 0; i < batchSize; ++i) + assert(mPPMs.size() == gImgFnames.size()); + for (int32_t i = 0; i < batchSize; ++i) { - readPPMFile(locateFile(imageList[i], mParams.dataDirs), mPPMs[i]); + readPPMFile(locateFile(gImgFnames[i], mParams.dataDirs), mPPMs[i]); } float* hostDataBuffer = static_cast(buffers.getHostBuffer(mParams.inputTensorNames[0])); // Host memory for input buffer - for (int i = 0, volImg = inputC * inputH * inputW; i < mParams.batchSize; ++i) + for (int32_t i = 0, volImg = inputC * inputH * inputW; i < mParams.batchSize; ++i) { - for (int c = 0; c < inputC; ++c) + for (int32_t c = 0; c < inputC; ++c) { // The color image to input should be in BGR order - for (unsigned j = 0, volChl = inputH * inputW; j < volChl; ++j) + for (uint32_t j = 0, volChl = inputH * inputW; j < volChl; ++j) { hostDataBuffer[i * volImg + c * volChl + j] = (2.0 / 255.0) * float(mPPMs[i].buffer[j * inputC + c]) - 1.0; @@ -323,38 +322,38 @@ bool SampleUffSSD::processInput(const samplesCommon::BufferManager& buffers) //! bool SampleUffSSD::verifyOutput(const samplesCommon::BufferManager& buffers) { - const int inputH = mInputDims.d[1]; - const int inputW = mInputDims.d[2]; - const int batchSize = mParams.batchSize; - const int keepTopK = mParams.keepTopK; + const int32_t inputH = mInputDims.d[1]; + const int32_t inputW = mInputDims.d[2]; + const int32_t batchSize = mParams.batchSize; + const int32_t keepTopK = mParams.keepTopK; const float visualThreshold = mParams.visualThreshold; - const int outputClsSize = mParams.outputClsSize; + const int32_t outputClsSize = mParams.outputClsSize; const float* detectionOut = static_cast(buffers.getHostBuffer(mParams.outputTensorNames[0])); - const int* keepCount = static_cast(buffers.getHostBuffer(mParams.outputTensorNames[1])); + const int32_t* keepCount = static_cast(buffers.getHostBuffer(mParams.outputTensorNames[1])); + // Read COCO class labels from file std::vector classes(outputClsSize); - - // Gather class labels - std::ifstream labelFile(locateFile(mParams.labelsFileName, mParams.dataDirs)); - std::string line; - int id = 0; - while (getline(labelFile, line)) { - classes[id++] = line; + std::ifstream labelFile(locateFile(mParams.labelsFileName, mParams.dataDirs)); + std::string line; + int32_t id = 0; + while (getline(labelFile, line)) + { + classes[id++] = line; + } } bool pass = true; - for (int p = 0; p < batchSize; ++p) + for (int32_t bi = 0; bi < batchSize; ++bi) { - int numDetections = 0; - // at least one correct detection + int32_t numDetections = 0; bool correctDetection = false; - for (int i = 0; i < keepCount[p]; ++i) + for (int32_t i = 0; i < keepCount[bi]; ++i) { - const float* det = &detectionOut[0] + (p * keepTopK + i) * 7; + const float* det = &detectionOut[0] + (bi * keepTopK + i) * 7; if (det[2] < visualThreshold) { continue; @@ -362,28 +361,30 @@ bool SampleUffSSD::verifyOutput(const samplesCommon::BufferManager& buffers) // Output format for each detection is stored in the below order // [image_id, label, confidence, xmin, ymin, xmax, ymax] - int detection = det[1]; + const int32_t detection = det[1]; assert(detection < outputClsSize); - std::string storeName = classes[detection] + "-" + std::to_string(det[2]) + ".ppm"; + const std::string outFname = classes[detection] + "-" + std::to_string(det[2]) + ".ppm"; numDetections++; - if ((p == 0 && classes[detection] == "dog") - || (p == 1 && (classes[detection] == "truck" || classes[detection] == "car"))) + + if ((bi == 0 && classes[detection] == "dog") + || (bi == 1 && (classes[detection] == "truck" || classes[detection] == "car"))) { correctDetection = true; } - sample::gLogInfo << "Detected " << classes[detection].c_str() << " in the image " << int(det[0]) << " (" - << mPPMs[p].fileName.c_str() << ")" - << " with confidence " << det[2] * 100.f << " and coordinates (" << det[3] * inputW << "," + sample::gLogInfo << "Detected " << classes[detection].c_str() << " in image " + << static_cast(det[0]) << " (" << mPPMs[bi].fileName.c_str() << ")" + << " with confidence " << det[2] * 100.f << " and coordinates (" << det[3] * inputW << ", " << det[4] * inputH << ")" - << ",(" << det[5] * inputW << "," << det[6] * inputH << ")." << std::endl; + << ", (" << det[5] * inputW << ", " << det[6] * inputH << ")." << std::endl; - sample::gLogInfo << "Result stored in " << storeName.c_str() << "." << std::endl; + sample::gLogInfo << "Result stored in: " << outFname.c_str() << std::endl; samplesCommon::writePPMFileWithBBox( - storeName, mPPMs[p], {det[3] * inputW, det[4] * inputH, det[5] * inputW, det[6] * inputH}); + outFname, mPPMs[bi], {det[3] * inputW, det[4] * inputH, det[5] * inputW, det[6] * inputH}); } + pass &= correctDetection; pass &= numDetections >= 1; } @@ -410,10 +411,11 @@ SampleUffSSDParams initializeSampleParams(const samplesCommon::Args& args) { params.dataDirs = args.dataDirs; } + params.uffFileName = "sample_ssd_relu6.uff"; params.labelsFileName = "ssd_coco_labels.txt"; params.inputTensorNames.push_back("Input"); - params.batchSize = 2; + params.batchSize = gImgFnames.size(); params.outputTensorNames.push_back("NMS"); params.outputTensorNames.push_back("NMS_1"); params.dlaCore = args.useDLACore; @@ -434,31 +436,32 @@ SampleUffSSDParams initializeSampleParams(const samplesCommon::Args& args) //! void printHelpInfo() { - std::cout - << "Usage: ./sample_uff_ssd [-h or --help] [-d or --datadir=] [--useDLACore=]" - << std::endl; + std::cout << "Usage: ./sample_uff_ssd [-h or --help] [-d or --datadir=] [--useDLACore=]" + << std::endl; std::cout << "--help Display help information" << std::endl; std::cout << "--datadir Specify path to a data directory, overriding the default. This option can be used " "multiple times to add multiple directories. If no data directories are given, the default is to use " "data/samples/ssd/ and data/ssd/" << std::endl; - std::cout << "--useDLACore=N Specify a DLA engine for layers that support DLA. Value can range from 0 to n-1, " - "where n is the number of DLA engines on the platform." + std::cout << "--useDLACore Specify a DLA engine for layers that support DLA. Value can range from 0 to N-1, " + "where N is the number of DLA engines on the platform." << std::endl; std::cout << "--fp16 Specify to run in fp16 mode." << std::endl; std::cout << "--int8 Specify to run in int8 mode." << std::endl; } -int main(int argc, char** argv) +int32_t main(int32_t argc, char** argv) { samplesCommon::Args args; - bool argsOK = samplesCommon::parseArgs(args, argc, argv); + const bool argsOK = samplesCommon::parseArgs(args, argc, argv); + if (!argsOK) { sample::gLogError << "Invalid arguments" << std::endl; printHelpInfo(); return EXIT_FAILURE; } + if (args.help) { printHelpInfo(); @@ -471,16 +474,18 @@ int main(int argc, char** argv) SampleUffSSD sample(initializeSampleParams(args)); - sample::gLogInfo << "Building and running a GPU inference engine for SSD" << std::endl; - + sample::gLogInfo << "Building inference engine for SSD" << std::endl; if (!sample.build()) { return sample::gLogger.reportFail(sampleTest); } + + sample::gLogInfo << "Running inference engine for SSD" << std::endl; if (!sample.infer()) { return sample::gLogger.reportFail(sampleTest); } + if (!sample.teardown()) { return sample::gLogger.reportFail(sampleTest); diff --git a/samples/opensource/trtexec/CMakeLists.txt b/samples/opensource/trtexec/CMakeLists.txt index 6eaeda12..f2eb2a3c 100644 --- a/samples/opensource/trtexec/CMakeLists.txt +++ b/samples/opensource/trtexec/CMakeLists.txt @@ -20,6 +20,7 @@ SET(SAMPLE_SOURCES ../../common/sampleReporting.cpp trtexec.cpp ) + set(SAMPLE_PARSERS "caffe" "uff" "onnx") include(../../CMakeSamplesTemplate.txt) diff --git a/samples/opensource/trtexec/trtexec.cpp b/samples/opensource/trtexec/trtexec.cpp index ab6d9a13..51243d14 100644 --- a/samples/opensource/trtexec/trtexec.cpp +++ b/samples/opensource/trtexec/trtexec.cpp @@ -18,7 +18,6 @@ #include #include #include -#include #include #include #include @@ -45,6 +44,33 @@ using namespace nvinfer1; using namespace sample; +using time_point = std::chrono::time_point; +using duration = std::chrono::duration; + +void printPerformanceProfile(const ReportingOptions& reporting, const InferenceEnvironment& iEnv, std::ostream& os) +{ + if (reporting.profile) + { + iEnv.profiler->print(sample::gLogInfo); + } + if (!reporting.exportProfile.empty()) + { + iEnv.profiler->exportJSONProfile(reporting.exportProfile); + } +} + +void printOutput(const ReportingOptions& reporting, const InferenceEnvironment& iEnv, std::ostream& os) +{ + if (reporting.output) + { + dumpOutputs(*iEnv.context.front(), *iEnv.bindings.front(), sample::gLogInfo); + } + if (!reporting.exportOutput.empty()) + { + exportJSONOutput(*iEnv.context.front(), *iEnv.bindings.front(), reporting.exportOutput); + } +} + int main(int argc, char** argv) { const std::string sampleName = "TensorRT.trtexec"; @@ -107,7 +133,8 @@ int main(int argc, char** argv) sample::setReportableSeverity(ILogger::Severity::kVERBOSE); } - cudaCheck(cudaSetDevice(options.system.device)); + setCudaDevice(options.system.device, sample::gLogInfo); + sample::gLogInfo << std::endl; initLibNvInferPlugins(&sample::gLogger.getTRTLogger(), ""); @@ -118,12 +145,25 @@ int main(int argc, char** argv) } InferenceEnvironment iEnv; + time_point buildStartTime{std::chrono::high_resolution_clock::now()}; iEnv.engine = getEngine(options.model, options.build, options.system, sample::gLogError); - if (!iEnv.engine) + time_point buildEndTime{std::chrono::high_resolution_clock::now()}; + if (iEnv.engine) + { + sample::gLogInfo << "Engine " << (options.build.load ? "loaded" : "built") << " in " + << duration(buildEndTime - buildStartTime).count() << " sec." << std::endl; + } + else { sample::gLogError << "Engine set up failed" << std::endl; return sample::gLogger.reportFail(sampleTest); } + + if (iEnv.engine.get()->isRefittable() && options.reporting.refit) + { + dumpRefittable(*iEnv.engine.get()); + } + if (options.inference.skip) { return sample::gLogger.reportPass(sampleTest); @@ -138,7 +178,7 @@ int main(int argc, char** argv) return sample::gLogger.reportFail(sampleTest); } - if (options.reporting.profile || !options.reporting.exportTimes.empty()) + if ((options.reporting.profile || !options.reporting.exportProfile.empty()) && !options.inference.rerun) { iEnv.profiler.reset(new Profiler); } @@ -149,32 +189,21 @@ int main(int argc, char** argv) return sample::gLogger.reportFail(sampleTest); } std::vector trace; - sample::gLogInfo << "Starting inference threads" << std::endl; + sample::gLogInfo << "Starting inference" << std::endl; runInference(options.inference, iEnv, options.system.device, trace); printPerformanceReport(trace, options.reporting, static_cast(options.inference.warmup), options.inference.batch, sample::gLogInfo); + printOutput(options.reporting, iEnv, sample::gLogInfo); - if (options.reporting.output) - { - dumpOutputs(*iEnv.context.front(), *iEnv.bindings.front(), sample::gLogInfo); - } - if (!options.reporting.exportOutput.empty()) - { - exportJSONOutput(*iEnv.context.front(), *iEnv.bindings.front(), options.reporting.exportOutput); - } - if (!options.reporting.exportTimes.empty()) - { - exportJSONTrace(trace, options.reporting.exportTimes); - } - if (options.reporting.profile) - { - iEnv.profiler->print(sample::gLogInfo); - } - if (!options.reporting.exportProfile.empty()) + if ((options.reporting.profile || !options.reporting.exportProfile.empty()) && options.inference.rerun) { - iEnv.profiler->exportJSONProfile(options.reporting.exportProfile); + auto* profiler = new Profiler; + iEnv.profiler.reset(profiler); + iEnv.context.front()->setProfiler(profiler); + runInference(options.inference, iEnv, options.system.device, trace); } + printPerformanceProfile(options.reporting, iEnv, sample::gLogInfo); return sample::gLogger.reportPass(sampleTest); } diff --git a/scripts/copyright-scan.py b/scripts/copyright-scan.py new file mode 100644 index 00000000..8389de4c --- /dev/null +++ b/scripts/copyright-scan.py @@ -0,0 +1,174 @@ +#!/usr/bin/env python +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import os +import re + +# Configuration + +copyright_year = "2020" + +extensions_p = (".py", ".sh", ".cmake", "CMakeLists") +extensions_c = (".c", ".cpp", ".h", ".hpp", ".cu") + +pattern_p = """#\s* +# Copyright \(c\) ([1-2][0-9]{3}),* NVIDIA CORPORATION.* +#\s* +# Licensed under the Apache License, Version 2.0 \(the "License"\);\s* +# you may not use this file except in compliance with the License.\s* +# You may obtain a copy of the License at\s* +#\s* +# http://www.apache.org/licenses/LICENSE-2.0\s* +#\s* +# Unless required by applicable law or agreed to in writing, software\s* +# distributed under the License is distributed on an "AS IS" BASIS,\s* +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\s* +# See the License for the specific language governing permissions and\s* +# limitations under the License.\s* +# +""" + +header_p = """# +# Copyright (c) {year}, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +""".format(year=copyright_year) + +pattern_c = """/\*\s* + \* Copyright \(c\) ([1-2][0-9]{3}),* NVIDIA CORPORATION.* + \*\s* + \* Licensed under the Apache License, Version 2.0 \(the "License"\);\s* + \* you may not use this file except in compliance with the License.\s* + \* You may obtain a copy of the License at\s* + \*\s* + \* http://www.apache.org/licenses/LICENSE-2.0\s* + \*\s* + \* Unless required by applicable law or agreed to in writing, software\s* + \* distributed under the License is distributed on an "AS IS" BASIS,\s* + \* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\s* + \* See the License for the specific language governing permissions and\s* + \* limitations under the License.\s* + \*/ +""" + +header_c = """/* + * Copyright (c) {year}, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +""".format(year=copyright_year) + +# Routines + +def parse_args(parser): + """ + Parse commandline arguments. + """ + parser.add_argument('-d', '--dir', type=str, required=True, help='Root directory to start the scan') + parser.add_argument('-f','--force-update', action='store_true', help='Force the header writes for all files') + parser.add_argument('--dry-run', action='store_true', help='Just perform a dry-run') + parser.add_argument('--max-depth', type=int,default=100, help='Maximum depth to recurse while scanning files.') + return parser + +def update(filename, args): + """ + Update copyright header for specified file + """ + if filename.endswith(extensions_p): + pattern = re.compile(pattern_p) + header = header_p + shebang = re.compile(r'^(\#\!.*\n)', re.MULTILINE) + elif filename.endswith(extensions_c): + pattern = re.compile(pattern_c) + header = header_c + shebang = None + else: + return + + with open(filename, "r+") as f: + data = f.read() + match = pattern.search(data) + if match: + year = match.group(1) + if copyright_year == year: + if args.force_update: + print(filename,": FORCED") + new_data = pattern.sub(header, data, count=1) + else: + print(filename,": SKIP") + return + else: + print(filename,": UPDATE (",year,"->",copyright_year,")") + new_data = pattern.sub(header, data, count=1) + else: + match = shebang.search(data) if shebang else None + if match: + print(filename,": ADD ( after",match.group(1),")") + new_data = shebang.sub(match.group(1)+header, data, count=1) + else: + print(filename,": ADD ( top )") + new_data = header+data + + if not args.dry_run: + with open(filename, "w") as f: + f.write(new_data) + +def copyright_scan(directory, depth, args, exclude_dirs=[]): + """ + Update copyright for TensorRT sources + """ + if directory in exclude_dirs: + return + for f in os.listdir(directory): + filename = os.path.join(directory,f) + if os.path.isdir(filename) and (depth > 0): + copyright_scan(filename, depth-1, args, exclude_dirs) + elif filename.endswith(extensions_p + extensions_c): + update(filename, args) + +def main(): + parser = argparse.ArgumentParser(description='TensorRT copyright scan') + parser = parse_args(parser) + args, _ = parser.parse_known_args() + + exclude_dirs = ["./third_party","./build","./parsers/onnx", "./include"] + copyright_scan(args.dir, args.max_depth, args, exclude_dirs) + +if __name__ == '__main__': + main() diff --git a/scripts/stubify.sh b/scripts/stubify.sh index ee114d00..008de8bf 100755 --- a/scripts/stubify.sh +++ b/scripts/stubify.sh @@ -14,6 +14,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # + # This short shell script will extract all the strong "text" symbols from the # shared library and create a new "stub" shared library with the same symbols. # The body of these functions will be empty and therefore have no dependencies. diff --git a/tools/Polygraphy/.gitignore b/tools/Polygraphy/.gitignore new file mode 100644 index 00000000..11614af2 --- /dev/null +++ b/tools/Polygraphy/.gitignore @@ -0,0 +1,115 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/tools/Polygraphy/CHANGELOG.md b/tools/Polygraphy/CHANGELOG.md new file mode 100644 index 00000000..143639e0 --- /dev/null +++ b/tools/Polygraphy/CHANGELOG.md @@ -0,0 +1,6 @@ +# Polygraphy Change Log + +Dates are in YYYY-MM-DD format. + +## v0.20.13 (2020-10-08) +- Initial public release of Polygraphy diff --git a/tools/Polygraphy/CONTRIBUTING.md b/tools/Polygraphy/CONTRIBUTING.md new file mode 100644 index 00000000..cb1e7168 --- /dev/null +++ b/tools/Polygraphy/CONTRIBUTING.md @@ -0,0 +1,54 @@ +# Contributing to Polygraphy + +## Table of Contents + +- [Design Principles](#design-principles) +- [Submitting Merge Requests](#submitting-merge-requests) + + +## Design Principles + +### Amazing Error Messages + +Error messages should ideally tell the user how to fix the error, or, failing that, +should try to make the cause of the error as obvious as possible. An overly verbose error +is better than a cryptic one. + +### Simple But Flexible + +The API should be as simple as possible, but also provide ways to completely customize +everything. +Loader composition is an example of this - advanced users can write their own loaders, +while still being able to use loaders provided by Polygraphy. + +### None Means Default + +Universally using `None` to indicate default value has some advantages: +- Makes it easier to write wrappers - instead of trying to match the default + values of the function being wrapped, users can just use `None` . + +- Can help prevent surprises caused by default value behavior in Python, as explained in + the [comment for default_value()](./polygraphy/util/misc.py) + +### Loader Naming Conventions + +- Loaders that convert from a source format to some target format should +follow the naming convention: `From`, e.g. `OnnxFromTfGraph`, `NetworkFromOnnxBytes` + +- Loaders that do not affect the format of their source should follow the naming convention: +``, e.g. `ModifyOnnx`, `SaveEngine` + +- For all other loaders, make sure the name is concise, but descriptive, e.g. `LoadPlugins`, +`CreateConfig` + + +## Submitting Merge Requests + +1. Create a separate branch for your feature or bug fix +2. Make your changes +3. Run Tests: + - Install prerequisite packages with: + - `python3 -m pip install -r tests/requirements.txt` + - `python3 -m pip install -r docs/requirements.txt` + - Run tests with: `make test` +4. Commit, push, and submit a merge request to the main branch diff --git a/tools/Polygraphy/Makefile b/tools/Polygraphy/Makefile new file mode 100644 index 00000000..c8d35984 --- /dev/null +++ b/tools/Polygraphy/Makefile @@ -0,0 +1,27 @@ +.PHONY: test test_noparallel clean build install_venv install docs + +# Tests also check that docs can build +test: docs + python3 -m pytest tests/ -v -x -nauto --dist=loadscope + +test_noparallel: docs + python3 -m pytest tests/ -v -x + +clean: + rm -rf dist/ build/ polygraphy.egg-info/ + +build: clean + python3 setup.py bdist_wheel + +install_common: build + -python3 -m pip install colored + +install_venv: install_common + python3 -m pip install --force-reinstall $(CURDIR)/dist/*.whl + +install: install_common + python3 -m pip install --force-reinstall $(CURDIR)/dist/*.whl --user + +docs: + mkdir -p build/docs + sphinx-build docs build/docs/ -j auto -W diff --git a/tools/Polygraphy/README.md b/tools/Polygraphy/README.md new file mode 100644 index 00000000..54cf06cf --- /dev/null +++ b/tools/Polygraphy/README.md @@ -0,0 +1,114 @@ +# Polygraphy: A Deep Learning Inference Prototyping and Debugging Toolkit + + +## Table of Contents + +- [Introduction](#introduction) +- [Installation](#installation) +- [Usage](#usage) +- [Examples](#examples) +- [Advanced](#advanced) + - [Using The API](#using-the-api) + - [Building API Docs](#building-api-docs) +- [Contributing](#contributing) + + +## Introduction + +Polygraphy is a toolkit designed to assist in running and debugging deep learning models +in various frameworks. It includes a [Python API](./polygraphy), and +[several command-line tools](./polygraphy/tools) built using this API. + + +## Installation + +**NOTE:** It is strongly recommended to install the `colored` module for colored output +from Polygraphy, as this can greatly improve readability. + +Each `backend` directory includes a `requirements.txt` file that specifies which packages +it depends on. You can install the requirements for whichever backends you're interested in +using: + +```bash +python3 -m pip install -r polygraphy/backend//requirements.txt +``` + +### Install From Package Index + +```bash +python3 -m pip install nvidia-pyindex +python3 -m pip install polygraphy +``` + +### Building From Source + +#### Using Make Targets + +```bash +make install +``` +Or, if installing inside a virtual environment: +```bash +make install_venv +``` + +#### Building Manually + +1. Build a wheel: + +```bash +python3 setup.py bdist_wheel +``` + +2. Install the wheel manually from **outside** the repository: + +```bash +python3 -m pip install polygraphy/dist/polygraphy-X.Y.Z-py2.py3-none-any.whl --user +``` +where `X.Y.Z` is the version number. + + +## Usage + +Polygraphy includes a command-line interface, [`polygraphy`](./bin/polygraphy), which provides various tools. +For usage information, run `polygraphy --help` + +For details on the various tools included in the Polygraphy toolkit, see the +[tools directory](./polygraphy/tools). + + +## Examples + +For examples of both the CLI and Python API, see the [examples directory](./examples). + + +## Advanced + +### Using The API + +For details on the Polygraphy Python API, see the [API directory](./polygraphy). +To view documentation about a specific class or function, you can view the +docstring with Python's `help()` function. + + +### Building API Docs + +To build the API documentation, first install required packages: + +```bash +python3 -m pip install -r docs/requirements.txt +``` + +and then use the `make` target to build docs: + +```bash +make docs +``` + +The HTML documentation will be generated under `build/docs` +To view the docs, open `build/docs/index.html` in a browser or HTML viewer. + + +## Contributing + +For information on how you can contribute to this project, see [CONTRIBUTING.md](./CONTRIBUTING.md) diff --git a/tools/Polygraphy/bin/polygraphy b/tools/Polygraphy/bin/polygraphy new file mode 100755 index 00000000..88530041 --- /dev/null +++ b/tools/Polygraphy/bin/polygraphy @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 + +""" +The Polygraphy CLI Toolkit + +Includes various subtools that can assist with prototyping and +debugging inference with deep learning models. See the help output +for details. +""" +import argparse +import os +import sys + +G_SCRIPT_FILE = os.path.realpath(__file__) +G_ROOT_DIR = os.path.join(os.path.dirname(G_SCRIPT_FILE), os.pardir) +sys.path.insert(0, G_ROOT_DIR) + + +import polygraphy +from polygraphy.logger import G_LOGGER +from polygraphy.tools import TOOL_REGISTRY +from polygraphy.tools.util import args as args_util +from polygraphy.util import misc + + +# All CLI tools should exit on errors rather than re-raising +G_LOGGER.exit_on_errors = True + +def main(): + parser = argparse.ArgumentParser(description="Polygraphy: A Deep Learning Debugging Toolkit") + parser.add_argument("-v", "--version", action="version", version=misc.str_from_module_info(polygraphy, name="Polygraphy")) + + subparsers = parser.add_subparsers(title="Tools", dest="tools") + subparsers.required = True + + for tool in TOOL_REGISTRY: + tool.setup_parser(subparsers) + + args, unknown = parser.parse_known_args() + args = args_util.setup(args, unknown) + + G_LOGGER.verbose("Running Command: {:}".format(" ".join(sys.argv))) + sys.exit(args.subcommand(args)) + + +if __name__ == "__main__": + main() diff --git a/tools/Polygraphy/docs/_static/img/nvlogo_white.png b/tools/Polygraphy/docs/_static/img/nvlogo_white.png new file mode 100644 index 00000000..5c458634 Binary files /dev/null and b/tools/Polygraphy/docs/_static/img/nvlogo_white.png differ diff --git a/tools/Polygraphy/docs/_static/style.css b/tools/Polygraphy/docs/_static/style.css new file mode 100644 index 00000000..176de2f5 --- /dev/null +++ b/tools/Polygraphy/docs/_static/style.css @@ -0,0 +1,3 @@ +.wy-nav-content { + max-width: 1100px !important; +} diff --git a/tools/Polygraphy/docs/backend/base/loader.rst b/tools/Polygraphy/docs/backend/base/loader.rst new file mode 100644 index 00000000..9532b679 --- /dev/null +++ b/tools/Polygraphy/docs/backend/base/loader.rst @@ -0,0 +1,5 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.base.loader diff --git a/tools/Polygraphy/docs/backend/base/runner.rst b/tools/Polygraphy/docs/backend/base/runner.rst new file mode 100644 index 00000000..87fe17e9 --- /dev/null +++ b/tools/Polygraphy/docs/backend/base/runner.rst @@ -0,0 +1,9 @@ +============ +Runners +============ + +.. autoclass:: polygraphy.backend.base.runner.BaseRunner + :inherited-members: + + .. automethod:: activate_impl + .. automethod:: deactivate_impl diff --git a/tools/Polygraphy/docs/backend/base/toc.rst b/tools/Polygraphy/docs/backend/base/toc.rst new file mode 100644 index 00000000..561b33d1 --- /dev/null +++ b/tools/Polygraphy/docs/backend/base/toc.rst @@ -0,0 +1,7 @@ +==== +Base +==== + +.. toctree:: + loader + runner diff --git a/tools/Polygraphy/docs/backend/common/loader.rst b/tools/Polygraphy/docs/backend/common/loader.rst new file mode 100644 index 00000000..14e9390f --- /dev/null +++ b/tools/Polygraphy/docs/backend/common/loader.rst @@ -0,0 +1,5 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.common.loader diff --git a/tools/Polygraphy/docs/backend/common/toc.rst b/tools/Polygraphy/docs/backend/common/toc.rst new file mode 100644 index 00000000..52e297ed --- /dev/null +++ b/tools/Polygraphy/docs/backend/common/toc.rst @@ -0,0 +1,6 @@ +========== +Common +========== + +.. toctree:: + loader diff --git a/tools/Polygraphy/docs/backend/onnx/loader.rst b/tools/Polygraphy/docs/backend/onnx/loader.rst new file mode 100644 index 00000000..d17b5ee5 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnx/loader.rst @@ -0,0 +1,5 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.onnx.loader diff --git a/tools/Polygraphy/docs/backend/onnx/runner.rst b/tools/Polygraphy/docs/backend/onnx/runner.rst new file mode 100644 index 00000000..0b1a4075 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnx/runner.rst @@ -0,0 +1,6 @@ +============ +Runners +============ + +.. automodule:: polygraphy.backend.onnx.runner + :inherited-members: diff --git a/tools/Polygraphy/docs/backend/onnx/toc.rst b/tools/Polygraphy/docs/backend/onnx/toc.rst new file mode 100644 index 00000000..ed9a88a4 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnx/toc.rst @@ -0,0 +1,6 @@ +==== +ONNX +==== + +.. toctree:: + loader diff --git a/tools/Polygraphy/docs/backend/onnxrt/loader.rst b/tools/Polygraphy/docs/backend/onnxrt/loader.rst new file mode 100644 index 00000000..70908e63 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnxrt/loader.rst @@ -0,0 +1,5 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.onnxrt.loader diff --git a/tools/Polygraphy/docs/backend/onnxrt/runner.rst b/tools/Polygraphy/docs/backend/onnxrt/runner.rst new file mode 100644 index 00000000..ca0bdd26 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnxrt/runner.rst @@ -0,0 +1,6 @@ +============ +Runners +============ + +.. automodule:: polygraphy.backend.onnxrt.runner + :inherited-members: diff --git a/tools/Polygraphy/docs/backend/onnxrt/toc.rst b/tools/Polygraphy/docs/backend/onnxrt/toc.rst new file mode 100644 index 00000000..d7ddac55 --- /dev/null +++ b/tools/Polygraphy/docs/backend/onnxrt/toc.rst @@ -0,0 +1,7 @@ +============ +ONNX Runtime +============ + +.. toctree:: + loader + runner diff --git a/tools/Polygraphy/docs/backend/tf/loader.rst b/tools/Polygraphy/docs/backend/tf/loader.rst new file mode 100644 index 00000000..b2197271 --- /dev/null +++ b/tools/Polygraphy/docs/backend/tf/loader.rst @@ -0,0 +1,5 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.tf.loader diff --git a/tools/Polygraphy/docs/backend/tf/runner.rst b/tools/Polygraphy/docs/backend/tf/runner.rst new file mode 100644 index 00000000..d1e9cb58 --- /dev/null +++ b/tools/Polygraphy/docs/backend/tf/runner.rst @@ -0,0 +1,6 @@ +============ +Runners +============ + +.. automodule:: polygraphy.backend.tf.runner + :inherited-members: diff --git a/tools/Polygraphy/docs/backend/tf/toc.rst b/tools/Polygraphy/docs/backend/tf/toc.rst new file mode 100644 index 00000000..f2f178ce --- /dev/null +++ b/tools/Polygraphy/docs/backend/tf/toc.rst @@ -0,0 +1,7 @@ +========== +TensorFlow +========== + +.. toctree:: + loader + runner diff --git a/tools/Polygraphy/docs/backend/toc.rst b/tools/Polygraphy/docs/backend/toc.rst new file mode 100644 index 00000000..e09b4a06 --- /dev/null +++ b/tools/Polygraphy/docs/backend/toc.rst @@ -0,0 +1,11 @@ +======== +Backends +======== + +.. toctree:: + base/toc + common/toc + onnx/toc + onnxrt/toc + tf/toc + trt/toc diff --git a/tools/Polygraphy/docs/backend/trt/calibrator.rst b/tools/Polygraphy/docs/backend/trt/calibrator.rst new file mode 100644 index 00000000..b35641b8 --- /dev/null +++ b/tools/Polygraphy/docs/backend/trt/calibrator.rst @@ -0,0 +1,5 @@ +============ +Calibrator +============ + +.. automodule:: polygraphy.backend.trt.calibrator diff --git a/tools/Polygraphy/docs/backend/trt/loader.rst b/tools/Polygraphy/docs/backend/trt/loader.rst new file mode 100644 index 00000000..1034de37 --- /dev/null +++ b/tools/Polygraphy/docs/backend/trt/loader.rst @@ -0,0 +1,6 @@ +============ +Loaders +============ + +.. automodule:: polygraphy.backend.trt.loader + :exclude-members: polygraphy.backend.trt.loader.BaseNetworkFromOnnx diff --git a/tools/Polygraphy/docs/backend/trt/runner.rst b/tools/Polygraphy/docs/backend/trt/runner.rst new file mode 100644 index 00000000..81af6c59 --- /dev/null +++ b/tools/Polygraphy/docs/backend/trt/runner.rst @@ -0,0 +1,6 @@ +============ +Runners +============ + +.. automodule:: polygraphy.backend.trt.runner + :inherited-members: diff --git a/tools/Polygraphy/docs/backend/trt/toc.rst b/tools/Polygraphy/docs/backend/trt/toc.rst new file mode 100644 index 00000000..fe42872a --- /dev/null +++ b/tools/Polygraphy/docs/backend/trt/toc.rst @@ -0,0 +1,8 @@ +========= +TensorRT +========= + +.. toctree:: + calibrator + loader + runner diff --git a/tools/Polygraphy/docs/common/data_structures.rst b/tools/Polygraphy/docs/common/data_structures.rst new file mode 100644 index 00000000..7a97e189 --- /dev/null +++ b/tools/Polygraphy/docs/common/data_structures.rst @@ -0,0 +1,4 @@ +=============== +Data Structures +=============== +.. automodule:: polygraphy.common.struct diff --git a/tools/Polygraphy/docs/common/exception.rst b/tools/Polygraphy/docs/common/exception.rst new file mode 100644 index 00000000..308539d5 --- /dev/null +++ b/tools/Polygraphy/docs/common/exception.rst @@ -0,0 +1,4 @@ +========== +Exceptions +========== +.. automodule:: polygraphy.common.exception diff --git a/tools/Polygraphy/docs/common/func.rst b/tools/Polygraphy/docs/common/func.rst new file mode 100644 index 00000000..f219df67 --- /dev/null +++ b/tools/Polygraphy/docs/common/func.rst @@ -0,0 +1,5 @@ +================ +Function Helpers +================ + +.. automodule:: polygraphy.common.func diff --git a/tools/Polygraphy/docs/common/toc.rst b/tools/Polygraphy/docs/common/toc.rst new file mode 100644 index 00000000..296cce3d --- /dev/null +++ b/tools/Polygraphy/docs/common/toc.rst @@ -0,0 +1,8 @@ +============ +Common +============ + +.. toctree:: + data_structures + exception + func diff --git a/tools/Polygraphy/docs/comparator/comparator.rst b/tools/Polygraphy/docs/comparator/comparator.rst new file mode 100644 index 00000000..8dd843b2 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/comparator.rst @@ -0,0 +1,5 @@ +========== +Comparator +========== + +.. autoclass:: polygraphy.comparator.comparator.Comparator diff --git a/tools/Polygraphy/docs/comparator/compare_func.rst b/tools/Polygraphy/docs/comparator/compare_func.rst new file mode 100644 index 00000000..66662775 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/compare_func.rst @@ -0,0 +1,5 @@ +==================== +Comparison Functions +==================== + +.. automodule:: polygraphy.comparator.compare diff --git a/tools/Polygraphy/docs/comparator/data_loader.rst b/tools/Polygraphy/docs/comparator/data_loader.rst new file mode 100644 index 00000000..01e12ff5 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/data_loader.rst @@ -0,0 +1,5 @@ +=========== +Data Loader +=========== + +.. automodule:: polygraphy.comparator.data_loader diff --git a/tools/Polygraphy/docs/comparator/data_structures.rst b/tools/Polygraphy/docs/comparator/data_structures.rst new file mode 100644 index 00000000..f7532ce8 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/data_structures.rst @@ -0,0 +1,5 @@ +================ +Data Structures +================ + +.. automodule:: polygraphy.comparator.struct diff --git a/tools/Polygraphy/docs/comparator/postprocess_func.rst b/tools/Polygraphy/docs/comparator/postprocess_func.rst new file mode 100644 index 00000000..61ab5048 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/postprocess_func.rst @@ -0,0 +1,5 @@ +========================= +Postprocessing Functions +========================= + +.. automodule:: polygraphy.comparator.postprocess diff --git a/tools/Polygraphy/docs/comparator/toc.rst b/tools/Polygraphy/docs/comparator/toc.rst new file mode 100644 index 00000000..c5a9ab29 --- /dev/null +++ b/tools/Polygraphy/docs/comparator/toc.rst @@ -0,0 +1,10 @@ +================== +Comparing Results +================== + +.. toctree:: + comparator + data_structures + compare_func + postprocess_func + data_loader diff --git a/tools/Polygraphy/docs/conf.py b/tools/Polygraphy/docs/conf.py new file mode 100644 index 00000000..0708240a --- /dev/null +++ b/tools/Polygraphy/docs/conf.py @@ -0,0 +1,88 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import sys +import os +ROOT_DIR = os.path.join(os.path.abspath(os.path.dirname(__file__)), os.path.pardir) +sys.path.insert(0, ROOT_DIR) +import polygraphy + +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.intersphinx', + 'sphinx.ext.autosummary', + 'sphinx.ext.napoleon', + 'sphinx.ext.mathjax', +] + +# Want to be able to generate docs with no dependencies installed +autodoc_mock_imports = ["tensorrt", "onnx", "numpy", "tensorflow", "onnx_graphsurgeon", "onnxruntime", "onnxtf", "tf2onnx"] + +autodoc_default_options = { + "members": True, + "show-inheritance": True, + "exclude-members": "activate_impl, deactivate_impl, BaseNetworkFromOnnx, BaseDataLoader", + "special-members": "__call__, __getitem__, __bool__", +} + +autodoc_member_order = "bysource" + +autodoc_inherit_docstrings = True + +autosummary_generate = True + +source_suffix = ['.rst'] + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = 'Polygraphy' +copyright = '2020, NVIDIA' +author = 'NVIDIA' + +version = polygraphy.__version__ +# The full version, including alpha/beta/rc tags. +release = version + +# Style +pygments_style = 'colorful' + +html_theme = 'sphinx_rtd_theme' + +# Use the TRT theme and NVIDIA logo +html_static_path = ['_static'] + +html_logo = '_static/img/nvlogo_white.png' + +# Hide source link +html_show_sourcelink = False + +# Output file base name for HTML help builder. +htmlhelp_basename = 'TensorRTdoc' + +# For constructor arguments to show up in Sphinx generated doc +autoclass_content = 'both' + +# Unlimited depth sidebar. +html_theme_options = { + 'navigation_depth': -1 +} + +html_sidebars = { '**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html'] } + +# Allows us to override the default page width in the Sphinx theme. +def setup(app): + app.add_css_file('style.css') diff --git a/tools/Polygraphy/docs/index.rst b/tools/Polygraphy/docs/index.rst new file mode 100644 index 00000000..d481d3a5 --- /dev/null +++ b/tools/Polygraphy/docs/index.rst @@ -0,0 +1,22 @@ +========== +Polygraphy +========== + +This page includes the Python API documentation for Polygraphy. Polygraphy is a toolkit +designed to assist in running and debugging deep learning models in various frameworks. + +For installation instructions, examples, and information about the CLI tools, +see `this page `_ instead. + +.. toctree:: + :hidden: + + self + +.. toctree:: + :caption: API Reference + :maxdepth: 2 + + backend/toc + common/toc + comparator/toc diff --git a/tools/Polygraphy/docs/requirements.txt b/tools/Polygraphy/docs/requirements.txt new file mode 100644 index 00000000..82133027 --- /dev/null +++ b/tools/Polygraphy/docs/requirements.txt @@ -0,0 +1,2 @@ +sphinx +sphinx_rtd_theme diff --git a/tools/Polygraphy/examples/api/01_comparing_frameworks/README.md b/tools/Polygraphy/examples/api/01_comparing_frameworks/README.md new file mode 100644 index 00000000..ac889b9d --- /dev/null +++ b/tools/Polygraphy/examples/api/01_comparing_frameworks/README.md @@ -0,0 +1,23 @@ +# Comparing Frameworks + + +## Introduction + +One of the core features of Polygraphy is comparison of model outputs across multiple +different backends. This makes it possible to check the accuracy of one backend with +respect to another. + +In this example, we'll look at how you can use the Polygraphy API to run inference +on a model using ONNX Runtime and TensorRT, and then compare the results. + + +## Running the Example + +1. Install prerequisites + a. Ensure that TensorRT is installed + b. Install other dependencies with `python3 -m pip install -r requirements.txt` + +2. Run the example + ```bash + python3 example.py + ``` diff --git a/tools/Polygraphy/examples/api/01_comparing_frameworks/example.py b/tools/Polygraphy/examples/api/01_comparing_frameworks/example.py new file mode 100644 index 00000000..b78d1e90 --- /dev/null +++ b/tools/Polygraphy/examples/api/01_comparing_frameworks/example.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script runs an identity model with ONNX-Runtime and TensorRT, +then compares outputs. +""" +from polygraphy.backend.trt import NetworkFromOnnxBytes, EngineFromNetwork, TrtRunner +from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnxBytes +from polygraphy.backend.common import BytesFromPath +from polygraphy.comparator import Comparator + +import os + +# Create loaders for both ONNX Runtime and TensorRT +MODEL = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, "models", "identity.onnx") + +load_serialized_onnx = BytesFromPath(MODEL) +build_onnxrt_session = SessionFromOnnxBytes(load_serialized_onnx) +build_engine = EngineFromNetwork(NetworkFromOnnxBytes(load_serialized_onnx)) + +# Create runners +runners = [ + TrtRunner(build_engine), + OnnxrtRunner(build_onnxrt_session), +] + +# Finally, run and compare the results. +run_results = Comparator.run(runners) +assert bool(Comparator.compare_accuracy(run_results)) diff --git a/tools/Polygraphy/examples/api/01_comparing_frameworks/requirements.txt b/tools/Polygraphy/examples/api/01_comparing_frameworks/requirements.txt new file mode 100644 index 00000000..05a21f2f --- /dev/null +++ b/tools/Polygraphy/examples/api/01_comparing_frameworks/requirements.txt @@ -0,0 +1,2 @@ +onnx +onnxruntime diff --git a/tools/Polygraphy/examples/api/02_using_real_data/README.md b/tools/Polygraphy/examples/api/02_using_real_data/README.md new file mode 100644 index 00000000..67a509ef --- /dev/null +++ b/tools/Polygraphy/examples/api/02_using_real_data/README.md @@ -0,0 +1,31 @@ +# Using Real Data + + +## Introduction + +The `Comparator` provided by Polygraphy can be useful for comparing a small number of +results across multiple runners, but is not well suited for validating a single runner +with a real dataset that includes labels or golden values - especially if the dataset is large. + +In such cases, it is recommended to use a runner directly instead. + +In this example, we use a `TrtRunner` directly to validate an identity model on +a trivial dataset. Unlike using the `Comparator`, using a runner gives you complete +freedom as to how you load your input data, as well as how you validate the results. + +Since all runners provide the same interface, you can freely drop-in other runners +without touching the rest of your validation code. For example, in this case, validating +the model using ONNX-Runtime would require changing just 2 lines (this is left as an +exercise for the reader). + + +## Running the Example + +1. Install prerequisites + a. Ensure that TensorRT is installed + b. Install other dependencies with `python3 -m pip install -r requirements.txt` + +2. Run the example + ```bash + python3 example.py + ``` diff --git a/tools/Polygraphy/examples/api/02_using_real_data/example.py b/tools/Polygraphy/examples/api/02_using_real_data/example.py new file mode 100644 index 00000000..657bbdad --- /dev/null +++ b/tools/Polygraphy/examples/api/02_using_real_data/example.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script uses the Polygraphy Runner API to validate the outputs +of an identity model using a trivial dataset. +""" +from polygraphy.backend.trt import NetworkFromOnnxPath, EngineFromNetwork, TrtRunner + +import numpy as np +import os + + +INPUT_SHAPE = (1, 1, 2, 2) +REAL_DATASET = [ # Definitely real data + np.ones(INPUT_SHAPE, dtype=np.float32), + np.zeros(INPUT_SHAPE, dtype=np.float32), + np.ones(INPUT_SHAPE, dtype=np.float32), + np.zeros(INPUT_SHAPE, dtype=np.float32), +] + +# For our identity network, the golden output values are the same as the input values. +# Though this network appears to do nothing, it can be incredibly useful in some cases (like here!). +GOLDEN_VALUES = REAL_DATASET + +MODEL = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, "models", "identity.onnx") + +build_engine = EngineFromNetwork(NetworkFromOnnxPath(MODEL)) + +# Activate the runner using a context manager. For TensorRT, this will build an engine, +# then destroy it upon exiting the context. +# NOTE: You can also use the activate() function for this, but you will need to make sure to +# deactivate() to avoid a memory leak. For that reason, a context manager is the safer option. +with TrtRunner(build_engine) as runner: + for (data, golden) in zip(REAL_DATASET, GOLDEN_VALUES): + outputs = runner.infer(feed_dict={"x": data}) + assert np.all(outputs["y"] == golden) diff --git a/tools/Polygraphy/examples/api/02_using_real_data/requirements.txt b/tools/Polygraphy/examples/api/02_using_real_data/requirements.txt new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/README.md b/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/README.md new file mode 100644 index 00000000..a39807a3 --- /dev/null +++ b/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/README.md @@ -0,0 +1,31 @@ +# Interoperating With TensorRT + + +## Introduction + +A key feature of Polygraphy is complete interoperability with TensorRT, as well as +with other backends. Since Polygraphy does not hide the underlying backend APIs, +it is possible to freely switch between using the Polygraphy API and a backend API, +such as TensorRT. + +In this example, we'll look at how you can retain access to the advanced functionality +provided by a backend without giving up the conveniences provided by Polygraphy - the +best of both worlds. + +Polygraphy provides an `extend` decorator which can be used to easily extend existing +Polygraphy loaders. This can be useful in many scenarios, but for this example, +we will focus on cases where you may want to: +- Modify the TensorRT network prior to building the engine +- Use a TensorRT builder flag not currently supported by Polygraphy + + +## Running the Example + +1. Install prerequisites + a. Ensure that TensorRT is installed + b. Install other dependencies with `python3 -m pip install -r requirements.txt` + +2. Run the example + ```bash + python3 example.py + ``` diff --git a/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/example.py b/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/example.py new file mode 100644 index 00000000..97449dc7 --- /dev/null +++ b/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/example.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script demonstrates how to use Polygraphy in conjunction with APIs +provided by a backend. Specifically, in this case, we use TensorRT APIs +to print the network name and enable FP16 mode. +""" +from polygraphy.backend.trt import NetworkFromOnnxPath, CreateConfig, EngineFromNetwork, TrtRunner +from polygraphy.common.func import extend + +import tensorrt as trt +import numpy as np +import os + + +MODEL = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, "models", "identity.onnx") + +# We can use the `extend` decorator to easily extend loaders provided by Polygraphy +# The parameters our decorated function takes should match the return values of the loader we are extending. + +# For `NetworkFromOnnxPath`, we can see from the API documentation that it returns a TensorRT +# builder, network and parser. That is what our function will receive. +@extend(NetworkFromOnnxPath(MODEL)) +def load_network(builder, network, parser): + # Here we can modify the network. For this example, we'll just set the network name. + network.name = "MyIdentity" + print("Network name: {:}".format(network.name)) + + +# In case a builder configuration option is missing from Polygraphy, we can easily set it using TensorRT APIs. +# Our function will receive a TensorRT builder config since that's what `CreateConfig` returns. +@extend(CreateConfig()) +def load_config(config): + # Polygraphy supports the fp16 flag, but in case it didn't, we could do this: + config.set_flag(trt.BuilderFlag.FP16) + + +# Since we have no further need of TensorRT APIs, we can come back to regular Polygraphy. +build_engine = EngineFromNetwork(load_network, config=load_config) + +with TrtRunner(build_engine) as runner: + runner.infer({"x": np.ones(shape=(1, 1, 2, 2), dtype=np.float32)}) diff --git a/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/requirements.txt b/tools/Polygraphy/examples/api/03_interoperating_with_tensorrt/requirements.txt new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/README.md b/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/README.md new file mode 100644 index 00000000..70f804fa --- /dev/null +++ b/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/README.md @@ -0,0 +1,44 @@ +# Int8 Calibration In TensorRT + + +## Introduction + +Int8 calibration in TensorRT involves providing a representative set of input data +to TensorRT as part of the engine building process. The +[calibration API](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Int8/Calibrator.html) +included in TensorRT requires the user to handle copying input data to the GPU, and +manage the calibration cache generated by TensorRT. + +While the TensorRT API provides a higher degree of control, we can greatly simplify the +process for many common use-cases. For that purpose, Polygraphy provides a calibrator, which +can be used either with Polygraphy loaders, or directly with TensorRT. In the latter +case, the Polygraphy calibrator behaves exactly like a normal TensorRT int8 calibrator. + +In this example, we'll look at how you can use Polygraphy's calibrator to calibrate a network +with (fake) calibration data, and how you can manage the calibration cache with just a single +parameter. + + +## Running the Example + +1. Install prerequisites + a. Ensure that TensorRT is installed + b. Install other dependencies with `python3 -m pip install -r requirements.txt` + +2. Run the example: + ```bash + python3 example.py + ``` + +3. The first time you run the example, it will create a calibration cache + called `identity-calib.cache`. If you run the example again, you should see that + it now uses the cache instead of running calibration again: + ```bash + python3 example.py + ``` + + +## Further Reading + +For more information on how int8 calibration works in TensorRT, see the +[developer guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#optimizing_int8_c) diff --git a/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/example.py b/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/example.py new file mode 100644 index 00000000..edf5ef64 --- /dev/null +++ b/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/example.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script demonstrates how to use the Calibrator API provided by Polygraphy +to calibrate a TensorRT engine to run in INT8 precision. +""" +from polygraphy.backend.trt import NetworkFromOnnxPath, CreateConfig, EngineFromNetwork, Calibrator, TrtRunner +from polygraphy.logger import G_LOGGER + +import numpy as np +import os + + +MODEL = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir, "models", "identity.onnx") +INPUT_SHAPE = (1, 1, 2, 2) + +# The data loader argument to Calibrator can be any iterable or generator that yields `feed_dict`s. +# A feed_dict is just a mapping of input names to corresponding inputs (as NumPy arrays). +# Calibration will continue until our data loader runs out of data (4 batches in this example). +def calib_data(): + for _ in range(4): + yield {"x": np.ones(shape=INPUT_SHAPE, dtype=np.float32)} # Totally real data + +# We can provide a path or file-like object if we want to cache calibration data. +# This lets us avoid running calibration the next time we build the engine. +calibrator = Calibrator(data_loader=calib_data(), cache="identity-calib.cache") +build_engine = EngineFromNetwork(NetworkFromOnnxPath(MODEL), config=CreateConfig(int8=True, calibrator=calibrator)) + +# When we activate our runner, it will calibrate and build the engine. If we want to +# see the logging output from TensorRT, we can temporarily increase logging verbosity: +with G_LOGGER.verbosity(G_LOGGER.VERBOSE): + with TrtRunner(build_engine) as runner: + feed_dict = {"x": np.ones(shape=INPUT_SHAPE, dtype=np.float32)} + outputs = runner.infer(feed_dict=feed_dict) + assert np.all(outputs["y"] == feed_dict["x"]) diff --git a/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/requirements.txt b/tools/Polygraphy/examples/api/04_int8_calibration_in_tensorrt/requirements.txt new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/README.md b/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/README.md new file mode 100644 index 00000000..7756b276 --- /dev/null +++ b/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/README.md @@ -0,0 +1,29 @@ +# Using The TensorRT Network API + + +## Introduction + +In addition to loading existing models, TensorRT allows you to define networks by hand +using the network API. + +In this example, we'll look at how you can use Polygraphy's `extend` decorator, covered in +[example 03](../03_interoperating_with_tensorrt), in conjunction with the `CreateNetwork` +loader to seamlessly integrate a network defined using TensorRT APIs with Polygraphy. + + +## Running the Example + +1. Install prerequisites + a. Ensure that TensorRT is installed + b. Install other dependencies with `python3 -m pip install -r requirements.txt` + +2. Run the example: + ```bash + python3 example.py + ``` + + +## Further Reading + +For more information on the TensorRT Network API, see the +[TensorRT API documentation](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/pyGraph.html) diff --git a/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/example.py b/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/example.py new file mode 100644 index 00000000..e2193f60 --- /dev/null +++ b/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/example.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python3 +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +This script demonstrates how to use the extend() API covered in example 03 +to construct a TensorRT network using the TensorRT Network API. +""" +from polygraphy.backend.trt import CreateNetwork, EngineFromNetwork, TrtRunner +from polygraphy.common.func import extend + +import tensorrt as trt +import numpy as np + + +INPUT_NAME = "input" +INPUT_SHAPE = (64, 64) +OUTPUT_NAME = "output" + +# Just like in example 03, we can use `extend` to add our own functionality to existing loaders. +# `CreateNetwork` will create an empty network, which we can then populate ourselves. +@extend(CreateNetwork()) +def create_network(builder, network): + # This network will add 1 to the input tensor + inp = network.add_input(name=INPUT_NAME, shape=INPUT_SHAPE, dtype=trt.float32) + ones = network.add_constant(shape=INPUT_SHAPE, weights=np.ones(shape=INPUT_SHAPE, dtype=np.float32)).get_output(0) + add = network.add_elementwise(inp, ones, op=trt.ElementWiseOperation.SUM).get_output(0) + add.name = OUTPUT_NAME + network.mark_output(add) + + +# After we've constructed the network, we can go back to using regular Polygraphy APIs. +build_engine = EngineFromNetwork(create_network) + +with TrtRunner(build_engine) as runner: + feed_dict = {INPUT_NAME: np.random.random_sample(INPUT_SHAPE).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs[OUTPUT_NAME] == (feed_dict[INPUT_NAME] + 1)) diff --git a/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/requirements.txt b/tools/Polygraphy/examples/api/05_using_tensorrt_network_api/requirements.txt new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/examples/api/README.md b/tools/Polygraphy/examples/api/README.md new file mode 100644 index 00000000..393d1db9 --- /dev/null +++ b/tools/Polygraphy/examples/api/README.md @@ -0,0 +1,4 @@ +# Polygraphy Python API Examples + +This directory includes examples that use the Polygraphy Python API. +For examples of the command-line tools, see the [cli](../cli/) directory instead. diff --git a/tools/Polygraphy/examples/cli/README.md b/tools/Polygraphy/examples/cli/README.md new file mode 100644 index 00000000..56c62738 --- /dev/null +++ b/tools/Polygraphy/examples/cli/README.md @@ -0,0 +1,4 @@ +# Polygraphy CLI Examples + +This directory includes examples that use the Polygraphy CLI. +For examples of the Python API, see the [api](../api/) directory instead. diff --git a/tools/Polygraphy/examples/cli/inspect/01_inspecting_a_tensorrt_network/README.md b/tools/Polygraphy/examples/cli/inspect/01_inspecting_a_tensorrt_network/README.md new file mode 100644 index 00000000..ddb2b104 --- /dev/null +++ b/tools/Polygraphy/examples/cli/inspect/01_inspecting_a_tensorrt_network/README.md @@ -0,0 +1,31 @@ +# Inspecting A TensorRT Network + +The `inspect model` subtool can automatically convert supported formats +into TensorRT networks, and then display them. + +For example: + +```bash +polygraphy inspect model ../../../models/identity.onnx \ + --mode=basic --display-as=trt +``` + +This will display something like: + +``` +[I] ==== TensorRT Network ==== + Name: Unnamed Network 0 | Explicit Batch Network + + ---- 1 Network Inputs ---- + {x [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Network Outputs ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Layers ---- + Layer 0 | (Unnamed Layer* 0) [Identity] [Op: LayerType.IDENTITY] + {x [dtype=float32, shape=(1, 1, 2, 2)]} + -> {y [dtype=float32, shape=(1, 1, 2, 2)]} +``` + +It is also possible to show detailed layer information, including layer attributes, using `--mode=full`. diff --git a/tools/Polygraphy/examples/cli/inspect/02_inspecting_a_tensorrt_engine/README.md b/tools/Polygraphy/examples/cli/inspect/02_inspecting_a_tensorrt_engine/README.md new file mode 100644 index 00000000..a4dbcb8b --- /dev/null +++ b/tools/Polygraphy/examples/cli/inspect/02_inspecting_a_tensorrt_engine/README.md @@ -0,0 +1,44 @@ +# Inspecting A TensorRT Engine + +The `inspect model` subtool can load and display information +about TensorRT engines, i.e. plan files: + +For example, first we'll generate an engine with dynamic shapes +and 2 profiles: + +```bash +polygraphy run ../../../models/identity.onnx --trt \ + --trt-min-shapes X,1x2x1x1 --trt-opt-shapes X,1x2x3x3 --trt-max-shapes X,1x2x5x5 \ + --trt-min-shapes X,1x2x2x2 --trt-opt-shapes X,1x2x4x4 --trt-max-shapes X,1x2x6x6 \ + --save-engine identity.engine +``` + +Next, we can inspect it: + +```bash +polygraphy inspect model identity.engine +``` + +This will display something like: + +``` +[I] ==== TensorRT Engine ==== + Name: Unnamed Network 0 | Explicit Batch Engine (2 layers) + + ---- 1 Engine Inputs ---- + {x [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Engine Outputs ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- Memory ---- + Workspace Memory: 0 bytes + + ---- 2 Profiles (2 Bindings Each) ---- + - Profile: 0 + Binding Index: 0 (Input) [Name: x] | Shapes: min=(1, 1, 2, 2), opt=(1, 1, 2, 2), max=(1, 1, 2, 2) + Binding Index: 1 (Output) [Name: y] | Shape: (1, 1, 2, 2) + - Profile: 1 + Binding Index: 2 (Input) [Name: x [profile 1]] | Shapes: min=(1, 1, 2, 2), opt=(1, 1, 2, 2), max=(1, 1, 2, 2) + Binding Index: 3 (Output) [Name: y [profile 1]] | Shape: (1, 1, 2, 2) +``` diff --git a/tools/Polygraphy/examples/cli/inspect/03_inspecting_an_onnx_model/README.md b/tools/Polygraphy/examples/cli/inspect/03_inspecting_an_onnx_model/README.md new file mode 100644 index 00000000..400051e4 --- /dev/null +++ b/tools/Polygraphy/examples/cli/inspect/03_inspecting_an_onnx_model/README.md @@ -0,0 +1,32 @@ +# Inspecting An ONNX Model + +The `inspect model` subtool can display ONNX models. + +For example: + +```bash +polygraphy inspect model ../../../models/identity.onnx --mode=basic +``` + +This will display something like: + +``` +[I] ==== ONNX Model ==== + Name: test_identity | Opset: 8 + + ---- 1 Graph Inputs ---- + {x [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Graph Outputs ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 0 Initializers ---- + {} + + ---- 1 Nodes ---- + Node 0 | [Op: Identity] + {x [dtype=float32, shape=(1, 1, 2, 2)]} + -> {y [dtype=float32, shape=(1, 1, 2, 2)]} +``` + +It is also possible to show detailed layer information, including layer attributes, using `--mode=full`. diff --git a/tools/Polygraphy/examples/cli/inspect/04_inspecting_a_tensorflow_graph/README.md b/tools/Polygraphy/examples/cli/inspect/04_inspecting_a_tensorflow_graph/README.md new file mode 100644 index 00000000..3846bc6c --- /dev/null +++ b/tools/Polygraphy/examples/cli/inspect/04_inspecting_a_tensorflow_graph/README.md @@ -0,0 +1,22 @@ +# Inspecting A TensorFlow Graph + +The `inspect model` subtool can display TensorFlow graphs. + +For example: + +```bash +polygraphy inspect model ../../../models/identity.pb --model-type=frozen +``` + +This will display something like: + +``` +[I] ==== TensorFlow Graph ==== + ---- 1 Graph Inputs ---- + {Input:0 [dtype=float32, shape=(1, 15, 25, 30)]} + + ---- 1 Graph Outputs ---- + {Identity_2:0 [dtype=float32, shape=(1, 15, 25, 30)]} + + ---- 4 Nodes ---- +``` diff --git a/tools/Polygraphy/examples/cli/inspect/05_inspecting_run_results/README.md b/tools/Polygraphy/examples/cli/inspect/05_inspecting_run_results/README.md new file mode 100644 index 00000000..c53bacfd --- /dev/null +++ b/tools/Polygraphy/examples/cli/inspect/05_inspecting_run_results/README.md @@ -0,0 +1,32 @@ +# Inspecting Run Results + +The `inspect results` subtool can display information about the +`RunResults` object generated by `Comparator.run()`: + +For example, first we'll generate some run results using 3 ONNX-Runtime runners: + +```bash +polygraphy run ../../../models/identity.onnx --onnxrt --onnxrt --onnxrt \ + --save-results onnxrt_results.pkl +``` + +Next, we can inspect them: + +```bash +polygraphy inspect results onnxrt_results.pkl +``` + +This will display something like: + +``` +[I] ==== Run Results (3 runners) ==== + + ---- Runner: onnxrt-runner-N0-09/17/20-10:21:38 (1 iterations) ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- Runner: onnxrt-runner-N1-09/17/20-10:21:38 (1 iterations) ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- Runner: onnxrt-runner-N2-09/17/20-10:21:38 (1 iterations) ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} +``` diff --git a/tools/Polygraphy/examples/cli/run/01_comparing_frameworks/README.md b/tools/Polygraphy/examples/cli/run/01_comparing_frameworks/README.md new file mode 100644 index 00000000..53f507f3 --- /dev/null +++ b/tools/Polygraphy/examples/cli/run/01_comparing_frameworks/README.md @@ -0,0 +1,10 @@ +# Comparing Frameworks + +You can use the `run` subtool to compare a model between different frameworks. +In the simplest case, you can supply a model, and one or more framework flags. + +For example, to compare an ONNX model between TensorRT and ONNX Runtime: + +```bash +polygraphy run ../../../models/identity.onnx --trt --onnxrt +``` diff --git a/tools/Polygraphy/examples/cli/run/02_comparing_across_runs/README.md b/tools/Polygraphy/examples/cli/run/02_comparing_across_runs/README.md new file mode 100644 index 00000000..23989c81 --- /dev/null +++ b/tools/Polygraphy/examples/cli/run/02_comparing_across_runs/README.md @@ -0,0 +1,22 @@ +# Comparing Across Runs + +In some cases, it may be be useful to compare results across different runs of `polygraphy run` - +for example, comparing between different machines, or comparing between multiple different versions +of the same library. + +For example, if you need to compare results between two different systems (let's call them +System A and System B), you can first save the results from System A: + + +```bash +polygraphy run ../../../models/identity.onnx --onnxrt \ + --save-results system_a_results.pkl +``` + +Next, you can run the model on System B, and load the results saved from +System A to compare against: + +```bash +polygraphy run ../../../models/identity.onnx --onnxrt \ + --load-results system_a_results.pkl +``` diff --git a/tools/Polygraphy/examples/cli/run/03_generating_a_comparison_script/README.md b/tools/Polygraphy/examples/cli/run/03_generating_a_comparison_script/README.md new file mode 100644 index 00000000..f74902fc --- /dev/null +++ b/tools/Polygraphy/examples/cli/run/03_generating_a_comparison_script/README.md @@ -0,0 +1,20 @@ + +# Generating A Script For Advanced Comparisons + +For more advanced requirements, you may want to use the [API](../../../../polygraphy). +Instead of writing a script from scratch, you can use `run`'s `--gen-script` option +to create a Python script that you can use as a starting point. + +First, generate a comparison script: + +```bash +polygraphy run ../../../models/identity.onnx --trt --onnxrt \ + --gen-script=compare_trt_onnxrt.py +``` + +The generated script will do exactly what the `run` command would otherwise do. +You are free to modify this script, and finally run it: + +```bash +python3 compare_trt_onnxrt.py +``` diff --git a/tools/Polygraphy/examples/cli/surgeon/01_isolating_subgraphs/README.md b/tools/Polygraphy/examples/cli/surgeon/01_isolating_subgraphs/README.md new file mode 100644 index 00000000..57ef1c20 --- /dev/null +++ b/tools/Polygraphy/examples/cli/surgeon/01_isolating_subgraphs/README.md @@ -0,0 +1,51 @@ +# Using Extract To Isolate A Subgraph + +The `surgeon extract` subtool can be used to extract a subgraph from a model with a single command. + +In this example, we'll extract a subgraph from a simple 2-layer identity model. +We will assume that we have already used `inspect model` with `--mode` to determine that +the input tensor of the subgraph we want is `identity_out_0`, and the output tensor is `identity_out_2`. + +Additionally, we'll assume that we don't know what the shapes or types of these tensors are, +and furthermore, that ONNX shape inference was not able to determine these either. + +When shapes and types are unknown, you can use `auto` to indicate that `extract` should +attempt to automatically determine these. +For inputs, we must specify both shape and data type, whereas outputs only require the data +type (hence `--inputs` requires 2 `auto`s and `--outputs` requires only 1). + +In our case, we can run: + +```bash +polygraphy surgeon extract ../../../models/2_layer_identity.onnx \ + --inputs identity_out_0,auto,auto \ + --outputs identity_out_2,auto \ + -o subgraph.onnx +``` + +**NOTE:** When even ONNX shape inference cannot determine shapes, `extract` will run inference on the model +using input data (you can control the shape of this input data using the `--model-inputs` argument). +This will cause the inputs of the resulting subgraph to have fixed shapes. You can change +these back to dynamic by using the extract command again on the subgraph, and specifying the same inputs, +but using shapes with dynamic dimensions, e.g. `--inputs identity_out_0,-1x-1,auto` + + +If we knew the shapes and/or data types, we could instead write: + +```bash +polygraphy surgeon extract ../../../models/2_layer_identity.onnx \ + --inputs identity_out_0,64x64,float32 \ + --outputs identity_out_2,float32 \ + -o subgraph.onnx +``` + +`subgraph.onnx` will be an ONNX model which contains only the subgraph whose input is +`identity_out_0` and whose output is `identity_out_2` - in this case, just a single node. + + +At this point, the model is ready for use. You can use `inspect` to confirm +whether it looks correct: + +```bash +polygraphy inspect model subgraph.onnx --mode=basic +``` diff --git a/tools/Polygraphy/examples/models/2_layer_identity.onnx b/tools/Polygraphy/examples/models/2_layer_identity.onnx new file mode 100644 index 00000000..f03a5cef Binary files /dev/null and b/tools/Polygraphy/examples/models/2_layer_identity.onnx differ diff --git a/tools/Polygraphy/examples/models/README.md b/tools/Polygraphy/examples/models/README.md new file mode 100644 index 00000000..3e719a54 --- /dev/null +++ b/tools/Polygraphy/examples/models/README.md @@ -0,0 +1,6 @@ +# Example Models + +This directory includes simple models used by various examples: +- `identity.onnx`: An ONNX model containing a single `Identity` op. +- `2_layer_identity.onnx`: An ONNX model containing two consecutive `Identity` ops. +- `identity.pb`: An TensorFlow frozen model containing a single `Identity` op. diff --git a/tools/Polygraphy/examples/models/identity.onnx b/tools/Polygraphy/examples/models/identity.onnx new file mode 100644 index 00000000..b7302044 --- /dev/null +++ b/tools/Polygraphy/examples/models/identity.onnx @@ -0,0 +1,15 @@ + backend-test:[ + +xy"Identity test_identityZ +x + + + + +b +y + + + + +B \ No newline at end of file diff --git a/tools/Polygraphy/examples/models/identity.pb b/tools/Polygraphy/examples/models/identity.pb new file mode 100644 index 00000000..69aeaae1 --- /dev/null +++ b/tools/Polygraphy/examples/models/identity.pb @@ -0,0 +1,17 @@ + +> +Input Placeholder* +dtype0* +shape: +$ +IdentityIdentityInput* +T0 +) + +Identity_1IdentityIdentity* +T0 ++ + +Identity_2Identity +Identity_1* +T0" \ No newline at end of file diff --git a/tools/Polygraphy/polygraphy/README.md b/tools/Polygraphy/polygraphy/README.md new file mode 100644 index 00000000..e51bc68b --- /dev/null +++ b/tools/Polygraphy/polygraphy/README.md @@ -0,0 +1,189 @@ +# Polygraphy Python API + +## Table of Contents + +- [Introduction](#introduction) +- [Backends](#backends) + - [Loaders](#loaders) + - [Runners](#runners) + - [Writing A Custom Runner](#writing-a-custom-runner) +- [Comparator](#comparator) + - [Data Loaders](#data-loaders) +- [Putting It All Together](#putting-it-all-together) +- [Examples](#examples) +- [Enabling PyTorch](#enabling-pytorch) + + +## Introduction + +**IMPORTANT:** The Python API is still not completely stable, and minor but breaking changes +may be made in future versions. + +The Polygraphy API consists broadly of two major components: +[`Backend`s](#backends) and the [`Comparator`](#comparator). + +**NOTE:** To help you get started with the API, you can use the [`run`](./tools/run/) tool to auto-generate +template scripts that use the Polygraphy API. + + +## Backends + +A Polygraphy backend provides an interface for a deep learning framework. +Backends are comprised of two components: Loaders and Runners. + + +### Loaders + +A `Loader` is used to load models for runners (see [`BaseLoadModel`](./backend/base/runner.py)). + +A `Loader` can be any `Callable` that takes no arguments and returns a model. +"Model" here is a generic term, and the specifics depend on the runner for which the loader has been implemented. + +Moreover, existing `Loader`s can be composed for more advanced behaviors. +For example, we can implement a conversion like `TensorFlow Frozen Model -> ONNX -> TensorRT Network -> TensorRT Engine`: +```python +from polygraphy.backend.tf import GraphFromFrozen +from polygraphy.backend.trt import EngineFromNetwork, NetworkFromOnnxBytes +from polygraphy.backend.onnx import OnnxFromTfGraph, BytesFromOnnx + +build_engine = EngineFromNetwork(NetworkFromOnnxBytes( + BytesFromOnnx(OnnxFromTfGraph(GraphFromFrozen("/path/to/model.pb"))))) +``` +We can now provide `build_engine` directly to a `TrtRunner`. + + +### Runners + +A `Runner` uses a loader to load a model and can then run inference (see [`BaseRunner`](./backend/base/runner.py)). + +**IMPORTANT:** Runners may reuse their output buffers. Thus, if you need to save outputs from multiple inferences, you should +make a copy of the outputs with `copy.deepcopy(outputs)`. + +To use a runner, you just need to activate it, and then call `infer()`. +Note that activating a runner can be very expensive, so you should minimize the +number of times you activate a runner - ideally do not do this more than once. + +It is recommended to use a context manager to activate and deactivate the +runner rather than calling the functions manually: +```python +from polygraphy.backend.trt import TrtRunner + +with TrtRunner(build_engine) as runner: + outputs = runner.infer(feed_dict={"input0": input_data}) +``` + +#### Writing A Custom Runner + +Generally, you do not need to write custom runners unless you want to support a new backend. + +In case you do, in the simplest case, you only need to implement two functions: +- `infer`: Accepts a dictionary of numpy buffers, runs inference, and finally returns a dictionary containing the outputs. +- `get_input_metadata`: Returns a [`TensorMetadata`](./common/struct.py) mapping input names to their shapes and data types. + You may use `None` to indicate dynamic dimensions. + +For more advanced runners, where some setup is required, you may also need to implement the `activate_impl()` and `deactivate_impl()` functions. + +For example, in the `TrtRunner`, engines are created in `activate_impl()` and destroyed in `deactivate_impl()`. +Importantly, the GPU is *not used at all* until these functions have been called (notice, for example, + that in the `TrtRunner`, the CUDA runtime library is only loaded in the `activate_impl()` function). +This allows the `Comparator` to optionally provide each runner with exclusive access to the GPU, to prevent any interference between runners. + + +## Comparator + +The `Comparator` is used to run inference for runners, and then compare accuracy (see [Comparator.py](./comparator/Comparator.py)). +This process is divided into two phases: + +1. Running inference: + ```python + run_results = Comparator.run(runners) + ``` + This function accepts a list of runners, and returns a `RunResults` object (see [Comparator.py](./comparator/Comparator.py)) + containing information about the outputs of each run. + It also accepts an optional `data_loader` argument to control the input data. If not provided, it will use the + default data loader. `Comparator.run()` continues until inputs from the data loader are exhausted. + +2. Comparing results: + ```python + Comparator.compare_accuracy(run_results) + ``` + This function accepts the results returned by `Comparator.run` and compares them between runners. + + +### Data Loaders + +A data loader is used by the `Comparator` to load input data to feed to each runner +(see [DataLoader.py](./comparator/DataLoader.py)). + +A data loader can be any generator or iterable that yields +a dictionary of input buffers. In the simplest case, this can just be a `list` of `dict`s. + +In case you don't know details about the inputs ahead of time, you can access the `input_metadata` +property in your data loader, which will be set to an `TensorMetadata` instance by the Comparator. + +**NOTE:** Polygraphy provides a default `DataLoader` class that uses numpy to generate random input buffers. +The input data can be bounded via parameters to the constructor. + +**IMPORTANT:** Data loaders are designed for scenarios where you need to compare a small number +of inputs across multiple runners. It is **not** a good idea to use a custom data loader +to validate a model with an entire dataset! Instead, runners should be used directly for such +cases (see the [example](../examples/api/02_using_real_data)). + + +## Putting It All Together + +Now that you know the basic components of Polygraphy, let's take a look at how they fit together. + +In this example, we will write a script that: +1. Loads a TensorFlow frozen model +2. Converts it to ONNX +3. Builds a TensorRT engine from the ONNX model +4. Bounds input values in the range `[0, 2]` +5. Runs inference using TensorFlow, ONNX-Runtime, and TensorRT +6. Compares the results and checks that they match + +```python +from polygraphy.backend.onnx import OnnxFromTfGraph, BytesFromOnnx +from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnxBytes +from polygraphy.backend.tf import TfRunner, GraphFromFrozen, SessionFromGraph +from polygraphy.backend.trt import TrtRunner, EngineFromNetwork, NetworkFromOnnxBytes +from polygraphy.comparator import Comparator, DataLoader + +# Convert the model into the various formats we care about. +load_frozen = GraphFromFrozen("/path/to/frozen/model.pb") +build_tf_session = SessionFromGraph(load_frozen) +export_serialized_onnx = BytesFromOnnx(OnnxFromTfGraph(load_frozen)) +build_onnxrt_session = SessionFromOnnxBytes(export_serialized_onnx) +build_engine = EngineFromNetwork(NetworkFromOnnxBytes(export_serialized_onnx)) + +# We want to run the model with TensorFlow, ONNX Runtime, and TensorRT. +runners = [ + TfRunner(build_tf_session), + OnnxrtRunner(build_onnxrt_session), + TrtRunner(build_engine), +] + +# For this model, assume inputs need to be bounded. +data_loader = DataLoader(int_range=(0, 2), float_range=(0.0, 2.0)) + +# Finally, run and check accuracy. +run_results = Comparator.run(runners, data_loader=data_loader) +assert bool(Comparator.compare_accuracy(run_results)) +``` + + +## Examples + +You can find complete code examples that use the Polygraphy Python API [here](../examples/api). + + +## Enabling PyTorch + +In order to enable PyTorch, you need to provide three things to the `PytRunner`: +1. A `BaseLoadPyt`: In the simplest case, this can be a callable that returns a `torch.nn.Module`. + +2. `input_metadata`: A `TensorMetadata` describing the inputs of the model. This maps input names to their shapes and data types. As with other runners, `None` may be used to indicate dynamic dimensions. + + **NOTE:** Other runners are able to automatically determine input metadata by inspecting the model definition, but because of the way PyTorch is implemented, it is difficult to write a generic function to determine model inputs from a `torch.nn.Module`. + +3. `output_names`: A list of output names. This is used by the `Comparator` to match `PytRunner` outputs to those of other runners. diff --git a/tools/Polygraphy/polygraphy/__init__.py b/tools/Polygraphy/polygraphy/__init__.py new file mode 100644 index 00000000..0558cd9d --- /dev/null +++ b/tools/Polygraphy/polygraphy/__init__.py @@ -0,0 +1 @@ +__version__ = "0.20.13" diff --git a/tools/Polygraphy/polygraphy/backend/__init__.py b/tools/Polygraphy/polygraphy/backend/__init__.py new file mode 100644 index 00000000..d2648f3e --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/__init__.py @@ -0,0 +1 @@ +from polygraphy.backend.base import BaseRunner, BaseLoadModel diff --git a/tools/Polygraphy/polygraphy/backend/base/__init__.py b/tools/Polygraphy/polygraphy/backend/base/__init__.py new file mode 100644 index 00000000..4c1f6dee --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/base/__init__.py @@ -0,0 +1,2 @@ +from polygraphy.backend.base.loader import BaseLoadModel +from polygraphy.backend.base.runner import BaseRunner diff --git a/tools/Polygraphy/polygraphy/backend/base/loader.py b/tools/Polygraphy/polygraphy/backend/base/loader.py new file mode 100644 index 00000000..b265319a --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/base/loader.py @@ -0,0 +1,21 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +class BaseLoadModel(object): + """ + Loads a model for a runner. + """ + pass diff --git a/tools/Polygraphy/polygraphy/backend/base/runner.py b/tools/Polygraphy/polygraphy/backend/base/runner.py new file mode 100644 index 00000000..59456063 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/base/runner.py @@ -0,0 +1,146 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.util import misc +from polygraphy.logger.logger import G_LOGGER, LogMode + +from collections import defaultdict +import time + + +class BaseRunner(object): + """ + The base class for runner objects. All runners should override the functions and attributes specified here. + """ + RUNNER_COUNTS = defaultdict(int) + + def __init__(self, name=None, prefix=None): + """ + Args: + name (str): + The name to use for this runner. + prefix (str): + The human-readable name prefix to use for this runner. + A runner count and timestamp will be appended to this prefix. + Only used if name is not provided. + """ + prefix = misc.default_value(prefix, "Runner") + if name is None: + count = BaseRunner.RUNNER_COUNTS[prefix] + BaseRunner.RUNNER_COUNTS[prefix] += 1 + name = "{:}-N{:}-{:}-{:}".format(prefix, count, time.strftime("%x"), time.strftime("%X")) + self.name = name + self.inference_time = None + + self.is_active = False + """bool: Whether this runner has been activated, either via context manager, or by calling ``activate()``.""" + + + def last_inference_time(self): + """ + Returns the total time required for the last call to ``infer()``. + + Returns: + float: The time in seconds, or None if runtime was not measured by the runner. + """ + if self.inference_time is None: + G_LOGGER.warning("inference_time was not set by this runner. Inference time will be incorrect!" + "To correctly compare runtimes, please set the inference_time property in the" + "infer() function", mode=LogMode.ONCE) + return None + return self.inference_time + + + def __enter__(self): + self.activate() + return self + + + def __exit__(self, exc_type, exc_value, traceback): + self.deactivate() + + + def activate_impl(self): + """ + Implementation for runner activation. Derived classes should override this function + rather than ``activate()``. + """ + pass + + + def activate(self): + """ + Activate the runner for inference. This may involve allocating GPU buffers, for example. + """ + if self.is_active: + G_LOGGER.warning("Runner is already active; Will not activate again. If you really want to " + "activate this runner twice, call activate_impl() directly") + return + + try: + self.activate_impl() + finally: + self.is_active = True + + + def infer(self, feed_dict): + """ + Runs inference using the provided feed_dict. + + Args: + feed_dict (OrderedDict[str, np.ndarray]): A mapping of input tensor names to corresponding input NumPy arrays. + + Returns: + OrderedDict[str, np.ndarray]: + A mapping of output tensor names to their corresponding NumPy arrays. + IMPORTANT: Runners may reuse these output buffers. Thus, if you need to save + outputs from multiple inferences, you should make a copy with ``copy.copy(outputs)``. + """ + raise NotImplementedError("BaseRunner is an abstract class") + + + def get_input_metadata(self): + """ + Returns information about the inputs of the model. + Shapes here may include dynamic dimensions, represented by ``None``. + Must be called only after activate() and before deactivate(). + + Returns: + TensorMetadata: Input names, shapes, and data types. + """ + raise NotImplementedError("BaseRunner is an abstract class") + + + def deactivate_impl(self): + """ + Implementation for runner deactivation. Derived classes should override this function + rather than ``deactivate()``. + """ + pass + + + def deactivate(self): + """ + Deactivate the runner. + """ + if not self.is_active: + G_LOGGER.warning("Runner is not active; Will not deactivate. If you really want to " + "deactivate this runner, call deactivate_impl() directly") + return + + try: + self.deactivate_impl() + finally: + self.is_active = False diff --git a/tools/Polygraphy/polygraphy/backend/cntk/__init__.py b/tools/Polygraphy/polygraphy/backend/cntk/__init__.py new file mode 100644 index 00000000..f5fbef95 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/cntk/__init__.py @@ -0,0 +1 @@ +from polygraphy.backend.cntk import CNTKRunner diff --git a/tools/Polygraphy/polygraphy/backend/cntk/requirements.txt b/tools/Polygraphy/polygraphy/backend/cntk/requirements.txt new file mode 100644 index 00000000..d491f1e4 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/cntk/requirements.txt @@ -0,0 +1 @@ +cntk diff --git a/tools/Polygraphy/polygraphy/backend/cntk/runner.py b/tools/Polygraphy/polygraphy/backend/cntk/runner.py new file mode 100644 index 00000000..4bfb2ea9 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/cntk/runner.py @@ -0,0 +1,50 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# EXPERIMENTAL +from polygraphy.logger.logger import G_LOGGER +from polygraphy.backend.base import BaseRunner + +from collections import OrderedDict +import time + +import cntk + + +class CNTKRunner(BaseRunner): + def __init__(self, model, name=None): + super().__init__(name=name, prefix="cntk-runner") + self.model = model + + + def activate_impl(self): + self.cntk_model = cntk.Function.load(self.model) + + self.inputs = OrderedDict() + for inp in self.cntk_model.arguments: + self.inputs[inp] = inp.shape + + + def infer(self, feed_dict): + start = time.time() + inference_outputs = self.cntk_model.eval(feed_dict) + end = time.time() + + out_dict = OrderedDict() + for out_node, out in zip(self.cntk_model.outputs, inference_outputs): + out_dict[out_node.name] = out + + self.inference_time = end - start + return out_dict diff --git a/tools/Polygraphy/polygraphy/backend/common/__init__.py b/tools/Polygraphy/polygraphy/backend/common/__init__.py new file mode 100644 index 00000000..d177e320 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/common/__init__.py @@ -0,0 +1 @@ +from polygraphy.backend.common.loader import BytesFromPath diff --git a/tools/Polygraphy/polygraphy/backend/common/loader.py b/tools/Polygraphy/polygraphy/backend/common/loader.py new file mode 100644 index 00000000..744e6a37 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/common/loader.py @@ -0,0 +1,38 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.base import BaseLoadModel + + +class BytesFromPath(BaseLoadModel): + def __init__(self, path): + """ + Functor that can load a file in binary mode ('rb'). + + Args: + path (str): The file path. + """ + self.path = path + + + def __call__(self): + """ + Loads a file in binary mode ('rb'). + + Returns: + bytes: The contents of the file. + """ + with open(self.path, "rb") as f: + return f.read() diff --git a/tools/Polygraphy/polygraphy/backend/onnx/__init__.py b/tools/Polygraphy/polygraphy/backend/onnx/__init__.py new file mode 100644 index 00000000..b826a227 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnx/__init__.py @@ -0,0 +1,17 @@ +from polygraphy.backend.onnx.loader import OnnxFromPath, OnnxFromTfGraph, ModifyOnnx, SaveOnnx, BytesFromOnnx +from polygraphy.backend.onnx.runner import OnnxTfRunner + + +def register_callback(): + from polygraphy.logger.logger import G_LOGGER + + def set_onnx_logging_level(sev): + import warnings + if sev >= G_LOGGER.INFO: + warnings.filterwarnings("ignore") + else: + warnings.filterwarnings("default") + + G_LOGGER.register_callback(set_onnx_logging_level) + +register_callback() diff --git a/tools/Polygraphy/polygraphy/backend/onnx/loader.py b/tools/Polygraphy/polygraphy/backend/onnx/loader.py new file mode 100644 index 00000000..a1bacfd4 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnx/loader.py @@ -0,0 +1,194 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.base import BaseLoadModel +from polygraphy.backend.onnx import util as onnx_util +from polygraphy.common import constants +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +# ONNX loaders return ONNX models in memory. +class OnnxFromPath(BaseLoadModel): + def __init__(self, path): + """ + Functor that loads an ONNX model from a file. + + Args: + path (str): The path from which to load the model. + """ + self.path = path + + + def __call__(self): + """ + Loads an ONNX model from a file. + + Returns: + onnx.ModelProto: The ONNX model + """ + import onnx + misc.log_module_info(onnx) + + G_LOGGER.verbose("Loading ONNX model: {:}".format(self.path)) + return onnx_util.check_model(onnx.load(self.path)) + + +class OnnxFromTfGraph(BaseLoadModel): + def __init__(self, graph, opset=None, optimize=None, fold_constant=None): + """ + Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a TensorFlow + graph and output names. + + + opset (int): The ONNX opset to use during conversion. + optimize (bool): Whether to use tf2onnx's graph optimization pass. + fold_constant (bool): Whether to fold constants in the TensorFlow Graph. Requires that ``optimize`` is also enabled. Defaults to True. + """ + self._graph = graph + self.opset = misc.default_value(opset, 11) + self.fold_constant = misc.default_value(fold_constant, True) + self.optimize = misc.default_value(optimize, True) + + if self.fold_constant and not self.optimize: + G_LOGGER.warning("`fold_constant` is enabled, but `optimize` is disabled. Constant folding will not be performed") + + + def __call__(self): + """ + Converts a TensorFlow model into ONNX. + + Returns: + onnx.ModelProto: The ONNX model. + """ + import tensorflow as tf + import tf2onnx + from polygraphy.backend.tf import util as tf_util + + misc.log_module_info(tf2onnx) + + (graph, output_names), _ = misc.try_call(self._graph) + input_names = list(tf_util.get_input_metadata(graph).keys()) + + if self.fold_constant: + G_LOGGER.info("Folding constants in graph using tf2onnx.tfonnx.tf_optimize") + graphdef = graph.as_graph_def() + if self.optimize: + graphdef = tf2onnx.tfonnx.tf_optimize(input_names, output_names, graph.as_graph_def(), fold_constant=self.fold_constant) + + with tf.Graph().as_default() as graph, tf.compat.v1.Session(graph=graph) as sess: + tf.import_graph_def(graphdef, name="") + + onnx_graph = tf2onnx.tfonnx.process_tf_graph(graph, input_names=input_names, output_names=output_names, opset=self.opset) + if self.optimize: + onnx_graph = tf2onnx.optimizer.optimize_graph(onnx_graph) + return onnx_util.check_model(onnx_graph.make_model("model")) + + +class ModifyOnnx(BaseLoadModel): + def __init__(self, model, do_shape_inference=None, outputs=None, exclude_outputs=None): + """ + Functor that modifies an ONNX model. + + Args: + model (Callable() -> onnx.ModelProto): A loader that can supply an ONNX model. + + outputs (Sequence[str]): + Names of tensors to mark as outputs. If provided, this will override the + existing model outputs. + If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked. + exclude_outputs (Sequence[str]): + Names of tensors to exclude as outputs. This can be useful in conjunction with + ``outputs=constants.MARK_ALL`` to omit outputs. + """ + self._model = model + self.do_shape_inference = misc.default_value(do_shape_inference, False) + self.outputs = outputs + self.exclude_outputs = exclude_outputs + + + def __call__(self): + """ + Modifies an ONNX model. + + Returns: + onnx.ModelProto: The modified ONNX model. + """ + model, _ = misc.try_call(self._model) + + if self.do_shape_inference: + model = onnx_util.infer_shapes(model) + + if self.outputs == constants.MARK_ALL: + G_LOGGER.verbose("Marking all ONNX tensors as outputs") + model = onnx_util.mark_layerwise(model) + elif self.outputs is not None: + model = onnx_util.mark_outputs(model, self.outputs) + + if self.exclude_outputs is not None: + model = onnx_util.unmark_outputs(model, self.exclude_outputs) + + return onnx_util.check_model(model) + + +class SaveOnnx(BaseLoadModel): + def __init__(self, model, path=None): + """ + Functor that saves an ONNX model to the specified path. + + Args: + model (Callable() -> onnx.ModelProto): A loader that can supply an ONNX model. + path (str): Path at which to write the ONNX model. + """ + self._model = model + self.path = path + + + def __call__(self): + """ + Saves an ONNX model to the specified path. + + Returns: + onnx.ModelProto: The model, after saving it. + """ + model, _ = misc.try_call(self._model) + misc.lazy_write(contents=lambda: model.SerializeToString(), path=self.path) + return model + + +class BytesFromOnnx(BaseLoadModel): + def __init__(self, model): + """ + Functor that serializes an ONNX model. + + Args: + model (Callable() -> onnx.ModelProto): A loader that can supply an ONNX model. + """ + self._model = model + + + def __call__(self): + """ + Serializes an ONNX model. + + Returns: + bytes: The serialized model. + """ + model, _ = misc.try_call(self._model) + return model.SerializeToString() diff --git a/tools/Polygraphy/polygraphy/backend/onnx/requirements.txt b/tools/Polygraphy/polygraphy/backend/onnx/requirements.txt new file mode 100644 index 00000000..b20b937c --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnx/requirements.txt @@ -0,0 +1 @@ +onnx diff --git a/tools/Polygraphy/polygraphy/backend/onnx/runner.py b/tools/Polygraphy/polygraphy/backend/onnx/runner.py new file mode 100644 index 00000000..f9dc2da8 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnx/runner.py @@ -0,0 +1,63 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import time +from collections import OrderedDict + +from polygraphy.backend.base import BaseRunner +from polygraphy.backend.onnx import util as onnx_util +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +class OnnxTfRunner(BaseRunner): + """ + Runs an ONNX model using the TensorFlow backend for ONNX. + """ + def __init__(self, model, name=None): + """ + Creates an ONNX-TF runner. + """ + super().__init__(name=name, prefix="onnxtf-runner") + import polygraphy.backend.tf # To set logging callback + self._model = model + + + def activate_impl(self): + self.model, _ = misc.try_call(self._model) + + import onnx_tf + G_LOGGER.info("Preparing ONNX-TF backend") + self.tf_rep = onnx_tf.backend.prepare(self.model) + + + def deactivate_impl(self): + del self.tf_rep + + + def infer(self, feed_dict): + start = time.time() + outputs = self.tf_rep.run(list(feed_dict.values())) + end = time.time() + + out_dict = OrderedDict() + for name, out in zip(self.tf_rep.outputs, outputs): + out_dict[name] = out + self.inference_time = end - start + return out_dict + + + def get_input_metadata(self): + return onnx_util.get_input_metadata(self.model.graph) diff --git a/tools/Polygraphy/polygraphy/backend/onnx/util.py b/tools/Polygraphy/polygraphy/backend/onnx/util.py new file mode 100644 index 00000000..132f949b --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnx/util.py @@ -0,0 +1,307 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import copy +from collections import OrderedDict + +from polygraphy.common import TensorMetadata +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +def check_model(model): + try: + import onnx + except: + G_LOGGER.warning("Could not import onnx module, skipping model check") + + try: + onnx.checker.check_model(model) + G_LOGGER.verbose("ONNX Checker Passed") + except onnx.checker.ValidationError as err: + G_LOGGER.warning("ONNX Checker exited with an error:\n{:}".format(err)) + finally: + return model + + +def infer_shapes(model): + try: + import onnx.shape_inference + except: + G_LOGGER.warning("Could not import onnx.shape_inference module, skipping shape inference") + + try: + model = onnx.shape_inference.infer_shapes(model) + G_LOGGER.verbose("ONNX Shape Inference completed successfully") + except Exception as err: + G_LOGGER.warning("ONNX shape inference exited with an error:\n{:}".format(err)) + finally: + return model + + +def all_tensor_names(model): + all_outputs = [output for node in model.graph.node if node.op_type != "Constant" for output in node.output] + all_outputs = misc.unique_list(all_outputs) + return all_outputs + + +def check_outputs_not_found(not_found, all_outputs): + if not_found: + G_LOGGER.critical("The following outputs: {:} were not found. " + "Note: Available tensors: {:}".format(not_found, all_outputs)) + + +def mark_outputs(model, outputs): + import onnx + + # Clear the old outputs + while model.graph.output: + model.graph.output.pop() + + all_outputs = all_tensor_names(model) + all_outputs_set = set(all_outputs) + + out_tensors = [] + not_found = set() + for output in outputs: + if output in all_outputs_set: + out_tensors.append(onnx.helper.make_empty_tensor_value_info(output)) + else: + not_found.add(output) + + check_outputs_not_found(not_found, all_outputs) + model.graph.output.extend(out_tensors) + return model + + +def mark_layerwise(model): + # Add all non-constant node outputs as graph outputs + model = mark_outputs(model, all_tensor_names(model)) + return model + + +def unmark_outputs(model, outputs): + outputs = set(outputs) + + cur_outputs = [] + while model.graph.output: + cur_outputs.append(model.graph.output.pop()) + cur_outputs = list(reversed(cur_outputs)) # Preserve ordering + + unmarked_outputs = set() + for out in cur_outputs: + if out.name not in outputs: + model.graph.output.extend([out]) + else: + unmarked_outputs.add(out.name) + + not_found = outputs - unmarked_outputs + check_outputs_not_found(not_found, [t.name for t in model.graph.output]) + return model + + +def get_shape(tensor): + import onnx + + shape = [] + if isinstance(tensor, onnx.TensorProto): + shape = tensor.dims + else: + for dim in tensor.type.tensor_type.shape.dim: + if dim.dim_param: + shape.append(-1) + else: + shape.append(dim.dim_value) + return shape + + +def get_dtype(tensor): + import onnx + + if isinstance(tensor, onnx.TensorProto): + onnx_type = tensor.data_type + else: + onnx_type = tensor.type.tensor_type.elem_type + if onnx_type in onnx.mapping.TENSOR_TYPE_TO_NP_TYPE: + return onnx.mapping.TENSOR_TYPE_TO_NP_TYPE[onnx_type] + return None + + +def get_values(tensor): + import onnx.numpy_helper + + return onnx.numpy_helper.to_array(tensor) + + +def get_tensor_metadata(tensors): + metadata = TensorMetadata() + for tensor in tensors: + metadata.add(name=tensor.name, dtype=get_dtype(tensor), shape=get_shape(tensor)) + return metadata + + +def get_input_metadata(graph): + # Some "inputs" are actually weights with initalizers, so we need to eliminate those. + initializer_names = set([tensor.name for tensor in graph.initializer]) + input_tensors = [tensor for tensor in graph.input if tensor.name not in initializer_names] + return get_tensor_metadata(input_tensors) + + +def get_output_metadata(graph): + return get_tensor_metadata(graph.output) + + +def str_from_onnx(model, mode="full"): + """ + Converts an ONNX Graph to a human-readable representation + + Args: + graph (onnx.GraphProto): The onnx graph. + mode (str): Controls what is displayed. Choices: ["none", "basic", "attrs", "full"] + + Returns: + str + """ + def get_opset(): + try: + return model.opset_import[0].version + except: + G_LOGGER.warning("Model does not contain opset information!") + return None + + onnx_str = "" + onnx_str += "Name: {:} | Opset: {:}\n".format(model.graph.name, get_opset()) + onnx_str += "\n" + + onnx_str += str_from_onnx_graph(model.graph, mode=mode, tensors={}) + return onnx_str + + +def str_from_onnx_graph(graph, mode, tensors, indent_level=0): + import onnx + + input_metadata = get_input_metadata(graph) + output_metadata = get_output_metadata(graph) + initializer_metadata = get_tensor_metadata(graph.initializer) + + # Subgraph inputs should remain separate from each other, hence copy the tensors map + tensors = copy.copy(tensors) + tensors.update(get_tensor_metadata(graph.value_info)) + tensors.update(initializer_metadata) + tensors.update(input_metadata) + tensors.update(output_metadata) + + graph_type = "Graph" if indent_level == 0 else "Subgraph" + + onnx_str = "" + onnx_str += "---- {:} {:} Inputs ----\n{:}\n\n".format(len(input_metadata), graph_type, input_metadata) + onnx_str += "---- {:} {:} Outputs ----\n{:}\n\n".format(len(output_metadata), graph_type, output_metadata) + + onnx_str += "---- {:} Initializers ----\n".format(len(initializer_metadata)) + if mode == "full": + for init in graph.initializer: + onnx_str += "Initializer | {:} [dtype={:}, shape={:}] | Values:\n{:}\n\n".format( + init.name, get_dtype(init), get_shape(init), misc.indent_block(str(get_values(init)))) + if not graph.initializer: + onnx_str += "\n" + elif mode != "none": + onnx_str += str(initializer_metadata) + onnx_str += "\n\n" + else: + onnx_str += "(Use --mode to display)" + onnx_str += "\n\n" + + + def metadata_from_names(names): + metadata = TensorMetadata() + for name in names: + dtype = None + shape = None + if name in tensors: + dtype, shape = tensors[name] + if name in initializer_metadata: + name = "Initializer | {:}".format(name) + metadata.add(name=name, dtype=dtype, shape=shape) + return metadata + + # Maps values from the AttributeType enum to their string representations, e.g., {1: "FLOAT"} + ATTR_TYPE_MAPPING = dict(zip(onnx.AttributeProto.AttributeType.values(), onnx.AttributeProto.AttributeType.keys())) + + # Maps an ONNX attribute to the corresponding Python property + ONNX_PYTHON_ATTR_MAPPING = { + "FLOAT": "f", + "INT": "i", + "STRING": "s", + "TENSOR": "t", + "GRAPH": "g", + "FLOATS": "floats", + "INTS": "ints", + "STRINGS": "strings", + } + + def attrs_to_dict(attrs): + attr_dict = OrderedDict() + for attr in attrs: + def process_attr(attr_str: str): + processed = getattr(attr, ONNX_PYTHON_ATTR_MAPPING[attr_str]) + if attr_str == "STRING": + processed = processed.decode() + elif attr_str == "TENSOR": + tensor_str = "Tensor: [dtype={:}, shape={:}]".format(get_dtype(processed), get_shape(processed)) + if mode == "full": + tensor_str += " | Values:\n" + misc.indent_block(str(get_values(processed))) + processed = tensor_str + elif attr_str == "GRAPH": + processed = "\n" + str_from_onnx_graph(processed, mode, tensors, indent_level=indent_level + 2) + elif attr_str == "FLOATS" or attr_str == "INTS": + # Proto hacky list to normal Python list + processed = [p for p in processed] + elif attr_str == "STRINGS": + processed = [p.decode() for p in processed] + return processed + + if attr.type in ATTR_TYPE_MAPPING: + attr_str = ATTR_TYPE_MAPPING[attr.type] + if attr_str in ONNX_PYTHON_ATTR_MAPPING: + attr_dict[attr.name] = process_attr(attr_str) + else: + G_LOGGER.warning("Attribute of type {:} is currently unsupported. Skipping attribute.".format(attr_str)) + else: + G_LOGGER.warning("Attribute type: {:} was not recognized. Was the graph generated with a newer IR " + "version than the installed `onnx` package? Skipping attribute.".format(attr.type)) + return attr_dict + + + onnx_str += "---- {:} Nodes ----\n".format(len(graph.node)) + if mode != "none": + for index, node in enumerate(graph.node): + input_info = metadata_from_names(node.input) + output_info = metadata_from_names(node.output) + + onnx_str += misc.str_from_layer("Node", index, node.name, node.op_type, input_info, output_info) + + if mode in ["attrs", "full"]: + attrs = attrs_to_dict(node.attribute) + if attrs: + onnx_str += misc.indent_block("---- Attributes ----") + "\n" + for key, val in attrs.items(): + if node.name: + onnx_str += "{:}.".format(node.name) + onnx_str += misc.indent_block("{:} = {:}".format(key, val)) + "\n" + onnx_str += "\n" + else: + onnx_str += "(Use --mode to display)" + + return misc.indent_block(onnx_str, indent_level) diff --git a/tools/Polygraphy/polygraphy/backend/onnxrt/__init__.py b/tools/Polygraphy/polygraphy/backend/onnxrt/__init__.py new file mode 100644 index 00000000..e9cb4932 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnxrt/__init__.py @@ -0,0 +1,2 @@ +from polygraphy.backend.onnxrt.loader import SessionFromOnnxBytes +from polygraphy.backend.onnxrt.runner import OnnxrtRunner diff --git a/tools/Polygraphy/polygraphy/backend/onnxrt/loader.py b/tools/Polygraphy/polygraphy/backend/onnxrt/loader.py new file mode 100644 index 00000000..1f792239 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnxrt/loader.py @@ -0,0 +1,41 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import onnxruntime +from polygraphy.backend.base import BaseLoadModel +from polygraphy.util import misc + +misc.log_module_info(onnxruntime) + +class SessionFromOnnxBytes(BaseLoadModel): + def __init__(self, model_bytes): + """ + Functor that builds an ONNX-Runtime inference session. + + Args: + model_bytes (Callable() -> bytes): A loader that can supply a serialized ONNX model. + """ + self._model_bytes = model_bytes + + + def __call__(self): + """ + Builds an ONNX-Runtime inference session. + + Returns: + onnxruntime.InferenceSession: The inference session. + """ + model_bytes, _ = misc.try_call(self._model_bytes) + return onnxruntime.InferenceSession(model_bytes) diff --git a/tools/Polygraphy/polygraphy/backend/onnxrt/requirements.txt b/tools/Polygraphy/polygraphy/backend/onnxrt/requirements.txt new file mode 100644 index 00000000..391b72eb --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnxrt/requirements.txt @@ -0,0 +1 @@ +onnxruntime diff --git a/tools/Polygraphy/polygraphy/backend/onnxrt/runner.py b/tools/Polygraphy/polygraphy/backend/onnxrt/runner.py new file mode 100644 index 00000000..59734556 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/onnxrt/runner.py @@ -0,0 +1,82 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import time +from collections import OrderedDict + +import numpy as np +from polygraphy.backend.base import BaseRunner +from polygraphy.common import TensorMetadata +from polygraphy.util import misc + + +class OnnxrtRunner(BaseRunner): + """ + Runs inference using an ONNX-Runtime inference session. + """ + def __init__(self, sess, name=None): + """ + Args: + sess (Callable() -> onnxruntime.InferenceSession): + A callable that can supply an ONNX-Runtime inferences session. + """ + super().__init__(name=name, prefix="onnxrt-runner") + self._sess = sess + + + def activate_impl(self): + self.sess, _ = misc.try_call(self._sess) + + + def deactivate_impl(self): + del self.sess + + + def infer(self, feed_dict): + start = time.time() + inference_outputs = self.sess.run(None, feed_dict) + end = time.time() + + out_dict = OrderedDict() + for node, out in zip(self.sess.get_outputs(), inference_outputs): + out_dict[node.name] = out + self.inference_time = end - start + return out_dict + + + def get_input_metadata(self): + ONNX_RT_TYPE_TO_NP = { + "tensor(double)": np.float64, + "tensor(float)": np.float32, + "tensor(float16)": np.float16, + "tensor(int16)": np.int16, + "tensor(int32)": np.int32, + "tensor(int64)": np.int64, + "tensor(int8)": np.int8, + "tensor(uint16)": np.uint16, + "tensor(uint32)": np.uint32, + "tensor(uint64)": np.uint64, + "tensor(uint8)": np.uint8, + "tensor(bool)": np.bool, + } + + def process_shape(shape): + # dims can be strings to indicate dynamic dimensions. Polygraphy uses None for the same purpose + return [None if not isinstance(elem, int) else elem for elem in shape] + + meta = TensorMetadata() + for node in self.sess.get_inputs(): + meta.add(node.name, dtype=ONNX_RT_TYPE_TO_NP[node.type], shape=process_shape(node.shape)) + return meta diff --git a/tools/Polygraphy/polygraphy/backend/pyt/__init__.py b/tools/Polygraphy/polygraphy/backend/pyt/__init__.py new file mode 100644 index 00000000..119ce26e --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/pyt/__init__.py @@ -0,0 +1,2 @@ +from polygraphy.backend.pyt.loader import BaseLoadPyt +from polygraphy.backend.pyt.runner import PytRunner diff --git a/tools/Polygraphy/polygraphy/backend/pyt/loader.py b/tools/Polygraphy/polygraphy/backend/pyt/loader.py new file mode 100644 index 00000000..2f89d261 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/pyt/loader.py @@ -0,0 +1,23 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.base import BaseLoadModel + +class BaseLoadPyt(BaseLoadModel): + def __call__(self): + """ + Returns a torch.nn.Module or subclass. + """ + raise NotImplementedError("PyTorch model loaders must be implemented on a per-model basis") diff --git a/tools/Polygraphy/polygraphy/backend/pyt/requirements.txt b/tools/Polygraphy/polygraphy/backend/pyt/requirements.txt new file mode 100644 index 00000000..12c6d5d5 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/pyt/requirements.txt @@ -0,0 +1 @@ +torch diff --git a/tools/Polygraphy/polygraphy/backend/pyt/runner.py b/tools/Polygraphy/polygraphy/backend/pyt/runner.py new file mode 100644 index 00000000..7946681c --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/pyt/runner.py @@ -0,0 +1,62 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import torch +from polygraphy.backend.base import BaseRunner +from polygraphy.util import misc + + +class PytRunner(BaseRunner): + def __init__(self, model, input_metadata, output_names, name=None): + """ + Args: + model (Callable() -> torch.nn.Module): + A model loader that returns a torch.nn.Module or subclass. + input_metadata (TensorMetadata): Mapping of input names to their data types and shapes. + output_names (List[str]): + A list of output names of the model. This information is used by the + Comparator to determine which outputs to compare. + + + name (str): + The human-readable name prefix to use for this runner. + A runner count and timestamp will be appended to this prefix. + """ + super().__init__(name=name, prefix="pytorch-runner") + self._model = model + self.input_metadata = input_metadata + self.output_names = output_names + + + def activate_impl(self): + self.model, _ = misc.try_call(self._model) + self.model.eval() + + + def infer(self, feed_dict): + with torch.no_grad(): + inputs = [torch.from_numpy(val.astype(dtype)).cuda() for (val, (dtype, _)) in zip(feed_dict.values(), self.input_metadata.values())] + start = time.time() + outputs = self.model(*inputs) + end = time.time() + + out_dict = OrderedDict() + for name, output in zip(self.output_names, outputs): + out_dict[name] = output.cpu().numpy() + return out_dict, end - start + + + def get_input_metadata(self): + return self.input_metadata diff --git a/tools/Polygraphy/polygraphy/backend/tf/__init__.py b/tools/Polygraphy/polygraphy/backend/tf/__init__.py new file mode 100644 index 00000000..c3756ab5 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/tf/__init__.py @@ -0,0 +1,30 @@ +from polygraphy.backend.tf.loader import OptimizeGraph, GraphFromKeras, GraphFromFrozen, GraphFromCkpt, UseTfTrt, ModifyGraph, SaveGraph, CreateConfig, SessionFromGraph +from polygraphy.backend.tf.runner import TfRunner + + +def register_callback(): + from polygraphy.logger.logger import G_LOGGER + + def set_tf_logging_level(sev): + import os + import tensorflow as tf + + if sev > G_LOGGER.WARNING: + tf_sev = tf.compat.v1.logging.ERROR + tf_logging_level = "3" + elif sev > G_LOGGER.INFO: + tf_sev = tf.compat.v1.logging.WARN + tf_logging_level = "2" + elif sev > G_LOGGER.VERBOSE: + tf_sev = tf.compat.v1.logging.INFO + tf_logging_level = "1" + else: + tf_sev = tf.compat.v1.logging.DEBUG + tf_logging_level = "0" + + tf.compat.v1.logging.set_verbosity(tf_sev) + os.environ['TF_CPP_MIN_LOG_LEVEL'] = tf_logging_level + + G_LOGGER.register_callback(set_tf_logging_level) # Will be registered when this runner is imported. + +register_callback() diff --git a/tools/Polygraphy/polygraphy/backend/tf/loader.py b/tools/Polygraphy/polygraphy/backend/tf/loader.py new file mode 100644 index 00000000..ec9df202 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/tf/loader.py @@ -0,0 +1,423 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Sets up everything needed to perform inference in TensorFlow. +import os + +import tensorflow as tf +from polygraphy.backend.base import BaseLoadModel +from polygraphy.backend.tf import util as tf_util +from polygraphy.common import constants +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +class OptimizeGraph(BaseLoadModel): + def __init__(self, graph): + """ + Functor that freezes a TensorFlow graph, and folds constants. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a TensorFlow graph and output names. + """ + self._graph = graph + + + def constfold(self, graphdef, output_names): + from tensorflow.core.protobuf import (config_pb2, meta_graph_pb2, + rewriter_config_pb2) + from tensorflow.python.framework import importer, ops + from tensorflow.python.grappler import tf_optimizer + from tensorflow.python.training import saver + + graph = ops.Graph() + with graph.as_default(): + output_collection = meta_graph_pb2.CollectionDef() + output_list = output_collection.node_list.value + for output in output_names: + output_list.append(output.encode("utf-8")) + + importer.import_graph_def(graphdef, name="") + metagraph = saver.export_meta_graph(graph_def=graph.as_graph_def(add_shapes=True), graph=graph) + metagraph.collection_def["train_op"].CopyFrom(output_collection) + + rewriter_config = rewriter_config_pb2.RewriterConfig() + rewriter_config.optimizers.extend(["constfold"]) + rewriter_config.meta_optimizer_iterations = (rewriter_config_pb2.RewriterConfig.ONE) + + session_config = config_pb2.ConfigProto() + session_config.graph_options.resave_options.CopyFrom(rewriter_config) + return tf_optimizer.OptimizeGraph(session_config, metagraph, graph_id=b"graph") + + + def __call__(self): + """ + Freezes a TensorFlow graph, and folds constants. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + (graph, output_names), _ = misc.try_call(self._graph) + with tf.Session(graph=graph) as sess: + sess.run(tf.initializers.global_variables()) + sess.run(tf.initializers.local_variables()) + + graphdef = sess.graph.as_graph_def() + removed = tf.graph_util.remove_training_nodes(graphdef) + G_LOGGER.ultra_verbose("Removed nodes: {:}".format(removed)) + + for node in graphdef.node: + if node.op == 'RefSwitch': + node.op = 'Switch' + for index in range(len(node.input)): + if 'moving_' in node.input[index]: + node.input[index] = node.input[index] + '/read' + elif node.op == 'AssignSub': + node.op = 'Sub' + if 'use_locking' in node.attr: del node.attr['use_locking'] + elif node.op == 'AssignAdd': + node.op = 'Add' + if 'use_locking' in node.attr: del node.attr['use_locking'] + elif node.op == 'Assign': + node.op = 'Identity' + if 'use_locking' in node.attr: del node.attr['use_locking'] + if 'validate_shape' in node.attr: del node.attr['validate_shape'] + if len(node.input) == 2: + # input0: ref: Should be from a Variable node. May be uninitialized. + # input1: value: The value to be assigned to the variable. + node.input[0] = node.input[1] + del node.input[1] + + # Strip port information from outputs + output_names = [name.split(":")[0] for name in output_names] + output_graph_def = tf.graph_util.convert_variables_to_constants(sess, graphdef, output_names) + output_graph_def = self.constfold(output_graph_def, output_names) + return GraphFromFrozen(output_graph_def)() + + +class GraphFromKeras(BaseLoadModel): + def __init__(self, path): + """ + Functor that loads a TensorFlow model from Keras. + + Args: + path (Union[str, h5py.File]): A path to the saved model, or the file object. + """ + self.path = path + + + def __call__(self): + """ + Loads a TensorFlow model from Keras. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + + from tensorflow.python import keras + from tensorflow.python.keras import backend + + model = keras.models.load_model(self.path) + graph = backend.get_session().graph + return graph, tf_util.get_graph_output_names(graph) + + +class GraphFromFrozen(BaseLoadModel): + def __init__(self, path): + """ + Functor that loads a TensorFlow frozen model. + + Args: + path (Union[str, tf.Graph, tf.GraphDef]): + A path to the frozen model, or a frozen TensorFlow graph or graphdef. + """ + self.path = path + + + def __call__(self): + """ + Loads a TensorFlow frozen model. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + graph = tf_util.load_graph(self.path) + return graph, tf_util.get_graph_output_names(graph) + + +class GraphFromCkpt(BaseLoadModel): + def __init__(self, dir, name=None): + """ + Functor that loads a TensorFlow model from a checkpoint. Note that in order to use checkpoints, + you must NOT use subprocesses in the Comparator. + + Args: + dir (str): Path to a directory containing checkpoints. + + + name (str): + The name of the checkpoint to load, not including the file extension. + For example, to load `model.meta`, the argument would be `model`. + """ + self.dir = dir + self.name = name + + + def __call__(self): + """ + Loads a TensorFlow model from a checkpoint. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + # If `name` is not provided, this expects that the directory contains a `checkpoint` file with the contents: + # + # model_checkpoint_path: "model" + # all_model_checkpoint_paths: "model" + # + # where "model" is the checkpoint name + if self.name is None: + G_LOGGER.verbose("Checkpoint name was not explicitly provided, searching for `checkpoint` file") + checkpoint = tf.train.get_checkpoint_state(self.dir) + if checkpoint is None: + ckpt_file_contents = '\nmodel_checkpoint_path: "model"\nall_model_checkpoint_paths: "model"\n' + G_LOGGER.critical("Checkpoint directory: {:} does not contain a `checkpoint` file, and the checkpoint name was" + "not provided. Please either create a checkpoint file with the contents:\n{:}" + "\nWhere `model` is the name of the checkpoint, or explicitly provide the name with" + "--ckpt, not including file extensions".format(self.dir, ckpt_file_contents)) + input_checkpoint = checkpoint.model_checkpoint_path + else: + input_checkpoint = os.path.join(self.dir, self.name) + + meta_file = input_checkpoint + '.meta' + with tf.Graph().as_default() as graph, tf.compat.v1.Session(graph=graph).as_default() as sess: + saver = tf.compat.v1.train.import_meta_graph(meta_file, clear_devices=True) + saver.restore(sess, input_checkpoint) + return graph, tf_util.get_graph_output_names(graph) + + +class UseTfTrt(BaseLoadModel): + def __init__(self, graph, max_workspace_size=None, fp16=None, int8=None, max_batch_size=None, + is_dynamic_op=False, minimum_segment_size=None): + + """ + Functor that optimizes a TensorFlow model using TF-TRT. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a TensorFlow graph and output names. + max_workspace_size (int): The maximum workspace size. + fp16 (bool): Whether to run in FP16 mode. + max_batch_size (int): The maximum batch size. + """ + self._graph = graph + self.max_workspace_size = misc.default_value(max_workspace_size, 1<<24) + self.fp16 = misc.default_value(fp16, False) + self.int8 = misc.default_value(int8, False) + self.max_batch_size = misc.default_value(max_batch_size, 1) + self.is_dynamic_op = is_dynamic_op + self.minimum_segment_size = misc.default_value(minimum_segment_size, 3) + + + def __call__(self): + """ + Optimizes a TensorFlow model using TF-TRT. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + from tensorflow.contrib import tensorrt as tf_trt + + (graph, output_names), _ = misc.try_call(self._graph) + + precision_mode = "FP16" if self.fp16 else "FP32" + precision_mode = "INT8" if self.int8 else precision_mode + + G_LOGGER.info("For TF-TRT, using outputs={:}, max_workspace_size_bytes={:}, max_batch_size={:}, " + "minimum_segment_size={:}, is_dynamic_op={:}, precision_mode={:}".format( + output_names, self.max_workspace_size, self.max_batch_size, self.minimum_segment_size, + self.is_dynamic_op, precision_mode)) + + graphdef = tf_trt.create_inference_graph(graph.as_graph_def(), outputs=output_names, + max_workspace_size_bytes=self.max_workspace_size, max_batch_size=self.max_batch_size, + minimum_segment_size=self.minimum_segment_size, is_dynamic_op=self.is_dynamic_op, precision_mode=precision_mode) + + segment_number = 0 + for node in graphdef.node: + if node.op == "TRTEngineOp": + engine = node.attr["serialized_segment"].s + segment_number += 1 + G_LOGGER.info("Found {:} engines in TFTRT graph".format(segment_number)) + + with tf.Graph().as_default() as graph: + tf.import_graph_def(graphdef, name="") + return graph, tf_util.get_graph_output_names(graph) + + +class ModifyGraph(BaseLoadModel): + def __init__(self, graph, outputs=None): + """ + Functor that modifies a TensorFlow graph. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a + TensorFlow graph and output names. + + + outputs (List[str]): + Names of output tensors. If provided, this will override the outputs + determined by the loader. + If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked. + """ + self._graph = graph + self.outputs = outputs + + + def __call__(self): + """ + Modifies a TensorFlow graph. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + (graph, outputs), _ = misc.try_call(self._graph) + + if self.outputs == constants.MARK_ALL: + outputs = list(tf_util.get_output_metadata(graph, layerwise=True).keys()) + elif self.outputs is not None: + outputs = self.outputs + + return graph, outputs + + +class SaveGraph(BaseLoadModel): + def __init__(self, graph, path=None, tensorboard_dir=None, engine_dir=None): + """ + Functor that writes out artifacts from a TensorFlow graph. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a + TensorFlow graph and output names. + + + path (str): Path at which to save the frozen graphdef. + tensorboard_dir (str): The directory in which to write TensorBoard visualizations. + engine_dir (str): The directory in which to save TF-TRT engines, + """ + self._graph = graph + self.path = path + self.tensorboard_dir = tensorboard_dir + self.engine_dir = engine_dir + + + def __call__(self): + """ + Writes out artifacts from a TensorFlow Graph. + + Returns: + Tuple[tf.Graph, Sequence[str]]: The TensorFlow graph, and the names of its outputs. + """ + (graph, outputs), _ = misc.try_call(self._graph) + + misc.lazy_write(contents=lambda: graph.as_graph_def().SerializeToString(), path=self.path) + if self.tensorboard_dir: + G_LOGGER.info("Writing tensorboard events to {:}".format(self.tensorboard_dir)) + train_writer = tf.compat.v1.summary.FileWriter(self.tensorboard_dir) + train_writer.add_graph(graph) + + if self.engine_dir is not None: + graphdef = graph.as_graph_def() + segment_number = 0 + for node in graphdef.node: + if node.op == "TRTEngineOp": + engine = node.attr["serialized_segment"].s + if self.engine_dir is not None: + misc.lazy_write(contents=engine, + path=os.path.join(self.engine_dir, "segment-{:}".format(segment_number))) + segment_number += 1 + + return graph, outputs + + +class CreateConfig(BaseLoadModel): + def __init__(self, gpu_memory_fraction=None, allow_growth=None, use_xla=None): + """ + Functor that creates a TensorFlow config. + + Args: + gpu_memory_fraction (float): + The fraction of GPU memory that will be made available to TensorFlow. + This should be a value between 0.0 and 1.0. + allow_growth (bool): Whether to allow GPU memory allocated by TensorFlow to grow. + use_xla (bool): Whether to attempt to enable XLA. + """ + self.gpu_memory_fraction = misc.default_value(gpu_memory_fraction, 0.9) + self.allow_growth = misc.default_value(allow_growth, False) + self.use_xla = misc.default_value(use_xla, False) + + + def __call__(self): + """ + Creates a TensorFlow config. + + Returns: + tf.ConfigProto: The TensorFlow config. + """ + + # Session configuration + gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=self.gpu_memory_fraction, + allow_growth=self.allow_growth) + config = tf.compat.v1.ConfigProto(gpu_options=gpu_options) + if self.use_xla: + config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 + G_LOGGER.verbose("Using gpu memory fraction: {:}, XLA: {:}".format(self.gpu_memory_fraction, self.use_xla)) + return config + + +class SessionFromGraph(BaseLoadModel): + def __init__(self, graph, config=None): + """ + Functor that creates a TensorFlow session that can be used for inference. + + Args: + graph (Callable() -> Tuple[tf.Graph, Sequence[str]]): + A callable that can supply a tuple containing a + TensorFlow graph and output names. + + + config (Callable() -> tf.ConfigProto): + """ + self.graph = graph + self.config = misc.default_value(config, CreateConfig()) + + + def __call__(self): + """ + Creates a TensorFlow session. + + Returns: + tf.Session: The TensorFlow session. + """ + config, _ = misc.try_call(self.config) + (graph, output_names), _ = misc.try_call(self.graph) + + with graph.as_default() as graph, tf.compat.v1.Session(graph=graph, config=config).as_default() as sess: + G_LOGGER.verbose("Using TensorFlow outputs: {:}".format(output_names)) + G_LOGGER.extra_verbose("Initializing variables in TensorFlow Graph") + sess.run(tf.compat.v1.initializers.global_variables()) + return sess, output_names diff --git a/tools/Polygraphy/polygraphy/backend/tf/requirements.txt b/tools/Polygraphy/polygraphy/backend/tf/requirements.txt new file mode 100644 index 00000000..fcd9b2b0 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/tf/requirements.txt @@ -0,0 +1 @@ +tensorflow<2.0 diff --git a/tools/Polygraphy/polygraphy/backend/tf/runner.py b/tools/Polygraphy/polygraphy/backend/tf/runner.py new file mode 100644 index 00000000..1e85940c --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/tf/runner.py @@ -0,0 +1,101 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Sets up everything needed to perform inference in TensorFlow. +import os +import time +from collections import OrderedDict + +import tensorflow as tf +from polygraphy.backend.base import BaseRunner +from polygraphy.backend.tf import util as tf_util +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + +misc.log_module_info(tf) + + +class TfRunner(BaseRunner): + """ + Runs inference using a TensorFlow session. + """ + def __init__(self, sess, timeline_dir=None, name=None): + """ + Args: + sess (Callable() -> Tuple[tf.Session, Sequence[str]]): + A callable that can supply a tuple containing a + TensorFlow session and output names. + + + timeline_dir (str): + Path to write a TensorFlow timeline. + Note that profiling may affect execution time. + name (str): + The human-readable name prefix to use for this runner. + A runner count and timestamp will be appended to this prefix. + """ + super().__init__(name=name, prefix="tf-runner") + + self._sess = sess + + self.timeline_dir = timeline_dir + self.num_inferences = 0 + self.run_options = None + self.run_metadata = None + if self.timeline_dir is not None: + # Enable profiling + G_LOGGER.warning("Profiling is enabled. This will impact performance") + self.run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) + self.run_metadata = tf.RunMetadata() + + + def activate_impl(self): + import tensorflow as tf + (self.sess, self.output_names), _ = misc.try_call(self._sess) + + + def get_input_metadata(self): + return tf_util.get_input_metadata(self.sess.graph) + + + def deactivate_impl(self): + self.sess.close() + del self.sess + + + def infer(self, feed_dict): + G_LOGGER.extra_verbose("Received feed_dict: {:}".format(feed_dict)) + start = time.time() + inference_outputs = self.sess.run(self.output_names, feed_dict=feed_dict, options=self.run_options, + run_metadata=self.run_metadata) + end = time.time() + + out_dict = OrderedDict() + for name, out in zip(self.output_names, inference_outputs): + out_dict[name] = out + self.inference_time = end - start + + def generate_timeline(): + from tensorflow.python.client import timeline + t1 = timeline.Timeline(self.run_metadata.step_stats) + return t1.generate_chrome_trace_format() + + if self.timeline_dir is not None: + misc.lazy_write(contents=generate_timeline, + path=os.path.join(self.timeline_dir, "run-{:}".format(self.num_inferences)), + mode="w") + self.num_inferences += 1 + + return out_dict diff --git a/tools/Polygraphy/polygraphy/backend/tf/util.py b/tools/Polygraphy/polygraphy/backend/tf/util.py new file mode 100644 index 00000000..c0fe931f --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/tf/util.py @@ -0,0 +1,176 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import defaultdict + +from polygraphy.common import TensorMetadata +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +def load_graph(path): + """ + Loads a TensorFlow frozen model. + + Args: + path (Union[str, tf.Graph, tf.GraphDef]): + A path to the frozen model, or a frozen TensorFlow graph or graphdef. + + Returns: + tf.Graph: The TensorFlow graph + """ + import tensorflow as tf + + if isinstance(path, tf.Graph): + return path + + if isinstance(path, str): + with open(path, "rb") as frozen_pb: + graphdef = tf.compat.v1.GraphDef() + graphdef.ParseFromString(frozen_pb.read()) + elif isinstance(path, tf.compat.v1.GraphDef): + graphdef = path + + with tf.Graph().as_default() as graph: + tf.import_graph_def(graphdef, name="") + return graph + + +def find_nodes_by_ops(graphdef, ops): + ops = set(ops) + return [node for node in graphdef.node if any([op in node.op for op in ops])] + + +def map_node_outputs(graphdef): + def sanitize_input_name(input_name): + # Strip port information and control symbol + split_input = input_name.split(":") + if len(split_input) > 1: + split_input.pop(-1) + return ":".join(split_input).replace('^', '') + + node_outputs = defaultdict(list) + for node in graphdef.node: + for input_name in node.input: + node_outputs[sanitize_input_name(input_name)].append(node) + return node_outputs + + +def get_tensor_metadata(tensors): + metadata = TensorMetadata() + for tensor in tensors: + try: + shape = [elem.value if hasattr(elem, "value") else elem for elem in tensor.shape] + except ValueError: + # Happens when rank is unknown + shape = None + metadata.add(tensor.name, dtype=tensor.dtype.as_numpy_dtype, shape=shape) + return metadata + + +def get_input_metadata(graph): + input_tensors = [] + input_nodes = find_nodes_by_ops(graph.as_graph_def(), ["Placeholder", "FIFOQueue"]) + G_LOGGER.verbose("Found input tensors: {:}".format(["{:}: {:}".format(n.name, n.op) for n in input_nodes])) + for node in input_nodes: + input_tensors.append(graph.get_tensor_by_name(node.name + ":0")) + + G_LOGGER.verbose("Retrieved TensorFlow input_tensors: {:}".format(input_tensors)) + return get_tensor_metadata(input_tensors) + + +def get_output_metadata(graph, layerwise=False): + graphdef = graph.as_graph_def() + + node_output_map = map_node_outputs(graphdef) + + def is_output_node(node): + # Make sure that we're not using hanging nodes as outputs - must have at least one input. + if len(node_output_map[node.name]) != 0 or len(node.input) == 0: + return False + + # Tensors with no shape cannot be outputs and TensorFlow doesn't like certain ops as outputs. + EXCLUDE_OPS = [ + "Switch", + "FusedBatchNorm", + "Assert", + "NextIteration", + "Enter", + "LoopCond", + "Exit", + "Print", + "Assign", + "NoOp", + "ReadVariableOp", + "VarIsInitializedOp", + "Const" + ] + + # Additionally, we sometimes need to exclude entire namespaces e.g. while loops. + EXCLUDE_NAMESPACES = ["while", "Assert"] + + if any([ex_op in node.op for ex_op in EXCLUDE_OPS]) or any([ns in node.name for ns in EXCLUDE_NAMESPACES]): + G_LOGGER.extra_verbose("Excluding {:}, op {:} is not a valid output op or is part of an excluded namespace " + "(Note: excluded namespaces: {:})".format(node.name, node.op, EXCLUDE_NAMESPACES)) + return False + + return True + + # For layerwise mode, every layer becomes an output. + if layerwise: + output_nodes = list(graphdef.node) + G_LOGGER.verbose("Running in layerwise mode. Marking {:} layers as potential outputs".format(len(output_nodes))) + else: + output_nodes = [node for node in graphdef.node if is_output_node(node)] + G_LOGGER.extra_verbose("Found likely output nodes: {:}".format(output_nodes)) + + output_tensors = [] + for node in output_nodes: + + tensor_name = node.name + ":0" + try: + tensor = graph.get_tensor_by_name(tensor_name) + output_tensors.append(tensor) + except KeyError: + G_LOGGER.warning("Could not import: {:}. Skipping.".format(tensor_name)) + if len(output_tensors) != len(output_nodes): + G_LOGGER.warning("Excluded {:} ops that don't seem like outputs. Use -vv/--super-verbose, or set " + "logging verbosity to EXTRA_VERBOSE to view them.".format(len(output_nodes) - len(output_tensors))) + + G_LOGGER.extra_verbose("Found output op types in graph: {:}".format(set([tensor.op.type for tensor in output_tensors]))) + G_LOGGER.verbose("Retrieved TensorFlow output_tensors: {:}".format(output_tensors)) + return get_tensor_metadata(output_tensors) + + +def get_graph_output_names(graph): + return list(get_output_metadata(graph).keys()) + + +def str_from_graph(graph, mode): + graph_str = "" + input_metadata = get_input_metadata(graph) + output_metadata = get_output_metadata(graph) + + graph_str += "---- {:} Graph Inputs ----\n{:}\n\n".format(len(input_metadata), input_metadata) + graph_str += "---- {:} Graph Outputs ----\n{:}\n\n".format(len(output_metadata), output_metadata) + graph_str += "---- {:} Nodes ----\n".format(len(graph.as_graph_def().node)) + if mode == "basic": + G_LOGGER.warning("Displaying layer information is unsupported for TensorFlow graphs. " + "Please use --mode=full if you would like to see the raw nodes") + if mode == "full": + for node in graph.as_graph_def().node: + graph_str += str(node) + "\n" + graph_str += "\n" + return misc.indent_block(graph_str, level=0) diff --git a/tools/Polygraphy/polygraphy/backend/trt/__init__.py b/tools/Polygraphy/polygraphy/backend/trt/__init__.py new file mode 100644 index 00000000..6d866f82 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/__init__.py @@ -0,0 +1,26 @@ +from polygraphy.backend.trt.loader import EngineFromBytes, NetworkFromOnnxBytes, NetworkFromOnnxPath, ModifyNetwork, Profile, CreateConfig, EngineFromNetwork, SaveEngine, LoadPlugins, CreateNetwork +from polygraphy.backend.trt.runner import TrtRunner +from polygraphy.backend.trt.calibrator import Calibrator +from polygraphy.backend.trt.util import TRT_LOGGER + + +def register_callback(): + from polygraphy.logger.logger import G_LOGGER + + def set_trt_logging_level(sev): + import tensorrt as trt + + if sev >= G_LOGGER.CRITICAL: + TRT_LOGGER.min_severity = trt.Logger.INTERNAL_ERROR + elif sev >= G_LOGGER.ERROR: + TRT_LOGGER.min_severity = trt.Logger.ERROR + elif sev >= G_LOGGER.INFO: + TRT_LOGGER.min_severity = trt.Logger.WARNING + elif sev >= G_LOGGER.VERBOSE: + TRT_LOGGER.min_severity = trt.Logger.INFO + else: + TRT_LOGGER.min_severity = trt.Logger.VERBOSE + + G_LOGGER.register_callback(set_trt_logging_level) # Will be registered when this runner is imported. + +register_callback() diff --git a/tools/Polygraphy/polygraphy/backend/trt/buffers.py b/tools/Polygraphy/polygraphy/backend/trt/buffers.py new file mode 100644 index 00000000..3e4cc574 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/buffers.py @@ -0,0 +1,72 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import OrderedDict + +import numpy as np +import tensorrt as trt +from polygraphy.backend.trt import util as trt_util +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import cuda + + +# This class always uses profile 0 binding names to refer to input and output buffers. +# Therefore, it is essentially agnostic to multiple profiles, which are instead handled in the runner. +class Buffers(object): + # Currently, buffers are reused across profiles. + @staticmethod + def from_engine(engine): + buffers = Buffers() + bindings_per_profile = trt_util.get_bindings_per_profile(engine) + for idx in range(bindings_per_profile): + binding = engine[idx] + dtype = trt.nptype(engine.get_binding_dtype(binding)) + buffers.device_buffers[binding] = cuda.DeviceBuffer(dtype=dtype) + if not engine.binding_is_input(binding): + buffers.outputs[binding] = np.empty(shape=tuple(), dtype=dtype) + G_LOGGER.extra_verbose("Created device buffers: {:}".format(buffers.device_buffers)) + return buffers + + + def __init__(self): + self.device_buffers = OrderedDict() + self.outputs = OrderedDict() + + + def copy_inputs(self, feed_dict, stream=None): + for name, buffer in feed_dict.items(): + self.device_buffers[name].copy_from(buffer, stream) + + + # Resizes all device buffers to match the shapes currently set on the provided context + def resize(self, engine, context, start_binding, end_binding): + for binding in range(start_binding, end_binding): + shape = tuple(context.get_binding_shape(binding)) + name = engine[binding - start_binding] # Use profile 0 binding names for all buffers. + self.device_buffers[name].resize(shape) + + + # Copies outputs from the device back to host. + def copy_outputs(self, stream=None): + for name, buffer in self.outputs.items(): + self.outputs[name] = self.device_buffers[name].copy_to(buffer, stream) + + + def bindings(self): + return [buf.address() for buf in self.device_buffers.values()] + + + def free(self): + [buf.free() for buf in self.device_buffers.values()] diff --git a/tools/Polygraphy/polygraphy/backend/trt/calibrator.py b/tools/Polygraphy/polygraphy/backend/trt/calibrator.py new file mode 100644 index 00000000..a41f2b09 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/calibrator.py @@ -0,0 +1,185 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import contextlib +import os +from collections import OrderedDict + +import tensorrt as trt +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc +from polygraphy.util.cuda import DeviceBuffer + + +def Calibrator(data_loader, cache=None, BaseClass=trt.IInt8MinMaxCalibrator, + batch_size=None): + """ + Supplies calibration data to TensorRT to calibrate the network for INT8 inference. + + Args: + data_loader (Generator -> OrderedDict[str, np.ndarray]): + A generator or iterable that yields a dictionary that maps input names to input NumPy buffers. + + In case you don't know details about the inputs ahead of time, you can access the + `input_metadata` property in your data loader, which will be set to an `TensorMetadata` instance. + Note that this does not work for generators or lists. + + The number of calibration batches is controlled by the number of items supplied + by the data loader. + + + cache (Union[str, file-like]): + Path or file-like object to save/load the calibration cache. + By default, the calibration cache is not saved. + BaseClass (type): + The type of calibrator to inherit from. + Defaults to trt.IInt8MinMaxCalibrator. + batch_size (int): + [DEPRECATED] The size of each batch provided by the data loader. + """ + class CalibratorClass(BaseClass): + """ + Calibrator that supplies calibration data to TensorRT to calibrate the network for INT8 inference. + """ + def __init__(self): + # Must explicitly initialize parent for any trampoline class! Will mysteriously segfault without this. + BaseClass.__init__(self) + + self.data_loader = data_loader + self._cache = cache + self.device_buffers = OrderedDict() + self.reset() + G_LOGGER.verbose("Created calibrator [cache={:}]".format(self._cache)) + + self.batch_size = misc.default_value(batch_size, 1) + + + def reset(self, input_metadata=None): + """ + Reset this calibrator for reuse. + The calibrator will clear any dynamic ranges cached from previous calibration runs, and will + attempt to rewind the data loader (note that generators cannot be rewound). + + Args: + input_metadata (TensorMetadata): + Mapping of input names to their data types and shapes. + Passed along to the data loader if provided. Generally should not be required + unless using Polygraphy's included `DataLoader` for this calibrator. + """ + if input_metadata is not None: + with contextlib.suppress(AttributeError): + self.data_loader.input_metadata = input_metadata + + # Attempt to reset data loader + self.data_loader_iter = iter(self.data_loader) + self.num_batches = 0 + + # Make sure calibrator will check the cache again when reset. + self.cache_contents = None + self.has_cached_scales = False + + + def get_batch_size(self): + return self.batch_size + + + def get_batch(self, names): + try: + host_buffers = next(self.data_loader_iter) + except StopIteration: + if not self.num_batches: + G_LOGGER.warning("Calibrator data loader provided no data. Possibilities include: (1) data loader " + "has no data to provide, (2) data loader was a generator, and the calibrator is being " + "reused across multiple loaders (generators cannot be rewound)") + return None + else: + self.num_batches += 1 + + for name, host_buffer in host_buffers.items(): + if name not in self.device_buffers: + self.device_buffers[name] = DeviceBuffer(shape=host_buffer.shape, dtype=host_buffer.dtype) + G_LOGGER.verbose("Allocated: {:}".format(self.device_buffers[name])) + if self.num_batches > 1: + G_LOGGER.warning("The calibrator data loader provided an extra input ({:}) compared to the last set of inputs.\n" + "Should this input be removed, or did you accidentally omit an input before?".format(name)) + + device_buffer = self.device_buffers[name] + device_buffer.copy_from(host_buffer) + return [device_buffer.address() for device_buffer in self.device_buffers.values()] + + + def read_calibration_cache(self): + def load_from_cache(): + if self._cache is None: + return None + + try: + if self._cache.seekable(): + self._cache.seek(0) + return self._cache.read() + except AttributeError: + if os.path.exists(self._cache): + G_LOGGER.info("Reading calibration cache from: {:}".format(self._cache), mode=LogMode.ONCE) + with open(self._cache, "rb") as f: + return f.read() + except: + # Cache is not readable + return None + + + if not self.has_cached_scales: + self.cache_contents = load_from_cache() + if not self.cache_contents: + G_LOGGER.warning("Calibration cache was provided, but is empty. Will regenerate scales by running calibration.", mode=LogMode.ONCE) + self.cache_contents = None + else: + self.has_cached_scales = True + return self.cache_contents + + + def write_calibration_cache(self, cache): + self.cache_contents = cache.tobytes() + self.has_cached_scales = True + + if self._cache is None: + return + + try: + if self._cache.seekable(): + self._cache.seek(0) + bytes_written = self._cache.write(self.cache_contents) + if bytes_written != len(self.cache_contents): + G_LOGGER.warning("Could not write entire cache. Note: cache contains {:} bytes, but only " + "{:} bytes were written".format(len(self.cache_contents), bytes_written)) + except AttributeError: + G_LOGGER.info("Writing calibration cache to: {:}".format(self._cache)) + with open(self._cache, "wb") as f: + f.write(self.cache_contents) + except: + # Cache is not writable + return + else: + self._cache.flush() + + + def free(self): + """ + Free the device buffers allocated for this calibrator. + """ + for device_buffer in self.device_buffers.values(): + device_buffer.free() + + + return CalibratorClass() diff --git a/tools/Polygraphy/polygraphy/backend/trt/loader.py b/tools/Polygraphy/polygraphy/backend/trt/loader.py new file mode 100644 index 00000000..3fefabe7 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/loader.py @@ -0,0 +1,497 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import contextlib +from collections import OrderedDict + +import tensorrt as trt +from polygraphy.backend.base import BaseLoadModel +from polygraphy.backend.trt import util as trt_util +from polygraphy.common import constants +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc + + +class LoadPlugins(BaseLoadModel): + def __init__(self, obj=None, plugins=None): + """ + A passthrough loader that loads plugins from the specified paths. + Passthrough here means that it can be used to wrap any other loader. The purpose of wrapping + another loader is that you can control the order of execution when lazily evaluating. + + For immediate evaluation, call the loader. For example: + + :: + # Note the `()` at the end + LoadPlugins(plugins=["/path/to/my/plugin.so", "/path/to/my/other_plugin.so"])() + + Args: + obj (object): + Any object to pass through this loader. + If ``obj`` is callable, parameters to __call__ will be forwarded + to ``obj`` when this loader is called. + plugins (List[str]): + A list of paths to plugin libraries to load before inference. + """ + self.obj = obj + self.plugins = plugins + + + def __call__(self, *args, **kwargs): + if self.plugins: + trt_util.load_plugins(self.plugins) + + ret, _ = misc.try_call(self.obj, *args, **kwargs) + return ret + + +class CreateNetwork(BaseLoadModel): + def __init__(self, explicit_precision=None, explicit_batch=None): + """ + Functor that creates an empty TensorRT network. + + Args: + explicit_precision (bool): + Whether to create the network with explicit precision enabled. Defaults to False + explicit_batch (bool): + Whether to create the network with explicit batch mode. Defaults to True. + """ + self.explicit_precision = misc.default_value(explicit_precision, False) + self.explicit_batch = misc.default_value(explicit_batch, True) + + + def __call__(self): + """ + Creates an empty TensorRT network. + + Returns: + (trt.Builder, trt.INetworkDefinition): The builder and empty network. + """ + builder = trt.Builder(trt_util.TRT_LOGGER) + network_flags = 0 + if self.explicit_batch: + network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + if self.explicit_precision: + network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION) + network = builder.create_network(flags=network_flags) + if network is None: + G_LOGGER.critical("Invalid network. See logging output above for details.") + return builder, network + + +class BaseNetworkFromOnnx(BaseLoadModel): + def __init__(self, explicit_precision, explicit_batch=None): + """ + Args: + explicit_precision (bool): Whether to create the network with explicit precision enabled. + """ + self.explicit_precision = misc.default_value(explicit_precision, False) + self.explicit_batch = misc.default_value(explicit_batch, True) + + + def __call__(self): + builder, network = CreateNetwork(explicit_precision=self.explicit_precision, explicit_batch=self.explicit_batch)() + parser = trt.OnnxParser(network, trt_util.TRT_LOGGER) + return builder, network, parser + + +class NetworkFromOnnxBytes(BaseNetworkFromOnnx): + def __init__(self, model_bytes, explicit_precision=None): + """ + Functor that parses an ONNX model to create a trt.INetworkDefinition. + + Args: + model_bytes (Callable() -> bytes): A loader that can supply a serialized ONNX model. + """ + super().__init__(explicit_precision) + self._model_bytes = model_bytes + + + def __call__(self): + """ + Parses an ONNX model. + + Returns: + (trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser): + A TensorRT network, as well as the builder used to create it, and the parser + used to populate it. + """ + builder, network, parser = super().__call__() + parser.parse(misc.try_call(self._model_bytes)[0]) + trt_util.check_onnx_parser_errors(parser) + return builder, network, parser + + +if misc.version(trt.__version__) >= misc.version("7.1"): + class NetworkFromOnnxPath(BaseNetworkFromOnnx): + def __init__(self, path, explicit_precision=None): + """ + Functor that parses an ONNX model to create a trt.INetworkDefinition. + This loader supports models with weights stored in an external location. + + Args: + path (str): The path from which to load the model. + """ + super().__init__(explicit_precision) + self.path = path + + + def __call__(self): + """ + Parses an ONNX model from a file. + + Returns: + (trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser): + A TensorRT network, as well as the builder used to create it, and the parser + used to populate it. + """ + builder, network, parser = super().__call__() + # We need to use parse_from_file for the ONNX parser to keep track of the location of the ONNX file for + # potentially parsing any external weights. + parser.parse_from_file(misc.try_call(self.path)[0]) + trt_util.check_onnx_parser_errors(parser) + return builder, network, parser +else: + class NetworkFromOnnxPath(NetworkFromOnnxBytes): + def __init__(self, path, explicit_precision=None): + """ + Functor that parses an ONNX model to create a trt.INetworkDefinition. + This loader supports models with weights stored in an external location. + + Args: + path (str): The path from which to load the model. + """ + from polygraphy.backend.common import BytesFromPath + load_model = BytesFromPath(misc.try_call(self.path)[0](self.path)) + super().__init__(load_model, explicit_precision) + + +class ModifyNetwork(BaseLoadModel): + def __init__(self, network, outputs=None, exclude_outputs=None): + """ + Functor that modifies a TensorRT ``INetworkDefinition``. + + Args: + network (Callable() -> trt.Builder, trt.INetworkDefinition): + A callable capable of returning a TensorRT Builder and INetworkDefinition. The callable may + have at most 3 return values if another object needs to be kept alive for the duration of the network, + e.g., in the case of a parser. The first and second return values must + always be the builder and network respectively. ModifyNetwork will never take ownership of these. + + outputs (Sequence[str]): + Names of tensors to mark as outputs. If provided, this will override the outputs + already marked in the network. + If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked. + exclude_outputs (Sequence[str]): + Names of tensors to exclude as outputs. This can be useful in conjunction with + ``outputs=constants.MARK_ALL`` to omit outputs. + """ + self._network = network + self.outputs = outputs + self.exclude_outputs = exclude_outputs + + + def __call__(self): + """ + Modifies a TensorRT ``INetworkDefinition``. + + Returns: + trt.INetworkDefinition: The modified network. + """ + ret, _ = misc.try_call(self._network) + builder, network, parser = misc.unpack_args(ret, num=3) + + if self.outputs == constants.MARK_ALL: + trt_util.mark_layerwise(network) + elif self.outputs is not None: + trt_util.mark_outputs(network, self.outputs) + + if self.exclude_outputs is not None: + trt_util.unmark_outputs(network, self.exclude_outputs) + + if parser is not None: + return builder, network, parser + else: + return builder, network + + +class ShapeTuple(object): + def __init__(self, min, opt, max): + """ + Represents a set of shapes for a single binding in a profile + + Args: + min (Tuple[int]): The minimum shape that the profile will support. + opt (Tuple[int]): The shape for which TensorRT will optimize the engine. + max (Tuple[int]): The maximum shape that the profile will support. + """ + self.min = min + self.opt = opt + self.max = max + + + def __str__(self): + return "(min={:}, opt={:}, max={:})".format(self.min, self.opt, self.max) + + + def __repr__(self): + return type(self).__name__ + self.__str__() + + +class Profile(OrderedDict): + """ + An ordered dictionary that represents a single optimization profile that + can be used to build an engine. + + More specifically, this is a OrderedDict[str, ShapeTuple] which maps binding + names to a set of min/opt/max shapes. + """ + def add(self, name, min, opt, max): + """ + A convenience function to add shapes for a single binding. + + Args: + name (str): The name of the binding. + min (Tuple[int]): The minimum shape that the profile will support. + opt (Tuple[int]): The shape for which TensorRT will optimize the engine. + max (Tuple[int]): The maximum shape that the profile will support. + + Returns: + self: + Which allows this function to be easily chained to add multiple bindings, + e.g., Profile().add(...).add(...) + """ + self[name] = ShapeTuple(min, opt, max) + return self + + def __getitem__(self, key): + """ + Retrieves the shapes registered for a given input name. + + Returns: + ShapeTuple: + A named tuple include ``min``, ``opt``, and ``max`` members for the shapes + corresponding to the input. + """ + if key not in self: + G_LOGGER.critical("Binding: {:} does not have shapes set in this profile".format(key)) + return super().__getitem__(key) + + +class CreateConfig(BaseLoadModel): + def __init__(self, max_workspace_size=None, tf32=None, fp16=None, int8=None, profiles=None, calibrator=None, strict_types=None): + """ + Functor that creates a TensorRT IBuilderConfig. + + Args: + max_workspace_size (int): The maximum workspace size, in bytes, when building the engine. + tf32 (bool): Whether to build the engine with TF32 precision enabled. Defaults to False. + fp16 (bool): Whether to build the engine with FP16 precision enabled. Defaults to False. + int8 (bool): Whether to build the engine with INT8 precision enabled. Defaults to False. + profiles (List[Profile]): + A list of optimization profiles to add to the configuration. Only needed for + networks with dynamic input shapes. If this is omitted for a network with + dynamic shapes, a default profile is created, where dynamic dimensions are + replaced with Polygraphy's DEFAULT_SHAPE_VALUE (defined in util/constants.py). + See `Profile` for details. + calibrator (trt.IInt8Calibrator): + An int8 calibrator. Only required in int8 mode when + the network does not have explicit precision. For networks with + dynamic shapes, the last profile provided (or default profile if + no profiles are provided) is used during calibration. + """ + self.max_workspace_size = misc.default_value(max_workspace_size, 1 << 24) + self.tf32 = misc.default_value(tf32, False) + self.fp16 = misc.default_value(fp16, False) + self.int8 = misc.default_value(int8, False) + self.profiles = misc.default_value(profiles, []) + self.calibrator = calibrator + self.strict_types = misc.default_value(strict_types, False) + + if self.calibrator is not None and not self.int8: + G_LOGGER.warning("A calibrator was provided to `CreateConfig`, but int8 mode was not enabled. " + "Did you mean to set `int8=True` to enable building with int8 precision?") + + + def __call__(self, builder, network): + """ + Creates a TensorRT IBuilderConfig that can be used by the EngineFromNetwork. + + Args: + builder (trt.Builder): + The TensorRT builder to use to create the configuration. + network (trt.INetworkDefinition): + The TensorRT network for which to create the config. The network is used to + automatically create a default optimization profile if none are provided. + + Returns: + trt.IBuilderConfig: The TensorRT builder configuration. + """ + config = builder.create_builder_config() + + calibration_profile = None + for profile in self.profiles: + calibration_profile = trt_util.build_profile(builder, network, profile) + config.add_optimization_profile(calibration_profile) + if not self.profiles: + calibration_profile = trt_util.build_default_profile(builder, network) + config.add_optimization_profile(calibration_profile) + + if self.profiles: + G_LOGGER.info("Configuring with profiles: {:}".format(self.profiles)) + + config.max_workspace_size = int(self.max_workspace_size) + + if self.strict_types: + config.set_flag(trt.BuilderFlag.STRICT_TYPES) + if not self.tf32: + with contextlib.suppress(AttributeError): + config.clear_flag(trt.BuilderFlag.TF32) + if self.fp16: + config.set_flag(trt.BuilderFlag.FP16) + if self.int8: + config.set_flag(trt.BuilderFlag.INT8) + if not network.has_explicit_precision: + if self.calibrator is not None: + input_metadata = trt_util.get_input_metadata_from_profile(calibration_profile, network) + with contextlib.suppress(AttributeError): + self.calibrator.reset(input_metadata) + config.int8_calibrator = self.calibrator + else: + G_LOGGER.warning("Network does not have explicit precision and no calibrator was provided. Please ensure " + "that tensors in the network have dynamic ranges set, or provide a calibrator in order to use int8 mode.") + return config + + +class EngineFromNetwork(BaseLoadModel): + def __init__(self, network, config=None): + """ + Functor that uses a TensorRT ``INetworkDefinition`` to build an engine. + + Args: + network (Callable() -> trt.Builder, trt.INetworkDefinition): + A callable capable of returning a TensorRT Builder and INetworkDefinition. The returned builder + and network are owned by EngineFromNetwork and should not be freed manually. The callable may + have at most 3 return values if another object needs to be kept alive for the duration of the network, + e.g., in the case of a parser. EngineFromNetwork will take ownership of the third return value, and, + like the network, it should not be freed by the callable. The first and second return values must + always be the builder and network respectively. + If instead of a loader, the network, builder, and optional parser arguments are provided directly, + then EngineFromNetwork will *not* deallocate them. + + + config (Callable(trt.Builder, trt.INetworkDefinition) -> trt.IBuilderConfig): + A callable that returns a TensorRT builder configuration. If not supplied, + a `CreateConfig` instance with default parameters is used. + """ + self._network = network + self._config = misc.default_value(config, CreateConfig()) + + + def __call__(self): + """ + Builds a TensorRT engine. + + Returns: + trt.ICudaEngine: The engine that was created. + """ + # If network is a callable, then we own its return value + ret, owning = misc.try_call(self._network) + builder, network, parser = misc.unpack_args(ret, num=3) + + with contextlib.ExitStack() as stack: + provided = "Builder and Network" if parser is None else "Builder, Network, and Parser" + if owning: + stack.enter_context(builder) + stack.enter_context(network) + if parser is not None: + stack.enter_context(parser) + else: + G_LOGGER.verbose("{:} were provided directly instead of via a Callable. This loader will not assume ownership. " + "Please ensure that they are freed.".format(provided)) + + network_log_mode = "full" if G_LOGGER.severity <= G_LOGGER.ULTRA_VERBOSE else "attrs" + G_LOGGER.super_verbose(lambda: ("Displaying TensorRT Network:\n" + trt_util.str_from_network(network, mode=network_log_mode))) + + config, _ = misc.try_call(self._config, builder, network) + G_LOGGER.info("Building engine with configuration: {:}".format(trt_util.str_from_config(config))) + engine = builder.build_engine(network, config) + if not engine: + G_LOGGER.critical("Invalid Engine. Please ensure the engine was built correctly") + + if hasattr(config.int8_calibrator, "free"): + config.int8_calibrator.free() + + return engine + + +class EngineFromBytes(BaseLoadModel): + def __init__(self, serialized_engine): + """ + Functor that deserializes an engine from a buffer. + + Args: + serialized_engine (Callable() -> Union[str, bytes]): + Either a loader that can supply a memory buffer, or a memory buffer itself. + For example, use a lambda: `lambda: open("/path/to/trt.engine", "rb").read()` for lazy evaluation, + or the contents of the file directly: `open("/path/to/trt.engine", "rb").read()` for immediate evaluation. + """ + self._serialized_engine = serialized_engine + + + def __call__(self): + """ + Deserializes an engine from a buffer. + + Returns: + trt.ICudaEngine: The deserialized engine. + """ + buffer, _ = misc.try_call(self._serialized_engine) + + trt.init_libnvinfer_plugins(trt_util.TRT_LOGGER, "") + with trt.Runtime(trt_util.TRT_LOGGER) as runtime: + engine = runtime.deserialize_cuda_engine(buffer) + if not engine: + G_LOGGER.critical("Could not load engine") + G_LOGGER.ultra_verbose(lambda: "Note: serialized_engine was: {:}".format(buffer)) + return engine + + +class SaveEngine(BaseLoadModel): + def __init__(self, engine, path=None): + """ + Functor that saves an engine to the provided path. + + Args: + engine (Callable() -> trt.ICudaEngine): + A callable that can supply a TensorRT engine. + + + path (str): The path at which to save the engine. + """ + self._engine = engine + self.path = path + + + def __call__(self): + """ + Saves an engine to the provided path. + + Returns: + trt.ICudaEngine: The engine that was saved. + """ + engine, _ = misc.try_call(self._engine) + misc.lazy_write(contents=lambda: engine.serialize(), path=self.path) + return engine diff --git a/tools/Polygraphy/polygraphy/backend/trt/requirements.txt b/tools/Polygraphy/polygraphy/backend/trt/requirements.txt new file mode 100644 index 00000000..616f29f0 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/requirements.txt @@ -0,0 +1 @@ +tensorrt diff --git a/tools/Polygraphy/polygraphy/backend/trt/runner.py b/tools/Polygraphy/polygraphy/backend/trt/runner.py new file mode 100644 index 00000000..035d31b9 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/runner.py @@ -0,0 +1,131 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import time + +import tensorrt as trt +from polygraphy.backend.base import BaseRunner +from polygraphy.backend.trt import util as trt_util +from polygraphy.backend.trt.buffers import Buffers +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import cuda, misc + +misc.log_module_info(trt) + + +class TrtRunner(BaseRunner): + """ + Runs inference using a TensorRT engine. + """ + def __init__(self, engine, name=None): + """ + Args: + engine (Callable() -> trt.ICudaEngine): + A callable that can supply a TensorRT engine. + If instead of a loader, the engine is provided directly, then the runner + will *not* take ownership of it, and therefore will not destroy it. + + + name (str): + The human-readable name prefix to use for this runner. + A runner count and timestamp will be appended to this prefix. + """ + super().__init__(name=name, prefix="trt-runner") + self._engine = engine + + + def activate_impl(self): + # If engine is a callable, then we own the engine + self.engine, self.owning = misc.try_call(self._engine) + + if not self.engine: + G_LOGGER.critical("Invalid Engine. Please ensure the engine was built correctly") + + if not self.owning: + G_LOGGER.verbose("Engine was provided directly instead of via a Callable. This runner will not assume ownership. " + "Please ensure the engine is freed.") + + self.buffers = Buffers.from_engine(self.engine) + self.stream = cuda.Stream() + + self.context = self.engine.create_execution_context() + + + def get_input_metadata(self): + bindings_per_profile = trt_util.get_bindings_per_profile(self.engine) + # This function always uses binding names of the 0th profile. + return trt_util.get_input_metadata_from_engine(self.engine, start_binding=0, end_binding=bindings_per_profile) + + + def deactivate_impl(self): + # Destroy the engine, and context. + with self.context: + pass + + if self.owning: + with self.engine: + pass + + self.buffers.free() + self.stream.free() + + + def infer(self, feed_dict): + def is_dynamic_shape_input(binding): + try: + self.engine.get_profile_shape_input(0, binding) + return True + except RuntimeError: + return False + + start_binding, end_binding = trt_util.get_active_profile_bindings(self.engine, self.context) + for name, inp in feed_dict.items(): + binding = start_binding + self.engine[name] + shape = inp.shape + # Only set shapes if required. + # get_shape/get_binding_shape will return what a shape input/data input is currently set to. + if is_dynamic_shape_input(binding): + G_LOGGER.verbose("Setting shape binding: {:} (index: {:}) to: {:}".format(name, binding, inp)) + if tuple(self.context.get_shape(binding)) != tuple(inp): + self.context.set_shape_input(binding, inp) + + elif misc.is_shape_dynamic(self.engine.get_binding_shape(binding)): + G_LOGGER.verbose("Setting binding: {:} (index: {:}) to shape: {:}".format(name, binding, shape)) + if tuple(self.context.get_binding_shape(binding)) != tuple(shape): + self.context.set_binding_shape(binding, shape) + + if not self.context.all_binding_shapes_specified: + G_LOGGER.critical("Some input shapes were not specified.\nNote: Network inputs are: {:}".format(self.get_input_metadata())) + if not self.context.all_shape_inputs_specified: + G_LOGGER.critical("Some shape inputs were not specified.\nNote: Network inputs are: {:}".format(self.get_input_metadata())) + + + # Inference + # Need to resize output buffers + self.buffers.resize(self.engine, self.context, start_binding=start_binding, end_binding=end_binding) + + start = time.time() + self.buffers.copy_inputs(feed_dict, self.stream) + # Need to offset bindings in case the active profile is not 0. + status = self.context.execute_async_v2(bindings=[0] * start_binding + self.buffers.bindings(), stream_handle=self.stream.address()) + if not status: + G_LOGGER.critical("Model execution failed. Please see the log messages above for details") + + self.buffers.copy_outputs(self.stream) + self.stream.synchronize() + end = time.time() + + self.inference_time = end - start + return self.buffers.outputs diff --git a/tools/Polygraphy/polygraphy/backend/trt/util.py b/tools/Polygraphy/polygraphy/backend/trt/util.py new file mode 100644 index 00000000..3763af5e --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt/util.py @@ -0,0 +1,445 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import contextlib +import ctypes + +import tensorrt as trt +from polygraphy.common import TensorMetadata +from polygraphy.common.constants import DEFAULT_SHAPE_VALUE +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + +TRT_LOGGER = trt.Logger() + + +def load_plugins(plugins): + for plugin in plugins: + G_LOGGER.info("Loading plugin library: {:}".format(plugin)) + ctypes.CDLL(plugin) + + +def check_onnx_parser_errors(parser): + if parser.num_errors > 0: + for index in range(parser.num_errors): + G_LOGGER.error(parser.get_error(index)) + G_LOGGER.critical("Could not parse ONNX correctly") + + +def get_layer_class_mapping(): + return { + trt.LayerType.CONVOLUTION: trt.IConvolutionLayer, + trt.LayerType.FULLY_CONNECTED: trt.IFullyConnectedLayer, + trt.LayerType.ACTIVATION: trt.IActivationLayer, + trt.LayerType.POOLING: trt.IPoolingLayer, + trt.LayerType.LRN: trt.ILRNLayer, + trt.LayerType.SCALE: trt.IScaleLayer, + trt.LayerType.SOFTMAX: trt.ISoftMaxLayer, + trt.LayerType.DECONVOLUTION: trt.IDeconvolutionLayer, + trt.LayerType.CONCATENATION: trt.IConcatenationLayer, + trt.LayerType.ELEMENTWISE: trt.IElementWiseLayer, + trt.LayerType.PLUGIN: trt.IPluginLayer, + trt.LayerType.RNN: trt.IRNNLayer, + trt.LayerType.UNARY: trt.IUnaryLayer, + trt.LayerType.PADDING: trt.IPaddingLayer, + trt.LayerType.SHUFFLE: trt.IShuffleLayer, + trt.LayerType.REDUCE: trt.IReduceLayer, + trt.LayerType.TOPK: trt.ITopKLayer, + trt.LayerType.GATHER: trt.IGatherLayer, + trt.LayerType.MATRIX_MULTIPLY: trt.IMatrixMultiplyLayer, + trt.LayerType.RAGGED_SOFTMAX: trt.IRaggedSoftMaxLayer, + trt.LayerType.CONSTANT: trt.IConstantLayer, + trt.LayerType.RNN_V2: trt.IRNNv2Layer, + trt.LayerType.IDENTITY: trt.IIdentityLayer, + trt.LayerType.PLUGIN_V2: trt.IPluginV2Layer, + trt.LayerType.SLICE: trt.ISliceLayer, + trt.LayerType.SHAPE: trt.IShapeLayer, + trt.LayerType.PARAMETRIC_RELU: trt.IParametricReLULayer, + trt.LayerType.RESIZE: trt.IResizeLayer, + trt.LayerType.TRIP_LIMIT: trt.ITripLimitLayer, + trt.LayerType.RECURRENCE: trt.IRecurrenceLayer, + trt.LayerType.ITERATOR: trt.IIteratorLayer, + trt.LayerType.LOOP_OUTPUT: trt.ILoopOutputLayer, + trt.LayerType.SELECT: trt.ISelectLayer, + trt.LayerType.FILL: trt.IFillLayer, + } + + +def get_input_metadata(network): + inputs = TensorMetadata() + for i in range(network.num_inputs): + tensor = network.get_input(i) + inputs.add(name=tensor.name, dtype=trt.nptype(tensor.dtype), shape=tensor.shape) + return inputs + + +def get_output_metadata(network): + outputs = TensorMetadata() + for i in range(network.num_outputs): + tensor = network.get_output(i) + outputs.add(name=tensor.name, dtype=trt.nptype(tensor.dtype), shape=tensor.shape) + return outputs + + +def str_from_network(network, mode="full"): + """ + Converts a TensorRT network to a human-readable representation + + Args: + network (trt.INetworkDefinition): The network. + mode (str): Controls what is displayed for each layer. Choices: ["none", "basic", "attrs", "full"] + + Returns: + str + """ + import numpy as np + + try: + LAYER_TYPE_CLASS_MAPPING = get_layer_class_mapping() + except AttributeError: + LAYER_TYPE_CLASS_MAPPING = {} + + def is_special_attribute(attr): + return attr.startswith("__") and attr.endswith("__") + + def is_valid_attribute(attr, layer): + if type(layer) == trt.IPoolingLayer or type(layer) == trt.IConvolutionLayer or type(layer) == trt.IDeconvolutionLayer: + if len(layer.get_input(0).shape) > 4: + # 3D pooling uses padding_nd + return attr not in ["padding", "stride", "window_size"] + if type(layer) == trt.IResizeLayer: + if layer.num_inputs > 1: + return attr not in ["scales"] + if type(layer) == trt.ISliceLayer: + if layer.num_inputs > 1: + return attr not in ["shape", "start", "stride"] + return True + + def get_layer_input_metadata(layer): + meta = TensorMetadata() + for i in range(layer.num_inputs): + inp = layer.get_input(i) + if inp: + meta.add(inp.name, trt.nptype(inp.dtype), inp.shape) + return meta + + def get_layer_output_metadata(layer): + meta = TensorMetadata() + for i in range(layer.num_outputs): + outp = layer.get_output(i) + if outp: + meta.add(outp.name, trt.nptype(outp.dtype), outp.shape) + return meta + + + network_str = "Name: {:} | {:} Batch Network{:}\n".format(network.name, + "Implicit" if hasattr(network, "has_implicit_batch_dimension") and network.has_implicit_batch_dimension else "Explicit", + " with Explicit Precision " if hasattr(network, "has_explicit_precision") and network.has_explicit_precision else "") + network_str += "\n" + + input_metadata = get_input_metadata(network) + network_str += "---- {:} Network Inputs ----\n{:}\n\n".format(len(input_metadata), input_metadata) + output_metadata = get_output_metadata(network) + network_str += "---- {:} Network Outputs ----\n{:}\n\n".format(len(output_metadata), output_metadata) + network_str += "---- {:} Layers ----\n".format(network.num_layers) + if mode != "none": + for index, layer in enumerate(network): + if layer.type in LAYER_TYPE_CLASS_MAPPING: + layer.__class__ = LAYER_TYPE_CLASS_MAPPING[layer.type] + + input_info = get_layer_input_metadata(layer) + output_info = get_layer_output_metadata(layer) + + network_str += misc.str_from_layer("Layer", index, layer.name, layer.type, input_info, output_info) + + if mode in ["attrs", "full"]: + # Exclude special attributes, as well as any attributes of the base layer class (those can be displayed above). + attrs = [attr for attr in dir(layer) if not is_special_attribute(attr) and not hasattr(trt.ILayer, attr) and is_valid_attribute(attr, layer)] + if attrs: + network_str += misc.indent_block("---- Attributes ----") + "\n" + for attr in attrs: + val = getattr(layer, attr) + if mode == "full" or not isinstance(val, np.ndarray): + if layer.name: + network_str += "{:}.".format(layer.name) + network_str += misc.indent_block("{:} = {:}".format(attr, val)) + "\n" + network_str += "\n" + else: + network_str += "(Use --mode to display)" + + return misc.indent_block(network_str, level=0) + + +def _get_network_outputs(network): + return [network.get_output(index).name for index in range(network.num_outputs)] + + +def check_outputs_not_found(not_found, all_outputs): + if not_found: + all_outputs = misc.unique_list(all_outputs) + G_LOGGER.critical("The following outputs: {:} were not found. " + "Note: Available tensors: {:}".format(not_found, all_outputs)) + + +def mark_outputs(network, outputs): + outputs = set(outputs) + all_outputs = [] + for layer in network: + for index in range(layer.num_outputs): + tensor = layer.get_output(index) + all_outputs.append(tensor.name) + # Clear all old outputs + if tensor.is_network_output: + network.unmark_output(tensor) + + if tensor.name in outputs: + if not tensor.is_network_output: + network.mark_output(tensor) + + marked_outputs = set(_get_network_outputs(network)) + not_found = outputs - marked_outputs + check_outputs_not_found(not_found, all_outputs) + + +def mark_layerwise(network): + # Layers within loops cannot be marked as network outputs. + LOOP_START_NAMES = ["TRIP_LIMIT", "ITERATOR"] + LOOP_END_NAMES = ["LOOP_OUTPUT"] + LOOP_START_LAYERS = [getattr(trt.LayerType, attr) for attr in LOOP_START_NAMES if hasattr(trt.LayerType, attr)] + LOOP_END_LAYERS = [getattr(trt.LayerType, attr) for attr in LOOP_END_NAMES if hasattr(trt.LayerType, attr)] + EXCLUDE_OUTPUT_LAYERS = [trt.LayerType.SHAPE, trt.LayerType.CONSTANT] + num_tensors_marked = 0 + in_loop = False + for layer in network: + if layer.type in LOOP_START_LAYERS: + G_LOGGER.warning("Loop detected. Please ensure the network is topologically sorted so that layers within " + "the loop body are not marked as network outputs in layerwise mode") + in_loop = True + elif layer.type in LOOP_END_LAYERS: + in_loop = False + + def should_mark_layer(): + return not in_loop and layer.type not in EXCLUDE_OUTPUT_LAYERS + + if should_mark_layer(): + for index in range(layer.num_outputs): + tensor = layer.get_output(index) + if not tensor.is_network_output: + G_LOGGER.verbose("Marking {:} as an output".format(tensor.name)) + network.mark_output(tensor) + num_tensors_marked += 1 + G_LOGGER.verbose("Marking {:} tensors as outputs".format(num_tensors_marked)) + + +def unmark_outputs(network, outputs): + outputs = set(outputs) + + unmarked_outputs = set() + for layer in network: + for index in range(layer.num_outputs): + tensor = layer.get_output(index) + if tensor.is_network_output and tensor.name in outputs: + network.unmark_output(tensor) + unmarked_outputs.add(tensor.name) + not_found = outputs - unmarked_outputs + check_outputs_not_found(not_found, _get_network_outputs(network)) + + +def str_from_config(config): + config_str = "max_workspace_size={:} bytes ({:.2f} MB) | ".format(config.max_workspace_size, config.max_workspace_size / (1024.0 ** 2)) + with contextlib.suppress(AttributeError): config_str += "tf32={:}, ".format(config.get_flag(trt.BuilderFlag.TF32)) + config_str += "fp16={:}, int8={:}, strict_types={:} | {:} profiles".format(config.get_flag(trt.BuilderFlag.FP16), + config.get_flag(trt.BuilderFlag.INT8), config.get_flag(trt.BuilderFlag.STRICT_TYPES), config.num_optimization_profiles) + return config_str + + +def check_profile(profile): + if not bool(profile): + G_LOGGER.critical("Profile is not valid, please provide profile data. Note: profile was: {:}".format(profile_shapes)) + return profile + + +def build_default_profile(builder, network, default_shape_value=None): + default_shape_value = misc.default_value(default_shape_value, DEFAULT_SHAPE_VALUE) + + def override_shape(shape): + return tuple([default_shape_value if misc.is_dimension_dynamic(dim) else dim for dim in shape]) + + trt_profile = builder.create_optimization_profile() + for idx in range(network.num_inputs): + inp = network.get_input(idx) + + with G_LOGGER.verbosity(): # WAR for spam from TRT + is_shape_tensor = inp.is_shape_tensor + + if is_shape_tensor: + rank = inp.shape[0] + shape = (default_shape_value, ) * rank + G_LOGGER.warning("Input shape-tensor: {:32} | Adjusted dynamic shape-tensor contents to: {:}. If this is incorrect, please provide a profile " + "that sets the values for this input shape-tensor.".format(inp.name, shape, rank), mode=LogMode.ONCE) + trt_profile.set_shape_input(inp.name, shape, shape, shape) + else: + shape = override_shape(inp.shape) + if override_shape(inp.shape) != inp.shape: + G_LOGGER.warning("Input tensor: {:32} | Adjusted dynamic shape: {:} to: {:}. If this is incorrect, please provide a profile " + "that sets the shape for this input tensor.".format(inp.name, inp.shape, shape), mode=LogMode.ONCE) + trt_profile.set_shape(inp.name, shape, shape, shape) + return check_profile(trt_profile) + + +def build_profile(builder, network, profile): + trt_profile = builder.create_optimization_profile() + unused_keys = set(profile.keys()) + for idx in range(network.num_inputs): + inp = network.get_input(idx) + if inp.name in unused_keys: + unused_keys.remove(inp.name) + + with G_LOGGER.verbosity(): # WAR for spam from TRT + is_shape_tensor = inp.is_shape_tensor + + if is_shape_tensor: + if inp.name in profile: + shapes = profile[inp.name] + trt_profile.set_shape_input(inp.name, shapes.min, shapes.opt, shapes.max) + G_LOGGER.extra_verbose("Input shape-tensor: {:32} | Setting values to min: {:}, opt: {:}, max: {:}".format(inp.name, shapes.min, shapes.opt, shapes.max)) + else: + G_LOGGER.warning("input shape-tensor: {:32} | No values provided. Assuming this is not a dynamic shape-tensor.".format(inp.name), mode=LogMode.ONCE) + elif misc.is_shape_dynamic(inp.shape): + shapes = profile[inp.name] + trt_profile.set_shape(inp.name, shapes.min, shapes.opt, shapes.max) + G_LOGGER.extra_verbose("Input tensor: {:32} | Setting shape to min: {:}, opt: {:}, max: {:}".format(inp.name, shapes.min, shapes.opt, shapes.max)) + + if unused_keys: + G_LOGGER.warning("Some inputs provided in the profile were unused: {:}".format(list(unused_keys))) + + return check_profile(trt_profile) + + +def get_input_metadata_from_profile(profile, network): + """ + Returns metadata about the inputs based on a profile + + Args: + profile (trt.IOptimizationProfile): The profile from which to retrieve input metada. + network (trt.INetworkDefinition): The network + + Returns: + TensorMetadata: A mapping of input names to their types and shapes. + """ + input_metadata = TensorMetadata() + for index in range(network.num_inputs): + tensor = network.get_input(index) + if tensor.is_shape_tensor: + shapes = profile.get_shape_input(tensor.name) + else: + shapes = profile.get_shape(tensor.name) + + if tuple(shapes[0]) != tuple(shapes[1]): + G_LOGGER.warning("In profile 0, min != max, using opt shapes for calibration") + # Always use opt shape + input_metadata.add(name=tensor.name, dtype=trt.nptype(tensor.dtype), shape=shapes[1]) + return input_metadata + + +def add_binding_to_metadata(engine, binding, metadata): + metadata.add( + name=engine[binding], + dtype=trt.nptype(engine.get_binding_dtype(binding)), + shape=list(engine.get_binding_shape(binding)) + ) + + +def get_input_metadata_from_engine(engine, start_binding, end_binding): + inputs = TensorMetadata() + for binding in range(start_binding, end_binding): + if engine.binding_is_input(binding): + add_binding_to_metadata(engine, binding, inputs) + return inputs + + +def get_output_metadata_from_engine(engine, start_binding, end_binding): + outputs = TensorMetadata() + for binding in range(start_binding, end_binding): + if not engine.binding_is_input(binding): + add_binding_to_metadata(engine, binding, outputs) + return outputs + + +def str_from_engine(engine): + bindings_per_profile = get_bindings_per_profile(engine) + engine_str = "Name: {:} | {:}{:} Batch Engine ({:} layers)\n".format(engine.name, + "Refittable " if engine.refittable else "", + "Implicit" if hasattr(engine, "has_implicit_batch_dimension") and engine.has_implicit_batch_dimension else "Explicit", + engine.num_layers) + engine_str += "\n" + + # Show metadata for the first profile (i.e. the dynamic shapes) + input_metadata = get_input_metadata_from_engine(engine, 0, bindings_per_profile) + engine_str += "---- {:} Engine Inputs ----\n{:}\n\n".format(len(input_metadata), input_metadata) + output_metadata = get_output_metadata_from_engine(engine, 0, bindings_per_profile) + engine_str += "---- {:} Engine Outputs ----\n{:}\n\n".format(len(output_metadata), output_metadata) + + engine_str += "---- Memory ----\nWorkspace Memory: {:} bytes\n\n".format(engine.max_workspace_size) + + engine_str += "---- {:} Profiles ({:} Bindings Each) ----\n".format(engine.num_optimization_profiles, bindings_per_profile) + for profile_index in range(engine.num_optimization_profiles): + engine_str += "- Profile: {:}\n".format(profile_index) + + max_width = max([len(binding) for binding in engine]) + 8 + for offset in range(bindings_per_profile): + binding = profile_index * bindings_per_profile + offset + name = "[Name: {:}]".format(engine.get_binding_name(binding)) + engine_str += misc.indent_block("Binding Index: {:} {:} {:<{max_width}}".format( + binding, "(Input) " if engine.binding_is_input(binding) else "(Output)", name, max_width=max_width)) + + if engine.binding_is_input(binding): + if engine.is_shape_binding(binding): + min_shape, opt_shape, max_shape = engine.get_profile_shape_input(profile_index, binding) + else: + min_shape, opt_shape, max_shape = engine.get_profile_shape(profile_index, binding) + engine_str += " | Shapes: min={:}, opt={:}, max={:}\n".format(min_shape, opt_shape, max_shape) + else: + engine_str += " | Shape: {:}".format(tuple(output_metadata[engine[offset]][1])) + engine_str += "\n" + return misc.indent_block(engine_str, level=0) + + +def get_bindings_per_profile(engine): + return engine.num_bindings // engine.num_optimization_profiles + + +def get_active_profile_bindings(engine, context): + """ + Gets the start and end binding indices for the active optimization profile. + + Args: + engine (trt.ICudaEngine): The engine in question. + context (trt.IExecutionContext): The context where the profile is currently set. + + Returns: + Tuple[int, int]: The start and end bindings indices, in that order + """ + active_profile = context.active_optimization_profile + bindings_per_profile = get_bindings_per_profile(engine) + + start_binding = bindings_per_profile * active_profile + end_binding = start_binding + bindings_per_profile + + G_LOGGER.ultra_verbose("Total # of Profiles: {:}, Bindings Per Profile: {:}, Active Profile: {:}, " + "Start Binding: {:}, End Binding: {:}".format( + engine.num_optimization_profiles, bindings_per_profile, + active_profile, start_binding, end_binding)) + return start_binding, end_binding diff --git a/tools/Polygraphy/polygraphy/backend/trt_legacy.py b/tools/Polygraphy/polygraphy/backend/trt_legacy.py new file mode 100644 index 00000000..42907af6 --- /dev/null +++ b/tools/Polygraphy/polygraphy/backend/trt_legacy.py @@ -0,0 +1,357 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from polygraphy.backend.base import BaseRunner, BaseLoadModel +from polygraphy.backend.trt.loader import BaseNetworkFromOnnx +from polygraphy.util.format import DataFormat, FormatManager +from polygraphy.common import TensorMetadata, constants +from polygraphy.backend.trt import util as trt_util +from polygraphy.backend.trt.util import TRT_LOGGER +from polygraphy.logger.logger import G_LOGGER +from polygraphy.util import misc, cuda + + +from collections import OrderedDict +import contextlib +import time +import os + +import tensorrt as trt +import numpy as np + +misc.log_module_info(trt) + +class LoadUffFile(BaseLoadModel): + def __init__(self, path, shapes, outputs): + self.path = path + self.shapes = shapes + self.outputs = outputs + + def __call__(self): + input_names = list(self.shapes.keys()) + input_shapes = list(self.shapes.values()) + with open(self.path, "rb") as f: + return f.read(), input_names, input_shapes, self.outputs + + +class ConvertToUff(BaseLoadModel): + def __init__(self, tf_loader, save_uff=None, preprocessor=None): + self.tf_loader = tf_loader + self.uff_path = save_uff + self.preprocessor = preprocessor + + def __call__(self): + """ + + save_uff (bool): Whether to write the generated UFF and corresponding PBTXT files. + """ + from polygraphy.backend.tf import util as tf_util + import uff + misc.log_module_info(uff) + + graph, output_names = self.tf_loader() + output_names = [name.split(":")[0] for name in output_names] + # GraphDefs don't have names, so we have to name it something generic. + output_filename = None if not self.uff_path else "out.uff" + + # Generate the UFF model and get information about the input_buffers/output_buffers. + uff_model, input_nodes, _ = uff.from_tensorflow(graph.as_graph_def(), return_graph_info=True, + quiet=(G_LOGGER.severity > G_LOGGER.VERBOSE), + debug_mode=(G_LOGGER.severity == G_LOGGER.EXTRA_VERBOSE), text=self.uff_path, + save_preprocessed=self.uff_path, output_filename=output_filename, + preprocessor=self.preprocessor) + + input_names = [node.name for node in input_nodes] + input_shapes = [tuple(int(dim.size) for dim in node.attr["shape"].shape.dim) for node in input_nodes] + return uff_model, input_names, input_shapes, output_names + + +class LoadNetworkFromUff(BaseLoadModel): + def __init__(self, uff_loader, uff_order=None): + self.uff_loader = uff_loader + self.uff_order = None + if uff_order: + self.uff_order = trt.UffInputOrder.NCHW if uff_order.lower() == "nchw" else trt.UffInputOrder.NHWC + + def __call__(self): + uff_model, input_names, input_shapes, output_names = self.uff_loader() + + builder = trt.Builder(TRT_LOGGER) + network = builder.create_network() + parser = trt.UffParser() + # Input names should come from the converter, as a preprocessing script may have been applied to the frozen model. + for name, shape in zip(input_names, input_shapes): + # Default order is NCHW, only set to NHWC if we're reasonably certain that it is. + input_order = self.uff_order + if not self.uff_order: + input_order = trt.UffInputOrder.NCHW + if FormatManager.determine_format(shape) == DataFormat.NHWC: + input_order = trt.UffInputOrder.NHWC + shape = shape[1:] + G_LOGGER.verbose("Registering UFF input: {:} with shape: {:} and input order: {:}".format(name, shape, input_order)) + parser.register_input(name, shape, input_order) + + if output_names and output_names != constants.MARK_ALL: + for name in output_names: + G_LOGGER.verbose("Registering UFF output: " + str(name)) + parser.register_output(name) + + G_LOGGER.info("Parsing UFF model with inputs: {:} and outputs: {:}".format(input_names, output_names)) + success = parser.parse_buffer(uff_model, network) + if not success: + G_LOGGER.critical("Could not parse UFF correctly") + return builder, network, parser, input_shapes[0][0] + + +class ParseNetworkFromOnnxLegacy(BaseNetworkFromOnnx): + def __init__(self, onnx_loader): + """ + Parses an ONNX model to create a trt.INetworkDefinition. This loader only supports the + implicit batch version of the parser. + + Args: + onnx_loader (Callable() -> onnx.ModelProto): A loader that can supply an ONNX model. + """ + super().__init__(explicit_precision=False, explicit_batch=False) + self.onnx_loader = onnx_loader + + + def __call__(self): + from polygraphy.backend.onnx import util as onnx_util + + builder, network, parser = super().__call__() + onnx_model, _ = misc.try_call(self.onnx_loader) + dtype, shape = list(onnx_util.get_input_metadata(onnx_model.graph).values())[0] + + parser.parse(onnx_model.SerializeToString()) + trt_util.check_onnx_parser_errors(parser) + + return builder, network, parser, shape[0] + + +class LoadNetworkFromCaffe(BaseLoadModel): + def __init__(self, deploy, model, outputs, batch_size=None, dtype=None): + self.deploy = deploy + self.model = model + if not self.model: + G_LOGGER.warning("No model file provided for Caffe model, random weights will be used. To avoid this, " + "please set the model paramater, or --model") + + if not outputs: + G_LOGGER.critical("Please set Caffe model outputs using the outputs parameter, or --trt-outputs. " + "Note: To determine possible outputs, try running: tail -n50 {:}".format(deploy)) + + self.outputs = outputs + self.dtype = misc.default_value(dtype, trt.float32) + self.batch_size = misc.default_value(batch_size, 1) + + def __call__(self): + builder = trt.Builder(TRT_LOGGER) + network = builder.create_network() + parser = trt.CaffeParser() + + model_tensors = parser.parse(deploy=self.deploy, model=self.model, network=network, dtype=self.dtype) + + if self.outputs and self.outputs != constants.MARK_ALL: + for output in self.outputs: + network.mark_output(model_tensors.find(output)) + + return builder, network, parser, self.batch_size + + +# Builds and tracks a single engine for a single network. +class TrtLegacyRunner(BaseRunner): + """ + A runner that can perform inference on a single TensorRT engine. + """ + # Simple helper data class that's a little nicer to use than a 2-tuple. + class HostDeviceMem(object): + def __init__(self, host_mem, device_mem): + self.host = host_mem + self.device = device_mem + + def __str__(self): + return "Host:" + str(self.host) + ", Device:" + str(self.device) + + def __init__(self, network_loader=None, max_workspace_size=None, max_batch_size=None, fp16=None, + tf32=None, load_engine=None, save_engine=None, layerwise=False, plugins=[], name=None): + """ + Creates a runner that manages a single TensorRT engine. + + + network_loader (BaseModelLoader): + A loader that returns a TRT builder, network, parser and input shapes. + max_workspace_size (int): The maximum workspace size. + max_batch_size (int): The maximum batch size. + fp16 (bool): Whether to run in fp16 mode + layerwise (bool): Whether to retrieve the outputs of every layer in the network. + name (str): + The human-readable name prefix to use for this runner. + A runner count and timestamp will be appended to this prefix. + """ + G_LOGGER.warning("TrtLegacyRunner is deprecated, and will be removed in a future release") + # Load any user-supplied plugin libraries. This must happen before everything else, including engine deserialization. + if plugins: + import ctypes + for plugin in plugins: + path = os.path.abspath(plugin) + G_LOGGER.info("Loading plugin library: {:}".format(path)) + ctypes.CDLL(path) + + # Choose a unique name for this runner. + super().__init__(name=name, prefix="trt-legacy-runner") + + # Save parameters for activate and deactivate. + self.network_loader = network_loader + self.max_workspace_size = misc.default_value(max_workspace_size, 1<<24) + self.fp16 = misc.default_value(fp16, False) + self.tf32 = misc.default_value(tf32, False) + self.load_engine = load_engine + + self.engine_path = save_engine + + self.layerwise = layerwise + self.max_batch_size = max_batch_size + + + def activate_impl(self): + """ + Vars: + engine (trt.ICudaEngine): + The engine tracked by this runner. The TrtLegacyRunner OWNS the engine it + manages, and therefore is responsible for it's destruction. Do not free the engine outside of the + runner, or it will result in a double free. + context (trt.IExecutionContext): The context used for inference. + input_buffers (Dict[str, TrtLegacyRunner.HostDeviceMem]): + A mapping of binding names to HostDeviceMem objects for input buffers. + output_buffers (Dict[str, TrtLegacyRunner.HostDeviceMem]): + A mapping of binding names to HostDeviceMem objects for output buffers. + bindings (List[int]): A list of device pointers for engine bindings. + stream (cuda.Stream): The CUDA stream that this runner will use for inference. + """ + # Only initialize GPU after this runner is activated. + # Allocates all buffers required for an engine, i.e. host/device input_buffers/output_buffers. + def allocate_buffers(engine): + input_buffers = OrderedDict() + output_buffers = OrderedDict() + bindings = [] + stream = cuda.Stream() + G_LOGGER.verbose("Using batch size: " + str(engine.max_batch_size) + " during buffer allocation") + for binding in engine: + shape = (engine.max_batch_size, ) + tuple(engine.get_binding_shape(binding)) + dtype = engine.get_binding_dtype(binding) + + device_mem = cuda.DeviceBuffer(shape=shape, dtype=trt.nptype(dtype)) + G_LOGGER.extra_verbose("Tensor: " + "{:40} | Allocated: {:}".format(binding, device_mem)) + + if engine.binding_is_input(binding): + input_buffers[binding] = TrtLegacyRunner.HostDeviceMem(None, device_mem) + else: + host_mem = np.empty(shape=shape, dtype=trt.nptype(dtype)) + output_buffers[binding] = TrtLegacyRunner.HostDeviceMem(host_mem, device_mem) + return input_buffers, output_buffers, stream + + # Always try reading the engine first, or, failing that, build it. + if self.load_engine: + with open(self.load_engine, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime: + G_LOGGER.info("Reading engine from {:}".format(self.load_engine)) + self.engine = runtime.deserialize_cuda_engine(f.read()) + else: + trt.init_libnvinfer_plugins(TRT_LOGGER, "") + builder, network, parser, model_batch_size = self.network_loader() + with builder, network, parser: + builder.max_batch_size = int(self.max_batch_size or model_batch_size or 1) + + config = builder.create_builder_config() + config.max_workspace_size = int(self.max_workspace_size) + + if not self.tf32: + with contextlib.suppress(AttributeError): config.clear_flag(trt.BuilderFlag.TF32) + if self.fp16: + config.flags = 1 << int(trt.BuilderFlag.FP16) + + if not network: + G_LOGGER.critical("Invalid network") + G_LOGGER.super_verbose(lambda: trt_util.str_from_network(network) or "Finished logging network") + + + if self.layerwise: + # In layerwise mode, every layer becomes an output. + G_LOGGER.info("Running in layerwise mode. Marking {:} layers as outputs".format(network.num_layers)) + for layer in network: + for index in range(layer.num_outputs): + out = layer.get_output(index) + if not out.is_network_output: + network.mark_output(out) + + G_LOGGER.info("Building engine: max workspace size={:} bytes, max batch size={:}, fp16={:}, " + "tf32={:}".format(builder.max_workspace_size, builder.max_batch_size, self.fp16, self.tf32)) + self.engine = builder.build_engine(network, config) + + + if not self.engine: + G_LOGGER.critical("Invalid Engine. Please ensure the engine was built correctly") + + if self.engine_path: + with open(self.engine_path, "wb") as f: + G_LOGGER.info("Writing engine to {:}".format(self.engine_path)) + f.write(self.engine.serialize()) + + self.context = self.engine.create_execution_context() + self.input_buffers, self.output_buffers, self.stream = allocate_buffers(self.engine) + + + def get_input_metadata(self): + inputs = TensorMetadata() + + for binding in self.engine: + if self.engine.binding_is_input(binding): + # Always prepend a dynamic batch dimension + inputs[binding] = (trt.nptype(self.engine.get_binding_dtype(binding)), [-1] + list(self.engine.get_binding_shape(binding))) + return inputs + + + def deactivate_impl(self): + # Destroy the engine and context. + with self.engine, self.context: + pass + [inp.device.free() for inp in self.input_buffers.values()] + [out.device.free() for out in self.output_buffers.values()] + self.stream.free() + + + def infer(self, feed_dict): + start = time.time() + [self.input_buffers[name].device.copy_from(buffer, self.stream) for name, buffer in feed_dict.items()] + # We will not run with smaller batch sizes than whatever the builder chose. + bindings = [buf.device.address() for buf in self.input_buffers.values()] + [buf.device.address() for buf in self.output_buffers.values()] + status = self.context.execute_async(batch_size=self.context.engine.max_batch_size, bindings=bindings, + stream_handle=self.stream.address()) + if not status: + G_LOGGER.critical("Model execution failed. Please see the log messages above for details") + + for out in self.output_buffers.values(): + out.host = out.device.copy_to(out.host, self.stream) + + self.stream.synchronize() + end = time.time() + + out_dict = OrderedDict() + for (name, out) in self.output_buffers.items(): + out_dict[name] = out.host + self.inference_time = end - start + return out_dict diff --git a/tools/Polygraphy/polygraphy/common/__init__.py b/tools/Polygraphy/polygraphy/common/__init__.py new file mode 100644 index 00000000..ac7bd29b --- /dev/null +++ b/tools/Polygraphy/polygraphy/common/__init__.py @@ -0,0 +1,2 @@ +from polygraphy.common.exception import PolygraphyException +from polygraphy.common.struct import TensorMetadata diff --git a/tools/Polygraphy/polygraphy/common/constants.py b/tools/Polygraphy/polygraphy/common/constants.py new file mode 100644 index 00000000..14f179f8 --- /dev/null +++ b/tools/Polygraphy/polygraphy/common/constants.py @@ -0,0 +1,21 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +DEFAULT_SHAPE_VALUE = 1 +DEFAULT_SEED = 1 + +TAB = " " * 4 # The one true tab + +MARK_ALL = "mark-all" diff --git a/tools/Polygraphy/polygraphy/common/exception.py b/tools/Polygraphy/polygraphy/common/exception.py new file mode 100644 index 00000000..4851d2ea --- /dev/null +++ b/tools/Polygraphy/polygraphy/common/exception.py @@ -0,0 +1,20 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +class PolygraphyException(Exception): + """ + Represents an exception raised by Polygraphy. + """ + pass diff --git a/tools/Polygraphy/polygraphy/common/func.py b/tools/Polygraphy/polygraphy/common/func.py new file mode 100644 index 00000000..c76f109f --- /dev/null +++ b/tools/Polygraphy/polygraphy/common/func.py @@ -0,0 +1,98 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import inspect + +from polygraphy.logger import G_LOGGER + + +def make_iterable(obj): + return obj if type(obj) == tuple else (obj, ) + + +def extend(extend_func): + """ + A decorator that uses the function it decorates to extend the function + provided as a parameter. + + This is best illustrated with an example: + :: + + def x(a0, a1, a2): + rv0 = [a0, a1, a2] + rv1 = None + return rv0, rv1 + + @extend(x) + def y(rv0, rv1): + rv0.append(-1) + + # We can now call `y` as if it were `x`, and we will receive + # the return values from `x` after any modifications by `y` + rv0, rv1 = y(1, 2, 3) + assert rv0 == [1, 2, 3, -1] + assert rv1 is None + + In this case, ``extend`` is essentially syntactic sugar for: + :: + + def y(a0, a1, a2): + rv0, rv1 = x(a0, a1, a2) + + # Body of `y` from previous section + rv0.append(-1) + + return rv0, rv1 + + If ``y`` does not return anything, or returns ``None``, then ``extend`` will + ensure that the return value of ``x`` is forwarded to the caller. + This means that ``y`` will provide exactly the same interface as ``x``. + + If `y` returns something other than ``None``, then its return value will be + provided to the caller, and the return value of ``x`` will be discarded. + + NOTE: This function will automatically unpack tuples returned by the function + being extended. Thus, the following implementation of ``x`` would behave just like + the one mentioned above: + :: + + def x(a0, a1, a2): + ret = (rv0, rv1) + return ret # Tuple will be unpacked, and `y` still sees 2 parameters + + Args: + extend_func (Callable): A callable to extend. + """ + def extend_decorator(func): + def extended_func(*args, **kwargs): + extend_func_retval = extend_func(*args, **kwargs) + extend_func_ret_tuple = make_iterable(extend_func_retval) + + func_args = inspect.getfullargspec(func).args + # Special case for when the extended function does not return anything + if len(func_args) == 0 and len(extend_func_ret_tuple) == 1 and extend_func_ret_tuple[0] is None: + func_retval = func() + elif len(extend_func_ret_tuple) == len(func_args): + func_retval = func(*extend_func_ret_tuple) + else: + G_LOGGER.critical("Function: {:} expected to receive {:} parameters from function: {:}, but " + "received: {:} instead".format(func.__name__, len(func_args), extend_func.__name__, extend_func_ret_tuple)) + + if func_retval is not None: + return func_retval + return extend_func_retval + + return extended_func + return extend_decorator diff --git a/tools/Polygraphy/polygraphy/common/struct.py b/tools/Polygraphy/polygraphy/common/struct.py new file mode 100644 index 00000000..219710fd --- /dev/null +++ b/tools/Polygraphy/polygraphy/common/struct.py @@ -0,0 +1,65 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import OrderedDict, namedtuple + +import numpy as np + +MetadataTuple = namedtuple("MetadataTuple", ["dtype", "shape"]) # Metadata for single tensor + +class TensorMetadata(OrderedDict): + """ + An OrderedDict[str, Tuple[np.dtype, Tuple[int]]] that maps input names to their data types and shapes. + """ + def add(self, name, dtype, shape): + """ + Convenience function for adding entries. + + Args: + name (str): The name of the input. + dtype (np.dtype): The data type of the input. + shape (Tuple[int]): + The shape of the input. Dynamic dimensions may + be indicated by negative values, ``None``, or a string. + + Returns: + The newly added entry. + """ + self[name] = MetadataTuple(dtype, shape) + return self + + + def __repr__(self): + ret = "TensorMetadata()" + for name, (dtype, shape) in self.items(): + ret += ".add('{:}', {:}, {:})".format(name, dtype, shape) + return ret + + + def __str__(self): + def str_from_single_meta(name, dtype, shape): + ret = "{:}".format(name) + meta_items = [] + if dtype is not None: + meta_items.append("dtype={:}".format(np.dtype(dtype).name)) + if shape is not None: + meta_items.append("shape={:}".format(tuple(shape))) + if meta_items: + ret += " [" + ", ".join(meta_items) + "]" + return ret + + sep = ", " + elems = [str_from_single_meta(name, dtype, shape) for name, (dtype, shape) in self.items()] + return "{" + sep.join(elems) + "}" diff --git a/tools/Polygraphy/polygraphy/comparator/__init__.py b/tools/Polygraphy/polygraphy/comparator/__init__.py new file mode 100644 index 00000000..66e84e9e --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/__init__.py @@ -0,0 +1,5 @@ +from polygraphy.comparator.comparator import Comparator +from polygraphy.comparator.compare import CompareFunc +from polygraphy.comparator.data_loader import DataLoader +from polygraphy.comparator.postprocess import PostprocessFunc +from polygraphy.comparator.struct import IterationResult, RunResults diff --git a/tools/Polygraphy/polygraphy/comparator/comparator.py b/tools/Polygraphy/polygraphy/comparator/comparator.py new file mode 100644 index 00000000..92d09e39 --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/comparator.py @@ -0,0 +1,336 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import contextlib +import copy +import queue +from multiprocessing import Process, Queue + +import numpy as np +from polygraphy.common import TensorMetadata +from polygraphy.comparator.compare import CompareFunc +from polygraphy.comparator.data_loader import DataLoader, DataLoaderCache +from polygraphy.comparator.struct import (AccuracyResult, IterationResult, + RunResults) +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + + +class Comparator(object): + @staticmethod + def run(runners, data_loader=None, warm_up=None, + use_subprocess=None, subprocess_timeout=None, + subprocess_polling_interval=None): + """ + Runs the supplied runners sequentially. + + Args: + data_loader (Generator -> OrderedDict[str, np.ndarray]): + A generator or iterable that yields a dictionary that maps input names to input numpy buffers. + In the simplest case, this can be a `List[Dict[str, np.ndarray]]` . + + In case you don't know details about the inputs ahead of time, you can access the + `input_metadata` property in your data loader, which will be set to an `TensorMetadata` + instance by this function. + Note that this does not work for generators or lists. + + The number of iterations run by this function is controlled by the number of items supplied + by the data loader. + + Defaults to an instance of `DataLoader`. + warm_up (int): + The number of warm up runs to perform for each runner before timing. + Defaults to 0. + use_subprocess (bool): + Whether each runner should be run in a subprocess. This allows each runner to have exclusive + access to the GPU. When using a subprocess, runners and loaders will never be modified. + subprocess_timeout (int): + The timeout before a subprocess is killed automatically. This is useful for handling processes + that never terminate. A value of None disables the timeout. Defaults to None. + subprocess_polling_interval (int): + The polling interval, in seconds, for checking whether a subprocess has completed or crashed. + In rare cases, omitting this parameter when subprocesses are enabled may cause this function + to hang indefinitely if the subprocess crashes. + A value of 0 disables polling. Defaults to 30 seconds. + + Returns: + RunResults: A mapping of runner names to the results of their inference. The ordering of `runners` is preserved in this mapping. + """ + warm_up = misc.default_value(warm_up, 0) + data_loader = misc.default_value(data_loader, DataLoader()) + use_subprocess = misc.default_value(use_subprocess, False) + subprocess_polling_interval = misc.default_value(subprocess_polling_interval, 30) + loader_cache = DataLoaderCache(data_loader) + + + def execute_runner(runner, loader_cache): + with runner as active_runner: + input_metadata = active_runner.get_input_metadata() + G_LOGGER.verbose("Runner: {:40} | Input Metadata:\n{:}".format(active_runner.name, misc.indent_block(input_metadata))) + loader_cache.set_input_metadata(input_metadata) + + if warm_up: + G_LOGGER.info("Runner: {:40} | Running {:} warm-up runs".format(active_runner.name, warm_up)) + try: + feed_dict = loader_cache[0] + except IndexError: + G_LOGGER.warning("{:} warm-up runs were requested, but data loader did not supply any data. " + "Skipping warm-up runs".format(warm_up)) + else: + G_LOGGER.ultra_verbose("Warm-up Input Buffers:\n{:}".format(misc.indent_block(feed_dict))) + # First do a few warm-up runs, and don't time them. + for i in range(warm_up): + active_runner.infer(feed_dict=feed_dict) + + # Then, actual iterations. + total_time = 0 + run_results = [] + for feed_dict in loader_cache: + G_LOGGER.extra_verbose(lambda: "Runner: {:40} | Feeding inputs:\n{:}".format(active_runner.name, misc.indent_block(feed_dict))) + outputs = active_runner.infer(feed_dict=feed_dict) + + runtime = active_runner.last_inference_time() + # Without a deep copy here, outputs will always reference the output of the last run + run_results.append(IterationResult(outputs=copy.deepcopy(outputs), runtime=runtime, runner_name=active_runner.name)) + + if len(run_results) == 1: + output_metadata = TensorMetadata() + for name, out in outputs.items(): + output_metadata.add(name, out.dtype, out.shape) + + G_LOGGER.verbose("Runner: {:40} | Output Metadata:\n{:}".format(active_runner.name, misc.indent_block(output_metadata)), mode=LogMode.ONCE) + G_LOGGER.extra_verbose(lambda: "Runner: {:40} | Inference Time: {:.3f} ms | Received outputs:\n{:}".format( + active_runner.name, runtime * 1000.0, misc.indent_block(outputs))) + + G_LOGGER.info("Runner: {:40} | Completed {:} iterations.".format(active_runner.name, len(run_results))) + return run_results + + + # Wraps execute_runner to use a queue. + def execute_runner_with_queue(runner_queue, runner, loader_cache): + run_results = None + try: + run_results = execute_runner(runner, loader_cache) + except: + # Cannot send the exception back, as it is not necessarily pickleable + import traceback + G_LOGGER.error(traceback.format_exc()) + misc.try_send_on_queue(runner_queue, run_results) + # After finishing, send the updated loader_cache back. + misc.try_send_on_queue(runner_queue, loader_cache) + + + # Do all inferences in one loop, then comparisons at a later stage. + # We run each runner in a separate process so that we can provide exclusive GPU access for each runner. + runner_queue = Queue() + run_results = RunResults() + for index, runner in enumerate(runners): + G_LOGGER.info("Runner: {:40} | Activating and starting inference".format(runner.name)) + if use_subprocess: + process = Process(target=execute_runner_with_queue, args=(runner_queue, runner, loader_cache)) + process.start() + + # If a subprocess hangs in a certain way, then process.join could block forever. Hence, + # we need to keep polling the process to make sure it really is alive. + run_results[runner.name] = None + while process.is_alive() and run_results[runner.name] is None: + try: + run_results[runner.name] = misc.try_receive_on_queue(runner_queue, timeout=subprocess_polling_interval / 2) + # Receive updated loader cache, or fall back if it could not be sent. + loader_cache = misc.try_receive_on_queue(runner_queue, timeout=subprocess_polling_interval / 2) + except queue.Empty: + G_LOGGER.extra_verbose("Polled subprocess - still running") + + try: + assert run_results[runner.name] is not None + process.join(subprocess_timeout) + except: + G_LOGGER.critical("Runner: {:40} | Terminated prematurely. Check the exception logged above. " + "If there is no exception logged above, make sure not to use the --use-subprocess " + "flag or set use_subprocess=False in Comparator.run().".format(runner.name)) + finally: + process.terminate() + + if loader_cache is None: + G_LOGGER.critical("Could not send data loader cache to runner subprocess. Please try disabling subprocesses " + "by removing the --use-subprocess flag, or setting use_subprocess=False in Comparator.run()") + else: + run_results[runner.name] = execute_runner(runner, loader_cache) + + G_LOGGER.verbose("Successfully ran: {:}".format([r.name for r in runners])) + return run_results + + + @staticmethod + def postprocess(run_results, postprocess_func): + """ + Applies post processing to all the outputs in the provided run results. + This is a convenience function to avoid the need for manual iteration over the run_results dictionary. + + Args: + run_results (RunResults): The result of Comparator.run(). + postprocess_func (Callable(IterationResult) -> IterationResult): + The function to apply to each ``IterationResult``. + + Returns: + RunResults: The updated run results. + """ + for runner_name, results in run_results.items(): + for index, result in enumerate(results): + results[index] = postprocess_func(result) + return run_results + + + @staticmethod + def default_comparisons(run_results): + # Sets up default comparisons - which is to compare each runner to the subsequent one. + all_runners = list(run_results.keys()) + return [(r1, r2) for r1, r2 in zip(all_runners[:-1], all_runners[1:])] + + + @staticmethod + def compare_accuracy(run_results, fail_fast=False, comparisons=None, compare_func=None): + """ + Args: + run_results (RunResults): The result of Comparator.run() + + + fail_fast (bool): Whether to exit after the first failure + comparisons (List[Tuple[str, str]]): + Comparisons to perform, specified by runner names. For example, [(r0, r1), (r1, r2)] + would compare the runner named r0 with r1, and r1 with r2. + By default, this compares each result to the subsequent one. + compare_func (Callable(IterationResult, IterationResult) -> OrderedDict[str, bool]): + A function that takes in two IterationResults, and returns a dictionary that maps output + names to a boolean (or anything convertible to a boolean) indicating whether outputs matched. + The order of arguments to this function is guaranteed to be the same as the ordering of the + tuples contained in `comparisons`. + + Returns: + AccuracyResult: + A summary of the results of the comparisons. The order of the keys (i.e. runner pairs) is + guaranteed to be the same as the order of `comparisons`. For more details, see the AccuracyResult + docstring (e.g. help(AccuracyResult)). + """ + def find_mismatched(match_dict): + return [name for name, matched in match_dict.items() if not bool(matched)] + + compare_func = misc.default_value(compare_func, CompareFunc.basic_compare_func()) + comparisons = misc.default_value(comparisons, Comparator.default_comparisons(run_results)) + + accuracy_result = AccuracyResult() + for runner0_name, runner1_name in comparisons: + G_LOGGER.info("Accuracy Comparison | {:} vs. {:}".format(runner0_name, runner1_name)) + with G_LOGGER.indent(): + results0, results1 = run_results[runner0_name], run_results[runner1_name] + runner_pair = (runner0_name, runner1_name) + accuracy_result[runner_pair] = [] + + num_iters = min(len(results0), len(results1)) + for iteration, (result0, result1) in enumerate(zip(results0, results1)): + if num_iters > 1: + G_LOGGER.info("Iteration: {:}".format(iteration)) + with contextlib.ExitStack() as stack: + if num_iters > 1: + stack.enter_context(G_LOGGER.indent()) + iteration_match_dict = compare_func(result0, result1) + accuracy_result[runner_pair].append(iteration_match_dict) + + mismatched_outputs = find_mismatched(iteration_match_dict) + if fail_fast and mismatched_outputs: + return accuracy_result + + G_LOGGER.extra_verbose("Finished comparing {:} with {:}".format(runner0_name, runner1_name,)) + + passed, failed, total = accuracy_result.stats(runner_pair) + pass_rate = accuracy_result.percentage(runner_pair) * 100.0 + if num_iters > 1 or len(comparisons) > 1: + msg = "Accuracy Summary | {:} vs. {:} | Passed: {:}/{:} iterations | Pass Rate: {:}%".format( + runner0_name, runner1_name, passed, total, pass_rate) + if passed == total: + G_LOGGER.success(msg) + else: + G_LOGGER.error(msg) + return accuracy_result + + + @staticmethod + def validate(run_results, check_finite=None, check_nan=None, fail_fast=None): + """ + Checks output validity. + + Args: + run_results (Dict[str, List[IterationResult]]): The result of Comparator.run(). + check_finite (bool): Whether to fail on non-finite values. Defaults to False. + check_nan (bool): Whether to fail on NaNs. Defaults to True. + fail_fast (bool): Whether to fail after the first invalid value. Defaults to False. + + Returns: + bool: True if all outputs were valid, False otherwise. + """ + check_finite = misc.default_value(check_finite, False) + check_nan = misc.default_value(check_nan, True) + fail_fast = misc.default_value(fail_fast, False) + + + def is_finite(output): + non_finite = np.logical_not(np.isfinite(output)) + if np.any(non_finite): + G_LOGGER.error("Encountered one or more non-finite values") + G_LOGGER.error("Note: Use -vv or set logging verbosity to EXTRA_VERBOSE to display non-finite values", mode=LogMode.ONCE) + G_LOGGER.extra_verbose("Note: non-finite values at:\n{:}".format(non_finite)) + G_LOGGER.extra_verbose("Note: non-finite values:\n{:}".format(output[non_finite])) + return False + return True + + + def is_not_nan(output): + nans = np.isnan(output) + if np.any(nans): + G_LOGGER.error("Encountered one or more NaNs") + G_LOGGER.error("Note: Use -vv or set logging verbosity to EXTRA_VERBOSE to display locations of NaNs", mode=LogMode.ONCE) + G_LOGGER.extra_verbose("Note: NaNs at:\n{:}".format(nans)) + return False + return True + + + all_valid = True + for runner_name, results in run_results.items(): + for result in results: + for output_name, output in result.items(): + G_LOGGER.info("Runner: {:40} | Validating output: {:} (check_finite={:}, check_nan={:})".format( + runner_name, output_name, check_finite, check_nan)) + + output_valid = True + with G_LOGGER.indent(): + if check_nan: + output_valid &= is_not_nan(output) + if check_finite: + output_valid &= is_finite(output) + + all_valid &= output_valid + + if output_valid: + G_LOGGER.success("Runner: {:40} | Output: {:} is valid".format(runner_name, output_name)) + else: + G_LOGGER.error("Runner: {:40} | Errors detected in output: {:}".format(runner_name, output_name)) + if fail_fast: + return False + + if all_valid: + G_LOGGER.success("Validation passed") + else: + G_LOGGER.error("Validation failed") + return all_valid diff --git a/tools/Polygraphy/polygraphy/comparator/compare.py b/tools/Polygraphy/polygraphy/comparator/compare.py new file mode 100644 index 00000000..15336cef --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/compare.py @@ -0,0 +1,298 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import OrderedDict + +import numpy as np +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + + +class OutputCompareResult(object): + """ + Represents the result of comparing a single output of a single iteration + between two runners. + """ + def __init__(self, passed, required_atol, required_rtol): + """ + Records the required tolerances for the results to be considered equivalent. + + Args: + passed (bool): Whether the error was within acceptable limits. + required_atol (float): The minimum required absolute tolerance to consider the outputs equivalent. + required_rtol (float): The minimum required relative tolerance to consider the outputs equivalent. + """ + self.passed = passed + self.required_atol = required_atol + self.required_rtol = required_rtol + + + def __bool__(self): + """ + Whether the output matched. + + Returns: + bool + """ + return self.passed + + + def __str__(self): + return "(atol={:}, rtol={:})".format(self.required_atol, self.required_rtol) + + +# Provides functions to compare two IterationResults +class CompareFunc(object): + """ + Provides functions that can be used to compare two `IterationResult` s. + """ + + @staticmethod + def basic_compare_func(check_shapes=None, rtol=None, atol=None, fail_fast=None, find_output_func=None): + """ + Creates a function that compares two IterationResults, and can be used as the `compare_func` argument + in ``Comparator.compare_accuracy``. + This function uses ``np.isclose`` to determine whether outputs match. + For details, see https://docs.scipy.org/doc/numpy/reference/generated/numpy.isclose.html. + + Args: + check_shapes (bool): + Whether shapes must match exactly. If this is False, this function may + permute or reshape outputs before comparison. Defaults to True. + rtol (float): + The relative tolerance to use when checking accuracy. Defaults to 1e-5. + atol (float): + The absolute tolerance to use when checking accuracy. Defaults to 1e-5. + fail_fast (bool): + Whether the function should exit immediately after the first failure. Defaults to False. + find_output_func (Callable(str, int, IterationResult) -> List[str]): + A callback that returns a list of output names to compare against from the provided + IterationResult, given an output name and index from another IterationResult. + The comparison function will always iterate over the output names of the + first IterationResult, expecting names from the second. A return value of + `[]` or `None` indicates that the output should be skipped. + + Returns: + Callable(IterationResult, IterationResult) -> OrderedDict[str, OutputCompareResult]: + A callable that returns a mapping of output names to `OutputCompareResult` s, indicating + whether the corresponding output matched. + """ + check_shapes = misc.default_value(check_shapes, True) + rtol = misc.default_value(rtol, 1e-5) + atol = misc.default_value(atol, 1e-5) + fail_fast = misc.default_value(fail_fast, False) + + + def compare_output(iter_result0, iter_result1): + """ + Compare the outputs of two runners from a single iteration. + + This function will always iterate over the output names of the first IterationResult, + and attempt to find corresponding output names in the second. + If no corresponding output name is found, the output is skipped. + If all output names are skipped, then this function raises an error. + + Args: + iter_result0 (IterationResult): The result of the first runner. + iter_result1 (IterationResult): The result of the second runner. + + Returns: + OrderedDict[str, OutputCompareResult]: + The name of the outputs compared, derived from the first IterationResult, + and whether they matched. If an output name is not found, it is omitted from this dictionary. + + Raises: + PolygraphyException: If all output names are skipped, and thus no outputs are compared. + """ + # Returns whether the outputs match + def check_outputs_match(out0, out0_name, out1, out1_name): + def compute_max(buffer): + if misc.is_empty_shape(buffer.shape): + return 0 + return np.amax(buffer) + + # Returns index of max value + def compute_argmax(buffer): + if misc.is_empty_shape(buffer.shape): + return 0 + return np.unravel_index(np.argmax(buffer), buffer.shape) + + def compute_min(buffer): + if misc.is_empty_shape(buffer.shape): + return 0 + return np.amin(buffer) + + # Returns index of min value + def compute_argmin(buffer): + if misc.is_empty_shape(buffer.shape): + return 0 + return np.unravel_index(np.argmin(buffer), buffer.shape) + + def compute_mean(buffer): + if misc.is_empty_shape(buffer.shape): + return 0 + return np.mean(buffer) + + + def compute_required(): + # The purpose of this function is to determine the minimum tolerances such that + # the outputs would be considered a match. + # The NumPy formula for np.isclose is absolute(out0 - out1) <= (atol + rtol * absolute(out1)) + # So, for both absolute/relative tolerance, given either one, + # we can compute the required value for the other: + # atol = absolute(out0 - out1) + # atol_if_rtol = absolute(out0 - out1) - rtol * absolute(out1) + # rtol = (absolute(out0 - out1) - atol) / absolute(out1) + if np.issubdtype(out0.dtype, np.bool_) and np.issubdtype(out1.dtype, np.bool_): + absdiff = np.logical_xor(out0, out1) + else: + absdiff = np.abs(out0 - out1) + absout1 = np.abs(out1) + required_atol = max(compute_max(absdiff), 0.0) + required_atol_if_rtol = max(compute_max(absdiff - rtol * absout1), 0.0) + # Suppress divide by 0 warnings + with np.testing.suppress_warnings() as sup: + sup.filter(RuntimeWarning) + required_rtol = max(compute_max((absdiff - atol) / absout1), 0.0) + return required_atol, required_atol_if_rtol, required_rtol + + + def log_mismatches(mismatches): + try: + with G_LOGGER.indent(): + G_LOGGER.super_verbose("Mismatches at:\n" + str(mismatches)) + G_LOGGER.extra_verbose("Runner: {:40} | Mismatched values:\n{:}".format(iter_result0.runner_name, out0[mismatches])) + G_LOGGER.extra_verbose("Runner: {:40} | Mismatched values:\n{:}".format(iter_result1.runner_name, out1[mismatches])) + except: + G_LOGGER.warning("Failing to log mismatches - this may be because the outputs are of different shapes") + + + try: + mismatches = np.logical_not(np.isclose(output0, output1, rtol=rtol, atol=atol)) + except Exception as err: + G_LOGGER.warning("Failed to compare outputs with:\n{:}\nSkipping".format(err)) + return False + + G_LOGGER.super_verbose("Runner: {:40} | Output: {:} (dtype={:}, shape={:}):\n{:}".format( + iter_result0.runner_name, out0_name, out0.dtype, out0.shape, misc.indent_block(out0))) + G_LOGGER.super_verbose("Runner: {:40} | Output: {:} (dtype={:}, shape={:}):\n{:}".format( + iter_result1.runner_name, out1_name, out1.dtype, out1.shape, misc.indent_block(out1))) + + failed = np.any(mismatches) + + try: + required_atol, required_atol_if_rtol, required_rtol = compute_required() + except Exception as err: + required_atol, required_atol_if_rtol, required_rtol = None, None, None + G_LOGGER.warning("Could not determine required tolerances due to an error:\n{:}".format(err)) + log_msg = "" + else: + log_msg = "Required tolerances: [atol={:.5g}] OR [rtol={:.5g}, atol={:.5g}] OR [rtol={:.5g}, atol={:.5g}]\n".format( + required_atol, rtol, required_atol_if_rtol, required_rtol, atol) + + log_msg += "Runner: {:40} | Stats: mean={:.5g}, min={:.5g} at {:}, max={:.5g} at {:}\n".format( + iter_result0.runner_name, compute_mean(out0), compute_min(out0), compute_argmin(out0), compute_max(out0), compute_argmax(out0)) + log_msg += "Runner: {:40} | Stats: mean={:.5g}, min={:.5g} at {:}, max={:.5g} at {:}\n".format( + iter_result1.runner_name, compute_mean(out1), compute_min(out1), compute_argmin(out1), compute_max(out1), compute_argmax(out1)) + + if failed: + log_mismatches(mismatches) + G_LOGGER.info(log_msg) + G_LOGGER.error("FAILED | Difference exceeds tolerance (rtol={:}, atol={:})".format(rtol, atol)) + else: + G_LOGGER.verbose(log_msg) + G_LOGGER.success("PASSED | Difference is within tolerance (rtol={:}, atol={:})".format(rtol, atol)) + + G_LOGGER.extra_verbose("Finished comparing: '{:}' (dtype={:}, shape={:}) [{:}] and '{:}' (dtype={:}, shape={:}) [{:}]" + .format(out0_name, out0.dtype, out0.shape, iter_result0.runner_name, out1_name, out1.dtype, out1.shape, iter_result1.runner_name)) + return OutputCompareResult(not failed, required_atol, required_rtol) + + + output_status = OrderedDict() # OrderedDict[str, bool] Maps output names to whether they matched. + + if not check_shapes: + G_LOGGER.info("Strict shape checking disabled. Will attempt to match output shapes before comparisons") + + + def default_find_output_func(output_name, index, iter_result): + found_name = misc.find_in_dict(output_name, iter_result, index) + if found_name is None: + return None + elif found_name != output_name: + exact_match = misc.find_in_dict(found_name, iter_result0) + if exact_match == found_name: + G_LOGGER.verbose("Will not compare {:} with {:}, since the former already has an exact match: {:}".format( + found_name, output_name, exact_match)) + return None # If the found output is being compared against another output already, skip this non-exact match + G_LOGGER.warning("Output names did not match exactly. Assuming {:} output: {:} " + "corresponds to output: {:}".format( + iter_result.runner_name, found_name, output_name)) + return [found_name] + + + nonlocal find_output_func + find_output_func = misc.default_value(find_output_func, default_find_output_func) + + for index, (out0_name, output0) in enumerate(iter_result0.items()): + out1_names = misc.default_value(find_output_func(out0_name, index, iter_result1), []) + + if len(out1_names) > 1: + G_LOGGER.info("Will attempt to compare output: '{:}' [{:}] with multiple outputs: '{:}' [{:}]".format( + out0_name, iter_result0.runner_name, list(out1_names), iter_result1.runner_name)) + + for out1_name in out1_names: + if out1_name is None or out1_name not in iter_result1: + G_LOGGER.warning("For output: '{:}' [{:}], skipping corresponding output: '{:}' [{:}], " + "since the output was not found".format(out0_name, iter_result0.runner_name, + out1_name, iter_result1.runner_name)) + continue + + output1 = iter_result1[out1_name] + G_LOGGER.info("Comparing Output: '{:}' (dtype={:}, shape={:}) with '{:}' (dtype={:}, shape={:})".format( + out0_name, output0.dtype, output0.shape, out1_name, output1.dtype, output1.shape)) + G_LOGGER.extra_verbose("Note: Comparing {:} vs. {:}".format(iter_result0.runner_name, iter_result1.runner_name)) + + with G_LOGGER.indent(): + if check_shapes and output0.shape != output1.shape: + G_LOGGER.error("Will not compare outputs of different shapes. Note: Output shapes are " + "{:} and {:}.".format(output0.shape, output1.shape)) + G_LOGGER.error("Note: Use --no-strict-shape-checking or set check_shapes=False to " + "attempt to compare values anyway.", mode=LogMode.ONCE) + outputs_match = False + else: + output1 = misc.try_match_shape(output1, output0.shape) + output0 = output0.reshape(output1.shape) + outputs_match = check_outputs_match(output0, out0_name, output1, out1_name) + + output_status[out0_name] = outputs_match + if fail_fast and not outputs_match: + return output_status + + mismatched_output_names = [name for name, matched in output_status.items() if not matched] + if mismatched_output_names: + G_LOGGER.error("FAILED | Mismatched outputs: {:}".format(mismatched_output_names)) + + # This is useful for catching cases were Polygraphy does something wrong with the runner output buffers + if not output_status and (bool(iter_result0.keys()) or bool(iter_result1.keys())): + r0_name = iter_result0.runner_name + r0_outs = list(iter_result0.keys()) + r1_name = iter_result1.runner_name + r1_outs = list(iter_result1.keys()) + G_LOGGER.critical("All outputs were skipped, no common outputs found! Note:\n{:} outputs: " + "{:}\n{:} outputs: {:}".format(r0_name, r0_outs, r1_name, r1_outs)) + + return output_status + + return compare_output diff --git a/tools/Polygraphy/polygraphy/comparator/data_loader.py b/tools/Polygraphy/polygraphy/comparator/data_loader.py new file mode 100644 index 00000000..8242e533 --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/data_loader.py @@ -0,0 +1,275 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import contextlib +from collections import OrderedDict + +import numpy as np +from polygraphy.common.constants import DEFAULT_SEED +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + + +class BaseDataLoader(object): + """ + Responsible for fetching or generating input data for runners. + """ + def __getitem__(self, index): + """ + Fetches or generates inputs. + + Args: + index (int): The index of inputs to fetch. For any given index, the inputs should always be the same. + + Vars: + input_metadata (TensorMetadata): + Mapping of input names to their data types and shapes. + This is set by the Comparator when the data loader is used. + + Returns: + OrderedDict[str, np.ndarray]: Mapping of input names to NumPy buffers containing data. + """ + raise NotImplementedError("BaseDataLoader is an abstract class") + + +class DataLoader(BaseDataLoader): + def __init__(self, seed=None, iterations=None, input_metadata=None, + int_range=None, float_range=None): + """ + Args: + seed (int): + The seed to use when generating random inputs. + Defaults to ``util.constants.DEFAULT_SEED``. + iterations (int): + The number of iterations for which to supply data. + Defaults to 1. + input_metadata (TensorMetadata): + A mapping of input names to their corresponding shapes and data types. + This will be used to determine what shapes to supply for inputs with dynamic shape, as + well as to set the data type of the generated inputs. + If either dtype or shape are None, then the value will be automatically determined. + For input shape tensors, i.e. inputs whose *value* describes a shape in the model, the + provided shape will be used to populate the values of the inputs, rather than to determine + their shape. + int_range (Tuple[int]): + A tuple containing exactly 2 integers, indicating the minimum and maximum integer values (inclusive) + the data loader should generate. If either value in the tuple is None, the default will be used + for that value. + If None is provided instead of a tuple, then the default values will be used for both the + minimum and maximum. + float_range (Tuple[float]): + A tuple containing exactly 2 floats, indicating the minimum and maximum float values (inclusive) + the data loader should generate. If either value in the tuple is None, the default will be used + for that value. + If None is provided instead of a tuple, then the default values will be used for both the + minimum and maximum. + """ + def default_tuple(tup, default): + if tup is None: + return default + new_tup = [] + for elem, default_elem in zip(tup, default): + new_tup.append(misc.default_value(elem, default_elem)) + return tuple(new_tup) + + self.seed = misc.default_value(seed, DEFAULT_SEED) + self.iterations = misc.default_value(iterations, 1) + self.user_input_metadata = misc.default_value(input_metadata, {}) + self.int_range = default_tuple(int_range, (1, 25)) + self.float_range = default_tuple(float_range, (-1.0, 1.0)) + self.input_metadata = None + + + def __getitem__(self, index): + """ + Randomly generates input data. + + Args: + index (int): + Since this class behaves like an iterable, it takes an index parameter. + Generated data is guaranteed to be the same for the same index. + + Returns: + OrderedDict[str, np.ndarray]: A mapping of input names to input numpy buffers. + """ + if index >= self.iterations: + raise IndexError() + + G_LOGGER.verbose("Generating data using numpy seed: {:}".format(self.seed + index)) + rng = np.random.RandomState(self.seed + index) + + + def get_static_shape(name, shape): + static_shape = shape + if misc.is_shape_dynamic(shape): + static_shape = misc.override_dynamic_shape(shape) + if static_shape != shape and name not in self.user_input_metadata: + if not misc.is_valid_shape_override(static_shape, shape): + G_LOGGER.critical("Input tensor: {:24} | Cannot override original shape: {:} to {:}".format(name, shape, static_shape)) + G_LOGGER.warning("Input tensor: {:24} | Adjusted shape: {:} to: {:}. If this is incorrect, please set input_metadata " + "or provide a custom data loader.".format(name, shape, static_shape), mode=LogMode.ONCE) + return static_shape + + + # Whether the user provided the values for a shape tensor input, + # rather than the shape of the input. + # If the shape is 1D, and has a value equal to the rank of the provided default shape, it is + # likely to be a shape tensor, and so its value, not shape, should be overriden. + def is_shape_tensor(name, dtype): + if name not in self.input_metadata or name not in self.user_input_metadata: + return False + + _, shape = self.input_metadata[name] + is_shape = np.issubdtype(dtype, np.integer) and (not misc.is_shape_dynamic(shape)) and (len(shape) == 1) + + user_shape = self.user_input_metadata[name][1] + is_shape &= len(user_shape) == shape[0] + # Can't have negative values in shapes + is_shape &= all([elem >= 0 for elem in user_shape]) + return is_shape + + + def generate_buffer(name, dtype, shape): + if is_shape_tensor(name, dtype): + buffer = np.array(shape, dtype=dtype) + G_LOGGER.info("Assuming {:} is a shape tensor. Setting input values to: {:}. If this is not correct, " + "please set it correctly in 'input_metadata' or by providing --input-shapes".format(name, buffer), mode=LogMode.ONCE) + elif np.issubdtype(dtype, np.integer): + # high is 1 greater than the max int drawn + buffer = rng.randint(low=self.int_range[0], high=self.int_range[1] + 1, size=shape, dtype=dtype) + elif np.issubdtype(dtype, np.bool_): + buffer = rng.randint(low=0, high=2, size=shape).astype(dtype) + else: + buffer = (rng.random_sample(size=shape) * (self.float_range[1] - self.float_range[0]) + self.float_range[0]).astype(dtype) + + buffer = np.array(buffer) # To handle scalars, since the above functions return a float if shape is (). + return buffer + + + if self.input_metadata is None and self.user_input_metadata is not None: + self.input_metadata = self.user_input_metadata + + buffers = OrderedDict() + for name, (dtype, shape) in self.input_metadata.items(): + if name in self.user_input_metadata: + user_dtype, user_shape = self.user_input_metadata[name] + + is_valid_shape_override = user_shape is not None and misc.is_valid_shape_override(user_shape, shape) + if not is_valid_shape_override and not is_shape_tensor(name, dtype): + G_LOGGER.warning("Input tensor: {:24} | Cannot use provided custom shape: {:}, since this input has " + "a static shape: {:}".format(name, user_shape, shape), mode=LogMode.ONCE) + else: + shape = misc.default_value(user_shape, shape) + + dtype = misc.default_value(user_dtype, dtype) + + static_shape = get_static_shape(name, shape) + buffers[name] = generate_buffer(name, dtype, shape=static_shape) + + # Warn about unused metadata + for name in self.user_input_metadata.keys(): + if name not in self.input_metadata: + msg = "Input tensor: {:24} | Metadata was provided, but the input does not exist in one or more runners.".format(name) + close_match = misc.find_in_dict(name, self.input_metadata) + if close_match: + msg += "\nMaybe you meant to set: {:}".format(close_match) + G_LOGGER.warning(msg) + + return buffers + + +# Caches data loaded by a DataLoader for use across multiple runners. +class DataLoaderCache(object): + def __init__(self, data_loader): + self.data_loader = data_loader + self.cache = [] # List[OrderedDict[str, np.ndarray]] + + + def __getitem__(self, iteration): + """ + Load the specified iteration from the cache if present, or generate using the data loader. + + Args: + iteration (int): The iteration whose data to retrieve. + """ + if iteration > len(self.cache): + raise IndexError() + + # Attempts to match existing input buffers to the requested input_metadata + def coerce_cached_input(index, name, dtype, shape): + cached_feed_dict = self.cache[iteration] + cached_name = misc.find_in_dict(name, cached_feed_dict, index) + assert cached_name is not None + + if cached_name != name: + G_LOGGER.warning("Input tensor: {:24} | Cached buffer name ({:}) does not match input name ({:}).".format( + name, cached_name, name)) + + buffer = cached_feed_dict[cached_name] + + if dtype != buffer.dtype: + G_LOGGER.warning("Input tensor: {:24} | Cached buffer dtype ({:}) does not match input dtype ({:}), attempting cast. ".format( + name, buffer.dtype, np.dtype(dtype).name)) + buffer = buffer.astype(dtype) + + if not misc.is_valid_shape_override(buffer.shape, shape): + G_LOGGER.warning("Input tensor: {:24} | Cached buffer shape ({:}) does not match input shape ({:}), attempting reshape. ".format( + name, buffer.shape, shape)) + buffer = misc.try_match_shape(buffer, shape) + + assert buffer.dtype == dtype and misc.is_valid_shape_override(buffer.shape, shape) + return buffer + + + feed_dict = OrderedDict() + + # Reload from data loader if needed + data_loader_feed_dict = None + + for index, (name, (dtype, shape)) in enumerate(self.input_metadata.items()): + try: + buffer = coerce_cached_input(index, name, dtype, shape) + except AssertionError: + G_LOGGER.warning("Could not reuse input: {:} across runners. Attempting to reload " + "inputs from the data loader. Note that this will only work if the data loader " + "supports random access.".format(name)) + try: + if data_loader_feed_dict is None: + data_loader_feed_dict = self.data_loader[iteration] + buffer = data_loader_feed_dict[name] + except: + G_LOGGER.critical("Could not reload inputs from data loader. Are the runners running the same model? " + "If not, please rewrite the data loader to support random access.") + feed_dict[name] = buffer + + return feed_dict + + + def set_input_metadata(self, input_metadata): + """ + Set the input metadata for the data loader. + + Args: + input_metadata (TensorMetadata): + Input Metadata, including shape and type information. The cache may attempt to transform inputs to + match the specified input_metadata when data already in the cache does not exactly match. + """ + self.input_metadata = input_metadata + with contextlib.suppress(AttributeError): self.data_loader.input_metadata = input_metadata + if not self.cache: + G_LOGGER.verbose("Loading inputs from data loader") + self.cache = list(self.data_loader) + if not self.cache: + G_LOGGER.warning("Data loader did not yield any input data.") diff --git a/tools/Polygraphy/polygraphy/comparator/postprocess.py b/tools/Polygraphy/polygraphy/comparator/postprocess.py new file mode 100644 index 00000000..d86b1532 --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/postprocess.py @@ -0,0 +1,62 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import numpy as np +from polygraphy.util import misc + + +class PostprocessFunc(object): + """ + Provides functions that can apply post-processing to `IterationResult` s. + """ + + @staticmethod + # This function returns a top_k function that can be used as a postprocess_func. + def topk_func(k=10, axis=-1, outputs=None, exclude=None): + """ + Creates a function that applies a Top-K operation to a IterationResult. + Top-K will return the indices of the k largest values in the array. + + Args: + k (int): + The number of indices to keep. + If this exceeds the axis length, it will be clamped. + Defaults to 10. + axis (int): + The axis along which to apply the topk. + Defaults to -1. + outputs (Sequence[str]): + Names of outputs to apply top-k to. + Defaults to all outputs. + exclude (Sequence[str]): + Names of outputs to exclude. Top-K will not be applied to these outputs. + + Returns: + Callable(IterationResult) -> IterationResult: The top-k function. + """ + exclude = set(misc.default_value(exclude, [])) + + # Top-K implementation. + def topk(run_result): + nonlocal outputs + outputs = set(misc.default_value(outputs, run_result.keys())) + + for name, output in run_result.items(): + if name in outputs and name not in exclude: + indices = np.argsort(-output, axis=axis) + axis_len = indices.shape[axis] + run_result[name] = np.take(indices, np.arange(0, min(k, axis_len)), axis=axis) + return run_result + return topk diff --git a/tools/Polygraphy/polygraphy/comparator/struct.py b/tools/Polygraphy/polygraphy/comparator/struct.py new file mode 100644 index 00000000..f0c48def --- /dev/null +++ b/tools/Polygraphy/polygraphy/comparator/struct.py @@ -0,0 +1,140 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import OrderedDict + +from polygraphy.util import misc + + +class IterationResult(OrderedDict): + def __init__(self, outputs=None, runtime=None, runner_name=None): + """ + An ordered dictionary containing the result of a running a single iteration of a runner. + + This maps output names to NumPy arrays, and preserves the output ordering from the runner. + + Also includes additional fields indicating the name of the runner which produced the + outputs, and the time required to do so. + + + Args: + outputs (Dict[str, np.array]): The outputs of this iteration, mapped to their names. + + + runtime (float): The time required for this iteration, in seconds. + runner_name (str): The name of the runner that produced this output. + """ + # IMPORTANT: This class must be pickleable. + initial = misc.default_value(outputs, {}) + # Before 3.6, OrderedDict.update() did not preserve ordering + for key, val in initial.items(): + self[key] = val + self.runtime = runtime + self.runner_name = misc.default_value(runner_name, "") + + +class RunResults(OrderedDict): + def __init__(self): + """ + An ordered dictionary that maps runners, by name, to zero or more IterationResults. + + More specifically, it is an OrderedDict[str, List[IterationResult]]. + """ + pass + + +class AccuracyResult(OrderedDict): + """ + An ordered dictionary including details about the result of `Comparator.compare_accuracy`. + + More specifically, it is an OrderedDict[Tuple[str, str], List[OrderedDict[str, bool]]] which maps a runner + pair (a tuple containing both runner names) to a list of dictionaries of booleans (or anything that can be + converted into a boolean, such as an OutputCompareResult), indicating whether there was a match in the outputs of + the corresponding iteration. The List[OrderedDict[str, bool]] is constructed from the dictionaries returned + by `compare_func` in `compare_accuracy`. + + For example, to see if there was a match in "output0" between "runner0" and + "runner1" on the 1st iteration, you would do the following: + :: + + output_name = "output0" + runner_pair = ("runner0", "runner1") + iteration = 0 + match = bool(accuracy_result[runner_pair][iteration][output_name]) + + In case there's a mismatch, you would be able to inspect the outputs in question by accessing + the results from Comparator.run() (assumed here to be called run_results): + :: + + runner0_output = run_results["runner0"][iteration][output_name] + runner1_output = run_results["runner1"][iteration][output_name] + """ + def __bool__(self): + """ + Whether all outputs matched for every iteration. + You can use this function to avoid manually checking each output. For example: + :: + + if accuracy_result: + print("All matched!") + + Returns: + bool + """ + return all([bool(match) for outs in self.values() for out in outs for match in out.values()]) + + + def _get_runner_pair(self, runner_pair): + return misc.default_value(runner_pair, list(self.keys())[0]) + + + def percentage(self, runner_pair=None): + """ + Returns the percentage of iterations that matched for the given pair of runners, + expressed as a decimal between 0.0 and 1.0. + + Always returns 1.0 when the number of iterations is 0, or when there are no runner comparisons. + + Args: + runner_pair ((str, str)): + A pair of runner names describing which runners to check. + Defaults to the first pair in the dictionary. + """ + if not list(self.keys()): + return 1.0 # No data in this result. + + matched, _, total = self.stats(runner_pair) + if not total: + return 1.0 # No iterations + return float(matched) / float(total) + + + def stats(self, runner_pair=None): + """ + Returns the number of iterations that matched, mismatched, and the total number of iterations. + + Args: + runner_pair ((str, str)): + A pair of runner names describing which runners to check. + Defaults to the first pair in the dictionary. + + Returns: + (int, int, int): Number of iterations that matched, mismatched, and total respectively. + """ + runner_pair = self._get_runner_pair(runner_pair) + outs = self[runner_pair] + matched = sum([all([match for match in out.values()]) for out in outs]) + total = len(outs) + return matched, total - matched, total diff --git a/tools/Polygraphy/polygraphy/logger/__init__.py b/tools/Polygraphy/polygraphy/logger/__init__.py new file mode 100644 index 00000000..8da78f8c --- /dev/null +++ b/tools/Polygraphy/polygraphy/logger/__init__.py @@ -0,0 +1 @@ +from polygraphy.logger.logger import G_LOGGER, LogMode diff --git a/tools/Polygraphy/polygraphy/logger/logger.py b/tools/Polygraphy/polygraphy/logger/logger.py new file mode 100644 index 00000000..67cfa934 --- /dev/null +++ b/tools/Polygraphy/polygraphy/logger/logger.py @@ -0,0 +1,291 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import inspect +import enum +import time +import sys +import os + + +try: + ModuleNotFoundError +except: + ModuleNotFoundError = ImportError + + +COLORED_MODULE_PRESENT = None +def has_colors(): + global COLORED_MODULE_PRESENT + if COLORED_MODULE_PRESENT is None: + try: + import colored + COLORED_MODULE_PRESENT = True + except (ImportError, ModuleNotFoundError): + COLORED_MODULE_PRESENT = False + print("[W] 'colored' module is not installed, will not use colors when logging. " + "To enable colors, please install the 'colored' module: python3 -m pip install colored") + return COLORED_MODULE_PRESENT + + +# Context manager to apply indentation to messages +class LoggerIndent(object): + def __init__(self, logger, indent): + self.logger = logger + self.old_indent = self.logger.logging_indent + self.indent = indent + + def __enter__(self): + self.logger.logging_indent = self.indent + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.logger.logging_indent = self.old_indent + + +# Context manager to temporarily set verbosity +class LoggerVerbosity(object): + def __init__(self, logger, severity): + self.logger = logger + self.old_severity = self.logger.severity + self.severity = severity + + def __enter__(self): + self.logger.severity = self.severity + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.logger.severity = self.old_severity + + +class LogMode(enum.IntEnum): + EACH = 0 # Log the message each time + ONCE = 1 # Log the message only once. The same message will not be logged again. + + +class Logger(object): + ULTRA_VERBOSE = -20 # Cast it into the flames! + SUPER_VERBOSE = -10 + EXTRA_VERBOSE = 0 + VERBOSE = 10 + INFO = 20 + SUCCESS = 21 + WARNING = 30 + ERROR = 40 + CRITICAL = 50 + + SEVERITY_LETTER_MAPPING = { + ULTRA_VERBOSE: "[U]", + SUPER_VERBOSE: "[P]", + EXTRA_VERBOSE: "[X]", + VERBOSE: "[V]", + INFO: "[I]", + SUCCESS: "[S]", + WARNING: "[W]", + ERROR: "[E]", + CRITICAL: "[C]", + } + + SEVERITY_COLOR_MAPPING = { + ULTRA_VERBOSE: "dark_green", + SUPER_VERBOSE: "green", + EXTRA_VERBOSE: "cyan", + VERBOSE: "dark_gray", + INFO: None, + SUCCESS: "light_green", + WARNING: "light_yellow", + ERROR: "light_red", + CRITICAL: "light_red", + } + + def __init__(self, severity=INFO, colors=True, letter=True, timestamp=False, line_info=False, exit_on_errors=False): + """ + Logger. + + Args: + severity (Logger.Severity): Messages below this severity are ignored. + colors (bool): Whether to use colored output. + letter (bool): Whether to prepend each logging message with a letter indicating it's severity. Defaults to True. + timestamp (bool): Whether to include a timestamp in the logging output. Defaults to False. + line_info (bool): Whether to include file and line number information in the logging output. Defaults to False. + """ + self._severity = severity + self.logging_indent = 0 + self.root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)) + self.once_logged = set() + self.colors = colors + self.letter = letter + self.timestamp = timestamp + self.line_info = line_info + self.exit_on_errors = exit_on_errors + self.logger_callbacks = [] + + + @property + def severity(self): + return self._severity + + + @severity.setter + def severity(self, value): + self._severity = value + for callback in self.logger_callbacks: + callback(self._severity) + + + def register_callback(self, callback): + """ + Registers a callback with the logger, which will be invoked when the logging severity is modified. + The callback is guaranteed to be called at least once in the register_callback function. + + Args: + callback (Callable(Logger.Severity)): A callback that accepts the current logger severity. + """ + callback(self._severity) + self.logger_callbacks.append(callback) + + + def indent(self, level=1): + """ + Returns a context manager that indents all strings logged by the specified amount. + """ + return LoggerIndent(self, level + self.logging_indent) + + + def verbosity(self, severity=CRITICAL): + """ + Returns a context manager that temporarily changes the severity of the logger for its duration. + + Args: + severity (Logger.Severity): + The severity to set the logger to. Defaults to Logger.CRITICAL, which will suppress all messages. + """ + return LoggerVerbosity(self, severity) + + + # If once is True, the logger will only log this message a single time. Useful in loops. + # message may be a callable which returns a message. This way, only if the message needs to be logged is it ever generated. + def log(self, message, severity, mode=LogMode.EACH, stack_depth=2): + def process_message(message, stack_depth): + def get_prefix(): + def get_line_info(): + adjusted_stack_depth = stack_depth + adjusted_stack_depth += 2 + module = inspect.getmodule(sys._getframe(adjusted_stack_depth)) + # Handle logging from the top-level of a module. + if not module: + adjusted_stack_depth -= 1 + module = inspect.getmodule(sys._getframe(adjusted_stack_depth)) + filename = module.__file__ + filename = os.path.relpath(filename, self.root_dir) + # If the file is not located in polygraphy, use its basename instead. + if os.pardir in filename: + filename = os.path.basename(filename) + return "[{:}:{:}] ".format(filename, sys._getframe(adjusted_stack_depth).f_lineno) + + prefix = "" + if self.letter: + prefix += Logger.SEVERITY_LETTER_MAPPING[severity] + " " + if self.timestamp: + prefix += "({:}) ".format(time.strftime("%X")) + if self.line_info: + prefix += get_line_info() + return prefix + + + def apply_indentation(prefix, message): + from polygraphy.common import constants + + message_lines = str(message).splitlines() + tab = constants.TAB * self.logging_indent + newline_tab = "\n" + tab + " " * len(prefix) + return tab + newline_tab.join([line for line in message_lines]) + + + def apply_color(message): + if self.colors and has_colors(): + import colored + color = Logger.SEVERITY_COLOR_MAPPING[severity] + return colored.stylize(message, [colored.fg(color)]) if color else message + return message + + + prefix = get_prefix() + message = apply_indentation(prefix, message) + return apply_color("{:}{:}".format(prefix, message)) + + + def should_log(message): + should = severity >= self._severity + if should and mode == LogMode.ONCE: + message_hash = hash(message) + should &= message_hash not in self.once_logged + self.once_logged.add(message_hash) + return should + + + if not should_log(message): + return + + if callable(message): + message = message() + message = str(message) + print(process_message(message, stack_depth=stack_depth)) + + + def ultra_verbose(self, message, mode=LogMode.EACH): + self.log(message, Logger.ULTRA_VERBOSE, mode=mode, stack_depth=3) + + + def super_verbose(self, message, mode=LogMode.EACH): + self.log(message, Logger.SUPER_VERBOSE, mode=mode, stack_depth=3) + + + def extra_verbose(self, message, mode=LogMode.EACH): + self.log(message, Logger.EXTRA_VERBOSE, mode=mode, stack_depth=3) + + + def verbose(self, message, mode=LogMode.EACH): + self.log(message, Logger.VERBOSE, mode=mode, stack_depth=3) + + + def info(self, message, mode=LogMode.EACH): + self.log(message, Logger.INFO, mode=mode, stack_depth=3) + + + def success(self, message, mode=LogMode.EACH): + self.log(message, Logger.SUCCESS, mode=mode, stack_depth=3) + + + def warning(self, message, mode=LogMode.EACH): + self.log(message, Logger.WARNING, mode=mode, stack_depth=3) + + + def error(self, message, mode=LogMode.EACH): + self.log(message, Logger.ERROR, mode=mode, stack_depth=3) + + + # Like error, but immediately exits. + def critical(self, message): + self.log(message, Logger.CRITICAL, stack_depth=3) + if self.exit_on_errors: + sys.exit(1) + else: + from polygraphy.common import PolygraphyException + raise PolygraphyException("Error encountered - see logging output for details") from None # Erase exception chain + + +global G_LOGGER +G_LOGGER = Logger() diff --git a/tools/Polygraphy/polygraphy/tools/README.md b/tools/Polygraphy/polygraphy/tools/README.md new file mode 100644 index 00000000..f1ba4fd4 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/README.md @@ -0,0 +1,37 @@ +# Polygraphy Tools + +## Table of Contents + +- [Introduction](#introduction) +- [Usage](#usage) +- [Examples](#examples) +- [Adding New Tools](#adding-new-tools) + + +## Introduction + +This directory includes command-line tools from the Polygraphy debugging toolkit. + +For more information about a specific tool, see the README in the corresponding directory here. + +Note that some of the tools included are still experimental. Any tool labeled `[EXPERIMENTAL]` +may be subject to backwards-incompatible changes, or even complete removal at any point in time. + + +## Usage + +All the tools provided by Polygraphy can be invoked using the polygraphy binary: [`bin/polygraphy`](../../bin/polygraphy) + +For usage information on a specific tool, you can see the help output with: `polygraphy -h` + + +## Examples + +For examples, see the corresponding subdirectory in [examples/cli](../../examples/cli) + + +## Adding New Tools + +You can add a new tool by adding a new file in this directory, creating a +class that extends the [`Tool` base class](./base/tool.py), and adding +the new tool to the [registry](./registry.py). diff --git a/tools/Polygraphy/polygraphy/tools/__init__.py b/tools/Polygraphy/polygraphy/tools/__init__.py new file mode 100644 index 00000000..f745f46d --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/__init__.py @@ -0,0 +1,2 @@ +from polygraphy.tools.base import Tool +from polygraphy.tools.registry import TOOL_REGISTRY diff --git a/tools/Polygraphy/polygraphy/tools/base/__init__.py b/tools/Polygraphy/polygraphy/tools/base/__init__.py new file mode 100644 index 00000000..a30d52db --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/base/__init__.py @@ -0,0 +1 @@ +from polygraphy.tools.base.tool import Tool diff --git a/tools/Polygraphy/polygraphy/tools/base/tool.py b/tools/Polygraphy/polygraphy/tools/base/tool.py new file mode 100644 index 00000000..523dad89 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/base/tool.py @@ -0,0 +1,45 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import argparse + +from polygraphy.tools.util import args as args_util + +class Tool(object): + def __init__(self): + self.__doc__ = None + self.name = None + + + def add_parser_args(self, parser): + pass + + + def setup_parser(self, subparsers): + add_help = True + tool_help = self.__doc__ if self.__doc__ is not None else "" + if not tool_help.strip(): + add_help = False + tool_help = "" + + parser = subparsers.add_parser(self.name, help=tool_help, add_help=add_help, description=tool_help) + parser.set_defaults(subcommand=self) + + self.add_parser_args(parser) + args_util.add_logger_args(parser) + + + def __call__(self, args): + raise NotImplementedError("Tool is an abstract class. Please implement this function for your tool") diff --git a/tools/Polygraphy/polygraphy/tools/inspect/README.md b/tools/Polygraphy/polygraphy/tools/inspect/README.md new file mode 100644 index 00000000..42f1b742 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/inspect/README.md @@ -0,0 +1,32 @@ +# Inspect + +## Table of Contents + +- [Introduction](#introduction) +- [Subtools](#subtools) +- [Usage](#usage) +- [Examples](#examples) + + +## Introduction + +The `inspect` tool can be used to display information about supported types of files. + + +## Subtools + +- `model` displays information about models, such as network structure. + This can be useful in case you are unfamiliar with the model, and need to know details about + the inputs or outputs, or layers. +- `results` displays information about `RunResults` from `Comparator.run()` (like those written by + `polygraphy run --save-outputs`). + + +## Usage + +See `polygraphy inspect -h` for usage information. + + +## Examples + +For examples, see [this directory](../../../examples/cli/inspect) diff --git a/tools/Polygraphy/polygraphy/tools/inspect/__init__.py b/tools/Polygraphy/polygraphy/tools/inspect/__init__.py new file mode 100644 index 00000000..cc39c15d --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/inspect/__init__.py @@ -0,0 +1 @@ +from polygraphy.tools.inspect.inspect import Inspect diff --git a/tools/Polygraphy/polygraphy/tools/inspect/inspect.py b/tools/Polygraphy/polygraphy/tools/inspect/inspect.py new file mode 100644 index 00000000..74b6e900 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/inspect/inspect.py @@ -0,0 +1,173 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.tools.util import args as args_util, misc as tool_util +from polygraphy.common import constants, TensorMetadata +from polygraphy.tools.base import Tool +from polygraphy.util import misc +from polygraphy.logger import G_LOGGER + + +def inspect_trt(args): + from polygraphy.backend.trt import util as trt_util + + if args.model_type == "engine": + if args.mode != "none": + G_LOGGER.warning("Displaying layer information for TensorRT engines is not currently supported") + + with tool_util.get_trt_serialized_engine_loader(args)() as engine: + engine_str = trt_util.str_from_engine(engine) + G_LOGGER.info("==== TensorRT Engine ====\n{:}".format(engine_str)) + else: + builder, network, parser = tool_util.get_trt_network_loader(args)() + with builder, network, parser: + network_str = trt_util.str_from_network(network, mode=args.mode) + G_LOGGER.info("==== TensorRT Network ====\n{:}".format(network_str)) + + +def inspect_onnx(args): + from polygraphy.backend.onnx import util as onnx_util + + onnx_model = tool_util.get_onnx_model_loader(args)() + model_str = onnx_util.str_from_onnx(onnx_model, mode=args.mode) + G_LOGGER.info("==== ONNX Model ====\n{:}".format(model_str)) + + +def inspect_tf(args): + from polygraphy.backend.tf import util as tf_util + + tf_graph, _ = tool_util.get_tf_model_loader(args)() + graph_str = tf_util.str_from_graph(tf_graph, mode=args.mode) + G_LOGGER.info("==== TensorFlow Graph ====\n{:}".format(graph_str)) + + +################################# SUBTOOLS ################################# + +class STModel(Tool): + """ + Display information about a model, including inputs and outputs, as well as layers and their attributes. + """ + def __init__(self): + self.name = "model" + + + def add_parser_args(self, parser): + parser.add_argument("--convert-to", "--display-as", help="Convert the model to the specified format before displaying", + choices=["onnx", "trt"], dest="display_as") + parser.add_argument("--mode", "--layer-info", help="Display layers: {{" + "'none': Display no layer information, " + "'basic': Display layer inputs and outputs, " + "'attrs': Display layer inputs, outputs and attributes, " + "'full': Display layer inputs, outputs, attributes, and weights" + "}}", + choices=["none", "basic", "attrs", "full"], dest="mode", default="none") + args_util.add_model_args(parser, model_required=True, inputs=False) + args_util.add_trt_args(parser, write=False, config=False, outputs=False) + args_util.add_tf_args(parser, tftrt=False, artifacts=False, runtime=False, outputs=False) + args_util.add_onnx_args(parser, outputs=False) + args_util.add_tf_onnx_args(parser) + + + def __call__(self, args): + func = None + + if args.model_type in ["frozen", "keras", "ckpt"]: + func = inspect_tf + + if args.model_type == "onnx" or args.display_as == "onnx": + func = inspect_onnx + + if args.model_type == "engine" or args.display_as == "trt": + func = inspect_trt + + if func is None: + G_LOGGER.critical("Could not determine how to display this model. Maybe you need to specify --display-as?") + func(args) + + +class STResults(Tool): + """ + Display information about results saved from Polygraphy's Comparator.run() + (for example, outputs saved by `--save-results` from `polygraphy run`). + """ + def __init__(self): + self.name = "results" + + + def add_parser_args(self, parser): + parser.add_argument("results", help="Path to a file containing Comparator.run() results from Polygraphy") + parser.add_argument("--all", help="Show information on all iterations present in the results instead of just the first", + action="store_true") + parser.add_argument("-s", "--show-values", help="Show values of output tensors instead of just metadata", action="store_true") + + + def __call__(self, args): + run_results = misc.pickle_load(args.results) + + def meta_from_iter_result(iter_result): + meta = TensorMetadata() + for name, arr in iter_result.items(): + meta.add(name, dtype=arr.dtype, shape=arr.shape) + return meta + + + results_str = "" + results_str += "==== Run Results ({:} runners) ====\n\n".format(len(run_results)) + + for runner_name, iters in run_results.items(): + results_str += "---- Runner: {:} ({:} iterations) ----\n".format(runner_name, len(iters)) + + for index, iter_result in enumerate(iters): + if args.show_values: + for name, arr in iter_result.items(): + results_str += "{:} [dtype={:}, shape={:}]\n{:}\n\n".format(name, arr.dtype, arr.shape, misc.indent_block(str(arr))) + else: + iter_meta = meta_from_iter_result(iter_result) + if len(iters) > 1 and args.all: + results_str += misc.indent_block("Iteration: {:} | ".format(index)) + results_str += "{:}\n".format(iter_meta) + + if not args.all: + break + results_str += "\n" + results_str = misc.indent_block(results_str, level=0) + G_LOGGER.info(results_str) + + +################################# MAIN TOOL ################################# + +class Inspect(Tool): + """ + Display information about supported files. + """ + def __init__(self): + self.name = "inspect" + + + def add_parser_args(self, parser): + subparsers = parser.add_subparsers(title="Inspection Subtools", dest="subtool") + subparsers.required = True + + SUBTOOLS = [ + STModel(), + STResults(), + ] + + for subtool in SUBTOOLS: + subtool.setup_parser(subparsers) + + + def __call__(self, args): + pass diff --git a/tools/Polygraphy/polygraphy/tools/precision/README.md b/tools/Polygraphy/polygraphy/tools/precision/README.md new file mode 100644 index 00000000..ec6ae663 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/precision/README.md @@ -0,0 +1,86 @@ +# [EXPERIMENTAL] Precision + +## Table of Contents + +- [Introduction](#introduction) +- [Usage](#usage) +- [Modes](#modes) +- [Examples](#examples) + - [Debugging An Inaccurate Network](#debugging-an-inaccurate-network) + + +## Introduction + +The `precision` tool can be used to determine which layers of a TensorRT network need to be +run in a higher precision in order to maintain the desired accuracy. + +The tool works by iteratively marking a subset of the layers in the network in the specified +higher precision (`float32` by default). Then, it checks the accuracy against golden outputs +and attempts to find the minimum number of layers that should be run in a higher precision. + +The golden outputs required can be generated separately by other Polygraphy tools, such as `polygraphy run` + +**NOTE:** You can greatly increase the speed of this tool by allowing it to create a calibration +cache. See step 2 in the [example](#debugging-an-inaccurate-network) for details. + + +## Usage + +See `polygraphy precision -h` for usage information. + + +## Modes + +This tool includes several different subtools, or modes, which control how it decides which layers +to mark in a higher precision. + +- `bisect`: Performs a binary search by marking groups of adjacent layers in the network. For example, it + may mark the first N layers in FP32. If the network passes, then it will attempt to mark only N/2 layers. + If that passes, it will mark N/4 layers, otherwise, 3N/4, and so on. +- `linear`: Performs a linear search by marking layers one at a time. For example, it may mark only the 1st layer + in FP32. If that fails, then it will mark the 2nd, then the 3rd, 4th, and so on. +- `worst-first`: Attempts to find the layers that introduce the most error (i.e. the layers that see the greatest + increase in required tolerance compared to the previous layer) and marks the top N worst layers first. + + **NOTE:** This mode depends on being able to mark every layer of the network as an output. Additionally, it + assumes that subsequent outputs come from subsequent layers, and therefore may not correctly find the worst layers + if layers produce multiple outputs. + + +## Examples + +### Debugging An Inaccurate Network + +In this example, we assume that `model.onnx` is a model that is not working when all layers +are marked to run in `int8` precision. + +1. Generate the golden outputs using ONNX Runtime: + + ```bash + polygraphy run model.onnx --onnxrt --save-results=golden.pkl + ``` + +2. Use `precision`'s `bisect` mode to perform a binary search over the model. + + ```bash + polygraphy precision bisect model.onnx --golden=golden.pkl --mode=forward --int8 --precision=float32 --calibration-cache=model_calibration.cache + ``` + + `--mode=forward` indicates that the algorithm will mark layers to run in a + higher precision starting from the inputs of the network. You can use `reverse` to + mark layers starting from the outputs of the networks. + + `--precision=float32` indicates that the higher precision to mark layers with should be + `trt.float32`. You can try using lower precisions like `float16` for better performance. + + `--calibration-cache` allows you to save calibration data to the disk, which eliminates the + need to generate it each time. It is **strongly recommended** to use this option, as it can greatly + speed up the process. + +3. After `precision` completes, if acceptable accuracy was achieved, you should see a message like: + ``` + [I] To achieve acceptable accuracy, try running the first 36 layers in DataType.FLOAT precision + ``` + + `precision` will also display information about tolerances + required for each iteration, and information about which layers it ran in a higher precision. diff --git a/tools/Polygraphy/polygraphy/tools/precision/__init__.py b/tools/Polygraphy/polygraphy/tools/precision/__init__.py new file mode 100644 index 00000000..e77ee522 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/precision/__init__.py @@ -0,0 +1 @@ +from polygraphy.tools.precision.precision import Precision diff --git a/tools/Polygraphy/polygraphy/tools/precision/precision.py b/tools/Polygraphy/polygraphy/tools/precision/precision.py new file mode 100644 index 00000000..40e0d109 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/precision/precision.py @@ -0,0 +1,305 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.tools.util import misc as tool_util, args as args_util +from polygraphy.tools.base import Tool +from polygraphy.util import misc +from polygraphy.logger import G_LOGGER + +from collections import OrderedDict +import math + + +################################# SUBTOOLS ################################# + + +# Subtool base class for accuracy checkers +class STCheckerBase(Tool): + def add_parser_args(self, parser, mode=True): + parser.add_argument("--golden", help="Golden outputs for accuracy comparison", required=True) + parser.add_argument("-s", "--show-output", help="Show logging output when checking network accuracy", action="store_true") + if mode: + parser.add_argument("--mode", help="How layers are marked to run in higher precision. " + "'forward' will start marking layers from network inputs, and 'reverse' will start " + "from the network outputs", choices=["forward", "reverse"], default="reverse") + parser.add_argument("-p", "--precision", help="Precision to use when marking layers to run in higher precision", + choices=["float32", "float16"], default="float32") + args_util.add_model_args(parser, model_required=True) + args_util.add_comparator_args(parser, iters=False, read=False, write=False, subprocess=False, fail_fast=False) + args_util.add_dataloader_args(parser) + args_util.add_trt_args(parser) + args_util.add_tf_args(parser, tftrt=False, artifacts=False, runtime=False) + args_util.add_onnx_args(parser, write=False) + args_util.add_tf_onnx_args(parser) + + + def __call__(self, args): + import tensorrt as trt + + if not args.calibration_cache: + G_LOGGER.warning("Not using a calibration cache. Using a calibration cache may significantly speed up the search process") + + self.precision = {"float32": trt.float32, "float16": trt.float16}[args.precision] + if self.precision == trt.float16 and not args.fp16: + args.fp16 = True + if self.precision == trt.float16 and not args.int8: + G_LOGGER.warning("Using float16 as the higher precision, but float16 is also the lowest precision available. Did you mean to set --int8 as well?") + + if not any([args.tf32, args.fp16, args.int8]): + G_LOGGER.critical("Please enable at least one precision besides float32 (e.g. --int8, --fp16)") + + if args.model_type == "engine": + G_LOGGER.critical("The precision tool cannot work with engines, as they cannot be modified. " + "Please provide a different format, such as an ONNX or TensorFlow model.") + + self.args = args + + self.golden = OrderedDict() + self.golden.update(misc.pickle_load(args.golden)) + + self.builder, self.network, self.parser = tool_util.get_trt_network_loader(args)() + with self.builder, self.network, self.parser: + indices = self.find() + + if indices is not None: + G_LOGGER.info("To achieve acceptable accuracy, try running layers: {:} in {:} precision".format( + indices, self.precision)) + else: + G_LOGGER.critical("Could not find a configuration that resulted in acceptable accuracy") + + + # Determines layer indices based on direction and number of layers to mark + def layer_indices(self, num_layers): + if self.args.mode == "forward": + start = 0 + end = num_layers + elif self.args.mode == "reverse": + start = self.network.num_layers - num_layers + end = self.network.num_layers + return range(start, end) + + + def mark_layers(self, indices): + def layer_to_str(layer): + outputs = [layer.get_output(i).name for i in range(layer.num_outputs)] + return "{:}: {:}".format(layer.name, outputs) + + # First, reset, since changes from the previous call will persist. + for layer in self.network: + layer.reset_precision() + + for index in indices: + layer = self.network.get_layer(index) + G_LOGGER.verbose("Running layer in higher precision: {:}".format(layer_to_str(layer))) + layer.precision = self.precision + G_LOGGER.info("Will run layer(s): {:} in {:} precision".format(indices, self.precision)) + + + def check_network(self, suffix): + """ + Checks whether the provided network is accurate compared to golden values. + + Returns: + OrderedDict[str, OutputCompareResult]: + A mapping of output names to an object describing whether they matched, and what the + required tolerances were. + """ + from polygraphy.comparator import Comparator, CompareFunc, DataLoader + from polygraphy.backend.trt import EngineFromNetwork, TrtRunner, ModifyNetwork, SaveEngine + + with G_LOGGER.verbosity(severity=G_LOGGER.severity if self.args.show_output else G_LOGGER.CRITICAL): + data_loader = tool_util.get_data_loader(self.args) + + self.args.strict_types = True # HACK: Override strict types so things actually run in the right precision. + config = tool_util.get_trt_config_loader(self.args, data_loader)(self.builder, self.network) + + suffix = "-{:}-{:}".format(suffix, self.precision) + engine_path = misc.insert_suffix(self.args.save_engine, suffix) + + self.builder, self.network, self.parser = ModifyNetwork((self.builder, self.network, self.parser), + outputs=self.args.trt_outputs)() + + engine_loader = SaveEngine(EngineFromNetwork((self.builder, self.network, self.parser), config), + path=engine_path) + + runners = [TrtRunner(engine_loader)] + + results = Comparator.run(runners, data_loader=data_loader) + if self.args.validate: + Comparator.validate(results) + results.update(self.golden) + + + compare_func = CompareFunc.basic_compare_func(atol=self.args.atol, rtol=self.args.rtol, check_shapes=not self.args.no_shape_check) + accuracy_result = Comparator.compare_accuracy(results, compare_func=compare_func) + + tolerances = list(accuracy_result.values())[0][0] # First iteration of first runner pair + for name, req_tol in tolerances.items(): + if bool(req_tol): + G_LOGGER.success("PASSED | Output: {:} | Required Tolerances: {:}".format(name, req_tol)) + else: + G_LOGGER.error("FAILED | Output: {:} | Required Tolerances: {:}".format(name, req_tol)) + return accuracy_result + + +class STWorstFirst(STCheckerBase): + """ + [EXPERIMENTAL] Progressively mark the worst layers (those that introduce the largest error) + to run in a higher precision. Assumes that the layerwise outputs + are topologically sorted. + """ + def __init__(self): + self.name = "worst-first" + + def add_parser_args(self, parser): + super().add_parser_args(parser, mode=False) + parser.add_argument("--top", help="Number of additional layers to mark each successive iteration", type=int, default=5) + + + def find(self): + def run(indices): + self.mark_layers(indices) + return self.check_network("-".join(map(str, indices))) + + + # Finds num worst indices in acc_results + def find_worst(num, acc_results): + acc_mapping = list(acc_results.values())[0][0] # First iteration of first runner-pair. + + # Compute for each layer: atol / prev_atol, to determine which layers contribute the greatest error. + # It is not enough to simply find the max(atol), because that doesn't account for error introduced + # by previous layers. + items = list(acc_mapping.items()) + ratios = [] + for (_, prev_tols), (outname, cur_tols) in zip(items[:-1], items[1:]): + ratio = cur_tols.required_atol / prev_tols.required_atol + ratios.append((ratio, outname)) + + # Mark more layers on each iteration + ratios = sorted(ratios, reverse=True)[:num] + G_LOGGER.verbose("Found worst {:} layers (Format: (error ratio, tensor name)): {:}".format(num, ratios)) + return [output_mapping[outname] for (ratio, outname) in ratios] + + + if not args_util.get(self.args, "trt_outputs"): + G_LOGGER.critical("worst-first requires all outputs to be marked as network outputs mode to determine where errors are being introduced. " + "Please enable --trt-outputs mark all, and ensure that your golden outputs also include layer-wise results") + + output_mapping = {} # Maps output tensor names to producer layer indices + for layer_index, layer in enumerate(self.network): + for out_index in range(layer.num_outputs): + output_mapping[layer.get_output(out_index).name] = layer_index + + indices = [] + acc_results = run(indices) + max_outputs = len(list(acc_results.values())[0][0]) - 1 + + iter_num = 0 + # indices will be at most one less than the number of layers, since we're comparing layers against subsequent ones. + while not bool(acc_results) and len(indices) < max_outputs: + iter_num += 1 + indices = find_worst(self.args.top * iter_num, acc_results) + acc_results = run(indices) + + if bool(acc_results): + return indices + + +class STLinear(STCheckerBase): + """ + Use a linear search to determine how many layers need to be run in + higher precision to achieve the desired accuracy. + """ + def __init__(self): + self.name = "linear" + + + def find(self): + which_layers = {"forward": "first", "reverse": "last"}[self.args.mode] + + for index in range(self.network.num_layers + 1): + indices = self.layer_indices(index) + self.mark_layers(indices) + success = self.check_network("{:}-{:}".format(which_layers, index)) + if success: + return indices + + +class STBisect(STCheckerBase): + """ + Use a binary search to determine how many layers need to be run in + higher precision to achieve the desired accuracy. + """ + def __init__(self): + self.name = "bisect" + + + def find(self): + which_layers = {"forward": "first", "reverse": "last"}[self.args.mode] + + num_layers = 0 + # Keep track of what works and what doesn't + known_good = self.network.num_layers + 1 + known_bad = 0 + + while known_good != known_bad and num_layers != known_good: + with G_LOGGER.indent(): + G_LOGGER.info("Last known good: {which_layers} {known_good} layer(s) in {precision} precision.\n" + "Last known bad: {which_layers} {known_bad} layer(s) in {precision} precision".format( + which_layers=which_layers, known_good=min(known_good, self.network.num_layers), + precision=self.precision, known_bad=known_bad)) + + indices = self.layer_indices(num_layers) + self.mark_layers(indices) + success = self.check_network("{:}-{:}".format(which_layers, num_layers)) + if success: + # Try something between + known_good = num_layers + else: + known_bad = num_layers + # Try something in between the known good value, and the known bad value. + num_layers = math.ceil((known_bad + known_good) / 2.0) + + if known_good <= self.network.num_layers: + return indices + + +################################# MAIN TOOL ################################# + +class Precision(Tool): + """ + [EXPERIMENTAL] Debug low precision models by suggesting which layers should be run in + higher precisions to preserve accuracy. + """ + def __init__(self): + self.name = "precision" + + + def add_parser_args(self, parser): + subparsers = parser.add_subparsers(title="Precision Modes", dest="mode") + subparsers.required = True + + PRECISION_SUBTOOLS = [ + STBisect(), + STLinear(), + STWorstFirst(), + ] + + for subtool in PRECISION_SUBTOOLS: + subtool.setup_parser(subparsers) + + + def __call__(self, args): + pass diff --git a/tools/Polygraphy/polygraphy/tools/registry.py b/tools/Polygraphy/polygraphy/tools/registry.py new file mode 100644 index 00000000..ea479df4 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/registry.py @@ -0,0 +1,53 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.tools.base import Tool +from polygraphy.logger import G_LOGGER +import importlib + +try: + ModuleNotFoundError +except: + ModuleNotFoundError = ImportError + +TOOL_REGISTRY = [] + + +class MissingTool(Tool): + def __init__(self, name, err): + self.name = name + self.__doc__ = "Error: Tool could not be loaded. Run 'polygraphy {:}' for details".format(self.name) + self.err = err + + + def __call__(self, args): + G_LOGGER.critical("Encountered an error when loading this tool:\n{:}".format(self.err)) + + +def try_register_tool(module, tool_class): + global TOOL_REGISTRY + + try: + toolmod = importlib.import_module(module) + tool = getattr(toolmod, tool_class)() + TOOL_REGISTRY.append(tool) + except Exception as err: + TOOL_REGISTRY.append(MissingTool(tool_class.lower(), err=err)) + + +try_register_tool("polygraphy.tools.run", "Run") +try_register_tool("polygraphy.tools.inspect", "Inspect") +try_register_tool("polygraphy.tools.surgeon", "Surgeon") +try_register_tool("polygraphy.tools.precision", "Precision") diff --git a/tools/Polygraphy/polygraphy/tools/run/README.md b/tools/Polygraphy/polygraphy/tools/run/README.md new file mode 100644 index 00000000..d978b3e8 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/run/README.md @@ -0,0 +1,22 @@ +# Run + +## Table of Contents + +- [Introduction](#introduction) +- [Usage](#usage) +- [Examples](#examples) + +## Introduction + +The `run` tool can be used to convert models, run inference, and compare results across frameworks. +It can also generate a Python script that uses the Polygraphy Python API to do the same. + + +## Usage + +See `polygraphy run -h` for usage information. + + +## Examples + +For examples, see [this directory](../../../examples/cli/run) diff --git a/tools/Polygraphy/polygraphy/tools/run/__init__.py b/tools/Polygraphy/polygraphy/tools/run/__init__.py new file mode 100644 index 00000000..d8507c2c --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/run/__init__.py @@ -0,0 +1 @@ +from polygraphy.tools.run.run import Run diff --git a/tools/Polygraphy/polygraphy/tools/run/run.py b/tools/Polygraphy/polygraphy/tools/run/run.py new file mode 100644 index 00000000..6bbe2127 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/run/run.py @@ -0,0 +1,266 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.tools.util.script import Script, Inline +from polygraphy.tools.util import args as args_util, misc as tool_util +from polygraphy.logger import G_LOGGER, LogMode +from polygraphy.common import constants +from polygraphy.tools.base import Tool +from polygraphy.util import misc +import polygraphy + +import argparse +import copy +import sys +import os + + +# Generate a summary line to add as a comment to the script +def generate_summary(model_file, runners, load_results): + def join_list(lst): + new_list = copy.copy(lst) + if len(new_list) > 1: + new_list[-1] = "and {:}".format(new_list[-1]) + return ", ".join(new_list) + + summary = "" + + if runners: + summary += "This script " + if len(runners) > 1: + summary += "compares " + else: + summary += "runs " + if model_file: + summary += "{:} ".format(model_file) + + runner_names = { + "trt": "TensorRT", + "trt_legacy": "TensorRT Legacy", + "tf": "TensorFlow", + "onnxrt": "ONNX Runtime", + "onnxtf": "ONNX-TensorFlow Backend", + "cntk": "CNTK" + } + runners = [runner_names[runner] for runner in runners] + summary += "between " if len(runners) > 1 else "using " + summary += join_list(runners) + + if load_results: + summary += "\nIt will check against outputs stored in {:}\n".format(join_list(load_results)) + + return summary + + + +def add_tf_runner(script, args): + script.add_import(imports=["TfRunner"], frm="polygraphy.backend.tf") + + graph_name = tool_util.add_tf_loader(script, args) + config_name = tool_util.add_tf_config_loader(script, args) + + script.add_import(imports=["SessionFromGraph"], frm="polygraphy.backend.tf") + loader_name = script.add_loader(Script.invoke("SessionFromGraph", graph_name, config=config_name), "build_tf_session") + + runner_str = Script.invoke("TfRunner", loader_name, timeline_path=args.save_timeline) + script.add_runner(runner_str) + + +def add_onnxrt_runner(script, args): + script.add_import(imports=["OnnxrtRunner"], frm="polygraphy.backend.onnxrt") + onnx_name = tool_util.add_serialized_onnx_loader(script, args) + + script.add_import(imports=["SessionFromOnnxBytes"], frm="polygraphy.backend.onnxrt") + loader_name = script.add_loader(Script.invoke("SessionFromOnnxBytes", onnx_name), "build_onnxrt_session") + + script.add_runner(Script.invoke("OnnxrtRunner", loader_name)) + + +def add_onnxtf_runner(script, args): + script.add_import(imports=["OnnxTfRunner", "OnnxFromPath"], frm="polygraphy.backend.onnx") + script.add_runner(Script.invoke("OnnxTfRunner", tool_util.add_onnx_loader(script, args, suffix="_onnxtf"))) + + +def add_cntk_runner(script, args): + script.add_import(imports=["CNTKRunner"], frm="polygraphy.backend.cntk") + script.add_runner(Script.invoke("CNTKRunner", args.model_file)) + + +def add_trt_runner(script, args, data_loader_name): + script.add_import(imports=["TrtRunner"], frm="polygraphy.backend.trt") + + if args.model_type == "engine": + loader_name = tool_util.add_trt_serialized_engine_loader(script, args) + else: + script.add_import(imports=["EngineFromNetwork"], frm="polygraphy.backend.trt") + loader_name = tool_util.add_trt_network_loader(script, args) + config_loader_name = tool_util.add_trt_config_loader(script, args, data_loader_name=data_loader_name) + loader_str = Script.invoke("EngineFromNetwork", loader_name, config=config_loader_name) + loader_name = script.add_loader(loader_str, "build_engine") + + SAVE_ENGINE = "SaveEngine" + save_engine = Script.invoke(SAVE_ENGINE, loader_name, path=args_util.get(args, "save_engine")) + if save_engine != Script.invoke(SAVE_ENGINE, loader_name): + script.add_import(imports=[SAVE_ENGINE], frm="polygraphy.backend.trt") + loader_name = script.add_loader(save_engine, "save_engine") + + script.add_runner(Script.invoke("TrtRunner", loader_name)) + + +def add_trt_legacy_runner(script, args): + script.add_import(imports=["TrtLegacyRunner"], frm="polygraphy.backend.trt_legacy") + G_LOGGER.warning("Legacy TensorRT runner only supports implicit batch TensorFlow/UFF, ONNX, and Caffe models") + + if args.model_type == "onnx": + script.add_import(imports=["ParseNetworkFromOnnxLegacy"], frm="polygraphy.backend.trt_legacy") + onnx_loader = tool_util.add_onnx_loader(script, args, disable_outputs=True) + loader_name = script.add_loader(Script.format_str("ParseNetworkFromOnnxLegacy({:})", onnx_loader), "parse_network_from_onnx_legacy") + elif args.model_type == "caffe": + script.add_import(imports=["LoadNetworkFromCaffe"], frm="polygraphy.backend.trt_legacy") + loader_name = script.add_loader(Script.format_str("LoadNetworkFromCaffe({:}, {:}, {:}, {:})", args.model_file, args.caffe_model, + args.trt_outputs, args.batch_size), "parse_network_from_caffe") + else: + script.add_import(imports=["LoadNetworkFromUff"], frm="polygraphy.backend.trt_legacy") + if args.model_type == "uff": + script.add_import(imports=["LoadUffFile"], frm="polygraphy.backend.trt_legacy") + shapes = {name: shape for name, (_, shape) in args.inputs.items()} + loader_name = script.add_loader(Script.format_str("LoadUffFile({:}, {:}, {:})", args.model_file, misc.default_value(shapes, {}), args.trt_outputs), "load_uff_file") + else: + script.add_import(imports=["ConvertToUff"], frm="polygraphy.backend.trt_legacy") + loader_name = script.add_loader(Script.format_str("ConvertToUff({:}, save_uff={:}, preprocessor={:})", tool_util.add_tf_loader(script, args), args.save_uff, args.preprocessor), "convert_to_uff") + loader_name = script.add_loader(Script.format_str("LoadNetworkFromUff({:}, uff_order={:})", loader_name, args.uff_order), "uff_network_loader") + + + runner_str = Script.format_str("TrtLegacyRunner({:}, {:}, {:}, fp16={:}, tf32={:}, load_engine={:}, save_engine={:}, layerwise={:}, plugins={:})", loader_name, args.workspace, args.batch_size, args.fp16, args.tf32, args.model_file if args.model_type == "engine" else None, args.save_engine, args_util.get(args, "trt_outputs")==constants.MARK_ALL, args.plugins) + script.add_runner(runner_str) + + + +def add_comparator(script, args, data_loader_name, cmd_run): + script.add_import(imports=["Comparator"], frm="polygraphy.comparator") + script.add_import(imports=["sys"]) + comparator_run = Script.invoke("Comparator.run", script.get_runners(), warm_up=args.warm_up, + data_loader=data_loader_name, use_subprocess=args.use_subprocess) + script.append_suffix(Script.format_str("\n# Runner Execution\nresults = {:}", Inline(comparator_run))) + + if args.load_results: + G_LOGGER.verbose("Will load runner results from: {:}".format(args.load_results)) + script.add_import(imports=["misc"], frm="polygraphy.util") + script.append_suffix(Script.format_str("\n# Load results\nfor load_output in {:}:\n{:}results.update(misc.pickle_load(load_output))", args.load_results, Inline(constants.TAB))) + + if args.save_results: + G_LOGGER.verbose("Will save runner results to: {:}".format(args.save_results)) + script.add_import(imports=["misc"], frm="polygraphy.util") + script.append_suffix(Script.format_str("\n# Save results\nmisc.pickle_save({:}, results)", args.save_results)) + + top_k = args_util.get(args, "top_k") + if top_k is not None: + script.add_import(imports=["PostprocessFunc"], frm="polygraphy.comparator") + script.append_suffix(Script.format_str("\n# Postprocessing - Apply Top-{:}\nresults = Comparator.postprocess(results, PostprocessFunc.topk_func(k={:}))", top_k, top_k)) + + script.append_suffix("\nsuccess = True") + + if len(args.runners) > 1 or args.load_results: # Only do comparisons if there's actually something to compare. + script.append_suffix("# Accuracy Comparison") + + compare_func_str = Script.invoke_if_nondefault("CompareFunc.basic_compare_func", rtol=args.rtol, atol=args.atol, + check_shapes=False if args.no_shape_check else None, + fail_fast=args.fail_fast) + compare_func = None + if compare_func_str: + script.add_import(imports=["CompareFunc"], frm="polygraphy.comparator") + compare_func = "compare_func" + script.append_suffix(Script.format_str("{:} = {:}", Inline(compare_func), Inline(compare_func_str))) + + compare_accuracy = Script.invoke("Comparator.compare_accuracy", Inline("results"), compare_func=Inline(compare_func) if compare_func is not None else None, + fail_fast=args.fail_fast) + script.append_suffix(Script.format_str("success &= bool({:})\n", Inline(compare_accuracy))) + if args.validate: + script.append_suffix("# Validation\nsuccess &= Comparator.validate(results)\n") + + if cmd_run is None: + cmd_run = Inline("' '.join(sys.argv)") + script.append_suffix(Script.format_str('# Report Results\ncmd_run={cmd}\nif success:\n G_LOGGER.success("PASSED | Command: {{}}".format(cmd_run))\nelse:\n G_LOGGER.error("FAILED | Command: {{}}".format(cmd_run))', cmd=cmd_run)) + script.append_suffix("sys.exit(0 if success else 1)") + + +# Generates a script based on command-line arguments +def build_script(args, cmd_run=None): + script = Script(summary=generate_summary(args.model_file, args.runners, args.load_results)) + tool_util.add_logger_settings(script, args) + + data_loader_name = tool_util.add_data_loader(script, args) + + for runner_arg in args.runners: + add_runner_func = { + "tf": add_tf_runner, + "onnxrt": add_onnxrt_runner, + "onnxtf": add_onnxtf_runner, + "cntk": add_cntk_runner, + "trt": lambda script, args: add_trt_runner(script, args, data_loader_name), + "trt_legacy": add_trt_legacy_runner, + }[runner_arg] + add_runner_func(script, args) + + add_comparator(script, args, data_loader_name=data_loader_name, cmd_run=cmd_run) + return str(script) + + +################################# TOOL ################################# + + +class Run(Tool): + """ + Run inference and compare results across backends. + """ + def __init__(self): + self.name = "run" + + + def add_parser_args(self, parser): + parser.add_argument("--gen", "--gen-script", help="Path to save a generated Python script, that will do exactly " + "what `run` would. When this option is enabled, `run` will just save the script and exit. " + "Use `-` to print the script to the standard output", + type=argparse.FileType("w"), dest="gen_script") + args_util.add_model_args(parser) + args_util.add_runner_args(parser) + args_util.add_comparator_args(parser, top_k=True) + args_util.add_dataloader_args(parser) + args_util.add_trt_args(parser, network_api=True) + args_util.add_trt_legacy_args(parser) + args_util.add_tf_args(parser) + args_util.add_onnx_args(parser) + args_util.add_tf_onnx_args(parser) + + + def __call__(self, args): + misc.log_module_info(polygraphy) + + script = build_script(args) + + if args.gen_script: + with args.gen_script: + args.gen_script.write(script) + + path = args.gen_script.name + # Somehow, piping fools isatty, e.g. `polygraphy run --gen-script - | cat` + if not args.gen_script.isatty() and path not in ["", ""]: + G_LOGGER.info("Writing script to: {:}".format(path)) + # Make file executable + os.chmod(path, os.stat(path).st_mode | 0o111) + else: + exec(script) + + return 0 diff --git a/tools/Polygraphy/polygraphy/tools/surgeon/README.md b/tools/Polygraphy/polygraphy/tools/surgeon/README.md new file mode 100644 index 00000000..8e3a5644 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/surgeon/README.md @@ -0,0 +1,123 @@ +# Surgeon + +## Table of Contents + +- [Introduction](#introduction) +- [Subtools](#subtools) +- [Usage](#usage) +- [Examples](#examples) +- [\[EXPERIMENTAL\] JSON Configuration Format](#experimental-json-configuration-format) + + +## Introduction + +The `surgeon` tool uses [ONNX-GraphSurgeon](https://github.com/NVIDIA/TensorRT/tree/master/tools/onnx-graphsurgeon) +to modify an ONNX model. + +Although this tool can only operate on and export ONNX models, it will automatically +attempt to convert supported formats to ONNX. + + +## Subtools + +`surgeon` provides subtools to perform different functions: + +- `extract` can extract subgraphs from models. It can also be used for changing the shapes/datatypes of the + model inputs/outputs by specifying the existing inputs/outputs, but with different shapes/datatypes. This + can be useful to make an input dimension dynamic, for example. + +- [EXPERIMENTAL] `prepare` and `operate` work together to perform a 2-stage process: + 1. `prepare` generates an editable JSON configuration file for a given model. + 2. `operate` accepts the model and applies modifications according to a JSON configuration file. + + For details on how to edit models using the JSON configuration file, see [Configuration Format](#configuration-format) + + **NOTE:** `prepare` and `operate` are best suited for interactive cases, such as debugging a single model. Configuration files are + model-specific, and cannot be shared across models. In cases where you need to apply a set of transformations to + multiple models, you should use the [ONNX-GraphSurgeon](https://github.com/NVIDIA/TensorRT/tree/master/tools/onnx-graphsurgeon) + Python API directly. + + +## Usage + +See `polygraphy surgeon -h` for usage information. + + +## Examples + +For examples, see [this directory](../../../examples/cli/surgeon) + + +## **[EXPERIMENTAL]** JSON Configuration Format + +The JSON configuration file generated by the `prepare` tool includes various fields that can be modified. + +**NOTE:** Tensor names can be changed, but this will cause any existing metadata to be omitted from the tensor. +Generally, there is no need to change tensor names, so this should hardly ever be an issue. + +An example configuration file is reproduced below: +```json +{ + "graph_inputs": [ + { + "name": "x", + "dtype": "float32", + "shape": [ + 1, + 1, + 2, + 2 + ] + } + ], + "graph_outputs": [ + { + "name": "y", + "dtype": "float32", + "shape": [ + 1, + 1, + 2, + 2 + ] + } + ], + "nodes": [ + { + "id": 0, + "name": "", + "op": "Identity", + "inputs": [ + "x" + ], + "outputs": [ + "y" + ] + } + ] +} +``` + +### Fields + +- `dtype`: Any string that maps to a numpy data type, such as `float16`, `int8`, `bool`, etc. +- `shape`: A list of integers. `-1` can be used to indicate a dynamic dimension + +### Graph Inputs + +The data types and shapes of the graph inputs can be freely modified. For graph inputs, shapes must be provided. + +### Graph Outputs + +Similar to graph inputs, graph output data types and shapes can be freely modified. +Unlike with graph inputs, shapes are not required for outputs, since they can be inferred +at runtime based on the input shapes. + +### Nodes + +**IMPORTANT:** You should *not* modify the `id` parameter of nodes. + +The `name`, `op`, `inputs`, and `outputs` fields can be freely modified. This provides a convenient way to reconnect +nodes in the graph and change their function. + +**NOTE:** Modiyfing node attributes is not supported at this time. diff --git a/tools/Polygraphy/polygraphy/tools/surgeon/__init__.py b/tools/Polygraphy/polygraphy/tools/surgeon/__init__.py new file mode 100644 index 00000000..9551e5f1 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/surgeon/__init__.py @@ -0,0 +1 @@ +from polygraphy.tools.surgeon.surgeon import Surgeon diff --git a/tools/Polygraphy/polygraphy/tools/surgeon/surgeon.py b/tools/Polygraphy/polygraphy/tools/surgeon/surgeon.py new file mode 100644 index 00000000..5d031ee0 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/surgeon/surgeon.py @@ -0,0 +1,351 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +from collections import OrderedDict + +import onnx +import onnx_graphsurgeon as gs +from polygraphy.common import TensorMetadata, constants +from polygraphy.logger import G_LOGGER +from polygraphy.tools.base import Tool +from polygraphy.tools.util import args as args_util +from polygraphy.tools.util import misc as tool_util +from polygraphy.util import misc + + +# Weights should be stored separately, JSON can just have a reference to a key. +class Config(OrderedDict): + @staticmethod + def from_graph(graph): + def names_from_tensors(tensors): + return [tensor.name for tensor in tensors] + + + def meta_from_tensors(tensors): + meta = [] + for tensor in tensors: + tensor_meta = {"name": tensor.name} + if tensor.dtype: + tensor_meta["dtype"] = misc.STR_FROM_NP_TYPE[tensor.dtype] + if tensor.shape: + tensor_meta["shape"] = tensor.shape + meta.append(tensor_meta) + return meta + + + config = Config() + config["graph_inputs"] = meta_from_tensors(graph.inputs) + config["graph_outputs"] = meta_from_tensors(graph.outputs) + + config["nodes"] = [] + for node_id, node in enumerate(graph.nodes): + node_info = { + "id": node_id, + "name": node.name, + "op": node.op, + "inputs": names_from_tensors(node.inputs), + "outputs": names_from_tensors(node.outputs), + } + config["nodes"].append(node_info) + return config + + +################################# SUBTOOLS ################################# + +class STSurgeonBase(Tool): + def add_parser_args(self, parser, gs=False, inputs=False, shape_inference_default=None, data=False): + if gs: + parser.add_argument("--no-cleanup", help="Skip cleanup and keep unused nodes in the graph", action="store_true") + parser.add_argument("--no-toposort", help="Skip topologically sorting the graph", action="store_true") + args_util.add_model_args(parser, model_required=True, inputs=inputs) + args_util.add_onnx_args(parser, write=False, outputs=False, shape_inference_default=shape_inference_default) + args_util.add_tf_onnx_args(parser) + if data: + args_util.add_dataloader_args(parser) + + + def setup(self, args): + onnx_model = tool_util.get_onnx_model_loader(args)() + return gs.import_onnx(onnx_model) + + +class STExtract(STSurgeonBase): + """ + Extract a subgraph based on the specified inputs and outputs. + """ + def __init__(self): + self.name = "extract" + + + def add_parser_args(self, parser): + parser.add_argument("-o", "--output", required=True, help="Path at which to write the ONNX model including only the subgraph") + + parser.add_argument("--inputs", dest="input_meta", help="Input metadata for subgraph (names, shapes, and data types). " + "Use 'auto' to make `extract` determine these automatically. Format: " + "--inputs ,,. " + "For example: --inputs input0,1x3x224x224,float32 input1,auto,auto. " + "If omitted, uses the current model inputs. Supported data types are: {:}".format(list(misc.NP_TYPE_FROM_STR.keys())), + nargs="+", default=None) + + parser.add_argument("--outputs", dest="output_meta", help="Output metadata for subgraph (names and data types). " + "Use 'auto' to make `extract` determine these automatically. Format: " + "--outputs ,. " + "For example: --outputs output0:float32 output1:auto. " + "If omitted, uses the current model outputs. Supported data types are: {:}".format(list(misc.NP_TYPE_FROM_STR.keys())), + nargs="+", default=None) + + super().add_parser_args(parser, gs=True, inputs="--model-inputs", shape_inference_default=True, data=True) + + + def __call__(self, args): + def missing_meta_tensors(input_metadata, output_metadata): + names = [] + for name, (dtype, shape) in input_metadata.items(): + if dtype is None or not shape: + names.append(name) + for name, (dtype, shape) in output_metadata.items(): + if dtype is None: + names.append(name) + return names + + + def update_meta_from_tensor_map(meta, tensor_map): + for name, (dtype, shape) in meta.items(): + tensor = tensor_map[name] + meta[name] = (dtype or tensor.dtype, shape or tensor.shape) + return meta + + + def meta_from_tensors(tensors): + meta = TensorMetadata() + for tensor in tensors: + meta.add(tensor.name, tensor.dtype, tensor.shape) + return meta + + + onnx_model = tool_util.get_onnx_model_loader(args)() + graph = gs.import_onnx(onnx_model) + tensor_map = graph.tensors() + + if args.input_meta: + input_metadata = update_meta_from_tensor_map(args_util.parse_meta(args.input_meta), tensor_map) + else: + input_metadata = meta_from_tensors(graph.inputs) + + if args.output_meta: + output_metadata = update_meta_from_tensor_map(args_util.parse_meta(args.output_meta, includes_shape=False), tensor_map) + else: + output_metadata = meta_from_tensors(graph.outputs) + + missing_tensors = missing_meta_tensors(input_metadata, output_metadata) + if missing_tensors: + # Use ONNX runtime with static shapes to infer shapes when all else fails + # Returns a TensorMetadata for all tensors in the graph. + def fallback_shape_inference(onnx_model): + from polygraphy.backend.onnx import BytesFromOnnx, ModifyOnnx + from polygraphy.backend.onnxrt import (OnnxrtRunner, + SessionFromOnnxBytes) + + load_model = ModifyOnnx(onnx_model, outputs=constants.MARK_ALL) + with OnnxrtRunner(SessionFromOnnxBytes(BytesFromOnnx(load_model))) as runner: + data_loader = tool_util.get_data_loader(args) + data_loader.input_metadata = runner.get_input_metadata() + outputs = runner.infer(feed_dict=data_loader[0]) + + meta = TensorMetadata() + for name, output in outputs.items(): + meta.add(name, output.dtype, output.shape) + return meta + + + def update_meta_from_meta(meta, golden_meta): + for name, (dtype, shape) in meta.items(): + if name in golden_meta: + (golden_dtype, golden_shape) = golden_meta[name] + meta[name] = (dtype or golden_dtype, shape or golden_shape) + G_LOGGER.verbose("Updated tensor: {:} metadata to: {:}".format(name, meta[name])) + return meta + + + G_LOGGER.warning("Some tensor shapes or dtypes are missing in the model. Note: Missing Tensors: {:}. " + "\nWill run inference to determine shapes. This will cause dynamic " + "dimensions to become static.\nTo avoid this, please provide metadata on the command-line. " + .format(missing_tensors)) + golden_meta = fallback_shape_inference(onnx_model) + input_metadata = update_meta_from_meta(input_metadata, golden_meta) + output_metadata = update_meta_from_meta(output_metadata, golden_meta) + + + # Set the graph inputs and outputs + graph.inputs.clear() + for name, (dtype, shape) in input_metadata.items(): + tensor = tensor_map[name] + tensor.dtype, tensor.shape = dtype, shape + tensor.inputs.clear() + graph.inputs.append(tensor) + + graph.outputs.clear() + for name, (dtype, shape) in output_metadata.items(): + tensor = tensor_map[name] + tensor.dtype, tensor.shape = dtype, shape + graph.outputs.append(tensor) + + G_LOGGER.info("Using Graph Inputs:\n{:}{:}".format(constants.TAB, graph.inputs)) + G_LOGGER.info("Using Graph Outputs:\n{:}{:}".format(constants.TAB, graph.outputs)) + + if not args.no_cleanup: + graph.cleanup() + if not args.no_toposort: + graph.toposort() + + onnx_model = gs.export_onnx(graph) + G_LOGGER.info("Writing model to: {output}. To see more details about the model, use: polygraphy inspect model {output} --mode=basic".format(output=args.output)) + onnx.save(onnx_model, args.output) + + +class STPrepare(STSurgeonBase): + """ + [EXPERIMENTAL] Prepare a JSON configuration file for a given model, + which can be edited and provided to `operate`. + """ + def __init__(self): + self.name = "prepare" + + + def add_parser_args(self, parser): + parser.add_argument("-o", "--output", help="Path to save JSON configuration for the model. " + "If omitted, the JSON configuration is printed to standard output.") + super().add_parser_args(parser) + + + def __call__(self, args): + graph = super().setup(args) + config = Config.from_graph(graph) + config_json = json.dumps(config, indent=constants.TAB) + G_LOGGER.info("Please do NOT modify the node 'id' values in the configuration file, or things may not work!") + + if args.output: + with open(args.output, "w") as f: + f.write(config_json) + else: + print(config_json) + + +class STOperate(STSurgeonBase): + """ + [EXPERIMENTAL] Modify a model according to the provided JSON configuration file. + """ + def __init__(self): + self.name = "operate" + + + def add_parser_args(self, parser): + parser.add_argument("-c", "--config", required=True, help="Path to JSON configuration that specifies how the model should be modified.") + parser.add_argument("-o", "--output", required=True, help="Path to save the model") + super().add_parser_args(parser, gs=True) + + + def __call__(self, args): + graph = super().setup(args) + with open(args.config, "r") as f: + config = json.loads(f.read()) + G_LOGGER.info("Please ensure you have not modified the node 'id' values in the configuration file, or things may not work!") + + tensor_map = graph.tensors() + + def get_tensor(name): + if name not in tensor_map: + G_LOGGER.verbose("Tensor: {:} does not exist in the model. Creating a new tensor".format(name)) + tensor_map[name] = gs.Variable(name) + return tensor_map[name] + + + def tensors_from_names(names): + tensors = [] + for name in names: + tensors.append(get_tensor(name)) + return tensors + + + def tensors_from_meta(meta, shape_optional=False): + tensors = [] + for tensor_meta in meta: + tensor = get_tensor(tensor_meta["name"]) + if "shape" in tensor_meta: + tensor.shape = tensor_meta["shape"] + elif not shape_optional: + G_LOGGER.critical("Could not find shape information for tensor: {:}".format(tensor.name)) + + if "dtype" in tensor_meta: + tensor.dtype = misc.NP_TYPE_FROM_STR[tensor_meta["dtype"]] + tensors.append(tensor) + return tensors + + + graph.inputs = tensors_from_meta(config["graph_inputs"]) + for inp in graph.inputs: + # Need to disconnect inputs of graph inputs, or nodes prior to them will remain + inp.inputs.clear() + + graph.outputs = tensors_from_meta(config["graph_outputs"], shape_optional=True) + + nodes = [] + for node_info in config["nodes"]: + if node_info["id"] > len(graph.nodes): + G_LOGGER.critical("Could not find node with ID: {:}. Were the node IDs modified in the config file?".format(node_info["id"])) + node = graph.nodes[node_info["id"]] + node.name = node_info["name"] + node.op = node_info["op"] + node.inputs = tensors_from_names(node_info["inputs"]) + node.outputs = tensors_from_names(node_info["outputs"]) + nodes.append(node) + + graph.nodes = nodes + + if not args.no_cleanup: + graph.cleanup() + if not args.no_toposort: + graph.toposort() + + onnx.save(gs.export_onnx(graph), args.output) + + +################################# MAIN TOOL ################################# + +class Surgeon(Tool): + """ + Modify models. + """ + def __init__(self): + self.name = "surgeon" + + + def add_parser_args(self, parser): + subparsers = parser.add_subparsers(title="Surgical Instruments", dest="instrument") + subparsers.required = True + + SURGEON_SUBTOOLS = [ + STExtract(), + STPrepare(), + STOperate(), + ] + + for subtool in SURGEON_SUBTOOLS: + subtool.setup_parser(subparsers) + + + def __call__(self, args): + pass diff --git a/tools/Polygraphy/polygraphy/tools/util/__init__.py b/tools/Polygraphy/polygraphy/tools/util/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/polygraphy/tools/util/args.py b/tools/Polygraphy/polygraphy/tools/util/args.py new file mode 100644 index 00000000..1761c0b8 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/util/args.py @@ -0,0 +1,434 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import argparse +import copy +import os + +from polygraphy.common import TensorMetadata, constants +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + +# The functions in this file include flags to control the set of options that are generated. + +def add_model_args(parser, model_required=False, inputs="--inputs"): + model_args = parser.add_argument_group("Model", "Model Options") + model_args.add_argument("model_file", help="Path to the model", nargs=None if model_required else '?') + model_args.add_argument("--model-type", help="The type of the input model: {{'frozen': TensorFlow frozen graph, 'keras': Keras model, " + "'ckpt': TensorFlow checkpoint directory, 'onnx': ONNX model, 'engine': TensorRT engine, 'uff': UFF file [deprecated], " + "'caffe': Caffe prototxt [deprecated]}}", choices=["frozen", "keras", "ckpt", "onnx", "uff", "caffe", "engine"], + default=None) + if inputs: + model_args.add_argument(inputs, inputs.replace("inputs", "input") + "-shapes", help="Model input(s) and their shape(s). Format: {arg_name} ,. " + "For example: {arg_name} image:1,1x3x224x224 other_input,10".format(arg_name=inputs), nargs="+", default=None, dest="inputs") + + +def add_dataloader_args(parser): + data_loader_args = parser.add_argument_group("Data Loader", "Options for modifying data used for inference") + data_loader_args.add_argument("--seed", metavar="SEED", help="Seed to use for random inputs", + type=int, default=None) + data_loader_args.add_argument("--int-min", help="Minimum integer value for random integer inputs", type=int, default=None) + data_loader_args.add_argument("--int-max", help="Maximum integer value for random integer inputs", type=int, default=None) + data_loader_args.add_argument("--float-min", help="Minimum float value for random float inputs", type=float, default=None) + data_loader_args.add_argument("--float-max", help="Maximum float value for random float inputs", type=float, default=None) + + +def add_comparator_args(parser, iters=True, accuracy=True, validate=True, read=True, write=True, fail_fast=True, subprocess=True, top_k=False): + comparator_args = parser.add_argument_group("Comparator", "Options for changing result comparison behavior") + if iters: + comparator_args.add_argument("--warm-up", metavar="NUM", help="Number of warm-up runs before timing inference", type=int, default=None) + comparator_args.add_argument("--iterations", metavar="NUM", help="Number of inference iterations", type=int, default=None) + if accuracy: + comparator_args.add_argument("--no-shape-check", help="Disable checking that output shapes match exactly", action="store_true", default=None) + comparator_args.add_argument("--rtol", metavar="RTOL", help="Relative tolerance for output comparison. See " + "https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html for details", type=float, default=None) + comparator_args.add_argument("--atol", metavar="ATOL", help="Absolute tolerance for output comparison. See " + "https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html for details", type=float, default=None) + if validate: + comparator_args.add_argument("--validate", help="Check outputs for NaNs", action="store_true", default=None) + if read: + comparator_args.add_argument("--load-results", help="Path(s) to load results from runners.", nargs="+", default=[]) + if write: + comparator_args.add_argument("--save-results", help="Path to save results from runners.", default=None) + if fail_fast: + comparator_args.add_argument("--fail-fast", help="Fail fast (stop comparing after the first failure)", action="store_true", default=None) + if subprocess: + comparator_args.add_argument("--use-subprocess", help="Run runners in isolated subprocesses. Cannot be used with a debugger", + action="store_true", default=None) + if top_k: + comparator_args.add_argument("--top-k", help="[EXPERIMENTAL] Apply Top-K (i.e. find indices of K largest values) to the outputs before comparing them.", type=int, default=None) + return comparator_args + + +def add_runner_args(parser): + # Appends to args.runners + class StoreRunnerOrdered(argparse.Action): + def __call__(self, parser, namespace, values, option_string=None): + if not hasattr(namespace, "runners"): + namespace.runners = [] + namespace.runners.append(option_string.lstrip("-").replace("-", "_")) + + runner_args = parser.add_argument_group("Runners", "Options for selecting runners. Zero or more runners may be specified") + runner_args.add_argument("--trt", help="Run inference using TensorRT", action=StoreRunnerOrdered, nargs=0) + runner_args.add_argument("--trt-legacy", help="Run inference using Legacy TensorRT Runner. Only supports networks using implicit batch mode", + action=StoreRunnerOrdered, nargs=0) + runner_args.add_argument("--tf", help="Run inference using TensorFlow", action=StoreRunnerOrdered, nargs=0) + runner_args.add_argument("--onnxrt", help="Run inference using ONNX Runtime", action=StoreRunnerOrdered, nargs=0) + runner_args.add_argument("--onnxtf", help="Run inference using the ONNX-TensorFlow Backend", action=StoreRunnerOrdered, nargs=0) + runner_args.add_argument("--cntk", help="[EXPERIMENTAL] Run inference on a CNTK model using CNTK", action=StoreRunnerOrdered, nargs=0) + + +def add_trt_args(parser, write=True, config=True, outputs=True, network_api=False): + trt_args = parser.add_argument_group("TensorRT", "Options for TensorRT") + if write: + trt_args.add_argument("--save-engine", help="Path to save a TensorRT engine file", default=None) + if config: + trt_args.add_argument("--trt-min-shapes", action='append', help="The minimum shapes the optimization profile(s) will support. " + "Specify this option once for each profile. If not provided, inference-time input shapes are used. " + "Format: --trt-min-shapes ,D0xD1x..xDN .. ,D0xD1x..xDN", nargs="+", default=[]) + trt_args.add_argument("--trt-opt-shapes", action='append', help="The shapes for which the optimization profile(s) will be most performant. " + "Specify this option once for each profile. If not provided, inference-time input shapes are used. " + "Format: --trt-opt-shapes ,D0xD1x..xDN .. ,D0xD1x..xDN", nargs="+", default=[]) + trt_args.add_argument("--trt-max-shapes", action='append', help="The maximum shapes the optimization profile(s) will support. " + "Specify this option once for each profile. If not provided, inference-time input shapes are used. " + "Format: --trt-max-shapes ,D0xD1x..xDN .. ,D0xD1x..xDN", nargs="+", default=[]) + + trt_args.add_argument("--tf32", help="Enable tf32 precision in TensorRT", action="store_true", default=None) + trt_args.add_argument("--fp16", help="Enable fp16 precision in TensorRT", action="store_true", default=None) + trt_args.add_argument("--int8", help="Enable int8 precision in TensorRT", action="store_true", default=None) + trt_args.add_argument("--strict-types", help="Enable strict types in TensorRT, forcing it to choose tactics based on the " + "layer precision set, even if another precision is faster.", action="store_true", default=None) + # Workspace uses float to enable scientific notation (e.g. 1e9) + trt_args.add_argument("--workspace", metavar="BYTES", help="Memory in bytes to allocate for the TensorRT builder's workspace", type=float, default=None) + trt_args.add_argument("--calibration-cache", help="Path to the calibration cache", default=None) + trt_args.add_argument("--plugins", help="Path(s) of additional plugin libraries to load", nargs="+", default=None) + trt_args.add_argument("--explicit-precision", help="Enable explicit precision mode", action="store_true", default=None) + trt_args.add_argument("--ext", help="Enable parsing ONNX models with externally stored weights", action="store_true", default=None) + if outputs: + trt_args.add_argument("--trt-outputs", help="Name(s) of TensorRT output(s). " + "Using '--trt-outputs mark all' indicates that all tensors should be used as outputs", nargs="+", default=None) + trt_args.add_argument("--trt-exclude-outputs", help="[EXPERIMENTAL] Name(s) of TensorRT output(s) to unmark as outputs. ", + nargs="+", default=None) + if network_api: + trt_args.add_argument("--network-api", help="[EXPERIMENTAL] Generated script will include placeholder code for defining a TensorRT Network using " + "the network API. Only valid if --gen/--gen-script is also enabled.", action="store_true", default=None) + + +def add_trt_legacy_args(parser): + trt_legacy_args = parser.add_argument_group("TensorRT Legacy", "[DEPRECATED] Options for TensorRT Legacy. Reuses TensorRT options, but does not support int8 mode, or dynamic shapes") + trt_legacy_args.add_argument("-p", "--preprocessor", help="The preprocessor to use for the UFF converter", default=None) + trt_legacy_args.add_argument("--uff-order", help="The order of the input", default=None) + trt_legacy_args.add_argument("--batch-size", metavar="SIZE", help="The batch size to use in TensorRT when it cannot be automatically determined", type=int, default=None) + trt_legacy_args.add_argument("--model", help="Model file for Caffe models. The deploy file should be provided as the model_file positional argument", dest="caffe_model") + trt_legacy_args.add_argument("--save-uff", help="Save intermediate UFF files", action="store_true", default=None) + + +def add_tf_args(parser, tftrt=True, artifacts=True, runtime=True, outputs=True): + tf_args = parser.add_argument_group("TensorFlow", "Options for TensorFlow") + tf_args.add_argument("--ckpt", help="[EXPERIMENTAL] Name of the checkpoint to load. Required if the `checkpoint` file is missing. Should not include file extension " + "(e.g. to load `model.meta` use `--ckpt=model`)", default=None) + if outputs: + tf_args.add_argument("--tf-outputs", help="Name(s) of TensorFlow output(s). " + "Using '--tf-outputs mark all' indicates that all tensors should be used as outputs", nargs="+", default=None) + if artifacts: + tf_args.add_argument("--save-pb", help="Path to save the TensorFlow frozen graphdef", default=None) + tf_args.add_argument("--save-tensorboard", help="[EXPERIMENTAL] Path to save a TensorBoard visualization", default=None) + tf_args.add_argument("--save-timeline", help="[EXPERIMENTAL] Directory to save timeline JSON files for profiling inference (view at chrome://tracing)", default=None) + if runtime: + tf_args.add_argument("--gpu-memory-fraction", help="Maximum percentage of GPU memory TensorFlow can allocate per process", type=float, default=None) + tf_args.add_argument("--allow-growth", help="Allow GPU memory allocated by TensorFlow to grow", action="store_true", default=None) + tf_args.add_argument("--xla", help="[EXPERIMENTAL] Attempt to run graph with xla", action="store_true", default=None) + tf_args.add_argument("--freeze-graph", help="[EXPERIMENTAL] Attempt to freeze the graph", action="store_true", default=None) + if tftrt: + tftrt_args = parser.add_argument_group("TensorFlow-TensorRT", "[UNTESTED] Options for TensorFlow-TensorRT Integration") + tftrt_args.add_argument("--tftrt", help="[UNTESTED] Enable TF-TRT integration", action="store_true", default=None) + tftrt_args.add_argument("--minimum-segment-size", help="Minimum length of a segment to convert to TensorRT", type=int, default=None) + tftrt_args.add_argument("--dynamic-op", help="Enable dynamic mode (defers engine build until runtime)", action="store_true", default=None) + + +def add_onnx_args(parser, write=True, outputs=True, shape_inference_default=None): + onnx_args = parser.add_argument_group("ONNX Options", "Options for ONNX") + if write: + onnx_args.add_argument("--save-onnx", help="Path to save the ONNX model", default=None) + + if shape_inference_default: + onnx_args.add_argument("--no-shape-inference", help="Disable ONNX shape inference when loading the model", action="store_true", default=None) + else: + onnx_args.add_argument("--shape-inference", help="Enable ONNX shape inference when loading the model", action="store_true", default=None) + + if outputs: + onnx_args.add_argument("--onnx-outputs", help="Name(s) of ONNX output(s). " + "Using '--onnx-outputs mark all' indicates that all tensors should be used as outputs", nargs="+", default=None) + onnx_args.add_argument("--onnx-exclude-outputs", help="[EXPERIMENTAL] Name(s) of ONNX output(s) to unmark as outputs.", nargs="+", default=None) + + +def add_tf_onnx_args(parser): + tf_onnx_args = parser.add_argument_group("TensorFlow-ONNX Options", "Options for TensorFlow-ONNX conversion") + tf_onnx_args.add_argument("--opset", help="Opset to use when converting to ONNX", default=None, type=int) + tf_onnx_args.add_argument("--no-const-folding", help="Do not fold constants in the TensorFlow graph prior to conversion", action="store_true", default=None) + + +def add_logger_args(parser): + logging_args = parser.add_argument_group("Logging", "Options for logging and debug output") + logging_args.add_argument("-v", "--verbose", help="Increase logging verbosity. Specify multiple times for higher verbosity", action="count", default=0) + logging_args.add_argument("--silent", help="Disable all output", action="store_true", default=None) + logging_args.add_argument("--log-format", help="Format for log messages: {{'timestamp': Include timestamp, 'line-info': Include file and line number, " + "'no-colors': Disable colors}}", choices=["timestamp", "line-info", "no-colors"], nargs="+", default=[]) + + +def get(args, attr): + """ + Gets a command-line argument if it exists, otherwise returns None. + + Args: + args: The command-line arguments. + attr (str): The name of the command-line argument. + """ + if hasattr(args, attr): + return getattr(args, attr) + return None + + +def parse_meta(meta_args, includes_shape=True, includes_dtype=True): + """ + Parses a list of tensor metadata arguments of the form ",," + `shape` and `dtype` are optional, but `dtype` must always come after `shape` if they are both enabled. + + Args: + meta_args (List[str]): A list of tensor metadata arguments from the command-line. + includes_shape (bool): Whether the arguments include shape information. + includes_dtype (bool): Whether the arguments include dtype information. + + Returns: + TensorMetadata: The parsed tensor metadata. + """ + SEP = "," + SHAPE_SEP = "x" + meta = TensorMetadata() + for orig_tensor_meta_arg in meta_args: + tensor_meta_arg = orig_tensor_meta_arg + + def pop_meta(name): + nonlocal tensor_meta_arg + tensor_meta_arg, _, val = tensor_meta_arg.rpartition(SEP) + if not tensor_meta_arg: + G_LOGGER.critical("Could not parse {:} from argument: {:}. Is it separated by a comma " + "(,) from the tensor name?".format(name, orig_tensor_meta_arg)) + if val.lower() == "auto": + val = None + return val + + + def parse_dtype(dtype): + if dtype is not None: + if dtype not in misc.NP_TYPE_FROM_STR: + G_LOGGER.critical("Could not understand data type: {:}. Please use one of: {:} or `auto`" + .format(dtype, list(misc.NP_TYPE_FROM_STR.keys()))) + dtype = misc.NP_TYPE_FROM_STR[dtype] + return dtype + + + def parse_shape(shape): + if shape is not None: + def parse_shape_dim(buf): + try: + buf = int(buf) + except: + pass + return buf + + + parsed_shape = [] + # Allow for quoted strings in shape dimensions + in_quotes = False + buf = "" + for char in shape.lower(): + if char in ["\"", "'"]: + in_quotes = not in_quotes + elif not in_quotes and char == SHAPE_SEP: + parsed_shape.append(parse_shape_dim(buf)) + buf = "" + else: + buf += char + # For the last dimension + parsed_shape.append(parse_shape_dim(buf)) + shape = tuple(parsed_shape) + return shape + + + name = None + dtype = None + shape = None + + if includes_dtype: + dtype = parse_dtype(pop_meta("data type")) + + if includes_shape: + shape = parse_shape(pop_meta("shape")) + + name = tensor_meta_arg + meta.add(name, dtype, shape) + return meta + + +# shapes is a TensorMetadata describing the runtime input shapes. +# Returns (List[Tuple[OrderedDict[str, List[int]]]) +def parse_profile_shapes(shapes, min_args, opt_args, max_args): + def get_shapes(lst, idx): + default_shapes = copy.copy(shapes) + if idx < len(lst): + default_shapes.update(parse_meta(lst[idx], includes_dtype=False)) + # Don't care about dtype, and need to override dynamic dimensions + default_shapes = {name: misc.override_dynamic_shape(shape) for name, (_, shape) in default_shapes.items()} + + for name, (_, shape) in shapes.items(): + if tuple(default_shapes[name]) != tuple(shape): + G_LOGGER.warning("Input tensor: {:} | For TensorRT profile, overriding shape: {:} to: {:}".format(name, shape, default_shapes[name]), mode=LogMode.ONCE) + + return default_shapes + + + num_profiles = max(len(min_args), len(opt_args), len(max_args)) + + # For cases where input shapes are provided, we have to generate a profile + if not num_profiles and shapes: + num_profiles = 1 + + + profiles = [] + for idx in range(num_profiles): + min_shapes = get_shapes(min_args, idx) + opt_shapes = get_shapes(opt_args, idx) + max_shapes = get_shapes(max_args, idx) + if sorted(min_shapes.keys()) != sorted(opt_shapes.keys()): + G_LOGGER.critical("Mismatch in input names between minimum shapes ({:}) and optimum shapes " + "({:})".format(list(min_shapes.keys()), list(opt_shapes.keys()))) + elif sorted(opt_shapes.keys()) != sorted(max_shapes.keys()): + G_LOGGER.critical("Mismatch in input names between optimum shapes ({:}) and maximum shapes " + "({:})".format(list(opt_shapes.keys()), list(max_shapes.keys()))) + + profiles.append((min_shapes, opt_shapes, max_shapes)) + return profiles + + +def setup_logger(args): + if args.verbose >= 4: + G_LOGGER.severity = G_LOGGER.ULTRA_VERBOSE + elif args.verbose == 3: + G_LOGGER.severity = G_LOGGER.SUPER_VERBOSE + elif args.verbose == 2: + G_LOGGER.severity = G_LOGGER.EXTRA_VERBOSE + elif args.verbose == 1: + G_LOGGER.severity = G_LOGGER.VERBOSE + + if args.silent: + G_LOGGER.severity = G_LOGGER.CRITICAL + + for fmt in args.log_format: + if fmt == "no-colors": + G_LOGGER.colors = False + elif fmt == "timestamp": + G_LOGGER.timestamp = True + elif fmt == "line-info": + G_LOGGER.line_info = True + + +def determine_model_type(args): + if get(args, "model_type") is not None: + return args.model_type.lower() + + if get(args, "model_file") is None: + return None + + def use_ext(ext_mapping): + file_ext = os.path.splitext(args.model_file)[-1] + if file_ext in ext_mapping: + return ext_mapping[file_ext] + + if get(args, "ckpt") or os.path.isdir(args.model_file): + return "ckpt" + elif "tf" in args.runners or "trt_legacy" in args.runners: + if args.caffe_model: + return "caffe" + ext_mapping = {".hdf5": "keras", ".uff": "uff", ".prototxt": "caffe", ".onnx": "onnx", ".engine": "engine", ".plan": "engine"} + return use_ext(ext_mapping) or "frozen" + else: + # When no framework is provided, some extensions can be ambiguous + ext_mapping = {".hdf5": "keras", ".graphdef": "frozen", ".onnx": "onnx", ".uff": "uff", ".engine": "engine", ".plan": "engine"} + model_type = use_ext(ext_mapping) + if model_type: + return model_type + + G_LOGGER.critical("Could not automatically determine model type for: {:}\n" + "Please explicitly specify the type with the --model-type option".format( + args.model_file)) + + +def setup(args, unknown): + """ + Prepares argument values for use. + """ + def exist(names): + return all([hasattr(args, name) for name in names]) + + def process_output_arg(name): + arg = get(args, name) + if arg is not None and len(arg) == 2 and arg == ["mark", "all"]: + arg = constants.MARK_ALL + setattr(args, name, arg) + + + if unknown: + G_LOGGER.critical("Unrecognized Options: {:}".format(unknown)) + + setup_logger(args) + + if not exist(["runners"]): # For when no runners are specified + args.runners = [] + + if get(args, "network_api") and not get(args, "gen_script"): + G_LOGGER.critical("Cannot use the --network-api option if --gen/--gen-script is not being used.") + elif get(args, "network_api") and "trt" not in args.runners: + args.runners.append("trt") + + if get(args, "model_file"): + G_LOGGER.verbose("Model: {:}".format(args.model_file)) + if not os.path.exists(args.model_file): + G_LOGGER.warning("Model path does not exist: {:}".format(args.model_file)) + args.model_file = os.path.abspath(args.model_file) + elif args.runners and get(args, "network_api") is None: + G_LOGGER.critical("One or more runners was specified, but no model file was provided. Make sure you've specified the model path, " + "and also that it's not being consumed as an argument for another parameter") + + args.model_type = determine_model_type(args) + + if get(args, "inputs"): + args.inputs = parse_meta(args.inputs, includes_dtype=False) # TensorMetadata + else: + args.inputs = TensorMetadata() + + if exist(["trt_min_shapes", "trt_opt_shapes", "trt_max_shapes"]): + args.profiles = parse_profile_shapes(args.inputs, args.trt_min_shapes, args.trt_opt_shapes, args.trt_max_shapes) + elif args.inputs: + args.profiles = parse_profile_shapes(args.inputs, [], [], []) + + if exist(["workspace"]): + args.workspace = int(args.workspace) if args.workspace is not None else args.workspace + + process_output_arg("tf_outputs") + process_output_arg("trt_outputs") + process_output_arg("onnx_outputs") + + return args diff --git a/tools/Polygraphy/polygraphy/tools/util/misc.py b/tools/Polygraphy/polygraphy/tools/util/misc.py new file mode 100644 index 00000000..0da3cea1 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/util/misc.py @@ -0,0 +1,364 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.tools.util.script import Script, Inline +from polygraphy.tools.util import args as args_util +from polygraphy.logger import G_LOGGER, LogMode +from polygraphy.common import constants +from polygraphy.util import misc + +import os + + +################################# SCRIPT HELPERS ################################# + + +def add_logger_settings(script, args): + # Always required since it is used to print the exit message. + script.append_preimport("from polygraphy.logger import G_LOGGER") + + logger_settings = [] + verbosity_count = args_util.get(args, "verbose") + if verbosity_count >= 4: + logger_settings.append("G_LOGGER.severity = G_LOGGER.ULTRA_VERBOSE") + elif verbosity_count == 3: + logger_settings.append("G_LOGGER.severity = G_LOGGER.SUPER_VERBOSE") + elif verbosity_count == 2: + logger_settings.append("G_LOGGER.severity = G_LOGGER.EXTRA_VERBOSE") + elif verbosity_count == 1: + logger_settings.append("G_LOGGER.severity = G_LOGGER.VERBOSE") + + if args_util.get(args, "silent"): + logger_settings.append("G_LOGGER.severity = G_LOGGER.CRITICAL") + + log_format = misc.default_value(args_util.get(args, "log_format"), []) + for fmt in args.log_format: + if fmt == "no-colors": + logger_settings.append("G_LOGGER.colors = False") + elif fmt == "timestamp": + logger_settings.append("G_LOGGER.timestamp = True") + elif fmt == "line-info": + logger_settings.append("G_LOGGER.line_info = True") + + for setting in logger_settings: + script.append_preimport(setting) + + +def _get_outputs_arg(script, args, name): + outputs = args_util.get(args, name) + if outputs == constants.MARK_ALL: + outputs = Inline("constants.MARK_ALL") + script.add_import(["constants"], frm="polygraphy.common") + return outputs + + +def add_tf_loader(script, args, disable_outputs=None, suffix=None): + if disable_outputs: + outputs = None + else: + outputs = _get_outputs_arg(script, args, "tf_outputs") + + model_file = args_util.get(args, "model_file") + model_type = args_util.get(args, "model_type") + + save_pb = args_util.get(args, "save_pb") + save_tensorboard = args_util.get(args, "save_tensorboard") + + if model_type == "ckpt": + G_LOGGER.verbose("Loading a TensorFlow checkpoint. Please ensure you are not using the --use-subprocess flag".format(model_file), mode=LogMode.ONCE) + script.add_import(imports=["GraphFromCkpt"], frm="polygraphy.backend.tf") + loader_id = "load_ckpt" + loader_str = Script.invoke("GraphFromCkpt", model_file, args_util.get(args, "ckpt")) + elif model_type == "keras": + script.add_import(imports=["GraphFromKeras"], frm="polygraphy.backend.tf") + loader_id = "load_keras" + loader_str = Script.invoke("GraphFromKeras", model_file) + else: + script.add_import(imports=["GraphFromFrozen"], frm="polygraphy.backend.tf") + G_LOGGER.verbose("Attempting to load as a frozen graph. If this is not correct, please specify --model-type", mode=LogMode.ONCE) + loader_id = "load_frozen" + loader_str = Script.invoke("GraphFromFrozen", model_file) + + loader_name = script.add_loader(loader_str, loader_id, suffix=suffix) + + if args_util.get(args, "freeze_graph"): + script.add_import(imports=["OptimizeGraph"], frm="polygraphy.backend.tf") + loader_name = script.add_loader(Script.invoke("OptimizeGraph", loader_name), "optimize_graph", suffix=suffix) + if args_util.get(args, "tftrt"): + script.add_import(imports=["UseTfTrt"], frm="polygraphy.backend.tf") + loader_str = Script.invoke("UseTfTrt", loader_name, max_workspace_size=args_util.get(args, "workspace"), fp16=args_util.get(args, "fp16"), int8=args_util.get(args, "int8"), + max_batch_size=args_util.get(args, "batch_size"), is_dynamic_op=args_util.get(args, "dynamic_op"), minimum_segment_size=args_util.get(args, "minimum_segment_size")) + loader_name = script.add_loader(loader_str, "use_tftrt", suffix=suffix) + + MODIFY_TF = "ModifyGraph" + modify_tf_str = Script.invoke(MODIFY_TF, loader_name, outputs=outputs) + if modify_tf_str != Script.invoke(MODIFY_TF, loader_name): + script.add_import(imports=[MODIFY_TF], frm="polygraphy.backend.tf") + loader_name = script.add_loader(modify_tf_str, "modify_tf") + + engine_dir = None + if args_util.get(args, "tftrt"): + engine_dir = args_util.get(args, "save_engine") + + WRITE_TF = "SaveGraph" + write_tf_str = Script.invoke(WRITE_TF, loader_name, path=save_pb, tensorboard_dir=save_tensorboard, engine_dir=engine_dir) + if write_tf_str != Script.invoke(WRITE_TF, loader_name): + script.add_import(imports=[WRITE_TF], frm="polygraphy.backend.tf") + loader_name = script.add_loader(write_tf_str, "save_tf") + + return loader_name + + +def add_tf_config_loader(script, args): + config_loader_str = Script.invoke_if_nondefault("CreateConfig", gpu_memory_fraction=args_util.get(args, "gpu_memory_fraction"), + allow_growth=args_util.get(args, "allow_growth"), use_xla=args_util.get(args, "xla")) + if config_loader_str is not None: + script.add_import(imports=["CreateConfig"], frm="polygraphy.backend.tf") + config_loader_name = script.add_loader(config_loader_str, "create_tf_config") + else: + config_loader_name = None + return config_loader_name + + + +def get_modify_onnx_str(script, args, loader_name, disable_outputs=None): + if disable_outputs: + outputs = None + exclude_outputs = None + else: + outputs = _get_outputs_arg(script, args, "onnx_outputs") + exclude_outputs = args_util.get(args, "onnx_exclude_outputs") + + if hasattr(args, "shape_inference"): + do_shape_inference = args_util.get(args, "shape_inference") + else: + do_shape_inference = None if args_util.get(args, "no_shape_inference") else True + + MODIFY_ONNX = "ModifyOnnx" + modify_onnx_str = Script.invoke(MODIFY_ONNX, loader_name, do_shape_inference=do_shape_inference, + outputs=outputs, exclude_outputs=exclude_outputs) + if modify_onnx_str != Script.invoke(MODIFY_ONNX, loader_name): + script.add_import(imports=[MODIFY_ONNX], frm="polygraphy.backend.onnx") + return modify_onnx_str + return None + + +def add_onnx_loader(script, args, disable_outputs=None, suffix=None): + if args_util.get(args, "model_type") == "onnx": + script.add_import(imports=["OnnxFromPath"], frm="polygraphy.backend.onnx") + loader_str = Script.invoke("OnnxFromPath", args_util.get(args, "model_file")) + loader_name = script.add_loader(loader_str, "load_onnx", suffix=suffix) + else: + G_LOGGER.verbose("Attempting to load as a TensorFlow model, using TF2ONNX to convert to ONNX. " + "If this is not correct, please specify --model-type", mode=LogMode.ONCE) + script.add_import(imports=["OnnxFromTfGraph"], frm="polygraphy.backend.onnx") + loader_str = Script.invoke("OnnxFromTfGraph", add_tf_loader(script, args, disable_outputs=True, suffix=suffix), + opset=args_util.get(args, "opset"), fold_constant=False if args_util.get(args, "no_const_folding") else None) + loader_name = script.add_loader(loader_str, "export_onnx_from_tf", suffix=suffix) + + modify_onnx_str = get_modify_onnx_str(script, args, loader_name, disable_outputs=disable_outputs) + if modify_onnx_str is not None: + loader_name = script.add_loader(modify_onnx_str, "modify_onnx") + + save_onnx = args_util.get(args, "save_onnx") + SAVE_ONNX = "SaveOnnx" + save_onnx_str = Script.invoke(SAVE_ONNX, loader_name, path=save_onnx) + if save_onnx_str != Script.invoke(SAVE_ONNX, loader_name): + script.add_import(imports=[SAVE_ONNX], frm="polygraphy.backend.onnx") + loader_name = script.add_loader(save_onnx_str, "save_onnx") + + return loader_name + + +def add_serialized_onnx_loader(script, args, disable_outputs=None): + model_file = args_util.get(args, "model_file") + + needs_modify = get_modify_onnx_str(script, args, "check_needs_modify", disable_outputs) is not None + should_import_raw = args_util.get(args, "model_type") == "onnx" and not needs_modify + + if should_import_raw: + script.add_import(imports=["BytesFromPath"], frm="polygraphy.backend.common") + onnx_loader = script.add_loader(Script.invoke("BytesFromPath", model_file), "load_serialized_onnx") + else: + script.add_import(imports=["BytesFromOnnx"], frm="polygraphy.backend.onnx") + onnx_loader = add_onnx_loader(script, args, disable_outputs=disable_outputs) + onnx_loader = script.add_loader(Script.invoke("BytesFromOnnx", onnx_loader), "serialize_onnx") + return onnx_loader + + +# If plugins are present, wrap the provided loader/object with LoadPlugins +def _wrap_if_plugins(script, args, obj_name): + plugins = args_util.get(args, "plugins") + if plugins: + script.add_import(imports=["LoadPlugins"], frm="polygraphy.backend.trt") + loader_str = Script.invoke("LoadPlugins", obj_name, plugins=plugins) + obj_name = script.add_loader(loader_str, "load_plugins") + return obj_name + + +def add_trt_network_loader(script, args): + model_file = args_util.get(args, "model_file") + outputs = _get_outputs_arg(script, args, "trt_outputs") + + if args_util.get(args, "network_api"): + CREATE_NETWORK_FUNC = Inline("create_network") + + script.add_import(imports=["CreateNetwork"], frm="polygraphy.backend.trt") + script.add_import(imports=["extend"], frm="polygraphy.common.func") + + script.append_prefix("# Manual TensorRT network creation") + script.append_prefix("@extend(CreateNetwork())") + script.append_prefix("def {:}(builder, network):".format(CREATE_NETWORK_FUNC)) + script.append_prefix("{tab}import tensorrt as trt\n".format(tab=constants.TAB)) + script.append_prefix("{tab}# Define your network here. Make sure to mark outputs!".format(tab=constants.TAB)) + net_inputs = args_util.get(args, "inputs") + if net_inputs: + for name, (dtype, shape) in net_inputs.items(): + script.append_prefix("{tab}{name} = network.add_input(name='{name}', shape={shape}, dtype=trt.float32) # TODO: Set dtype".format( + name=name, shape=shape, tab=constants.TAB)) + script.append_prefix("{tab}# TODO: network.mark_output(...)\n".format(tab=constants.TAB)) + return CREATE_NETWORK_FUNC + + + if args_util.get(args, "ext"): + script.add_import(imports=["NetworkFromOnnxPath"], frm="polygraphy.backend.trt") + loader_str = Script.invoke("NetworkFromOnnxPath", _wrap_if_plugins(script, args, model_file), explicit_precision=args_util.get(args, "explicit_precision")) + loader_name = script.add_loader(loader_str, "parse_network_from_onnx") + else: + script.add_import(imports=["NetworkFromOnnxBytes"], frm="polygraphy.backend.trt") + onnx_loader = add_serialized_onnx_loader(script, args, disable_outputs=True) + loader_str = Script.invoke("NetworkFromOnnxBytes", _wrap_if_plugins(script, args, onnx_loader), explicit_precision=args_util.get(args, "explicit_precision")) + loader_name = script.add_loader(loader_str, "parse_network_from_onnx") + + MODIFY_NETWORK = "ModifyNetwork" + modify_network_str = Script.invoke(MODIFY_NETWORK, loader_name, outputs=outputs, exclude_outputs=args_util.get(args, "trt_exclude_outputs")) + if modify_network_str != Script.invoke(MODIFY_NETWORK, loader_name): + script.add_import(imports=[MODIFY_NETWORK], frm="polygraphy.backend.trt") + loader_name = script.add_loader(modify_network_str, "modify_network") + + return loader_name + + +def add_trt_config_loader(script, args, data_loader_name): + profiles = [] + for (min_shape, opt_shape, max_shape) in args_util.get(args, "profiles"): + profile_str = "Profile()" + for name in min_shape.keys(): + profile_str += Script.format_str(".add({:}, min={:}, opt={:}, max={:})", name, min_shape[name], opt_shape[name], max_shape[name]) + profiles.append(Inline(profile_str)) + if profiles: + script.add_import(imports=["Profile"], frm="polygraphy.backend.trt") + sep = Inline("\n{:}".format(constants.TAB)) + profiles = Script.format_str("[{:}{:}\n]", sep, Inline((",{:}".format(sep)).join(profiles))) + profile_name = script.add_loader(profiles, "profiles") + else: + profile_name = None + + calibrator = None + if args_util.get(args, "int8"): + script.add_import(imports=["DataLoader"], frm="polygraphy.comparator") + script.add_import(imports=["Calibrator"], frm="polygraphy.backend.trt") + calibrator = Script.invoke("Calibrator", data_loader=Inline(data_loader_name) if data_loader_name else Inline("DataLoader()"), + cache=args_util.get(args, "calibration_cache")) + + config_loader_str = Script.invoke_if_nondefault("CreateTrtConfig", max_workspace_size=args_util.get(args, "workspace"), tf32=args_util.get(args, "tf32"), + fp16=args_util.get(args, "fp16"), int8=args_util.get(args, "int8"), strict_types=args_util.get(args, "strict_types"), + profiles=profile_name, calibrator=Inline(calibrator) if calibrator else None) + if config_loader_str is not None: + script.add_import(imports=["CreateConfig as CreateTrtConfig"], frm="polygraphy.backend.trt") + config_loader_name = script.add_loader(config_loader_str, "create_trt_config") + else: + config_loader_name = None + return config_loader_name + + +def add_trt_serialized_engine_loader(script, args): + script.add_import(imports=["EngineFromBytes"], frm="polygraphy.backend.trt") + script.add_import(imports=["BytesFromPath"], frm="polygraphy.backend.common") + + load_engine = script.add_loader(Script.invoke("BytesFromPath", args_util.get(args, "model_file")), "load_engine") + return script.add_loader(Script.invoke("EngineFromBytes", _wrap_if_plugins(script, args, load_engine)), "deserialize_engine") + + +def add_data_loader(script, args): + def omit_none_tuple(tup): + if all([elem is None for elem in tup]): + return None + return tup + + int_range = omit_none_tuple(tup=(args_util.get(args, "int_min"), args_util.get(args, "int_max"))) + float_range = omit_none_tuple(tup=(args_util.get(args, "float_min"), args_util.get(args, "float_max"))) + + input_metadata_str = Inline(repr(args_util.get(args, "inputs"))) if args_util.get(args, "inputs") else None + if input_metadata_str: + script.add_import(imports=["TensorMetadata"], frm="polygraphy.common") + + data_loader = Script.invoke_if_nondefault("DataLoader", seed=args_util.get(args, "seed"), iterations=args_util.get(args, "iterations"), + input_metadata=input_metadata_str, int_range=int_range, float_range=float_range) + if data_loader is not None: + data_loader_name = Inline("data_loader") + script.add_import(imports=["DataLoader"], frm="polygraphy.comparator") + script.append_prefix(Script.format_str("\n# Inference Inputs Loader\n{:} = {:}\n", data_loader_name, Inline(data_loader))) + else: + data_loader_name = None + return data_loader_name + + +################################# PYTHON HELPERS ################################# + + +def get_tf_model_loader(args): + script = Script() + loader_name = add_tf_loader(script, args) + exec(str(script), globals(), locals()) + return locals()[loader_name] + + +def get_onnx_model_loader(args): + script = Script() + loader_name = add_onnx_loader(script, args) + exec(str(script), globals(), locals()) + return locals()[loader_name] + + +def get_trt_network_loader(args): + script = Script() + loader_name = add_trt_network_loader(script, args) + exec(str(script), globals(), locals()) + return locals()[loader_name] + + +def get_trt_config_loader(args, data_loader): + script = Script() + loader_name = add_trt_config_loader(script, args, data_loader_name="data_loader") + exec(str(script), globals(), locals()) + return locals()[loader_name] + + +def get_trt_serialized_engine_loader(args): + script = Script() + loader_name = add_trt_serialized_engine_loader(script, args) + exec(str(script), globals(), locals()) + return locals()[loader_name] + + +def get_data_loader(args): + script = Script() + data_loader_name = add_data_loader(script, args) + if data_loader_name is None: # All arguments are default + from polygraphy.comparator import DataLoader + return DataLoader() + exec(str(script), globals(), locals()) + return locals()[data_loader_name] diff --git a/tools/Polygraphy/polygraphy/tools/util/script.py b/tools/Polygraphy/polygraphy/tools/util/script.py new file mode 100644 index 00000000..d2e43f05 --- /dev/null +++ b/tools/Polygraphy/polygraphy/tools/util/script.py @@ -0,0 +1,264 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.logger import G_LOGGER, LogMode +from polygraphy.util import misc +from polygraphy.common import constants +import polygraphy + +from collections import OrderedDict, defaultdict +import time +import sys +import os + +# Special marker that indicates a string should be literally copied into the script, not wrapped in quotes +class Inline(str): + pass + +# Processes arguments for an object in the generated script. +def process_args(args, kwargs): + def process_arg(arg): + if arg is not None: + if isinstance(arg, str) and not isinstance(arg, Inline): + return "'{:}'".format(arg) + if isinstance(arg, OrderedDict): + return dict(arg) + return arg + args = [process_arg(arg) for arg in args] + kwargs = {key: process_arg(val) for key, val in kwargs.items()} + return args, kwargs + + +def invoke_impl(type_str, *args, **kwargs): + args, kwargs = process_args(args, kwargs) + obj_str = "{:}(".format(type_str) + obj_str += ", ".join(args) + + all_defaults = all([arg is None for arg in args]) + is_first = len(args) == 0 + + for key, val in kwargs.items(): + if val is not None: + all_defaults = False + if not is_first: + obj_str += ", " + obj_str += "{:}={:}".format(key, val) + is_first = False + obj_str += ")" + return obj_str, all_defaults + + +################################# SCRIPT ################################## +# Used to generate a script that uses the Polygraphy API. + +class Script(object): + @staticmethod + def format_str(base_str, *args, **kwargs): + """ + Like str.format(), but includes string arguments with quotes, unless + they are marked Inline. + + Examples: + format_str("{:} is my name", "polygraphy") + -> "'polygraphy' is my name" + + format_str("{:} is my name", Inline("polygraphy")) + -> "polygraphy is my name" + """ + args, kwargs = process_args(args, kwargs) + return base_str.format(*args, **kwargs) + + + @staticmethod + def invoke(type_str, *args, **kwargs): + """ + Creates a string representation that will invoke the specified object, + with the specified arguments. + + Args: + type_str (str): A string representing the object that should be invoked. + *args and **kwargs: + Arguments to pass along to the object. If a keyword argument + is set to None, it will be omitted. + + Returns: + str: A string representation that invokes the object specified. + + Examples: + Script.invoke("MyClass", 0, 1, last=3) + -> "MyClass(0, 1, last=3)" + + Script.invoke("my_func", 0, 1, last=None) + -> "my_func(0, 1)" + """ + return invoke_impl(type_str, *args, **kwargs)[0] + + + @staticmethod + def invoke_if_nondefault(type_str, *args, **kwargs): + """ + Similar to `invoke`, but will return None if all arguments are None. + + Examples: + Script.invoke("MyClass", 0, 1, last=3) + -> "MyClass(0, 1, last=3)" + + Script.invoke("my_func", None, None, last=None) + -> None + """ + obj_str, all_defaults = invoke_impl(type_str, *args, **kwargs) + if all_defaults: + return None + return obj_str + + + def __init__(self, summary=None): + """ + Represents a Python script that uses the Polygraphy API. + + + summary (str): + A summary of what the script does, which will be included in the script as a comment. + """ + self.imports = set() + self.from_imports = defaultdict(set) # Dict[str, List[str]] Maps from module to imported components (e.g. {"polygraphy.util": ["misc", "cli"]}) + self.loaders = OrderedDict() # Dict[str, str] Maps a string constructing a loader to a name. + self.loader_count = defaultdict(int) # Dict[str, int] Maps loader_id to the number of loaders sharing that ID + self.runners = [] # List[str] + self.preimport = [] # List[str] + self.prefix = [] # List[str] + self.suffix = [] # List[str] + self.summary = summary + + + def add_import(self, imports, frm=None): + """ + Adds imports to this script + + Args: + imports (List[str]): List of components to import + frm (str): Module frm which to import + """ + if frm: + self.from_imports[frm].update(imports) + else: + self.imports.update(imports) + + + def add_loader(self, loader_str, loader_id, suffix=None): + """ + Adds a loader to the script. + If the loader is a duplicate, returns the existing loader instead. + + Args: + loader_str (str): A string constructing the loader. + loader_id (str): A short human-friendly identifier for the loader + + Returns: + str: The name of the loader added. + """ + suffix = misc.default_value(suffix, "") + + if loader_str in self.loaders: + return self.loaders[loader_str] + + unique_name = loader_id + suffix + if self.loader_count[unique_name]: + unique_name = "{:}_{:}".format(unique_name, self.loader_count[loader_id]) + unique_name = Inline(unique_name) + + self.loader_count[loader_id] += 1 + self.loaders[loader_str] = unique_name + return unique_name + + + def get_runners(self): + return Inline("runners") + + + def add_runner(self, runner_str): + """ + Adds a runner to the script. + + Args: + runner_str (str): A string constructing the runner + """ + self.runners.append(runner_str) + + + def append_preimport(self, line): + """ + Append a line to the pre-import prefix of the script + + Args: + line (str): The line to append. + """ + self.preimport.append(line) + + + def append_prefix(self, line): + """ + Append a line to the prefix of the script + + Args: + line (str): The line to append. + """ + self.prefix.append(line) + + + def append_suffix(self, line): + """ + Append a line to the suffix of the script + + Args: + line (str): The line to append. + """ + self.suffix.append(line) + + + def __str__(self): + script = "#!/usr/bin/env python3\n" + script += "# Template auto-generated by polygraphy [v{:}] on {:} at {:}\n".format( + polygraphy.__version__, time.strftime("%D"), time.strftime("%H:%M:%S")) + script += "# Generation Command: {:}\n".format(" ".join(sys.argv)) + if self.summary: + script += "# " + "\n# ".join(self.summary.splitlines()) + "\n" + script += "\n".join(self.preimport) + ("\n\n" if self.preimport else "") + + for imp in sorted(self.imports): + script += "import {:}\n".format(imp) + for frm, imps in sorted(self.from_imports.items()): + imps = sorted(imps) + script += "from {:} import {:}\n".format(frm, ", ".join(imps)) + script += "\n" + + script += "\n".join(self.prefix) + ("\n" if self.prefix else "") + + if self.loaders: + script += "# Loaders\n" + for loader, loader_name in self.loaders.items(): + script += "{:} = {:}\n".format(loader_name, loader) + script += "\n" + + script += "# Runners\n" + script += "{:} = [\n".format(self.get_runners()) + for runner in self.runners: + script += "{:}{:},\n".format(constants.TAB, runner) + script += "]\n" + + script += "\n".join(self.suffix) + "\n" + + G_LOGGER.super_verbose("Created script:\n{:}".format(script)) + return script diff --git a/tools/Polygraphy/polygraphy/util/__init__.py b/tools/Polygraphy/polygraphy/util/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/polygraphy/util/cuda.py b/tools/Polygraphy/polygraphy/util/cuda.py new file mode 100644 index 00000000..4f1fcc21 --- /dev/null +++ b/tools/Polygraphy/polygraphy/util/cuda.py @@ -0,0 +1,200 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.logger.logger import G_LOGGER, LogMode +from polygraphy.util import misc + +import ctypes + +import numpy as np + + +class Cuda(object): + class MemcpyKind(object): + HostToHost = ctypes.c_int(0) + HostToDevice = ctypes.c_int(1) + DeviceToHost = ctypes.c_int(2) + DeviceToDevice = ctypes.c_int(3) + Default = ctypes.c_int(4) + + + def __init__(self): + self.handle = ctypes.CDLL("libcudart.so") + if not self.handle: + G_LOGGER.critical("Could not load the CUDA runtime library. Is it on your loader path?") + + + def check(self, status): + if status != 0: + G_LOGGER.critical("CUDA Error: {:}. To figure out what this means, refer to " + "https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038".format(status)) + + + def create_stream(self): + stream = ctypes.c_void_p(None) + self.check(self.handle.cudaStreamCreate(ctypes.byref(stream))) + return stream + + + def stream_synchronize(self, stream): + self.check(self.handle.cudaStreamSynchronize(stream)) + + + def destroy_stream(self, stream): + self.check(self.handle.cudaStreamDestroy(stream)) + + + def malloc(self, nbytes): + ptr = ctypes.c_void_p(None) + self.check(self.handle.cudaMalloc(ctypes.byref(ptr), nbytes)) + return ptr + + + def free(self, ptr): + self.check(self.handle.cudaFree(ptr)) + + + def htod(self, dst, src, nbytes, stream=None): + if stream is not None: + self.check(self.handle.cudaMemcpyAsync(dst, src, nbytes, Cuda.MemcpyKind.HostToDevice, stream)) + else: + self.check(self.handle.cudaMemcpy(dst, src, nbytes, Cuda.MemcpyKind.HostToDevice)) + + + def dtoh(self, dst, src, nbytes, stream=None): + if stream is not None: + self.check(self.handle.cudaMemcpyAsync(dst, src, nbytes, Cuda.MemcpyKind.DeviceToHost, stream)) + else: + self.check(self.handle.cudaMemcpy(dst, src, nbytes, Cuda.MemcpyKind.DeviceToHost)) + + +G_CUDA = None +def wrapper(): + global G_CUDA + G_CUDA = misc.default_value(G_CUDA, Cuda()) + return G_CUDA + + +class Stream(object): + def __init__(self): + self.handle = wrapper().create_stream() + + + def free(self): + wrapper().destroy_stream(self.handle) + + + def synchronize(self): + wrapper().stream_synchronize(self.handle) + + + def address(self): + return self.handle.value + + +def try_get_stream_handle(stream): + if stream is None: + return None + return stream.handle + + +class DeviceBuffer(object): + def __init__(self, shape=None, dtype=None): + """ + Represents a buffer on the GPU. + + Args: + shape (Tuple[int]): The initial shape of the buffer. + dtype (np.dtype): The data type of the buffer. + """ + self.shape = misc.default_value(shape, tuple()) + self.dtype = misc.default_value(dtype, np.float32) + self.allocated_nbytes = 0 + self._ptr = ctypes.c_void_p(None) + self.resize(self.shape) + + + def address(self): + return self._ptr.value + + + def allocate(self, nbytes): + if nbytes: + self._ptr = wrapper().malloc(nbytes) + self.allocated_nbytes = nbytes + + + def free(self): + wrapper().free(self._ptr) + self.shape = tuple() + self.allocated_nbytes = 0 + self._ptr = ctypes.c_void_p(None) + + + def resize(self, shape): + nbytes = misc.volume(shape) * np.dtype(self.dtype).itemsize + if nbytes > self.allocated_nbytes: + self.free() + self.allocate(nbytes) + self.shape = shape + + + def _check_dtype_matches(self, host_buffer): + if host_buffer.dtype != self.dtype: + G_LOGGER.warning("Host buffer type: {:} does not match the type of device buffer: {:}. " + "This may cause CUDA errors!".format(host_buffer.dtype, self.dtype)) + + + def copy_from(self, host_buffer, stream=None): + if host_buffer.nbytes: + # When copying from a host buffer, we always adopt its dtype + self.dtype = host_buffer.dtype + self._check_dtype_matches(host_buffer) + self.resize(host_buffer.shape) + buffer = np.ascontiguousarray(host_buffer.ravel()) + host_ptr = buffer.ctypes.data_as(ctypes.c_void_p) + wrapper().htod(dst=self._ptr, src=host_ptr, nbytes=host_buffer.nbytes, stream=try_get_stream_handle(stream)) + + + def copy_to(self, host_buffer, stream=None): + """ + Copies from this device buffer to the provided host buffer. + Host buffer must be contiguous in memory (see np.ascontiguousarray). + + Args: + host_buffer (np.ndarray): The host buffer to copy into. + stream (Stream): + A Stream instance (see util/cuda.py). Performs a synchronous copy if no stream is provided. + + Returns: + np.ndarray: The host buffer, possibly reallocated if the provided buffer was too small. + """ + nbytes = misc.volume(self.shape) * np.dtype(self.dtype).itemsize + self._check_dtype_matches(host_buffer) + + try: + host_buffer.resize(self.shape, refcheck=False) + except ValueError: + host_buffer = np.empty(self.shape, dtype=np.dtype(self.dtype)) + + if nbytes: + host_ptr = host_buffer.ctypes.data_as(ctypes.c_void_p) + wrapper().dtoh(dst=host_ptr, src=self._ptr, nbytes=nbytes, stream=try_get_stream_handle(stream)) + host_buffer = host_buffer.reshape(self.shape) + return host_buffer + + + def __str__(self): + return "DeviceBuffer[(dtype={:}, shape={:}), ptr={:}]".format(np.dtype(self.dtype).name, self.shape, hex(self._ptr.value)) diff --git a/tools/Polygraphy/polygraphy/util/format.py b/tools/Polygraphy/polygraphy/util/format.py new file mode 100644 index 00000000..15beac7c --- /dev/null +++ b/tools/Polygraphy/polygraphy/util/format.py @@ -0,0 +1,112 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.logger.logger import G_LOGGER +import enum + +# TRT does not include batch dimension. +class DataFormat(enum.IntEnum): + UNKNOWN = 0 + NW = 1 + NHW = 2 + CHW = 3 + NHWC = 4 + NCHW = 5 + +# This class is responsible for deducing the format of a shape, +# and converting it to the desired format (specified as a DataFormat). +class FormatManager(object): + # NOTE: New permutations should be added in this function, as it will automatically generate the inverses. + def _generate_permutations(): + def is_invertible(perm): + return min(perm) >= 0 and max(perm) < len(perm) + + def inverse_permutation(perm): + inverse = [perm[index] for index in perm] + return inverse + + # Inverse permutations are generated automatically below. + # We use -1 to denote that a dummy dimension of 1 should be inserted in the convert function. + initial_permutations = { + (DataFormat.NCHW, DataFormat.NCHW): (0, 1, 2, 3), + (DataFormat.NHWC, DataFormat.NHWC): (0, 1, 2, 3), + (DataFormat.NHWC, DataFormat.NCHW): (0, 3, 1, 2), + (DataFormat.CHW, DataFormat.CHW): (0, 1, 2), + (DataFormat.NCHW, DataFormat.CHW): (1, 2, 3), + (DataFormat.NHWC, DataFormat.CHW): (3, 1, 2), + (DataFormat.NHW, DataFormat.CHW): (-1, 1, 2), + (DataFormat.NW, DataFormat.CHW): (-1, -1, 1), + } + permutations = {} + for (f1, f2), perm in initial_permutations.items(): + permutations[(f1, f2)] = perm + if is_invertible(perm): + permutations[(f2, f1)] = inverse_permutation(perm) + return permutations + + # Dict[Tuple[DataFormat, DataFormat], Tuple[int]] + # This provides the correct permutation for various data format conversions. + DATA_PERMUTATIONS = _generate_permutations() + + @staticmethod + def determine_format(shape): + """ + Guesses the data format of a given shape. + + Args: + shape (Tuple[int]): The shape, including batch dimension. + + Returns: + DataFormat: The determined data format. + """ + # The smaller this ratio, the closer a and b are. + def minmax_ratio(a, b): + return abs(max(a, b) / min(a, b)) + + # Assume all shapes include batch dimension + if len(shape) == 4: + # Typically, H and W are quite close, so if minmax_ratio(0, 1) > minmax_ratio(1, 2), then we assume CHW. + if minmax_ratio(shape[1], shape[2]) > minmax_ratio(shape[2], shape[3]): + return DataFormat.NCHW + return DataFormat.NHWC + elif len(shape) == 3: + return DataFormat.NHW + elif len(shape) == 2: + return DataFormat.NW + else: + G_LOGGER.warning("Cannot determine format for " + str(shape) + + ". Currently only implemented for input_buffers with 1-3 non-batch dimensions. Please update this function!") + return DataFormat.UNKNOWN + + # Get the permutation required to transpose old_format to new_format + @staticmethod + def permutation(old_format, new_format): + return FormatManager.DATA_PERMUTATIONS[(old_format, new_format)] + + @staticmethod + def convert(shape, new_format): + """ + Permutes a shape from one format to another. + + Args: + shape (Tuple[int]): The shape to convert. + new_format (DataFormat): The desired format of the shape. + + Returns: + Tuple[int]: A new shape in the correct format. + """ + old_format = FormatManager.determine_format(shape) + perm = FormatManager.permutation(old_format, new_format) + return [shape[index] if index != -1 else 1 for index in perm] diff --git a/tools/Polygraphy/polygraphy/util/misc.py b/tools/Polygraphy/polygraphy/util/misc.py new file mode 100644 index 00000000..44dff967 --- /dev/null +++ b/tools/Polygraphy/polygraphy/util/misc.py @@ -0,0 +1,430 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.util.format import DataFormat, FormatManager +from polygraphy.logger import G_LOGGER +from polygraphy.common import constants + +from collections import OrderedDict +import pickle +import zlib +import sys +import os + +import numpy as np + + +NP_TYPE_FROM_STR = {np.dtype(dtype).name: np.dtype(dtype) for dtype in np.sctypeDict.values()} +STR_FROM_NP_TYPE = {dtype: name for name, dtype in NP_TYPE_FROM_STR.items()} + + +def version(version_str): + return tuple([int(num) for num in version_str.split(".")]) + + +def find_in_dict(name, mapping, index=None): + """ + Attempts to partially match keys in a dictionary. Checks for exact matches and + substring matches, falling back to index based matching. + + Args: + name (str): The key to search for. + mapping (dict): The dictionary to search in. + index (int): An index to fall back to if the key could not be found by name. + + Returns: + str: The key found in the dict, or None if it could not be found. + """ + G_LOGGER.ultra_verbose("Searching for key: {:}. Fallback index is set to {:}".format(name, index)) + if name in mapping: + return name + for key in mapping.keys(): + if name.lower() in key.lower() or key.lower() in name.lower(): + return key + if index is not None and index >= 0 and index < len(mapping.keys()): + return list(mapping.keys())[index] + return None + + +def unique_list(sequence): + """ + Creates a list without duplicate elements, preserving order. + + Args: + sequence (Sequence): The sequence to make unique + + Returns: + list: A list containing the same elements as sequence, in the same order, but without duplicates. + """ + return list(OrderedDict.fromkeys(sequence)) + + +# default_value exists to solve issues that might result from Python's normal default arguments. +# Specifically, consider the following class: +# +# class MyClass(object): +# def __init__(self, value=[]): +# self.value = value +# +# This leads to unexpected behavior when the default value is used: +# +# >>> x = MyClass() +# >>> x.value.append("SHOULD NOT BE IN Y") +# >>> y = MyClass() +# >>> y.value +# ['SHOULD NOT BE IN Y'] +# +# If we rewrite the class using default value: +# +# class MyClass(object): +# def __init__(self, value=None): +# self.value = default_value(value, []) +# +# Then we get the expected behavior: +# +# >>> x = MyClass() +# >>> x.value.append("SHOULD NOT BE IN Y") +# >>> y = MyClass() +# >>> y.value +# [] +def default_value(value, default): + """ + Returns a specified default value if the provided value is None. + + Args: + value (object): The value. + default (object): The default value to use if value is None. + + Returns: + object: Either value, or the default. + """ + return value if value is not None else default + + +def unpack_args(args, num): + """ + Extracts the specified number of arguments from a tuple, padding with + `None` if the tuple length is insufficient. + + Args: + args (Tuple[object]): The tuple of arguments + num (int): The number of elements desired. + + Returns: + Tuple[object]: A tuple containing `num` arguments, padded with `None` if `len(args) < num` + """ + args += (None, ) * (num - len(args)) + return args[0:num] + +## +## Shapes +## + +def is_dimension_dynamic(dim): + is_dim_str = not isinstance(dim, int) + return dim is None or is_dim_str or dim < 0 + + +def num_dynamic_dimensions(shape): + return len([dim for dim in shape if is_dimension_dynamic(dim)]) + + +def is_shape_dynamic(shape): + return num_dynamic_dimensions(shape) > 0 + + +def is_valid_shape_override(new_shape, original_shape): + ranks_same = len(original_shape) == len(new_shape) + overrides_valid = all([odim == ndim or is_dimension_dynamic(odim) for odim, ndim in zip(original_shape, new_shape)]) + return ranks_same and overrides_valid + + +def override_dynamic_shape(shape): + return [constants.DEFAULT_SHAPE_VALUE if is_dimension_dynamic(elem) else elem for elem in shape] + + +def shapes_match(shape0, shape1): + return len(shape0) == len(shape1) and all([s0 == s1 for s0, s1 in zip(shape0, shape1)]) + + +def volume(obj): + vol = 1 + for elem in obj: + vol *= elem + return vol + + +def is_empty_shape(shape): + return volume(shape) == 0 + +## +## Compression and Serialization +## + +class Compressed(object): + """ + Represents an object compressed by zlib + """ + def __init__(self, cobj): + self.bytes = cobj + + +def is_compressed(obj): + return isinstance(obj, Compressed) + + +def compress(obj): + G_LOGGER.verbose("Compressing {} object".format(type(obj))) + return Compressed(zlib.compress(obj)) + + +def decompress(compressed): + G_LOGGER.verbose("Decompressing bytes") + return zlib.decompress(compressed.bytes) + + +def pickle_load(path): + with open(path, "rb") as f: + return pickle.loads(f.read()) + + +def pickle_save(path, obj): + with open(path, "wb") as f: + return f.write(pickle.dumps(obj)) + + +PIPE_MAX_SEND_BYTES = 1 << 31 +"""The maximum number of bytes that can be sent at once over a queue""" + + +def send_on_queue(queue, obj): + obj = pickle.dumps(obj) + + if sys.getsizeof(obj) > PIPE_MAX_SEND_BYTES: + G_LOGGER.warning("Object size ({:} bytes) exceeds maximum size that can be sent over queues ({:} bytes). " + "Attempting to compress - this may take some time. If this does not work or you want to avoid " + "the compression overhead, you should disable subprocesses by omitting the --use-subprocess flag, " + "or by setting use_subprocess=False in Comparator.run().".format(sys.getsizeof(obj), PIPE_MAX_SEND_BYTES)) + obj = compress(obj) + + assert sys.getsizeof(obj) <= PIPE_MAX_SEND_BYTES + + G_LOGGER.ultra_verbose("Sending: {:} on queue".format(obj)) + queue.put(obj) + + +def try_send_on_queue(queue, obj): + """ + Attempts to send an object over the queue, compressing it if needed. + In the event the object cannot be sent, sends `None` instead. + + Args: + queue (queue.Queue): The queue to send the object over. + obj (object): The object to send. + """ + try: + send_on_queue(queue, obj) + except Exception as err: + G_LOGGER.warning("Could not send object on queue: {:}\nSending None instead.".format(err)) + queue.put(None) + + +def receive_on_queue(queue, timeout=None): + G_LOGGER.extra_verbose("Waiting for data to become available on queue") + obj = queue.get(block=True, timeout=timeout) + if is_compressed(obj): + obj = decompress(obj) + obj = pickle.loads(obj) + G_LOGGER.ultra_verbose("Received {:} on queue".format(obj)) + return obj + + +def try_receive_on_queue(queue, timeout=None): + try: + obj = receive_on_queue(queue, timeout) + if obj is None: + G_LOGGER.warning("Received {:} on the queue. This likely means that there was an error in sending " + "the object over the queue. You may want to run with use_subprocess=False in Comparator.run() " + "or omit the --use-subprocess flag to prevent further issues.".format(obj)) + return obj + except Exception as err: + G_LOGGER.warning("Could not receive on queue: {:}\nYou may want to run with use_subprocess=False in Comparator.run() " + "or omit the --use-subprocess flag to prevent further issues.".format(err)) + return None + + +def try_call(func, *args, **kwargs): + """ + Attempts to invoke a function with arguments. If `func` is not callable, then returns `func` + The second return value of this function indicates whether the argument was a callable. + """ + if callable(func): + ret = func(*args, **kwargs) + return ret, True + return func, False + + +## +## File creation +## + +def insert_suffix(path, suffix): + """ + Inserts the provided suffix into the given path, before any file extensions. + + Returns: + str: The path, with suffix inserted, or None if no path was provided. + """ + if path is None: + return None + path, ext = os.path.splitext(path) + return "".join([path, suffix, ext]) + + +def lazy_write(contents, path, mode="wb"): + """ + Writes a file to the specified path. + + Args: + contents (Callable() -> bytes): + Either a bytes-like object that can be written to disk, or a callable which will return such an object. + path (str): The path to write to. + + + mode(str): The mode to use when writing. Defaults to "wb". + + Returns: + str: The complete file path, or `None` if nothing was written. + """ + if path is not None: + dir_path = os.path.dirname(path) + if dir_path and not os.path.exists(dir_path): + G_LOGGER.verbose("{:} does not exist, creating now.".format(dir_path)) + os.makedirs(dir_path, exist_ok=True) + + contents, _ = try_call(contents) + + with open(path, mode) as f: + G_LOGGER.info("Writing to {:}".format(path)) + f.write(contents) + return path + return None + + +def try_match_shape(arr, shape): + """ + Attempts to permute or reshape the array so its shape matches the specified shape. + This is a no-op if the array is already the correct shape. + + Args: + arr (np.ndarray): The array to reshape. + shape (Tuple[int]): The shape to use. May contain at most 1 dynamic dimension. + + Returns: + np.ndarray: The reshaped array. + """ + def is_rank_same(arr, shape): + return len(shape) == len(arr.shape) + + def try_reshape(arr, shape): + try: + arr = arr.reshape(shape) + G_LOGGER.verbose("Reshaped array to shape: {:}".format(arr.shape)) + except ValueError: + G_LOGGER.warning("Could not reshape array (shape: {:}) to {:}. Skipping reshape.".format(arr.shape, shape)) + return arr + + def try_permute(arr, shape): + try: + perm = FormatManager.permutation(FormatManager.determine_format(arr.shape), FormatManager.determine_format(shape)) + G_LOGGER.verbose("Permuting shape: {:} using permutation {:}".format(arr.shape, perm)) + arr = np.transpose(arr, perm) + except Exception as err: + # FormatManager may not recognize the format or be able generate the permutation for the format combination + G_LOGGER.extra_verbose("Skipping permutation due to {:}".format(err)) + return arr + + # Override any dynamic dimensions in the shape with concrete shapes from the array. + def try_fix_shape(arr, shape): + if num_dynamic_dimensions(shape) == 1: + try: + static_dims = [dim for dim in shape if not is_dimension_dynamic(dim)] + determined_dim = volume(arr.shape) // volume(static_dims) + except ZeroDivisionError: + determined_dim = 0 + shape = [determined_dim if is_dimension_dynamic(elem) else elem for elem in shape] + elif is_rank_same(arr, shape): + shape = [arr_shape_elem if is_dimension_dynamic(elem) else elem for elem, arr_shape_elem in zip(shape, arr.shape)] + return shape + + if shape == arr.shape: + return arr + + # When ranks are unequal, we try to squeeze first + if not is_rank_same(arr, shape): + shape = [elem for elem in shape if elem != 1] + arr = np.squeeze(arr) + + if is_shape_dynamic(shape): + shape = try_fix_shape(arr, shape) + + # If the rank is still not the same, do a reshape on the second + if not is_rank_same(arr, shape): + arr = try_reshape(arr, shape) + + # Next, permute if the ranks now match + if is_rank_same(arr, shape): + arr = try_permute(arr, shape) + + # Do a final reshape after the outputs have been permuted. + arr = try_reshape(arr, shape) + return arr + + +def str_from_module_info(module, name=None): + name = default_value(name, "Loaded Module: {:<14}".format(module.__name__)) + paths = str(list(map(os.path.realpath, module.__path__))) + return "{:} | Version: {:<8} | Path: {:}".format(name, str(module.__version__), paths) + + +def log_module_info(module, name=None, severity=G_LOGGER.VERBOSE): + G_LOGGER.log(str_from_module_info(module, name), severity=severity) + + +def str_from_layer(prefix, index, name, op, input_info, output_info): + layer_str = "{:} {:<4} | {:} [Op: {:}]\n".format(prefix, index, name, op) + layer_str += "{tab}{:}".format(input_info, tab=constants.TAB) + if input_info and output_info: + layer_str += "\n" + constants.TAB + else: + layer_str += " " + layer_str += "-> {:}\n".format(output_info) + return layer_str + + +def indent_block(block, level=1): + """ + Indents the provided block of text. + + Args: + block (str): The text to indent. + level (int): The number of tabs to indent with. + + Returns: + str: The indented block. + """ + tab = constants.TAB * level + sep = "\n{:}".format(tab) + return tab + sep.join(str(block).splitlines()) diff --git a/tools/Polygraphy/setup.cfg b/tools/Polygraphy/setup.cfg new file mode 100644 index 00000000..2a9acf13 --- /dev/null +++ b/tools/Polygraphy/setup.cfg @@ -0,0 +1,2 @@ +[bdist_wheel] +universal = 1 diff --git a/tools/Polygraphy/setup.py b/tools/Polygraphy/setup.py new file mode 100644 index 00000000..cfe0d75e --- /dev/null +++ b/tools/Polygraphy/setup.py @@ -0,0 +1,56 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import sys +import polygraphy +from setuptools import setup, find_packages + +ROOT_DIR = os.path.abspath(os.path.dirname(__file__)) +BIN_DIR = os.path.join(ROOT_DIR, "bin") + +def no_publish(): + blacklist = ['register'] + for cmd in blacklist: + if cmd in sys.argv: + raise RuntimeError("Command \"{}\" blacklisted".format(cmd)) + + +REQUIRED_PACKAGES = [ + "numpy", +] + +def main(): + no_publish() + setup( + name="polygraphy", + version=polygraphy.__version__, + description="Polygraphy: A Deep Learning Inference Prototyping and Debugging Toolkit", + long_description=open("README.md", "r", encoding="utf-8").read(), + url="https://github.com/NVIDIA/TensorRT/tree/master/tools/polygraphy", + author="NVIDIA", + author_email="svc_tensorrt@nvidia.com", + classifiers=[ + 'Intended Audience :: Developers', + 'Programming Language :: Python :: 3', + ], + install_requires=REQUIRED_PACKAGES, + packages=find_packages(), + scripts=[os.path.join(BIN_DIR, "polygraphy")], + zip_safe=True, + ) + +if __name__ == '__main__': + main() diff --git a/tools/Polygraphy/tests/backend/__init__.py b/tools/Polygraphy/tests/backend/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/backend/onnx/__init__.py b/tools/Polygraphy/tests/backend/onnx/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/backend/onnx/test_loader.py b/tools/Polygraphy/tests/backend/onnx/test_loader.py new file mode 100644 index 00000000..d1a52977 --- /dev/null +++ b/tools/Polygraphy/tests/backend/onnx/test_loader.py @@ -0,0 +1,79 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.onnx import OnnxFromTfGraph, OnnxFromPath, ModifyOnnx, SaveOnnx +from polygraphy.backend.tf import GraphFromFrozen +from polygraphy.common import constants +from polygraphy.logger import G_LOGGER + +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.common import check_file_non_empty + +import tempfile +import pytest +import os + + +class TestLoggerCallbacks(object): + @pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys()) + def test_set_severity(self, sev): + G_LOGGER.severity = sev + + +class TestOnnxFileLoader(object): + def test_basic(self): + loader = OnnxFromPath(ONNX_MODELS["identity"].path) + loader() + + +class TestExportOnnxFromTf(object): + def test_no_optimize(self): + loader = OnnxFromTfGraph(TF_MODELS["identity"].loader, optimize=False, fold_constant=False) + model = loader() + + + def test_opset(self): + loader = OnnxFromTfGraph(TF_MODELS["identity"].loader, opset=9) + model = loader() + assert model.opset_import[0].version == 9 + + +class TestModifyOnnx(object): + def test_layerwise(self): + loader = ModifyOnnx(OnnxFromPath(ONNX_MODELS["identity_identity"].path), outputs=constants.MARK_ALL) + model = loader() + assert len(model.graph.output) == 2 + + + def test_custom_outputs(self): + loader = ModifyOnnx(OnnxFromPath(ONNX_MODELS["identity_identity"].path), outputs=["identity_out_0"]) + model = loader() + assert len(model.graph.output) == 1 + assert model.graph.output[0].name == "identity_out_0" + + + def test_exclude_outputs_with_layerwise(self): + loader = ModifyOnnx(OnnxFromPath(ONNX_MODELS["identity_identity"].path), outputs=constants.MARK_ALL, exclude_outputs=["identity_out_2"]) + model = loader() + assert len(model.graph.output) == 1 + assert model.graph.output[0].name == "identity_out_0" + + +class TestSaveOnnx(object): + def test_save_onnx(self): + with tempfile.NamedTemporaryFile() as outpath: + loader = SaveOnnx(OnnxFromPath(ONNX_MODELS["identity"].path), path=outpath.name) + loader() + check_file_non_empty(outpath.name) diff --git a/tools/Polygraphy/tests/backend/onnx/test_runner.py b/tools/Polygraphy/tests/backend/onnx/test_runner.py new file mode 100644 index 00000000..dc78b083 --- /dev/null +++ b/tools/Polygraphy/tests/backend/onnx/test_runner.py @@ -0,0 +1,33 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.onnx import OnnxTfRunner, OnnxFromPath, BytesFromOnnx + +from tests.models.meta import ONNX_MODELS + + +class TestOnnxTfRunner(object): + def test_can_name_runner(self): + NAME = "runner" + runner = OnnxTfRunner(None, name=NAME) + assert runner.name == NAME + + + def test_basic(self): + model = ONNX_MODELS["identity"] + with OnnxTfRunner(OnnxFromPath(model.path)) as runner: + assert runner.is_active + model.check_runner(runner) + assert not runner.is_active diff --git a/tools/Polygraphy/tests/backend/onnx/test_util.py b/tools/Polygraphy/tests/backend/onnx/test_util.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/Polygraphy/tests/backend/onnx/test_util.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/Polygraphy/tests/backend/onnxrt/__init__.py b/tools/Polygraphy/tests/backend/onnxrt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/backend/onnxrt/test_runner.py b/tools/Polygraphy/tests/backend/onnxrt/test_runner.py new file mode 100644 index 00000000..b77ec149 --- /dev/null +++ b/tools/Polygraphy/tests/backend/onnxrt/test_runner.py @@ -0,0 +1,52 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnxBytes +from polygraphy.logger import G_LOGGER + +from tests.models.meta import ONNX_MODELS + +import pytest + + +class TestLoggerCallbacks(object): + @pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys()) + def test_set_severity(self, sev): + G_LOGGER.severity = sev + + +class TestOnnxrtRunner(object): + def test_can_name_runner(self): + NAME = "runner" + runner = OnnxrtRunner(None, name=NAME) + assert runner.name == NAME + + + def test_basic(self): + model = ONNX_MODELS["identity"] + with OnnxrtRunner(SessionFromOnnxBytes(model.loader)) as runner: + assert runner.is_active + model.check_runner(runner) + assert not runner.is_active + + + def test_dim_param_converted_to_int_shape(self): + model = ONNX_MODELS["dim_param"] + with OnnxrtRunner(SessionFromOnnxBytes(model.loader)) as runner: + input_meta = runner.get_input_metadata() + # In Polygraphy, we only use None to indicate a dynamic input dimension - not strings. + for name, (dtype, shape) in input_meta.items(): + for dim in shape: + assert dim is None or isinstance(dim, int) diff --git a/tools/Polygraphy/tests/backend/test_tensorrt_legacy.py b/tools/Polygraphy/tests/backend/test_tensorrt_legacy.py new file mode 100644 index 00000000..537df299 --- /dev/null +++ b/tools/Polygraphy/tests/backend/test_tensorrt_legacy.py @@ -0,0 +1,39 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.trt_legacy import TrtLegacyRunner, LoadNetworkFromUff, ConvertToUff, ParseNetworkFromOnnxLegacy + +from tests.models.meta import TF_MODELS, ONNX_MODELS + +import numpy as np +import pytest + + +@pytest.mark.parametrize("fp16", [True, False], ids=lambda x: "fp16" if x else "") +@pytest.mark.parametrize("tf32", [True, False], ids=lambda x: "tf32" if x else "") +def test_uff_identity(fp16, tf32): + model = TF_MODELS["identity"] + loader = model.loader + with TrtLegacyRunner(LoadNetworkFromUff(ConvertToUff(loader)), fp16=fp16, tf32=tf32) as runner: + assert runner.is_active + feed_dict = {"Input": np.random.random_sample(size=(1, 15, 25, 30)).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs["Identity_2"] == feed_dict["Input"]) + assert not runner.is_active + + +def test_can_construct_onnx_loader(): + model = ONNX_MODELS["identity"].path + loader = ParseNetworkFromOnnxLegacy(model) diff --git a/tools/Polygraphy/tests/backend/tf/__init__.py b/tools/Polygraphy/tests/backend/tf/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/backend/tf/test_loader.py b/tools/Polygraphy/tests/backend/tf/test_loader.py new file mode 100644 index 00000000..b5018bde --- /dev/null +++ b/tools/Polygraphy/tests/backend/tf/test_loader.py @@ -0,0 +1,73 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.tf import GraphFromFrozen, ModifyGraph, SaveGraph +from polygraphy.common import constants +from polygraphy.logger import G_LOGGER + +from tests.models.meta import TF_MODELS +from tests.common import check_file_non_empty + +import tensorflow as tf +import tempfile +import pytest +import os + + +class TestLoggerCallbacks(object): + @pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys()) + def test_set_severity(self, sev): + G_LOGGER.severity = sev + + +class TestFrozenGraphLoader(object): + def test_load_graph(self): + with tf.compat.v1.Graph().as_default() as graph: + inp = tf.placeholder(shape=(1, 1, 1, 1), dtype=tf.float32) + out = tf.identity(inp) + + graph, outputs = GraphFromFrozen(graph)() + assert graph + assert outputs + + + def test_load_pb(self): + tf_loader = GraphFromFrozen(TF_MODELS["identity"].path) + tf_loader() + + +class TestModifyGraph(object): + def test_layerwise(self): + load_frozen = GraphFromFrozen(TF_MODELS["identity"].path) + modify_tf = ModifyGraph(load_frozen, outputs=constants.MARK_ALL) + + graph, outputs = modify_tf() + assert graph + assert outputs + + +class TestSaveGraph(object): + def test_save_pb(self): + with tempfile.NamedTemporaryFile() as outpath: + tf_loader = SaveGraph(GraphFromFrozen(TF_MODELS["identity"].path), path=outpath.name) + tf_loader() + check_file_non_empty(outpath.name) + + + def test_save_tensorboard(self): + with tempfile.TemporaryDirectory() as outdir: + tf_loader = SaveGraph(GraphFromFrozen(TF_MODELS["identity"].path), tensorboard_dir=outdir) + tf_loader() + assert os.path.exists(tf_loader.tensorboard_dir) diff --git a/tools/Polygraphy/tests/backend/tf/test_runner.py b/tools/Polygraphy/tests/backend/tf/test_runner.py new file mode 100644 index 00000000..f2346663 --- /dev/null +++ b/tools/Polygraphy/tests/backend/tf/test_runner.py @@ -0,0 +1,47 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.tf import TfRunner, SessionFromGraph + +from tests.models.meta import TF_MODELS +from tests.common import check_file_non_empty + +import tempfile +import pytest +import os + + +class TestTfRunner(object): + def test_can_name_runner(self): + NAME = "runner" + runner = TfRunner(None, name=NAME) + assert runner.name == NAME + + + def test_basic(self): + model = TF_MODELS["identity"] + with TfRunner(SessionFromGraph(model.loader)) as runner: + assert runner.is_active + model.check_runner(runner) + assert not runner.is_active + + + @pytest.mark.skip(reason="Non-trivial to set up - requires CUPTI") + def test_save_timeline(self): + model = TF_MODELS["identity"] + with tempfile.NamedTemporaryFile() as outpath: + with TfRunner(SessionFromGraph(model.loader), allow_growth=True, save_timeline=outpath.name) as runner: + model.check_runner(runner) + check_file_non_empty(outpath.name) diff --git a/tools/Polygraphy/tests/backend/trt/__init__.py b/tools/Polygraphy/tests/backend/trt/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/backend/trt/test_buffers.py b/tools/Polygraphy/tests/backend/trt/test_buffers.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/Polygraphy/tests/backend/trt/test_buffers.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/Polygraphy/tests/backend/trt/test_calibrator.py b/tools/Polygraphy/tests/backend/trt/test_calibrator.py new file mode 100644 index 00000000..ef619f35 --- /dev/null +++ b/tools/Polygraphy/tests/backend/trt/test_calibrator.py @@ -0,0 +1,137 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.trt import EngineFromBytes, EngineFromNetwork, CreateConfig, NetworkFromOnnxBytes, NetworkFromOnnxPath, ModifyNetwork, Calibrator, Profile, SaveEngine, LoadPlugins +from polygraphy.backend.trt import util as trt_util +from polygraphy.common import PolygraphyException, constants +from polygraphy.comparator import DataLoader + +from tests.models.meta import ONNX_MODELS +from tests.common import version, check_file_non_empty + +import tensorrt as trt +import numpy as np +import tempfile +import pytest +import os + + +@pytest.fixture(scope="session") +def identity_builder_network(): + builder, network, parser = NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader)() + with builder, network, parser: + yield builder, network + + +class TestCalibrator(object): + def test_calibrator_iterable_data(self, identity_builder_network): + builder, network = identity_builder_network + NUM_BATCHES = 2 + + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] * NUM_BATCHES + calibrator = Calibrator(data) + + create_config = CreateConfig(int8=True, calibrator=calibrator) + loader = EngineFromNetwork((builder, network), create_config) + with loader(): + assert calibrator.num_batches == NUM_BATCHES + + + def test_calibrator_generator_data(self, identity_builder_network): + builder, network = identity_builder_network + NUM_BATCHES = 2 + + def generate_data(): + for item in [np.ones((1, 1, 2, 2), dtype=np.float32)] * NUM_BATCHES: + yield {"x": item} + calibrator = Calibrator(generate_data()) + + create_config = CreateConfig(int8=True, calibrator=calibrator) + loader = EngineFromNetwork((builder, network), create_config) + with loader(): + assert calibrator.num_batches == NUM_BATCHES + + + # We want the calibrator to inter-op with TRT APIs seamlessly + def test_calibrator_outside_polygraphy(self, identity_builder_network): + builder, network = identity_builder_network + NUM_BATCHES = 2 + + def generate_data(): + for item in [np.ones((1, 1, 2, 2), dtype=np.float32)] * NUM_BATCHES: + yield {"x": item} + calibrator = Calibrator(generate_data()) + + config = builder.create_builder_config() + config.set_flag(trt.BuilderFlag.INT8) + config.int8_calibrator = calibrator + + with builder.build_engine(network, config) as engine: + assert engine + + + def test_calibrator_with_path_name_cache(self, identity_builder_network): + builder, network = identity_builder_network + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] + + with tempfile.NamedTemporaryFile() as cache: + create_config = CreateConfig(int8=True, calibrator=Calibrator(data, cache=cache.name)) + with EngineFromNetwork((builder, network), create_config)(): + check_file_non_empty(cache.name) + + + @pytest.mark.parametrize("mode", ["wb+", "rb", "wb"]) + def test_calibrator_with_file_object_cache(self, identity_builder_network, mode): + builder, network = identity_builder_network + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] + + with tempfile.NamedTemporaryFile(mode=mode) as cache: + create_config = CreateConfig(int8=True, calibrator=Calibrator(data, cache=cache)) + with EngineFromNetwork((builder, network), create_config)(): + if mode != "rb": + check_file_non_empty(cache.name) + + + # read_calibration_cache should work even if an explicit cache is not provided + # This way, it is possible to calibrate quickly when calibrating multiple times. + def test_calibrator_caches_without_explicit_cache(self, identity_builder_network): + builder, network = identity_builder_network + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] + + calibrator = Calibrator(data) + # First, populate the cache + create_config = CreateConfig(int8=True, calibrator=calibrator) + with EngineFromNetwork((builder, network), create_config)(): + pass + + # Check that the internal cache is populated + assert calibrator.read_calibration_cache() + + + def test_calibrator_rechecks_cache_on_reset(self, identity_builder_network): + builder, network = identity_builder_network + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] + + with tempfile.NamedTemporaryFile(mode="wb+") as cache: + calibrator = Calibrator(data, cache=cache.name) + # First, populate the cache + create_config = CreateConfig(int8=True, calibrator=calibrator) + with EngineFromNetwork((builder, network), create_config)(): + pass + + # Ensure that now the calibrator will read from the cache when reset + calibrator.reset() + assert not calibrator.has_cached_scales + assert len(calibrator.read_calibration_cache()) == os.stat(cache.name).st_size diff --git a/tools/Polygraphy/tests/backend/trt/test_loader.py b/tools/Polygraphy/tests/backend/trt/test_loader.py new file mode 100644 index 00000000..1fb1bd32 --- /dev/null +++ b/tools/Polygraphy/tests/backend/trt/test_loader.py @@ -0,0 +1,253 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.trt import EngineFromBytes, EngineFromNetwork, CreateConfig, NetworkFromOnnxBytes, NetworkFromOnnxPath, ModifyNetwork, Calibrator, Profile, SaveEngine, LoadPlugins +from polygraphy.backend.trt import util as trt_util +from polygraphy.common import PolygraphyException, constants +from polygraphy.comparator import DataLoader + +from tests.models.meta import ONNX_MODELS +from tests.common import version, check_file_non_empty + +import tensorrt as trt +import numpy as np +import tempfile +import pytest +import os + + +@pytest.fixture(scope="session") +def identity_engine(): + network_loader = NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader) + engine_loader = EngineFromNetwork(network_loader, CreateConfig()) + with engine_loader() as engine: + yield engine + + +@pytest.fixture(scope="session") +def identity_builder_network(): + builder, network, parser = NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader)() + with builder, network, parser: + yield builder, network + + +@pytest.fixture(scope="session") +def load_identity(): + return NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader) + + +@pytest.fixture(scope="session") +def load_identity_identity(): + return NetworkFromOnnxBytes(ONNX_MODELS["identity_identity"].loader) + + +class TestLoadPlugins(object): + def test_can_load_libnvinfer_plugins(self): + def get_plugin_names(): + return [pc.name for pc in trt.get_plugin_registry().plugin_creator_list] + + loader = LoadPlugins(plugins=["libnvinfer_plugin.so"]) + loader() + assert get_plugin_names() + + +class TestSerializedEngineLoader(object): + def test_serialized_engine_loader_from_lambda(self, identity_engine): + with tempfile.NamedTemporaryFile() as outpath: + with open(outpath.name, "wb") as f: + f.write(identity_engine.serialize()) + + loader = EngineFromBytes(lambda: open(outpath.name, "rb").read()) + with loader() as engine: + assert isinstance(engine, trt.ICudaEngine) + + + def test_serialized_engine_loader_from_buffer(self, identity_engine): + loader = EngineFromBytes(identity_engine.serialize()) + with loader() as engine: + assert isinstance(engine, trt.ICudaEngine) + + +class TestOnnxNetworkLoader(object): + def test_loader(self): + builder, network, parser = NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader)() + with builder, network, parser: + assert not network.has_implicit_batch_dimension + assert not network.has_explicit_precision + + + def test_loader_explicit_precision(self): + builder, network, parser = NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader, explicit_precision=True)() + with builder, network, parser: + assert not network.has_implicit_batch_dimension + assert network.has_explicit_precision + + +@pytest.mark.skipif(version(trt.__version__) < version("7.1.0.0"), reason="API was added in TRT 7.1") +class TestNetworkFromOnnxPath(object): + def test_loader(self): + builder, network, parser = NetworkFromOnnxPath(ONNX_MODELS["identity"].path)() + with builder, network, parser: + assert not network.has_implicit_batch_dimension + assert not network.has_explicit_precision + + + def test_loader_explicit_precision(self): + builder, network, parser = NetworkFromOnnxPath(ONNX_MODELS["identity"].path, explicit_precision=True)() + with builder, network, parser: + assert not network.has_implicit_batch_dimension + assert network.has_explicit_precision + + +class TestModifyNetwork(object): + def test_layerwise(self, load_identity_identity): + load_network = ModifyNetwork(load_identity_identity, outputs=constants.MARK_ALL) + builder, network, parser = load_network() + with builder, network, parser: + for layer in network: + for index in range(layer.num_outputs): + assert layer.get_output(index).is_network_output + + + def test_custom_outputs(self, load_identity_identity): + builder, network, parser = ModifyNetwork(load_identity_identity, outputs=["identity_out_0"])() + with builder, network, parser: + assert network.num_outputs == 1 + assert network.get_output(0).name == "identity_out_0" + + + def test_exclude_outputs_with_layerwise(self, load_identity_identity): + builder, network, parser = ModifyNetwork(load_identity_identity, outputs=constants.MARK_ALL, exclude_outputs=["identity_out_2"])() + with builder, network, parser: + assert network.num_outputs == 1 + assert network.get_output(0).name == "identity_out_0" + + +class TestProfile(object): + def test_can_add(self): + profile = Profile() + min, opt, max = (1, 1), (2, 2), (4, 4) + profile.add("input", min=min, opt=opt, max=max) + shape_tuple = profile["input"] + assert shape_tuple.min == min + assert shape_tuple.opt == opt + assert shape_tuple.max == max + + +class TestConfigLoader(object): + def test_defaults(self, identity_builder_network): + builder, network = identity_builder_network + loader = CreateConfig() + config = loader(builder, network) + assert config.max_workspace_size == 1 << 24 + if version(trt.__version__) > version("7.1.0.0"): + assert not config.get_flag(trt.BuilderFlag.TF32) + assert not config.get_flag(trt.BuilderFlag.FP16) + assert not config.get_flag(trt.BuilderFlag.INT8) + assert config.num_optimization_profiles == 1 + assert config.int8_calibrator is None + + + def test_workspace_size(self, identity_builder_network): + builder, network = identity_builder_network + loader = CreateConfig(max_workspace_size=0) + config = loader(builder, network) + assert config.max_workspace_size == 0 + + + @pytest.mark.parametrize("flag", [True, False]) + def test_strict_types(self, identity_builder_network, flag): + builder, network = identity_builder_network + loader = CreateConfig(strict_types=flag) + config = loader(builder, network) + assert config.get_flag(trt.BuilderFlag.STRICT_TYPES) == flag + + + @pytest.mark.parametrize("flag", [True, False]) + def test_tf32(self, identity_builder_network, flag): + builder, network = identity_builder_network + loader = CreateConfig(tf32=flag) + config = loader(builder, network) + if version(trt.__version__) > version("7.1.0.0"): + assert config.get_flag(trt.BuilderFlag.TF32) == flag + + + @pytest.mark.parametrize("flag", [True, False]) + def test_fp16(self, identity_builder_network, flag): + builder, network = identity_builder_network + loader = CreateConfig(fp16=flag) + config = loader(builder, network) + assert config.get_flag(trt.BuilderFlag.FP16) == flag + + + @pytest.mark.parametrize("flag", [True, False]) + def test_int8(self, identity_builder_network, flag): + builder, network = identity_builder_network + loader = CreateConfig(int8=flag) + config = loader(builder, network) + assert config.get_flag(trt.BuilderFlag.INT8) == flag + + + def test_calibrator_metadata_set(self, identity_builder_network): + builder, network = identity_builder_network + calibrator = Calibrator(DataLoader()) + loader = CreateConfig(int8=True, calibrator=calibrator) + config = loader(builder, network) + assert config.int8_calibrator + assert "x" in calibrator.data_loader.input_metadata + + + def test_multiple_profiles(self, identity_builder_network): + builder, network = identity_builder_network + profiles = [ + Profile().add("x", (1, 2, 1, 1), (1, 2, 2, 2), (1, 2, 4, 4)), + Profile().add("x", (1, 2, 4, 4), (1, 2, 8, 8), (1, 2, 16, 16)), + ] + loader = CreateConfig(profiles=profiles) + config = loader(builder, network) + assert config.num_optimization_profiles == 2 + + +class TestEngineFromNetwork(object): + def test_can_build_with_parser_owning(self): + loader = EngineFromNetwork(NetworkFromOnnxBytes(ONNX_MODELS["identity"].loader)) + with loader(): + pass + + + def test_can_build_without_parser_non_owning(self, identity_builder_network): + builder, network = identity_builder_network + loader = EngineFromNetwork((builder, network)) + with loader(): + pass + + + def test_can_build_with_calibrator(self, identity_builder_network): + builder, network = identity_builder_network + calibrator = Calibrator(DataLoader()) + create_config = CreateConfig(int8=True, calibrator=calibrator) + loader = EngineFromNetwork((builder, network), create_config) + with loader(): + pass + # Calibrator buffers should be freed after the build + assert all([buf.allocated_nbytes == 0 for buf in calibrator.device_buffers.values()]) + + +class TestSaveEngine(object): + def test_save_engine(self, load_identity): + with tempfile.NamedTemporaryFile() as outpath: + engine_loader = SaveEngine(EngineFromNetwork(load_identity), path=outpath.name) + with engine_loader() as engine: + check_file_non_empty(outpath.name) diff --git a/tools/Polygraphy/tests/backend/trt/test_runner.py b/tools/Polygraphy/tests/backend/trt/test_runner.py new file mode 100644 index 00000000..a4da35ce --- /dev/null +++ b/tools/Polygraphy/tests/backend/trt/test_runner.py @@ -0,0 +1,75 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.trt import TrtRunner, EngineFromNetwork, CreateConfig, NetworkFromOnnxBytes, Profile +from polygraphy.logger import G_LOGGER + +from tests.models.meta import ONNX_MODELS +from tests.common import version + +import tensorrt as trt + +import pytest + + +class TestLoggerCallbacks(object): + @pytest.mark.parametrize("sev", G_LOGGER.SEVERITY_LETTER_MAPPING.keys()) + def test_set_severity(self, sev): + G_LOGGER.severity = sev + + +class TestTrtRunner(object): + def test_can_name_runner(self): + NAME = "runner" + runner = TrtRunner(None, name=NAME) + assert runner.name == NAME + + + def test_basic(self): + model = ONNX_MODELS["identity"] + network_loader = NetworkFromOnnxBytes(model.loader) + with TrtRunner(EngineFromNetwork(network_loader)) as runner: + assert runner.is_active + model.check_runner(runner) + assert not runner.is_active + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_multiple_profiles(self): + model = ONNX_MODELS["dynamic_identity"] + shapes = [(1, 2, 4, 4), (1, 2, 8, 8), (1, 2, 16, 16)] + network_loader = NetworkFromOnnxBytes(model.loader) + profiles = [ + Profile().add("X", (1, 2, 1, 1), (1, 2, 2, 2), (1, 2, 4, 4)), + Profile().add("X", *shapes), + ] + config_loader = CreateConfig(profiles=profiles) + with TrtRunner(EngineFromNetwork(network_loader, config_loader)) as runner: + runner.context.active_optimization_profile = 1 + for shape in shapes: + model.check_runner(runner, {"X": shape}) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_empty_tensor_with_dynamic_input_shape_tensor(self): + model = ONNX_MODELS["empty_tensor_expand"] + shapes = [(1, 2, 0, 3, 0), (2, 2, 0, 3, 0), (4, 2, 0, 3, 0)] + network_loader = NetworkFromOnnxBytes(model.loader) + profiles = [Profile().add("new_shape", *shapes)] + config_loader = CreateConfig(profiles=profiles) + + with TrtRunner(EngineFromNetwork(network_loader, config_loader)) as runner: + for shape in shapes: + model.check_runner(runner, {"new_shape": shape}) diff --git a/tools/Polygraphy/tests/backend/trt/test_util.py b/tools/Polygraphy/tests/backend/trt/test_util.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/Polygraphy/tests/backend/trt/test_util.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/Polygraphy/tests/common.py b/tools/Polygraphy/tests/common.py new file mode 100644 index 00000000..e8ed056d --- /dev/null +++ b/tools/Polygraphy/tests/common.py @@ -0,0 +1,24 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os + + +def version(version_str): + return tuple([int(num) for num in version_str.split(".")]) + + +def check_file_non_empty(path): + assert os.stat(path).st_size > 0 diff --git a/tools/Polygraphy/tests/common/test_func.py b/tools/Polygraphy/tests/common/test_func.py new file mode 100644 index 00000000..6cf91047 --- /dev/null +++ b/tools/Polygraphy/tests/common/test_func.py @@ -0,0 +1,132 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.common import func + + +class TestExtend(object): + def test_override_rv(self): + def x(): + return 1 + + # Since y explicitly returns something, the return value of x is discarded. + @func.extend(x) + def y(elem): + assert elem == 1 + return 2 + + assert y() == 2 + + + def test_extend_named_parameters(self): + def x(arg0, arg1): + return arg0, arg1 + + @func.extend(x) + def y(elem0, elem1): + pass + + arg0, arg1 = y(arg1=1, arg0=0) + assert arg0 == 0 + assert arg1 == 1 + + + def test_extend_0_args_1_rv(self): + def x(): + return 1 + + @func.extend(x) + def y(elem): + assert elem == 1 + + assert y() == 1 + + + def test_extend_0_args_2_rv(self): + def x(): + return 1, 2 + + @func.extend(x) + def y(elem0, elem1): + assert elem0 == 1 + assert elem1 == 2 + + assert y() == (1, 2) + + + def test_extend_1_args_0_rv(self): + def x(arg0): + pass + + @func.extend(x) + def y(): + pass + + y(1) + + + def test_extend_1_args_1_rv(self): + def x(arg0): + assert arg0 == 1 + return 3 + + @func.extend(x) + def y(elem): + assert elem == 3 + + assert y(1) == 3 + + + def test_extend_2_args_2_rv(self): + def x(arg0, arg1): + assert arg0 == -1 + assert arg1 == -1 + return 1, 2 + + @func.extend(x) + def y(elem0, elem1): + assert elem0 == 1 + assert elem1 == 2 + + assert y(-1, -1) == (1, 2) + + + def test_extend_can_modify_rv(self): + def x(): + return [] + + @func.extend(x) + def y(lst): + lst.extend([1, 2, 3]) + + assert x() == [] + assert y() == [1, 2, 3] + + + def test_extend_can_modify_rv_objects(self): + class ModifiableObj(object): + def __init__(self): + self.value = 0 + + + def x(): + return ModifiableObj() + + @func.extend(x) + def y(mo): + mo.value = 1 + + assert x().value == 0 + assert y().value == 1 diff --git a/tools/Polygraphy/tests/common/test_struct.py b/tools/Polygraphy/tests/common/test_struct.py new file mode 100644 index 00000000..af13d518 --- /dev/null +++ b/tools/Polygraphy/tests/common/test_struct.py @@ -0,0 +1,38 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import numpy as np +from polygraphy.common import TensorMetadata + + +class TestTensorMetadata(object): + def test_str(self): + meta = TensorMetadata().add("X", dtype=np.float32, shape=(64, 64)) + assert str(meta) == "{X [dtype=float32, shape=(64, 64)]}" + + + def test_str_no_dtype(self): + meta = TensorMetadata().add("X", dtype=None, shape=(64, 64)) + assert str(meta) == "{X [shape=(64, 64)]}" + + + def test_str_no_shape(self): + meta = TensorMetadata().add("X", dtype=np.float32, shape=None) + assert str(meta) == "{X [dtype=float32]}" + + + def test_str_no_meta(self): + meta = TensorMetadata().add("X", dtype=None, shape=None) + assert str(meta) == "{X}" diff --git a/tools/Polygraphy/tests/comparator/__init__.py b/tools/Polygraphy/tests/comparator/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/comparator/test_comparator.py b/tools/Polygraphy/tests/comparator/test_comparator.py new file mode 100644 index 00000000..5875251d --- /dev/null +++ b/tools/Polygraphy/tests/comparator/test_comparator.py @@ -0,0 +1,142 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.onnx import OnnxFromTfGraph, BytesFromOnnx +from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnxBytes +from polygraphy.comparator import Comparator, CompareFunc, PostprocessFunc, RunResults, IterationResult, DataLoader +from polygraphy.backend.trt import TrtRunner, EngineFromNetwork, NetworkFromOnnxBytes +from polygraphy.common import PolygraphyException +from polygraphy.backend.tf import TfRunner, SessionFromGraph + +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.common import version + +import subprocess as sp +import tensorrt as trt +import numpy as np +import pytest + + +class TestComparator(object): + def test_warmup_runs(self): + onnx_loader = ONNX_MODELS["identity"].loader + runner = OnnxrtRunner(SessionFromOnnxBytes(onnx_loader)) + run_results = Comparator.run([runner], warm_up=2) + assert len(run_results[runner.name]) == 1 + + + def test_list_as_data_loader(self): + onnx_loader = ONNX_MODELS["identity"].loader + runner = OnnxrtRunner(SessionFromOnnxBytes(onnx_loader), name="onnx_runner") + + data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] * 2 + run_results = Comparator.run([runner], data_loader=data) + iter_results = run_results["onnx_runner"] + assert len(iter_results) == 2 + for actual, expected in zip(iter_results, data): + assert np.all(actual['y'] == expected['x']) + + + def test_generator_as_data_loader(self): + onnx_loader = ONNX_MODELS["identity"].loader + runner = OnnxrtRunner(SessionFromOnnxBytes(onnx_loader), name="onnx_runner") + + def data(): + for feed_dict in [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] * 2: + yield feed_dict + + run_results = Comparator.run([runner], data_loader=data()) + iter_results = run_results["onnx_runner"] + assert len(iter_results) == 2 + for actual, expected in zip(iter_results, data()): + assert np.all(actual['y'] == expected['x']) + + + def test_multiple_runners(self): + load_tf = TF_MODELS["identity"].loader + build_tf_session = SessionFromGraph(load_tf) + load_serialized_onnx = BytesFromOnnx(OnnxFromTfGraph(load_tf)) + build_onnxrt_session = SessionFromOnnxBytes(load_serialized_onnx) + load_engine = EngineFromNetwork(NetworkFromOnnxBytes(load_serialized_onnx)) + + runners = [ + TfRunner(build_tf_session), + OnnxrtRunner(build_onnxrt_session), + TrtRunner(load_engine), + ] + + run_results = Comparator.run(runners) + compare_func = CompareFunc.basic_compare_func(check_shapes=version(trt.__version__) >= version("7.0")) + assert bool(Comparator.compare_accuracy(run_results, compare_func=compare_func)) + assert len(list(run_results.values())[0]) == 1 # Default number of iterations + + + def test_postprocess(self): + onnx_loader = ONNX_MODELS["identity"].loader + run_results = Comparator.run([OnnxrtRunner(SessionFromOnnxBytes(onnx_loader))], use_subprocess=True) + # Output shape is (1, 1, 2, 2) + postprocessed = Comparator.postprocess(run_results, postprocess_func=PostprocessFunc.topk_func(k=1, axis=-1)) + for name, results in postprocessed.items(): + for result in results: + for name, output in result.items(): + assert output.shape == (1, 1, 2, 1) + + + # When there is an unpickleable exception in the subprocess, the Comparator should be able to recover and exit gracefully. + def test_errors_do_not_hang(self): + # Should error because interface is not implemented correctly. + class FakeRunner(object): + def __init__(self): + self.name = "fake" + + runners = [FakeRunner()] + with pytest.raises(PolygraphyException): + Comparator.run(runners, use_subprocess=True, subprocess_polling_interval=1) + + + # When there is an unpickleable exception in the subprocess, the Comparator should be able to recover and exit gracefully. + def test_segfault_does_not_hang(self): + def raise_called_process_error(): + class UnpickleableException(sp.CalledProcessError): + pass + + raise UnpickleableException(-11, ["simulate", "segfault"]) + + runners = [TrtRunner(EngineFromNetwork(raise_called_process_error))] + with pytest.raises(PolygraphyException): + Comparator.run(runners, use_subprocess=True, subprocess_polling_interval=1) + + + def test_multirun_outputs_are_different(self): + onnx_loader = ONNX_MODELS["identity"].loader + runner = OnnxrtRunner(SessionFromOnnxBytes(onnx_loader)) + run_results = Comparator.run([runner], data_loader=DataLoader(iterations=2)) + + iteration0 = run_results[runner.name][0] + iteration1 = run_results[runner.name][1] + for name in iteration0.keys(): + assert np.any(iteration0[name] != iteration1[name]) + + + def test_validate_nan(self): + run_results = RunResults() + run_results["fake-runner"] = [IterationResult(outputs={"x": np.array(np.nan)})] + assert not Comparator.validate(run_results) + + + def test_validate_inf(self): + run_results = RunResults() + run_results["fake-runner"] = [IterationResult(outputs={"x": np.array(np.inf)})] + assert not Comparator.validate(run_results, check_finite=True) diff --git a/tools/Polygraphy/tests/comparator/test_compare.py b/tools/Polygraphy/tests/comparator/test_compare.py new file mode 100644 index 00000000..35bc2fab --- /dev/null +++ b/tools/Polygraphy/tests/comparator/test_compare.py @@ -0,0 +1,28 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import numpy as np +from polygraphy.comparator import CompareFunc, IterationResult + + +class TestCompareFunc(object): + def test_basic_compare_func_can_compare_bool(self): + iter_result0 = IterationResult(outputs={"output": np.zeros((4, 4), dtype=np.bool)}) + iter_result1 = IterationResult(outputs={"output": np.ones((4, 4), dtype=np.bool)}) + + compare_func = CompareFunc.basic_compare_func() + acc = compare_func(iter_result0, iter_result1) + + assert not acc["output"] diff --git a/tools/Polygraphy/tests/comparator/test_data_loader.py b/tools/Polygraphy/tests/comparator/test_data_loader.py new file mode 100644 index 00000000..ad265564 --- /dev/null +++ b/tools/Polygraphy/tests/comparator/test_data_loader.py @@ -0,0 +1,137 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.comparator import DataLoader +from polygraphy.comparator.data_loader import DataLoaderCache +from polygraphy.common import TensorMetadata +from polygraphy.util import misc +from polygraphy.logger import G_LOGGER + +from tests.models.meta import ONNX_MODELS + +from collections import OrderedDict +import numpy as np + + +class TestDataLoader(object): + def test_can_override_shape(self): + model = ONNX_MODELS["dynamic_identity"] + + shape = (1, 1, 4, 5) + custom_input_metadata = TensorMetadata().add("X", dtype=None, shape=shape) + data_loader = DataLoader(input_metadata=custom_input_metadata) + # Simulate what the comparator does + data_loader.input_metadata = model.input_metadata + + feed_dict = data_loader[0] + assert tuple(feed_dict["X"].shape) == shape + + + def test_range_min_max_equal(self): + RANGE_VAL = 1 + data_loader = DataLoader(input_metadata=TensorMetadata().add("X", dtype=np.int32, shape=(1, 1)), + int_range=(RANGE_VAL, RANGE_VAL)) + feed_dict = data_loader[0] + assert np.all(feed_dict["X"] == RANGE_VAL) + + + def test_shape_tensor_detected(self): + INPUT_DATA = (1, 2, 3) + input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, )) + # This contains the shape values + overriden_meta = TensorMetadata().add("X", dtype=np.int32, shape=INPUT_DATA) + data_loader = DataLoader(input_metadata=overriden_meta) + data_loader.input_metadata = input_meta + + feed_dict = data_loader[0] + assert np.all(feed_dict["X"] == INPUT_DATA) # values become INPUT_DATA + + + def test_no_shape_tensor_false_positive_negative_dims(self): + INPUT_DATA = (-100, 2, 4) + # This should NOT be detected as a shape tensor + input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, )) + overriden_meta = TensorMetadata().add("X", dtype=np.int32, shape=INPUT_DATA) + data_loader = DataLoader(input_metadata=overriden_meta) + data_loader.input_metadata = input_meta + + feed_dict = data_loader[0] + assert feed_dict["X"].shape == (3, ) # Shape IS (3, ), because this is NOT a shape tensor + assert np.any(feed_dict["X"] != INPUT_DATA) # Contents are not INPUT_DATA, since it's not treated as a shape value + + + def test_no_shape_tensor_false_positive_float(self): + INPUT_DATA = (-100, -50, 0) + # Float cannot be a shape tensor + input_meta = TensorMetadata().add("X", dtype=np.float32, shape=(3, )) + overriden_meta = TensorMetadata().add("X", dtype=np.float32, shape=INPUT_DATA) + data_loader = DataLoader(input_metadata=overriden_meta) + data_loader.input_metadata = input_meta + + feed_dict = data_loader[0] + assert feed_dict["X"].shape == (3, ) # Values are NOT (3, ) + assert np.any(feed_dict["X"] != INPUT_DATA) # Values are NOT (3, ) + + + def test_non_user_provided_inputs_never_shape_tensors(self): + # If the user didn't provide metadata, then the value can never be a shape tensor. + input_meta = TensorMetadata().add("X", dtype=np.int32, shape=(3, )) + data_loader = DataLoader() + data_loader.input_metadata = input_meta + + feed_dict = data_loader[0] + assert feed_dict["X"].shape == (3, ) # Treat as a normal tensor + + +class TestDataLoaderCache(object): + def test_can_cast_dtype(self): + # Ensure that the data loader can only be used once + def load_data(): + yield {"X": np.ones((1, 1), dtype=np.float32)} + cache = DataLoaderCache(load_data()) + + fp32_meta = TensorMetadata().add("X", dtype=np.float32, shape=(1, 1)) + cache.set_input_metadata(fp32_meta) + feed_dict = cache[0] + assert feed_dict["X"].dtype == np.float32 + + fp64_meta = TensorMetadata().add("X", dtype=np.float64, shape=(1, 1)) + cache.set_input_metadata(fp64_meta) + feed_dict = cache[0] + assert feed_dict["X"].dtype == np.float64 + + + # If one input isn't in the cache, we shouldn't give up looking + # for other inputs + def test_will_not_give_up_on_first_cache_miss(self): + SHAPE = (32, 32) + + DATA = [OrderedDict()] + DATA[0]["X"] = np.zeros(SHAPE, dtype=np.int64) + DATA[0]["Y"] = np.zeros(SHAPE, dtype=np.int64) + + cache = DataLoaderCache(DATA) + cache.set_input_metadata(TensorMetadata().add("X", np.int64, shape=SHAPE).add("Y", np.int64, SHAPE)) + + # Populate the cache with bad X but good Y + cache.cache[0] = OrderedDict() + cache.cache[0]["X"] = np.ones((64, 64), dtype=np.int64) + cache.cache[0]["Y"] = np.ones(SHAPE, dtype=np.int64) + + feed_dict = cache[0] + # Cache cannot reuse X, so it'll reload - we'll get all 0s from the data loader + assert np.all(feed_dict["X"] == 0) + # Cache can reuse Y, even though it's after X, so we'll get ones from the cache + assert np.all(feed_dict["Y"] == 1) diff --git a/tools/Polygraphy/tests/comparator/test_postprocess.py b/tools/Polygraphy/tests/comparator/test_postprocess.py new file mode 100644 index 00000000..bd043b8e --- /dev/null +++ b/tools/Polygraphy/tests/comparator/test_postprocess.py @@ -0,0 +1,32 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import numpy as np +from polygraphy.comparator import PostprocessFunc + + +class TestTopK(object): + def test_basic(self): + arr = np.array([1, 2, 3, 4, 5], dtype=np.float32) + func = PostprocessFunc.topk_func(k=3) + top_k = func({"x": arr}) + assert np.all(top_k["x"] == [4, 3, 2]) + + + def test_k_can_exceed_array_len(self): + arr = np.array([1, 2, 3, 4, 5], dtype=np.float32) + func = PostprocessFunc.topk_func(k=10) + top_k = func({"x": arr}) + assert np.all(top_k["x"] == [4, 3, 2, 1, 0]) diff --git a/tools/Polygraphy/tests/models/__init__.py b/tools/Polygraphy/tests/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/models/and.onnx b/tools/Polygraphy/tests/models/and.onnx new file mode 100644 index 00000000..823a8fad --- /dev/null +++ b/tools/Polygraphy/tests/models/and.onnx @@ -0,0 +1,17 @@ + backend-test:_ + +x +yand"And +test_and2dZ +x +   + +Z +y +   + +b +and +   + +B \ No newline at end of file diff --git a/tools/Polygraphy/tests/models/dim_param.onnx b/tools/Polygraphy/tests/models/dim_param.onnx new file mode 100644 index 00000000..8a5a26d8 Binary files /dev/null and b/tools/Polygraphy/tests/models/dim_param.onnx differ diff --git a/tools/Polygraphy/tests/models/dynamic_identity.onnx b/tools/Polygraphy/tests/models/dynamic_identity.onnx new file mode 100644 index 00000000..59f843a7 --- /dev/null +++ b/tools/Polygraphy/tests/models/dynamic_identity.onnx @@ -0,0 +1,15 @@ + backend_test:y + +XY"Identityonnx_dynamic_identityZ& +X! + + + +height +widthb& +Y! + + + +height +widthB diff --git a/tools/Polygraphy/tests/models/empty_tensor_expand.onnx b/tools/Polygraphy/tests/models/empty_tensor_expand.onnx new file mode 100644 index 00000000..59758437 Binary files /dev/null and b/tools/Polygraphy/tests/models/empty_tensor_expand.onnx differ diff --git a/tools/Polygraphy/tests/models/identity.onnx b/tools/Polygraphy/tests/models/identity.onnx new file mode 100644 index 00000000..b7302044 --- /dev/null +++ b/tools/Polygraphy/tests/models/identity.onnx @@ -0,0 +1,15 @@ + backend-test:[ + +xy"Identity test_identityZ +x + + + + +b +y + + + + +B \ No newline at end of file diff --git a/tools/Polygraphy/tests/models/identity_identity.onnx b/tools/Polygraphy/tests/models/identity_identity.onnx new file mode 100644 index 00000000..dede9e51 Binary files /dev/null and b/tools/Polygraphy/tests/models/identity_identity.onnx differ diff --git a/tools/Polygraphy/tests/models/identity_with_initializer.onnx b/tools/Polygraphy/tests/models/identity_with_initializer.onnx new file mode 100644 index 00000000..5acfa08b Binary files /dev/null and b/tools/Polygraphy/tests/models/identity_with_initializer.onnx differ diff --git a/tools/Polygraphy/tests/models/meta.py b/tools/Polygraphy/tests/models/meta.py new file mode 100644 index 00000000..7aae873b --- /dev/null +++ b/tools/Polygraphy/tests/models/meta.py @@ -0,0 +1,93 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.backend.tf import GraphFromFrozen +from polygraphy.backend.common import BytesFromPath +from polygraphy.common import TensorMetadata +from polygraphy.util import misc + +import numpy as np +import os + + +def model_path(name): + MODELS_BASE_PATH = os.path.abspath(os.path.dirname(__file__)) + return os.path.join(MODELS_BASE_PATH, name) + + +class Model(object): + def __init__(self, path, LoaderType, check_runner, input_metadata=None): + self.path = path + self.loader = LoaderType(self.path) + self.check_runner = check_runner + self.input_metadata = input_metadata + + +def check_tf_identity(runner): + feed_dict = {"Input:0": np.random.random_sample(size=(1, 15, 25, 30)).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs["Identity_2:0"] == feed_dict["Input:0"]) + + +TF_MODELS = { + "identity": Model(path=model_path("tf_identity.pb"), LoaderType=GraphFromFrozen, check_runner=check_tf_identity), +} + + +def check_identity(runner): + feed_dict = {"x": np.random.random_sample(size=(1, 1, 2, 2)).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs["y"] == feed_dict["x"]) + + +def check_identity_identity(runner): + feed_dict = {"X": np.random.random_sample(size=(64, 64)).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs["identity_out_2"] == feed_dict["X"]) + + +def check_dynamic_identity(runner, shapes): + feed_dict = {"X": np.random.random_sample(size=shapes["X"]).astype(np.float32)} + outputs = runner.infer(feed_dict) + assert np.all(outputs["Y"] == feed_dict["X"]) + + +def check_empty_tensor_expand(runner, shapes): + shape = shapes["new_shape"] + feed_dict = {"data": np.array((0, )), "new_shape": np.array(shape, dtype=np.int32)} + outputs = runner.infer(feed_dict) + # Empty tensor will still be empty after broadcast + assert outputs["expanded"].shape == shape + assert misc.volume(outputs["expanded"].shape) == 0 + + +# scan +# "and": Model(path=model_path("and.onnx"), LoaderType=OnnxFileLoader), +# "pow_scalar": Model(path=model_path("pow_scalar.onnx"), LoaderType=OnnxFileLoader), +def no_check_implemented(runner): + raise NotImplementedError("No check_runner implemented for this model") + + +ONNX_MODELS = { + "identity": Model(path=model_path("identity.onnx"), LoaderType=BytesFromPath, check_runner=check_identity, input_metadata=TensorMetadata().add("x", dtype=np.float32, shape=(1, 1, 2, 2))), + "identity_identity": Model(path=model_path("identity_identity.onnx"), LoaderType=BytesFromPath, check_runner=check_identity_identity), + "dynamic_identity": Model(path=model_path("dynamic_identity.onnx"), LoaderType=BytesFromPath, check_runner=check_dynamic_identity, input_metadata=TensorMetadata().add("X", dtype=np.float32, shape=(1, 1, -1, -1))), + "empty_tensor_expand": Model(path=model_path("empty_tensor_expand.onnx"), LoaderType=BytesFromPath, check_runner=check_empty_tensor_expand), + + "scan": Model(path=model_path("scan.onnx"), LoaderType=BytesFromPath, check_runner=no_check_implemented), + "dim_param": Model(path=model_path("dim_param.onnx"), LoaderType=BytesFromPath, check_runner=no_check_implemented), + "tensor_attr": Model(path=model_path("tensor_attr.onnx"), LoaderType=BytesFromPath, check_runner=no_check_implemented), + "identity_with_initializer": Model(path=model_path("identity_with_initializer.onnx"), LoaderType=BytesFromPath, check_runner=no_check_implemented), +} diff --git a/tools/Polygraphy/tests/models/pow_scalar.onnx b/tools/Polygraphy/tests/models/pow_scalar.onnx new file mode 100644 index 00000000..b9eaabe2 Binary files /dev/null and b/tools/Polygraphy/tests/models/pow_scalar.onnx differ diff --git a/tools/Polygraphy/tests/models/scan.onnx b/tools/Polygraphy/tests/models/scan.onnx new file mode 100644 index 00000000..f2ec1423 --- /dev/null +++ b/tools/Polygraphy/tests/models/scan.onnx @@ -0,0 +1,43 @@ +:¼ +à +initial +xyz"Scan*­ +body2¡ + +sum_in +nextsum_out"Add + +sum_outscan_out"Identity scan_bodyZ +sum_in + + +Z +next + + +b +sum_out + + +b +scan_out + + + * +num_scan_inputs graphZ +initial + + +Z +x +  + +b +y + + +b +z +  + +B diff --git a/tools/Polygraphy/tests/models/tensor_attr.onnx b/tools/Polygraphy/tests/models/tensor_attr.onnx new file mode 100644 index 00000000..54b1fcb2 Binary files /dev/null and b/tools/Polygraphy/tests/models/tensor_attr.onnx differ diff --git a/tools/Polygraphy/tests/models/tf_identity.pb b/tools/Polygraphy/tests/models/tf_identity.pb new file mode 100644 index 00000000..69aeaae1 --- /dev/null +++ b/tools/Polygraphy/tests/models/tf_identity.pb @@ -0,0 +1,17 @@ + +> +Input Placeholder* +dtype0* +shape: +$ +IdentityIdentityInput* +T0 +) + +Identity_1IdentityIdentity* +T0 ++ + +Identity_2Identity +Identity_1* +T0" \ No newline at end of file diff --git a/tools/Polygraphy/tests/requirements.txt b/tools/Polygraphy/tests/requirements.txt new file mode 100644 index 00000000..6b4b7bc8 --- /dev/null +++ b/tools/Polygraphy/tests/requirements.txt @@ -0,0 +1,10 @@ +onnx +tensorflow-gpu < 2.0 +onnx-tf +pytest +tf2onnx +onnxruntime==1.0.0 +colored +numpy +pytest-xdist +onnx_graphsurgeon diff --git a/tools/Polygraphy/tests/test_examples.py b/tools/Polygraphy/tests/test_examples.py new file mode 100644 index 00000000..0933a952 --- /dev/null +++ b/tools/Polygraphy/tests/test_examples.py @@ -0,0 +1,154 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.logger.logger import G_LOGGER + +from tests.common import version + +import subprocess as sp +import pytest +import copy +import os + +import tensorrt as trt + + +ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.pardir)) +EXAMPLES_ROOT = os.path.join(ROOT_DIR, "examples") + +# Extract any ``` blocks from the README +# Each block is stored as a separate string in the returned list +def load_code_blocks_from_readme(readme): + def ignore_command(cmd): + return "pip" in cmd + + commands = [] + with open(readme, 'r') as f: + in_command_block = False + for line in f.readlines(): + if not in_command_block and "```" in line: + commands.append([]) + in_command_block = True + elif in_command_block: + if "```" in line: + in_command_block = False + elif not ignore_command(line): + commands[-1].append(line.rstrip()) + # commands is List[List[str]] - flatten: + commands = ["\n".join(command) for command in commands] + return commands + + +class Example(object): + def __init__(self, path_components, artifact_names=[]): + self.path = os.path.join(EXAMPLES_ROOT, *path_components) + self.artifacts = [os.path.join(self.path, name) for name in artifact_names] + + + def __enter__(self): + readme = os.path.join(self.path, "README.md") + return load_code_blocks_from_readme(readme) + + + def run(self, command): + G_LOGGER.info("Running: {:} from cwd: {:}".format(command, self.path)) + env = copy.copy(os.environ) + env["PYTHONPATH"] = ROOT_DIR + env["PATH"] = os.path.join(ROOT_DIR, "bin") + os.path.pathsep + env["PATH"] + # Remove whitespace args and escaped newlines + command = [arg for arg in command.strip().split(" ") if arg.strip() and arg != "\\\n"] + status = sp.run(command, cwd=self.path, env=env, stdout=sp.PIPE, stderr=sp.PIPE, universal_newlines=True) + assert status.returncode == 0, status.stdout + "\n" + status.stderr + return status + + + def __exit__(self, exc_type, exc_value, traceback): + """ + Checks for and removes artifacts expected by this example + """ + for artifact in self.artifacts: + print("Checking for the existence of artifact: {:}".format(artifact)) + assert os.path.exists(artifact) + os.remove(artifact) + + + def __str__(self): + return os.path.relpath(self.path, EXAMPLES_ROOT) + + +API_EXAMPLES = [ + Example(["api", "01_comparing_frameworks"]), + Example(["api", "02_using_real_data"]), + Example(["api", "03_interoperating_with_tensorrt"]), + Example(["api", "04_int8_calibration_in_tensorrt"], artifact_names=["identity-calib.cache"]), + Example(["api", "05_using_tensorrt_network_api"]), +] + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +@pytest.mark.parametrize("example", API_EXAMPLES, ids=lambda case: str(case)) +def test_api_examples(example): + with example as commands: + for command in commands: + example.run(command) + + +CLI_EXAMPLES = [ + # Run + Example(["cli", "run", "01_comparing_frameworks"]), + Example(["cli", "run", "02_comparing_across_runs"], artifact_names=["system_a_results.pkl"]), + Example(["cli", "run", "03_generating_a_comparison_script"], artifact_names=["compare_trt_onnxrt.py"]), + # Surgeon + Example(["cli", "surgeon", "01_isolating_subgraphs"], artifact_names=["subgraph.onnx"]), +] + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +@pytest.mark.parametrize("example", CLI_EXAMPLES, ids=lambda case: str(case)) +def test_cli_examples(example): + with example as commands: + for command in commands: + example.run(command) + + +CLI_INSPECT_EXAMPLES = [ + Example(["cli", "inspect", "01_inspecting_a_tensorrt_network"]), + Example(["cli", "inspect", "02_inspecting_a_tensorrt_engine"], artifact_names=["identity.engine"]), + Example(["cli", "inspect", "03_inspecting_an_onnx_model"]), + Example(["cli", "inspect", "04_inspecting_a_tensorflow_graph"]), + Example(["cli", "inspect", "05_inspecting_run_results"], artifact_names=["onnxrt_results.pkl"]), +] + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +@pytest.mark.parametrize("example", CLI_INSPECT_EXAMPLES, ids=lambda case: str(case)) +def test_cli_inspect_examples(example): + # Last block should be the expected output, and last command should generate it. + with example as blocks: + commands, expected_output = blocks[:-1], blocks[-1] + for command in commands: + actual_output = example.run(command).stdout + + print(actual_output) + # Makes reading the diff way easier + actual_lines = actual_output.splitlines() + expected_lines = expected_output.splitlines() + assert len(actual_lines) == len(expected_lines) + + # Indicates lines that may not match exactly + NON_EXACT_LINE_MARKERS = ["---- Runner: ", " Layer"] + + for index, (actual_line, expected_line) in enumerate(zip(actual_lines, expected_lines)): + # Skip whitespace, and lines that include runner names (since those have timestamps) + if expected_line.strip() and all([marker not in expected_line for marker in NON_EXACT_LINE_MARKERS]): + print("Checking line {:}: {:}".format(index, expected_line)) + assert actual_line == expected_line diff --git a/tools/Polygraphy/tests/test_tools.py b/tools/Polygraphy/tests/test_tools.py new file mode 100644 index 00000000..1daddeed --- /dev/null +++ b/tools/Polygraphy/tests/test_tools.py @@ -0,0 +1,324 @@ +from polygraphy.util import misc +from polygraphy.logger import G_LOGGER + +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.common import version, check_file_non_empty + +import subprocess as sp +import tensorrt as trt +import tempfile +import pytest +import copy +import glob +import sys +import os + + +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) +BIN_DIR = os.path.join(ROOT_DIR, "bin") +Polygraphy = os.path.join(BIN_DIR, "polygraphy") + + + +def check_subprocess(status): + assert not status.returncode + + +def run_subtool(subtool, additional_opts): + cmd = [sys.executable, Polygraphy, subtool] + additional_opts + G_LOGGER.info("Running command: {:}".format(" ".join(cmd))) + check_subprocess(sp.run(cmd)) + + +def run_polygraphy_run(additional_opts=[]): + run_subtool("run", additional_opts) + + +def run_polygraphy_inspect(additional_opts=[]): + run_subtool("inspect", additional_opts) + + +def run_polygraphy_precision(additional_opts=[]): + run_subtool("precision", additional_opts) + + +def run_polygraphy_surgeon(additional_opts=[]): + run_subtool("surgeon", additional_opts) + + +# +# INSPECT MODEL +# + +@pytest.fixture(scope="module", params=[None, "basic", "full"]) +def run_inspect_model(request): + flags = ["--layer-info={:}".format(request.param)] if request.param else [] + yield lambda additional_opts: run_polygraphy_inspect(["model"] + flags + additional_opts) + + +def test_polygraphy_inspect_model_trt_sanity(run_inspect_model): + run_inspect_model([ONNX_MODELS["identity"].path, "--display-as=trt"]) + + +def test_polygraphy_inspect_model_trt_engine_sanity(run_inspect_model): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--save-engine", outpath.name]) + run_inspect_model([outpath.name, "--model-type=engine"]) + + +def test_polygraphy_inspect_model_onnx_sanity(run_inspect_model): + run_inspect_model([ONNX_MODELS["identity"].path]) + + +def test_polygraphy_inspect_model_onnx_subgraphs(run_inspect_model): + run_inspect_model([ONNX_MODELS["scan"].path]) + + +def test_polygraphy_inspect_model_tf_sanity(run_inspect_model): + run_inspect_model([TF_MODELS["identity"].path, "--model-type=frozen"]) + + +# +# INSPECT RESULTS +# + +def test_polygraphy_inspect_results_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_inspect(["results", outpath.name]) + + +# +# PRECISION +# + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_bisect_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_precision(["bisect", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_linear_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_precision(["linear", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_worst_first_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name, "--onnx-outputs", "mark", "all"]) + run_polygraphy_precision(["worst-first", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8", "--trt-outputs", "mark", "all"]) + + +# +# SURGEON +# + +def test_polygraphy_surgeon_sanity(): + with tempfile.NamedTemporaryFile() as configpath, tempfile.NamedTemporaryFile() as modelpath: + run_polygraphy_surgeon(["prepare", ONNX_MODELS["identity"].path, "-o", configpath.name]) + run_polygraphy_surgeon(["operate", ONNX_MODELS["identity"].path, "-c", configpath.name, "-o", modelpath.name]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +def test_polygraphy_surgeon_extract_sanity(): + with tempfile.NamedTemporaryFile() as modelpath: + run_polygraphy_surgeon(["extract", ONNX_MODELS["identity_identity"].path, "-o", modelpath.name, "--inputs", "identity_out_0,auto,auto"]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +def test_polygraphy_surgeon_extract_fallback_shape_inference(): + with tempfile.NamedTemporaryFile() as modelpath: + # Force fallback shape inference by disabling ONNX shape inference + run_polygraphy_surgeon(["extract", ONNX_MODELS["identity_identity"].path, "-o", modelpath.name, "--inputs", + "identity_out_0,auto,auto", "--outputs", "identity_out_2,auto", "--no-shape-inference"]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +# +# RUN +# + +def test_polygraphy_run_gen_script(): + with tempfile.NamedTemporaryFile(mode="w") as f: + run_polygraphy_run(["--gen-script={:}".format(f.name), ONNX_MODELS["identity"].path]) + with open(f.name, "r") as script: + print(script.read()) + env = copy.deepcopy(os.environ) + env.update({"PYTHONPATH": ROOT_DIR}) + check_subprocess(sp.run([sys.executable, f.name], env=env)) + + +def test_logger_verbosity(): + run_polygraphy_run(["--silent"]) + + +def test_trt_legacy_uff(): + run_polygraphy_run([TF_MODELS["identity"].path, "--trt-legacy"]) + + +@pytest.mark.skipif(version(trt.__version__) >= version("7.0"), reason="Unsupported in TRT 7.0 and later") +def test_trt_legacy_onnx(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt-legacy"]) + + +def test_trt(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt"]) + + +def test_trt_plugins(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--plugins", "libnvinfer_plugin.so"]) + + +def test_trt_custom_outputs(): + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--trt", "--trt-outputs", "identity_out_0"]) + + +def test_trt_layerwise_outputs(): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--trt", "--trt-outputs", "mark", "all", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 2 + + +@pytest.mark.skipif(version(trt.__version__) < version("7.1.0.0"), reason="API was added in TRT 7.1") +def test_trt_onnx_ext(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--ext"]) + + +def test_trt_int8(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--int8"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_input_shape(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--inputs", "X,1x2x4x4"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_dynamic_input_shape(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--inputs", "X,1x2x-1x4"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_explicit_profile(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--inputs", "X,1x2x1x1", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_explicit_profile_implicit_runtime_shape(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_explicit_profile_opt_runtime_shapes_differ(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--inputs", "X,1x2x2x2", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x3x3", "--trt-max-shapes", "X,1x2x4x4"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_trt_multiple_profiles(): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1", "--trt-min-shapes", "X,1x2x4x4", "--trt-opt-shapes", "X,1x2x4x4", "--trt-max-shapes", "X,1x2x4x4"]) + + +def test_trt_int8_calibration_cache(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--int8", "--calibration-cache", outpath.name]) + check_file_non_empty(outpath.name) + + +def test_trt_save_load_engine(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--save-engine", outpath.name]) + check_file_non_empty(outpath.name) + run_polygraphy_run(["--trt", outpath.name, "--model-type=engine"]) + + +def test_tf(): + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5"]) + + +def test_tf_save_pb(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-pb", outpath.name]) + check_file_non_empty(outpath.name) + + +def test_tf_save_tensorboard(): + with tempfile.TemporaryDirectory() as outdir: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-tensorboard", outdir]) + files = glob.glob("{:}{:}*".format(outdir, os.path.sep)) + assert len(files) == 1 + + +@pytest.mark.skip(reason="Non-trivial to set up - requires CUPTI") +def test_tf_save_timeline(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-timeline", outpath.name]) + timelines = glob.glob(misc.insert_suffix(outpath.name, "*")) + for timeline in timelines: + check_file_non_empty(timeline) + + +@pytest.mark.skip(reason="Non-trivial to set up") +def test_tftrt(): + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--tftrt"]) + + +def test_tf2onnxrt(): + run_polygraphy_run([TF_MODELS["identity"].path, "--onnxrt", "--model-type=frozen"]) + + +def test_tf2onnx_save_onnx(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--onnxrt", "--model-type=frozen", "--save-onnx", outpath.name]) + check_file_non_empty(outpath.name) + import onnx + assert onnx.load(outpath.name) + + +def test_onnx_tf(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxtf"]) + + +def test_onnx_rt(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt"]) + + +def test_onnx_rt_custom_outputs(): + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--onnxrt", "--onnx-outputs", "identity_out_0"]) + + +def test_onnx_rt_layerwise_outputs(): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--onnxrt", "--onnx-outputs", "mark", "all", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 2 + + +def test_0_iterations(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--iterations=0"]) + + +def test_custom_tolerance(): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--iterations=0", "--atol=1.0", "--rtol=1.0"]) + + +def test_save_load_outputs(): + with tempfile.NamedTemporaryFile() as outfile0, tempfile.NamedTemporaryFile() as outfile1: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outfile0.name]) + run_polygraphy_run(["--load-results", outfile0.name, "--save-results", outfile1.name]) # Copy + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--load-results", outfile0.name, outfile1.name]) + # Should work even with no runners specified + run_polygraphy_run([ONNX_MODELS["identity"].path, "--load-results", outfile0.name, outfile1.name]) + # Should work with only one file + run_polygraphy_run([ONNX_MODELS["identity"].path, "--load-results", outfile0.name]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_runner_coexistence(): + run_polygraphy_run([TF_MODELS["identity"].path, "--model-type=frozen", "--tf", "--onnxrt", "--trt"]) diff --git a/tools/Polygraphy/tests/tools/__init__.py b/tools/Polygraphy/tests/tools/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/tools/common.py b/tools/Polygraphy/tests/tools/common.py new file mode 100644 index 00000000..d764b5dd --- /dev/null +++ b/tools/Polygraphy/tests/tools/common.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import subprocess as sp +import sys +import os + +from polygraphy.logger import G_LOGGER + +ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)) +BIN_DIR = os.path.join(ROOT_DIR, "bin") +polygraphy = os.path.join(BIN_DIR, "polygraphy") + + +def check_subprocess(status): + assert not status.returncode + + +def run_subtool(subtool, additional_opts, disable_verbose=False): + cmd = [sys.executable, polygraphy, subtool] + additional_opts + if not disable_verbose: + cmd += ["-vvvvv"] + G_LOGGER.info("Running command: {:}".format(" ".join(cmd))) + status = sp.run(cmd, stdout=sp.PIPE, stderr=sp.PIPE) + check_subprocess(status) + return status + + +def run_polygraphy_run(additional_opts=[], disable_verbose=False): + return run_subtool("run", additional_opts, disable_verbose) + + +def run_polygraphy_inspect(additional_opts=[], disable_verbose=False): + return run_subtool("inspect", additional_opts, disable_verbose) + + +def run_polygraphy_precision(additional_opts=[], disable_verbose=False): + return run_subtool("precision", additional_opts, disable_verbose) + + +def run_polygraphy_surgeon(additional_opts=[], disable_verbose=False): + return run_subtool("surgeon", additional_opts, disable_verbose) diff --git a/tools/Polygraphy/tests/tools/test_args.py b/tools/Polygraphy/tests/tools/test_args.py new file mode 100644 index 00000000..7c42b5df --- /dev/null +++ b/tools/Polygraphy/tests/tools/test_args.py @@ -0,0 +1,56 @@ + +import numpy as np +from polygraphy.tools.util.args import parse_meta + + +class TestParseMeta(object): + def test_parse_shape_only(self): + meta_args = ["input0,1x3x224x224"] + meta = parse_meta(meta_args, includes_dtype=False) + assert meta["input0"].shape == (1, 3, 224, 224) + assert meta["input0"].dtype is None + + + def test_parse_shape_single_dim(self): + meta_args = ["input0,1"] + meta = parse_meta(meta_args, includes_dtype=False) + assert meta["input0"].shape == (1, ) + + + def test_parse_dtype_only(self): + meta_args = ["input0,float32"] + meta = parse_meta(meta_args, includes_shape=False) + assert meta["input0"].shape is None + assert meta["input0"].dtype == np.float32 + + + def test_parse_shape_dtype(self): + meta_args = ["input0,1x3x224x224,float32"] + meta = parse_meta(meta_args) + assert meta["input0"].shape == (1, 3, 224, 224) + assert meta["input0"].dtype == np.float32 + + + def test_parse_shape_dtype_auto(self): + meta_args = ["input0,auto,auto"] + meta = parse_meta(meta_args) + assert meta["input0"].shape is None + assert meta["input0"].dtype is None + + + def test_parse_shape_with_dim_param_single_quote(self): + meta_args = ["input0,'batch'x3x224x224"] + meta = parse_meta(meta_args, includes_dtype=False) + assert meta["input0"].shape == ("batch", 3, 224, 224) + + + def test_parse_shape_with_dim_param_double_quote(self): + meta_args = ['input0,"batch"x3x224x224'] + meta = parse_meta(meta_args, includes_dtype=False) + assert meta["input0"].shape == ("batch", 3, 224, 224) + + + def test_parse_shape_with_dim_param_including_x(self): + meta_args = ["input0,'batchx'x3x224x224"] + meta = parse_meta(meta_args, includes_dtype=False) + assert meta["input0"].shape == ("batchx", 3, 224, 224) diff --git a/tools/Polygraphy/tests/tools/test_inspect.py b/tools/Polygraphy/tests/tools/test_inspect.py new file mode 100644 index 00000000..0bb0904c --- /dev/null +++ b/tools/Polygraphy/tests/tools/test_inspect.py @@ -0,0 +1,286 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import copy +import glob +import os +import subprocess as sp +import sys +import tempfile +from textwrap import dedent + +import pytest +from polygraphy.logger import G_LOGGER +from polygraphy.util import misc +from tests.common import check_file_non_empty, version +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.tools.common import (run_polygraphy_inspect, run_polygraphy_run, + run_subtool) + +# +# INSPECT MODEL +# + +@pytest.fixture(scope="module", params=["none", "basic", "attrs", "full"]) +def run_inspect_model(request): + yield lambda additional_opts: run_polygraphy_inspect(["model"] + ["--mode={:}".format(request.param)] + additional_opts) + + +# ONNX cases +ONNX_CASES = [ + ["identity", "none", + """ + [I] ==== ONNX Model ==== + Name: test_identity | Opset: 8 + + ---- 1 Graph Inputs ---- + {x [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Graph Outputs ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 0 Initializers ---- + (Use --mode to display) + + ---- 1 Nodes ---- + (Use --mode to display) + """ + ], + ["identity", "basic", + """ + [I] ==== ONNX Model ==== + Name: test_identity | Opset: 8 + + ---- 1 Graph Inputs ---- + {x [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 1 Graph Outputs ---- + {y [dtype=float32, shape=(1, 1, 2, 2)]} + + ---- 0 Initializers ---- + {} + + ---- 1 Nodes ---- + Node 0 | [Op: Identity] + {x [dtype=float32, shape=(1, 1, 2, 2)]} + -> {y [dtype=float32, shape=(1, 1, 2, 2)]} + """ + ], + ["identity_with_initializer", "basic", + """ + [I] ==== ONNX Model ==== + Name: onnx_graphsurgeon | Opset: 11 + + ---- 0 Graph Inputs ---- + {} + + ---- 1 Graph Outputs ---- + {Y [dtype=float32, shape=(2, 2)]} + + ---- 1 Initializers ---- + {X [dtype=float32, shape=(2, 2)]} + + ---- 1 Nodes ---- + Node 0 | [Op: Identity] + {Initializer | X [dtype=float32, shape=(2, 2)]} + -> {Y [dtype=float32, shape=(2, 2)]} + """ + ], + ["identity_with_initializer", "full", + """ + [I] ==== ONNX Model ==== + Name: onnx_graphsurgeon | Opset: 11 + + ---- 0 Graph Inputs ---- + {} + + ---- 1 Graph Outputs ---- + {Y [dtype=float32, shape=(2, 2)]} + + ---- 1 Initializers ---- + Initializer | X [dtype=float32, shape=[2, 2]] | Values: + [[1. 1.] + [1. 1.]] + + ---- 1 Nodes ---- + Node 0 | [Op: Identity] + {Initializer | X [dtype=float32, shape=(2, 2)]} + -> {Y [dtype=float32, shape=(2, 2)]} + """ + ], + ["tensor_attr", "basic", + """ + [I] ==== ONNX Model ==== + Name: onnx_graphsurgeon | Opset: 11 + + ---- 0 Graph Inputs ---- + {} + + ---- 1 Graph Outputs ---- + {const_out [dtype=float32, shape=(14, 14)]} + + ---- 0 Initializers ---- + {} + + ---- 1 Nodes ---- + Node 0 | [Op: Constant] + {} -> {const_out [dtype=float32, shape=(14, 14)]} + """ + ], + ["tensor_attr", "attrs", + """ + [I] ==== ONNX Model ==== + Name: onnx_graphsurgeon | Opset: 11 + + ---- 0 Graph Inputs ---- + {} + + ---- 1 Graph Outputs ---- + {const_out [dtype=float32, shape=(14, 14)]} + + ---- 0 Initializers ---- + {} + + ---- 1 Nodes ---- + Node 0 | [Op: Constant] + {} -> {const_out [dtype=float32, shape=(14, 14)]} + ---- Attributes ---- + value = Tensor: [dtype=float32, shape=[14, 14]] + """ + ], + ["tensor_attr", "full", + """ + [I] ==== ONNX Model ==== + Name: onnx_graphsurgeon | Opset: 11 + + ---- 0 Graph Inputs ---- + {} + + ---- 1 Graph Outputs ---- + {const_out [dtype=float32, shape=(14, 14)]} + + ---- 0 Initializers ---- + + ---- 1 Nodes ---- + Node 0 | [Op: Constant] + {} -> {const_out [dtype=float32, shape=(14, 14)]} + ---- Attributes ---- + value = Tensor: [dtype=float32, shape=[14, 14]] | Values: + [[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] + [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]] + """ + ], + ["scan", "full", + """ + [I] ==== ONNX Model ==== + Name: graph | Opset: 10 + + ---- 2 Graph Inputs ---- + {initial [dtype=float32, shape=(2,)], x [dtype=float32, shape=(3, 2)]} + + ---- 2 Graph Outputs ---- + {y [dtype=float32, shape=(2,)], z [dtype=float32, shape=(3, 2)]} + + ---- 0 Initializers ---- + + ---- 1 Nodes ---- + Node 0 | [Op: Scan] + {initial [dtype=float32, shape=(2,)], x [dtype=float32, shape=(3, 2)]} + -> {y [dtype=float32, shape=(2,)], z [dtype=float32, shape=(3, 2)]} + ---- Attributes ---- + body = + ---- 2 Subgraph Inputs ---- + {sum_in [dtype=float32, shape=(2,)], next [dtype=float32, shape=(2,)]} + + ---- 2 Subgraph Outputs ---- + {sum_out [dtype=float32, shape=(2,)], scan_out [dtype=float32, shape=(2,)]} + + ---- 0 Initializers ---- + + ---- 2 Nodes ---- + Node 0 | [Op: Add] + {sum_in [dtype=float32, shape=(2,)], next [dtype=float32, shape=(2,)]} + -> {sum_out [dtype=float32, shape=(2,)]} + + Node 1 | [Op: Identity] + {sum_out [dtype=float32, shape=(2,)]} + -> {scan_out [dtype=float32, shape=(2,)]} + + num_scan_inputs = 1 + """ + ], +] + +@pytest.mark.parametrize("case", ONNX_CASES, ids=lambda case: "{:}-{:}".format(case[0], case[1])) +def test_polygraphy_inspect_model_onnx(run_inspect_model, case): + model, mode, expected = case + status = run_polygraphy_inspect(["model", ONNX_MODELS[model].path, "--mode={:}".format(mode)], disable_verbose=True) + + expected = dedent(expected).strip() + actual = status.stdout.decode() + + print("Actual output:\n{:}".format(actual)) + for acline, exline in zip(actual.splitlines(), expected.splitlines()): + acline = acline.rstrip() + exline = exline.rstrip() + print("Checking line : {:}".format(acline)) + print("Expecting line: {:}".format(exline)) + assert acline == exline + + +@pytest.mark.parametrize("model", ["identity", "scan", "tensor_attr"]) +def test_polygraphy_inspect_model_trt_sanity(run_inspect_model, model): + import tensorrt as trt + + if model == "tensor_attr" and version(trt.__version__) < version("7.2"): + pytest.skip("Models with constant outputs were not supported before 7.2") + + if model == "scan" and version(trt.__version__) < version("7.0"): + pytest.skip("Scan was not supported until 7.0") + + run_inspect_model([ONNX_MODELS[model].path, "--display-as=trt"]) + + +def test_polygraphy_inspect_model_trt_engine_sanity(run_inspect_model): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--save-engine", outpath.name]) + run_inspect_model([outpath.name, "--model-type=engine"]) + + +def test_polygraphy_inspect_model_tf_sanity(run_inspect_model): + run_inspect_model([TF_MODELS["identity"].path, "--model-type=frozen"]) + + +# +# INSPECT RESULTS +# + +@pytest.mark.parametrize("opts", [[], ["--show-values"]]) +def test_polygraphy_inspect_results(opts): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_inspect(["results", outpath.name] + opts) diff --git a/tools/Polygraphy/tests/tools/test_precision.py b/tools/Polygraphy/tests/tools/test_precision.py new file mode 100644 index 00000000..e1e65dec --- /dev/null +++ b/tools/Polygraphy/tests/tools/test_precision.py @@ -0,0 +1,51 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import copy +import glob +import os +import subprocess as sp +import sys +import tempfile + +import pytest +from polygraphy.logger import G_LOGGER +from polygraphy.util import misc + +import tensorrt as trt +from tests.common import check_file_non_empty, version +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.tools.common import run_subtool, run_polygraphy_precision, run_polygraphy_run + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_bisect_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_precision(["bisect", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_linear_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name]) + run_polygraphy_precision(["linear", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8"]) + + +@pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") +def test_polygraphy_precision_worst_first_sanity(): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outpath.name, "--onnx-outputs", "mark", "all"]) + run_polygraphy_precision(["worst-first", ONNX_MODELS["identity"].path, "--golden", outpath.name, "--int8", "--trt-outputs", "mark", "all"]) diff --git a/tools/Polygraphy/tests/tools/test_run.py b/tools/Polygraphy/tests/tools/test_run.py new file mode 100644 index 00000000..4505e7c7 --- /dev/null +++ b/tools/Polygraphy/tests/tools/test_run.py @@ -0,0 +1,247 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import copy +import glob +import os +import subprocess as sp +import sys +import tempfile + +import pytest +from polygraphy.logger import G_LOGGER +from polygraphy.util import misc + +import tensorrt as trt +from tests.common import check_file_non_empty, version +from tests.models.meta import ONNX_MODELS, TF_MODELS +from tests.tools.common import (ROOT_DIR, check_subprocess, run_polygraphy_run, + run_subtool) + + +class TestGen(object): + def test_polygraphy_run_gen_script(self): + with tempfile.NamedTemporaryFile(mode="w") as f: + run_polygraphy_run(["--gen-script={:}".format(f.name), ONNX_MODELS["identity"].path]) + with open(f.name, "r") as script: + print(script.read()) + env = copy.deepcopy(os.environ) + env.update({"PYTHONPATH": ROOT_DIR}) + check_subprocess(sp.run([sys.executable, f.name], env=env)) + + +class TestLogging(object): + def test_logger_verbosity(self): + run_polygraphy_run(["--silent"]) + + +class TestTrtLegacy(object): + def test_trt_legacy_uff(self): + run_polygraphy_run([TF_MODELS["identity"].path, "--trt-legacy"]) + + + @pytest.mark.skipif(version(trt.__version__) >= version("7.0"), reason="Unsupported in TRT 7.0 and later") + def test_trt_legacy_onnx(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt-legacy"]) + + +class TestTrt(object): + def test_trt(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt"]) + + + def test_trt_plugins(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--plugins", "libnvinfer_plugin.so"]) + + + def test_trt_custom_outputs(self): + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--trt", "--trt-outputs", "identity_out_0"]) + + + def test_trt_layerwise_outputs(self): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--trt", "--trt-outputs", "mark", "all", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 2 + assert "identity_out_0" in result + assert "identity_out_2" in result + + + def test_trt_exclude_outputs_with_layerwise(self): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--trt", "--trt-outputs", "mark", "all", "--trt-exclude-outputs", "identity_out_2", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 1 + assert "identity_out_0" in result + + + @pytest.mark.skipif(version(trt.__version__) < version("7.1.0.0"), reason="API was added in TRT 7.1") + def test_trt_onnx_ext(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--ext"]) + + + def test_trt_int8(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--int8"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_input_shape(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--input-shapes", "X,1x2x4x4"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_dynamic_input_shape(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--input-shapes", "X,1x2x-1x4"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_explicit_profile(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--input-shapes", "X,1x2x1x1", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_explicit_profile_implicit_runtime_shape(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_explicit_profile_opt_runtime_shapes_differ(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--input-shapes", "X,1x2x2x2", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x3x3", "--trt-max-shapes", "X,1x2x4x4"]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_trt_multiple_profiles(self): + run_polygraphy_run([ONNX_MODELS["dynamic_identity"].path, "--trt", "--onnxrt", "--trt-min-shapes", "X,1x2x1x1", "--trt-opt-shapes", "X,1x2x1x1", "--trt-max-shapes", "X,1x2x1x1", "--trt-min-shapes", "X,1x2x4x4", "--trt-opt-shapes", "X,1x2x4x4", "--trt-max-shapes", "X,1x2x4x4"]) + + + def test_trt_int8_calibration_cache(self): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--int8", "--calibration-cache", outpath.name]) + check_file_non_empty(outpath.name) + + + def test_trt_save_load_engine(self): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--trt", "--save-engine", outpath.name]) + check_file_non_empty(outpath.name) + run_polygraphy_run(["--trt", outpath.name, "--model-type=engine"]) + + +class TestTf(object): + def test_tf(self): + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5"]) + + + def test_tf_save_pb(self): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-pb", outpath.name]) + check_file_non_empty(outpath.name) + + + def test_tf_save_tensorboard(self): + with tempfile.TemporaryDirectory() as outdir: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-tensorboard", outdir]) + files = glob.glob("{:}{:}*".format(outdir, os.path.sep)) + assert len(files) == 1 + + + @pytest.mark.skip(reason="Non-trivial to set up - requires CUPTI") + def test_tf_save_timeline(self): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--gpu-memory-fraction=0.5", "--save-timeline", outpath.name]) + timelines = glob.glob(misc.insert_suffix(outpath.name, "*")) + for timeline in timelines: + check_file_non_empty(timeline) + + + @pytest.mark.skip(reason="Non-trivial to set up") + def test_tftrt(self): + run_polygraphy_run([TF_MODELS["identity"].path, "--tf", "--tftrt"]) + + +class TestOnnxTf(object): + def test_onnx_tf(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxtf"]) + + +class TestOnnxrt(object): + def test_tf2onnxrt(self): + run_polygraphy_run([TF_MODELS["identity"].path, "--onnxrt", "--model-type=frozen"]) + + + def test_tf2onnx_save_onnx(self): + with tempfile.NamedTemporaryFile() as outpath: + run_polygraphy_run([TF_MODELS["identity"].path, "--onnxrt", "--model-type=frozen", "--save-onnx", outpath.name]) + check_file_non_empty(outpath.name) + import onnx + assert onnx.load(outpath.name) + + + def test_onnx_rt(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt"]) + + + def test_onnx_rt_custom_outputs(self): + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--onnxrt", "--onnx-outputs", "identity_out_0"]) + + + def test_onnx_rt_layerwise_outputs(self): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--onnxrt", "--onnx-outputs", "mark", "all", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 2 + assert "identity_out_0" in result + assert "identity_out_2" in result + + + def test_onnx_rt_exclude_outputs_with_layerwise(self): + with tempfile.NamedTemporaryFile() as outfile0: + run_polygraphy_run([ONNX_MODELS["identity_identity"].path, "--onnxrt", "--onnx-outputs", "mark", "all", "--onnx-exclude-outputs", "identity_out_2", "--save-results", outfile0.name]) + results = misc.pickle_load(outfile0.name) + [result] = list(results.values())[0] + assert len(result) == 1 + assert "identity_out_0" in result + + +class TestOther(object): + def test_0_iterations(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--iterations=0"]) + + + def test_custom_tolerance(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--iterations=0", "--atol=1.0", "--rtol=1.0"]) + + + def test_top_k(self): + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--top-k=5"]) + + + def test_save_load_outputs(self): + with tempfile.NamedTemporaryFile() as outfile0, tempfile.NamedTemporaryFile() as outfile1: + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--save-results", outfile0.name]) + run_polygraphy_run(["--load-results", outfile0.name, "--save-results", outfile1.name]) # Copy + run_polygraphy_run([ONNX_MODELS["identity"].path, "--onnxrt", "--load-results", outfile0.name, outfile1.name]) + # Should work even with no runners specified + run_polygraphy_run([ONNX_MODELS["identity"].path, "--load-results", outfile0.name, outfile1.name]) + # Should work with only one file + run_polygraphy_run([ONNX_MODELS["identity"].path, "--load-results", outfile0.name]) + + + @pytest.mark.skipif(version(trt.__version__) < version("7.0"), reason="Unsupported for TRT 6") + def test_runner_coexistence(self): + run_polygraphy_run([TF_MODELS["identity"].path, "--model-type=frozen", "--tf", "--onnxrt", "--trt"]) diff --git a/tools/Polygraphy/tests/tools/test_surgeon.py b/tools/Polygraphy/tests/tools/test_surgeon.py new file mode 100644 index 00000000..31a19c7d --- /dev/null +++ b/tools/Polygraphy/tests/tools/test_surgeon.py @@ -0,0 +1,46 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import tempfile + +from tests.models.meta import ONNX_MODELS +from tests.tools.common import run_polygraphy_run, run_polygraphy_surgeon + + +def test_polygraphy_surgeon_sanity(): + with tempfile.NamedTemporaryFile() as configpath, tempfile.NamedTemporaryFile() as modelpath: + run_polygraphy_surgeon(["prepare", ONNX_MODELS["identity"].path, "-o", configpath.name]) + run_polygraphy_surgeon(["operate", ONNX_MODELS["identity"].path, "-c", configpath.name, "-o", modelpath.name]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +def test_polygraphy_surgeon_extract_sanity(): + with tempfile.NamedTemporaryFile() as modelpath: + run_polygraphy_surgeon(["extract", ONNX_MODELS["identity_identity"].path, "-o", modelpath.name, "--inputs", "identity_out_0,auto,auto"]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +def test_polygraphy_surgeon_extract_fallback_shape_inference(): + with tempfile.NamedTemporaryFile() as modelpath: + # Force fallback shape inference by disabling ONNX shape inference + run_polygraphy_surgeon(["extract", ONNX_MODELS["identity_identity"].path, "-o", modelpath.name, "--inputs", + "identity_out_0,auto,auto", "--outputs", "identity_out_2,auto", "--no-shape-inference"]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) + + +def test_polygraphy_surgeon_extract_sanity_dim_param(): + with tempfile.NamedTemporaryFile() as modelpath: + run_polygraphy_surgeon(["extract", ONNX_MODELS["dim_param"].path, "-o", modelpath.name]) + run_polygraphy_run([modelpath.name, "--model-type=onnx", "--onnxrt"]) diff --git a/tools/Polygraphy/tests/util/__init__.py b/tools/Polygraphy/tests/util/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tools/Polygraphy/tests/util/test_cuda.py b/tools/Polygraphy/tests/util/test_cuda.py new file mode 100644 index 00000000..1058c7d9 --- /dev/null +++ b/tools/Polygraphy/tests/util/test_cuda.py @@ -0,0 +1,105 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import ctypes + +import numpy as np +import pytest +from polygraphy.util import misc +from polygraphy.util.cuda import DeviceBuffer, Stream + + +class ResizeTestCase(object): + # *_bytes is the size of the allocated buffer, old/new are the apparent shapes of the buffer. + def __init__(self, old, old_size, new, new_size): + self.old = old + self.old_bytes = old_size * np.float32().itemsize + self.new = new + self.new_bytes = new_size * np.float32().itemsize + +RESIZES = [ + ResizeTestCase(tuple(), 1, (1, 1, 1), 1), # Reshape (no-op) + ResizeTestCase((2, 2, 2), 8, (1, 1), 8), # Resize to smaller buffer + ResizeTestCase((2, 2, 2), 8, (9, 9), 81), # Resize to larger buffer +] + + +class TestDeviceBuffer(object): + @pytest.mark.parametrize("shapes", RESIZES) + def test_device_buffer_resize(self, shapes): + buf = DeviceBuffer(shapes.old) + assert buf.allocated_nbytes == shapes.old_bytes + assert buf.shape == shapes.old + buf.resize(shapes.new) + assert buf.allocated_nbytes == shapes.new_bytes + assert buf.shape == shapes.new + + + def test_device_buffer_memcpy_async(self): + stream = Stream() + arr = np.ones((1, 384), dtype=np.int32) + + buf = DeviceBuffer() + buf.resize(arr.shape) + buf.copy_from(arr) + + new_arr = np.empty((1, 384), dtype=np.int32) + buf.copy_to(new_arr, stream) + + stream.synchronize() + + assert np.all(new_arr == arr) + + + def test_device_buffer_memcpy_sync(self): + arr = np.ones((1, 384), dtype=np.int32) + + buf = DeviceBuffer() + buf.resize(arr.shape) + buf.copy_from(arr) + + new_arr = np.empty((1, 384), dtype=np.int32) + buf.copy_to(new_arr) + + assert np.all(new_arr == arr) + + + def test_device_buffer_free(self): + buf = DeviceBuffer(shape=(64, 64), dtype=np.float32) + assert buf.allocated_nbytes == 64 * 64 * np.float32().itemsize + + buf.free() + assert buf.allocated_nbytes == 0 + assert buf.shape == tuple() + + + def test_empty_tensor_to_host(self): + buf = DeviceBuffer(shape=(5, 2, 0, 3, 0), dtype=np.float32) + assert misc.volume(buf.shape) == 0 + + host_buf = np.empty(tuple(), dtype=np.float32) + assert misc.volume(host_buf.shape) == 1 + + host_buf = buf.copy_to(host_buf) + assert host_buf.shape == buf.shape + assert host_buf.nbytes == 0 + assert misc.volume(host_buf.shape) == 0 + + +class TestStream(object): + def test_handle_is_ctypes_ptr(self): + # If the handle is not a c_void_p (e.g. just an int), then it may cause segfaults when used. + stream = Stream() + assert isinstance(stream.handle, ctypes.c_void_p) diff --git a/tools/Polygraphy/tests/util/test_format.py b/tools/Polygraphy/tests/util/test_format.py new file mode 100644 index 00000000..2d0b6d2f --- /dev/null +++ b/tools/Polygraphy/tests/util/test_format.py @@ -0,0 +1,37 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.logger import G_LOGGER + +from polygraphy.util.format import FormatManager, DataFormat + +import pytest + + +class FormatTestCase: + def __init__(self, shape, format): + self.shape = shape + self.format = format + +EXPECTED_FORMATS = [ + FormatTestCase((1, 3, 480, 960), DataFormat.NCHW), + FormatTestCase((1, 3, 224, 224), DataFormat.NCHW), + FormatTestCase((1, 224, 224, 3), DataFormat.NHWC), + FormatTestCase((1, 9, 9, 3), DataFormat.NHWC), +] + +@pytest.mark.parametrize("test_case", EXPECTED_FORMATS) +def test_format_deduction(test_case): + assert test_case.format == FormatManager.determine_format(test_case.shape) diff --git a/tools/Polygraphy/tests/util/test_misc.py b/tools/Polygraphy/tests/util/test_misc.py new file mode 100644 index 00000000..251b52c5 --- /dev/null +++ b/tools/Polygraphy/tests/util/test_misc.py @@ -0,0 +1,102 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from polygraphy.util import misc + +import numpy as np +import pytest + + +VOLUME_CASES = [ + ((1, 1, 1), 1), + ((2, 3, 4), 24), + (tuple(), 1), +] + +@pytest.mark.parametrize("case", VOLUME_CASES) +def test_volume(case): + it, vol = case + assert misc.volume(it) == vol + + +class FindInDictCase(object): + def __init__(self, name, map, index, expected): + self.name = name + self.map = map + self.index = index + self.expected = expected + +FIND_IN_DICT_CASES = [ + FindInDictCase("resnet50_v1.5/output/Softmax:0", map={"resnet50_v1.5/output/Softmax:0": "x"}, index=None, expected="resnet50_v1.5/output/Softmax:0"), + FindInDictCase("resnet50_v1.5/output/Softmax:0", map={"resnet50_v1.5/output/softmax:0": "x"}, index=None, expected="resnet50_v1.5/output/softmax:0"), +] + +@pytest.mark.parametrize("case", FIND_IN_DICT_CASES) +def test_find_in_dict(case): + actual = misc.find_in_dict(case.name, case.map, case.index) + assert actual == case.expected + + +SHAPE_OVERRIDE_CASES = [ + ((1, 3, 224, 224), (None, 3, 224, 224), True), +] + +@pytest.mark.parametrize("case", SHAPE_OVERRIDE_CASES) +def test_is_valid_shape_override(case): + override, shape, expected = case + assert misc.is_valid_shape_override(new_shape=override, original_shape=shape) == expected + + +SHAPE_MATCHING_CASES = [ + (np.zeros((1, 1, 3, 3)), (3, 3), (3, 3)), # Squeeze array shape + (np.zeros((1, 3, 3, 1)), (1, 1, 3, 3), (1, 1, 3, 3)), # Permute + (np.zeros((3, 3)), (1, 1, 3, 3), (3, 3)), # Squeeze specified shape + (np.zeros((3, 3)), (-1, 3), (3, 3)), # Infer dynamic + (np.zeros((3 * 224 * 224)), (None, 3, 224, 224), (1, 3, 224, 224)), # Reshape and Permute + (np.zeros((1, 3, 224, 224)), (None, 224, 224, 3), (1, 224, 224, 3)), # Permute +] + +@pytest.mark.parametrize("case", SHAPE_MATCHING_CASES) +def test_shape_matching(case): + out, shape, expected_shape = case + out = misc.try_match_shape(out, shape) + assert out.shape == expected_shape + + + +UNPACK_ARGS_CASES = [ + ((0, 1, 2), 3, (0, 1, 2)), # no extras + ((0, 1, 2), 4, (0, 1, 2, None)), # 1 extra + ((0, 1, 2), 2, (0, 1)), # 1 fewer +] + +@pytest.mark.parametrize("case", UNPACK_ARGS_CASES) +def test_unpack_args(case): + args, num, expected = case + assert misc.unpack_args(args, num) == expected + + +UNIQUE_LIST_CASES = [ + ([], []), + ([3, 1, 2], [3, 1, 2]), + ([1, 2, 3, 2, 1], [1, 2, 3]), + ([0, 0, 0, 0, 1, 0, 0], [0, 1]), + ([5, 5, 5, 5, 5], [5]), +] + +@pytest.mark.parametrize("case", UNIQUE_LIST_CASES) +def test_unique_list(case): + lst, expected = case + assert misc.unique_list(lst) == expected diff --git a/tools/onnx-graphsurgeon/CHANGELOG.md b/tools/onnx-graphsurgeon/CHANGELOG.md index 61c486d4..953b4874 100644 --- a/tools/onnx-graphsurgeon/CHANGELOG.md +++ b/tools/onnx-graphsurgeon/CHANGELOG.md @@ -1,14 +1,35 @@ -# ONNX GraphSurgeon changelog history +# ONNX GraphSurgeon Change Log Dates are in YYYY-MM-DD format. -## vNext +## v0.2.7 (2020-09-29) +### Changed +- Changed the default value of `remove_unused_node_outputs` in `cleanup()` to `False`, as a value of `True` can lead to unintuitive behavior, + especially with looping constructs like `Scan` and `Loop`. + + +## v0.2.6 (2020-09-25) +### Fixed +- Fixed a bug where calling `graph.tensors()` would cause the inputs or outputs of some tensors to be modified. + +### Changed +- Changed `SynchronizedList.__add__()` no longer modifies the left operand. + + +## v0.2.5 (2020-09-21) +### Fixed +- Fixed a bug where nodes including subgraphs whose inputs/outputs had the same names as the node's inputs/outputs would not be imported correctly. + + +## v0.2.4 (2020-09-14) ### Fixed - `fold_constants()` will no longer fail if there is nothing to fold in the graph +- `cleanup()` will now properly remove the producer nodes of graph inputs. +- Fixed a bug where graph input/output tensors not attached to nodes would not be correctly exported. -## v0.2.3 +## v0.2.3 (2020-06-17) ### Added - `Graph.register()` now accepts an `opsets` argument so that functions can be registered for specific opsets. diff --git a/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md b/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md index 22484602..c4cbf520 100644 --- a/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md +++ b/tools/onnx-graphsurgeon/examples/01_creating_a_model/README.md @@ -1,7 +1,9 @@ # Creating An ONNX Model -ONNX GraphSurgeon includes an [IR (intermediate representation)](../../README.md#ir) -that can be exported to ONNX. Additionally, the IR exposes a simple API so that it is +## Introduction + +ONNX GraphSurgeon includes an [IR (intermediate representation)](../../README.md#ir) +that can be exported to ONNX. Additionally, the IR exposes a simple API so that it is possible to build graphs manually. This example creates an ONNX model containing a single GlobalLpPool node. diff --git a/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md index 43242ce8..63500600 100644 --- a/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md +++ b/tools/onnx-graphsurgeon/examples/02_creating_a_model_with_initializer/README.md @@ -1,5 +1,7 @@ # Creating An ONNX Model With An Initializer +## Introduction + This example creates an ONNX model containing a single Convolution node with weights. `Constant`s in ONNX GraphSurgeon are automatically exported as initializers in the ONNX graph. diff --git a/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md index 1dc29060..ab7bc8ec 100644 --- a/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md +++ b/tools/onnx-graphsurgeon/examples/03_isolating_a_subgraph/README.md @@ -1,6 +1,8 @@ # Isolating A Subgraph -This example first generates a very basic model, +## Introduction + +This example first generates a basic model, then extracts a subgraph from this model. Isolating a subgraph from a model is as simple as modifying the inputs and @@ -8,7 +10,7 @@ outputs of the graph, running `graph.cleanup()`, and then re-exporting the graph We do **not** need to know which nodes, initializers, or intermediate tensors we want - marking the inputs and outputs is sufficient for ONNX GraphSurgeon to be able -to deduce the other information automatically. +to determine the other information automatically. ## Running the example diff --git a/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md index 2a77b1de..319b4a54 100644 --- a/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md +++ b/tools/onnx-graphsurgeon/examples/04_modifying_a_model/README.md @@ -1,6 +1,8 @@ # Modiyfing A Model -This example first generates a very basic model, +## Introduction + +This example first generates a basic model, then modifies the resulting model in various ways. By importing an ONNX graph into the [ONNX GraphSurgeon IR](../../README.md#ir), it is diff --git a/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md b/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md index ba68bf9c..905b0e51 100644 --- a/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md +++ b/tools/onnx-graphsurgeon/examples/05_folding_constants/README.md @@ -1,5 +1,7 @@ # Folding Constants +## Introduction + This example first generates a a model with several operations that can be evaluated prior to inference time, then folds these operations and exports a new model. diff --git a/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md b/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md index bdd38ffa..2da0014f 100644 --- a/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md +++ b/tools/onnx-graphsurgeon/examples/06_removing_nodes/README.md @@ -1,11 +1,15 @@ # Removing A Node -This example first generates a very basic model, and then removes a node in the +## Introduction + +This example first generates a basic model, and then removes a node in the middle of the model, and reconnects the remaining nodes in the graph. In order to remove a node, we replace the output tensors of its input node with its own outputs, and then remove its own connection to its outputs. +## Node Removal Basics + For example, for a graph with the following structure: Node0 @@ -26,8 +30,8 @@ In order to remove `Node1`, The resulting graph looks like this: - Node0 - | + Node0 + | Tensor1 Tensor0 | | Node2 Node1 diff --git a/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md index e48664e1..a69a07ef 100644 --- a/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md +++ b/tools/onnx-graphsurgeon/examples/07_creating_a_model_with_the_layer_api/README.md @@ -1,5 +1,7 @@ # Creating A Model Using The Graph Layer API +## Introduction + This example uses the `Graph.layer()` function in conjunction with `Graph.register()` to demonstrate how to construct complicated ONNX models more easily. @@ -9,10 +11,8 @@ insert the node in the graph. For details, see the `help` output for `Graph.laye **Note**: You still need to set `Graph` inputs and outputs yourself! - `Graph.layer()` can be used to implement your own functions that can be registered with `Graph.register()`. For example, to implement a `graph.add` function that inserts an "Add" operation into the graph, you could write: - ```python @gs.Graph.register() def add(self, a, b): @@ -20,7 +20,6 @@ def add(self, a, b): ``` and invoke it like so: - ```python [Y] = graph.add(*graph.add(X, B), C) ``` diff --git a/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/README.md b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/README.md new file mode 100644 index 00000000..e92d73b0 --- /dev/null +++ b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/README.md @@ -0,0 +1,86 @@ +# Replacing A Subgraph + +This example first generates a model consisting of a `Min` op followed by a +`Max`, and then uses the `graph.layer()` and `graph.register()` APIs seen in +[example 07](../07_creating_a_model_with_the_layer_api) to create a function +that can be used to replace this subgraph with a `Clip` op. + +This can be useful, for example, to enable TensorRT plugins with ONNX. + + +## Subgraph Replacement Basics + +The process of replacing a subgraph involves 3 steps. For example, for a graph +with the following structure: + + Tensor0 + | + Node0 + | + Tensor1 Tensor2 + \ / + Node1 + | + Tensor3 + | + Node2 + +In order to replace the subgraph consisting of [`Node0`, `Node1`], we need to: + +1. Disconnect the **outputs** of the subgraph inputs: `Tensor0` and `Tensor2` + + That means we need to delete the edge between `Tensor0` and `Node0`, + and between `Tensor2` and `Node1`. + +2. Disconnect the **inputs** of the subgraph outputs: `Tensor3` + + That means we need to delete the edge between `Node1` and `Tensor3`. + +This will leave us with a graph like this: + + Tensor0 Tensor2 + + Tensor3 + | + Node2 + +And the now disconnected subgraph: + + Node0 + | + Tensor1 + \ + Node1 + +3. Lastly, we need to insert our node such that it has inputs: [`Tensor0`, `Tensor2`] + and outputs: [`Tensor3`, ]. + +After this step, we have our final graph (`cleanup()` will remove the dangling subgraph): + + Tensor0 Tensor2 + \ / + MyNewNode0 + | + Tensor3 + | + Node2 + +## Running the example + +1. Generate a the model including `Min` and `Max` ops by running: + ```bash + python3 generate.py + ``` + + The generated model will compute `max(min(x, 6), 0)` and look like this: + + ![../resources/08_model.onnx.png](../resources/08_model.onnx.png) + +2. Replace the subgraph with a `Clip` op by running: + ```bash + python3 replace.py + ``` + + The final model will include a `clip(x, min=0, max=6)` and look like this: + + ![../resources/08_replaced.onnx.png](../resources/08_replaced.onnx.png) diff --git a/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/generate.py b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/generate.py new file mode 100644 index 00000000..5e252b69 --- /dev/null +++ b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/generate.py @@ -0,0 +1,52 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import onnx_graphsurgeon as gs +import numpy as np +import onnx + +# Register functions to make graph generation easier +@gs.Graph.register() +def min(self, *args): + return self.layer(op="Min", inputs=args, outputs=["min_out"])[0] + +@gs.Graph.register() +def max(self, *args): + return self.layer(op="Max", inputs=args, outputs=["max_out"])[0] + +@gs.Graph.register() +def identity(self, inp): + return self.layer(op="Identity", inputs=[inp], outputs=["identity_out"])[0] + + +# Generate the graph +graph = gs.Graph() + +graph.inputs = [gs.Variable("input", shape=(4, 4), dtype=np.float32)] + +# Clip values to [0, 6] +MIN_VAL = np.array(0, np.float32) +MAX_VAL = np.array(6, np.float32) + +# Add identity nodes to make the graph structure a bit more interesting +inp = graph.identity(graph.inputs[0]) +max_out = graph.max(graph.min(inp, MAX_VAL), MIN_VAL) +graph.outputs = [graph.identity(max_out), ] + +# Graph outputs must include dtype information +graph.outputs[0].to_variable(dtype=np.float32, shape=(4, 4)) + +onnx.save(gs.export_onnx(graph), "model.onnx") diff --git a/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/replace.py b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/replace.py new file mode 100644 index 00000000..483d5be0 --- /dev/null +++ b/tools/onnx-graphsurgeon/examples/08_replacing_a_subgraph/replace.py @@ -0,0 +1,55 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import onnx_graphsurgeon as gs +import numpy as np +import onnx + + +# Here we'll register a function to do all the subgraph-replacement heavy-lifting. +# NOTE: Since registered functions are entirely reusable, it may be a good idea to +# refactor them into a separate module so you can use them across all your models. +@gs.Graph.register() +def replace_with_clip(self, inputs, outputs): + # Disconnect output nodes of all input tensors + for inp in inputs: + inp.outputs.clear() + + # Disconnet input nodes of all output tensors + for out in outputs: + out.inputs.clear() + + # Insert the new node. + return self.layer(op="Clip", inputs=inputs, outputs=outputs) + + +# Now we'll do the actual replacement +graph = gs.import_onnx(onnx.load("model.onnx")) + +tmap = graph.tensors() +# You can figure out the input and output tensors using Netron. In our case: +# Inputs: [inp, MIN_VAL, MAX_VAL] +# Outputs: [max_out] +inputs = [tmap["identity_out_0"], tmap["onnx_graphsurgeon_constant_5"], tmap["onnx_graphsurgeon_constant_2"]] +outputs = [tmap["max_out_6"]] + +graph.replace_with_clip(inputs, outputs) + +# Remove the now-dangling subgraph. +graph.cleanup().toposort() + +# That's it! +onnx.save(gs.export_onnx(graph), "replaced.onnx") diff --git a/tools/onnx-graphsurgeon/examples/resources/08_model.onnx.png b/tools/onnx-graphsurgeon/examples/resources/08_model.onnx.png new file mode 100644 index 00000000..b9fffb5b Binary files /dev/null and b/tools/onnx-graphsurgeon/examples/resources/08_model.onnx.png differ diff --git a/tools/onnx-graphsurgeon/examples/resources/08_replaced.onnx.png b/tools/onnx-graphsurgeon/examples/resources/08_replaced.onnx.png new file mode 100644 index 00000000..7e7bd70d Binary files /dev/null and b/tools/onnx-graphsurgeon/examples/resources/08_replaced.onnx.png differ diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py index 217eadfc..3e91762a 100644 --- a/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py +++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/__init__.py @@ -4,4 +4,4 @@ from onnx_graphsurgeon.ir.graph import Graph from onnx_graphsurgeon.ir.node import Node -__version__ = "0.2.3" +__version__ = "0.2.7" diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py index b2a1122e..d3f03257 100644 --- a/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py +++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/importers/onnx_importer.py @@ -86,7 +86,7 @@ def import_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto]) -> @staticmethod - def import_node(onnx_node: onnx.NodeProto, tensor_map: "OrderedDict[str, Tensor]") -> Node: + def import_node(onnx_node: onnx.NodeProto, tensor_map: "OrderedDict[str, Tensor]", subgraph_tensor_map: "OrderedDict[str, Tensor]") -> Node: def attrs_to_dict(attrs): attr_dict = OrderedDict() for attr in attrs: @@ -97,10 +97,9 @@ def process_attr(attr_str: str): elif attr_str == "TENSOR": processed = OnnxImporter.import_tensor(processed) elif attr_str == "GRAPH": - processed = OnnxImporter.import_graph(processed, tensor_map) + processed = OnnxImporter.import_graph(processed, misc.combine_dicts(tensor_map, subgraph_tensor_map)) elif attr_str == "FLOATS" or attr_str == "INTS": - # Proto hacky list to normal Python list - processed = [p for p in processed] + processed = list(processed) elif attr_str == "STRINGS": processed = [p.decode() for p in processed] return processed @@ -116,33 +115,42 @@ def process_attr(attr_str: str): return attr_dict # Optional inputs/outputs are represented by empty tensors. All other tensors should already have been populated during shape inference. - def check_tensor(name: str): - if name not in tensor_map: - if name: - G_LOGGER.debug("Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor.".format(name)) - tensor_map[name] = Variable(name) - else: - # Empty tensors are not tracked by the graph, as these represent optional inputs/outputs that have been omitted. - G_LOGGER.verbose("Generating empty tensor") - return Variable.empty() - return tensor_map[name] + def get_tensor(name: str, check_outer_graph=True): + # Prioritize the subgraph even if check_outer_graph is set + if name in subgraph_tensor_map: + return subgraph_tensor_map[name] + + if check_outer_graph and name in tensor_map: + return tensor_map[name] + + if not name: + # Empty tensors are not tracked by the graph, as these represent optional inputs/outputs that have been omitted. + G_LOGGER.verbose("Generating empty tensor") + return Variable.empty() + + G_LOGGER.verbose("Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor.".format(name)) + subgraph_tensor_map[name] = Variable(name) + return subgraph_tensor_map[name] + # Retrieve Tensors for node inputs/outputs. Only empty tensors should need to be newly added. def retrieve_node_inputs() -> List[Tensor]: inputs = [] # List[Tensor] for input_name in onnx_node.input: - inputs.append(check_tensor(input_name)) + inputs.append(get_tensor(input_name)) return inputs def retrieve_node_outputs() -> List[Tensor]: outputs = [] # List[Tensor] for output_name in onnx_node.output: - outputs.append(check_tensor(output_name)) + # Node outputs cannot come from the outer graph, they must be created within the inner graph. + outputs.append(get_tensor(output_name, check_outer_graph=False)) return outputs return Node(op=onnx_node.op_type, name=onnx_node.name, attrs=attrs_to_dict(onnx_node.attribute), inputs=retrieve_node_inputs(), outputs=retrieve_node_outputs()) + @staticmethod def import_graph(onnx_graph: onnx.GraphProto, tensor_map: "OrderedDict[str, Tensor]"=None, opset=None) -> Graph: """ @@ -155,21 +163,28 @@ def import_graph(onnx_graph: onnx.GraphProto, tensor_map: "OrderedDict[str, Tens tensor_map (OrderedDict[str, Tensor]): A mapping of tensor names to Tensors. This is generally only useful for subgraph import. opset (int): The ONNX opset to use for this graph. """ - # Tensor map should not be modified - may be from outer graph - tensor_map = copy.copy(misc.default_value(tensor_map, OrderedDict())) + tensor_map = copy.copy(misc.default_value(tensor_map, OrderedDict())) # Outer graph tensors, read-only + subgraph_tensor_map = OrderedDict() # Tensors in this subgraph - # Retrieves a Tensor from tensor_map if present, otherwise imports the tensor + # Retrieves a Tensor from subgraph_tensor_map or the outer graph (tensor_map) if present, otherwise imports the tensor # If overwrite=True, this function will overwrite previously imported tensors # if the new tensor has more information available. - def get_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto], overwrite=False) -> Tensor: - if onnx_tensor.name not in tensor_map: - tensor_map[onnx_tensor.name] = OnnxImporter.import_tensor(onnx_tensor) - elif overwrite: - tensor = OnnxImporter.import_tensor(onnx_tensor) - if isinstance(tensor_map[onnx_tensor.name], Variable): - tensor_map[onnx_tensor.name].dtype = tensor_map[onnx_tensor.name].dtype or tensor.dtype - tensor_map[onnx_tensor.name].shape = tensor_map[onnx_tensor.name].shape or tensor.shape - return tensor_map[onnx_tensor.name] + def get_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto], overwrite=False, check_outer_graph=True) -> Tensor: + # Prioritize the subgraph even if check_outer_graph is set + if onnx_tensor.name in subgraph_tensor_map: + if overwrite: + tensor = OnnxImporter.import_tensor(onnx_tensor) + if isinstance(subgraph_tensor_map[onnx_tensor.name], Variable): + subgraph_tensor_map[onnx_tensor.name].dtype = subgraph_tensor_map[onnx_tensor.name].dtype or tensor.dtype + subgraph_tensor_map[onnx_tensor.name].shape = subgraph_tensor_map[onnx_tensor.name].shape or tensor.shape + return subgraph_tensor_map[onnx_tensor.name] + + if check_outer_graph and onnx_tensor.name in tensor_map: + return tensor_map[onnx_tensor.name] + + subgraph_tensor_map[onnx_tensor.name] = OnnxImporter.import_tensor(onnx_tensor) + return subgraph_tensor_map[onnx_tensor.name] + # Import initializers contents into Constants. G_LOGGER.debug("Importing initializers") @@ -184,24 +199,25 @@ def get_tensor(onnx_tensor: Union[onnx.ValueInfoProto, onnx.TensorProto], overwr get_tensor(tensor, overwrite=True) # Import graph inputs and outputs. Initializers are not considered to be inputs. + # Graph inputs and outputs can never come from the outer graph! initializer_names = set([tensor.name for tensor in onnx_graph.initializer]) G_LOGGER.debug("Importing graph inputs") graph_inputs = [] # List[Tensor] for inp in onnx_graph.input: if inp.name not in initializer_names: - tensor = get_tensor(inp) + tensor = get_tensor(inp, check_outer_graph=False) graph_inputs.append(tensor) G_LOGGER.debug("Importing graph outputs") graph_outputs = [] # List[Tensor] for out in onnx_graph.output: - tensor = get_tensor(out) + tensor = get_tensor(out, check_outer_graph=False) graph_outputs.append(tensor) G_LOGGER.debug("Importing nodes") nodes = [] # List[Node] for onnx_node in onnx_graph.node: - node = OnnxImporter.import_node(onnx_node, tensor_map) + node = OnnxImporter.import_node(onnx_node, tensor_map, subgraph_tensor_map) nodes.append(node) return Graph(nodes=nodes, inputs=graph_inputs, outputs=graph_outputs, name=onnx_graph.name, doc_string=onnx_graph.doc_string, opset=opset) diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py index 58063e2a..9e8a8bec 100644 --- a/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py +++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/ir/graph.py @@ -168,21 +168,27 @@ def _get_used_node_ids(self): return used_node_ids, used_tensors - def cleanup(self, remove_unused_node_outputs=True): + def cleanup(self, remove_unused_node_outputs=False): """ Removes unused nodes and tensors from the graph. A node or tensor is considered unused if it does not contribute to any of the graph outputs. + Additionally, any producer nodes of graph input tensors are removed from the graph. + Note: This function will never modify graph output tensors. Optional Args: remove_unused_node_outputs (bool): Whether to remove unused output tensors of nodes. This will never remove - empty tensor outputs. If this is set to False, outputs of nodes kept in the graph will not be modified. + empty tensor outputs. Defaults to False. Returns: self """ with self.node_ids(): + # Graph inputs cannot have producers + for inp in self.inputs: + inp.inputs.clear() + used_node_ids, used_tensors = self._get_used_node_ids() inputs = [] @@ -209,7 +215,10 @@ def cleanup(self, remove_unused_node_outputs=True): def is_hanging_tensor(tensor): return not tensor.is_empty() and len(tensor.outputs) == 0 and tensor.name not in graph_output_names - [node.outputs.remove(out) for out in node.outputs if is_hanging_tensor(out)] + to_remove = [out for out in node.outputs if is_hanging_tensor(out)] + for out in to_remove: + if out in node.outputs: + node.outputs.remove(out) self.nodes = nodes return self @@ -284,11 +293,21 @@ def add_to_tensor_map(tensor): if check_duplicates and tensor.name in tensor_map and not (tensor_map[tensor.name] is tensor): G_LOGGER.critical("Found distinct tensors that share the same name:\n[id: {:}] {:}\n[id: {:}] {:}" .format(id(tensor_map[tensor.name]), tensor_map[tensor.name], id(tensor), tensor)) + tensor_map[tensor.name] = tensor + + # I/O tensors may not be attached to nodes. + for io_tensor in self.inputs: + add_to_tensor_map(io_tensor) + for node in self.nodes: for tensor in node.inputs + node.outputs: add_to_tensor_map(tensor) + + for io_tensor in self.outputs: + add_to_tensor_map(io_tensor) + return tensor_map diff --git a/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py index 029c3475..efb3429e 100644 --- a/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py +++ b/tools/onnx-graphsurgeon/onnx_graphsurgeon/util/misc.py @@ -52,6 +52,16 @@ def default_value(value, default): return value if value is not None else default +def combine_dicts(dict0, dict1): + """ + Combine two dictionaries. Values in the second will overwrite values in the first. + """ + combined = OrderedDict() + combined.update(dict0) + combined.update(dict1) + return combined + + # Special type of list that synchronizes contents with another list. # Concrete example: Assume some node, n, contains an input tensor, t. If we remove t from n.inputs, # we also need to remove n from t.outputs. To avoid having to do this manually, we use SynchronizedList, @@ -119,9 +129,7 @@ def clear(self): def __add__(self, other_list: List[object]): - new_list = SynchronizedList(self.parent_obj, self.field_name, self) - new_list += other_list - return new_list + return list(self) + list(other_list) def __iadd__(self, other_list: List[object]): diff --git a/tools/onnx-graphsurgeon/setup.py b/tools/onnx-graphsurgeon/setup.py index 951326f3..baeca408 100644 --- a/tools/onnx-graphsurgeon/setup.py +++ b/tools/onnx-graphsurgeon/setup.py @@ -37,12 +37,18 @@ def main(): version=onnx_graphsurgeon.__version__, description="ONNX GraphSurgeon", long_description=open("README.md", "r", encoding="utf-8").read(), + license="Apache 2.0", url="https://github.com/nvidia/tensorrt/tools/onnx-graphsurgeon", - author="Pranav Marathe", - author_email="pranavm@nvidia.com", + author="NVIDIA", + author_email="svc_tensorrt@nvidia.com", classifiers=[ 'Intended Audience :: Developers', 'Programming Language :: Python :: 3', + "Programming Language :: Python :: 3.4", + "Programming Language :: Python :: 3.5", + "Programming Language :: Python :: 3.6", + "Programming Language :: Python :: 3.7", + "Programming Language :: Python :: 3.8", ], install_requires=REQUIRED_PACKAGES, packages=find_packages(), diff --git a/tools/onnx-graphsurgeon/tests/models/initializer_is_output.onnx b/tools/onnx-graphsurgeon/tests/models/initializer_is_output.onnx new file mode 100644 index 00000000..0645124a Binary files /dev/null and b/tools/onnx-graphsurgeon/tests/models/initializer_is_output.onnx differ diff --git a/tools/onnx-graphsurgeon/tests/models/nested_dup_names.onnx b/tools/onnx-graphsurgeon/tests/models/nested_dup_names.onnx new file mode 100644 index 00000000..35ca1074 Binary files /dev/null and b/tools/onnx-graphsurgeon/tests/models/nested_dup_names.onnx differ diff --git a/tools/onnx-graphsurgeon/tests/onnx_models.py b/tools/onnx-graphsurgeon/tests/onnx_models.py index 7cf8d69c..6f95d07f 100644 --- a/tools/onnx-graphsurgeon/tests/onnx_models.py +++ b/tools/onnx-graphsurgeon/tests/onnx_models.py @@ -43,21 +43,41 @@ def load(self): return onnx.load(self.path) def assert_equal(self, graph: Graph): - - assert graph.inputs == self.inputs G_LOGGER.debug("Graph inputs matched") + # Break down fields to make debugging failures easier. for actual, expected in zip(graph.nodes, self.nodes): - G_LOGGER.debug("Actual Node: {:}.\n\nExpected Node: {:}".format(actual, expected)) - # Break down fields to make debugging failures easier. + def check_tensor_io(actensor, extensor): + def check_list(aclist, exlist): + G_LOGGER.debug("Actual node list: {:}\n\nExpected node list: {:}".format(aclist, exlist)) + assert len(aclist) == len(exlist) + for acnode, exnode in zip(aclist, exlist): + assert acnode == exnode + + G_LOGGER.debug("Checking tensor: {:} inputs".format(actensor.name)) + check_list(actensor.inputs, extensor.inputs) + G_LOGGER.debug("Checking tensor: {:} outputs".format(actensor.name)) + check_list(actensor.outputs, extensor.outputs) + + + G_LOGGER.debug("Actual Node: {:}\n\nExpected Node: {:}".format(actual, expected)) assert actual.op == expected.op assert actual.inputs == expected.inputs + # Check I/O of input tensors + for acinp, exinp in zip(actual.inputs, expected.inputs): + check_tensor_io(acinp, exinp) + assert actual.outputs == expected.outputs + # Check I/O of output tensors + for acout, exout in zip(actual.outputs, expected.outputs): + check_tensor_io(acout, exout) + assert actual.name == expected.name - for (akey, aval), (ekey, eval) in zip(actual.attrs.items(), expected.attrs.items()): - assert akey == ekey - assert aval == eval + assert len(actual.attrs) == len(expected.attrs) + for (ackey, acval), (exkey, exval) in zip(actual.attrs.items(), expected.attrs.items()): + assert ackey == exkey + assert acval == exval assert actual == expected G_LOGGER.debug("Graph nodes matched") @@ -65,6 +85,10 @@ def assert_equal(self, graph: Graph): G_LOGGER.debug("Graph outputs matched") + def __str__(self): + return os.path.basename(self.path) + + def identity_model(): path = os.path.join(TEST_ROOT, "models", "identity.onnx") model = onnx.load(path) @@ -146,3 +170,32 @@ def scan_model(): attrs["num_scan_inputs"] = 1 scan_node = Node(op="Scan", inputs=inputs, outputs=outputs, attrs=attrs) return Model(path, inputs=inputs, outputs=outputs, nodes=[scan_node], opset=OnnxImporter.get_opset(model)) + + +def initializer_is_output_model(): + path = os.path.join(TEST_ROOT, "models", "initializer_is_output.onnx") + model = onnx.load(path) + + X = Constant(name="X", values=np.ones((64, 64), dtype=np.float32)) + + return Model(path, inputs=[], outputs=[X], nodes=[], opset=OnnxImporter.get_opset(model)) + + +# Node includes a subgraph whose I/O names are the same as that of the node. +def nested_dup_names(): + path = os.path.join(TEST_ROOT, "models", "nested_dup_names.onnx") + model = onnx.load(path) + + # Inner + subgraph_inputs = [Variable("X", shape=(2, 2), dtype=np.float32)] + subgraph_outputs = [Variable("Y", shape=(2, 2), dtype=np.float32)] + + subgraph_node = Node(op="Identity", inputs=subgraph_inputs, outputs=subgraph_outputs) + subgraph = Graph(nodes=[subgraph_node], inputs=subgraph_inputs, outputs=subgraph_outputs) + + # Outer - problem happens if outer node has same I/O names as subgraph + inputs = [Variable("X", shape=(2, 2), dtype=np.float32)] + outputs = [Variable("Y", shape=(2, 2), dtype=np.float32)] + + node = Node(op="Nested", inputs=inputs, outputs=outputs, attrs={"body": subgraph}) + return Model(path, inputs=inputs, outputs=outputs, nodes=[node], opset=11) diff --git a/tools/onnx-graphsurgeon/tests/test_examples.py b/tools/onnx-graphsurgeon/tests/test_examples.py index 54a2ade3..750c2892 100644 --- a/tools/onnx-graphsurgeon/tests/test_examples.py +++ b/tools/onnx-graphsurgeon/tests/test_examples.py @@ -44,6 +44,7 @@ def __init__(self, name, infer=True): ("05_folding_constants", [Artifact("model.onnx"), Artifact("folded.onnx")]), ("06_removing_nodes", [Artifact("model.onnx", infer=False), Artifact("removed.onnx")]), ("07_creating_a_model_with_the_layer_api", [Artifact("model.onnx")]), + ("08_replacing_a_subgraph", [Artifact("model.onnx"), Artifact("replaced.onnx")]), ] # Extract any ``` blocks from the README diff --git a/tools/onnx-graphsurgeon/tests/test_exporters.py b/tools/onnx-graphsurgeon/tests/test_exporters.py index 80d5cbf9..ecf5cdb1 100644 --- a/tools/onnx-graphsurgeon/tests/test_exporters.py +++ b/tools/onnx-graphsurgeon/tests/test_exporters.py @@ -18,7 +18,7 @@ from onnx_graphsurgeon.importers.onnx_importer import OnnxImporter from onnx_graphsurgeon.logger.logger import G_LOGGER -from onnx_models import identity_model, lstm_model, scan_model, dim_param_model +from onnx_models import identity_model, lstm_model, scan_model, dim_param_model, initializer_is_output_model from onnx_graphsurgeon.ir.tensor import Tensor, Constant, Variable from onnx_graphsurgeon.ir.graph import Graph @@ -123,7 +123,7 @@ def test_export_node(self): # See test_importers for import correctness checks # This function first imports an ONNX graph, and then re-exports it with no changes. # The exported ONNX graph should exactly match the original. - @pytest.mark.parametrize("model", [identity_model(), lstm_model(), scan_model(), dim_param_model()]) + @pytest.mark.parametrize("model", [identity_model(), lstm_model(), scan_model(), dim_param_model(), initializer_is_output_model()]) def test_export_graph(self, model): onnx_graph = model.load().graph graph = OnnxImporter.import_graph(onnx_graph) diff --git a/tools/onnx-graphsurgeon/tests/test_importers.py b/tools/onnx-graphsurgeon/tests/test_importers.py index dca0217c..6ee5ab29 100644 --- a/tools/onnx-graphsurgeon/tests/test_importers.py +++ b/tools/onnx-graphsurgeon/tests/test_importers.py @@ -18,7 +18,7 @@ from onnx_graphsurgeon.logger.logger import G_LOGGER from onnx_graphsurgeon.ir.tensor import Tensor, Variable, Constant -from onnx_models import identity_model, lstm_model, scan_model, dim_param_model +from onnx_models import identity_model, lstm_model, scan_model, dim_param_model, initializer_is_output_model, nested_dup_names from collections import OrderedDict import onnx.shape_inference @@ -74,7 +74,7 @@ def test_import_node(self): strings_attr = ["constant", "and", "variable"] onnx_node = onnx.helper.make_node(op, inputs, outputs, float_attr=float_attr, int_attr=int_attr, str_attr=str_attr, tensor_attr=tensor_attr, floats_attr=floats_attr, ints_attr=ints_attr, strings_attr=strings_attr) - node = OnnxImporter.import_node(onnx_node, OrderedDict()) + node = OnnxImporter.import_node(onnx_node, OrderedDict(), OrderedDict()) assert node.op == op assert node.attrs["float_attr"] == float_attr assert node.attrs["int_attr"] == int_attr @@ -86,7 +86,10 @@ def test_import_node(self): assert node.attrs["strings_attr"] == strings_attr - @pytest.mark.parametrize("model", [identity_model(), lstm_model(), scan_model()]) + @pytest.mark.parametrize("model", + [identity_model(), lstm_model(), scan_model(), dim_param_model(), initializer_is_output_model(), nested_dup_names()], + ids=lambda model: str(model) + ) def test_import_graph(self, model): graph = OnnxImporter.import_graph(model.load().graph) model.assert_equal(graph) diff --git a/tools/onnx-graphsurgeon/tests/test_ir.py b/tools/onnx-graphsurgeon/tests/test_ir.py index 9d7bd5e6..210042bb 100644 --- a/tools/onnx-graphsurgeon/tests/test_ir.py +++ b/tools/onnx-graphsurgeon/tests/test_ir.py @@ -291,7 +291,6 @@ def test_add(self, field_names): nlist = nlist + self.tensors for tensor in self.tensors: assert tensor in nlist - assert getattr(tensor, tensor_field)[0] == self.node @pytest.mark.parametrize("field_names", [("inputs", "outputs"), ("outputs", "inputs")]) def test_iadd(self, field_names): @@ -540,16 +539,29 @@ def test_layer_with_arrays(self): assert graph.nodes[-1].outputs == outputs - def test_tensors(self): - graph, nodes, tensors = tensors_linear_graph() + # Calling `tensors()` should not modify tensors in the graph. + def test_tensors_does_not_modify_tensors(self): + graph, _, _ = tensors_linear_graph() graph_tensors = graph.tensors() + # Generate a new graph to compare against + _, _, tensors = tensors_linear_graph() + + assert set(tensors.keys()) == set(graph_tensors.keys()) + for name, tensor in tensors.items(): - assert name in graph_tensors - assert tensor is graph_tensors[name] + graph_tensor = graph_tensors[name] + assert tensor == graph_tensor + assert tensor.inputs == graph_tensor.inputs + assert tensor.outputs == graph_tensor.outputs - for name, tensor in graph_tensors.items(): - assert name in tensors - assert tensor is tensors[name] + + # Check that tensors includes tensors not attached to nodes + def test_tensors_includes_non_node_tensors(self): + X = Constant("X", values=np.ones(shape=(64, 64), dtype=np.float32)) + graph = Graph(inputs=[], outputs=[X]) + tensor_map = graph.tensors() + assert "X" in tensor_map + assert tensor_map["X"] == X def test_tensors_check_duplicates(self): @@ -603,7 +615,7 @@ def test_cleanup_multi_tier(self): assert tensor.name not in tensor_map - def test_cleanup_intermediate_tensors(self): + def test_cleanup_remove_unused_node_outputs(self): graph, _ = toposort_linear_graph() graph.toposort() graph_output = graph.outputs[0] @@ -613,11 +625,23 @@ def test_cleanup_intermediate_tensors(self): # Since it does not contribute to graph outputs, it should be removed. graph.nodes[1].outputs.append(dummy) - graph.cleanup() + graph.cleanup(remove_unused_node_outputs=True) assert dummy not in graph.nodes[1].outputs assert graph.outputs[0] == graph_output # Graoh outputs will never be removed + def test_cleanup_graph_input_producers(self): + graph, _ = toposort_linear_graph() + tensor_map = graph.tensors() + assert "x" in tensor_map + + graph.inputs = [tensor_map["intermediate0"]] + + graph.cleanup() + cleaned_tensor_map = graph.tensors() + assert "x" not in cleaned_tensor_map + + def test_cleanup_independent_path(self): graph, _ = toposort_linear_graph() # Build out a path totally unrelated to rest of the graph diff --git a/tools/onnx-graphsurgeon/tests/test_util.py b/tools/onnx-graphsurgeon/tests/test_util.py new file mode 100644 index 00000000..288a9455 --- /dev/null +++ b/tools/onnx-graphsurgeon/tests/test_util.py @@ -0,0 +1,24 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from onnx_graphsurgeon.util import misc + + +def test_combine_dicts_second_overwrites_first(): + x = {"a": 1} + y = {"a": 2} + z = misc.combine_dicts(x, y) + assert z["a"] == 2 diff --git a/tools/pytorch-quantization/.coveragerc b/tools/pytorch-quantization/.coveragerc new file mode 100644 index 00000000..39279661 --- /dev/null +++ b/tools/pytorch-quantization/.coveragerc @@ -0,0 +1,10 @@ +[run] +omit = pytorch_quantization/utils/amp_wrapper.py, pytorch_quantization/utils/quant_logging.py, *__init__.py + +[report] +exclude_lines = + def backward + RNNReLUCell + RNNTanhCell + GRU + \ No newline at end of file diff --git a/tools/pytorch-quantization/.gitignore b/tools/pytorch-quantization/.gitignore new file mode 100644 index 00000000..8684bfa4 --- /dev/null +++ b/tools/pytorch-quantization/.gitignore @@ -0,0 +1,157 @@ +## copied from PyTorch + +.coverage +.mypy_cache +*/*.pyc +*/*.so* +*/**/__pycache__ +*/**/*.dylib* +*/**/*.pyc +*/**/*.pyd +*/**/*.so* +*/**/**/*.pyc +*/**/**/**/*.pyc +*/**/**/**/**/*.pyc +aten/build/ +aten/src/ATen/Config.h +aten/src/ATen/cuda/CUDAConfig.h +build/ +dist/ + + +# IPython notebook checkpoints +.ipynb_checkpoints + +# Editor temporaries +*.swn +*.swo +*.swp +*.swm +*~ + +# macOS dir files +.DS_Store + +# Symbolic files +tools/shared/cwrap_common.py + +# Ninja files +.ninja_deps +.ninja_log +compile_commands.json +*.egg-info/ +docs/source/scripts/activation_images/ + +## General + +# Compiled Object files +*.slo +*.lo +*.o +*.cuo +*.obj + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Compiled protocol buffers +*.pb.h +*.pb.cc +*_pb2.py + +# Compiled python +*.pyc +*.pyd + +# Compiled MATLAB +*.mex* + +# IPython notebook checkpoints +.ipynb_checkpoints + +# Editor temporaries +*.swn +*.swo +*.swp +*~ + +# Sublime Text settings +*.sublime-workspace +*.sublime-project + +# Eclipse Project settings +*.*project +.settings + +# QtCreator files +*.user + +# PyCharm files +.idea + +# OSX dir files +.DS_Store + +## Caffe2 + +# build, distribute, and bins (+ python proto bindings) +build +build_host_protoc +build_android +build_ios +/build_* +.build_debug/* +.build_release/* +distribute/* +*.testbin +*.bin +cmake_build +.cmake_build +gen +.setuptools-cmake-build +.pytest_cache +aten/build/* + +# Bram +plsdontbreak + +# Generated documentation +docs/_site +docs/gathered +_site +doxygen +docs/dev + +# LevelDB files +*.sst +*.ldb +LOCK +LOG* +CURRENT +MANIFEST-* + +# generated version file +caffe2/version.py + +# setup.py intermediates +.eggs +caffe2.egg-info + +# Atom/Watchman required file +.watchmanconfig + +# BEGIN NOT-CLEAN-FILES (setup.py handles this marker. Do not change.) +# +# Below files are not deleted by "setup.py clean". + +# Visual Studio Code files +.vscode +.vs diff --git a/tools/pytorch-quantization/.pylintrc b/tools/pytorch-quantization/.pylintrc new file mode 100644 index 00000000..314a66ce --- /dev/null +++ b/tools/pytorch-quantization/.pylintrc @@ -0,0 +1,407 @@ +[MASTER] + +# Specify a configuration file. +#rcfile= + +# Python code to execute, usually for sys.path manipulation such as +# pygtk.require(). +#init-hook= + +# Add files or directories to the blacklist. They should be base names, not +# paths. +ignore=CVS + +# Add files or directories matching the regex patterns to the blacklist. 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Joining a lot of strings can lead to a maximum recursion error in +# Pylint and this flag can prevent that. It has one side effect, the resulting +# AST will be different than the one from reality. This option is deprecated +# and it will be removed in Pylint 2.0. +optimize-ast=no + + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED +confidence= + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +#enable= + +# Disable the message, report, category or checker with the given id(s). 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If you want to run only the classes checker, but have +# no Warning level messages displayed, use"--disable=all --enable=classes +# --disable=W" +disable=old-raise-syntax,unpacking-in-except,using-cmp-argument,filter-builtin-not-iterating,nonzero-method,dict-iter-method,basestring-builtin,unicode-builtin,oct-method,cmp-method,delslice-method,round-builtin,range-builtin-not-iterating,cmp-builtin,old-division,old-octal-literal,raising-string,coerce-method,input-builtin,buffer-builtin,parameter-unpacking,hex-method,map-builtin-not-iterating,getslice-method,execfile-builtin,long-builtin,print-statement,raw_input-builtin,apply-builtin,standarderror-builtin,xrange-builtin,setslice-method,import-star-module-level,intern-builtin,indexing-exception,reduce-builtin,no-absolute-import,long-suffix,coerce-builtin,suppressed-message,dict-view-method,metaclass-assignment,backtick,unichr-builtin,useless-suppression,old-ne-operator,zip-builtin-not-iterating,reload-builtin,file-builtin,next-method-called,import-error + + +[REPORTS] + +# Set the output format. Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Put messages in a separate file for each module / package specified on the +# command line instead of printing them on stdout. Reports (if any) will be +# written in a file name "pylint_global.[txt|html]". This option is deprecated +# and it will be removed in Pylint 2.0. +files-output=no + +# Tells whether to display a full report or only the messages +reports=yes + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# A regular expression matching the name of dummy variables (i.e. expectedly +# not used). +dummy-variables-rgx=(_+[a-zA-Z0-9]*?$)|dummy + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid to define new builtins when possible. +additional-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_,_cb + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six.moves,future.builtins + + +[BASIC] + +# Good variable names which should always be accepted, separated by a comma +good-names=i,j,k,ex,Run,_,x,lr,s + +# Bad variable names which should always be refused, separated by a comma +bad-names=foo,bar,baz,toto,tutu,tata + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +property-classes=abc.abstractproperty + +# Regular expression matching correct class names +class-rgx=[A-Z_][a-zA-Z0-9]+$ + +# Naming hint for class names +class-name-hint=[A-Z_][a-zA-Z0-9]+$ + +# Regular expression matching correct function names +function-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming hint for function names +function-name-hint=[a-z_][a-z0-9_]{2,30}$ + +# Regular expression matching correct attribute names +attr-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming hint for attribute names +attr-name-hint=[a-z_][a-z0-9_]{2,30}$ + +# Regular expression matching correct constant names +const-rgx=(([A-Z_][A-Z0-9_]*)|(__.*__))$ + +# Naming hint for constant names +const-name-hint=(([A-Z_][A-Z0-9_]*)|(__.*__))$ + +# Regular expression matching correct module names +module-rgx=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ + +# Naming hint for module names +module-name-hint=(([a-z_][a-z0-9_]*)|([A-Z][a-zA-Z0-9]+))$ + +# Regular expression matching correct variable names +variable-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming hint for variable names +variable-name-hint=[a-z_][a-z0-9_]{2,30}$ + +# Regular expression matching correct argument names +argument-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming hint for argument names +argument-name-hint=[a-z_][a-z0-9_]{2,30}$ + +# Regular expression matching correct class attribute names +class-attribute-rgx=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ + +# Naming hint for class attribute names +class-attribute-name-hint=([A-Za-z_][A-Za-z0-9_]{2,30}|(__.*__))$ + +# Regular expression matching correct inline iteration names +inlinevar-rgx=[A-Za-z_][A-Za-z0-9_]*$ + +# Naming hint for inline iteration names +inlinevar-name-hint=[A-Za-z_][A-Za-z0-9_]*$ + +# Regular expression matching correct method names +method-rgx=[a-z_][a-z0-9_]{2,30}$ + +# Naming hint for method names +method-name-hint=[a-z_][a-z0-9_]{2,30}$ + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=^_ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=-1 + + +[ELIF] + +# Maximum number of nested blocks for function / method body +max-nested-blocks=5 + + +[SPELLING] + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=4 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=120 + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=^\s*(# )??$ + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=no + +# List of optional constructs for which whitespace checking is disabled. `dict- +# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\n222: 2}. +# `trailing-comma` allows a space between comma and closing bracket: (a, ). +# `empty-line` allows space-only lines. +no-space-check=trailing-comma,dict-separator + +# Maximum number of lines in a module +max-module-lines=1000 + +# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1 +# tab). +indent-string=' ' + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# Expected format of line ending, e.g. empty (any line ending), LF or CRLF. +expected-line-ending-format= + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=FIXME,XXX,TODO + + +[LOGGING] + +# Logging modules to check that the string format arguments are in logging +# function parameter format +logging-modules=logging + + +[TYPECHECK] + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. 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This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + + +[DESIGN] + +# Maximum number of arguments for function / method +max-args=10 + +# Argument names that match this expression will be ignored. Default to name +# with leading underscore +ignored-argument-names=_.* + +# Maximum number of locals for function / method body +max-locals=15 + +# Maximum number of return / yield for function / method body +max-returns=6 + +# Maximum number of branch for function / method body +max-branches=12 + +# Maximum number of statements in function / method body +max-statements=50 + +# Maximum number of parents for a class (see R0901). +max-parents=7 + +# Maximum number of attributes for a class (see R0902). +max-attributes=7 + +# Minimum number of public methods for a class (see R0903). +min-public-methods=2 + +# Maximum number of public methods for a class (see R0904). +max-public-methods=20 + +# Maximum number of boolean expressions in a if statement +max-bool-expr=5 + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=Exception diff --git a/tools/pytorch-quantization/.style.yapf b/tools/pytorch-quantization/.style.yapf new file mode 100644 index 00000000..a8a5b55e --- /dev/null +++ b/tools/pytorch-quantization/.style.yapf @@ -0,0 +1,7 @@ +[style] +based_on_style = google +indent_width = 4 +COLUMN_LIMIT = 120 +SPACES_BEFORE_COMMENT = 2 +EACH_DICT_ENTRY_ON_SEPARATE_LINE = 1 +CONTINUATION_INDENT_WIDTH = 4 diff --git a/tools/pytorch-quantization/CONTRIBUTING.md b/tools/pytorch-quantization/CONTRIBUTING.md new file mode 100644 index 00000000..eb67f19e --- /dev/null +++ b/tools/pytorch-quantization/CONTRIBUTING.md @@ -0,0 +1,119 @@ +# Contributing guidelines + +## Style + +#### C++ coding style + +Follow [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html), with max-line-length extended to 120. + +Run `cpplint` before committing code. + +#### Python coding style + +Follow [Google Python Style Guide,](https://google.github.io/styleguide/pyguide.html) with max-line-length extended to 120. Exceptions are allowed if it feels more natural to follow Pytorch style. For example, Pytorch allows import relative path, also class name. + +Run `pylint` before committing code. It doesn't mean every issue has to be corrected nor check has to be manually disabled. Just make sure you are aware of the remaining issues and you are comfort with all of them. But, don't leave lint error there. Disable it explicitly if it is not a real error. + +Install `pylint` + +```bash +pip install pylint +``` + +To check a file with `pylint`: + +```bash +pylint --rcfile=.pylintrc myfile.py +``` + +#### Yapf + +[yapf](https://github.com/google/yapf/) is an auto format tool owned by Google (not a Google product). To save the time of arguing code style during code review, use yapf to format the code is a good option. Note that it doesn't reformat comment. + +Install `yapf` + +```bash +pip install yapf +``` + +Format code with yapf + +```bash +yapf myfile.py --style .style.yapf +``` + +There are Sublime and Vim plugins. + +## Test + +Use [googletest](https://github.com/google/googletest) for c++ code. + +Use [pytest](https://docs.pytest.org/en/latest/) for python code. + +To run all the tests: + +```shell +pytest --verbose +``` + +To run a particular test file + +```shell +pytest --verbose mytestfile.py +``` + + + +## Conventions + +Quantization is a very overloaded word, many things related to it can create a lot of confusions. Let's try to avoid confusions as much as possible by following existing conventions. Generally, if there is a similar Tensorflow or numpy function, follow its convention. Though Tensorflow uses `quantized`, `quantization` and `quant`, let's stick with the shortest one only. + +### Naming + +##### Function and class name + +When developing quantized version of a function or module, add`Quant` to class name, add `quant_` to function name, e.g. + +```python +class Linear(...) +class QuantLinear(...) + +def linear(...) +def quant_linear(...) +``` + +##### Variable name + +Add prefix `quant_mode_`, `num_bits_` etc. to name of tensors will be quantized, e.g. + +```python +def matmul(a, b) +def quant_matmul(a, b) +``` + +Don't use prefix/suffix `weight` or `act` if tensor being quantized doesn't have them explicitly in name. From function's perspective, it takes tensors, not necessarily weight and activation tensors. e.g. `a` and `b` of `matmul` can both be either weight or activation. + +##### Quantization mode + +There only convention here we can adopt is `per_channel`. Other things, like there is no convention to follow of per row/column scale of matrix multiply. And though we usually absolute max value based scaling factor, there are other ways to decide it, like KL-divergence. + +Our API design is flexible enough to support any granularity of quantization. The main concept is `axis`. + +```python +# axis=None means per tensor +# For 2d convolution weight with layout KCRS, axis=(1, 2, 3) means perchannel quantization +# more example below +QUANT_DESC_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8) +QUANT_DESC_UNSIGNED_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8, unsigned=True) +QUANT_DESC_8BIT_CONV1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONV3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONVTRANSPOSE1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) +QUANT_DESC_8BIT_CONVTRANSPOSE2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) +QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) + +``` + +### Misc + diff --git a/tools/pytorch-quantization/LICENSE b/tools/pytorch-quantization/LICENSE new file mode 100644 index 00000000..0049c303 --- /dev/null +++ b/tools/pytorch-quantization/LICENSE @@ -0,0 +1,283 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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All rights reserved. + + All contributions by Facebook: + Copyright (c) 2016 Facebook Inc. + + All contributions by Google: + Copyright (c) 2015 Google Inc. + All rights reserved. + + All contributions by Yangqing Jia: + Copyright (c) 2015 Yangqing Jia + All rights reserved. + + All contributions from Caffe: + Copyright(c) 2013, 2014, 2015, the respective contributors + All rights reserved. + + All other contributions: + Copyright(c) 2015, 2016 the respective contributors + All rights reserved. + + Caffe2 uses a copyright model similar to Caffe: each contributor holds + copyright over their contributions to Caffe2. The project versioning records + all such contribution and copyright details. 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Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America + and IDIAP Research Institute nor the names of its contributors may be + used to endorse or promote products derived from this software without + specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE + ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE + LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR + CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF + SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS + INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN + CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) + ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. diff --git a/tools/pytorch-quantization/MANIFEST.in b/tools/pytorch-quantization/MANIFEST.in new file mode 100644 index 00000000..7525ad75 --- /dev/null +++ b/tools/pytorch-quantization/MANIFEST.in @@ -0,0 +1,3 @@ +include LICENSE +include requirements.txt +recursive-include src * diff --git a/tools/pytorch-quantization/README.md b/tools/pytorch-quantization/README.md new file mode 100644 index 00000000..9980a35b --- /dev/null +++ b/tools/pytorch-quantization/README.md @@ -0,0 +1,73 @@ +## Quantization Basics + +We use scale only symmetric quantization +```math +\mathbf{y_q}=rn(\frac{2^{k-1}-1}{\alpha}clip(\mathbf{y},-\alpha,\alpha)) +``` +where $`k`$ is number of bits to use for $`\mathbf{y_q}`$, $`\alpha`$ is absolute maximum quantized range of $`\mathbf{y}`$, *rn* is standard round to nearest, and *clip* is standard clip function which saturate values to $`[-\alpha, \alpha]`$. Note that $`\alpha`$ is not necessary a scalar, it can be any shape that can be broadcasted to $`\mathbf{y}`$. See https://arxiv.org/abs/2004.09602 for more detail. + +The key concept is $`\alpha`$ which defines quantization for given number of bits. It is named `amax` (maximum absolute value) throughout the entire code base. + +"Fake" (emulated) quantization is usually used in quantization training. In "fake" mode, values are still represented in floating point, but only limited number of distinct values in a given range. For example, in 8bit, there are 255 values in $`[-\alpha, \alpha]`$ . Conceptually, "fake" mode is same as quantize then dequantize. It can be defined as +```math +\mathbf{y_{fq}}=\frac{\alpha}{2^{k-1}-1}rn(\frac{2^{k-1}-1}{\alpha}clip(\mathbf{y},-\alpha,\alpha)) +``` + + +## Install + +```shell +git clone https://github.com/NVIDIA/TensorRT.git +cd tools/pytorch-quantization +python setup.py install +``` + +## Usage + +#### Quantize an existing model + +A model can be post training quantized by simply replacing `nn.ConvNd` and `nn.Linear` with `QuantConvNd` and `QuantLinear`. By default, it will be quantized to signed 8bit and compute amax in-flight for each batch. See a LeNet example below: + +```python +import torch.nn as nn +import torch.nn.functional as F + +from pytorch_quantization.nn import QuantConv2d, QuantLinear +from pytorch_quantization.tensor_quant import QuantDescriptor + +class LeNet(nn.Module): + def __init__(self): + super(LeNet, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5) + self.conv2 = nn.Conv2d(10, 20, kernel_size=5) + self.fc1 = nn.Linear(320, 50) + self.fc2 = nn.Linear(50, 10) + + def forward(self, x): + x = F.relu(F.max_pool2d(self.conv1(x), 2)) + x = F.relu(F.max_pool2d(self.conv2(x), 2)) + x = x.view(-1, 320) + x = F.relu(self.fc1(x)) + x = F.dropout(x, training=self.training) + x = self.fc2(x) + return F.log_softmax(x, dim=1) + + +class QuantLeNet(nn.Module): + def __init__(self): + super(QuantLeNet, self).__init__() + self.conv1 = QuantConv2d(1, 10, kernel_size=5) + self.conv2 = QuantConv2d(10, 20, kernel_size=5) + self.fc1 = QuantLinear(320, 50) + self.fc2 = QuantLinear(50, 10) + + def forward(self, x): + x = F.relu(F.max_pool2d(self.conv1(x), 2)) + x = F.relu(F.max_pool2d(self.conv2(x), 2)) + x = x.view(-1, 320) + x = F.relu(self.fc1(x)) + x = F.dropout(x, training=self.training) + x = self.fc2(x) + return F.log_softmax(x, dim=1) +``` + diff --git a/tools/pytorch-quantization/docs/Makefile b/tools/pytorch-quantization/docs/Makefile new file mode 100644 index 00000000..69fe55ec --- /dev/null +++ b/tools/pytorch-quantization/docs/Makefile @@ -0,0 +1,19 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/tools/pytorch-quantization/docs/source/calib.rst b/tools/pytorch-quantization/docs/source/calib.rst new file mode 100644 index 00000000..69109e0a --- /dev/null +++ b/tools/pytorch-quantization/docs/source/calib.rst @@ -0,0 +1,22 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.calib +=================================== + +.. automodule:: pytorch_quantization.calib +.. currentmodule:: pytorch_quantization.calib + +:hidden:`MaxCalibrator` +~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: MaxCalibrator + :members: + :inherited-members: + +:hidden:`HistogramCalibrator` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: HistogramCalibrator + :members: + :inherited-members: diff --git a/tools/pytorch-quantization/docs/source/conf.py b/tools/pytorch-quantization/docs/source/conf.py new file mode 100644 index 00000000..a8a298f2 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/conf.py @@ -0,0 +1,226 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) +import sphinx_glpi_theme + +# -- Project information ----------------------------------------------------- + +project = 'pytorch-quantization' +copyright = '2020, NVIDIA' + +# The short X.Y version +version = '2.0.0' +# The full version, including alpha/beta/rc tags +release = 'master' + +author = 'NVIDIA' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +needs_sphinx = '1.8' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', +] + +# Napoleon settings +napoleon_google_docstring = True +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = False +napoleon_use_admonition_for_examples = True +napoleon_use_admonition_for_notes = True +napoleon_use_admonition_for_references = False +napoleon_use_ivar = True +napoleon_use_param = False +napoleon_use_rtype = False + + + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +#html_theme = 'sphinx_rtd_theme' +html_theme = 'glpi' +html_theme_path = sphinx_glpi_theme.get_html_themes_path() + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'pytorch-quantizationdoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'pytorch-quantization.tex', 'pytorch-quantization Documentation', + 'NVIDIA', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'pytorch-quantization', 'pytorch-quantization Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'pytorch-quantization', 'pytorch-quantization Documentation', + author, 'pytorch-quantization', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + + +# -- Extension configuration ------------------------------------------------- +# Disable docstring inheritance +autodoc_inherit_docstrings = False + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = { + 'python': ('https://docs.python.org/', None), + 'numpy': ('https://docs.scipy.org/doc/numpy/', None), +} diff --git a/tools/pytorch-quantization/docs/source/functional.rst b/tools/pytorch-quantization/docs/source/functional.rst new file mode 100644 index 00000000..1e60d01a --- /dev/null +++ b/tools/pytorch-quantization/docs/source/functional.rst @@ -0,0 +1,17 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.nn.functional +=================================== + +.. automodule:: pytorch_quantization.nn.functional +.. currentmodule:: pytorch_quantization.nn.functional + +:hidden:`ClipFunction` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: ClipFunction + :members: + + +``clip`` is alias of ``ClipFunction.apply`` \ No newline at end of file diff --git a/tools/pytorch-quantization/docs/source/index.rst b/tools/pytorch-quantization/docs/source/index.rst new file mode 100644 index 00000000..4cea4a7c --- /dev/null +++ b/tools/pytorch-quantization/docs/source/index.rst @@ -0,0 +1,33 @@ +pytorch-quantization's documentation +================================================ + +.. toctree:: + :maxdepth: 1 + :caption: User Guide + + userguide + +.. toctree:: + :caption: Tutorials + :titlesonly: + :maxdepth: 2 + + tutorials/quant_resnet50 + tutorials/creating_custom_quantized_modules + + +.. toctree:: + :maxdepth: 1 + :caption: Package Reference + + calib + nn + functional + optim + tensor_quant + utils + +Indices +================== + +* :ref:`genindex` diff --git a/tools/pytorch-quantization/docs/source/nn.rst b/tools/pytorch-quantization/docs/source/nn.rst new file mode 100644 index 00000000..5b15c7f6 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/nn.rst @@ -0,0 +1,140 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.nn +=================================== + +.. automodule:: pytorch_quantization.nn +.. currentmodule:: pytorch_quantization.nn + +:hidden:`TensorQuantizer` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: TensorQuantizer + :members: + +Quantized Modules +---------------------------------- +.. currentmodule:: pytorch_quantization.nn.modules.quant_conv + +_QuantConvNd +~~~~~~~~~~~~ +.. autoclass:: _QuantConvNd + :members: + +.. currentmodule:: pytorch_quantization.nn + +:hidden:`QuantConv1d` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantConv1d + :members: + +:hidden:`QuantConv2d` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantConv2d + :members: + +:hidden:`QuantConv3d` +~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantConv3d + :members: + +:hidden:`QuantConvTranspose1d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantConvTranspose1d + :members: + +:hidden:`QuantConvTranspose2d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +.. autoclass:: QuantConvTranspose2d + :members: + +:hidden:`QuantConvTranspose3d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantConvTranspose3d + :members: + +:hidden:`QuantLinear` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantLinear + :members: + +:hidden:`QuantMaxPool1d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantMaxPool1d + :members: + +:hidden:`QuantMaxPool2d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantMaxPool2d + :members: + +:hidden:`QuantMaxPool3d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantMaxPool3d + :members: + +:hidden:`QuantAvgPool1d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAvgPool1d + :members: + +:hidden:`QuantAvgPool2d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAvgPool2d + :members: + +:hidden:`QuantAvgPool3d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAvgPool3d + :members: + +:hidden:`QuantAdaptiveAvgPool1d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAdaptiveAvgPool1d + :members: + +:hidden:`QuantAdaptiveAvgPool2d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAdaptiveAvgPool2d + :members: + +:hidden:`QuantAdaptiveAvgPool3d` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantAdaptiveAvgPool3d + :members: + +:hidden:`Clip` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: Clip + :members: + +:hidden:`QuantLSTM` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantLSTM + :members: + +:hidden:`QuantLSTMCell` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantLSTMCell + :members: diff --git a/tools/pytorch-quantization/docs/source/optim.rst b/tools/pytorch-quantization/docs/source/optim.rst new file mode 100644 index 00000000..06be0865 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/optim.rst @@ -0,0 +1,10 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.optim.helper +=================================== + +.. automodule:: pytorch_quantization.optim.helper + :members: + :undoc-members: + :show-inheritance: diff --git a/tools/pytorch-quantization/docs/source/tensor_quant.rst b/tools/pytorch-quantization/docs/source/tensor_quant.rst new file mode 100644 index 00000000..a19148ef --- /dev/null +++ b/tools/pytorch-quantization/docs/source/tensor_quant.rst @@ -0,0 +1,31 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.tensor_quant +=================================== + +.. automodule:: pytorch_quantization.tensor_quant +.. currentmodule:: pytorch_quantization.tensor_quant + + +:hidden:`QuantDescriptor` +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: QuantDescriptor + :members: + +:hidden:`ScaledQuantDescriptor` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: ScaledQuantDescriptor + :members: + +:hidden:`TensorQuantFunction` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: TensorQuantFunction + :members: + +``tensor_quant`` is alias of ``TensorQuantFunction.apply`` + +``fake_tensor_quant`` is alias of ``FakeTensorQuantFunction.apply`` diff --git a/tools/pytorch-quantization/docs/source/tutorials/creating_custom_quantized_modules.rst b/tools/pytorch-quantization/docs/source/tutorials/creating_custom_quantized_modules.rst new file mode 100644 index 00000000..2ffe5fa3 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/tutorials/creating_custom_quantized_modules.rst @@ -0,0 +1,169 @@ +Creating Custom Quantized Modules +================================= + +There are several quantized modules provided by the quantization tool as follows: + +- QuantConv1d, QuantConv2d, QuantConv3d, QuantConvTranspose1d, QuantConvTranspose2d, QuantConvTranspose3d +- QuantLinear +- QuantAvgPool1d, QuantAvgPool2d, QuantAvgPool3d, QuantMaxPool1d, QuantMaxPool2d, QuantMaxPool3d + +To quantize a module, we need to quantize the input and weights if present. Following are 3 major use-cases: + +#. Create quantized wrapper for modules that have only inputs +#. Create quantized wrapper for modules that have inputs as well as weights. +#. Directly add the ``TensorQuantizer`` module to the inputs of an operation in the model graph. + +The first two methods are very useful if it's needed to automatically replace the original modules (nodes in the graph) with their quantized versions. The third method could be useful when it's required to manually add the quantization to the model graph at very specific places (more manual, more control). + +Let's see each use-case with examples below. + +Quantizing Modules With Only Inputs +----------------------------------- +A suitable example would be quantizing the ``pooling`` module variants. + +Essentially, we need to provide a wrapper function that takes the original module and adds the ``TensorQuantizer`` module around it so that the input is first quantized and then fed into the original module. + +- Create the wrapper by subclassing the original module (``pooling.MaxPool2d``) along with the utilities module (``_utils.QuantInputMixin``). + +.. code:: python + + class QuantMaxPool2d(pooling.MaxPool2d, _utils.QuantInputMixin): + +- The ``__init__.py`` function would call the original module's init function and provide it with the corresponding arguments. There would be just one additional argument using ``**kwargs`` which contains the quantization configuration information. The ``QuantInputMixin`` utility contains the method ``pop_quant_desc_in_kwargs`` which extracts this configuration information from the input or returns a default if that input is ``None``. Finally the ``init_quantizer`` method is called that initializes the ``TensorQuantizer`` module which would quantize the inputs. + +.. code:: python + + def __init__(self, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False, **kwargs): + super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation, + return_indices, ceil_mode) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + +- After the initialization, the ``forward`` function needs to be defined in our wrapper module that would actually quantize the inputs using the ``_input_quantizer`` that was initialized in the ``__init__`` function forwarding the inputs to the base module using ``super`` call. + +.. code:: python + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantMaxPool2d, self).forward(quant_input) + +- Finally, we need to define a getter method for the ``_input_quantizer``. This could, for example, be used to disable the quantization for a particular module using ``module.input_quantizer.disable()`` which is helpful while experimenting with different layer quantization configuration. + +.. code:: python + + @property + def input_quantizer(self): + return self._input_quantizer + +A complete quantized pooling module would look like following: + +.. code:: python + + class QuantMaxPool2d(pooling.MaxPool2d, _utils.QuantInputMixin): + """Quantized 2D maxpool""" + def __init__(self, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False, **kwargs): + super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation, + return_indices, ceil_mode) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantMaxPool2d, self).forward(quant_input) + + @property + def input_quantizer(self): + return self._input_quantizer + +Quantizing Modules With Weights and Inputs +------------------------------------------ +We give an example of quantizing the ``torch.nn.Linear`` module. It follows that the only additional change from the previous example of quantizing pooling modules is that we'd need to accomodate the quantization of weights in the Linear module. + +- We create the quantized linear module as follows: + +.. code:: python + + class QuantLinear(nn.Linear, _utils.QuantMixin): + +- In the ``__init__`` function, we first use the ``pop_quant_desc_in_kwargs`` function to extract the quantization descriptors for both inputs and weights. Second, we initialize the ``TensorQuantizer`` modules for both inputs and weights using these quantization descriptors. + +.. code:: python + + def __init__(self, in_features, out_features, bias=True, **kwargs): + super(QuantLinear, self).__init__(in_features, out_features, bias) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + + self.init_quantizer(quant_desc_input, quant_desc_weight) + +- Also, override the ``forward`` function call and pass the inputs and weights through ``_input_quantizer`` and ``_weight_quantizer`` respectively before passing the quantized arguments to the actual ``F.Linear`` call. This step adds the actual input/weight ``TensorQuantizer`` to the module and eventually the model. + +.. code:: python + + def forward(self, input): + quant_input = self._input_quantizer(input) + quant_weight = self._weight_quantizer(self.weight) + + output = F.linear(quant_input, quant_weight, bias=self.bias) + + return output + +- Also similar to the ``Linear`` module, we add the getter methods for the ``TensorQuantizer`` modules associated with inputs/weights. This could be used to, for example, disable the quantization mechanism by calling ``module_obj.weight_quantizer.disable()`` + +.. code:: python + + @property + def input_quantizer(self): + return self._input_quantizer + + @property + def weight_quantizer(self): + return self._weight_quantizer + +- With all of the above changes, the quantized Linear module would look like following: + +.. code:: python + + class QuantLinear(nn.Linear, _utils.QuantMixin): + + def __init__(self, in_features, out_features, bias=True, **kwargs): + super(QuantLinear, self).__init__(in_features, out_features, bias) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + + self.init_quantizer(quant_desc_input, quant_desc_weight) + + def forward(self, input): + quant_input = self._input_quantizer(input) + quant_weight = self._weight_quantizer(self.weight) + + output = F.linear(quant_input, quant_weight, bias=self.bias) + + return output + + @property + def input_quantizer(self): + return self._input_quantizer + + @property + def weight_quantizer(self): + return self._weight_quantizer + + +Directly Quantizing Inputs In Graph +----------------------------------- +It is also possible to directly quantize graph inputs without creating wrappers as explained above. + +Here's an example: + +.. code:: python + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input) + + quant_input = quantizer(test_input) + + out = F.adaptive_avg_pool2d(quant_input, 3) + +Assume that there is a ``F.adaptive_avg_pool2d`` operation in the graph and we'd like to quantize this operation. In the example above, we use ``TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input)`` to define a quantizer that we then use to actually quantize the ``test_input`` and then feed this quantized input to the ``F.adaptive_avg_pool2d`` operation. Note that this quantizer is the same as the ones we used earlier while created quantized versions of torch's modules. diff --git a/tools/pytorch-quantization/docs/source/tutorials/quant_resnet50.rst b/tools/pytorch-quantization/docs/source/tutorials/quant_resnet50.rst new file mode 100644 index 00000000..4db3bede --- /dev/null +++ b/tools/pytorch-quantization/docs/source/tutorials/quant_resnet50.rst @@ -0,0 +1,200 @@ +Quantizing Resnet50 +=================== + +Create a quantized model +------------------------ + +Import the necessary python modules: + +.. code:: python + + import torch + import torch.utils.data + from torch import nn + + from pytorch_quantization import nn as quant_nn + from pytorch_quantization import calib + from pytorch_quantization.tensor_quant import QuantDescriptor + + from torchvision import models + + sys.path.append("path to torchvision/references/classification/") + from train import evaluate, train_one_epoch, load_data + +Adding quantized modules +~~~~~~~~~~~~~~~~~~~~~~~~ + +The first step is to add quantizer modules to the neural network graph. +This package provides a number of quantized layer modules, which contain quantizers for inputs and weights. +e.g. ``quant_nn.QuantLinear``, which can be used in place of ``nn.Linear``. +These quantized layers can be substituted automatically, via monkey-patching, or by manually modifying the model definition. + +Automatic layer substitution is done with ``quant_modules``. This should be called before model creation. + +.. code:: python + + from pytorch_quantization import quant_modules + quant_modules.initialize() + +This will apply to all instances of each module. +If you do not want all modules to be quantized you should instead substitute the quantized modules manually. +Stand-alone quantizers can also be added to the model with ``quant_nn.TensorQuantizer``. + +Post training quantization +-------------------------- + +For efficient inference, we want to select a fixed range for each quantizer. +Starting with a pre-trained model, the simplest way to do this is by calibration. + + +Calibration +~~~~~~~~~~~ + +We will use histogram based calibration for activations and the default max calibration for weights. + +.. code:: python + + quant_desc_input = QuantDescriptor(calib_method='histogram') + quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input) + + model = models.resnet50(pretrained=True) + model.cuda() + +To collect activation histograms we must feed sample data in to the model. +First, create ImageNet dataloaders as done in the training script. +Then, enable calibration in each quantizer and feed training data in to the model. +1024 samples (2 batches of 512) should be sufficient to estimate the distribution of activations. +Use training data for calibration so that validation also measures generalization of the selected ranges. + +.. code:: python + + data_path = "PATH to imagenet" + batch_size = 512 + + traindir = os.path.join(data_path, 'train') + valdir = os.path.join(data_path, 'val') + dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, False, False) + + data_loader = torch.utils.data.DataLoader( + dataset, batch_size=batch_size, + sampler=train_sampler, num_workers=4, pin_memory=True) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, batch_size=batch_size, + sampler=test_sampler, num_workers=4, pin_memory=True) + +.. code:: python + + def collect_stats(model, data_loader, num_batches): + """Feed data to the network and collect statistic""" + + # Enable calibrators + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + module.disable_quant() + module.enable_calib() + else: + module.disable() + + for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches): + model(image.cuda()) + if i >= num_batches: + break + + # Disable calibrators + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + module.enable_quant() + module.disable_calib() + else: + module.enable() + + def compute_amax(model, **kwargs): + # Load calib result + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + if isinstance(module._calibrator, calib.MaxCalibrator): + module.load_calib_amax() + else: + module.load_calib_amax(**kwargs) + print(F"{name:40}: {module}") + model.cuda() + + # It is a bit slow since we collect histograms on CPU + with torch.no_grad(): + collect_stats(model, data_loader, num_batches=2) + compute_amax(model, method="percentile", percentile=99.99) + +After calibration is done, quantizers will have ``amax`` set, which represents the absolute maximum input value representable in the quantized space. +By default, weight ranges are per channel while activation ranges are per tensor. +We can see the condensed amaxes by printing each ``TensorQuantizer`` module. + +:: + + conv1._input_quantizer : TensorQuantizer(8bit fake per-tensor amax=2.6400 calibrator=MaxCalibrator(track_amax=False) quant) + conv1._weight_quantizer : TensorQuantizer(8bit fake axis=(0) amax=[0.0000, 0.7817](64) calibrator=MaxCalibrator(track_amax=False) quant) + layer1.0.conv1._input_quantizer : TensorQuantizer(8bit fake per-tensor amax=6.8645 calibrator=MaxCalibrator(track_amax=False) quant) + layer1.0.conv1._weight_quantizer : TensorQuantizer(8bit fake axis=(0) amax=[0.0000, 0.7266](64) calibrator=MaxCalibrator(track_amax=False) quant) + ... + +Evaluate the calibrated model +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Next we will evaluate the classification accuracy of our post training quantized model on the ImageNet validation set. + +.. code:: python + + criterion = nn.CrossEntropyLoss() + with torch.no_grad(): + evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20) + + # Save the model + torch.save(model.state_dict(), "/tmp/quant_resnet50-calibrated.pth") + +This should yield 76.1% top-1 accuracy, which is close the the pre-trained model accuracy of 76.2%. + +Use different calibration +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can try different calibrations without recollecting the histograms, and see which one gets the best accuracy. + +.. code:: python + + with torch.no_grad(): + compute_amax(model, method="percentile", percentile=99.9) + evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20) + + with torch.no_grad(): + for method in ["mse", "entropy"]: + print(F"{method} calibration") + compute_amax(model, method=method) + evaluate(model, criterion, data_loader_test, device="cuda", print_freq=20) + + +MSE and entropy should both get over 76%. 99.9% clips too many values for resnet50 and will get slightly lower accuracy. + +Quantized fine tuning +--------------------- + +Optionally, we can fine-tune the calibrated model to improve accuracy further. + +.. code:: python + + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) + + # Training takes about one and half hour per epoch on a single V100 + train_one_epoch(model, criterion, optimizer, data_loader, "cuda", 0, 100) + + # Save the model + torch.save(model.state_dict(), "/tmp/quant_resnet50-finetuned.pth") + +After one epoch of fine-tuning, we can achieve over 76.4% top-1 accuracy. +Fine-tuning for more epochs with learning rate annealing can improve accuracy further. +For example, fine-tuning for 15 epochs with cosine annealing starting with a learning rate of 0.001 can get over 76.7%. +It should be noted that the same fine-tuning schedule will improve the accuracy of the unquantized model as well. diff --git a/tools/pytorch-quantization/docs/source/userguide.rst b/tools/pytorch-quantization/docs/source/userguide.rst new file mode 100644 index 00000000..8b4e8ef8 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/userguide.rst @@ -0,0 +1,262 @@ +Basic Functionalities +--------------------- + +Quantization function +~~~~~~~~~~~~~~~~~~~~~ + +``tensor_quant`` and ``fake_tensor_quant`` are 2 basic functions to +quantize a tensor. ``fake_tensor_quant`` returns fake quantized tensor +(float value). ``tensor_quant`` returns quantized tensor (integer value) +and scale. + +.. code:: python + + tensor_quant(inputs, amax, num_bits=8, output_dtype=torch.float, unsigned=False) + fake_tensor_quant(inputs, amax, num_bits=8, output_dtype=torch.float, unsigned=False) + +Example: + +.. code:: python + + from pytorch_quantization import tensor_quant + + # Generate random input. With fixed seed 12345, x should be + # tensor([0.9817, 0.8796, 0.9921, 0.4611, 0.0832, 0.1784, 0.3674, 0.5676, 0.3376, 0.2119]) + torch.manual_seed(12345) + x = torch.rand(10) + + # fake quantize tensor x. fake_quant_x will be + # tensor([0.9843, 0.8828, 0.9921, 0.4609, 0.0859, 0.1797, 0.3672, 0.5703, 0.3359, 0.2109]) + fake_quant_x = tensor_quant.fake_tensor_quant(x, x.abs().max()) + + # quantize tensor x. quant_x will be + # tensor([126., 113., 127., 59., 11., 23., 47., 73., 43., 27.]) + # with scale=128.0057 + quant_x, scale = tensor_quant.tensor_quant(x, x.abs().max()) + +Backward of both functions are defined as `Straight-Through Estimator (STE) `_. + +Descriptor and quantizer +~~~~~~~~~~~~~~~~~~~~~~~~ + +``QuantDescriptor`` defines how a tensor should be quantized. There are +also some predefined ``QuantDescriptor``, e.g. +``QUANT_DESC_8BIT_PER_TENSOR`` and +``QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL``. + +``TensorQuantizer`` is the module for quantizing tensors and defined by +``QuantDescriptor``. + +.. code:: python + + from pytorch_quantization.tensor_quant import QuantDescriptor + from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer + + quant_desc = QuantDescriptor(num_bits=4, fake_quant=False, axis=(0), unsigned=True) + quantizer = TensorQuantizer(quant_desc) + + torch.manual_seed(12345) + x = torch.rand(10, 9, 8, 7) + + quant_x = quantizer(x) + +If ``amax`` is given in the :func:`QuantDescriptor `, :func:`TensorQuantizer ` will use it to quantize. Otherwise, :func:`TensorQuantizer ` will compute amax then quantize. amax will be computed w.r.t ``axis`` specified. Note that ``axis`` of QuantDescriptor specify remaining axis as oppsed to axis of `max() `_. + +Quantized module +~~~~~~~~~~~~~~~~ + +There are 2 major types of module, ``Conv`` and ``Linear``. Both can +replace ``torch.nn`` version and apply quantization on both weight and +activation. + +Both take ``quant_desc_input`` and ``quant_desc_weight`` in addition to +arguments of the original module. + +.. code:: python + + from torch import nn + + from pytorch_quantization import tensor_quant + import pytorch_quantization.nn as quant_nn + + # pytorch's module + fc1 = nn.Linear(in_features, out_features, bias=True) + conv1 = nn.Conv2d(in_channels, out_channels, kernel_size) + + # quantized version + quant_fc1 = quant_nn.Linear( + in_features, out_features, bias=True, + quant_desc_input=tensor_quant.QUANT_DESC_8BIT_PER_TENSOR, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW) + quant_conv1 = quant_nn.Conv2d( + in_channels, out_channels, kernel_size, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL) + +Post training quantization +-------------------------- + +A model can be post training quantized by simply by calling ``quant_modules.initialize()`` + +.. code:: python + + from pytorch_quantization import quant_modules + model = torchvision.models.resnet50() + +If a model is not entirely defined by module, than TensorQuantizer should be +manually created and added to the right place in the model. + +Calibration +~~~~~~~~~~~ + +Calibration is the TensorRT terminology of passing data samples to the +quantizer and deciding the best amax for activations. We support 3 +calibration method: + +- ``max``: Simply use global maximum absolute value +- ``entropy``: TensorRT's entropy calibration +- ``percentile``: Get rid of out lier based on given percentile. +- ``mse``: MSE(Mean Square Error) based calibration + +In above ResNet50 example, calibration method is set to ``mse``, it can +be used as the following example: + +.. code:: python + + # Find the TensorQuantizer and enable calibration + for name, module in model.named_modules(): + if name.endswith('_input_quantizer'): + module.enable_calib() + module.disable_quant() # Use full precision data to calibrate + + # Feeding data samples + model(x) + # ... + + # Finalize calibration + for name, module in model.named_modules(): + if name.endswith('_input_quantizer'): + module.load_calib_amax() + module.enable_quant() + + # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process + model.cuda() + + # Keep running the quantized model + # ... + +Quantized Fine Tuning +--------------------- + +Quantized fine tuning is based on Straight Through Estimator (STE) +derivative approximation. It is some time known as “quantization aware +trainingâ€. We don’t use the name because it doesn’t reflect the +underneath assumption. If anything, it makes training being “unaware†of +quantization because of the STE approximation. + +After calibration is done, quantized fine tuning is simply select a +training schedule and continue training the calibrated model. Usually, +it doesn’t need to fine tune very long. We usually use around 10% of the +original training schedule, starting at 1% of the initial training +learning rate, and a cosine annealing learning rate schedule that +follows the decreasing half of a cosine period, down to 1% of the +initial fine tuning learning rate (0.01% of the initial training +learning rate). + +Some recommendations +~~~~~~~~~~~~~~~~~~~~ + +Quantized fine tuning (Essentially a discrete numerical optimization +problem) is not a solved problem mathematically. Based on our +experience, here are some recommendations: + +- For STE approximation to work well, it is better to use small + learning rate. Large learning rate is more likely to enlarge the + variance introduced by STE approximation and destroy the trained + network. +- Do not change quantization representation (scale) during training, at + least not too frequently. Changing scale every step, it is + effectively like changing data format (e8m7, e5m10, e3m4, et.al) + every step, which will easily affect convergence. + +Export to ONNX +-------------- + +The goal of exporting to ONNX is to deploy inference by TensorRT, not +ONNX runtime. So we only export fake quantized model into a form TensorRT will take. Fake +quantization will be broken into a pair of +QuantizeLinear/DequantizeLinear ONNX ops. In future, TensorRT will take +the graph, and execute it in int8 in the most optimized way to its +capability. + +Pytorch doesn’t support exporting fake quantize ops to ONNX yet, but the +code is simple. Add the following code to +``torch/onnx/symbolic_opset10.py`` + +.. code:: python + + @parse_args('v', 't', 'i', 'i', 'i') + def fake_quantize_per_tensor_affine(g, inputs, scale, zero_point, quant_min=-128, quant_max=127): + if quant_min not in [0, -128] or quant_max not in [127, 255]: + raise TypeError("ONNX defines [0, 255] for quint8 and [-128, 127] for int8, got [{}, {}]".format( + quant_min, quant_max)) + scale = scale.float() # Avoid exportor generating double type + zero_point = torch.tensor(zero_point, dtype=torch.int8) # ONNX requires zero_point to be tensor + return g.op("DequantizeLinear", g.op("QuantizeLinear", inputs, scale, zero_point), scale, zero_point) + + @parse_args('v', 'v', 'v', 'i', 'i', 'i') + def fake_quantize_per_channel_affine(g, inputs, scale, zero_point, axis, quant_min=-128, quant_max=127): + if quant_min not in [0, -128] or quant_max not in [127, 255]: + raise TypeError("ONNX defines [0, 255] for quint8 and [-128, 127] for int8, got [{}, {}]".format( + quant_min, quant_max)) + # ONNX defines zero_point to be int8 or uint8 + if quant_min == 0: + zero_point = g.op("Cast", zero_point, to_i=sym_help.cast_pytorch_to_onnx['Byte']) + else: + zero_point = g.op("Cast", zero_point, to_i=sym_help.cast_pytorch_to_onnx['Char']) + return g.op( + "DequantizeLinear", + g.op("QuantizeLinear", inputs, scale, zero_point, axis_i=axis), + scale, zero_point, axis_i=axis) + +Then set static member of TensorQuantizer to use Pytorch’s own fake +quantization functions + +.. code:: python + + from pytorch_quantization import nn as quant_nn + quant_nn.TensorQuantizer.use_fb_fake_quant = True + +Fake quantized model can now be exported to ONNX as other models, follow +the instructions in +`torch.onnx `__. +For example: + +.. code:: python + + from pytorch_quantization import nn as quant_nn + from pytorch_quantization import quant_modules + quant_nn.TensorQuantizer.use_fb_fake_quant = True + + quant_modules.initialize() + model = torchvision.models.resnet50() + # load the calibrated model + state_dict = torch.load("quant_resnet50-entropy-1024.pth", map_location="cpu") + model.load_state_dict(state_dict) + model.cuda() + + dummy_input = torch.randn(128, 3, 224, 224, device='cuda') + + input_names = [ "actual_input_1" ] + output_names = [ "output1" ] + + # enable_onnx_checker needs to be disabled. See notes below. + torch.onnx.export( + model, dummy_input, "quant_resnet50.onnx", verbose=True, opset_version=10, enable_onnx_checker=False) + +.. Note:: + + Note that ``axis`` is added to ``QuantizeLinear`` and ``DequantizeLinear`` in opset13 + which hasn't been released yet, so that ONNX runtime doesn't support it yet. + + If only per tensor quantization is used, the generated graph can be run + by ONNX runtime on CPU. \ No newline at end of file diff --git a/tools/pytorch-quantization/docs/source/utils.rst b/tools/pytorch-quantization/docs/source/utils.rst new file mode 100644 index 00000000..8023f252 --- /dev/null +++ b/tools/pytorch-quantization/docs/source/utils.rst @@ -0,0 +1,30 @@ +.. role:: hidden + :class: hidden-section + +pytorch_quantization.utils +=================================== + + +pytorch_quantization.utils.amp\_wrapper +------------------------------------------ + +.. automodule:: pytorch_quantization.utils.amp_wrapper + :members: + :undoc-members: + :show-inheritance: + +pytorch_quantization.utils.quant\_logging +-------------------------------------------- + +.. automodule:: pytorch_quantization.utils.quant_logging + :members: + :undoc-members: + :show-inheritance: + +pytorch_quantization.utils.reduce\_amax +------------------------------------------ + +.. automodule:: pytorch_quantization.utils.reduce_amax + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/tools/pytorch-quantization/examples/calibrate_quant_resnet50.ipynb b/tools/pytorch-quantization/examples/calibrate_quant_resnet50.ipynb new file mode 100644 index 00000000..9315a0f8 --- /dev/null +++ b/tools/pytorch-quantization/examples/calibrate_quant_resnet50.ipynb @@ -0,0 +1,747 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import os\n", + "import sys\n", + "import time\n", + "\n", + "import torch\n", + "import torch.utils.data\n", + "from torch import nn\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "import torchvision\n", + "from torchvision import transforms\n", + "\n", + "from pytorch_quantization import nn as quant_nn\n", + "from pytorch_quantization import calib\n", + "from pytorch_quantization.tensor_quant import QuantDescriptor\n", + "\n", + "from absl import logging\n", + "logging.set_verbosity(logging.FATAL) # Disable logging as they are too noisy in notebook\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# For simplicity, import train and eval functions from the train script from torchvision instead of copything them here\n", + "# Download torchvision from https://github.com/pytorch/vision\n", + "sys.path.append(\"/raid/skyw/models/torchvision/references/classification/\")\n", + "from train import evaluate, train_one_epoch, load_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set default QuantDescriptor to use histogram based calibration for activation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "quant_desc_input = QuantDescriptor(calib_method='histogram')\n", + "quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input)\n", + "quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize quantized modules" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pytorch_quantization import quant_modules\n", + "quant_modules.initialize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create model with pretrained weight" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ResNet(\n", + " (conv1): QuantConv2d(\n", + " 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (downsample): Sequential(\n", + " (0): QuantConv2d(\n", + " 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic 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eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (layer2): 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bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " (5): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (layer4): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (downsample): Sequential(\n", + " (0): QuantConv2d(\n", + " 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " (2): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): QuantLinear(\n", + " in_features=2048, out_features=1000, bias=True\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=dynamic calibrator=HistogramCalibrator quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=dynamic calibrator=MaxCalibrator quant)\n", + " )\n", + ")" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = torchvision.models.resnet50(pretrained=True, progress=False)\n", + "model.cuda()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create data loader" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading data\n", + "Loading training data\n", + "Took 3.580507755279541\n", + "Loading validation data\n", + "Creating data loaders\n" + ] + } + ], + "source": [ + "data_path = \"/raid/data/imagenet/imagenet_pytorch\"\n", + "batch_size = 512\n", + "\n", + "traindir = os.path.join(data_path, 'train')\n", + "valdir = os.path.join(data_path, 'val')\n", + "dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, False, False)\n", + "\n", + "data_loader = torch.utils.data.DataLoader(\n", + " dataset, batch_size=batch_size,\n", + " sampler=train_sampler, num_workers=4, pin_memory=True)\n", + "\n", + "data_loader_test = torch.utils.data.DataLoader(\n", + " dataset_test, batch_size=batch_size,\n", + " sampler=test_sampler, num_workers=4, pin_memory=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calibrate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def collect_stats(model, data_loader, num_batches):\n", + " \"\"\"Feed data to the network and collect statistic\"\"\"\n", + "\n", + " # Enable calibrators\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " module.disable_quant()\n", + " module.enable_calib()\n", + " else:\n", + " module.disable()\n", + "\n", + " for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches):\n", + " model(image.cuda())\n", + " if i >= num_batches:\n", + " break\n", + "\n", + " # Disable calibrators\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " module.enable_quant()\n", + " module.disable_calib()\n", + " else:\n", + " module.enable()\n", + " \n", + "def compute_amax(model, **kwargs):\n", + " # Load calib result\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, quant_nn.TensorQuantizer):\n", + " if module._calibrator is not None:\n", + " if isinstance(module._calibrator, calib.MaxCalibrator):\n", + " module.load_calib_amax()\n", + " else:\n", + " module.load_calib_amax(**kwargs)\n", + "# print(F\"{name:40}: {module}\")\n", + " model.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [04:50<00:00, 111.13s/it]" + ] + } + ], + "source": [ + "# It is a bit slow since we collect histograms on CPU\n", + "with torch.no_grad():\n", + " collect_stats(model, data_loader, num_batches=2)\n", + " compute_amax(model, method=\"percentile\", percentile=99.99)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now evaluate the calibrated model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test: [ 0/98] eta: 0:05:53 loss: 0.5656 (0.5656) acc1: 85.7422 (85.7422) acc5: 96.0938 (96.0938) time: 3.6079 data: 2.8152 max mem: 5880\n", + "Test: [20/98] eta: 0:01:07 loss: 0.6741 (0.6825) acc1: 82.8125 (82.4219) acc5: 95.8984 (95.7682) time: 0.7343 data: 0.0002 max mem: 5882\n", + "Test: [40/98] eta: 0:00:46 loss: 0.6995 (0.7157) acc1: 80.0781 (81.4024) acc5: 96.0938 (95.7412) time: 0.7226 data: 0.0002 max mem: 5882\n", + "Test: [60/98] eta: 0:00:29 loss: 1.1064 (0.8590) acc1: 71.4844 (78.2627) acc5: 91.0156 (94.1150) time: 0.7259 data: 0.0002 max mem: 5882\n", + "Test: [80/98] eta: 0:00:13 loss: 1.1220 (0.9372) acc1: 72.4609 (76.7072) acc5: 89.6484 (93.1375) time: 0.7220 data: 0.0002 max mem: 5882\n", + "Test: Total time: 0:01:13\n", + " * Acc@1 76.138 Acc@5 92.916\n" + ] + } + ], + "source": [ + "criterion = nn.CrossEntropyLoss()\n", + "with torch.no_grad():\n", + " evaluate(model, criterion, data_loader_test, device=\"cuda\", print_freq=20)\n", + " \n", + "# Save the model\n", + "torch.save(model.state_dict(), \"/tmp/quant_resnet50-calibrated.pth\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We can also try different calibrations and see which one works the best" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test: [ 0/98] eta: 0:05:27 loss: 0.6037 (0.6037) acc1: 84.9609 (84.9609) acc5: 95.3125 (95.3125) time: 3.3411 data: 2.6190 max mem: 5882\n", + "Test: [20/98] eta: 0:01:06 loss: 0.6760 (0.7041) acc1: 81.2500 (81.7522) acc5: 95.7031 (95.4892) time: 0.7243 data: 0.0002 max mem: 5882\n", + "Test: [40/98] eta: 0:00:45 loss: 0.7241 (0.7351) acc1: 79.1016 (80.7784) acc5: 95.8984 (95.4459) time: 0.7243 data: 0.0002 max mem: 5882\n", + "Test: [60/98] eta: 0:00:29 loss: 1.1162 (0.8793) acc1: 71.4844 (77.6383) acc5: 90.8203 (93.7948) time: 0.7204 data: 0.0002 max mem: 5882\n", + "Test: [80/98] eta: 0:00:13 loss: 1.1498 (0.9603) acc1: 71.4844 (76.0368) acc5: 89.4531 (92.7156) time: 0.7164 data: 0.0002 max mem: 5882\n", + "Test: Total time: 0:01:12\n", + " * Acc@1 75.438 Acc@5 92.486\n" + ] + } + ], + "source": [ + "with torch.no_grad():\n", + " compute_amax(model, method=\"percentile\", percentile=99.9)\n", + " evaluate(model, criterion, data_loader_test, device=\"cuda\", print_freq=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse calibration\n", + "Test: [ 0/98] eta: 0:06:34 loss: 0.5700 (0.5700) acc1: 85.1562 (85.1562) acc5: 96.2891 (96.2891) time: 4.0243 data: 3.3231 max mem: 5882\n", + "Test: [20/98] eta: 0:01:08 loss: 0.6758 (0.6838) acc1: 82.8125 (82.5707) acc5: 96.0938 (95.7868) time: 0.7204 data: 0.0002 max mem: 5882\n", + "Test: [40/98] eta: 0:00:46 loss: 0.7047 (0.7163) acc1: 80.2734 (81.4834) acc5: 96.2891 (95.7746) time: 0.7178 data: 0.0002 max mem: 5882\n", + "Test: [60/98] eta: 0:00:29 loss: 1.1127 (0.8585) acc1: 71.0938 (78.3395) acc5: 90.8203 (94.1278) time: 0.7192 data: 0.0002 max mem: 5882\n", + "Test: [80/98] eta: 0:00:13 loss: 1.1261 (0.9367) acc1: 72.6562 (76.7530) acc5: 89.8438 (93.1785) time: 0.7176 data: 0.0002 max mem: 5882\n", + "Test: Total time: 0:01:13\n", + " * Acc@1 76.186 Acc@5 92.926\n", + "entropy calibration\n", + "Test: [ 0/98] eta: 0:05:28 loss: 0.5648 (0.5648) acc1: 85.3516 (85.3516) acc5: 96.0938 (96.0938) time: 3.3558 data: 2.6268 max mem: 5882\n", + "Test: [20/98] eta: 0:01:05 loss: 0.6724 (0.6815) acc1: 82.8125 (82.5428) acc5: 95.8984 (95.7589) time: 0.7196 data: 0.0002 max mem: 5882\n", + "Test: [40/98] eta: 0:00:45 loss: 0.7090 (0.7149) acc1: 80.6641 (81.4929) acc5: 96.0938 (95.7269) time: 0.7214 data: 0.0002 max mem: 5882\n", + "Test: [60/98] eta: 0:00:29 loss: 1.1077 (0.8571) acc1: 72.0703 (78.3779) acc5: 90.6250 (94.0798) time: 0.7198 data: 0.0002 max mem: 5882\n", + "Test: [80/98] eta: 0:00:13 loss: 1.1253 (0.9356) acc1: 72.2656 (76.7626) acc5: 90.0391 (93.1231) time: 0.7192 data: 0.0002 max mem: 5882\n", + "Test: Total time: 0:01:12\n", + " * Acc@1 76.206 Acc@5 92.900\n" + ] + } + ], + "source": [ + "with torch.no_grad():\n", + " for method in [\"mse\", \"entropy\"]:\n", + " print(F\"{method} calibration\")\n", + " compute_amax(model, method=method)\n", + " evaluate(model, criterion, data_loader_test, device=\"cuda\", print_freq=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/pytorch-quantization/examples/finetune_quant_resnet50.ipynb b/tools/pytorch-quantization/examples/finetune_quant_resnet50.ipynb new file mode 100644 index 00000000..400270b1 --- /dev/null +++ b/tools/pytorch-quantization/examples/finetune_quant_resnet50.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following the calibrate_quant_resnet50 example, now we fine tune the model" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Logging before flag parsing goes to stderr.\n", + "W0608 21:25:39.018203 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.019082 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.019555 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.020030 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.020492 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.020947 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n", + "W0608 21:25:39.021392 140228493526848 tensor_quant.py:96] Meaning of axis has changed since v2.0. Make sure to update.\n" + ] + } + ], + "source": [ + "import datetime\n", + "import os\n", + "import sys\n", + "import time\n", + "\n", + "import torch\n", + "import torch.utils.data\n", + "from torch import nn\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "import torchvision\n", + "from torchvision import transforms\n", + "\n", + "from pytorch_quantization import nn as quant_nn\n", + "from pytorch_quantization import calib\n", + "from pytorch_quantization.tensor_quant import QuantDescriptor\n", + "\n", + "from absl import logging\n", + "logging.set_verbosity(logging.FATAL) # Disable logging as they are too noisy in notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# For simplicity, import train and eval functions from the train script from torchvision instead of copything them here\n", + "sys.path.append(\"/raid/skyw/models/torchvision/references/classification/\")\n", + "from train import evaluate, train_one_epoch, load_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "QuantResNet(\n", + " (conv1): QuantConv2d(\n", + " 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=2.6387 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0000, 0.7817](64) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=2.9730 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0000, 0.7266](64) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.0971 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0000, 0.4679](64) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.3318 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " 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(_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=0.9257 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0455, 0.2792](256) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=0.8117 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0000, 0.3126](1024) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " (4): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.4199 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0598, 0.2722](256) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=0.9274 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0459, 0.1919](256) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=0.8702 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0160, 0.3161](1024) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " (5): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.4258 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0618, 0.3995](256) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.2256 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0498, 0.2236](256) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.3560 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0180, 0.3288](1024) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (layer4): Sequential(\n", + " (0): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.3915 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0744, 0.3415](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.1571 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0432, 0.3993](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.1295 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0198, 0.3546](2048) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (downsample): Sequential(\n", + " (0): QuantConv2d(\n", + " 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.3915 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0197, 0.6413](2048) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=3.9348 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0482, 0.7003](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.1277 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0379, 0.2257](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=0.8992 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0211, 0.2427](2048) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " (2): Bottleneck(\n", + " (conv1): QuantConv2d(\n", + " 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=5.2181 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0601, 0.4541](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): QuantConv2d(\n", + " 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.2051 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0338, 0.1416](512) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): QuantConv2d(\n", + " 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=1.1045 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.0119, 0.2798](2048) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): QuantLinear(\n", + " in_features=2048, out_features=1000, bias=True\n", + " (_input_quantizer): TensorQuantizer(8bit fake per-tensor amax=5.5345 calibrator=MaxCalibrator(track_amax=False) quant)\n", + " (_weight_quantizer): TensorQuantizer(8bit fake axis=0 amax=[0.1716, 0.7371](1000) calibrator=MaxCalibrator(track_amax=False) quant)\n", + " )\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pytorch_quantization import quant_modules\n", + "quant_modules.initialize()\n", + "\n", + "# Create and load the calibrated model\n", + "model = torchvision.models.resnet50()\n", + "model.load_state_dict(torch.load(\"/tmp/quant_resnet50-calibrated.pth\"))\n", + "model.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"/raid/data/imagenet/imagenet_pytorch\"\n", + "\n", + "traindir = os.path.join(data_path, 'train')\n", + "valdir = os.path.join(data_path, 'val')\n", + "dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, False, False)\n", + "\n", + "data_loader = torch.utils.data.DataLoader(\n", + " dataset, batch_size=256,\n", + " sampler=train_sampler, num_workers=4, pin_memory=True)\n", + "\n", + "data_loader_test = torch.utils.data.DataLoader(\n", + " dataset_test, batch_size=256,\n", + " sampler=test_sampler, num_workers=4, pin_memory=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantized fine tuning\n", + "Let's fine tune the model with fake quantization. We only fine tune for 1 epoch as an example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n", + "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1)\n", + "\n", + "data_loader = torch.utils.data.DataLoader(\n", + " dataset, batch_size=128,\n", + " sampler=train_sampler, num_workers=16, pin_memory=True)\n", + "\n", + "# Training takes about one and half hour per epoch on single V100\n", + "train_one_epoch(model, criterion, optimizer, data_loader, \"cuda\", 0, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the fine tuned model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test: [ 0/196] eta: 0:09:36 loss: 0.4680 (0.4680) acc1: 85.9375 (85.9375) acc5: 98.0469 (98.0469) time: 2.9406 data: 2.1852 max mem: 16096\n", + "Test: [ 10/196] eta: 0:01:58 loss: 0.6694 (0.6522) acc1: 83.2031 (82.9545) acc5: 96.0938 (96.1293) time: 0.6346 data: 0.1988 max mem: 16096\n", + "Test: [ 20/196] eta: 0:01:30 loss: 0.6738 (0.6733) acc1: 82.0312 (82.4777) acc5: 95.7031 (95.7961) time: 0.3928 data: 0.0001 max mem: 16096\n", + "Test: [ 30/196] eta: 0:01:18 loss: 0.6219 (0.6322) acc1: 84.3750 (83.9718) acc5: 95.7031 (96.0181) time: 0.3859 data: 0.0001 max mem: 16096\n", + "Test: [ 40/196] eta: 0:01:10 loss: 0.6801 (0.6750) acc1: 81.6406 (82.5934) acc5: 95.7031 (95.9604) time: 0.3861 data: 0.0001 max mem: 16096\n", + "Test: [ 50/196] eta: 0:01:04 loss: 0.6937 (0.6724) acc1: 80.0781 (82.3529) acc5: 96.8750 (96.0938) time: 0.3834 data: 0.0001 max mem: 16096\n", + "Test: [ 60/196] eta: 0:00:58 loss: 0.7149 (0.6849) acc1: 80.0781 (81.9864) acc5: 96.4844 (96.1066) time: 0.3854 data: 0.0001 max mem: 16096\n", + "Test: [ 70/196] eta: 0:00:53 loss: 0.6616 (0.6716) acc1: 80.8594 (82.2843) acc5: 96.4844 (96.1983) time: 0.3859 data: 0.0001 max mem: 16096\n", + "Test: [ 80/196] eta: 0:00:48 loss: 0.6510 (0.6968) acc1: 81.2500 (81.7467) acc5: 95.7031 (95.9201) time: 0.3860 data: 0.0001 max mem: 16096\n", + "Test: [ 90/196] eta: 0:00:44 loss: 0.9469 (0.7444) acc1: 76.1719 (80.6834) acc5: 92.5781 (95.4370) time: 0.3868 data: 0.0001 max mem: 16096\n", + "Test: [100/196] eta: 0:00:39 loss: 1.1594 (0.7964) acc1: 70.7031 (79.5521) acc5: 90.6250 (94.8755) time: 0.3864 data: 0.0001 max mem: 16096\n", + "Test: [110/196] eta: 0:00:35 loss: 1.1594 (0.8214) acc1: 72.2656 (79.0365) acc5: 91.4062 (94.6298) time: 0.3836 data: 0.0001 max mem: 16096\n", + "Test: [120/196] eta: 0:00:31 loss: 0.9820 (0.8389) acc1: 76.1719 (78.7771) acc5: 92.1875 (94.3634) time: 0.3856 data: 0.0001 max mem: 16096\n", + "Test: [130/196] eta: 0:00:26 loss: 1.0825 (0.8705) acc1: 72.6562 (77.9610) acc5: 91.4062 (94.0303) time: 0.3866 data: 0.0001 max mem: 16096\n", + "Test: [140/196] eta: 0:00:22 loss: 1.1088 (0.8889) acc1: 72.2656 (77.6125) acc5: 91.4062 (93.8137) time: 0.3879 data: 0.0001 max mem: 16096\n", + "Test: [150/196] eta: 0:00:18 loss: 1.1069 (0.9059) acc1: 73.0469 (77.2998) acc5: 91.0156 (93.5586) time: 0.3914 data: 0.0002 max mem: 16096\n", + "Test: [160/196] eta: 0:00:14 loss: 1.1360 (0.9197) acc1: 73.0469 (77.0380) acc5: 90.2344 (93.3472) time: 0.3898 data: 0.0002 max mem: 16096\n", + "Test: [170/196] eta: 0:00:10 loss: 1.2171 (0.9371) acc1: 71.8750 (76.6265) acc5: 89.8438 (93.1721) time: 0.3845 data: 0.0002 max mem: 16096\n", + "Test: [180/196] eta: 0:00:06 loss: 1.2493 (0.9527) acc1: 68.7500 (76.2992) acc5: 90.2344 (93.0205) time: 0.3815 data: 0.0001 max mem: 16096\n", + "Test: [190/196] eta: 0:00:02 loss: 1.0816 (0.9511) acc1: 71.4844 (76.3089) acc5: 92.1875 (93.0465) time: 0.3736 data: 0.0001 max mem: 16096\n", + "Test: Total time: 0:01:17\n", + " * Acc@1 76.426 Acc@5 93.080\n" + ] + } + ], + "source": [ + "with torch.no_grad():\n", + " evaluate(model, criterion, data_loader_test, device=\"cuda\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After only 1 epoch of quantized fine tuning, top-1 improved from ~76.1 to 76.426. Train longer with lr anealing can improve accuracy futher" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tools/pytorch-quantization/examples/torchvision/classification/classification_flow.py b/tools/pytorch-quantization/examples/torchvision/classification/classification_flow.py new file mode 100644 index 00000000..2dc58d0f --- /dev/null +++ b/tools/pytorch-quantization/examples/torchvision/classification/classification_flow.py @@ -0,0 +1,444 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import datetime +import os +import sys +import time +import argparse +import warnings + +import torch +import torch.utils.data +from torch import nn + +from tqdm import tqdm + +import torchvision +from torchvision import transforms +from torchvision.models.utils import load_state_dict_from_url + +from pytorch_quantization import nn as quant_nn +from pytorch_quantization import calib +from pytorch_quantization.tensor_quant import QuantDescriptor +from pytorch_quantization import quant_modules + +import onnxruntime +import numpy as np + +from prettytable import PrettyTable + +# The following path assumes running in nvcr.io/nvidia/pytorch:20.08-py3 +sys.path.insert(0,"/opt/pytorch/vision/references/classification/") + +# Import functions from torchvision reference +try: + from train import evaluate, train_one_epoch, load_data, utils +except Exception as e: + raise ModuleNotFoundError( + "Add https://github.com/pytorch/vision/blob/master/references/classification/ to PYTHONPATH") + +def get_parser(): + """ + Creates an argument parser. + """ + parser = argparse.ArgumentParser(description='Classification quantization flow script') + + parser.add_argument('--data-dir', '-d', type=str, help='input data folder', required=True) + parser.add_argument('--model-name', '-m', default='resnet50', help='model name: default resnet50') + parser.add_argument('--disable-pcq', '-dpcq', action="store_true", help='disable per-channel quantization for weights') + parser.add_argument('--out-dir', '-o', default='/tmp', help='output folder: default /tmp') + parser.add_argument('--print-freq', '-pf', type=int, default=20, help='evaluation print frequency: default 20') + parser.add_argument('--threshold', '-t', type=float, default=-1.0, help='top1 accuracy threshold (less than 0.0 means no comparison): default -1.0') + + parser.add_argument('--batch-size-train', type=int, default=128, help='batch size for training: default 128') + parser.add_argument('--batch-size-test', type=int, default=128, help='batch size for testing: default 128') + parser.add_argument('--batch-size-onnx', type=int, default=1, help='batch size for onnx: default 1') + + parser.add_argument('--seed', type=int, default=12345, help='random seed: default 12345') + + checkpoint = parser.add_mutually_exclusive_group(required=True) + checkpoint.add_argument('--ckpt-path', default='', type=str, + help='path to latest checkpoint (default: none)') + checkpoint.add_argument('--ckpt-url', default='', type=str, + help='url to latest checkpoint (default: none)') + checkpoint.add_argument('--pretrained', action="store_true") + + parser.add_argument('--num-calib-batch', default=4, type=int, + help='Number of batches for calibration. 0 will disable calibration. (default: 4)') + parser.add_argument('--num-finetune-epochs', default=0, type=int, + help='Number of epochs to fine tune. 0 will disable fine tune. (default: 0)') + parser.add_argument('--calibrator', type=str, choices=["max", "histogram"], default="max") + parser.add_argument('--percentile', nargs='+', type=float, default=[99.9, 99.99, 99.999, 99.9999]) + parser.add_argument('--sensitivity', action="store_true", help="Build sensitivity profile") + parser.add_argument('--evaluate-onnx', action="store_true", help="Evaluate exported ONNX") + + return parser + +def prepare_model( + model_name, + data_dir, + per_channel_quantization, + batch_size_train, + batch_size_test, + batch_size_onnx, + calibrator, + pretrained=True, + ckpt_path=None, + ckpt_url=None): + """ + Prepare the model for the classification flow. + Arguments: + model_name: name to use when accessing torchvision model dictionary + data_dir: directory with train and val subdirs prepared "imagenet style" + per_channel_quantization: iff true use per channel quantization for weights + note that this isn't currently supported in ONNX-RT/Pytorch + batch_size_train: batch size to use when training + batch_size_test: batch size to use when testing in Pytorch + batch_size_onnx: batch size to use when testing with ONNX-RT + calibrator: calibration type to use (max/histogram) + + pretrained: if true a pretrained model will be loaded from torchvision + ckpt_path: path to load a model checkpoint from, if not pretrained + ckpt_url: url to download a model checkpoint from, if not pretrained and no path was given + * at least one of {pretrained, path, url} must be valid + + The method returns a the following list: + [ + Model object, + data loader for training, + data loader for Pytorch testing, + data loader for onnx testing + ] + """ + # Use 'spawn' to avoid CUDA reinitialization with forked subprocess + torch.multiprocessing.set_start_method('spawn') + + ## Initialize quantization, model and data loaders + if per_channel_quantization: + quant_desc_input = QuantDescriptor(calib_method=calibrator) + quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input) + else: + ## Force per tensor quantization for onnx runtime + quant_desc_input = QuantDescriptor(calib_method=calibrator, axis=None) + quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantConvTranspose2d.set_default_quant_desc_input(quant_desc_input) + quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input) + + quant_desc_weight = QuantDescriptor(calib_method=calibrator, axis=None) + quant_nn.QuantConv2d.set_default_quant_desc_weight(quant_desc_weight) + quant_nn.QuantConvTranspose2d.set_default_quant_desc_weight(quant_desc_weight) + quant_nn.QuantLinear.set_default_quant_desc_weight(quant_desc_weight) + + quant_modules.initialize() + + model = torchvision.models.__dict__[model_name](pretrained=pretrained) + if not pretrained: + if ckpt_path: + checkpoint = torch.load(ckpt_path) + else: + checkpoint = load_state_dict_from_url(ckpt_url) + if 'state_dict' in checkpoint.keys(): + checkpoint = checkpoint['state_dict'] + elif 'model' in checkpoint.keys(): + checkpoint = checkpoint['model'] + model.load_state_dict(checkpoint) + model.eval() + model.cuda() + + ## Prepare the data loaders + traindir = os.path.join(data_dir, 'train') + valdir = os.path.join(data_dir, 'val') + dataset, dataset_test, train_sampler, test_sampler = load_data(traindir, valdir, False, False) + + data_loader_train = torch.utils.data.DataLoader( + dataset, batch_size=batch_size_train, + sampler=train_sampler, num_workers=16, pin_memory=True) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, batch_size=batch_size_test, + sampler=test_sampler, num_workers=4, pin_memory=True) + + data_loader_onnx = torch.utils.data.DataLoader( + dataset_test, batch_size=batch_size_onnx, + sampler=test_sampler, num_workers=4, pin_memory=True) + + return model, data_loader_train, data_loader_test, data_loader_onnx + +def main(cmdline_args): + parser = get_parser() + args = parser.parse_args(cmdline_args) + print(parser.description) + print(args) + + torch.manual_seed(args.seed) + np.random.seed(args.seed) + + ## Prepare the pretrained model and data loaders + model, data_loader_train, data_loader_test, data_loader_onnx = prepare_model( + args.model_name, + args.data_dir, + not args.disable_pcq, + args.batch_size_train, + args.batch_size_test, + args.batch_size_onnx, + args.calibrator, + args.pretrained, + args.ckpt_path, + args.ckpt_url) + + ## Initial accuracy evaluation + criterion = nn.CrossEntropyLoss() + with torch.no_grad(): + print('Initial evaluation:') + top1_initial = evaluate(model, criterion, data_loader_test, device="cuda", print_freq=args.print_freq) + + ## Calibrate the model + with torch.no_grad(): + calibrate_model( + model=model, + model_name=args.model_name, + data_loader=data_loader_train, + num_calib_batch=args.num_calib_batch, + calibrator=args.calibrator, + hist_percentile=args.percentile, + out_dir=args.out_dir) + + ## Evaluate after calibration + if args.num_calib_batch > 0: + with torch.no_grad(): + print('Calibration evaluation:') + top1_calibrated = evaluate(model, criterion, data_loader_test, device="cuda", print_freq=args.print_freq) + else: + top1_calibrated = -1.0 + + ## Build sensitivy profile + if args.sensitivity: + build_sensitivity_profile(model, criterion, data_loader_test) + + ## Finetune the model + for epoch in range(args.num_finetune_epochs): + finetune_model(model, data_loader_train) + + if args.num_finetune_epochs > 0: + ## Evaluate after finetuning + with torch.no_grad(): + print('Finetune evaluation:') + top1_finetuned = evaluate(model, criterion, data_loader_test, device="cuda") + else: + top1_finetuned = -1.0 + + ## Export to ONNX + onnx_filename = args.out_dir + '/' + args.model_name + ".onnx" + top1_onnx = -1.0 + if export_onnx(model, onnx_filename, args.batch_size_onnx, not args.disable_pcq) and args.evaluate_onnx: + ## Validate ONNX and evaluate + top1_onnx = evaluate_onnx(onnx_filename, data_loader_onnx, criterion, args.print_freq) + + ## Print summary + print("Accuracy summary:") + table = PrettyTable(['Stage','Top1']) + table.align['Stage'] = "l" + table.add_row( [ 'Initial', "{:.2f}".format(top1_initial) ] ) + table.add_row( [ 'Calibrated', "{:.2f}".format(top1_calibrated) ] ) + table.add_row( [ 'Finetuned', "{:.2f}".format(top1_finetuned) ] ) + table.add_row( [ 'ONNX', "{:.2f}".format(top1_onnx) ] ) + print(table) + + ## Compare results + if args.threshold >= 0.0: + if args.evaluate_onnx and top1_onnx < 0.0: + print("Failed to export/evaluate ONNX!") + return 1 + if args.num_finetune_epochs > 0: + if top1_finetuned >= (top1_onnx - args.threshold): + print("Accuracy threshold was met!") + else: + print("Accuracy threshold was missed!") + return 1 + + return 0 + +def evaluate_onnx(onnx_filename, data_loader, criterion, print_freq): + """Evaluate accuracy on the given ONNX file using the provided data loader and criterion. + The method returns the average top-1 accuracy on the given dataset. + """ + print("Loading ONNX file: ", onnx_filename) + ort_session = onnxruntime.InferenceSession(onnx_filename) + with torch.no_grad(): + metric_logger = utils.MetricLogger(delimiter=" ") + header = 'Test:' + with torch.no_grad(): + for image, target in metric_logger.log_every(data_loader, print_freq, header): + image = image.to("cpu", non_blocking=True) + image_data = np.array(image) + input_data = image_data + + # run the data through onnx runtime instead of torch model + input_name = ort_session.get_inputs()[0].name + raw_result = ort_session.run([], {input_name: input_data}) + output = torch.tensor((raw_result[0])) + + loss = criterion(output, target) + acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) + batch_size = image.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + # gather the stats from all processes + metric_logger.synchronize_between_processes() + + print(' ONNXRuntime: Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5)) + return metric_logger.acc1.global_avg + +def export_onnx(model, onnx_filename, batch_onnx, per_channel_quantization): + model.eval() + quant_nn.TensorQuantizer.use_fb_fake_quant = True # We have to shift to pytorch's fake quant ops before exporting the model to ONNX + + if per_channel_quantization: + # Before opset13 is supported by Pytorch and ONNX runtime, we'll have to use the work around that + # pretends opset12 supports per channel. + # Use scripts/patch_onnx_export.sh to patch pytorch. + opset_version = 12 + else: + opset_version = 12 + + # Export ONNX for multiple batch sizes + print("Creating ONNX file: " + onnx_filename) + dummy_input = torch.randn(batch_onnx, 3, 224, 224, device='cuda') #TODO: switch input dims by model + try: + torch.onnx.export(model, dummy_input, onnx_filename, verbose=False, opset_version=opset_version, enable_onnx_checker=False, do_constant_folding=True) + except ValueError: + warnings.warn(UserWarning("Per-channel quantization is not yet supported in Pytorch/ONNX RT (requires ONNX opset 13)")) + print("Failed to export to ONNX") + return False + + return True + +def calibrate_model(model, model_name, data_loader, num_calib_batch, calibrator, hist_percentile, out_dir): + """ + Feed data to the network and calibrate. + Arguments: + model: classification model + model_name: name to use when creating state files + data_loader: calibration data set + num_calib_batch: amount of calibration passes to perform + calibrator: type of calibration to use (max/histogram) + hist_percentile: percentiles to be used for historgram calibration + out_dir: dir to save state files in + """ + + if num_calib_batch > 0: + print("Calibrating model") + with torch.no_grad(): + collect_stats(model, data_loader, num_calib_batch) + + if not calibrator == "histogram": + compute_amax(model, method="max") + calib_output = os.path.join( + out_dir, + F"{model_name}-max-{num_calib_batch*data_loader.batch_size}.pth") + torch.save(model.state_dict(), calib_output) + else: + for percentile in hist_percentile: + print(F"{percentile} percentile calibration") + compute_amax(model, method="percentile") + calib_output = os.path.join( + out_dir, + F"{model_name}-percentile-{percentile}-{num_calib_batch*data_loader.batch_size}.pth") + torch.save(model.state_dict(), calib_output) + + for method in ["mse", "entropy"]: + print(F"{method} calibration") + compute_amax(model, method=method) + calib_output = os.path.join( + out_dir, + F"{model_name}-{method}-{num_calib_batch*data_loader.batch_size}.pth") + torch.save(model.state_dict(), calib_output) + +def collect_stats(model, data_loader, num_batches): + """Feed data to the network and collect statistics""" + # Enable calibrators + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + module.disable_quant() + module.enable_calib() + else: + module.disable() + + # Feed data to the network for collecting stats + for i, (image, _) in tqdm(enumerate(data_loader), total=num_batches): + model(image.cuda()) + if i >= num_batches: + break + + # Disable calibrators + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + module.enable_quant() + module.disable_calib() + else: + module.enable() + +def compute_amax(model, **kwargs): + # Load calib result + for name, module in model.named_modules(): + if isinstance(module, quant_nn.TensorQuantizer): + if module._calibrator is not None: + if isinstance(module._calibrator, calib.MaxCalibrator): + module.load_calib_amax() + else: + module.load_calib_amax(**kwargs) + print(F"{name:40}: {module}") + model.cuda() + +def finetune_model(model, data_loader): + """Finetune the model""" + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.SGD(model.parameters(), lr=0.0001) + lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) + + # Training a single epch + train_one_epoch(model, criterion, optimizer, data_loader, "cuda", 0, 100) + +def build_sensitivity_profile(model, criterion, data_loader_test): + quant_layer_names = [] + for name, module in model.named_modules(): + if name.endswith("_quantizer"): + module.disable() + layer_name = name.replace("._input_quantizer", "").replace("._weight_quantizer", "") + if layer_name not in quant_layer_names: + quant_layer_names.append(layer_name) + for i, quant_layer in enumerate(quant_layer_names): + print("Enable", quant_layer) + for name, module in model.named_modules(): + if name.endswith("_quantizer") and quant_layer in name: + module.enable() + print(F"{name:40}: {module}") + with torch.no_grad(): + evaluate(model, criterion, data_loader_test, device="cuda") + for name, module in model.named_modules(): + if name.endswith("_quantizer") and quant_layer in name: + module.disable() + print(F"{name:40}: {module}") + +if __name__ == '__main__': + res = main(sys.argv[1:]) + exit(res) diff --git a/tools/pytorch-quantization/pytorch_quantization/__init__.py b/tools/pytorch-quantization/pytorch_quantization/__init__.py new file mode 100644 index 00000000..0562898c --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/__init__.py @@ -0,0 +1,19 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from absl import logging +logging.use_absl_handler() diff --git a/tools/pytorch-quantization/pytorch_quantization/calib/__init__.py b/tools/pytorch-quantization/pytorch_quantization/calib/__init__.py new file mode 100644 index 00000000..787ebaaf --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/calib/__init__.py @@ -0,0 +1,23 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""``pytorch_quantization.calib`` provides Calibrator classes that +collect data statistics and determine pytorch_quantization parameters. +""" + +from .max import MaxCalibrator +from .histogram import HistogramCalibrator diff --git a/tools/pytorch-quantization/pytorch_quantization/calib/calibrator.py b/tools/pytorch-quantization/pytorch_quantization/calib/calibrator.py new file mode 100644 index 00000000..5cc7049a --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/calib/calibrator.py @@ -0,0 +1,60 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Abstract base class for calibrators""" + + +class _Calibrator(): + """Abstract base class of calibrators + Args: + num_bits: An integer. Number of bits of quantization. + axis: A tuple. see QuantDescriptor. + unsigned: A boolean. using unsigned quantization. + + Readonly Properties: + axis: + """ + def __init__(self, num_bits, axis, unsigned): + self._num_bits = num_bits + self._axis = axis + self._unsigned = unsigned + + def collect(self, x): + """Abstract method: collect tensor statistics used to compute amax + + Args: + x: A tensor + """ + raise NotImplementedError + + def reset(self): + """Abstract method: reset calibrator to initial state""" + raise NotImplementedError + + def compute_amax(self, *args, **kwargs): + """Abstract method: compute the amax from the collected data + + Returns: + amax: a tensor + """ + raise NotImplementedError + + def __repr__(self): + s = "num_bits={_num_bits}" + s += " axis={_axis}" + s += " unsigned={_unsigned}" + return s.format(**self.__dict__) diff --git a/tools/pytorch-quantization/pytorch_quantization/calib/histogram.py b/tools/pytorch-quantization/pytorch_quantization/calib/histogram.py new file mode 100644 index 00000000..8ee198eb --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/calib/histogram.py @@ -0,0 +1,257 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Histogram based calibrators""" +from collections import Counter +import numpy as np +from scipy.stats import entropy + +from absl import logging + +import torch + +from pytorch_quantization.calib.calibrator import _Calibrator +from pytorch_quantization.tensor_quant import fake_tensor_quant + +class HistogramCalibrator(_Calibrator): + """Unified histogram calibrator + + Histogram will be only collected once. compute_amax() performs entropy, percentile, or mse + calibration based on arguments + + Args: + num_bits: An integer. Number of bits of quantization. + axis: A tuple. see QuantDescriptor. + unsigned: A boolean. using unsigned quantization. + num_bins: An integer. Number of histograms bins. Default 2048. + grow_method: A string. DEPRECATED. default None. + skip_zeros: A boolean. If True, skips zeros when collecting data for histogram. Default False. + """ + def __init__(self, num_bits, axis, unsigned, num_bins=2048, grow_method=None, skip_zeros=False): + super(HistogramCalibrator, self).__init__(num_bits, axis, unsigned) + self._num_bins = num_bins + self._skip_zeros = skip_zeros + + self._calib_bin_edges = None + self._calib_hist = None + + if axis is not None: + raise NotImplementedError("Calibrator histogram collection only supports per tensor scaling") + + if grow_method is not None: + logging.warning("grow_method is deprecated. Got %s, ingored!", grow_method) + + def collect(self, x): + """Collect histogram""" + if torch.min(x) < 0.: + logging.log_first_n( + logging.INFO, + ("Calibrator encountered negative values. It shouldn't happen after ReLU. " + "Make sure this is the right tensor to calibrate."), + 1) + x = x.abs() + x_np = x.cpu().detach().numpy() + + if self._skip_zeros: + x_np = x_np[np.where(x_np != 0)] + + if self._calib_bin_edges is None and self._calib_hist is None: + # first time it uses num_bins to compute histogram. + self._calib_hist, self._calib_bin_edges = np.histogram(x_np, bins=self._num_bins) + else: + temp_amax = np.max(x_np) + if temp_amax > self._calib_bin_edges[-1]: + # increase the number of bins + width = self._calib_bin_edges[1] - self._calib_bin_edges[0] + # NOTE: np.arange may create an extra bin after the one containing temp_amax + new_bin_edges = np.arange(self._calib_bin_edges[-1] + width, temp_amax + width, width) + self._calib_bin_edges = np.hstack((self._calib_bin_edges, new_bin_edges)) + hist, self._calib_bin_edges = np.histogram(x_np, bins=self._calib_bin_edges) + hist[:len(self._calib_hist)] += self._calib_hist + self._calib_hist = hist + + def reset(self): + """Reset the collected histogram""" + self._calib_bin_edges = None + self._calib_hist = None + + def _compute_amax_entropy(self, stride, start_bin): + """Returns amax that minimizes KL-Divergence of the collected histogram""" + + # If calibrator hasn't collected any data, return none + if self._calib_bin_edges is None and self._calib_hist is None: + return None + + def _normalize_distr(distr): + summ = np.sum(distr) + if summ != 0: + distr = distr / summ + + bins = self._calib_hist[:] + bins[0] = bins[1] + + total_data = np.sum(bins) + + divergences = [] + arguments = [] + + # we are quantizing to 128 values + sign if num_bits=8 + nbins = 1 << (self._num_bits - 1 + int(self._unsigned)) + + starting = start_bin + stop = len(bins) + + new_density_counts = np.zeros(nbins, dtype=np.float64) + + for i in range(starting, stop + 1, stride): + new_density_counts.fill(0) + space = np.linspace(0, i, num=nbins + 1) + digitized_space = np.digitize(range(i), space) - 1 + + digitized_space[bins[:i] == 0] = -1 + + for idx, digitized in enumerate(digitized_space): + if digitized != -1: + new_density_counts[digitized] += bins[idx] + + counter = Counter(digitized_space) + for key, val in counter.items(): + if key != -1: + new_density_counts[key] = new_density_counts[key] / val + + new_density = np.zeros(i, dtype=np.float64) + for idx, digitized in enumerate(digitized_space): + if digitized != -1: + new_density[idx] = new_density_counts[digitized] + + total_counts_new = np.sum(new_density) + np.sum(bins[i:]) + _normalize_distr(new_density) + + reference_density = np.array(bins[:len(digitized_space)]) + reference_density[-1] += np.sum(bins[i:]) + + total_counts_old = np.sum(reference_density) + if round(total_counts_new) != total_data or round(total_counts_old) != total_data: + raise RuntimeError("Count mismatch! total_counts_new={}, total_counts_old={}, total_data={}".format( + total_counts_new, total_counts_old, total_data)) + + _normalize_distr(reference_density) + + ent = entropy(reference_density, new_density) + divergences.append(ent) + arguments.append(i) + + divergences = np.array(divergences) + logging.debug("divergences={}".format(divergences)) + last_argmin = len(divergences) - 1 - np.argmin(divergences[::-1]) + calib_amax = self._calib_bin_edges[last_argmin * stride + starting] + calib_amax = torch.tensor(calib_amax.item()) #pylint: disable=not-callable + + return calib_amax + + def _compute_amax_mse(self, stride, start_bin): + """Returns amax that minimizes MSE of the collected histogram""" + + # If calibrator hasn't collected any data, return none + if self._calib_bin_edges is None and self._calib_hist is None: + return None + + counts = torch.from_numpy(self._calib_hist[:]).float() + edges = torch.from_numpy(self._calib_bin_edges[:]).float() + centers = (edges[1:] + edges[:-1])/2 + + mses = [] + arguments = [] + + for i in range(start_bin, len(centers), stride): + + amax = centers[i] + quant_centers = fake_tensor_quant(centers, amax, self._num_bits, self._unsigned) + + mse = ((quant_centers - centers)**2 * counts).mean() + + mses.append(mse) + arguments.append(i) + + logging.debug("mses={}".format(mses)) + argmin = np.argmin(mses) + calib_amax = centers[arguments[argmin]] + + return calib_amax + + def _compute_amax_percentile(self, percentile): + """Returns amax that clips the percentile fraction of collected data""" + + if percentile < 0 or percentile > 100: + raise ValueError("Invalid percentile. Must be in range 0 <= percentile <= 100.") + + # If calibrator hasn't collected any data, return none + if self._calib_bin_edges is None and self._calib_hist is None: + return None + + total = self._calib_hist.sum() + cdf = np.cumsum(self._calib_hist / total) + idx = np.searchsorted(cdf, percentile/100) + calib_amax = self._calib_bin_edges[idx] + calib_amax = torch.tensor(calib_amax.item()) #pylint: disable=not-callable + + return calib_amax + + def compute_amax( + self, method: str, *, stride: int = 1, start_bin: int = 128, percentile: float = 99.99): + """Compute the amax from the collected histogram + + Args: + method: A string. One of ['entropy', 'mse', 'percentile'] + + Keyword Arguments: + stride: An integer. Default 1 + start_bin: An integer. Default 128 + percentils: A float number between [0, 100]. Default 99.99. + + Returns: + amax: a tensor + """ + if method == 'entropy': + calib_amax = self._compute_amax_entropy(stride, start_bin) + elif method == 'mse': + calib_amax = self._compute_amax_mse(stride, start_bin) + elif method == 'percentile': + calib_amax = self._compute_amax_percentile(percentile) + else: + raise TypeError("Unknown calibration method {}".format(method)) + + return calib_amax + + # pylint:disable=missing-docstring + def __str__(self): + s = "HistogramCalibrator(" + if self._calib_bin_edges is None: + bin_edge_str = "None" + else: + bin_edge_str = "[{:.3f}, ..., {:.3f}]({})".format( + self._calib_bin_edges[0], self._calib_bin_edges[-1], len(self._calib_bin_edges)) + s += "calib_bin_edges={})".format(bin_edge_str) + return s + + def __repr__(self): + s = "HistogramCalibrator(" + s += super(HistogramCalibrator, self).__repr__() + s += " calib_bin_edges={_calib_bin_edges}" + s += " calib_hist={_calib_hist})" + return s.format(**self.__dict__) + # pylint:enable=missing-docstring diff --git a/tools/pytorch-quantization/pytorch_quantization/calib/max.py b/tools/pytorch-quantization/pytorch_quantization/calib/max.py new file mode 100644 index 00000000..c135a73c --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/calib/max.py @@ -0,0 +1,108 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Calibrator that returns the absolute max of all collected tensors""" +from absl import logging +import torch + +from pytorch_quantization.calib.calibrator import _Calibrator +from pytorch_quantization import utils as quant_utils + +class MaxCalibrator(_Calibrator): + """Max calibrator, tracks the maximum value globally + + Args: + calib_desc: A MaxCalibDescriptor. + num_bits: An integer. Number of bits of quantization. + axis: A tuple. see QuantDescriptor. + unsigned: A boolean. using unsigned quantization. + + Readonly Properties: + amaxs: A list of amax. Numpy array is saved as it is likely to be used for some plot. + """ + def __init__(self, num_bits, axis, unsigned, track_amax=False): + super(MaxCalibrator, self).__init__(num_bits, axis, unsigned) + self._track_amax = track_amax + if self._track_amax: + self._amaxs = [] # shall we have a better name? + self._calib_amax = None + + # pylint:disable=missing-docstring + @property + def amaxs(self): + return self._amaxs + # pylint:enable=missing-docstring + + def collect(self, x): + """Tracks the absolute max of all tensors + + Args: + x: A tensor + + Raises: + RuntimeError: If amax shape changes + """ + if torch.min(x) < 0.: + logging.log_first_n( + logging.INFO, + ("Calibrator encountered negative values. It shouldn't happen after ReLU. " + "Make sure this is the right tensor to calibrate."), + 1) + x = x.abs() + + # Swap axis to reduce. + axis = self._axis if isinstance(self._axis, (list, tuple)) else [self._axis] + reduce_axis = [] + for i in range(x.dim()): + if not i in axis: + reduce_axis.append(i) + local_amax = quant_utils.reduce_amax(x, axis=reduce_axis).detach() + if self._calib_amax is None: + self._calib_amax = local_amax + else: + if local_amax.shape != self._calib_amax.shape: + raise RuntimeError("amax shape changed!") + self._calib_amax.copy_(torch.max(self._calib_amax, local_amax).data) + + if self._track_amax: + self._amaxs.append(local_amax.cpu().numpy()) + + def reset(self): + """Reset the collected absolute max""" + self._calib_amax = None + + def compute_amax(self): + """Return the absolute max of all tensors collected""" + return self._calib_amax + + # pylint:disable=missing-docstring + def __str__(self): + s = "MaxCalibrator(" + s += "track_amax={_track_amax}" + s += ")" + return s.format(**self.__dict__) + + def __repr__(self): + s = "MaxCalibrator(" + s += super(MaxCalibrator, self).__repr__() + s += " calib_amax={_calib_amax}" + s += " track_amax={_track_amax}" + if self._track_amax: + s += " amaxs={_amaxs}" + s += ")" + return s.format(**self.__dict__) + # pylint:enable=missing-docstring diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/__init__.py b/tools/pytorch-quantization/pytorch_quantization/nn/__init__.py new file mode 100644 index 00000000..e1935d5b --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/__init__.py @@ -0,0 +1,25 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from pytorch_quantization.nn.modules.tensor_quantizer import * +from pytorch_quantization.nn.modules.quant_conv import * +from pytorch_quantization.nn.modules.quant_linear import * +from pytorch_quantization.nn.modules.quant_pooling import * +from pytorch_quantization.nn.modules.clip import * +from pytorch_quantization.nn.modules.quant_rnn import * +from pytorch_quantization.nn.modules.quant_bert import * +from pytorch_quantization.nn.modules.quant_instancenorm import * diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/_functions/__init__.py b/tools/pytorch-quantization/pytorch_quantization/nn/_functions/__init__.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/_functions/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/_functions/quant_rnn.py b/tools/pytorch-quantization/pytorch_quantization/nn/_functions/quant_rnn.py new file mode 100644 index 00000000..92050f37 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/_functions/quant_rnn.py @@ -0,0 +1,285 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""RNN implementation in python +Originally copied from https://github.com/pytorch/pytorch/blob/v0.4.1/torch/nn/_functions/rnn.py +with following modification + fusedBackend is removed + CudnnRNN is removed + Hack for ONNX in RNN() is removed +Only LSTM is quantized. Other paths are excluded in __all__ +""" + +import warnings +from torch.autograd import NestedIOFunction +from torch.nn import functional as F +import torch +import itertools +from functools import partial + +__all__ = ["LSTMCell", "RNN"] + +def RNNReLUCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None): + hy = F.relu(F.linear(input, w_ih, b_ih) + F.linear(hidden, w_hh, b_hh)) + return hy + + +def RNNTanhCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None): + hy = torch.tanh(F.linear(input, w_ih, b_ih) + F.linear(hidden, w_hh, b_hh)) + return hy + + +def LSTMCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None, input_quantizer=None, weight_quantizer=None): + """Quantized LSTM Cell + + The assumption is at inference time, only one fused gemm will be launched for one time step Weights of 4 gates + are fused together, and activation from layer and recurrent paths are fused togather. ``input_quantizer`` will be + applied on the fused activation tensor. And ``weight_quantizer`` will be applied on the fused weight tensor. + """ + + hx, cx = hidden + if input_quantizer is not None: + input, hx = input_quantizer(torch.cat([input, hx], 1)).split([input.size()[1], hx.size()[1]], 1) + if weight_quantizer is not None: + w_ih, w_hh = weight_quantizer(torch.cat([w_ih, w_hh], 1)).split([w_ih.size()[1], w_hh.size()[1]], 1) + gates = F.linear(input, w_ih, b_ih) + F.linear(hx, w_hh, b_hh) + + ingate, forgetgate, cellgate, outgate = gates.chunk(4, 1) + + ingate = torch.sigmoid(ingate) + forgetgate = torch.sigmoid(forgetgate) + cellgate = torch.tanh(cellgate) + outgate = torch.sigmoid(outgate) + + cy = (forgetgate * cx) + (ingate * cellgate) + hy = outgate * torch.tanh(cy) + + return hy, cy + + +def GRUCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None): + gi = F.linear(input, w_ih, b_ih) + gh = F.linear(hidden, w_hh, b_hh) + i_r, i_i, i_n = gi.chunk(3, 1) + h_r, h_i, h_n = gh.chunk(3, 1) + + resetgate = torch.sigmoid(i_r + h_r) + inputgate = torch.sigmoid(i_i + h_i) + newgate = torch.tanh(i_n + resetgate * h_n) + hy = newgate + inputgate * (hidden - newgate) + + return hy + + +def StackedRNN(inners, num_layers, lstm=False, dropout=0, train=True): + + num_directions = len(inners) + total_layers = num_layers * num_directions + + def forward(input, hidden, weight, batch_sizes, input_quantizers, weight_quantizers): + assert(len(weight) == total_layers) + next_hidden = [] + + if lstm: + hidden = list(zip(*hidden)) + + for i in range(num_layers): + all_output = [] + for j, inner in enumerate(inners): + l = i * num_directions + j + + hy, output = inner(input, hidden[l], weight[l], batch_sizes, + input_quantizer=input_quantizers[l], weight_quantizer=weight_quantizers[l]) + next_hidden.append(hy) + all_output.append(output) + + input = torch.cat(all_output, input.dim() - 1) + + if dropout != 0 and i < num_layers - 1: + input = F.dropout(input, p=dropout, training=train, inplace=False) + + if lstm: + next_h, next_c = zip(*next_hidden) + next_hidden = ( + torch.cat(next_h, 0).view(total_layers, *next_h[0].size()), + torch.cat(next_c, 0).view(total_layers, *next_c[0].size()) + ) + else: + next_hidden = torch.cat(next_hidden, 0).view( + total_layers, *next_hidden[0].size()) + + return next_hidden, input + + return forward + + +def Recurrent(inner, reverse=False): + def forward(input, hidden, weight, batch_sizes, input_quantizer, weight_quantizer): + output = [] + steps = range(input.size(0) - 1, -1, -1) if reverse else range(input.size(0)) + for i in steps: + hidden = inner(input[i], hidden, *weight, + input_quantizer=input_quantizer, weight_quantizer=weight_quantizer) + # hack to handle LSTM + output.append(hidden[0] if isinstance(hidden, tuple) else hidden) + + if reverse: + output.reverse() + output = torch.cat(output, 0).view(input.size(0), *output[0].size()) + + return hidden, output + + return forward + + +def variable_recurrent_factory(inner, reverse=False): + if reverse: + return VariableRecurrentReverse(inner) + else: + return VariableRecurrent(inner) + + +def VariableRecurrent(inner): + def forward(input, hidden, weight, batch_sizes, input_quantizer, weight_quantizer): + + output = [] + input_offset = 0 + last_batch_size = batch_sizes[0] + hiddens = [] + flat_hidden = not isinstance(hidden, tuple) + if flat_hidden: + hidden = (hidden,) + for batch_size in batch_sizes: + step_input = input[input_offset:input_offset + batch_size] + input_offset += batch_size + + dec = last_batch_size - batch_size + if dec > 0: + hiddens.append(tuple(h[-dec:] for h in hidden)) + hidden = tuple(h[:-dec] for h in hidden) + last_batch_size = batch_size + + if flat_hidden: + hidden = (inner(step_input, hidden[0], *weight, + input_quantizer=input_quantizer, weight_quantizer=weight_quantizer),) + else: + hidden = inner(step_input, hidden, *weight, + input_quantizer=input_quantizer, weight_quantizer=weight_quantizer) + + output.append(hidden[0]) + hiddens.append(hidden) + hiddens.reverse() + + hidden = tuple(torch.cat(h, 0) for h in zip(*hiddens)) + assert hidden[0].size(0) == batch_sizes[0] + if flat_hidden: + hidden = hidden[0] + output = torch.cat(output, 0) + + return hidden, output + + return forward + + +def VariableRecurrentReverse(inner): + def forward(input, hidden, weight, batch_sizes, input_quantizer, weight_quantizer): + output = [] + input_offset = input.size(0) + last_batch_size = batch_sizes[-1] + initial_hidden = hidden + flat_hidden = not isinstance(hidden, tuple) + if flat_hidden: + hidden = (hidden,) + initial_hidden = (initial_hidden,) + hidden = tuple(h[:batch_sizes[-1]] for h in hidden) + for i in reversed(range(len(batch_sizes))): + batch_size = batch_sizes[i] + inc = batch_size - last_batch_size + if inc > 0: + hidden = tuple(torch.cat((h, ih[last_batch_size:batch_size]), 0) + for h, ih in zip(hidden, initial_hidden)) + last_batch_size = batch_size + step_input = input[input_offset - batch_size:input_offset] + input_offset -= batch_size + + if flat_hidden: + hidden = (inner(step_input, hidden[0], *weight, + input_quantizer=input_quantizer, weight_quantizer=weight_quantizer),) + else: + hidden = inner(step_input, hidden, *weight, + input_quantizer=input_quantizer, weight_quantizer=weight_quantizer) + output.append(hidden[0]) + + output.reverse() + output = torch.cat(output, 0) + if flat_hidden: + hidden = hidden[0] + return hidden, output + + return forward + + +def AutogradRNN(mode, input_size, hidden_size, num_layers=1, batch_first=False, + dropout=0, train=True, bidirectional=False, variable_length=False, + dropout_state=None, flat_weight=None, + input_quantizers=None, weight_quantizers=None): + + if mode == 'RNN_RELU': + cell = RNNReLUCell + elif mode == 'RNN_TANH': + cell = RNNTanhCell + elif mode == 'LSTM': + cell = LSTMCell + elif mode == 'GRU': + cell = GRUCell + else: + raise Exception('Unknown mode: {}'.format(mode)) + + rec_factory = variable_recurrent_factory if variable_length else Recurrent + + if bidirectional: + layer = (rec_factory(cell), rec_factory(cell, reverse=True)) + else: + layer = (rec_factory(cell),) + + func = StackedRNN(layer, + num_layers, + (mode == 'LSTM'), + dropout=dropout, + train=train) + + def forward(input, weight, hidden, batch_sizes, input_quantizers, weight_quantizers): + if batch_first and not variable_length: + input = input.transpose(0, 1) + + nexth, output = func(input, hidden, weight, batch_sizes, input_quantizers, weight_quantizers) + + if batch_first and not variable_length: + output = output.transpose(0, 1) + + return output, nexth + + return forward + + +def RNN(*args, **kwargs): + + def forward(input, *fargs, **fkwargs): + func = AutogradRNN(*args, **kwargs) + return func(input, *fargs, **fkwargs) + + return forward diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/functional.py b/tools/pytorch-quantization/pytorch_quantization/nn/functional.py new file mode 100644 index 00000000..76b027a3 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/functional.py @@ -0,0 +1,63 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Some supportive functions""" +from absl import logging + +import torch +from torch.autograd import Function + +from pytorch_quantization.utils import amp_wrapper as amp + + +class ClipFunction(Function): + """An universal tensor clip function + + Pytorch's clamp() only supports scalar range and doesn't support broadcast. This implementation uses min/max which + is more genaral. The gradient is defined according to IBM's PACT paper https://arxiv.org/abs/1805.06085, which is + also the behavior of Tensorflow's clip_by_value() + """ + + @staticmethod + def forward(ctx, input, clip_value_min, clip_value_max): + output = torch.min(input, clip_value_max) + output = torch.max(output, clip_value_min) + ctx.save_for_backward(input, clip_value_min, clip_value_max) + return output + + @staticmethod + def backward(ctx, grad_output): + input, clip_value_min, clip_value_max = ctx.saved_tensors + min_mask = (input > clip_value_min).to(grad_output.dtype) + max_mask = (input < clip_value_max).to(grad_output.dtype) + grad_input = grad_output * min_mask * max_mask + + if clip_value_min.requires_grad or clip_value_max.requires_grad: + logging.log_first_n(logging.WARNING, "Learning clip min/max is experimental, use at your own risk :).", 1) + if clip_value_min.numel() != 1 or clip_value_max.numel() != 1: + raise ValueError("Learnable min/max can only be scalar, got size %s and %s." % (clip_value_min.size(), + clip_value_max.size())) + + # Ensure the dtypes of min/max grads matches the input dtype + # This might be necessary if running w/ AMP which will cast to fp32 before `sum()` + grad_clip_value_min = (grad_output * (1. - min_mask)).sum().to(clip_value_min.dtype) if clip_value_min.requires_grad else None + grad_clip_value_max = (grad_output * (1. - max_mask)).sum().to(clip_value_min.dtype) if clip_value_max.requires_grad else None + + return grad_input, grad_clip_value_min, grad_clip_value_max + + +clip = amp.promote_function(ClipFunction.apply) diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/__init__.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/__init__.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/_utils.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/_utils.py new file mode 100644 index 00000000..b49a8a8d --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/_utils.py @@ -0,0 +1,164 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Some helper functions for implementing quantized modules""" +import copy +import inspect + +from absl import logging + +from torch import nn + +from pytorch_quantization.nn import TensorQuantizer +from pytorch_quantization.tensor_quant import QuantDescriptor, QUANT_DESC_8BIT_PER_TENSOR + + +class QuantMixin(): + """Mixin class for adding basic quantization logic to quantized modules""" + + default_quant_desc_input = QUANT_DESC_8BIT_PER_TENSOR + default_quant_desc_weight = QUANT_DESC_8BIT_PER_TENSOR + + @classmethod + def set_default_quant_desc_input(cls, value): + """ + Args: + value: An instance of :class:`QuantDescriptor ` + """ + if not isinstance(value, QuantDescriptor): + raise ValueError("{} is not an instance of QuantDescriptor!") + cls.default_quant_desc_input = copy.deepcopy(value) + + @classmethod + def set_default_quant_desc_weight(cls, value): + """ + Args: + value: An instance of :class:`QuantDescriptor ` + """ + if not isinstance(value, QuantDescriptor): + raise ValueError("{} is not an instance of QuantDescriptor!") + cls.default_quant_desc_weight = copy.deepcopy(value) + + def init_quantizer(self, quant_desc_input, quant_desc_weight, num_layers=None): + """Helper function for __init__ of quantized module + + Create input and weight quantizer based on quant_desc passed by kwargs, or default of the class. + + Args: + quant_desc_input: An instance of :class:`QuantDescriptor ` + quant_desc_weight: An instance of :class:`QuantDescriptor ` + num_layers: An integer. Default None. If not None, create a list of quantizers. + """ + if not inspect.stack()[1].function == "__init__": + raise TypeError("{} should be only called by __init__ of quantized module.".format(__name__)) + self._fake_quant = True + if (not quant_desc_input.fake_quant) or (not quant_desc_weight.fake_quant): + raise ValueError("Only fake quantization is supported!") + + logging.info("Input is %squantized to %d bits in %s with axis %s!", "" + if not quant_desc_input.fake_quant else "fake ", + quant_desc_input.num_bits, self.__class__.__name__, quant_desc_input.axis) + logging.info("Weight is %squantized to %d bits in %s with axis %s!", "" + if not quant_desc_weight.fake_quant else "fake ", + quant_desc_weight.num_bits, self.__class__.__name__, quant_desc_weight.axis) + + if num_layers is None: + self._input_quantizer = TensorQuantizer(quant_desc_input) + self._weight_quantizer = TensorQuantizer(quant_desc_weight) + else: + self._input_quantizers = nn.ModuleList([TensorQuantizer(quant_desc_input) for _ in range(num_layers)]) + self._weight_quantizers = nn.ModuleList([TensorQuantizer(quant_desc_weight) for _ in range(num_layers)]) + + # pylint:disable=missing-docstring + @property + def input_quantizer(self): + return self._input_quantizer + + @property + def weight_quantizer(self): + return self._weight_quantizer + # pylint:enable=missing-docstring + + +class QuantInputMixin(): + """Mixin class for adding basic quantization logic to quantized modules""" + + default_quant_desc_input = QUANT_DESC_8BIT_PER_TENSOR + + @classmethod + def set_default_quant_desc_input(cls, value): + """ + Args: + value: An instance of :class:`QuantDescriptor ` + """ + if not isinstance(value, QuantDescriptor): + raise ValueError("{} is not an instance of QuantDescriptor!") + cls.default_quant_desc_input = copy.deepcopy(value) + + def init_quantizer(self, quant_desc_input): + """Helper function for __init__ of simple quantized module + + Create input quantizer based on quant_desc passed by kwargs, or default of the class. + + Args: + quant_desc_input: An instance of :class:`QuantDescriptor ` + """ + if not inspect.stack()[1].function == "__init__": + raise TypeError("{} should be only called by __init__ of quantized module.".format(__name__)) + self._fake_quant = True + if not quant_desc_input.fake_quant: + raise ValueError("Only fake quantization is supported!") + + logging.info("Input is %squantized to %d bits in %s with axis %s!", "" + if not quant_desc_input.fake_quant else "fake ", + quant_desc_input.num_bits, self.__class__.__name__, quant_desc_input.axis) + + self._input_quantizer = TensorQuantizer(quant_desc_input) + + # pylint:disable=missing-docstring + @property + def input_quantizer(self): + return self._input_quantizer + # pylint:enable=missing-docstring + + +def pop_quant_desc_in_kwargs(quant_cls, input_only=False, **kwargs): + """Pop quant descriptors in kwargs + + If there is no descriptor in kwargs, the default one in quant_cls will be used + + Arguments: + quant_cls: A class that has default quantization descriptors + input_only: A boolean. If True, pop quant_desc_input only, not quant_desc_weight. Default false. + + Keyword Arguments: + quant_desc_input: An instance of :class:`QuantDescriptor `. + Quantization descriptor of input. + quant_desc_weight: An instance of :class:`QuantDescriptor `. + Quantization descriptor of weight. + """ + quant_desc_input = kwargs.pop('quant_desc_input', quant_cls.default_quant_desc_input) + if not input_only: + quant_desc_weight = kwargs.pop('quant_desc_weight', quant_cls.default_quant_desc_weight) + + # Check if anything is left in **kwargs + if kwargs: + raise TypeError("Unused keys: {}".format(kwargs.keys())) + + if input_only: + return quant_desc_input + return quant_desc_input, quant_desc_weight diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/clip.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/clip.py new file mode 100644 index 00000000..8955056a --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/clip.py @@ -0,0 +1,58 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Implement a clip module as pytorch only has a simple clamp function """ +import torch +from torch import nn +from torch.nn.parameter import Parameter + +from pytorch_quantization.nn import functional as QF + +__all__ = ['Clip'] + +class Clip(nn.Module): + """Clip tensor + + Args: + clip_value_min: A number or tensor of lower bound to clip + clip_value_max: A number of tensor of upper bound to clip + learn_min: A boolean. If True, learn min. clip_value_min will be used to initialize. Default False + learn_max: A boolean. Similar as learn_min but for max. + + Raises: + ValueError: + """ + + def __init__(self, clip_value_min, clip_value_max, learn_min=False, learn_max=False): + super(Clip, self).__init__() + if learn_min: + if not isinstance(clip_value_min, float) and clip_value_min.size != 1: + raise ValueError("clip_value_min/clip_value_max must be scalar for initilizing learnable range.") + self.clip_value_min = Parameter(torch.tensor(clip_value_min)) # pylint: disable=not-callable + else: + self.clip_value_min = clip_value_min + + if learn_max: + if not isinstance(clip_value_max, float) and clip_value_max.size != 1: + raise ValueError("clip_value_min/clip_value_max must be scalar for initilizing learnable range.") + self.clip_value_max = Parameter(torch.tensor(clip_value_max)) # pylint: disable=not-callable + else: + self.clip_value_max = clip_value_max + + def forward(self, inputs): + outputs = QF.clip(inputs, self.clip_value_min, self.clip_value_max) + return outputs diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_bert.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_bert.py new file mode 100644 index 00000000..b390824d --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_bert.py @@ -0,0 +1,139 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Quantized BERT Self Attention + +This method has been adapted from the transformers repo: +https://github.com/huggingface/transformers/tree/v2.9.1 +""" + + +import math + +import torch +from torch import nn + +from pytorch_quantization import nn as quant_nn +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer + +__all__ = ["QuantBertSelfAttention"] + +class QuantBertSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + self.output_attentions = config.output_attentions + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + # Quantized implementations of torch.nn.Linear modules + self.query = quant_nn.QuantLinear(config.hidden_size, self.all_head_size) + self.key = quant_nn.QuantLinear(config.hidden_size, self.all_head_size) + self.value = quant_nn.QuantLinear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + # Additional quantizers that will be needed to quantize the inputs to the torch.matmul() operation in the + # forward method. Since it's a simple operation and no quantized version of it exists, the inputs to this + # operation could be manually quantized to realize a quantized mat-mul operation. + self.matmul_q_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input) + self.matmul_k_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input) + self.matmul_v_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input) + self.matmul_a_input_quantizer = TensorQuantizer(quant_nn.QuantLinear.default_quant_desc_input) + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + if encoder_hidden_states is not None: + mixed_key_layer = self.key(encoder_hidden_states) + mixed_value_layer = self.value(encoder_hidden_states) + attention_mask = encoder_attention_mask + else: + mixed_key_layer = self.key(hidden_states) + mixed_value_layer = self.value(hidden_states) + + query_layer = self.transpose_for_scores(mixed_query_layer) + key_layer = self.transpose_for_scores(mixed_key_layer) + value_layer = self.transpose_for_scores(mixed_value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # Quantized matrix multiplication. Achieved by quantizing the inputs to torch.matmul(). + attention_scores = torch.matmul( + self.matmul_q_input_quantizer(query_layer), + self.matmul_k_input_quantizer(key_layer.transpose(-1, -2))) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + # Quantized matrix multiplication. Achieved by quantizing the inputs to torch.matmul(). + context_layer = torch.matmul( + self.matmul_a_input_quantizer(attention_probs), + self.matmul_v_input_quantizer(value_layer)) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,) + return outputs diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_conv.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_conv.py new file mode 100644 index 00000000..c9fa93ce --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_conv.py @@ -0,0 +1,398 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Quantized convolution +Base code is from nn.Conv, details of Module and original argument can be found there. +Module names are intentionally kept same as unquantized version so that they can be dropped into preexisting model +easily, and load pretrained weight. Aliases with Quant prefix are defined and are encouraged to be used explicitly +when start scratch. +""" + +import torch +import torch.nn +import torch.nn.functional as F +from torch.nn.modules.utils import _single, _pair, _triple +from torch.nn.modules.conv import _ConvTransposeNd + +from pytorch_quantization import tensor_quant + +from . import _utils + +__all__ = [ + "Conv2d", "QuantConv2d", "Conv3d", "QuantConv3d", "Conv1d", "QuantConv1d", "ConvTranspose1d", "ConvTranspose2d", + "ConvTranspose3d", "QuantConvTranspose1d", "QuantConvTranspose2d", "QuantConvTranspose3d" +] + + +class _QuantConvNd(torch.nn.modules.conv._ConvNd, _utils.QuantMixin): + """base class of quantized Conv inherited from _ConvNd + + Comments of original arguments can be found in torch.nn.modules.conv + + Arguments: + quant_desc_input: An instance of :class:`QuantDescriptor `. + Quantization descriptor of input. + quant_desc_weight: An instance of :class:`QuantDescriptor `. + Quantization descriptor of weight. + + Raises: + ValueError: If unsupported arguments are passed in. + + Readonly properties: + - input_quantizer: + - weight_quantizer: + + Static methods: + - set_default_quant_desc_input: Set default_quant_desc_input + - set_default_quant_desc_weight: Set default_quant_desc_weight + """ + + default_quant_desc_input = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + + def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, + groups, bias, padding_mode, quant_desc_input, quant_desc_weight): + super(_QuantConvNd, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, + transposed, output_padding, groups, bias, padding_mode) + self.init_quantizer(quant_desc_input, quant_desc_weight) + + def _quant(self, input): + """Apply quantization on input and weight + + Function called by the classes lower in the hierarchy, which actually performs the quantization before forward + in the derivate class the particular Function. + + Arguments: + input: in_features to quantize + Returns: + A tuple: (quant_in_feature, quant_weight) + """ + quant_input = self._input_quantizer(input) + quant_weight = self._weight_quantizer(self.weight) + + return (quant_input, quant_weight) + + +class QuantConv2d(_QuantConvNd): + """Quantized 2D conv""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + **kwargs): + + kernel_size = _pair(kernel_size) + stride = _pair(stride) + padding = _pair(padding) + dilation = _pair(dilation) + + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, False, + _pair(0), groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input): + # the actual quantization happens in the next level of the class hierarchy + quant_input, quant_weight = self._quant(input) + + if self.padding_mode == 'circular': + expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2, + (self.padding[0] + 1) // 2, self.padding[0] // 2) + output = F.conv2d(F.pad(quant_input, expanded_padding, mode='circular'), + quant_weight, self.bias, self.stride, + _pair(0), self.dilation, self.groups) + else: + output = F.conv2d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation, + self.groups) + + return output + + +class QuantConv3d(_QuantConvNd): + """Quantized 3D Conv""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONV3D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + **kwargs): + + kernel_size = _triple(kernel_size) + stride = _triple(stride) + padding = _triple(padding) + dilation = _triple(dilation) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConv3d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, False, + _triple(0), groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input): + # the actual quantization happens in the next level of the class hierarchy + quant_input, quant_weight = self._quant(input) + + if self.padding_mode == 'circular': + expanded_padding = ((self.padding[2] + 1) // 2, self.padding[2] // 2, + (self.padding[1] + 1) // 2, self.padding[1] // 2, + (self.padding[0] + 1) // 2, self.padding[0] // 2) + output = F.conv3d(F.pad(quant_input, expanded_padding, mode='circular'), + quant_weight, self.bias, self.stride, _triple(0), + self.dilation, self.groups) + else: + output = F.conv3d(quant_input, quant_weight, self.bias, self.stride, self.padding, self.dilation, + self.groups) + + return output + + +class QuantConv1d(_QuantConvNd): + """Quantized 1D Conv""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONV1D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + **kwargs): + + kernel_size = _single(kernel_size) + stride = _single(stride) + padding = _single(padding) + dilation = _single(dilation) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConv1d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, False, + _single(0), groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input): + # the actual quantization happens in the next level of the class hierarchy + quant_input, quant_weight = self._quant(input) + + if self.padding_mode == 'circular': + expanded_padding = ((self.padding[0] + 1) // 2, self.padding[0] // 2) + output = F.conv1d(F.pad(quant_input, expanded_padding, mode='circular'), + quant_weight, self.bias, self.stride, + _single(0), self.dilation, self.groups) + else: + output = F.conv1d(quant_input, quant_weight, self.bias, self.stride, + self.padding, self.dilation, self.groups) + + return output + + +class _QuantConvTransposeNd(torch.nn.modules.conv._ConvTransposeNd, _utils.QuantMixin): + """base class of quantized Transposed Conv inherited from _ConvTransposeNd + + Comments of original arguments can be found in torch.nn.modules.conv + + Arguments: + quant_desc_input: An instance of :class:`QuantDescriptor `. + Quantization descriptor of input. + quant_desc_weight: An instance of :class:`QuantDescriptor `. + Quantization descriptor of weight. + + Raises: + ValueError: If unsupported arguments are passed in. + + Readonly properties: + - input_quantizer: + - weight_quantizer: + + Static methods: + - set_default_quant_desc_input: Set default_quant_desc_input + - set_default_quant_desc_weight: Set default_quant_desc_weight + """ + + default_quant_desc_input = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + def __init__(self, in_channels, out_channels, kernel_size, stride, + padding, dilation, transposed, output_padding, + groups, bias, padding_mode, quant_desc_input, quant_desc_weight): + super(_QuantConvTransposeNd, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, + transposed, output_padding, groups, bias, padding_mode) + self.init_quantizer(quant_desc_input, quant_desc_weight) + + def _quant(self, input): + """Apply quantization on input and weight + + Function called by the classes lower in the hierarchy, which actually performs the quantization before forward + in the derivate class the particular Function. + + Arguments: + input: in_features to quantize + Returns: + A tuple: (quant_in_feature, quant_weight) + """ + quant_input = self._input_quantizer(input) + quant_weight = self._weight_quantizer(self.weight) + + return (quant_input, quant_weight) + + +class QuantConvTranspose1d(_QuantConvTransposeNd): + """Quantized ConvTranspose1d""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE1D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + output_padding=0, + groups=1, + bias=True, + dilation=1, + padding_mode='zeros', + **kwargs): + kernel_size = _single(kernel_size) + stride = _single(stride) + padding = _single(padding) + dilation = _single(dilation) + output_padding = _single(output_padding) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConvTranspose1d, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, + True, output_padding, groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input, output_size=None): + if self.padding_mode != 'zeros': + raise ValueError('Only `zeros` padding mode is supported for QuantConvTranspose1d') + + output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) + + quant_input, quant_weight = self._quant(input) + output = F.conv_transpose1d(quant_input, quant_weight, self.bias, self.stride, self.padding, output_padding, + self.groups, self.dilation) + return output + + +class QuantConvTranspose2d(_QuantConvTransposeNd): + """Quantized ConvTranspose2d""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE2D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + output_padding=0, + groups=1, + bias=True, + dilation=1, + padding_mode='zeros', + **kwargs): + kernel_size = _pair(kernel_size) + stride = _pair(stride) + padding = _pair(padding) + dilation = _pair(dilation) + output_padding = _pair(output_padding) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConvTranspose2d, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, + True, output_padding, groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input, output_size=None): + if self.padding_mode != 'zeros': + raise ValueError('Only `zeros` padding mode is supported for QuantConvTranspose2d') + + output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) + + quant_input, quant_weight = self._quant(input) + output = F.conv_transpose2d(quant_input, quant_weight, self.bias, self.stride, self.padding, output_padding, + self.groups, self.dilation) + + return output + + +class QuantConvTranspose3d(_QuantConvTransposeNd): + """Quantized ConvTranspose3d""" + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + output_padding=0, + groups=1, + bias=True, + dilation=1, + padding_mode='zeros', + **kwargs): + kernel_size = _triple(kernel_size) + stride = _triple(stride) + padding = _triple(padding) + dilation = _triple(dilation) + output_padding = _triple(output_padding) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + super(QuantConvTranspose3d, self).__init__( + in_channels, out_channels, kernel_size, stride, padding, dilation, + True, output_padding, groups, bias, padding_mode, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + + def forward(self, input, output_size=None): + if self.padding_mode != 'zeros': + raise ValueError('Only `zeros` padding mode is supported for QuantConvTranspose3d') + + output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) + + quant_input, quant_weight = self._quant(input) + output = F.conv_transpose3d(quant_input, quant_weight, self.bias, self.stride, self.padding, output_padding, + self.groups, self.dilation) + + return output + + +# Define alias with Quant prefix +_ConvNd = _QuantConvNd +Conv1d = QuantConv1d +Conv2d = QuantConv2d +Conv3d = QuantConv3d +ConvTranspose1d = QuantConvTranspose1d +ConvTranspose2d = QuantConvTranspose2d +ConvTranspose3d = QuantConvTranspose3d diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_instancenorm.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_instancenorm.py new file mode 100644 index 00000000..b864dcc2 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_instancenorm.py @@ -0,0 +1,78 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""Quantized instance normalization module + Base code is from nn.InstanceNorm, details of the module can be found from the offical repo. +""" + +from torch.nn.modules.batchnorm import _NormBase +import torch.nn.functional as F +from torch.nn.modules import instancenorm + +from pytorch_quantization.nn import TensorQuantizer +from pytorch_quantization import tensor_quant +from . import _utils + +__all__ = [ + "QuantInstanceNorm1d", "QuantInstanceNorm2d", "QuantInstanceNorm3d" +] + +class QuantInstanceNorm1d(instancenorm.InstanceNorm1d, _utils.QuantInputMixin): + r"""Applies Quantized Instance Normalization over a 3D input + """ + def __init__( + self, num_features: int, eps: float = 1e-5, momentum: float = 0.1, affine: bool = False, + track_running_stats: bool = False, **kwargs): + super(QuantInstanceNorm1d, self).__init__( + num_features, eps, momentum, affine, track_running_stats) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantInstanceNorm1d, self).forward(quant_input) + + +class QuantInstanceNorm2d(instancenorm.InstanceNorm2d, _utils.QuantInputMixin): + r"""Applies Quantized Instance Normalization over a 4D input + """ + def __init__( + self, num_features: int, eps: float = 1e-5, momentum: float = 0.1, affine: bool = False, + track_running_stats: bool = False, **kwargs): + super(QuantInstanceNorm2d, self).__init__( + num_features, eps, momentum, affine, track_running_stats) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantInstanceNorm2d, self).forward(quant_input) + + +class QuantInstanceNorm3d(instancenorm.InstanceNorm3d, _utils.QuantInputMixin): + r"""Applies Quantized Instance Normalization over a 5D input + """ + def __init__( + self, num_features: int, eps: float = 1e-5, momentum: float = 0.1, affine: bool = False, + track_running_stats: bool = False, **kwargs): + super(QuantInstanceNorm3d, self).__init__( + num_features, eps, momentum, affine, track_running_stats) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantInstanceNorm3d, self).forward(quant_input) diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_linear.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_linear.py new file mode 100644 index 00000000..c8ef5b11 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_linear.py @@ -0,0 +1,77 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Quantized Linear""" +from torch import nn +from torch.nn import functional as F + +from pytorch_quantization import tensor_quant + +from . import _utils + +__all__ = ["Linear", "QuantLinear"] + +class QuantLinear(nn.Linear, _utils.QuantMixin): + """Quantized version of nn.Linear + + Apply quantized linear to the incoming data, y = dequant(quant(x)quant(A)^T + b). + + Keep Module name "Linear" instead of "QuantLinear" so that it can be easily dropped into preexisting model and load + pretrained weights. An alias "QuantLinear" is defined below. The base code is a copy of nn.Linear, see detailed + comment of original arguments there. + + Quantization descriptors are passed in in kwargs. If not presents, default_quant_desc_input and + default_quant_desc_weight are used. + + Keyword Arguments: + quant_desc_input: An instance of :class:`QuantDescriptor `. + Quantization descriptor of input. + quant_desc_wegiht: An instance of :class:`QuantDescriptor `. + Quantization descriptor of weight. + + Raises: + ValueError: If unsupported arguments are passed in. + KeyError: If unsupported kwargs are passed in. + + Readonly properties: + - input_quantizer: + - weight_quantizer: + + Static methods: + - set_default_quant_desc_input: Set default_quant_desc_input + - set_default_quant_desc_weight: Set default_quant_desc_weight + """ + + default_quant_desc_input = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW + + def __init__(self, in_features, out_features, bias=True, **kwargs): + super(QuantLinear, self).__init__(in_features, out_features, bias) + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + + self.init_quantizer(quant_desc_input, quant_desc_weight) + + def forward(self, input): + quant_input = self._input_quantizer(input) + quant_weight = self._weight_quantizer(self.weight) + + output = F.linear(quant_input, quant_weight, bias=self.bias) + + return output + + +Linear = QuantLinear diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_pooling.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_pooling.py new file mode 100644 index 00000000..1c1a7a2c --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_pooling.py @@ -0,0 +1,162 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Quantized Pooling +Base code is from nn.pooling, details of Module and original argument can be found there. +Module names are intentionally kept same as unquantized version so that they can be dropped into preexisting model +easily, and load pretrained weight. Aliases with Quant prefix are defined and are encouraged to be used explicitly +when start scratch. +""" + +from torch.nn.modules import pooling + +from . import _utils + +__all__ = [ + "MaxPool1d", "QuantMaxPool1d", "MaxPool2d", "QuantMaxPool2d", "MaxPool3d", "QuantMaxPool3d", + "AvgPool1d", "QuantAvgPool1d", "AvgPool2d", "QuantAvgPool2d", "AvgPool3d", "QuantAvgPool3d", + "AdaptiveAvgPool1d", "QuantAdaptiveAvgPool1d", "AdaptiveAvgPool2d", "QuantAdaptiveAvgPool2d", + "AdaptiveAvgPool3d", "QuantAdaptiveAvgPool3d" +] + +class QuantMaxPool1d(pooling.MaxPool1d, _utils.QuantInputMixin): + """Quantized 1D maxpool""" + def __init__(self, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False, **kwargs): + super(QuantMaxPool1d, self).__init__(kernel_size, stride, padding, dilation, + return_indices, ceil_mode) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantMaxPool1d, self).forward(quant_input) + +class QuantMaxPool2d(pooling.MaxPool2d, _utils.QuantInputMixin): + """Quantized 2D maxpool""" + def __init__(self, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False, **kwargs): + super(QuantMaxPool2d, self).__init__(kernel_size, stride, padding, dilation, + return_indices, ceil_mode) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantMaxPool2d, self).forward(quant_input) + +class QuantMaxPool3d(pooling.MaxPool3d, _utils.QuantInputMixin): + """Quantized 3D maxpool""" + def __init__(self, kernel_size, stride=None, padding=0, dilation=1, + return_indices=False, ceil_mode=False, **kwargs): + super(QuantMaxPool3d, self).__init__(kernel_size, stride, padding, dilation, + return_indices, ceil_mode) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantMaxPool3d, self).forward(quant_input) + + +class QuantAvgPool1d(pooling.AvgPool1d, _utils.QuantInputMixin): + """Quantized 1D average pool""" + def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, + count_include_pad=True, **kwargs): + super(QuantAvgPool1d, self).__init__(kernel_size, stride, padding, ceil_mode, + count_include_pad) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAvgPool1d, self).forward(quant_input) + +class QuantAvgPool2d(pooling.AvgPool2d, _utils.QuantInputMixin): + """Quantized 2D average pool""" + def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, + count_include_pad=True, divisor_override=None, **kwargs): + super(QuantAvgPool2d, self).__init__(kernel_size, stride, padding, ceil_mode, + count_include_pad, divisor_override) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAvgPool2d, self).forward(quant_input) + + +class QuantAvgPool3d(pooling.AvgPool3d, _utils.QuantInputMixin): + """Quantized 3D average pool""" + def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, + count_include_pad=True, divisor_override=None, **kwargs): + super(QuantAvgPool3d, self).__init__(kernel_size, stride, padding, ceil_mode, + count_include_pad, divisor_override) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAvgPool3d, self).forward(quant_input) + + +class QuantAdaptiveAvgPool1d(pooling.AdaptiveAvgPool1d, _utils.QuantInputMixin): + """Quantized 1D adaptive average pool""" + def __init__(self, output_size, **kwargs): + super(QuantAdaptiveAvgPool1d, self).__init__(output_size) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAdaptiveAvgPool1d, self).forward(quant_input) + + +class QuantAdaptiveAvgPool2d(pooling.AdaptiveAvgPool2d, _utils.QuantInputMixin): + """Quantized 2D adaptive average pool""" + def __init__(self, output_size, **kwargs): + super(QuantAdaptiveAvgPool2d, self).__init__(output_size) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAdaptiveAvgPool2d, self).forward(quant_input) + + +class QuantAdaptiveAvgPool3d(pooling.AdaptiveAvgPool3d, _utils.QuantInputMixin): + """Quantized 3D adaptive average pool""" + def __init__(self, output_size, **kwargs): + super(QuantAdaptiveAvgPool3d, self).__init__(output_size) + quant_desc_input = _utils.pop_quant_desc_in_kwargs(self.__class__, input_only=True, **kwargs) + self.init_quantizer(quant_desc_input) + + def forward(self, input): + quant_input = self._input_quantizer(input) + return super(QuantAdaptiveAvgPool3d, self).forward(quant_input) + + +# Define alias with Quant prefix +MaxPool1d = QuantMaxPool1d +MaxPool2d = QuantMaxPool2d +MaxPool3d = QuantMaxPool3d +AvgPool1d = QuantAvgPool1d +AvgPool2d = QuantAvgPool2d +AvgPool3d = QuantAvgPool3d +AdaptiveAvgPool1d = QuantAdaptiveAvgPool1d +AdaptiveAvgPool2d = QuantAdaptiveAvgPool2d +AdaptiveAvgPool3d = QuantAdaptiveAvgPool3d diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_rnn.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_rnn.py new file mode 100644 index 00000000..e392cb46 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/quant_rnn.py @@ -0,0 +1,458 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""RNN implementation in python +originally copied from https://github.com/pytorch/pytorch/blob/v0.4.1/torch/nn/modules/rnn.py + backend is changed to _functions/rnn.py +""" +import math +import torch +import warnings +import itertools +import numbers + +from torch import nn +from torch.nn import Parameter +from torch.nn.utils.rnn import PackedSequence + +from pytorch_quantization import tensor_quant +from pytorch_quantization.nn._functions import quant_rnn + +from . import _utils + +__all__ = ["QuantLSTM", "QuantLSTMCell", "LSTM", "LSTMCell"] + +class QuantRNNBase(nn.Module, _utils.QuantMixin): + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW + + def __init__(self, mode, input_size, hidden_size, + num_layers=1, bias=True, batch_first=False, + dropout=0, bidirectional=False, **kwargs): + super(QuantRNNBase, self).__init__() + self.mode = mode + self.input_size = input_size + self.hidden_size = hidden_size + self.num_layers = num_layers + self.bias = bias + self.batch_first = batch_first + self.dropout = dropout + self.dropout_state = {} + self.bidirectional = bidirectional + num_directions = 2 if bidirectional else 1 + + if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \ + isinstance(dropout, bool): + raise ValueError("dropout should be a number in range [0, 1] " + "representing the probability of an element being " + "zeroed") + if dropout > 0 and num_layers == 1: + warnings.warn("dropout option adds dropout after all but last " + "recurrent layer, so non-zero dropout expects " + "num_layers greater than 1, but got dropout={} and " + "num_layers={}".format(dropout, num_layers)) + + if mode == 'LSTM': + gate_size = 4 * hidden_size + elif mode == 'GRU': + gate_size = 3 * hidden_size + else: + gate_size = hidden_size + + self._all_weights = [] + for layer in range(num_layers): + for direction in range(num_directions): + layer_input_size = input_size if layer == 0 else hidden_size * num_directions + + w_ih = Parameter(torch.Tensor(gate_size, layer_input_size)) + w_hh = Parameter(torch.Tensor(gate_size, hidden_size)) + b_ih = Parameter(torch.Tensor(gate_size)) + b_hh = Parameter(torch.Tensor(gate_size)) + layer_params = (w_ih, w_hh, b_ih, b_hh) + + suffix = '_reverse' if direction == 1 else '' + param_names = ['weight_ih_l{}{}', 'weight_hh_l{}{}'] + if bias: + param_names += ['bias_ih_l{}{}', 'bias_hh_l{}{}'] + param_names = [x.format(layer, suffix) for x in param_names] + + for name, param in zip(param_names, layer_params): + setattr(self, name, param) + self._all_weights.append(param_names) + + self.flatten_parameters() + self.reset_parameters() + + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + self.init_quantizer(quant_desc_input, quant_desc_weight, num_layers=num_layers * (1 + bidirectional)) + + def flatten_parameters(self): + """Resets parameter data pointer so that they can use faster code paths. + + Right now, this works only if the module is on the GPU and cuDNN is enabled. + Otherwise, it's a no-op. + """ + any_param = next(self.parameters()).data + if not any_param.is_cuda or not torch.backends.cudnn.is_acceptable(any_param): + self._data_ptrs = [] + return + + # If any parameters alias, we fall back to the slower, copying code path. This is + # a sufficient check, because overlapping parameter buffers that don't completely + # alias would break the assumptions of the uniqueness check in + # Module.named_parameters(). + unique_data_ptrs = set(p.data_ptr() for l in self.all_weights for p in l) + if len(unique_data_ptrs) != sum(len(l) for l in self.all_weights): + self._data_ptrs = [] + return + + with torch.cuda.device_of(any_param): + import torch.backends.cudnn.rnn as rnn + + weight_arr = list(itertools.chain.from_iterable(self.all_weights)) + weight_stride0 = len(self.all_weights[0]) + + # NB: This is a temporary hack while we still don't have Tensor + # bindings for ATen functions + with torch.no_grad(): + # NB: this is an INPLACE function on weight_arr, that's why the + # no_grad() is necessary. + weight_buf = torch._cudnn_rnn_flatten_weight( + weight_arr, weight_stride0, + self.input_size, rnn.get_cudnn_mode(self.mode), self.hidden_size, self.num_layers, + self.batch_first, bool(self.bidirectional)) + + self._param_buf_size = weight_buf.size(0) + self._data_ptrs = list(p.data.data_ptr() for p in self.parameters()) + + def _apply(self, fn): + ret = super(QuantRNNBase, self)._apply(fn) + self.flatten_parameters() + return ret + + def reset_parameters(self): + stdv = 1.0 / math.sqrt(self.hidden_size) + for weight in self.parameters(): + weight.data.uniform_(-stdv, stdv) + + def check_forward_args(self, input, hidden, batch_sizes): + is_input_packed = batch_sizes is not None + expected_input_dim = 2 if is_input_packed else 3 + if input.dim() != expected_input_dim: + raise RuntimeError( + 'input must have {} dimensions, got {}'.format( + expected_input_dim, input.dim())) + if self.input_size != input.size(-1): + raise RuntimeError( + 'input.size(-1) must be equal to input_size. Expected {}, got {}'.format( + self.input_size, input.size(-1))) + + if is_input_packed: + mini_batch = int(batch_sizes[0]) + else: + mini_batch = input.size(0) if self.batch_first else input.size(1) + + num_directions = 2 if self.bidirectional else 1 + expected_hidden_size = (self.num_layers * num_directions, + mini_batch, self.hidden_size) + + def check_hidden_size(hx, expected_hidden_size, msg='Expected hidden size {}, got {}'): + if tuple(hx.size()) != expected_hidden_size: + raise RuntimeError(msg.format(expected_hidden_size, tuple(hx.size()))) + + if self.mode == 'LSTM': + check_hidden_size(hidden[0], expected_hidden_size, + 'Expected hidden[0] size {}, got {}') + check_hidden_size(hidden[1], expected_hidden_size, + 'Expected hidden[1] size {}, got {}') + else: + check_hidden_size(hidden, expected_hidden_size) + + def forward(self, input, hx=None): + is_packed = isinstance(input, PackedSequence) + if is_packed: + input, batch_sizes, sorted_indices, unsorted_indices = input + max_batch_size = batch_sizes[0] + max_batch_size = int(max_batch_size) + else: + batch_sizes = None + max_batch_size = input.size(0) if self.batch_first else input.size(1) + + if hx is None: + num_directions = 2 if self.bidirectional else 1 + hx = input.new_zeros(self.num_layers * num_directions, + max_batch_size, self.hidden_size, + requires_grad=False) + if self.mode == 'LSTM': + hx = (hx, hx) + + has_flat_weights = list(p.data.data_ptr() for p in self.parameters()) == self._data_ptrs + if has_flat_weights: + first_data = next(self.parameters()).data + assert first_data.storage().size() == self._param_buf_size + flat_weight = first_data.new().set_(first_data.storage(), 0, torch.Size([self._param_buf_size])) + else: + flat_weight = None + + self.check_forward_args(input, hx, batch_sizes) + func = quant_rnn.RNN( + self.mode, + self.input_size, + self.hidden_size, + num_layers=self.num_layers, + batch_first=self.batch_first, + dropout=self.dropout, + train=self.training, + bidirectional=self.bidirectional, + dropout_state=self.dropout_state, + variable_length=is_packed, + flat_weight=flat_weight + ) + output, hidden = func(input, self.all_weights, hx, batch_sizes, self._input_quantizers, self._weight_quantizers) + if is_packed: + output = PackedSequence(output, batch_sizes) + return output, hidden + + def extra_repr(self): + s = '{input_size}, {hidden_size}' + if self.num_layers != 1: + s += ', num_layers={num_layers}' + if self.bias is not True: + s += ', bias={bias}' + if self.batch_first is not False: + s += ', batch_first={batch_first}' + if self.dropout != 0: + s += ', dropout={dropout}' + if self.bidirectional is not False: + s += ', bidirectional={bidirectional}' + return s.format(**self.__dict__) + + def __setstate__(self, d): + super(QuantRNNBase, self).__setstate__(d) + self.__dict__.setdefault('_data_ptrs', []) + if 'all_weights' in d: + self._all_weights = d['all_weights'] + if isinstance(self._all_weights[0][0], str): + return + num_layers = self.num_layers + num_directions = 2 if self.bidirectional else 1 + self._all_weights = [] + for layer in range(num_layers): + for direction in range(num_directions): + suffix = '_reverse' if direction == 1 else '' + weights = ['weight_ih_l{}{}', 'weight_hh_l{}{}', 'bias_ih_l{}{}', 'bias_hh_l{}{}'] + weights = [x.format(layer, suffix) for x in weights] + if self.bias: + self._all_weights += [weights] + else: + self._all_weights += [weights[:2]] + + @property + def all_weights(self): + return [[getattr(self, weight) for weight in weights] for weights in self._all_weights] + + +class QuantRNN(QuantRNNBase): + r"""Applies a multi-layer Elman RNN with `tanh` or `ReLU` non-linearity to an + input sequence. + """ + + def __init__(self, *args, **kwargs): + if 'nonlinearity' in kwargs: + if kwargs['nonlinearity'] == 'tanh': + mode = 'RNN_TANH' + elif kwargs['nonlinearity'] == 'relu': + mode = 'RNN_RELU' + else: + raise ValueError("Unknown nonlinearity '{}'".format( + kwargs['nonlinearity'])) + del kwargs['nonlinearity'] + else: + mode = 'RNN_TANH' + + super(QuantRNN, self).__init__(mode, *args, **kwargs) + + +class QuantLSTM(QuantRNNBase): + r"""Applies a multi-layer long short-term memory (LSTM) RNN to an input + sequence. + """ + + def __init__(self, *args, **kwargs): + super(QuantLSTM, self).__init__('LSTM', *args, **kwargs) + + +class GRU(QuantRNNBase): + r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. + """ + + def __init__(self, *args, **kwargs): + super(GRU, self).__init__('GRU', *args, **kwargs) + +class QuantRNNCellBase(nn.Module, _utils.QuantMixin): + + default_quant_desc_weight = tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW + + def extra_repr(self): + s = '{input_size}, {hidden_size}' + if 'bias' in self.__dict__ and self.bias is not True: + s += ', bias={bias}' + if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh": + s += ', nonlinearity={nonlinearity}' + return s.format(**self.__dict__) + + def check_forward_input(self, input): + if input.size(1) != self.input_size: + raise RuntimeError( + "input has inconsistent input_size: got {}, expected {}".format( + input.size(1), self.input_size)) + + def check_forward_hidden(self, input, hx, hidden_label=''): + if input.size(0) != hx.size(0): + raise RuntimeError( + "Input batch size {} doesn't match hidden{} batch size {}".format( + input.size(0), hidden_label, hx.size(0))) + + if hx.size(1) != self.hidden_size: + raise RuntimeError( + "hidden{} has inconsistent hidden_size: got {}, expected {}".format( + hidden_label, hx.size(1), self.hidden_size)) + + +class QuantRNNCell(QuantRNNCellBase): + r"""An Elman RNN cell with tanh or ReLU non-linearity. + """ + + def __init__(self, input_size, hidden_size, bias=True, nonlinearity="tanh"): + super(QuantRNNCell, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.bias = bias + self.nonlinearity = nonlinearity + self.weight_ih = Parameter(torch.Tensor(hidden_size, input_size)) + self.weight_hh = Parameter(torch.Tensor(hidden_size, hidden_size)) + if bias: + self.bias_ih = Parameter(torch.Tensor(hidden_size)) + self.bias_hh = Parameter(torch.Tensor(hidden_size)) + else: + self.register_parameter('bias_ih', None) + self.register_parameter('bias_hh', None) + self.reset_parameters() + + def reset_parameters(self): + stdv = 1.0 / math.sqrt(self.hidden_size) + for weight in self.parameters(): + weight.data.uniform_(-stdv, stdv) + + def forward(self, input, hx=None): + self.check_forward_input(input) + if hx is None: + hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) + self.check_forward_hidden(input, hx) + if self.nonlinearity == "tanh": + func = quant_rnn.RNNTanhCell + elif self.nonlinearity == "relu": + func = quant_rnn.RNNReLUCell + else: + raise RuntimeError( + "Unknown nonlinearity: {}".format(self.nonlinearity)) + + return func( + input, hx, + self.weight_ih, self.weight_hh, + self.bias_ih, self.bias_hh, + ) + + +class QuantLSTMCell(QuantRNNCellBase): + r"""A long short-term memory (LSTM) cell. + """ + + def __init__(self, input_size, hidden_size, bias=True, **kwargs): + super(QuantLSTMCell, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.bias = bias + self.weight_ih = Parameter(torch.Tensor(4 * hidden_size, input_size)) + self.weight_hh = Parameter(torch.Tensor(4 * hidden_size, hidden_size)) + if bias: + self.bias_ih = Parameter(torch.Tensor(4 * hidden_size)) + self.bias_hh = Parameter(torch.Tensor(4 * hidden_size)) + else: + self.register_parameter('bias_ih', None) + self.register_parameter('bias_hh', None) + self.reset_parameters() + quant_desc_input, quant_desc_weight = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs) + self.init_quantizer(quant_desc_input, quant_desc_weight) + + def reset_parameters(self): + stdv = 1.0 / math.sqrt(self.hidden_size) + for weight in self.parameters(): + weight.data.uniform_(-stdv, stdv) + + def forward(self, input, hx=None): + self.check_forward_input(input) + if hx is None: + hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) + hx = (hx, hx) + self.check_forward_hidden(input, hx[0], '[0]') + self.check_forward_hidden(input, hx[1], '[1]') + return quant_rnn.LSTMCell( + input, hx, + self.weight_ih, self.weight_hh, + self.bias_ih, self.bias_hh, + self._input_quantizer, self._weight_quantizer + ) + + +class GRUCell(QuantRNNCellBase): + r"""A gated recurrent unit (GRU) cell + """ + + def __init__(self, input_size, hidden_size, bias=True): + super(GRUCell, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.bias = bias + self.weight_ih = Parameter(torch.Tensor(3 * hidden_size, input_size)) + self.weight_hh = Parameter(torch.Tensor(3 * hidden_size, hidden_size)) + if bias: + self.bias_ih = Parameter(torch.Tensor(3 * hidden_size)) + self.bias_hh = Parameter(torch.Tensor(3 * hidden_size)) + else: + self.register_parameter('bias_ih', None) + self.register_parameter('bias_hh', None) + self.reset_parameters() + + def reset_parameters(self): + stdv = 1.0 / math.sqrt(self.hidden_size) + for weight in self.parameters(): + weight.data.uniform_(-stdv, stdv) + + def forward(self, input, hx=None): + self.check_forward_input(input) + if hx is None: + hx = input.new_zeros(input.size(0), self.hidden_size, requires_grad=False) + self.check_forward_hidden(input, hx) + return quant_rnn.GRUCell( + input, hx, + self.weight_ih, self.weight_hh, + self.bias_ih, self.bias_hh, + ) + +LSTM = QuantLSTM +LSTMCell = QuantLSTMCell diff --git a/tools/pytorch-quantization/pytorch_quantization/nn/modules/tensor_quantizer.py b/tools/pytorch-quantization/pytorch_quantization/nn/modules/tensor_quantizer.py new file mode 100644 index 00000000..fd3f32c7 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/nn/modules/tensor_quantizer.py @@ -0,0 +1,402 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""TensorQuantizer Module""" +import math +from absl import logging + +import torch +from torch import nn + +from pytorch_quantization.tensor_quant import QuantDescriptor, tensor_quant, fake_tensor_quant +from pytorch_quantization.nn.modules.clip import Clip + +from pytorch_quantization import calib + +import pytorch_quantization.utils as quant_utils + +__all__ = ['TensorQuantizer'] + +class TensorQuantizer(nn.Module): + """Tensor quantizer module + + This module uses tensor_quant or fake_tensor_quant function to quantize a tensor. And wrappers variable, moving + statistics we'd want when training a quantized network. + + Experimental features: + ``clip`` stage learns range before enabling quantization. + ``calib`` stage runs calibration + + Args: + quant_desc: An instance of :func:`QuantDescriptor `. + disabled: A boolean. If True, by pass the whole module returns input. Default False. + if_quant: A boolean. If True, run main quantization body. Default True. + if_clip: A boolean. If True, clip before quantization and learn amax. Default False. + if_calib: A boolean. If True, run calibration. Not implemented yet. Settings of calibration will probably + go to :func:`QuantDescriptor `. + + Raises: + + Readonly Properties: + - axis: + - fake_quant: + - scale: + - step_size: + + Mutable Properties: + - num_bits: + - unsigned: + - amax: + """ + + # An experimental static switch for using pytorch's native fake quantization + # Primary usage is to export to ONNX + use_fb_fake_quant = False + + def __init__(self, quant_desc=QuantDescriptor(), disabled=False, if_quant=True, if_clip=False, if_calib=False): + """Initialize quantizer and set up required variables""" + super(TensorQuantizer, self).__init__() + # Expand quant_desc. Use quant_desc.dict would be eaiser, but adding one-by-one explicitly gives more control + self._num_bits = quant_desc.num_bits + self._fake_quant = quant_desc.fake_quant + self._axis = quant_desc.axis + self._scale_amax = quant_desc.scale_amax + self._learn_amax = quant_desc.learn_amax + self._unsigned = quant_desc.unsigned + self._narrow_range = quant_desc.narrow_range + + self._scale = None if not quant_desc.fake_quant else 1. + self._disabled = disabled + self._if_quant = if_quant + self._if_clip = False + self._if_calib = if_calib + + if quant_desc.amax is not None: + self.register_buffer('_amax', torch.tensor(quant_desc.amax)) + + # Clip module consumes a lot of memory, so only create it if learn_amax is True + if self._learn_amax: + init_amax = quant_desc.amax if quant_desc.amax is not None else 1. + self.clip = Clip(-init_amax, init_amax, learn_min=True, learn_max=True) + # It makes more sense to enable clip stage (which learns amax) if learn_amax is true + self.enable_clip() + if if_clip: + self.enable_clip() + + if quant_desc.calib_method == "histogram": + logging.info("Creating histogram calibrator") + self._calibrator = calib.HistogramCalibrator( + num_bits=self._num_bits, axis=self._axis, unsigned=self._unsigned) + elif quant_desc.calib_method == "max": + logging.info("Creating Max calibrator") + self._calibrator = calib.MaxCalibrator(num_bits=self._num_bits, axis=self._axis, unsigned=self._unsigned) + + # pylint:disable=missing-docstring + @property + def num_bits(self): + return self._num_bits + + @property + def unsigned(self): + return self._unsigned + + @property + def scale(self): + if self._fake_quant: + logging.error("Fake quantize mode doesn't use scale explicitly!") + if self._scale is None: + logging.critical("Accessing scale before quantizing any tensor!") + return self._scale + + @property + def amax(self): + if not hasattr(self, "_amax"): + return None + return self._amax + + @property + def step_size(self): + if not hasattr(self, "_amax"): + logging.error("step_size is undefined under dynamic amax mode!") + return None + return self._amax / (2.0**(self._num_bits - 1 + int(self._unsigned)) - 1.0) + + @property + def axis(self): + return self._axis + + @property + def fake_quant(self): + return self._fake_quant + + @property + def narrow_range(self): + return self._narrow_range + + def disable(self): + """Bypass the module""" + self._disabled = True + + def enable(self): + self._disabled = False + + def disable_clip(self): + """Disable clip stage""" + self._if_clip = False + self.clip.clip_value_min.required_grad = False + self.clip.clip_value_max.required_grad = False + + def enable_clip(self): + """Enable clip stage""" + logging.warning("Enable `clip` stage for amax learning.") + if not self._learn_amax: + raise ValueError("learn_amax is False. Cannot enable clip.") + self.clip.clip_value_min.required_grad = True + self.clip.clip_value_max.required_grad = True + self._if_clip = True + + def disable_calib(self): + logging.warning("Disable {}".format(self._calibrator.__class__.__name__)) + self._if_calib = False + + def enable_calib(self): + if self._calibrator is None: + raise ValueError("Calibrator was not created, cannot enable calibration.") + logging.info("Enable {}".format(self._calibrator.__class__.__name__)) + self._if_calib = True + + def disable_quant(self): + logging.info("Disable `quant` stage.") + self._if_quant = False + + def enable_quant(self): + logging.info("Enable `quant` stage.") + self._if_quant = True + + @amax.setter + def amax(self, value): + if value is None: + logging.error("Setting amax no None is meaningless.") + else: + if isinstance(value, torch.Tensor): + logging.warning("amax setter is not designed to take tensor.") + if not hasattr(self, "_amax"): + self.register_buffer('_amax', torch.tensor(value)) + else: + value = torch.tensor(value, device=self._amax.device) + if self._amax.shape != value.shape: + raise TypeError("Changing shape when setting amax is not allowed.") + self._amax.data.copy_(value.data) + + @num_bits.setter + def num_bits(self, value): + self._num_bits = value + + @unsigned.setter + def unsigned(self, value): + self._unsigned = value + + @narrow_range.setter + def narrow_range(self, value): + self._narrow_range = value + + # pylint:enable=missing-docstring + def load_calib_amax(self, *args, **kwargs): + """Load amax from calibrator. + + Updates the amax buffer with value computed by the calibrator, creating it if necessary. + *args and **kwargs are directly passed to compute_amax, except "strict" in kwargs. Refer to + compute_amax for more details. + """ + strict = kwargs.pop("strict", True) + if getattr(self, '_calibrator', None) is None: + raise RuntimeError("Calibrator not created.") + calib_amax = self._calibrator.compute_amax(*args, **kwargs) + if calib_amax is None: + err_msg = "Calibrator returned None." + if not strict: + logging.warning(err_msg) + logging.warning("Set amax to NaN!") + calib_amax = torch.tensor(math.nan) + else: + raise RuntimeError(err_msg) + logging.warning("Load calibrated amax, shape={}.".format(calib_amax.shape)) + logging.log_first_n( + logging.WARNING, "Call .cuda() if running on GPU after loading calibrated amax.", 1) + if not hasattr(self, '_amax'): + self.register_buffer('_amax', calib_amax.data) + else: + self._amax.copy_(calib_amax) + + def init_learn_amax(self): + """Initialize learned amax from fixed amax""" + if self._learn_amax is False: + raise RuntimeError("Called init_learn_amax with learn_amax=False.") + logging.warning("Load amax as initial value for amax learning!") + if self._amax.numel() != 1: + logging.warning("Per channel learned amax not supported. Initializing with max(amax).") + init_amax = torch.max(self._amax) + else: + init_amax = self._amax + self.clip.clip_value_min.data.copy_(-init_amax.data) + self.clip.clip_value_max.data.copy_(init_amax.data) + + def _get_amax(self, inputs): + """get amax from buffer or compute it dynamically.""" + if hasattr(self, '_amax'): + amax = self._amax + else: + if self._axis is None: + reduce_axis = None + else: + reduce_axis = [] + # Swap axis to reduce + axis = self._axis if isinstance(self._axis, (list, tuple)) else [self._axis] + for i in range(inputs.dim()): + if not i in axis: + reduce_axis.append(i) + amax = quant_utils.reduce_amax(inputs, axis=reduce_axis, keepdims=True).detach() + if self._scale_amax is not None: + amax = amax.detach() * self._scale_amax + + return amax + + def _fb_fake_quant(self, inputs, amax): + """Native pytorch fake quantization.""" + logging.log_first_n(logging.WARNING, "Use Pytorch's native experimental fake quantization.", 1) + bound = (1 << (self._num_bits - 1 + int(self._unsigned))) - 1 + # To be consistent with ONNX, full range is used. e.g. range is [-128, 127] in int8 + if amax.numel() == 1: + outputs = torch.fake_quantize_per_tensor_affine( + inputs, amax.item() / bound, 0, + -bound - 1 if not self._unsigned else 0, bound) + else: + amax_sequeeze = amax.squeeze().detach() + if len(amax_sequeeze.shape) != 1: + raise TypeError("Pytorch's native quantization doesn't support multiple axes") + quant_dim = list(amax.shape).index(list(amax_sequeeze.shape)[0]) + scale = amax_sequeeze / bound + outputs = torch.fake_quantize_per_channel_affine( + inputs, scale.data, torch.zeros_like(scale, dtype=torch.long).data, quant_dim, + -bound - 1 if not self._unsigned else 0, bound) + + return outputs + + def _quant_forward(self, inputs): + """Quantized forward pass.""" + if self._learn_amax: + inputs = self.clip(inputs) + amax = torch.max(-self.clip.clip_value_min, self.clip.clip_value_max).detach() + else: + amax = self._get_amax(inputs) + + if self._fake_quant: + if not TensorQuantizer.use_fb_fake_quant: + outputs = fake_tensor_quant(inputs, amax, self._num_bits, self._unsigned, self._narrow_range) + else: + outputs = self._fb_fake_quant(inputs, amax) + else: + outputs, self._scale = tensor_quant(inputs, amax, self._num_bits, self._unsigned) + + return outputs + + def forward(self, inputs): + """Apply tensor_quant function to inputs + + Args: + inputs: A Tensor of type float32. + + Returns: + outputs: A Tensor of type output_dtype + """ + if self._disabled: + return inputs + + outputs = inputs + + if self._if_calib: + if self._calibrator is None: + raise RuntimeError("Calibrator was not created.") + # Shape is only know when it sees the first tensor + self._calibrator.collect(inputs) + + if self._if_clip: + if not self._learn_amax: + raise RuntimeError("Clip without learning amax is not implemented.") + outputs = self.clip(inputs) + + if self._if_quant: + outputs = self._quant_forward(inputs) + + return outputs + + def _short_amax(self, fmt='.4f'): + """Short description of amax + + Returns: + 'dynamic': if _amax is not registered + 'amax': if _amax is per-tensor + '[min, max](size)': if _amax is per-channel + """ + if not hasattr(self, '_amax'): + return 'dynamic' + if self._amax.numel() == 1: + return '{:{fmt}}'.format(self._amax.item(), fmt=fmt) + return '[{:{fmt}}, {:{fmt}}]({})'.format(self._amax.min().item(), self._amax.max().item(), + self._amax.numel(), fmt=fmt) + + def extra_repr(self): + if self._disabled: + return "disabled" + s = "{}{}bit".format("unsigned " if self._unsigned else "", self._num_bits) + s += " narrow" if (self._narrow_range) else "" + s += " fake" if (self._fake_quant) else "" + s += " axis={}".format(self._axis) if self._axis is not None else " per-tensor" + s += " amax={}".format(self._short_amax()) + s += " *{}".format(self._scale_amax) if self._scale_amax else "" + s += " learned" if (self._learn_amax) else "" + s += " calibrator={}".format(self._calibrator.__class__.__name__) if (self._calibrator is not None) else "" + s += " scale={}".format(self._scale) if self._scale is not None else "" + s += " quant" if (self._if_quant) else "" + s += " clip" if (self._if_clip) else "" + s += " calib" if (self._if_calib) else "" + return s + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + """Overloaded module function + + Adds warnings during state_dict loading. + A workaround is implemented for loading amax from checkpoint and only supports CUDA. + + Args: + state_dict: A dict containing the state of the top level module + prefix: A string that prefixes all of this modules state in state_dict, e.g. 'model.conv1.' + """ + dst_has_amax = '_amax' in self._buffers + src_has_amax = prefix + '_amax' in state_dict + + if not src_has_amax and dst_has_amax: + logging.error("{}: No amax in state_dict.".format(prefix[:-1])) + elif src_has_amax and not dst_has_amax: + logging.debug(("{}: No '_amax' buffer to load amax into." + " '_amax` will be created as WAR for now. " + "This behavior will change in future.").format(prefix[:-1])) + self.register_buffer("_amax", state_dict[prefix + '_amax'].data.cuda()) + elif src_has_amax and dst_has_amax: + logging.warning("{}: Overwriting amax.".format(prefix[:-1])) + + super(TensorQuantizer, self)._load_from_state_dict(state_dict, prefix, *args, **kwargs) diff --git a/tools/pytorch-quantization/pytorch_quantization/optim/__init__.py b/tools/pytorch-quantization/pytorch_quantization/optim/__init__.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/optim/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/pytorch_quantization/optim/helper.py b/tools/pytorch-quantization/pytorch_quantization/optim/helper.py new file mode 100644 index 00000000..2c94d2af --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/optim/helper.py @@ -0,0 +1,131 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Helper functions for quant optimizer/trainer""" + +import re + +from absl import logging + +def match_parameters(model, patterns): + """Returns an generator over module parameters if name matches key + + It is useful to group parameters, and apply different functions to different group. This function provides an easy + way to group them. + + Args: + model: A Module + patterns: A list of strings that will be used to match parameter names. If parameter name contains any pattern, + it will be yield + + Yields: + param: Module parameters + """ + for name, param in model.named_parameters(): + for pattern in patterns: + if re.search(pattern, name): + yield param + +def group_parameters(model, patterns_list, lrs=None, momentums=None, weight_decays=None): + """Group parameters for using per-parameters option in optimizer + + Returns a list of dict that matches Pytorch optimizer fashion, see + https://pytorch.org/docs/stable/optim.html#per-parameter-options for more details. + + Example: + >>> [ + >>> {'params': model.base.parameters()}, + >>> {'params': model.classifier.parameters(), 'lr': 1e-3} + >>> ] + Parameters will be grouped w.r.t first level of the keys_list. e.g. `keys_list=[['conv1', 'conv2'], ['conv3']]` will + return 2 groups, one with `conv1` and `conv2` in name, and the other with `conv3` in name. + + If lr, momentum or weight_decay are supplied, they will be added to the group as well. + + + Args: + model: A module + patterns_list: A list of list of strings. WARNING: patters must be EXCLUSIVE, the function doesn't + perform exclusive check. + lrs: A list of float with same length as keys_list or None. + momentums: A list of float with same length as keys_list or None. + weight_decays: A list of float with same length as keys_list or None. + + Returns: + param_group: A list of dict + + """ + param_groups = [] + for pattern in patterns_list: + if not isinstance(pattern, list): + raise TypeError("patterns_list must be list of list of patterns") + param_groups.append({'params': match_parameters(model, pattern)}) + + if lrs is not None: + if len(lrs) != len(patterns_list): + raise TypeError("len(lrs) must match len(patterns_list)") + for i, lr in enumerate(lrs): + param_groups[i]['lr'] = lr + + if momentums is not None: + if len(momentums) != len(patterns_list): + raise TypeError("len(momentums) must match len(patterns_list)") + for i, momentum in enumerate(momentums): + param_groups[i]['momentum'] = momentum + + if weight_decays is not None: + if len(weight_decays) != len(patterns_list): + raise TypeError("len(weight_decays) must match len(patterns_list)") + for i, weight_decay in enumerate(weight_decays): + param_groups[i]['weight_decay'] = weight_decay + + return param_groups + +def freeze_parameters(model, patterns): + """Set requires_grad to False if patterns match name + + Args: + model: A Module + patterns: A list of strings that will be used to match parameter names. If parameter name contains any pattern, + it will be frozen. + """ + for name, param in model.named_parameters(): + for pattern in patterns: + if re.search(pattern, name): + logging.warning("Freeze %s.", name) + param.requires_grad = False + +def quant_weight_inplace(model): + """Make quantization inplace + + Search for quantized modules including QuantConvNd and QuantLinear, make weight quantization in place using + weight_quantizer. + + Most publications of quantization aware training uses STE by default, which is really an approximation of + derivative of the nondifferentiable quantization function, which works to some extended but by no means the F=ma of + the problem. + Inplace quantization can be used to implement relax-and-round, which is a common method in Discrete Optimization's + or Integer Programming, see more explanation in + p4hw://hw/doc/gpu/compute/DL/reduced_precision_study/fine.tune.4.int8/Train.for.Quantization.md + """ + for name, module in model.named_modules(): + if hasattr(module, '_weight_quantizer') and module.weight_quantizer is not None: + if not module.weight_quantizer.fake_quant: + logging.warning(("In-place real quantization is VERY dangerous and should be used for inference only. " + "Make sure that is the desired behavior.")) + logging.warning("In-place quantize weight of %s", name) + module.weight.data.copy_(module.weight_quantizer(module.weight)) diff --git a/tools/pytorch-quantization/pytorch_quantization/quant_modules.py b/tools/pytorch-quantization/pytorch_quantization/quant_modules.py new file mode 100644 index 00000000..fcbbbbb1 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/quant_modules.py @@ -0,0 +1,147 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +"""Dynamically replace the modules with quantized versions.""" + +from collections import namedtuple +import torch +from pytorch_quantization import nn as quant_nn + +# Definition of the named tuple that is used to store mapping of the quantized modules +_quant_entry = namedtuple('quant_entry', 'orig_mod mod_name replace_mod') + +# Global member of the file that contains the mapping of quantized modules +_DEFAULT_QUANT_MAP = [_quant_entry(torch.nn, "Conv1d", quant_nn.QuantConv1d), + _quant_entry(torch.nn, "Conv2d", quant_nn.QuantConv2d), + _quant_entry(torch.nn, "Conv3d", quant_nn.QuantConv3d), + _quant_entry(torch.nn, "ConvTranspose1d", quant_nn.QuantConvTranspose1d), + _quant_entry(torch.nn, "ConvTranspose2d", quant_nn.QuantConvTranspose2d), + _quant_entry(torch.nn, "ConvTranspose3d", quant_nn.QuantConvTranspose3d), + _quant_entry(torch.nn, "Linear", quant_nn.QuantLinear), + _quant_entry(torch.nn, "LSTM", quant_nn.QuantLSTM), + _quant_entry(torch.nn, "LSTMCell", quant_nn.QuantLSTMCell)] + +class QuantModuleReplacementHelper(): + """To help replace torch.nn modules with quantized versions. + + This module is used to replace (by monkey patching) the torch.nn modules with their + quantized versions as provided by either tool's internal implementation or any other + user provided custom module. + + Attributes: + orginal_func_map: A dict. Maintains the original torch.nn module mapping. + quant_support_list: A list. Contains the names of modules for which a quantized + version is provided by the tool. + quant_map: A dict. Contains the map of the module name and its quantized versions. + quant_switch_opt: A dict. A map to indicate which modules to be left unreplaced with + their quantized versions. This dict is updated by a list provided from the user + which indicates the modules to leave out in monkey patching. + + """ + def __init__(self): + + # Will hold the original modules to be replaced back + self.orginal_func_map = set() + + # Maintains the list of supported quantized modules by the tool as default + self.default_quant_map = _DEFAULT_QUANT_MAP + + # Will hold the final quantized modules after checking if user supplied any + # custom quantized functions. + self.quant_map = set() + + def prepare_state(self, float_module_list=None, custom_map=None): + """ + Prepare the internal variables that would used in the monkey patching mechanism later. + 1. Set up the list of quantized modules that are supported by the tool for torch.nn. + 2. Set up the custom mapping for modules other than torch.nn. + 3. Use the float_module_list to switch off the monkey patching replacement for user indicated modules + """ + + # For the default quantized modules supported, generate the quant_map + for item in self.default_quant_map: + if float_module_list is not None and item.mod_name in float_module_list: + # Skip this module if this is present in the float_module_list + continue + else: + # append the modules into the variable that will be used in monkey patching + self.quant_map.add(item) + # also store the original module to be used in reverse monkey patching + self.orginal_func_map.add(_quant_entry(item.orig_mod, item.mod_name, + getattr(item.orig_mod, item.mod_name))) + + # Add custom modules to the quant_map + if custom_map is not None: + for item in custom_map: + # append the custom modules to the list that will be used in monkey patching + # Note that we convert a tuple to a named tuple here + self.quant_map.add(_quant_entry(item[0], item[1], item[2])) + # also store the original module in another list which will be used to reverse monkey patching + self.orginal_func_map.add(_quant_entry(item[0], item[1], getattr(item[0], item[1]))) + + def apply_quant_modules(self): + """ + For the modules registered in the quant_map, simply monkey patch them and also store the + original modules so that they could be later replaced back. + """ + for entry in self.quant_map: + setattr(entry.orig_mod, entry.mod_name, entry.replace_mod) + + def restore_float_modules(self): + """ + Reverse the effect of monkey patch by using the orginal_func_map to replace back the + original modules. + """ + for entry in self.orginal_func_map: + setattr(entry.orig_mod, entry.mod_name, entry.replace_mod) + +def initialize(float_module_list=None, custom_quant_modules=None): + """Dynamic module replacement using monkey patching. + + Dynamically monkey patches the modules with their quantized versions. Internally, the + state is maintained by a helper class object which helps in replacing the original + modules back. + + Args: + float_module_list: A list. User supplied list which indicates which modules to not monkey patch. + custom_quant_modules: A dict. A mapping provided by user to indicate any other module apart + from torch.nn and its corresponding quantized version. + + Returns: + nothing. + + Typical usage example: + + # Define the deny list for torch.nn modules and custom map for modules other than torch.nn. + float_module_list = ["Linear"] + custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)] + ## Monkey patch the modules + pytorch_quantization.quant_modules.initialize(float_module_list, custom_modules) + ## Use the quantized modules + pytorch_quantization.quant_modules.deactivate() + """ + _quant_module_helper_object.prepare_state(float_module_list, custom_quant_modules) + _quant_module_helper_object.apply_quant_modules() + +def deactivate(): + """Dynamic module replacement which reverses the monkey patching. + + Dynamically replaces back the original modules that were monkey patched earlier + in the initialize() function call using helper class object which maintains the state. + """ + _quant_module_helper_object.restore_float_modules() + +# Global object that maintains the state of the modules that are replaced. +_quant_module_helper_object = QuantModuleReplacementHelper() diff --git a/tools/pytorch-quantization/pytorch_quantization/tensor_quant.py b/tools/pytorch-quantization/pytorch_quantization/tensor_quant.py new file mode 100644 index 00000000..ee862a6b --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/tensor_quant.py @@ -0,0 +1,428 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Basic tensor quantization functions""" +import numpy as np +import yaml + +from absl import logging + +import torch +from torch.autograd import Function + +from pytorch_quantization.utils import amp_wrapper as amp + + +class ScaledQuantDescriptor(): + """Supportive descriptor of quantization + + Describe how a tensor should be quantized. A QuantDescriptor and a tensor defines a quantized tensor. + + Args: + num_bits: An integer. Number of bits of quantization. It is used to calculate scaling factor. Default 8. + name: Seems a nice thing to have + + Keyword Arguments: + fake_quant: A boolean. If True, use fake quantization mode. Default True. + axis: None, int or tuple of int. axes which will have its own max for computing scaling factor. + If None (the default), use per tensor scale. + Must be in the range [-rank(input_tensor), rank(input_tensor)). + e.g. For a KCRS weight tensor, quant_axis=(0) will yield per channel scaling. + Default None. + amax: A float or list/ndarray of floats of user specified absolute max range. If supplied, + ignore quant_axis and use this to quantize. If learn_amax is True, will be used to initialize + learnable amax. Default None. + learn_amax: A boolean. If True, learn amax. Default False. + scale_amax: A float. If supplied, multiply amax by scale_amax. Default None. It is useful for some + quick experiment. + calib_method: A string. One of ["max", "histogram"] indicates which calibration to use. Except the simple + max calibration, other methods are all hisogram based. Default "max". + unsigned: A Boolean. If True, use unsigned. Default False. + + Raises: + TypeError: If unsupported type is passed in. + + Read-only properties: + - fake_quant: + - name: + - learn_amax: + - scale_amax: + - axis: + - calib_method: + - num_bits: + - amax: + - unsigned: + """ + + def __init__(self, num_bits=8, name=None, **kwargs): + if not isinstance(num_bits, int): + raise TypeError("num_bits must be an integer, not {}.".format(type(num_bits))) + if num_bits < 0: + raise ValueError("num_bits must be >= 0, not {}.".format(num_bits)) + if num_bits == 0: + logging.error("num_bits is 0. This will result in the tensor being quantized to all zeros." + " This mode should only be used for debugging purposes.") + self._num_bits = num_bits + if not isinstance(name, str) and name is not None: + raise TypeError("name must be a string or None, not {}.".format(type(name))) + self._name = name + + self._fake_quant = kwargs.pop('fake_quant', True) + self._axis = kwargs.pop('axis', None) + if self._axis is not None: + logging.debug("Meaning of axis has changed since v2.0. Make sure to update.") + self._learn_amax = kwargs.pop('learn_amax', False) + if self._learn_amax and self._axis is not None: + raise TypeError( + "axis is ignored and must be None when learn_amax is true, got {}.".format(type(self._axis))) + amax = kwargs.pop('amax', None) + if amax is not None: + if not isinstance(amax, float) and not isinstance( + amax, list) and not isinstance(amax, np.ndarray): + raise TypeError("amax must be float, list or ndarray, not {}".format(type(amax))) + # Make it single precision array + self._amax = np.array(amax, dtype=np.float32) + else: + self._amax = amax + + self._scale_amax = kwargs.pop('scale_amax', None) + self._calib_method = kwargs.pop('calib_method', "max") + self._unsigned = kwargs.pop('unsigned', False) + self._narrow_range = kwargs.pop('narrow_range', True) + + if kwargs: + raise TypeError("Unused keys: {}".format(kwargs.keys())) + + # pylint:disable=missing-docstring + @property + def num_bits(self): + return self._num_bits + + @property + def fake_quant(self): + return self._fake_quant + + @property + def axis(self): + return self._axis + + @property + def amax(self): + return self._amax + + @property + def learn_amax(self): + return self._learn_amax + + @property + def scale_amax(self): + return self._scale_amax + + @property + def name(self): + return self._name + + @property + def calib_method(self): + return self._calib_method + + @property + def unsigned(self): + return self._unsigned + + @property + def narrow_range(self): + return self._narrow_range + # pylint:enable=missing-docstring + + def __str__(self): + s = (self._name + ': ') if self._name is not None else 'QuantDescriptor' + s += "({}{}bit".format("unsigned " if self._unsigned else "", self._num_bits) + s += " fake" if self._fake_quant else " real" + s += " axis={}".format(self._axis if self._axis is not None else " per-tensor") + if isinstance(self._amax, torch.Tensor): + s += " amax={}".format(np.array2string(self._amax.cpu().numpy().flatten(), edgeitems=1, + formatter={'all': "{:.2e}".format})) + elif self._amax is not None: + s += " amax={_amax}" + s += " full_range" + if self._learn_amax: + s += " learn_amax" + if self._scale_amax: + s += " scale_amax={_scale_amax}" + s += ")" + return s.format(**self.__dict__) + + def __eq__(self, rhs): + """Compare 2 descriptors""" + return self.__dict__ == rhs.__dict__ + + def dict(self): + """Serialize to dict + + The build-in __dict__ method returns all the attributes, which includes those have default value and have + protected prefix "_". This method only returns those have values other than the default one and don't have _ in + key. Construct a instance by dict returned by this method should get exactly the same instance. + """ + obj_dict = {} + obj_dict['num_bits'] = self._num_bits + obj_dict['name'] = self._name + + if not self._fake_quant: + obj_dict['fake_quant'] = self._fake_quant + if self._axis is not None: + obj_dict['axis'] = self._axis + if self._amax is not None: + obj_dict['amax'] = self._amax.tolist() + if self._scale_amax is not None: + obj_dict['scale_amax'] = self._scale_amax + if self._learn_amax: + obj_dict['learn_amax'] = self._learn_amax + if self._unsigned: + obj_dict['unsigned'] = self._unsigned + + return obj_dict + + def to_yaml(self): + """Create yaml serialization + Some attributes need special treatment to have human readable form, including amax, axis. + """ + obj_dict = self.dict() + + if "axis" in obj_dict: + obj_dict['axis'] = list(obj_dict['axis']) + return yaml.dump(obj_dict, width=120) + + @classmethod + def from_yaml(cls, yaml_str): + """Create descriptor from yaml str""" + obj_dict = yaml.safe_load(yaml_str) + + if 'axis' in obj_dict: + obj_dict['axis'] = tuple(obj_dict['axis']) + + quant_desc = cls(**obj_dict) + + return quant_desc + +QuantDescriptor = ScaledQuantDescriptor + +# Predefined descriptors +QUANT_DESC_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8) +QUANT_DESC_UNSIGNED_8BIT_PER_TENSOR = QuantDescriptor(num_bits=8, unsigned=True) +QUANT_DESC_8BIT_CONV1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONV3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW = QuantDescriptor(num_bits=8, axis=(0)) +QUANT_DESC_8BIT_CONVTRANSPOSE1D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) +QUANT_DESC_8BIT_CONVTRANSPOSE2D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) +QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL = QuantDescriptor(num_bits=8, axis=(1)) + + +class TensorQuantFunction(Function): + """A universal tensor quantization function + + Take an input tensor, output an quantized tensor. The granularity of scale can be interpreted from the + shape of amax. + output_dtype indicates whether the quantized value will be stored in integer or float. The reason we want to store + it in float is the pytorch function takes the quantized value may not accept integer input, e.g. Conv2D. + + It uses 2^num_bits -1 values instead of 2^num_bits. e.g., for num_bits=8, it uses [-127, 127] instead of [-128, 127] + """ + + @staticmethod + def forward(ctx, inputs, amax, num_bits=8, unsigned=False, narrow_range=True): + """ + + Follow tensorflow convention, max value is passed in and used to decide scale, instead of inputing scale + directly. Though inputing scale directly may be more natural to use. + + Args: + ctx: A Context object to store tensors for backward. + inputs: A Tensor of type float32. + amax: A Tensor of type float32. Inputs will be quantized within range [-amax, amax] + amax will be broadcasted to inputs tensor. + num_bits: A integer used to calculate scaling factor, scale = (2^(num_bits-1) - 1) / max + Effectively, it indicates how many integer bits is used to represent the value. Default 8. + output_dtype: A type of Tensor. torch.int32 or torch.float32. + unsigned: A boolean. Use unsigned integer range. E.g. [0, 255] for num_bits=8. Default False. + narrow_range: A boolean. Use symmetric integer range for signed quantization + E.g. [-127,127] instead of [-128,127] for num_bits=8. Default True. + + Returns: + outputs: A Tensor of type output_dtype. + scale: A Tensor of type float32. outputs / scale will dequantize outputs tensor. + + Raises: + ValueError: + """ + ctx.save_for_backward(inputs, amax) + outputs, scale = _tensor_quant(inputs, amax, num_bits, unsigned, narrow_range) + # Check if scale overflows FP16 + if outputs.dtype == torch.half and scale.max() > 65504: + raise ValueError("scale is too large for FP16 with amax={}".format(amax)) + return outputs, scale.to(inputs.dtype) + + @staticmethod + def backward(ctx, grad_outputs, grad_scale): + """ + Implements straight through estimation with clipping. For -amax <= input <= amax + the gradient passes straight through, otherwise the gradient is zero. + + Args: + ctx: A Context object with saved tensors from forward. + grad_outputs: A tensor of gradient of outputs. + grad_scale: A tensor of gradient of scale. + + Returns: + grad_inputs: A tensor of gradient. + """ + inputs, amax = ctx.saved_tensors + zero = grad_outputs.new_zeros(1) # create a zero tensor with the same type and device + grad_inputs = torch.where(inputs.abs() <= amax, grad_outputs, zero) + return grad_inputs, None, None, None, None + + +class FakeTensorQuantFunction(Function): + """Fake version of TensorQuantFunction + See comments of TensorQuantFunction, arguments are the same. + """ + + @staticmethod + def forward(ctx, inputs, amax, num_bits=8, unsigned=False, narrow_range=True): + ctx.save_for_backward(inputs, amax) + outputs, scale = _tensor_quant(inputs, amax, num_bits, unsigned, narrow_range) + return outputs / scale.to(inputs.dtype) + + @staticmethod + def backward(ctx, grad_outputs): + inputs, amax = ctx.saved_tensors + zero = grad_outputs.new_zeros(1) + grad_inputs = torch.where(inputs.abs() <= amax, grad_outputs, zero) + return grad_inputs, None, None, None, None + +def _tensor_quant(inputs, amax, num_bits=8, unsigned=False, narrow_range=True): + """Shared function body between TensorQuantFunction and FakeTensorQuantFunction""" + # Fine scale, per channel scale will be handled by broadcasting, which could be tricky. Pop a warning. + if isinstance(amax, torch.Tensor) and inputs.dim() != amax.dim(): + logging.debug("amax %s has different shape than inputs %s. Make sure broadcast works as expected!", + amax.size(), inputs.size()) + + logging.debug("{} bits quantization on shape {} tensor.".format(num_bits, inputs.size())) + + if unsigned: + if inputs.min() < 0.: + raise TypeError("Negative values encountered in unsigned quantization.") + + # Computation must be in FP32 to prevent potential over flow. + input_dtype = inputs.dtype + if inputs.dtype == torch.half: + inputs = inputs.float() + if amax.dtype == torch.half: + amax = amax.float() + + min_amax = amax.min() + if min_amax < 0: + raise ValueError("Negative values in amax") + + max_bound = torch.tensor((2.0**(num_bits - 1 + int(unsigned))) - 1.0, device=amax.device) + if unsigned: + min_bound = 0 + elif narrow_range: + min_bound = -max_bound + else: + min_bound = -max_bound - 1 + scale = max_bound / amax + + epsilon = 1. / (1<<24) + if min_amax <= epsilon: # Treat amax smaller than minimum representable of fp16 0 + zero_amax_mask = (amax <= epsilon) + scale[zero_amax_mask] = 0 # Value quantized with amax=0 should all be 0 + + outputs = torch.clamp((inputs * scale).round_(), min_bound, max_bound) + + if min_amax <= epsilon: + scale[zero_amax_mask] = 1. # Return 1 makes more sense for values quantized to 0 with amax=0 + + if input_dtype == torch.half: + outputs = outputs.half() + + return outputs, scale + +class FakeAffineTensorQuantFunction(Function): + """Fake version of affine quantization + + gemmlowp style scale+shift quantization. See more details in + https://github.com/google/gemmlowp/blob/master/doc/quantization.md. + + We DO NOT recommend affine quantization on weights for performance reason. There might be value to affine quantize + activation as it can be cancelled by bias and comes with no performance penalty. This functionality is only added + for experimental purpose. + """ + + @staticmethod + def forward(ctx, inputs, min_range, max_range, num_bits=8): + """ + + As it will be only applied on activation with per tensor granularity, broadcast is not needed. + + Args: + ctx: Pytorch convention. + inputs: A Tensor of type float32. + min_range: A float. + max_range: A float. + num_bits: An integer + + Returns: + outputs: A Tensor of type output_dtype + """ + logging.debug("{} bits quantization on shape {} tensor.".format(num_bits, inputs.size())) + ctx.save_for_backward(inputs, min_range, max_range) + + step_size = (max_range - min_range) / (2.0**num_bits - 1) + + min_bound = -2.0**(num_bits - 1) + max_bound = 2.0**(num_bits - 1) - 1 + + quant_zero = torch.round(min_range / step_size) - min_bound + quantized = torch.round(inputs / step_size) - quant_zero + quantized = torch.clamp(quantized, min_bound, max_bound) + + outputs = (quantized + quant_zero) * step_size + + return outputs + + @staticmethod + def backward(ctx, grad_outputs): + """ + Args: + ctx: Pytorch convention. + grad_output: A tensor of gradient of outputs + + Returns: + grad_inputs: A tensor of gradient + """ + + inputs, min_range, max_range = ctx.saved_tensors + zero = grad_outputs.new_zeros(1) + grad_inputs = torch.where((inputs <= max_range)*(inputs >= min_range), grad_outputs, zero) + return grad_inputs, None, None, None + + +tensor_quant = amp.promote_function(TensorQuantFunction.apply) +fake_tensor_quant = amp.promote_function(FakeTensorQuantFunction.apply) +fake_affine_tensor_quant = amp.promote_function(FakeAffineTensorQuantFunction.apply) diff --git a/tools/pytorch-quantization/pytorch_quantization/utils/__init__.py b/tools/pytorch-quantization/pytorch_quantization/utils/__init__.py new file mode 100644 index 00000000..177d41ee --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/utils/__init__.py @@ -0,0 +1,20 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Main entry of all utils""" + +from .reduce_amax import reduce_amax diff --git a/tools/pytorch-quantization/pytorch_quantization/utils/amp_wrapper.py b/tools/pytorch-quantization/pytorch_quantization/utils/amp_wrapper.py new file mode 100644 index 00000000..fe085c61 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/utils/amp_wrapper.py @@ -0,0 +1,37 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""This makes the amp decorators work as normal when amp is available, and otherwise act as no-op pass-throughs.""" + +from absl import logging + +try: + import apex.amp as amp + def half_function(fn): + return amp.half_function(fn) + def float_function(fn): + return amp.float_function(fn) + def promote_function(fn): + return amp.promote_function(fn) +except Exception: + logging.error("AMP is not avaialble.") + def half_function(fn): + return fn + def float_function(fn): + return fn + def promote_function(fn): + return fn diff --git a/tools/pytorch-quantization/pytorch_quantization/utils/quant_logging.py b/tools/pytorch-quantization/pytorch_quantization/utils/quant_logging.py new file mode 100644 index 00000000..b5b8b2bf --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/utils/quant_logging.py @@ -0,0 +1,26 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""A WAR for codes that messes up logging format""" + +import logging + +def reset_logger_handler(): + """Remove all handler in root logger""" + root_logger = logging.getLogger() + while root_logger.handlers: + root_logger.removeHandler(root_logger.handlers[0]) diff --git a/tools/pytorch-quantization/pytorch_quantization/utils/reduce_amax.py b/tools/pytorch-quantization/pytorch_quantization/utils/reduce_amax.py new file mode 100644 index 00000000..13c36c17 --- /dev/null +++ b/tools/pytorch-quantization/pytorch_quantization/utils/reduce_amax.py @@ -0,0 +1,62 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Function to get absolute maximum of a tensor +Follow numpy fashion, which is more generic as pytorch's +""" + +import torch + +def reduce_amax(input, axis=None, keepdims=True): + """Compute the absolute maximum value of a tensor. + + Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, + the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, + the reduced dimensions are retained with length 1. + + .. note:: + Gradient computeation is disabled as this function is never meant learning reduces amax + + Args: + input: Input tensor + axis: The dimensions to reduce. None or int or tuple of ints. If None (the default), + reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)). + keepdims: A boolean. If true, retains reduced dimensions with length 1. Default True + granularity: DEPRECTED. specifies if the statistic has to be calculated at tensor or channel granularity + + Returns: + The reduced tensor. + + Raises: + ValueError: Any axis which doesn't make sense or is not supported + ValueError: If unknown granularity is passed in. + """ + with torch.no_grad(): + output = input.abs() + if axis is None: + output = torch.max(output) + else: + if isinstance(axis, int): + output, _ = torch.max(output, dim=axis, keepdim=keepdims) + else: + if isinstance(axis, tuple) and len(axis) > input.dim(): + raise ValueError("Cannot reduce more axes than tensor's dim.") + for i in axis: + output, _ = torch.max(output, dim=i, keepdim=True) + if not keepdims or output.numel() == 1: + output.squeeze_() + return output diff --git a/tools/pytorch-quantization/requirements.txt b/tools/pytorch-quantization/requirements.txt new file mode 100644 index 00000000..655349eb --- /dev/null +++ b/tools/pytorch-quantization/requirements.txt @@ -0,0 +1,5 @@ +numpy +absl-py>=0.7.0 +scipy +sphinx_glpi_theme +prettytable diff --git a/tools/pytorch-quantization/scripts/onnx_export_per_channel.patch b/tools/pytorch-quantization/scripts/onnx_export_per_channel.patch new file mode 100644 index 00000000..8e443c59 --- /dev/null +++ b/tools/pytorch-quantization/scripts/onnx_export_per_channel.patch @@ -0,0 +1,14 @@ +@parse_args('v', 'v', 'v', 'i', 'i', 'i') +def fake_quantize_per_channel_affine(g, inputs, scale, zero_point, axis, quant_min=-128, quant_max=127): + if quant_min not in [0, -128] or quant_max not in [127, 255]: + raise TypeError("ONNX defines [0, 255] for quint8 and [-128, 127] for int8, got [{}, {}]".format( + quant_min, quant_max)) + # ONNX defines zero_point to be int8 or uint8 + if quant_min == 0: + zero_point = g.op("Cast", zero_point, to_i=sym_help.cast_pytorch_to_onnx['Byte']) + else: + zero_point = g.op("Cast", zero_point, to_i=sym_help.cast_pytorch_to_onnx['Char']) + return g.op( + "DequantizeLinear", + g.op("QuantizeLinear", inputs, scale, zero_point, axis_i=axis), + scale, zero_point, axis_i=axis) diff --git a/tools/pytorch-quantization/scripts/onnx_export_per_tensor.patch b/tools/pytorch-quantization/scripts/onnx_export_per_tensor.patch new file mode 100644 index 00000000..5e9aa730 --- /dev/null +++ b/tools/pytorch-quantization/scripts/onnx_export_per_tensor.patch @@ -0,0 +1,8 @@ +@parse_args('v', 't', 'i', 'i', 'i') +def fake_quantize_per_tensor_affine(g, inputs, scale, zero_point, quant_min=-128, quant_max=127): + if quant_min not in [0, -128] or quant_max not in [127, 255]: + raise TypeError("ONNX defines [0, 255] for quint8 and [-128, 127] for int8, got [{}, {}]".format( + quant_min, quant_max)) + scale = scale.float() # Avoid exportor generating double type + zero_point = torch.tensor(zero_point, dtype=torch.int8) # ONNX requires zero_point to be tensor + return g.op("DequantizeLinear", g.op("QuantizeLinear", inputs, scale, zero_point), scale, zero_point) diff --git a/tools/pytorch-quantization/scripts/patch_onnx_export.sh b/tools/pytorch-quantization/scripts/patch_onnx_export.sh new file mode 100755 index 00000000..a901f456 --- /dev/null +++ b/tools/pytorch-quantization/scripts/patch_onnx_export.sh @@ -0,0 +1,73 @@ +#!/bin/bash +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Patching script for supporting fake quantization in torch ONNX export +# See docs/source/userguide.rst for details + +PatchFile() { + TARGET_FILE=$1 + PATCH_FILE=$2 + if [ $(diff --unchanged-line-format= --old-line-format= --new-line-format='%L' "$TARGET_FILE" "$PATCH_FILE" | wc -l) -eq "0" ]; then + echo "$TARGET_FILE is already patched with $PATCH_FILE" + else + printf "\n#Fake quantization export\n" >> $TARGET_FILE + cat $PATCH_FILE >> $TARGET_FILE + echo "$TARGET_FILE was patched." + fi +} + +# Check if the patch file exists +CWD="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +PATCH_C="${CWD}/onnx_export_per_channel.patch" +if [ -f $PATCH_C ]; then + echo "Found patch: $PATCH_C" +else + echo "$PATCH_C does not exist." + exit 1 +fi +PATCH_T="${CWD}/onnx_export_per_tensor.patch" +if [ -f $PATCH_T ]; then + echo "Found patch: $PATCH_T" +else + echo "$PATCH_T does not exist." + exit 1 +fi + +# Check if the ONNX opset file exists +DIR=$(pip show torch | grep Location | cut -d' ' -f 2) +FILE13="${DIR}/torch/onnx/symbolic_opset13.py" +FILE10="${DIR}/torch/onnx/symbolic_opset10.py" + +# Patch per tensor +if [ -f $FILE10 ]; then + # Patch per tensor in opset 10 + PatchFile "$FILE10" "$PATCH_T" +else + echo "File ${FILE10} does not exist." + exit 1 +fi + +# Patch per channel +if [ -f $FILE13 ]; then + # Patch in opset 13 + PatchFile "$FILE13" "$PATCH_C" +else + # Patch in opset 10 + PatchFile "$FILE10" "$PATCH_C" +fi + +echo "Done." +exit 0 diff --git a/tools/pytorch-quantization/setup.cfg b/tools/pytorch-quantization/setup.cfg new file mode 100644 index 00000000..ed5822d9 --- /dev/null +++ b/tools/pytorch-quantization/setup.cfg @@ -0,0 +1,6 @@ +[aliases] +test=pytest + +[tool:pytest] +addopts = --verbose +python_files = tests/*.py diff --git a/tools/pytorch-quantization/setup.py b/tools/pytorch-quantization/setup.py new file mode 100644 index 00000000..ed8a6e8b --- /dev/null +++ b/tools/pytorch-quantization/setup.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Simple setup script""" + +import os +from setuptools import setup, find_packages +from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension + +abspath = os.path.dirname(os.path.realpath(__file__)) + +with open(os.path.join(abspath, "requirements.txt")) as f: + requirements = f.read().splitlines() + +setup( + name="pytorch_quantization", + version="2.0.0", + description="NVIDIA Pytorch quantization toolkit", + packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]), + setup_requires=["pytest-runner"], + tests_require=["pytest"], + + install_requires=requirements, + ext_modules=[ + CUDAExtension( + name="pytorch_quantization.cuda_ext", + sources=[os.path.join(abspath, "src/tensor_quant.cpp"), + os.path.join(abspath, "src/tensor_quant_gpu.cu")]) + ], + cmdclass={ + "build_ext": BuildExtension + }, + zip_safe=False, + + long_description=open("README.md", "r", encoding="utf-8").read(), + url="https://github.com/nvidia/tensorrt/tools/pytorch-quantization", + author="NVIDIA", + author_email="nvidia@nvidia.com", + +) diff --git a/tools/pytorch-quantization/src/tensor_quant.cpp b/tools/pytorch-quantization/src/tensor_quant.cpp new file mode 100644 index 00000000..7a149589 --- /dev/null +++ b/tools/pytorch-quantization/src/tensor_quant.cpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +#include + +void fake_tensor_quant_cuda_inplace(at::Tensor, at::Tensor, int, bool); +at::Tensor fake_tensor_quant_cuda(at::Tensor, at::Tensor, int, bool); +at::Tensor fake_tensor_quant_with_axis_cuda(at::Tensor, at::Tensor, int, int, bool); +float bits_to_bound(int, int); + +void fake_tensor_quant_inplace(at::Tensor inputs, at::Tensor amax, int num_bits=8, bool is_unsigned=false) { + TORCH_CHECK(amax.numel(), 1); + + float bound = bits_to_bound(num_bits, is_unsigned); + float scale = bound / amax.data_ptr()[0]; + for (int i = 0; i < inputs.numel(); ++i) { + float output = round(inputs.data_ptr()[i] * scale); + output = output > bound ? bound : output; + output = output < -bound ? -bound : output; + + inputs.data_ptr()[i] = output / scale; + } +} + +void fake_tensor_quant_(at::Tensor inputs, at::Tensor amax, int num_bits=8, bool is_unsigned=false) { + TORCH_CHECK(amax.numel(), 1); + if (inputs.type().is_cuda()) { + fake_tensor_quant_cuda_inplace(inputs, amax, num_bits, is_unsigned); + } else { + fake_tensor_quant_inplace(inputs, amax, num_bits, is_unsigned); + } +} + +at::Tensor fake_tensor_quant(at::Tensor inputs, at::Tensor amax, int num_bits=8, bool is_unsigned=false) { + TORCH_CHECK(amax.numel(), 1); + if (inputs.type().is_cuda()) { + return fake_tensor_quant_cuda(inputs, amax, num_bits, is_unsigned); + } else { + auto outputs = torch::clone(inputs); + fake_tensor_quant_inplace(outputs, amax, num_bits, is_unsigned); + return outputs; + } +} + +at::Tensor fake_tensor_quant_with_axis( + at::Tensor inputs, at::Tensor amax, int axis, int num_bits=8, bool is_unsigned=false) { + TORCH_CHECK(amax.numel(), inputs.size(axis)); + if (inputs.type().is_cuda()) { + return fake_tensor_quant_with_axis_cuda( + inputs, amax, axis, num_bits, is_unsigned); + } else { + throw std::runtime_error("axis is only supported on GPU."); + } +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fake_tensor_quant_", &fake_tensor_quant_, "Fake Tensor Quant Inplace", py::arg("inputs"), py::arg("amax"), + py::arg("num_bits")=8, py::arg("unsigned")=false); + m.def("fake_tensor_quant", &fake_tensor_quant, "Fake Tensor Quant", py::arg("inputs"), py::arg("amax"), + py::arg("num_bits")=8, py::arg("unsigned")=false); + m.def("fake_tensor_quant_with_axis", &fake_tensor_quant_with_axis, + "Fake Tensor Quant with axis", py::arg("inputs"), py::arg("amax"), + py::arg("axis"), py::arg("num_bits")=8, py::arg("unsigned")=false); +} diff --git a/tools/pytorch-quantization/src/tensor_quant_gpu.cu b/tools/pytorch-quantization/src/tensor_quant_gpu.cu new file mode 100644 index 00000000..ef1eed2c --- /dev/null +++ b/tools/pytorch-quantization/src/tensor_quant_gpu.cu @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +#include +#include + +#include +#include +#include +#include + +#define BLOCK_SIZE 128 + +__host__ __device__ float bits_to_bound(int num_bits, int is_unsigned) { + float bound = (1 << (num_bits - 1 + int(is_unsigned))) - 1; + return bound; +} + +template __device__ T fake_tensor_quant_device(T input, T amax, int bound); + +template <> +__device__ float fake_tensor_quant_device(float input, float amax, int bound) { + float scale = bound / amax; + float output = round(input * scale); + output = output > bound ? bound : output; + output = output < -bound ? -bound : output; + + return output / scale; +} + +template <> +__device__ at::Half fake_tensor_quant_device(at::Half input, at::Half amax, int bound) { + float output = fake_tensor_quant_device(__half2float(input), __half2float(amax), bound); + + return __float2half(output); +} + +// Sepcialize double only to pass Aten dispatch macros +template <> +__device__ double fake_tensor_quant_device(double input, double amax, int bound) { + float output = fake_tensor_quant_device(input, amax, bound); + + return output; +} + +template +__global__ void fake_tensor_quant_kernel( + const T *inputs, size_t n, T *outputs, + const T *amax, int num_bits=8, bool is_unsigned=false) { + int tid = blockIdx.x * blockDim.x + threadIdx.x; + + if (tid < n) { + float bound = bits_to_bound(num_bits, is_unsigned); + outputs[tid] = fake_tensor_quant_device(inputs[tid], amax[0], bound); + } +} + +void fake_tensor_quant_cuda_inplace(at::Tensor inputs, at::Tensor amax, int num_bits=8, bool is_unsigned=false) { + size_t numel = inputs.numel(); + AT_DISPATCH_FLOATING_TYPES_AND_HALF(inputs.type().scalarType(), "fake_tensor_quant_cuda_inplace", [&] { + fake_tensor_quant_kernel<<>>( + inputs.data_ptr(), numel, inputs.data_ptr(), + amax.data_ptr(), num_bits, is_unsigned); + }); +} + +at::Tensor fake_tensor_quant_cuda(at::Tensor inputs, at::Tensor amax, int num_bits=8, bool is_unsigned=false) { + size_t numel = inputs.numel(); + auto outputs = torch::zeros_like(inputs); + AT_DISPATCH_FLOATING_TYPES_AND_HALF(inputs.type().scalarType(), "fake_tensor_quant_cuda", [&] { + fake_tensor_quant_kernel<<>>( + inputs.data_ptr(), numel, outputs.data_ptr(), + amax.data_ptr(), num_bits, is_unsigned); + }); + + return outputs; +} + +__global__ void fake_tensor_quant_with_axis_cuda_kernel( + const float *inputs, size_t n, float *outputs, + const float *amax, int axis_size, int outer_size, int num_bits=8, bool is_unsigned=false) { + int tid = blockIdx.x * blockDim.x + threadIdx.x; + + float bound = bits_to_bound(num_bits, is_unsigned); + + for (int idx = 4 * tid; idx < 4 * (tid + 1) && idx < n; ++idx) { + int axis_idx = (idx / outer_size) % axis_size; + + outputs[idx] = fake_tensor_quant_device(inputs[idx], amax[axis_idx], bound); + } +} + +at::Tensor fake_tensor_quant_with_axis_cuda( + at::Tensor inputs, at::Tensor amax, int axis, int num_bits=8, bool is_unsigned=false) { + auto outputs = torch::empty_like(inputs); + size_t numel = inputs.numel(); + int axis_size = inputs.size(axis); + + int outer_size = 1; + for (int i = axis + 1; i < inputs.dim(); ++i) { + outer_size *= inputs.size(i); + } + + fake_tensor_quant_with_axis_cuda_kernel<<>>( + inputs.data_ptr(), numel, outputs.data_ptr(), amax.data_ptr(), axis_size, outer_size, + num_bits, is_unsigned); + return outputs; +} + diff --git a/tools/pytorch-quantization/tests/__init__.py b/tools/pytorch-quantization/tests/__init__.py new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/pytorch-quantization/tests/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/tests/calibrator_test.py b/tools/pytorch-quantization/tests/calibrator_test.py new file mode 100644 index 00000000..57bc3a46 --- /dev/null +++ b/tools/pytorch-quantization/tests/calibrator_test.py @@ -0,0 +1,305 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Tests of calibrators""" +import pytest +import numpy as np + +import torch + +from pytorch_quantization import utils as quant_utils +from pytorch_quantization import calib +import tests.utils as test_utils +from tests.fixtures import verbose + +np.random.seed(12345) +torch.manual_seed(12345) + +# pylint:disable=missing-docstring, no-self-use + +class TestMaxCalibrator(): + + def test_simple_run(self): + max_calibrator = calib.MaxCalibrator(8, None, False) + + x_1 = torch.rand(129).cuda() + x_2 = torch.rand(127).cuda() + max_calibrator.collect(x_1) + max_calibrator.collect(x_2) + + test_utils.compare(max_calibrator.compute_amax(), torch.max(x_1.max(), x_2.max()), atol=0, rtol=0, ctol=0) + + # Nothing to test other than creation + max_calibrator = calib.MaxCalibrator(8, None, True) + + def test_fine_grain(self): + axis = 0 + reducs_axis = (1, 2, 3) + max_calibrator = calib.MaxCalibrator(8, axis, False) + + x_1 = torch.rand(31, 63, 7, 7).cuda() + x_2 = torch.rand(31, 63, 7, 7).cuda() + max_calibrator.collect(x_1) + max_calibrator.collect(x_2) + + assert max_calibrator.compute_amax().shape[0] == 31 + + test_utils.compare(max_calibrator.compute_amax(), + quant_utils.reduce_amax(torch.max(x_1, x_2), axis=reducs_axis), + atol=0, rtol=0, ctol=0) + + max_calibrator.reset() + assert max_calibrator.compute_amax() is None + + def test_raises(self): + axis = 0 + max_calibrator = calib.MaxCalibrator(8, axis, False) + + x_2 = torch.rand(32, 63, 7, 7).cuda() + x_3 = torch.rand(33, 63, 7, 7).cuda() + max_calibrator.collect(x_2) + with pytest.raises(RuntimeError, match="shape changed"): + max_calibrator.collect(x_3) + + def test_track_amax(self): + max_calibrator = calib.MaxCalibrator(8, None, False, track_amax=True) + + x_1 = torch.rand(129).cuda() + x_2 = torch.rand(127).cuda() + max_calibrator.collect(x_1) + max_calibrator.collect(x_2) + + test_utils.compare(max_calibrator.compute_amax(), torch.max(x_1.max(), x_2.max()), atol=0, rtol=0, ctol=0) + np.testing.assert_array_equal(max_calibrator.amaxs[0], x_1.max().cpu().numpy()) + np.testing.assert_array_equal(max_calibrator.amaxs[1], x_2.max().cpu().numpy()) + + def test_repr(self): + max_calibrator = calib.MaxCalibrator(8, None, False, track_amax=True) + repr(max_calibrator) + +class TestHistogramCalibrator(): + + def test_grow(self, verbose): + x_1 = torch.tensor([0, 255, 255, 255, 255, 255]).cuda() + x_2 = torch.tensor([0, 255, 255, 255, 255, 256]).cuda() + + hist_calibrator = calib.HistogramCalibrator(8, None, False, grow_method='stretch') + hist_calibrator.collect(x_1) + hist_calibrator.collect(x_2) + + amax = hist_calibrator.compute_amax(method='entropy') + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be closer to 256 because the last bin gets stretched to (~255, 257) + assert (amax - 255.).abs() < (amax - 256.).abs() + + hist_calibrator = calib.HistogramCalibrator(8, None, False, grow_method='append') + hist_calibrator.collect(x_1) + hist_calibrator.collect(x_2) + + amax = hist_calibrator.compute_amax(method='mse') + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be closer to 255 + assert (amax - 255.).abs() < 0.5 + + def test_skip_zeros(self, verbose): + x_1 = torch.tensor([0, 0, 0, 0, 0, 1, 2, 3, 4, 5]) + x_2 = torch.tensor([0, 0, 0, 0, 0, 6, 7, 8, 9, 10]) + + calibrator = calib.HistogramCalibrator(8, None, False, skip_zeros=True) + calibrator.collect(x_1) + calibrator.collect(x_2) + + amax = calibrator.compute_amax("percentile", percentile=50) + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be close to 5 + assert (amax - 5.).abs() < 10/2048 + +class TestEntropyCalibrator(): + + def test_one_tensor(self, verbose): + hist_calibrator = calib.HistogramCalibrator(8, None, False, grow_method='stretch') + + x_2 = torch.rand(11, 7, 3, 3).cuda() # uniform in (0,1) + x_2[1, 1, 1, 1] = 10. # create outlier + hist_calibrator.collect(x_2) + + # Don't have a better test metric. One outlier 10 should be discared by KL-divergence + amax = hist_calibrator.compute_amax("entropy") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + assert amax < 1.1 + + def test_unsigned(self, verbose): + hist_calibrator = calib.HistogramCalibrator(8, None, True, grow_method='stretch') + + x_2 = torch.rand(11, 7, 3, 3).cuda() # uniform in (0,1) + x_2[1, 1, 1, 1] = 10. # create outlier + hist_calibrator.collect(x_2) + + amax = hist_calibrator.compute_amax("entropy") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + assert amax < 1.1 + + def test_two_tensor(self, verbose): + hist_calibrator = calib.HistogramCalibrator(8, None, False, grow_method='stretch') + + x_2 = torch.rand(11, 7, 3, 3).cuda() # uniform in (0,1) + x_2[1, 1, 1, 1] = 10. # create outlier + + + x_2 = torch.rand(11, 7, 3, 3).cuda() # uniform in (0,1) + x_2[1, 1, 1, 1] = 10. # create outlier + hist_calibrator.collect(x_2) + x_3 = torch.rand(11, 7, 3, 3).cuda() + hist_calibrator.collect(x_3) + + # Don't have a better test metric. One outlier 10 should be discared by KL-divergence + amax = hist_calibrator.compute_amax("entropy") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + assert amax < 1.1 + + def test_repr(self): + hist_calibrator = calib.HistogramCalibrator(8, None, True) + repr(hist_calibrator) + +class TestMSECalibrator(): + + def test_one_tensor(self, verbose): + calibrator = calib.HistogramCalibrator(8, None, False) + + x_1 = torch.ones(11, 7, 3, 3).cuda() * 255. + x_1[1, 1, 1, 1] = 256. # create an outlier + calibrator.collect(x_1) + + amax = calibrator.compute_amax("mse") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be closer to 255 + assert (amax - 255.).abs() < (amax - 256.).abs() + + def test_unsigned_one_tensor(self, verbose): + calibrator = calib.HistogramCalibrator(8, None, True) + + x_1 = torch.ones(11, 7, 3, 3).cuda() * 512. + x_1[1, 1, 1, 1] = 513. # create an outlier + calibrator.collect(x_1) + + amax = calibrator.compute_amax("mse") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be closer to 512 + assert (amax - 512.).abs() < (amax - 513.).abs() + + def test_two_tensor(self, verbose): + calibrator = calib.HistogramCalibrator(8, None, False) + + x_1 = torch.ones(11, 7, 3, 3).cuda() * 255. + x_1[1, 1, 1, 1] = 256. # create an outlier + calibrator.collect(x_1) + x_2 = torch.ones(11, 7, 3, 3).cuda() * 255. + calibrator.collect(x_2) + + amax = calibrator.compute_amax("mse") + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be closer to 255 + assert (amax - 255.).abs() < (amax - 256.).abs() + + def test_repr(self): + calibrator = calib.HistogramCalibrator(8, None, False) + repr(calibrator) + +class TestPercentileCalibrator(): + + def test_one_tensor(self, verbose): + calibrator = calib.HistogramCalibrator(8, None, False) + + x_1 = torch.arange(100) + calibrator.collect(x_1) + + amax = calibrator.compute_amax("percentile", percentile=90) + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be approximately 89 + assert (amax - 89.).abs() < 100/1024 + + def test_unsigned_one_tensor(self, verbose): + calibrator = calib.HistogramCalibrator( 8, None, True) + + x_1 = torch.arange(100) + calibrator.collect(x_1) + + amax = calibrator.compute_amax("percentile", percentile=80) + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be approximately 79 + assert (amax - 79.).abs() < 100/2048 + + def test_two_tensor(self, verbose): + calibrator = calib.HistogramCalibrator(8, None, False) + + x_1 = torch.arange(100) + calibrator.collect(x_1) + x_2 = torch.arange(0, 50, 0.5) + calibrator.collect(x_2) + amax = calibrator.compute_amax("percentile", percentile=99) + + if verbose: + print('amax={:.4f}'.format(amax.item()), end=' ') + + # amax should be approximately 97 + assert (amax - 97.).abs() < 100/1024 + + def test_repr(self): + calibrator = calib.HistogramCalibrator(8, None, False) + repr(calibrator) + + def test_range(self): + calibrator = calib.HistogramCalibrator(8, None, False) + x_1 = torch.arange(100) + calibrator.collect(x_1) + with pytest.raises(ValueError, match="range"): + calibrator.compute_amax("percentile", percentile=-10) + with pytest.raises(ValueError, match="range"): + calibrator.compute_amax("percentile", percentile=200) diff --git a/tools/pytorch-quantization/tests/classification_flow_test.py b/tools/pytorch-quantization/tests/classification_flow_test.py new file mode 100644 index 00000000..6a9abe22 --- /dev/null +++ b/tools/pytorch-quantization/tests/classification_flow_test.py @@ -0,0 +1,72 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Tests of the classification flow""" +import os +import subprocess +import sys +from os import path +import glob +import pytest +# pylint:disable=missing-docstring, no-self-use + +class TestClassificationFlow(): + + def test_resnet50(self, request, pytestconfig): + dir_path = os.path.dirname(os.path.realpath(__file__)) + dataset_dir = pytestconfig.getoption('--data-dir') + + # skip if the data dir flag was not set + if not dataset_dir: + pytest.skip("Prepare required dataset and use --data-dir option to enable") + + # Verify data dir exists + if not path.exists(dataset_dir): + print("Dataset path %s doesn't exist"%(dataset_dir), file=sys.stderr) + assert path.exists(dataset_dir) + + # Append required paths to PYTHONPATH + test_env = os.environ.copy() + if 'PYTHONPATH' not in test_env: + test_env['PYTHONPATH'] = "" + + # Add project root and torchvision to the path (assuming running in nvcr.io/nvidia/pytorch:20.08-py3) + test_env['PYTHONPATH'] += ":/opt/pytorch/vision/references/classification/:%s/../"%(dir_path) + + # Add requirement egg files manually to path since we're spawning a new process (downloaded by setuptools) + for egg in glob.glob(dir_path + "/../.eggs/*.egg"): + test_env['PYTHONPATH'] += ":%s"%(egg) + + # Run in a subprocess to avoid contaminating the module state for other test cases + ret = subprocess.run( + [ + 'python3', dir_path + '/../examples/torchvision/classification/classification_flow.py', + '--data-dir', dataset_dir, + '--model', 'resnet50', '--pretrained', + '-dpcq', + '-t', '0.5', + '--num-finetune-epochs', '1', + '--evaluate-onnx', + ], + env=test_env, + check=False, stdout=subprocess.PIPE) + + # If the test failed dump the output to stderr for better logging + if ret.returncode != 0: + print(ret.stdout, file=sys.stderr) + + assert ret.returncode == 0 diff --git a/tools/pytorch-quantization/tests/clip_test.py b/tools/pytorch-quantization/tests/clip_test.py new file mode 100644 index 00000000..a9ba8688 --- /dev/null +++ b/tools/pytorch-quantization/tests/clip_test.py @@ -0,0 +1,68 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of Clip module.""" + +import pytest +import numpy as np + +import torch + +from pytorch_quantization.nn.modules import clip + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +np.random.seed(1234) +torch.manual_seed(1234) + +# pylint:disable=missing-docstring, no-self-use + + +class TestClip(): + + def test_simple_run(self): + x_np = np.random.rand(1023).astype(np.float32) + x_torch = torch.Tensor(x_np) + clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7)) + clip_x_np = np.clip(x_np, 0.3, 0.7) + clip_x_torch = clip_op(x_torch) + np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np) + + def test_raise(self): + with pytest.raises(ValueError, match="must be scalar"): + clip_op = clip.Clip(torch.tensor(0.3), torch.tensor(0.7), learn_min=True) + + def test_backward(self): + x = torch.randn(3, 7, requires_grad=True) + x.retain_grad() + + min_value = 0.3 + max_value = 0.7 + clip_op = clip.Clip(min_value, max_value, learn_min=True, learn_max=True) + clip_x = clip_op(x) + clip_x.retain_grad() + + labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda() + criterion = torch.nn.CrossEntropyLoss() + loss = criterion(clip_x, labels) + + loss.backward() + + assert x.grad.cpu()[x < min_value].sum() == 0 + assert x.grad.cpu()[x > max_value].sum() == 0 + assert torch.equal(clip_x.grad[(x > min_value) & (x < max_value)], x.grad[(x > min_value) & (x < max_value)]) diff --git a/tools/pytorch-quantization/tests/conftest.py b/tools/pytorch-quantization/tests/conftest.py new file mode 100644 index 00000000..0202530e --- /dev/null +++ b/tools/pytorch-quantization/tests/conftest.py @@ -0,0 +1,24 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""local configuration for pytests""" + +import pytest + +def pytest_addoption(parser): + parser.addoption('--data-dir', type=str, dest="data_dir", + default='', help="set dataset dir for tests") diff --git a/tools/pytorch-quantization/tests/fixtures/__init__.py b/tools/pytorch-quantization/tests/fixtures/__init__.py new file mode 100644 index 00000000..36a4e8f5 --- /dev/null +++ b/tools/pytorch-quantization/tests/fixtures/__init__.py @@ -0,0 +1,21 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +import pytest +@pytest.fixture +def verbose(request): + return request.config.getoption("verbose") diff --git a/tools/pytorch-quantization/tests/fixtures/models.py b/tools/pytorch-quantization/tests/fixtures/models.py new file mode 100644 index 00000000..1fd432c9 --- /dev/null +++ b/tools/pytorch-quantization/tests/fixtures/models.py @@ -0,0 +1,68 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Model used for tests""" + +import pytest +import torch.nn as nn +import torch.nn.functional as F +from pytorch_quantization.nn import QuantConv2d, QuantLinear +from pytorch_quantization.tensor_quant import QuantDescriptor + +class LeNet(nn.Module): + def __init__(self, **kwargs): + super(LeNet, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5, **kwargs) + self.conv2 = nn.Conv2d(10, 20, kernel_size=5, **kwargs) + self.fc1 = nn.Linear(320, 50, **kwargs) + self.fc2 = nn.Linear(50, 10, **kwargs) + + def forward(self, x): + x = F.relu(F.max_pool2d(self.conv1(x), 2)) + x = F.relu(F.max_pool2d(self.conv2(x), 2)) + x = x.view(-1, 320) + x = F.relu(self.fc1(x)) + x = F.dropout(x, training=self.training) + x = self.fc2(x) + return F.log_softmax(x, dim=1) + + +class QuantLeNet(nn.Module): + def __init__(self, **kwargs): + super(QuantLeNet, self).__init__() + self.conv1 = QuantConv2d(1, 10, kernel_size=5, **kwargs) + self.conv2 = QuantConv2d(10, 20, kernel_size=5, **kwargs) + self.fc1 = QuantLinear(320, 50, **kwargs) + self.fc2 = QuantLinear(50, 10, **kwargs) + + def forward(self, x): + x = F.relu(F.max_pool2d(self.conv1(x), 2)) + x = F.relu(F.max_pool2d(self.conv2(x), 2)) + x = x.view(-1, 320) + x = F.relu(self.fc1(x)) + x = F.dropout(x, training=self.training) + x = self.fc2(x) + return F.log_softmax(x, dim=1) + +@pytest.fixture +def resnet18(): + import torchvision + return torchvision.models.resnet18() + +@pytest.fixture +def quant_lenet(): + return QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor()) diff --git a/tools/pytorch-quantization/tests/functional_test.py b/tools/pytorch-quantization/tests/functional_test.py new file mode 100644 index 00000000..96ef4049 --- /dev/null +++ b/tools/pytorch-quantization/tests/functional_test.py @@ -0,0 +1,93 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of supportive functions""" + +import pytest +import numpy as np + +import torch + +import pytorch_quantization.nn.functional as QF + +np.random.seed(1234) +torch.manual_seed(1234) + +# pylint:disable=missing-docstring, no-self-use + +torch.set_default_tensor_type('torch.cuda.FloatTensor') + + +class TestClip(): + + def test_simple_run(self): + x_np = np.random.rand(1023).astype(np.float32) + x_torch = torch.Tensor(x_np) + clip_x_np = np.clip(x_np, 0.3, 0.7) + clip_x_torch = QF.clip(x_torch, torch.tensor(0.3), torch.tensor(0.7)) + np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np) + + def test_raise(self): + x = torch.randn(3, 7, requires_grad=True) + + min_value = torch.Tensor(3, 7) + max_value = torch.Tensor(3, 7) + min_value.requires_grad = True + max_value.requires_grad = True + clip_x = QF.clip(x, min_value, max_value) + + labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda() + criterion = torch.nn.CrossEntropyLoss() + loss = criterion(clip_x, labels) + with pytest.raises(RuntimeError, match="can only be scalar"): + loss.backward() + + def test_broadcast(self): + """Test broadcast behavior by randomly picked shuffling of np.random.rand""" + x_np = np.random.rand(1023, 4, 5, 6).astype(np.float32) - 0.5 + x_torch = torch.Tensor(x_np) + min_value = np.random.rand(1, 4, 1, 1).astype(np.float32) * 0.1 - 0.2 + max_value = np.random.rand(1, 4, 1, 1).astype(np.float32) * 10 + 0.5 + clip_x_np = np.clip(x_np, min_value, max_value) + clip_x_torch = QF.clip(x_torch, torch.tensor(min_value), torch.tensor(max_value)) + np.testing.assert_array_equal(clip_x_torch.cpu().numpy(), clip_x_np) + + def test_backward(self): + x = torch.randn(3, 1025, requires_grad=True) + x.retain_grad() + + min_value = torch.tensor(0.3) + max_value = torch.tensor(0.7) + min_value.requires_grad = True + max_value.requires_grad = True + min_value.retain_grad() + max_value.retain_grad() + clip_x = QF.clip(x, min_value, max_value) + clip_x.retain_grad() + + labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda() + criterion = torch.nn.CrossEntropyLoss() + loss = criterion(clip_x, labels) + loss.backward() + + np.testing.assert_array_almost_equal( + clip_x.grad[x < min_value].sum().cpu().numpy(), min_value.grad.cpu().numpy(), decimal=6) + np.testing.assert_array_almost_equal( + clip_x.grad[x > max_value].sum().cpu().numpy(), max_value.grad.cpu().numpy(), decimal=6) + assert x.grad.cpu()[x < min_value].sum() == 0 + assert x.grad.cpu()[x > max_value].sum() == 0 + assert torch.equal(clip_x.grad[(x > min_value) & (x < max_value)], x.grad[(x > min_value) & (x < max_value)]) diff --git a/tools/pytorch-quantization/tests/integration_test.py b/tools/pytorch-quantization/tests/integration_test.py new file mode 100644 index 00000000..cc3b3772 --- /dev/null +++ b/tools/pytorch-quantization/tests/integration_test.py @@ -0,0 +1,169 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of integrating Quant layers into a network""" + +import pytest + +import numpy as np + +import torch +import torch.nn.functional as F +import torch.optim as optim +from apex.amp import _amp_state + +from pytorch_quantization import tensor_quant +from pytorch_quantization import quant_modules +from pytorch_quantization import nn as quant_nn +from pytorch_quantization.tensor_quant import QuantDescriptor +from tests.fixtures.models import LeNet, QuantLeNet +from tests.fixtures import verbose + +np.random.seed(12345) # seed 1234 causes 1 number mismatch at 6th decimal in one of the tests + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +# pylint:disable=missing-docstring, no-self-use + +class TestNetwork(): + """test basic operations of quantized network""" + + def test_simple_build(self): + """test instantiation""" + quant_model = QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor()) + for name, module in quant_model.named_modules(): + if "quantizer" in name: + module.disable() + + input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc) + + input_desc = QuantDescriptor(amax=6.) + weight_desc = QuantDescriptor(amax=1.) + quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc) + + + def test_forward(self): + """test forward pass with random data""" + input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc) + output = quant_model(torch.empty(16, 1, 28, 28)) + + def test_backward(self): + """test one iteration with random data and labels""" + input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + quant_model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc) + optimizer = optim.SGD(quant_model.parameters(), lr=0.01) + optimizer.zero_grad() + output = quant_model(torch.empty(16, 1, 28, 28)) + loss = F.nll_loss(output, torch.randint(10, (16,), dtype=torch.int64)) + loss.backward() + optimizer.step() + + def test_amp_fp16(self): + """test one iteration with random data and labels""" + try: + from apex import amp + except ImportError: + pytest.skip("AMP is not available.") + input_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + weight_desc = tensor_quant.QUANT_DESC_8BIT_PER_TENSOR + model = QuantLeNet(quant_desc_input=input_desc, quant_desc_weight=weight_desc) + optimizer = optim.SGD(model.parameters(), lr=0.01) + model, optimizer = amp.initialize(model, optimizer, opt_level="O1") + optimizer.zero_grad() + output = model(torch.empty(16, 1, 28, 28)) + loss = F.nll_loss(output, torch.randint(10, (16,), dtype=torch.int64)) + with amp.scale_loss(loss, optimizer) as scaled_loss: + scaled_loss.backward() + optimizer.step() + assert loss.dtype == torch.float32 + _amp_state.handle._deactivate() + + def test_quant_module_replacement(self): + """test monkey patching of modules with their quantized versions""" + lenet = LeNet() + qlenet = QuantLeNet() + + mod_list = [type(mod) for name, mod in lenet.named_modules()] + mod_list = mod_list[1:] + qmod_list = [type(mod) for name, mod in qlenet.named_modules()] + qmod_list = qmod_list[1:] + + # Before any monkey patching, the networks should be different + assert(mod_list != qmod_list) + + # Monkey patch the modules + no_replace_list = ["Linear"] + custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)] + + quant_modules.initialize(no_replace_list, custom_quant_modules) + + lenet = LeNet() + qlenet = QuantLeNet() + + mod_list = [type(mod) for name, mod in lenet.named_modules()] + mod_list = mod_list[1:] + qmod_list = [type(mod) for name, mod in qlenet.named_modules()] + qmod_list = qmod_list[1:] + + # After monkey patching, the networks should be same + assert(mod_list == qmod_list) + + # Reverse monkey patching + quant_modules.deactivate() + + lenet = LeNet() + qlenet = QuantLeNet() + + mod_list = [type(mod) for name, mod in lenet.named_modules()] + mod_list = mod_list[1:] + qmod_list = [type(mod) for name, mod in qlenet.named_modules()] + qmod_list = qmod_list[1:] + + # After reversing monkey patching, the networks should again be different + assert(mod_list != qmod_list) + + def test_calibration(self): + quant_model = QuantLeNet(quant_desc_input=QuantDescriptor(), quant_desc_weight=QuantDescriptor()).cuda() + + for name, module in quant_model.named_modules(): + if name.endswith("_quantizer"): + if module._calibrator is not None: + module.disable_quant() + module.enable_calib() + else: + module.disable() + print(F"{name:40}: {module}") + + quant_model(torch.rand(16, 1, 224, 224, device="cuda")) + + # Load calib result and disable calibration + for name, module in quant_model.named_modules(): + if name.endswith("_quantizer"): + if module._calibrator is not None: + module.load_calib_amax() + module.enable_quant() + module.disable_calib() + else: + module.enable() + quant_model.cuda() + diff --git a/tools/pytorch-quantization/tests/license_test.py b/tools/pytorch-quantization/tests/license_test.py new file mode 100644 index 00000000..a0c38515 --- /dev/null +++ b/tools/pytorch-quantization/tests/license_test.py @@ -0,0 +1,81 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""test the license of source files.""" + +import pytest +from pathlib import Path +from filecmp import cmp + +# pylint:disable=missing-docstring, no-self-use + +class TestLicense(): + + def test_license(self): + root = Path(__file__).parent.parent.absolute() + root_len = len(str(root)) + + # Collect files ending with relevant extensions + file_list = [] + file_types = ['*.py', '*.cpp', '*.cu', '*.h', '*.hpp', '*.c', '*.sh'] + for ft in file_types: + file_list += list(root.rglob(ft)) + + # Trim files from build folders + build_folders = ['build', 'dist', '.eggs', '.vscode'] + build_files = [] + for src_file in file_list: + local_path = str(src_file.parents[0])[root_len : ] + for folder in build_folders: + if folder in local_path: + build_files.append(src_file) + + for bf in build_files: + file_list.remove(bf) + + print (f"Found {len(file_list)} source files") + + cpp_header = (root / 'tests' / 'license_test_header_cpp.txt').open().readlines() + py_header = (root / 'tests' / 'license_test_header_py.txt').open().readlines() + sh_header = (root / 'tests' / 'license_test_header_sh.txt').open().readlines() + + invalid_files = [] + for f in file_list: + with open(f) as src_file: + src_lines = src_file.readlines() + + if f.suffix == '.py': + header = py_header + elif f.suffix == '.sh': + header = sh_header + else: + header = cpp_header + + num_lines = len(header) + if len(src_lines) < num_lines: + invalid_files.append(f) + continue + + for i in range(num_lines): + if src_lines[i] != header[i]: + invalid_files.append(f) + break + + if len(invalid_files) > 0: + for f in invalid_files: + print(f"The file {f} has an invalid header!") + raise AssertionError("%d files have invalid headers!" % (len(invalid_files))) diff --git a/tools/pytorch-quantization/tests/license_test_header_cpp.txt b/tools/pytorch-quantization/tests/license_test_header_cpp.txt new file mode 100644 index 00000000..5a80993e --- /dev/null +++ b/tools/pytorch-quantization/tests/license_test_header_cpp.txt @@ -0,0 +1,15 @@ +/* + * Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ diff --git a/tools/pytorch-quantization/tests/license_test_header_py.txt b/tools/pytorch-quantization/tests/license_test_header_py.txt new file mode 100644 index 00000000..b2638cb6 --- /dev/null +++ b/tools/pytorch-quantization/tests/license_test_header_py.txt @@ -0,0 +1,15 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/tests/license_test_header_sh.txt b/tools/pytorch-quantization/tests/license_test_header_sh.txt new file mode 100644 index 00000000..9776c4fb --- /dev/null +++ b/tools/pytorch-quantization/tests/license_test_header_sh.txt @@ -0,0 +1,16 @@ +#!/bin/bash +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/tools/pytorch-quantization/tests/optim_helper_test.py b/tools/pytorch-quantization/tests/optim_helper_test.py new file mode 100644 index 00000000..521f74f3 --- /dev/null +++ b/tools/pytorch-quantization/tests/optim_helper_test.py @@ -0,0 +1,110 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Tests of helper functions for quant optimizer""" + +import numpy as np +import pytest + + +import torch.optim as optim + +from pytorch_quantization.optim import helper +from pytorch_quantization.tensor_quant import QuantDescriptor +from .fixtures.models import QuantLeNet +from .fixtures.models import resnet18 + +# pylint:disable=missing-docstring, no-self-use + +class TestMatchParameters(): + + def test_single_key(self, resnet18): + param = helper.match_parameters(resnet18, ['downsample.0.weight']) + assert len(list(param)) == 3 + + def test_multi_keys(self, resnet18): + param = list(helper.match_parameters(resnet18, ['conv1', 'downsample'])) + assert len(param) == 18 + + def test_regex(self, resnet18): + param = helper.match_parameters(resnet18, ['downsample.*.weight$']) + assert len(list(param)) == 6 + + param = helper.match_parameters(resnet18, ['downsample.*.wei$']) + assert not list(param) + + +class TestGroupParameters(): + + def test_single_key(self, resnet18): + param_groups = helper.group_parameters(resnet18, [['downsample.1.weight']]) + assert len(list(param_groups[0]['params'])) == 3 + + def test_lr_momentum_decay(self, resnet18): + lrs = [0.01, 0.001] + momentums = [0.02, 0.002] + weight_decays = [0.03, 0.003] + param_groups = helper.group_parameters( + resnet18, [['conv1.*weight'], ['downsample.*.weight']], lrs, momentums, weight_decays) + + assert param_groups[0]['lr'] == lrs[0] + assert param_groups[1]['lr'] == lrs[1] + assert param_groups[0]['momentum'] == momentums[0] + assert param_groups[1]['momentum'] == momentums[1] + assert param_groups[0]['weight_decay'] == weight_decays[0] + assert param_groups[1]['weight_decay'] == weight_decays[1] + + def test_optimizer_feed(self, resnet18): + """Feed grouped parameters to optimizer, see what happens""" + lrs = [0.01, 0.001] + momentums = [0.02, 0.002] + weight_decays = [0.03, 0.003] + param_groups = helper.group_parameters( + resnet18, [['conv1.*weight'], ['downsample.*.weight']], lrs, momentums, weight_decays) + optimizer = optim.SGD(param_groups) + optimizer.step() + + def test_raises(self): + with pytest.raises(TypeError, match="must be list of list of patterns"): + helper.group_parameters(None, [['downsample.1.weight'], 'conv1']) + + with pytest.raises(TypeError, match="must match"): + helper.group_parameters(None, [['downsample.1.weight'], ['conv1']], lrs=[0.1]) + + with pytest.raises(TypeError, match="must match"): + helper.group_parameters(None, [['downsample.1.weight'], ['conv1']], momentums=[0.1]) + + with pytest.raises(TypeError, match="must match"): + helper.group_parameters(None, [['downsample.1.weight'], ['conv1']], weight_decays=[0.1]) + + +class TestFreezeParameters(): + + def test_simple(self, resnet18): + helper.freeze_parameters(resnet18, ['downsample.0.weight']) + for name, param in resnet18.named_parameters(): + if 'downsample.0.weight' in name: + assert not param.requires_grad + +class TestQuantWeightInPlace(): + + def test_simple(self): + quant_lenet = QuantLeNet( + quant_desc_input=QuantDescriptor(), + quant_desc_weight=QuantDescriptor()) + quant_lenet.eval() + helper.quant_weight_inplace(quant_lenet) diff --git a/tools/pytorch-quantization/tests/print_test.py b/tools/pytorch-quantization/tests/print_test.py new file mode 100644 index 00000000..1a4dec6d --- /dev/null +++ b/tools/pytorch-quantization/tests/print_test.py @@ -0,0 +1,59 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""test for str and repr +Make sure things can print and in a nice form. Put all the print tests together so that running this test file alone +can inspect all the print messages in the project +""" + +import torch +from torch import nn + +from pytorch_quantization import calib +from pytorch_quantization import tensor_quant +from pytorch_quantization import nn as quant_nn +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer + +# pylint:disable=missing-docstring, no-self-use + +class TestPrint(): + + def test_print_descriptor(self): + test_desc = tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL + print(test_desc) + + def test_print_tensor_quantizer(self): + test_quantizer = TensorQuantizer() + print(test_quantizer) + + def test_print_module(self): + class _TestModule(nn.Module): + def __init__(self): + super(_TestModule, self).__init__() + self.conv = nn.Conv2d(33, 65, 3) + self.quant_conv = quant_nn.Conv2d(33, 65, 3) + self.linear = nn.Linear(33, 65) + self.quant_linear = quant_nn.Linear(33, 65) + + test_module = _TestModule() + print(test_module) + + def test_print_calibrator(self): + print(calib.MaxCalibrator(7, 1, False)) + hist_calibrator = calib.HistogramCalibrator(8, None, True) + hist_calibrator.collect(torch.rand(10)) + print(hist_calibrator) diff --git a/tools/pytorch-quantization/tests/quant_conv_test.py b/tools/pytorch-quantization/tests/quant_conv_test.py new file mode 100644 index 00000000..a25a77e5 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_conv_test.py @@ -0,0 +1,556 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantConv module. +Mose tests check the functionality of all the combinations in Quant conv against the corresponding functionalities in +tensor_quant. There are tests for all the three QuantConv1D, QuantConv2D, and QuantConv3D +""" +import pytest +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F + +from pytorch_quantization import tensor_quant +from pytorch_quantization.tensor_quant import QuantDescriptor +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer +from pytorch_quantization import utils as quant_utils +from pytorch_quantization.nn.modules import quant_conv +import tests.utils as test_utils + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +torch.backends.cudnn.deterministic = True + +np.random.seed(1234) + +# pylint:disable=missing-docstring, no-self-use + +_NUM_IN_CHANNELS = 13 +_NUM_OUT_CHANNELS = 17 + +class TestQuantConv2D(): + #Quantizing weight + + def test_no_quant(self): + + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256, 256) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_tensor(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor()) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256, 256) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256, 256) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1, 1)) + + out1 = F.conv2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_in_feature_fake_quant(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256, 256) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.conv2d(quant_input, quant_conv_object.weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=False, quant_desc_weight=QuantDescriptor()) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1, 1)) + + out1 = F.conv2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3) + + quant_conv_object = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv2d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONV2D_WEIGHT_PER_CHANNEL) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1, 1)) + + out1 = F.conv2d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 24, 24).cuda() + + torch.manual_seed(12345) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(12345) + fake_quant_conv2d = quant_conv.QuantConv2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(0))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(12345) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(12345) + conv2d = nn.Conv2d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv2d(test_input) + output = conv2d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-6, atol=1.5e-4) + + + def test_set_default_quant_desc(self): + quant_conv_layer = quant_conv.Conv2d(32, 257, 3) + assert quant_conv_layer.input_quantizer._axis == None + assert quant_conv_layer.weight_quantizer._axis == (0) + + # set default to a different one + quant_desc_input = QuantDescriptor(num_bits=11) + quant_desc_weight = QuantDescriptor(num_bits=13, axis=(1)) + quant_conv.QuantConv2d.set_default_quant_desc_input(quant_desc_input) + quant_conv.QuantConv2d.set_default_quant_desc_weight(quant_desc_weight) + + # Create one with default descriptor + quant_conv_layer = quant_conv.Conv2d(32, 257, 3) + # Check quant_desc in quantizer created with default descriptor + assert quant_conv_layer.input_quantizer._num_bits == quant_desc_input.num_bits + assert quant_conv_layer.weight_quantizer._axis == quant_desc_weight.axis + + # Test default is per class + quant_conv_layer = quant_conv.Conv3d(31, 255, 5) + assert quant_conv_layer.input_quantizer._num_bits != quant_desc_input.num_bits + assert quant_conv_layer.weight_quantizer._axis != quant_desc_weight.axis + + # Reset default + quant_conv.QuantConv2d.set_default_quant_desc_input(QuantDescriptor()) + quant_conv.QuantConv2d.set_default_quant_desc_weight(QuantDescriptor(axis=(0))) + + def test_unused_kwargs(self): + with pytest.raises(TypeError, match="Unused keys"): + quant_conv.Conv2d(32, 257, 3, descriptor='oops') + +class TestQuantConv1D(): + + def test_no_quant(self): + kernel_size = 8 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_tensor(self): + kernel_size = 8 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor()) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor(axis=(0))) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(1, 2)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_input(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(20, _NUM_IN_CHANNELS, 50) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.conv1d(quant_input, quant_conv_object.weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=False, quant_desc_weight=QuantDescriptor()) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor(axis=(0))) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1)) + + out1 = F.conv1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3, axis=(0)) + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_weight=QuantDescriptor(axis=(0))) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1)) + + out1 = F.conv1d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 24).cuda() + + torch.manual_seed(12345) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(12345) + fake_quant_conv1d = quant_conv.QuantConv1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(0))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(12345) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(12345) + conv1d = nn.Conv1d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv1d(test_input) + output = conv1d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-5, atol=1e-4) + + +class TestQuantConv3D(): + #Quantizing weight + + def test_no_quant(self): + kernel_size = 8 + + quant_conv_object = quant_conv.QuantConv3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 8, 8, 8) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv3d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3, axis=(0)) + + quant_conv_object = quant_conv.QuantConv3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 8, 8, 8) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv3d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConv3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_weight=QuantDescriptor(axis=(0))) + test_input = torch.randn(8, _NUM_IN_CHANNELS, 8, 8, 8) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant( + weight_copy, + torch.max(torch.abs(weight_copy).view(_NUM_OUT_CHANNELS, -1), dim=1, keepdim=True)[0].view( + _NUM_OUT_CHANNELS, 1, 1, 1, 1)) + + out1 = F.conv3d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 24, 24, 24).cuda() + + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + fake_quant_conv3d = quant_conv.QuantConv3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(0))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + conv3d = nn.Conv3d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv3d(test_input) + output = conv3d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-6, atol=2e-4) diff --git a/tools/pytorch-quantization/tests/quant_conv_transposed_test.py b/tools/pytorch-quantization/tests/quant_conv_transposed_test.py new file mode 100644 index 00000000..0770d7c4 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_conv_transposed_test.py @@ -0,0 +1,521 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantConv module. +Test for QuantConvTransposed +""" +import pytest +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F + +from pytorch_quantization import tensor_quant +from pytorch_quantization.tensor_quant import QuantDescriptor +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer +from pytorch_quantization import utils as quant_utils +from pytorch_quantization.nn.modules import quant_conv +import tests.utils as test_utils + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +torch.backends.cudnn.deterministic = True + +np.random.seed(1234) + +# pylint:disable=missing-docstring, no-self-use + +_NUM_IN_CHANNELS = 13 +_NUM_OUT_CHANNELS = 17 + + +class TestQuantConvTranspose2D(): + + def test_no_quant(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv_transpose2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_tensor(self): + kernel_size = 8 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor()) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(256, _NUM_IN_CHANNELS, 32, 32) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv_transpose2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE2D_WEIGHT_PER_CHANNEL) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256, 256) + + weight_copy = quant_conv_object.weight.clone() + + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2, 3)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose2d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_input(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(20, _NUM_IN_CHANNELS, 50, 50) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.conv_transpose2d(quant_input, quant_conv_object.weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=False, quant_desc_weight=QuantDescriptor()) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv_transpose2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor(axis=(1))) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2, 3)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3, axis=(1)) + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv_transpose2d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_weight=QuantDescriptor(axis=(1))) + test_input = torch.randn(2, _NUM_IN_CHANNELS, 2, 2) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2, 3)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose2d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32).cuda() + + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + fake_quant_conv2d = quant_conv.QuantConvTranspose2d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(1))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + conv2d = nn.ConvTranspose2d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv2d(test_input) + output = conv2d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-5, atol=2e-4) + + +class TestQuantConvTranspose3D(): + + def test_no_quant(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32, 32) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv_transpose3d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3, axis=(1)) + + quant_conv_object = quant_conv.QuantConvTranspose3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16, 16, 16) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv_transpose3d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE3D_WEIGHT_PER_CHANNEL) + test_input = torch.randn(2, _NUM_IN_CHANNELS, 2, 2, 2) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2, 3, 4)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose3d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 32, 32, 32).cuda() + + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + fake_quant_conv3d = quant_conv.QuantConvTranspose3d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(1))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + conv3d = nn.ConvTranspose3d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv3d(test_input) + output = conv3d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-5, atol=2e-4) + + +class TestQuantConvTranspose1D(): + + def test_no_quant(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.input_quantizer.disable() + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 32) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_copy + + out1 = F.conv_transpose1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_tensor(self): + kernel_size = 8 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor()) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(256, _NUM_IN_CHANNELS, 32) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv_transpose1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_CONVTRANSPOSE1D_WEIGHT_PER_CHANNEL) + quant_conv_object.input_quantizer.disable() + test_input = torch.randn(16, _NUM_IN_CHANNELS, 256) + + weight_copy = quant_conv_object.weight.clone() + + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose1d(test_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_input(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False) + quant_conv_object.weight_quantizer.disable() + test_input = torch.randn(20, _NUM_IN_CHANNELS, 50) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.conv_transpose1d(quant_input, quant_conv_object.weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=False, quant_desc_weight=QuantDescriptor()) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.conv_transpose1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_weight=QuantDescriptor(axis=(1))) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_prec(self): + kernel_size = 3 + + quant_desc_input = QuantDescriptor(num_bits=4) + quant_desc_weight = QuantDescriptor(num_bits=3, axis=(1)) + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + test_input = torch.randn(16, _NUM_IN_CHANNELS, 16) + + test_input_quantizer = TensorQuantizer(quant_desc_input) + weight_quantizer = TensorQuantizer(quant_desc_weight) + + quant_input = test_input_quantizer(test_input) + + weight_copy = quant_conv_object.weight.clone() + quant_weight = weight_quantizer(weight_copy) + + out1 = F.conv_transpose1d(quant_input, quant_weight) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_bias(self): + kernel_size = 3 + + quant_conv_object = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_weight=QuantDescriptor(axis=(1))) + test_input = torch.randn(2, _NUM_IN_CHANNELS, 2) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + weight_copy = quant_conv_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=(0, 2)) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.conv_transpose1d(quant_input, quant_weight, bias=quant_conv_object.bias) + out2 = quant_conv_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_against_unquantized(self): + kernel_size = 3 + test_input = torch.randn(16, _NUM_IN_CHANNELS, 24).cuda() + + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + fake_quant_conv1d = quant_conv.QuantConvTranspose1d( + _NUM_IN_CHANNELS, + _NUM_OUT_CHANNELS, + kernel_size, + bias=True, + quant_desc_input=QuantDescriptor(num_bits=16), + quant_desc_weight=QuantDescriptor(num_bits=16, axis=(1))) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + conv1d = nn.ConvTranspose1d(_NUM_IN_CHANNELS, _NUM_OUT_CHANNELS, kernel_size, bias=True) + + fake_quant_output = fake_quant_conv1d(test_input) + output = conv1d(test_input) + + test_utils.compare(fake_quant_output, output, rtol=1e-5, atol=1e-4) diff --git a/tools/pytorch-quantization/tests/quant_instancenorm_test.py b/tools/pytorch-quantization/tests/quant_instancenorm_test.py new file mode 100644 index 00000000..6ebf89f0 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_instancenorm_test.py @@ -0,0 +1,198 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantInstanceNorm module. +Mose tests check the functionality of all the combinations in Quant instancenorm against the corresponding functionalities in +tensor_quant. There are tests for all the three QuantInstaceNorm1D, QuantInstanceNorm2D, and QuantInstanceNorm3D +""" +import pytest +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F + +from pytorch_quantization import tensor_quant +from pytorch_quantization.tensor_quant import QuantDescriptor +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer +from pytorch_quantization import utils as quant_utils +from pytorch_quantization.nn.modules import quant_instancenorm +#import tests.utils as test_utils + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +torch.backends.cudnn.deterministic = True + +np.random.seed(1234) + +# pylint:disable=missing-docstring, no-self-use +NUM_CHANNELS = 15 + +class TestQuantInstanceNorm1D(): + + def test_no_quant(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True) + quant_instancenorm_object.input_quantizer.disable() + + test_input = torch.randn(8, NUM_CHANNELS, 128) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(test_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor()) + + test_input = torch.randn(8, NUM_CHANNELS, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm1d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor(axis=(1))) + + test_input = torch.randn(8, NUM_CHANNELS, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, + torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0]) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + +class TestQuantInstanceNorm2D(): + + def test_no_quant(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True) + quant_instancenorm_object.input_quantizer.disable() + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(test_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor()) + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm2d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor(axis=(1))) + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, + torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0].max(3, keepdim=True)[0]) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + + +class TestQuantInstanceNorm3D(): + + def test_no_quant(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True) + quant_instancenorm_object.input_quantizer.disable() + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(test_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor()) + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + + quant_instancenorm_object = quant_instancenorm.QuantInstanceNorm3d(NUM_CHANNELS, affine=True, + quant_desc_input=QuantDescriptor(axis=(1))) + + test_input = torch.randn(8, NUM_CHANNELS, 128, 128, 128) + quant_input = tensor_quant.fake_tensor_quant(test_input, + torch.abs(test_input).max(0, keepdim=True)[0].max(2, keepdim=True)[0] + .max(3, keepdim=True)[0].max(4, keepdim=True)[0]) + + out1 = quant_instancenorm_object(test_input) + out2 = F.instance_norm(quant_input, + quant_instancenorm_object.running_mean, + quant_instancenorm_object.running_var, + quant_instancenorm_object.weight, + quant_instancenorm_object.bias) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) diff --git a/tools/pytorch-quantization/tests/quant_linear_test.py b/tools/pytorch-quantization/tests/quant_linear_test.py new file mode 100644 index 00000000..54326a09 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_linear_test.py @@ -0,0 +1,230 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantLinear module. +Most tests check the functionality of all the combinations in Quant Linear against the corresponding functionalities +in tensor_quant. +""" +import pytest +import numpy as np + +import torch +import torch.nn.functional as F +from torch import nn + +from pytorch_quantization import tensor_quant +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer +from pytorch_quantization import utils as quant_utils +from pytorch_quantization.nn.modules import quant_linear +import tests.utils as test_utils + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +np.random.seed(1234) +torch.manual_seed(1234) + +# pylint:disable=missing-docstring, no-self-use + + +class TestQuantLinear(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_linear_object = quant_linear.QuantLinear( + 7, 9, bias=False, quant_desc_weight=tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + #Quantizing weight + def test_weight_fake_per_tensor(self): + with torch.cuda.device(0): + size = 256 + quant_linear_object = quant_linear.QuantLinear( + size, + size, + bias=False, + quant_desc_weight=tensor_quant.QuantDescriptor(axis=None)) + quant_linear_object.input_quantizer.disable() + test_input = torch.randn(size, size) + + weight_copy = quant_linear_object.weight.clone() + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.linear(test_input, quant_weight) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_weight_fake_per_channel(self): + size_in = 255 + size_out = 257 + quant_linear_object = quant_linear.QuantLinear( + size_in, size_out, bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW) + quant_linear_object.input_quantizer.disable() + test_input = torch.randn(32, size_in) + + weight_copy = quant_linear_object.weight.clone() + amax = quant_utils.reduce_amax(weight_copy, axis=1, keepdims=True) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, amax) + + out1 = F.linear(test_input, quant_weight) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + # Quantizing activations + def test_test_input_fake_per_tensor(self): + size_in = 255 + size_out = 257 + quant_linear_object = quant_linear.QuantLinear( + size_in, size_out, bias=False) + quant_linear_object.weight_quantizer.disable() + test_input = torch.randn(32, size_in) + + weight_copy = quant_linear_object.weight.clone() + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.linear(quant_input, weight_copy) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor(self): + """quantize everything, activations will scaled per tensor in ALL cases""" + size_in = 255 + size_out = 257 + quant_linear_object = quant_linear.QuantLinear( + size_in, size_out, bias=False, quant_desc_weight=tensor_quant.QuantDescriptor()) + test_input = torch.randn(32, size_in) + + weight_copy = quant_linear_object.weight.clone() + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.linear(quant_input, quant_weight) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_tensor_with_bias(self): + """quantize everything, activations will scaled per tensor in ALL cases""" + size_in = 255 + size_out = 257 + quant_linear_object = quant_linear.QuantLinear( + size_in, size_out, bias=False, quant_desc_weight=tensor_quant.QuantDescriptor()) + test_input = torch.randn(32, 17, 93, size_in) # Test input other than 2 dimensional + + weight_copy = quant_linear_object.weight.clone() + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, torch.max(torch.abs(weight_copy))) + + out1 = F.linear(quant_input, quant_weight, bias=quant_linear_object.bias) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel(self): + """quantize everything, activations will scaled per tensor in ALL cases""" + size_in = 255 + size_out = 257 + quant_linear_object = quant_linear.QuantLinear(size_in, size_out, bias=False, + quant_desc_weight=tensor_quant.QUANT_DESC_8BIT_LINEAR_WEIGHT_PER_ROW) + test_input = torch.randn(32, size_in) + + weight_copy = quant_linear_object.weight.clone() + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + quant_weight = tensor_quant.fake_tensor_quant(weight_copy, + torch.max(torch.abs(weight_copy), dim=1, keepdim=True)[0]) + + out1 = F.linear(quant_input, quant_weight) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_per_channel_other_precs(self): + """Test some precisions other than 8bit.""" + size_in = 255 + size_out = 257 + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=4) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=3) + quant_linear_object = quant_linear.QuantLinear( + size_in, + size_out, + bias=False, + quant_desc_input=quant_desc_input, + quant_desc_weight=quant_desc_weight) + weight_quantizer = TensorQuantizer(quant_desc_weight) + test_input_quantizer = TensorQuantizer(quant_desc_input) + + test_input = torch.randn(32, size_in) + + weight_copy = quant_linear_object.weight.clone() + quant_input = test_input_quantizer(test_input) + quant_weight = weight_quantizer(weight_copy) + + out1 = F.linear(quant_input, quant_weight) + out2 = quant_linear_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_fake_quant_against_unquantized(self): + """ + Quantized Linear should introduce bounded error compare to Linear + """ + size_in = 255 + size_out = 257 + test_input = torch.randn(32, size_in).cuda() + + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + quant_linear_layer = quant_linear.QuantLinear( + size_in, + size_out, + bias=True, + quant_desc_input=tensor_quant.QuantDescriptor(num_bits=16), + quant_desc_weight=tensor_quant.QuantDescriptor(num_bits=16, axis=0)) + + # Reset seed. Make sure weight and bias are the same + torch.manual_seed(1234) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + linear_layer = nn.Linear(size_in, size_out, bias=True) + + quant_out_features = quant_linear_layer(test_input) + out_features = linear_layer(test_input) + + # The difference between Linear and QuantLinear should be bounded in a range + # Small values which become 0 after quantization lead to large relative errors. rtol and atol could be + # much smaller without those values + np.testing.assert_allclose( + quant_out_features.detach().cpu().numpy(), out_features.detach().cpu().numpy(), rtol=0.01, atol=1e-4) + + def test_set_default_quant_desc(self): + quant_linear_layer = quant_linear.QuantLinear(32, 257) + assert quant_linear_layer.input_quantizer.axis == None + assert quant_linear_layer.weight_quantizer.axis == (0) + + # set default to a different one + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=11) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=13, axis=1) + quant_linear.Linear.set_default_quant_desc_input(quant_desc_input) + quant_linear.Linear.set_default_quant_desc_weight(quant_desc_weight) + + # Create one with default descriptor + quant_linear_layer = quant_linear.QuantLinear(32, 257) + # Check quant_desc in quantizer created with default descriptor + assert quant_linear_layer.input_quantizer.num_bits == quant_desc_input.num_bits + assert quant_linear_layer.weight_quantizer.axis == quant_desc_weight.axis + + def test_unused_kwargs(self): + with pytest.raises(TypeError, match="Unused keys"): + quant_linear_layer = quant_linear.QuantLinear(32, 257, descriptor='oops') diff --git a/tools/pytorch-quantization/tests/quant_modules_test.py b/tools/pytorch-quantization/tests/quant_modules_test.py new file mode 100644 index 00000000..08458934 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_modules_test.py @@ -0,0 +1,82 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Tests of Quant Module Replacement""" +import pytest +import numpy as np + +import torch + +from pytorch_quantization import nn as quant_nn +from pytorch_quantization import quant_modules +from pytorch_quantization.quant_modules import QuantModuleReplacementHelper +import tests.utils as test_utils +from tests.fixtures import verbose + +# pylint:disable=missing-docstring, no-self-use + +class TestQuantModuleReplace(): + + def test_simple_default_args(self): + replacement_helper = QuantModuleReplacementHelper() + replacement_helper.prepare_state() + replacement_helper.apply_quant_modules() + + # Linear module should not be replaced with its quantized version + assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3))) + assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3))) + + replacement_helper.restore_float_modules() + + def test_with_no_replace_list(self): + no_replace_list = ["Linear"] + custom_quant_modules = None + replacement_helper = QuantModuleReplacementHelper() + replacement_helper.prepare_state(no_replace_list, custom_quant_modules) + replacement_helper.apply_quant_modules() + + # Linear module should not be replaced with its quantized version + assert(type(quant_nn.QuantLinear(16, 256, 3)) != type(torch.nn.Linear(16, 256, 3))) + assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3))) + + replacement_helper.restore_float_modules() + + def test_with_custom_quant_modules(self): + no_replace_list = ["Linear"] + custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)] + replacement_helper = QuantModuleReplacementHelper() + replacement_helper.prepare_state(no_replace_list, custom_quant_modules) + replacement_helper.apply_quant_modules() + + # Although no replace list indicates Linear module should not be replaced with its + # quantized version, since the custom_quant_modules still contains the Linear module's + # mapping, it will replaced. + assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3))) + assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3))) + + replacement_helper.restore_float_modules() + + def test_initialize_deactivate(self): + no_replace_list = ["Linear"] + custom_quant_modules = [(torch.nn, "Linear", quant_nn.QuantLinear)] + + quant_modules.initialize(no_replace_list, custom_quant_modules) + + assert(type(quant_nn.QuantLinear(16, 256, 3)) == type(torch.nn.Linear(16, 256, 3))) + assert(type(quant_nn.QuantConv2d(16, 256, 3)) == type(torch.nn.Conv2d(16, 256, 3))) + + quant_modules.deactivate() diff --git a/tools/pytorch-quantization/tests/quant_pooling_test.py b/tools/pytorch-quantization/tests/quant_pooling_test.py new file mode 100644 index 00000000..aadd2ab9 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_pooling_test.py @@ -0,0 +1,306 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantPooling module. +Most tests check the functionality of all the combinations in Quant Pooling against the corresponding functionalities +in tensor_quant. +""" +import pytest +import numpy as np + +import torch +import torch.nn.functional as F + +from pytorch_quantization import tensor_quant +from pytorch_quantization.nn.modules import quant_pooling + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') + +np.random.seed(1234) +torch.manual_seed(1234) + +# pylint:disable=missing-docstring, no-self-use +class TestQuantMaxPool1d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantMaxPool1d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.max_pool1d(quant_input, 3, 1, 0, 1, False, False) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantMaxPool2d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_variable_bits(self): + # Repeat checking the output for variable number of bits to QuantDescriptor + for bits in [2, 4, 6]: + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits) + + quant_pooling.QuantMaxPool2d.set_default_quant_desc_input(quant_desc_input) + quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits) + + out1 = F.max_pool2d(quant_input, 3, 1, 0, 1, False, False) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_fake_quant_disable(self): + quant_pooling_object = quant_pooling.QuantMaxPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_pooling_object.input_quantizer.disable() + + out1 = F.max_pool2d(test_input, 3, 1, 0, 1, False, False) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantMaxPool3d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantMaxPool3d(kernel_size=3, stride=1) + + test_input = torch.randn(5, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.max_pool3d(quant_input, 3, 1, 0, 1, False, False) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAvgPool1d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAvgPool1d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.avg_pool1d(quant_input, 3, 1, 0, False, True) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAvgPool2d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_variable_bits(self): + # Repeat checking the output for variable number of bits to QuantDescriptor + for bits in [2, 4, 6]: + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits) + + quant_pooling.QuantAvgPool2d.set_default_quant_desc_input(quant_desc_input) + quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits) + + out1 = F.avg_pool2d(quant_input, 3, 1, 0, False, True, None) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_fake_quant_disable(self): + quant_pooling_object = quant_pooling.QuantAvgPool2d(kernel_size=3, stride=1) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_pooling_object.input_quantizer.disable() + + out1 = F.avg_pool2d(test_input, 3, 1, 0, False, True, None) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAvgPool3d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAvgPool3d(kernel_size=3, stride=1) + + test_input = torch.randn(5, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.avg_pool3d(quant_input, 3, 1, 0, False, True, None) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAdaptiveAvgPool1d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool1d(output_size=3) + + test_input = torch.randn(1, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.adaptive_avg_pool1d(quant_input, 3) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAdaptiveAvgPool2d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.adaptive_avg_pool2d(quant_input, 3) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_variable_bits(self): + # Repeat checking the output for variable number of bits to QuantDescriptor + for bits in [2, 4, 6]: + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=bits) + + quant_pooling.QuantAdaptiveAvgPool2d.set_default_quant_desc_input(quant_desc_input) + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input)), bits) + + out1 = F.adaptive_avg_pool2d(quant_input, 3) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + + def test_input_fake_quant_disable(self): + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool2d(output_size=3) + + test_input = torch.randn(1, 5, 5, 5, dtype=torch.double) + + quant_pooling_object.input_quantizer.disable() + + out1 = F.adaptive_avg_pool2d(test_input, 3) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) + +class TestQuantAdaptiveAvgPool3d(): + + def test_raise(self): + with pytest.raises(ValueError) as excinfo: + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3, + quant_desc_input= + tensor_quant.QuantDescriptor(fake_quant=False)) + assert "Only fake quantization is supported" in str(excinfo.value) + + # Quantizing activations + def test_input_fake_quant(self): + quant_pooling_object = quant_pooling.QuantAdaptiveAvgPool3d(output_size=3) + + test_input = torch.randn(5, 5, 5, 5, dtype=torch.double) + + quant_input = tensor_quant.fake_tensor_quant(test_input, torch.max(torch.abs(test_input))) + + out1 = F.adaptive_avg_pool3d(quant_input, 3) + out2 = quant_pooling_object(test_input) + np.testing.assert_array_equal(out1.detach().cpu().numpy(), out2.detach().cpu().numpy()) diff --git a/tools/pytorch-quantization/tests/quant_rnn_test.py b/tools/pytorch-quantization/tests/quant_rnn_test.py new file mode 100644 index 00000000..812339e7 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_rnn_test.py @@ -0,0 +1,519 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of QuantRNN module. +""" +import pytest + +import torch +from torch import nn + +import numpy as np + +from pytorch_quantization.nn.modules import quant_rnn +from pytorch_quantization import tensor_quant + +from tests.fixtures import verbose + +from . import utils + +# make everything run on the GPU +torch.set_default_tensor_type('torch.cuda.FloatTensor') +# change default type to double if utils.compare flags a small error, may just be floating point rounding error +# torch.set_default_tensor_type('torch.cuda.DoubleTensor') + +np.random.seed(1234) +torch.manual_seed(1234) +if torch.cuda.is_available(): + torch.cuda.manual_seed_all(1234) + +# pylint: disable=no-self-use, missing-docstring, redefined-builtin, bad-continuation + +# global state for saving/loading test vectors +SAVE_VECTORS = 0 +VECTOR_FILE = 'tests/quant_rnn_test_vectors.pt' +if SAVE_VECTORS: + TEST_VECTORS = dict() +else: + TEST_VECTORS = torch.load(VECTOR_FILE) + + +class TestQuantLSTMCell(): + """ + tests for quant_rnn.QuantLSTMCell + default parameters in QuantLSTMCell: + bias=True, + num_bits_weight=8, quant_mode_weight='per_channel', + num_bits_input=8, quant_mode_input='per_tensor' + + Tests of real quantization mode (nonfake) are disabled as it is not fully supported yet. + """ + + def test_basic_forward(self, verbose): + """Do a forward pass on the cell module and see if anything catches fire.""" + batch = 7 + input_size = 11 + hidden_size = 9 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=8) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=8, axis=(1,)) + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + quant_rnn_object._input_quantizer.disable() + quant_rnn_object._weight_quantizer.disable() + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_rnn_object(input, hx=(hidden, cell)) + + def test_no_quant_input_hidden(self, verbose): + """QuantLSTM with quantization disabled vs. pytorch LSTM for input and hidden inputs.""" + batch = 17 + input_size = 13 + hidden_size = 7 + + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=False) + quant_rnn_object._input_quantizer.disable() + quant_rnn_object._weight_quantizer.disable() + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=False) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + ref_hout, ref_cout = ref_rnn_object(input, hx=(hidden, cell)) + + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_no_quant_input_hidden_bias(self, verbose): + """QuantLSTMCell with quantization disabled vs. pytorch LSTMCell for input, hidden inputs and bias.""" + batch = 19 + input_size = 11 + hidden_size = 3 + + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=True) + quant_rnn_object._input_quantizer.disable() + quant_rnn_object._weight_quantizer.disable() + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=True) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + ref_hout, ref_cout = ref_rnn_object(input, hx=(hidden, cell)) + + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_against_unquantized(self, verbose): + """Quantization should introduce bounded error utils.compare to pytorch implementation.""" + batch = 9 + input_size = 13 + hidden_size = 7 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=16) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=16, axis=(1,)) + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=False) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + ref_hout, ref_cout = ref_rnn_object(input, hx=(hidden, cell)) + + # The difference between reference and quantized should be bounded in a range + # Small values which become 0 after quantization lead to large relative errors. rtol and atol could be + # much smaller without those values + utils.compare(quant_hout, ref_hout, rtol=1e-4, atol=1e-4) + utils.compare(quant_cout, ref_cout, rtol=1e-4, atol=1e-4) + + # check that quantization introduces some error + utils.assert_min_mse(quant_hout, ref_hout, tol=1e-20) + utils.assert_min_mse(quant_cout, ref_cout, tol=1e-20) + + def test_quant_input_hidden(self, verbose): + """QuantLSTMCell vs. manual input quantization + pytorchLSTMCell.""" + batch = 15 + input_size = 121 + hidden_size = 51 + num_bits = 4 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=False) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits) + + ref_hout, ref_cout = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_quant_input_hidden_bias(self, verbose): + """QuantLSTMCell vs. manual input quantization + pytorchLSTMCell + bias should not be quantized + """ + batch = 9 + input_size = 23 + hidden_size = 31 + num_bits = 7 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=True, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=True) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits) + + ref_hout, ref_cout = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_quant_different_prec(self, verbose): + """QuantLSTMCell vs. manual input quantization + pytorch LSTMCell + different input and weight precisions + """ + batch = 27 + input_size = 11 + hidden_size = 10 + num_bits_weight = 4 + num_bits_input = 8 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits_input) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits_weight) + quant_rnn_object = quant_rnn.QuantLSTMCell(input_size, hidden_size, bias=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTMCell(input_size, hidden_size, bias=False) + + input = torch.randn(batch, input_size) + hidden = torch.randn(batch, hidden_size) + cell = torch.randn(batch, hidden_size) + + quant_hout, quant_cout = quant_rnn_object(input, hx=(hidden, cell)) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits_input) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits_weight) + + ref_hout, ref_cout = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + +class TestQuantLSTM(): + """ + tests for quant_rnn.QuantLSTM + default parameters in QuantLSTM: + bias=True, + quant_weight=True, bits_weight=8, fake_quantTrue, quant_mode_weight='channel', + quant_input=True, bits_acts=8, quant_mode_input='tensor' + + Tests of real quantization mode (nonfake) are disabled as it is not fully supported yet. + """ + + def test_basic_forward(self, verbose): + """Do a forward pass on the layer module and see if anything catches fire.""" + batch = 5 + input_size = 13 + hidden_size = 31 + seq_len = 1 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=8) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=8, axis=(1,)) + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + quant_rnn_object(input, hx=(hidden, cell)) + + def test_no_quant(self, verbose): + """QuantLSTM with quantization disabled vs. pytorch LSTM.""" + batch = 11 + input_size = 14 + hidden_size = 22 + seq_len = 1 + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False) + quant_rnn_object._input_quantizers[0].disable() + quant_rnn_object._weight_quantizers[0].disable() + ref_rnn_object = nn.LSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(input) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_no_quant_input_hidden(self, verbose): + """QuantLSTM with quantization disabled vs. pytorch LSTM for input and hidden inputs.""" + batch = 13 + input_size = 19 + hidden_size = 20 + seq_len = 1 + + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False) + quant_rnn_object._input_quantizers[0].disable() + quant_rnn_object._weight_quantizers[0].disable() + ref_rnn_object = nn.LSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(input, hx=(hidden, cell)) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_no_quant_all_modes(self, verbose): + """QuantLSTM with quantization disabled vs. pytorch LSTM for all modes.""" + + def testcase(input_size, hidden_size, seq_len, batch, num_layers, bias, batch_first, dropout, bidirectional): + + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, + num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, + bidirectional=bidirectional) + + num_quantizers = num_layers * 2 if bidirectional else num_layers + for i in range(num_quantizers): + quant_rnn_object._input_quantizers[i].disable() + quant_rnn_object._weight_quantizers[i].disable() + + ref_rnn_object = nn.LSTM(input_size, hidden_size, + num_layers=num_layers, bias=bias, batch_first=batch_first, dropout=dropout, + bidirectional=bidirectional) + + # copy state from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(seq_len, batch, input_size) + num_directions = 2 if bidirectional else 1 + hidden = torch.randn(num_layers*num_directions, batch, hidden_size) + cell = torch.randn(num_layers*num_directions, batch, hidden_size) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(input, hx=(hidden, cell)) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + # test various permuatations of the following parameters: + # size, num_layers, bias, batch_first, dropout, bidirectional + testcase(32, 27, 1, 1, 1, False, False, 0, False) + testcase(19, 63, 1, 1, 2, False, False, 0, False) + testcase(11, 41, 1, 1, 1, True, False, 0, False) + testcase(33, 31, 1, 1, 1, False, True, 0, False) + # testcase(32, 32, 1, 1, 2, False, False, 0.5, False) #TODO(pjudd) this fails look into dropout seeding + testcase(73, 13, 1, 1, 1, False, False, 0, True) + + def test_against_unquantized(self, verbose): + """Quantization should introduce bounded error utils.compare to pytorch implementation.""" + batch = 21 + input_size = 33 + hidden_size = 25 + seq_len = 1 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=16) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=16, axis=(1,)) + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTM(input_size, hidden_size, + num_layers=1, bias=False, batch_first=False, dropout=0, bidirectional=False) + + # copy weights from one rnn to the other + ref_rnn_object.load_state_dict(quant_rnn_object.state_dict()) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(input, hx=(hidden, cell)) + + # The difference between reference and quantized should be bounded in a range + # Small values which become 0 after quantization lead to large relative errors. rtol and atol could be + # much smaller without those values + utils.compare(quant_out, ref_out, rtol=1e-4, atol=1e-4) + utils.compare(quant_hout, ref_hout, rtol=1e-4, atol=1e-4) + utils.compare(quant_cout, ref_cout, rtol=1e-4, atol=1e-4) + + # check that quantization introduces some error + utils.assert_min_mse(quant_out, ref_out, tol=1e-20) + utils.assert_min_mse(quant_hout, ref_hout, tol=1e-20) + utils.assert_min_mse(quant_cout, ref_cout, tol=1e-20) + + def test_quant_input_hidden(self, verbose): + """QuantLSTM vs. manual input quantization + pytorchLSTM.""" + batch = 13 + input_size = 17 + hidden_size = 7 + seq_len = 1 + num_bits = 6 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, num_layers=1, bias=False, + batch_first=False, dropout=0, bidirectional=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTM(input_size, hidden_size, num_layers=1, bias=False, + batch_first=False, dropout=0, bidirectional=False) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_quant_input_hidden_bias(self, verbose): + """QuantLSTM vs. manual input quantization + pytorchLSTM.""" + batch = 17 + input_size = 13 + hidden_size = 7 + seq_len = 1 + num_bits = 5 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits) + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, num_layers=1, bias=True, + batch_first=False, dropout=0, bidirectional=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTM(input_size, hidden_size, num_layers=1, bias=True, + batch_first=False, dropout=0, bidirectional=False) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + def test_quant_different_prec(self, verbose): + """QuantLSTM vs. manual input quantization + pytorchLSTM.""" + batch = 22 + input_size = 23 + hidden_size = 24 + seq_len = 1 + num_bits_weight = 4 + num_bits_input = 8 + + quant_desc_input = tensor_quant.QuantDescriptor(num_bits=num_bits_input) + quant_desc_weight = tensor_quant.QuantDescriptor(num_bits=num_bits_weight) + quant_rnn_object = quant_rnn.QuantLSTM(input_size, hidden_size, num_layers=1, bias=False, + batch_first=False, dropout=0, bidirectional=False, + quant_desc_input=quant_desc_input, quant_desc_weight=quant_desc_weight) + ref_rnn_object = nn.LSTM(input_size, hidden_size, num_layers=1, bias=False, + batch_first=False, dropout=0, bidirectional=False) + + input = torch.randn(seq_len, batch, input_size) + hidden = torch.randn(seq_len, batch, hidden_size) + cell = torch.randn(seq_len, batch, hidden_size) + + quant_input, quant_hidden = utils.quantize_by_range_fused((input, hidden), num_bits_input) + + utils.copy_state_and_quantize_fused(ref_rnn_object, quant_rnn_object, num_bits_weight) + + quant_out, (quant_hout, quant_cout) = quant_rnn_object(input, hx=(hidden, cell)) + ref_out, (ref_hout, ref_cout) = ref_rnn_object(quant_input, hx=(quant_hidden, cell)) + + utils.compare(quant_out, ref_out) + utils.compare(quant_hout, ref_hout) + utils.compare(quant_cout, ref_cout) + + +class TestEpilogue(): + """Run after all tests to save globals.""" + + def test_save_vectors(self, verbose): + """Save test vectors to file.""" + if SAVE_VECTORS: + torch.save(TEST_VECTORS, VECTOR_FILE) + raise Exception('Saved test vectors to {}, for testing set SAVE_VECTORS = 0'.format(VECTOR_FILE)) diff --git a/tools/pytorch-quantization/tests/quant_rnn_test_vectors.pt b/tools/pytorch-quantization/tests/quant_rnn_test_vectors.pt new file mode 100644 index 00000000..69987516 Binary files /dev/null and b/tools/pytorch-quantization/tests/quant_rnn_test_vectors.pt differ diff --git a/tools/pytorch-quantization/tests/quant_utils_test.py b/tools/pytorch-quantization/tests/quant_utils_test.py new file mode 100644 index 00000000..8afbee19 --- /dev/null +++ b/tools/pytorch-quantization/tests/quant_utils_test.py @@ -0,0 +1,51 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Test pytorch_quantization.utils""" +import pytest +import numpy as np + +import torch +from pytorch_quantization import utils as quant_utils +from tests.fixtures import verbose + +np.random.seed(12345) + +# pylint:disable=missing-docstring, no-self-use + +class TestQuantUtils(): + + def test_reduce_amax(self): + x_np = (np.random.rand(3, 7, 11, 13, 17) - 0.1).astype(np.float32) + x_torch = torch.tensor(x_np) + + # Test reduce to one value + amax_np = np.max(np.abs(x_np)) + amax_torch = quant_utils.reduce_amax(x_torch) + np.testing.assert_array_equal(amax_np, amax_torch.cpu().numpy()) + + # Test different axis + axes = [(1, 2, 3), (0, 2, 3), (0, 3), (0, 1, 3, 4)] + for axis in axes: + keepdims = np.random.rand() > 0.5 + amax_np = np.max(np.abs(x_np), axis=axis, keepdims=keepdims) + amax_torch = quant_utils.reduce_amax(x_torch, axis=axis, keepdims=keepdims) + np.testing.assert_array_almost_equal(amax_np, amax_torch.cpu().numpy()) + + with pytest.raises(ValueError) as excinfo: + quant_utils.reduce_amax(x_torch, axis=(0, 1, 2, 3, 4, 5)) + assert "Cannot reduce more axes" in str(excinfo.value) diff --git a/tools/pytorch-quantization/tests/tensor_quant_test.py b/tools/pytorch-quantization/tests/tensor_quant_test.py new file mode 100644 index 00000000..e05432bc --- /dev/null +++ b/tools/pytorch-quantization/tests/tensor_quant_test.py @@ -0,0 +1,373 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of tensor quantization function and module""" +import pytest +import numpy as np + +import torch +from torch.nn.parameter import Parameter + +from pytorch_quantization import calib +from pytorch_quantization import cuda_ext +from pytorch_quantization import tensor_quant +from pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer + +import tests.utils as test_utils +from tests.fixtures import verbose + +np.random.seed(123456) # seed 1234 causes 1 number mismatch at 6th decimal in one of the tests + +# pylint:disable=missing-docstring, no-self-use + + +class TestTensorQuant(): + + def test_simple_run(self): + """ quantizer passes gradcheck + """ + x = Parameter(torch.randn(2, 3, dtype=torch.float64).cuda()) * 100 + tensor_quant.tensor_quant(x, torch.max(torch.abs(x)), 7) + + def test_per_tensor_scale(self): + """ tensor_quant matches numpy quantization + """ + torch.set_default_tensor_type('torch.cuda.FloatTensor') # Test on GPU + x_np = np.random.rand(1023) + x_torch = torch.Tensor(x_np) + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np))) + quant_x_torch, _ = tensor_quant.tensor_quant(x_torch, torch.max(torch.abs(x_torch))) + np.testing.assert_array_equal(quant_x_torch.cpu().numpy(), quant_x_np) + torch.set_default_tensor_type('torch.FloatTensor') + + def test_per_channel_scale(self): + """ fake_tensor_quant performs per channel quantization + """ + x_np = np.random.rand(15, 15, 64, 128).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + + # Pytorch filter layout seems to be KCRS, reduce max to shape [K, 1, 1, 1] to test per channel scale + # Shrink max a little, so that clip behavior is tested + amax_x_np = 0.7 * np.max(np.abs(x_np), axis=(1, 2, 3), keepdims=True) + # Pytorch's max function doesn't support reduces multiple axis, and returns (max, argmax) tuple, + # so it has to be reduced by multiple torch.max + amax_x_torch = 0.7 * torch.max( + torch.max(torch.max(x_torch, dim=1, keepdim=True)[0], dim=2, keepdim=True)[0], dim=3, keepdim=True)[0] + + quant_x_np = test_utils.quant_np(x_np, amax_x_np) + quant_x_torch, _ = tensor_quant.tensor_quant(x_torch, amax_x_torch) + + # np.testing.assert_array_equal(quant_x_torch.cpu().numpy(), quant_x_np) + # Pytorch numerics is not the same as numpy, it will be off by 1 + np.testing.assert_array_less(np.abs(quant_x_torch.cpu().numpy() - quant_x_np), 2) + if verbose: + mismatches = np.where(np.abs(quant_x_torch.cpu().numpy() - quant_x_np) >= 1) + print("Mismatches:") + print(" Original: ", x_np[mismatches]) + print(" numpy: ", quant_x_np[mismatches]) + print(" Pytorch: ", quant_x_torch.cpu().numpy()[mismatches]) + + def test_backward(self): + """ tensor_quant implements straight through estimator on the backward pass + Note: this does not work for integer output_dtype + """ + x = torch.randn(3, 7, requires_grad=True).cuda() + labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda() + quant_x, _ = tensor_quant.tensor_quant(x, x.abs().max(), 7) + float_quant_x = quant_x.type(torch.FloatTensor).cuda() + x.retain_grad() + float_quant_x.retain_grad() + criterion = torch.nn.CrossEntropyLoss().cuda() + loss = criterion(float_quant_x, labels) + loss.backward() + np.testing.assert_array_equal(float_quant_x.grad.cpu().numpy(), x.grad.cpu().numpy()) + + def test_unsigned(self): + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np) + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np)), num_bits=9, fake=False) + quant_x_torch, _ = tensor_quant.tensor_quant(x_torch, torch.max(torch.abs(x_torch)), 8, True) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np) + + x_torch = torch.randn(3, 7) + with pytest.raises(TypeError, match="Negative values encountered"): + tensor_quant.tensor_quant(x_torch, torch.max(torch.abs(x_torch)), 8, True) + + def test_overflow_fp16(self): + x_torch = torch.randn(1023).cuda().half() + with pytest.raises(ValueError, match="scale is too large for FP16"): + quant_x_torch, scale = tensor_quant.tensor_quant(x_torch, torch.tensor(1e-4).cuda().half(), 8, False) + + def test_clip_gradient(self): + x = torch.randn(3, 7, requires_grad=True).cuda() + x.retain_grad() + amax = x.abs().max() / 2 + x_in_range = (-amax <= x) * (x <= amax) + quant_x, _ = tensor_quant.tensor_quant(x, amax, 8) + loss = torch.sum((quant_x - 0.5)**2) + loss.backward() + np.testing.assert_array_equal(x.grad.cpu().numpy() != 0, x_in_range.cpu().numpy()) + + def test_full_range(self): + """ fake_tensor_quant uses the full integer range when narrow=False + """ + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + amax = np.max(np.abs(x_np)) + quant_x_np = test_utils.quant_np(x_np, amax, num_bits=9, fake=False, narrow_range=False) + quant_x_torch, _ = tensor_quant.tensor_quant(x_torch, torch.max(torch.abs(x_torch)), 8, True, False) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np) + +class TestFakeTensorQuant(): + + def test_simple_run(self): + x = Parameter(torch.randn(3, 7).cuda()) + tensor_quant.fake_tensor_quant(x, torch.max(torch.abs(x))) + + def test_per_tensor_scale(self): + """ fake_tensor_quant matches numpy quantization + """ + x_np = np.random.rand(13).astype('float32') + print(x_np) + x_torch = torch.Tensor(x_np).cuda() + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np)), fake=True) + quant_x_torch = tensor_quant.fake_tensor_quant(x_torch, torch.max(torch.abs(x_torch))) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np) + + def test_per_channel_scale(self): + """ fake_tensor_quant performs per channel quantization + """ + x_np = np.random.rand(15, 15, 64, 128).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + + # Pytorch filter layout seems to be KCRS, reduce max to shape [K, 1, 1, 1] to test per channel scale + # Shrink max a little, so that clip behavior is tested + amax_x_np = 0.9 * np.max(np.abs(x_np), axis=(1, 2, 3), keepdims=True) + # Pytorch's max function doesn't support reduces multiple axis, and returns (max, argmax) tuple, + # so it has to be reduced by multiple torch.max + amax_x_torch = 0.9 * torch.max( + torch.max(torch.max(x_torch, dim=1, keepdim=True)[0], dim=2, keepdim=True)[0], dim=3, keepdim=True)[0] + + quant_x_np = test_utils.quant_np(x_np, amax_x_np, fake=True) + quant_x_torch = tensor_quant.fake_tensor_quant(x_torch, amax_x_torch) + + # Pytorch numerics is not the same as numpy, results will be off a little + # np.testing.assert_array_equal(quant_x_torch.cpu().numpy(), quant_x_np) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np, decimal=2) + if verbose: + mismatches = np.where(np.abs(quant_x_torch.cpu().numpy() - quant_x_np) >= 1e-5) + print("Mismatches:") + print(" Original: ", x_np[mismatches]) + print(" numpy: ", quant_x_np[mismatches]) + print(" Pytorch: ", quant_x_torch.cpu().numpy()[mismatches]) + + def test_backward(self): + """ fake_tensor_quant implements straight through estimator on the backward pass + """ + x = torch.randn(3, 7, requires_grad=True).cuda() + labels = torch.randint(6, (3,)).type(torch.LongTensor).cuda() + quant_x = tensor_quant.fake_tensor_quant(x, torch.max(torch.abs(x)), 7) + x.retain_grad() + quant_x.retain_grad() + criterion = torch.nn.CrossEntropyLoss().cuda() + loss = criterion(quant_x, labels) + loss.backward() + np.testing.assert_array_equal(quant_x.grad.cpu().numpy(), x.grad.cpu().numpy()) + + def test_unsigned(self): + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np)), num_bits=9, fake=True) + quant_x_torch = tensor_quant.fake_tensor_quant(x_torch, torch.max(torch.abs(x_torch)), 8, True) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np) + + def test_cuda_ext(self): + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + + for num_bits in [3, 4, 5, 7, 8, 11]: + for unsigned in [True, False]: + test_utils.compare( + cuda_ext.fake_tensor_quant(x_torch, torch.max(torch.abs(x_torch)), num_bits, unsigned), + tensor_quant.fake_tensor_quant(x_torch, torch.max(torch.abs(x_torch)), num_bits, unsigned), + rtol=0, atol=0) + + # Test fp16 + x_np_fp16 = np.random.rand(1023).astype('float16') + x_torch_fp16 = torch.Tensor(x_np_fp16).cuda().half() + test_utils.compare( + cuda_ext.fake_tensor_quant(x_torch_fp16, torch.max(torch.abs(x_torch_fp16))), + tensor_quant.fake_tensor_quant(x_torch_fp16, torch.max(torch.abs(x_torch_fp16))), + rtol=0, atol=0) + + def test_cuda_ext_with_axis(self): + x_np = np.random.rand(3, 4, 5, 6).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + + # amax along axis 1 + amax_torch = torch.tensor([0.8, 0.9, 0.7, 0.6], device="cuda") + + for num_bits in [3, 4, 5, 7, 8, 11]: + for unsigned in [True, False]: + cuda_ext_out = cuda_ext.fake_tensor_quant_with_axis(x_torch, amax_torch, 1, num_bits, unsigned) + pytorch_out = tensor_quant.fake_tensor_quant(x_torch, amax_torch.view(1, -1, 1, 1), num_bits, unsigned) + test_utils.compare(cuda_ext_out, pytorch_out, rtol=0, atol=0) + + def test_cuda_ext_inplace(self): + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np)), fake=True) + cuda_ext.fake_tensor_quant_(x_torch, torch.max(torch.abs(x_torch))) + np.testing.assert_array_equal(x_torch.cpu().numpy(), quant_x_np) + + # Test fp16 + x_np_fp16 = np.random.rand(1023).astype('float16') + x_torch_fp16 = torch.Tensor(x_np_fp16).cuda().half() + quant_x_np_fp16 = test_utils.quant_np(x_np_fp16, np.max(np.abs(x_np_fp16)), fake=True) + cuda_ext.fake_tensor_quant_(x_torch_fp16, torch.max(torch.abs(x_torch_fp16))) + np.testing.assert_array_almost_equal(x_torch_fp16.cpu().numpy(), quant_x_np_fp16, decimal=2) + + def test_overflow_fp16(self): + x_torch = torch.randn(1023).cuda().half() + quant_x_torch = tensor_quant.fake_tensor_quant(x_torch, torch.tensor(1e-4).cuda().half(), 8, False) + assert not (torch.isinf(quant_x_torch).any() or torch.isnan(quant_x_torch).any()) + + def test_clip_gradient(self): + x = torch.randn(3, 7, requires_grad=True).cuda() + x.retain_grad() + amax = x.abs().max() / 2 + x_in_range = (-amax <= x) * (x <= amax) + quant_x = tensor_quant.fake_tensor_quant(x, amax, 8) + loss = torch.sum((quant_x - 0.5)**2) + loss.backward() + np.testing.assert_array_equal(x.grad.cpu().numpy() != 0, x_in_range.cpu().numpy()) + + def test_full_range(self): + """ fake_tensor_quant uses the full integer range when narrow=False + """ + x_np = np.random.rand(1023).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + amax = np.max(np.abs(x_np)) + quant_x_np = test_utils.quant_np(x_np, amax, num_bits=9, fake=True, narrow_range=False) + quant_x_torch = tensor_quant.fake_tensor_quant(x_torch, torch.max(torch.abs(x_torch)), 8, True, False) + np.testing.assert_array_almost_equal(quant_x_torch.cpu().numpy(), quant_x_np) + +class TestQuantDescriptor(): + + def test_scaled_mode(self): + num_bits = np.random.randint(0, 16) + + test_quant_desc = tensor_quant.QuantDescriptor(num_bits=num_bits) + assert test_quant_desc.num_bits == num_bits + assert test_quant_desc.axis is None + assert test_quant_desc.amax is None + assert not test_quant_desc.learn_amax + + axis = (0, 1, 3) + test_quant_desc = tensor_quant.QuantDescriptor(axis=axis) + assert test_quant_desc.num_bits == 8 # default value + assert test_quant_desc.axis == axis + assert test_quant_desc.amax is None + + amax = 0.7 + test_quant_desc = tensor_quant.QuantDescriptor(amax=amax, unsigned=True) + assert test_quant_desc.axis is None + assert test_quant_desc.amax == np.float32(amax) + assert test_quant_desc.unsigned + + amax = 0.7 + test_quant_desc = tensor_quant.QuantDescriptor(amax=amax, learn_amax=True) + assert test_quant_desc.amax == np.float32(amax) + assert test_quant_desc.learn_amax + + # Test the print string once if verbose is set. + if verbose: + print(test_quant_desc) + + with pytest.raises(TypeError, match="must be float, list or ndarray"): + tensor_quant.QuantDescriptor(amax='oops') + + with pytest.raises(TypeError, match="amax must be float, list or ndarray"): + tensor_quant.QuantDescriptor(amax='oops', learn_amax=True) + + with pytest.raises(TypeError, match="axis is ignored and must be None"): + tensor_quant.QuantDescriptor(axis=(1, 2), amax=0.7, learn_amax=True) + + def test_amax(self): + test_quant_desc = tensor_quant.QuantDescriptor() + assert test_quant_desc.amax is None + + test_quant_desc = tensor_quant.QuantDescriptor(amax=1.2) + assert isinstance(test_quant_desc.amax, np.ndarray) + np.testing.assert_array_equal(test_quant_desc.amax, np.float32(1.2)) + + test_quant_desc = tensor_quant.QuantDescriptor(amax=[1.3, 1.4]) + assert isinstance(test_quant_desc.amax, np.ndarray) + np.testing.assert_array_equal(test_quant_desc.amax, np.float32([1.3, 1.4])) + + with pytest.raises(TypeError, match="must be float, list or ndarray"): + tensor_quant.QuantDescriptor(amax='oops') + + def test_from_to_dict(self): + quant_desc_1 = tensor_quant.QuantDescriptor( + num_bits=2, name='a', fake_quant=True, axis=(1, 2), + amax=3.1415926536) + quant_desc_2 = tensor_quant.QuantDescriptor(**quant_desc_1.dict()) + if verbose: + print(quant_desc_1.dict()) + assert quant_desc_1 == quant_desc_2 + + quant_desc_1 = tensor_quant.QuantDescriptor(num_bits=2, amax=0.1, unsigned=True) + quant_desc_2 = tensor_quant.QuantDescriptor(**quant_desc_1.dict()) + assert quant_desc_1 == quant_desc_2 + + def test_from_to_yaml(self): + quant_desc_1 = tensor_quant.QuantDescriptor( + num_bits=2, name='a', fake_quant=True, axis=(1, 2), + amax=3.1415926536) + quant_desc_2 = tensor_quant.QuantDescriptor.from_yaml(quant_desc_1.to_yaml()) + if verbose: + print(quant_desc_1.to_yaml()) + assert quant_desc_1 == quant_desc_2 + + quant_desc_1 = tensor_quant.QuantDescriptor(num_bits=2, amax=0.1) + quant_desc_2 = tensor_quant.QuantDescriptor.from_yaml(quant_desc_1.to_yaml()) + assert quant_desc_1 == quant_desc_2 + + +class TestFakeAffineTensorQuant(): + + def test_simple_run(self, verbose): + x = np.array([-1., -13., -101., -128., 0., 2., 5., 13., 93., 111., 127.], dtype=np.float32) + torch_x = torch.tensor(x).cuda() + quant_x = tensor_quant.fake_affine_tensor_quant(torch_x, torch.min(torch_x), torch.max(torch_x)) + + if verbose: + print(quant_x) + + np.testing.assert_array_almost_equal(quant_x.cpu().numpy(), x) + + def test_clip_gradient(self): + x = torch.randn(3, 7, requires_grad=True).cuda() + x.retain_grad() + xmin = x.min() / 2 + xmax = x.max() / 2 + x_in_range = (xmin <= x) * (x <= xmax) + quant_x = tensor_quant.fake_affine_tensor_quant(x, xmin, xmax, 8) + loss = torch.sum((quant_x - 0.5)**2) + loss.backward() + np.testing.assert_array_equal(x.grad.cpu().numpy() != 0, x_in_range.cpu().numpy()) diff --git a/tools/pytorch-quantization/tests/tensor_quantizer_test.py b/tools/pytorch-quantization/tests/tensor_quantizer_test.py new file mode 100644 index 00000000..6a13a985 --- /dev/null +++ b/tools/pytorch-quantization/tests/tensor_quantizer_test.py @@ -0,0 +1,244 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""tests of tensor quantizer""" +import yaml +import pytest +import numpy as np + +import torch + +from pytorch_quantization import tensor_quant +from pytorch_quantization import calib +from pytorch_quantization.nn.modules import tensor_quantizer +from pytorch_quantization import utils as quant_utils +import tests.utils as test_utils +from tests.fixtures import verbose + +np.random.seed(12345) + +# pylint:disable=missing-docstring, no-self-use + +class TestTensorQuantizer(): + + def test_simple_run(self): + """Quantizer calls fake_tensor_quant by default""" + x = torch.randn(3, 7).cuda() + amax_x = torch.max(torch.abs(x)) + fn_quant_x = tensor_quant.fake_tensor_quant(x, amax_x) + quantizer = tensor_quantizer.TensorQuantizer() + module_quant_x = quantizer(x) + np.testing.assert_array_equal(fn_quant_x.cpu().numpy(), module_quant_x.cpu().numpy()) + + def test_simple_run_no_fake(self): + """Quantizer fake_quant=False calls tensor_quant and sets the scale property""" + x = torch.randn(3, 7).cuda() + amax_x = torch.max(torch.abs(x)) + fn_quant_x, fn_scale = tensor_quant.tensor_quant(x, amax_x) + quantizer = tensor_quantizer.TensorQuantizer(tensor_quant.QuantDescriptor(num_bits=8, fake_quant=False)) + module_quant_x = quantizer(x) + module_scale = quantizer.scale + np.testing.assert_array_equal(fn_quant_x.cpu().numpy(), module_quant_x.cpu().numpy()) + np.testing.assert_array_equal(fn_scale.cpu().numpy(), module_scale.cpu().numpy()) + + def test_per_tensor_scale(self): + """Quantizer performs expected quantization""" + x_np = np.random.rand(1023) + x_torch = torch.Tensor(x_np) + quant_x_np = test_utils.quant_np(x_np, np.max(np.abs(x_np))) + quantizer = tensor_quantizer.TensorQuantizer(tensor_quant.QuantDescriptor(num_bits=8, fake_quant=False)) + module_quant_x = quantizer(x_torch) + np.testing.assert_array_equal(module_quant_x.cpu().numpy(), quant_x_np) + + def test_per_channel_scale(self, verbose): + """Quantizer performs per channel scaling""" + x_np = np.random.rand(15, 15, 64, 128).astype('float32') + x_torch = torch.Tensor(x_np).cuda() + + # Pytorch filter layout seems to be KCRS, reduce max to shape [K, 1, 1, 1] to test per channel scale + # Shrink max a little, so that clip behavior is tested + amax_x_np = 0.7 * np.max(np.abs(x_np), axis=(1, 2, 3), keepdims=True) + + quant_x_np = test_utils.quant_np(x_np, amax_x_np) + quantizer = tensor_quantizer.TensorQuantizer( + tensor_quant.QuantDescriptor(num_bits=8, axis=(0), fake_quant=False, scale_amax=0.7)) + quantizer.cuda() + module_quant_x = quantizer(x_torch) + + # np.testing.assert_array_equal(quant_x_torch.cpu().numpy(), quant_x_np) + # Pytorch numerics is not the same as numpy, it will be off by 1 + error = np.abs(module_quant_x.cpu().numpy() - quant_x_np) + np.testing.assert_array_less(error, 2) + if verbose: + mismatches = np.where(error >= 1) + print("Mismatches:") + print(" Original: ", x_np[mismatches]) + print(" numpy: ", quant_x_np[mismatches]) + print(" TensorQuantizer: ", module_quant_x.cpu().numpy()[mismatches]) + + def test_learn_amax(self): + """Test the clip implied by learn_amax""" + x_np = np.random.rand(1023).astype(np.float32) + x_torch = torch.Tensor(x_np) + amax = 0.5 + quant_x_np = test_utils.quant_np(x_np, 0.5, fake=True) + quantizer = tensor_quantizer.TensorQuantizer( + tensor_quant.QuantDescriptor(num_bits=8, amax=amax, learn_amax=True)) + assert hasattr(quantizer, 'clip') + module_quant_x = quantizer(x_torch) + np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np) + + def test_clip_mode(self): + """Test the clip stage only""" + x_np = np.random.rand(1023).astype(np.float32) + x_torch = torch.Tensor(x_np) + amax = 0.5 + clip_x_np = np.clip(x_np, -amax, amax) + quantizer = tensor_quantizer.TensorQuantizer( + tensor_quant.QuantDescriptor(amax=amax, learn_amax=True), if_quant=False, if_clip=True) + assert hasattr(quantizer, 'clip') + module_clip_x = quantizer(x_torch) + np.testing.assert_array_equal(module_clip_x.cpu().detach().numpy(), clip_x_np) + + def test_scale_amax(self): + x_np = np.random.rand(1023).astype(np.float32) + x_torch = torch.Tensor(x_np) + amax = 0.5 + scale_amax = 0.9 + quant_x_np = test_utils.quant_np(x_np, amax * scale_amax, fake=True) + quantizer = tensor_quantizer.TensorQuantizer( + tensor_quant.QuantDescriptor(num_bits=8, amax=amax, scale_amax=scale_amax)) + module_quant_x = quantizer(x_torch) + np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np) + + # Test twice. There was a but in scale amax logic that modify the amax every time + module_quant_x = quantizer(x_torch) + np.testing.assert_array_equal(module_quant_x.cpu().detach().numpy(), quant_x_np) + + def test_disable(self): + x = torch.randn(3, 7).cuda() + amax_x = torch.max(torch.abs(x)) + quantizer = tensor_quantizer.TensorQuantizer(disabled=True) + module_quant_x = quantizer(x) + np.testing.assert_array_equal(x.cpu().numpy(), module_quant_x.cpu().numpy()) + + def test_state_loading(self): + """Test quant_desc loading via state_dict""" + amax = [3.142, 2.718] + quant_desc1 = tensor_quant.QuantDescriptor(amax=amax) + quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1) + + # copy state + quantizer1.load_state_dict(quantizer1.state_dict()) + np.testing.assert_array_equal(quantizer1.amax.detach().cpu().numpy(), quant_desc1.amax) + + def test_properties(self): + quant_desc1 = tensor_quant.QuantDescriptor(amax=3.14) + quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1) + quantizer1.amax = 0.577 + + assert quantizer1.amax.detach().cpu().numpy() == np.float32(0.577) + np.testing.assert_array_equal(quantizer1.amax.detach().cpu().numpy(), quantizer1.amax) + assert quantizer1.step_size == 0.577 / 127. + + quant_desc2 = tensor_quant.QuantDescriptor() + quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2) + amax_np = np.array([3.142, 2.718], dtype=np.float32) + quantizer2.amax = amax_np + np.testing.assert_array_equal(quantizer2.amax.detach().cpu().numpy(), amax_np) + + quant_desc3 = tensor_quant.QuantDescriptor() + quantizer3 = tensor_quantizer.TensorQuantizer(quant_desc3) + assert quantizer3.amax is None + + def test_init_calib(self): + quant_desc2 = tensor_quant.QuantDescriptor(axis=(0, 1)) + quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2, if_calib=True).cuda() + + x_2 = torch.rand(127, 63, 7, 7).cuda() + quantizer2(x_2) + quantizer2.load_calib_amax() + + assert quantizer2.amax.numel() == 127 * 63 + + def test_max_calib(self): + axis = 0 + reduce_axis = (1, 2, 3) + quant_desc1 = tensor_quant.QuantDescriptor(axis=axis) + quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1).cuda() + quantizer1.enable_calib() + + quant_desc1 = tensor_quant.QuantDescriptor(axis=axis) + quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1).cuda() + quantizer1.enable_calib() + + with pytest.raises(RuntimeError, match="Calibrator returned None"): + quantizer1.load_calib_amax() + + x_1 = torch.rand(127, 63, 7, 7).cuda() + x_2 = torch.rand(127, 63, 7, 7).cuda() + quantizer1(x_1) + quantizer1(x_2) + quantizer1.disable_calib() + + global_amax = torch.max( + quant_utils.reduce_amax(x_1, axis=reduce_axis, keepdims=True), + quant_utils.reduce_amax(x_2, axis=reduce_axis, keepdims=True)) + test_utils.compare(quantizer1._calibrator.compute_amax(), global_amax, atol=0, rtol=0, ctol=0) + + quantizer1.load_calib_amax() + test_utils.compare(quantizer1.amax, global_amax, atol=0, rtol=0, ctol=0) + + quant_desc2 = tensor_quant.QuantDescriptor(learn_amax=True) + quantizer2 = tensor_quantizer.TensorQuantizer(quant_desc2).cuda() + quantizer2.enable_calib() + quantizer2(x_1) + quantizer2(x_2) + + quantizer2.load_calib_amax() + quantizer2.init_learn_amax() + test_utils.compare(quantizer2.clip.clip_value_min, -torch.max(global_amax), atol=0, rtol=0, ctol=0) + test_utils.compare(quantizer2.clip.clip_value_max, torch.max(global_amax), atol=0, rtol=0, ctol=0) + + def test_entropy_and_percentile_calib(self): + """Don't really have a good way to test it.""" + quant_desc1 = tensor_quant.QuantDescriptor(calib_method='histogram') + quantizer1 = tensor_quantizer.TensorQuantizer(quant_desc1, if_calib=True, if_quant=False).cuda() + + x_1 = torch.rand(3, 63, 7, 7).cuda() + x_2 = torch.rand(3, 63, 7, 7).cuda() + quantizer1(x_1) + quantizer1(x_2) + + quantizer1.load_calib_amax("entropy") + test_utils.compare(quantizer1._calibrator.compute_amax("entropy"), quantizer1.amax, atol=0, rtol=0, ctol=0) + quantizer1._calibrator.reset() + + quantizer1(x_1) + quantizer1(x_2) + + quantizer1.load_calib_amax("percentile", percentile=99.99) + test_utils.compare(quantizer1._calibrator.compute_amax( + "percentile", percentile=99.99), quantizer1.amax, atol=0, rtol=0, ctol=0) + + def test_setters(self): + quantizer = tensor_quantizer.TensorQuantizer() + quantizer.num_bits = 7 + quantizer.unsigned = True + + assert quantizer.num_bits == 7 + assert quantizer.unsigned diff --git a/tools/pytorch-quantization/tests/utils.py b/tools/pytorch-quantization/tests/utils.py new file mode 100644 index 00000000..cedc1e06 --- /dev/null +++ b/tools/pytorch-quantization/tests/utils.py @@ -0,0 +1,128 @@ +# +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Utils for testing quantization.""" +import numpy as np +from scipy.spatial import distance + +import torch + +from pytorch_quantization import tensor_quant + +def quantize_by_range(x, num_bits): + """Quantize torch tensor by range to num_bits with symmetric zero-mean quantizer.""" + amax = x.abs().max() + x_q = tensor_quant.fake_tensor_quant(x, amax, num_bits) + return x_q + +def quantize_by_range_fused(x_tuple, num_bits): + """Quantize multiple torch tensors by combined range to num_bits with symmetric zero-mean quantizer.""" + # compute aggregate amax across all tensors + amax = max([x.abs().max() for x in x_tuple]) + # quantize each tensor with the aggregate amax + x_q_tuple = tuple(tensor_quant.fake_tensor_quant(x, amax, num_bits) for x in x_tuple) + return x_q_tuple + +def copy_state_and_quantize(dst, src, num_bits): + """Copy src to dst, quantize all 'weight' entries to num_bits.""" + src_state_dict = src.state_dict() + dst_state_dict = dict() + for key in src_state_dict: + if 'weight' in key: + dst_state_dict[key] = quantize_by_range(src_state_dict[key], num_bits) + else: + dst_state_dict[key] = src_state_dict[key].clone() + + dst.load_state_dict(dst_state_dict) + +def copy_state_and_quantize_fused(dst, src, num_bits): + """Copy src to dst, quantize all 'weight' entries to num_bits using the aggregate amax.""" + src_state_dict = src.state_dict() + dst_state_dict = dict() + + # compute aggregate amax across all weight tensors + amax = 0 + for key in src_state_dict: + if 'weight' in key: + amax = max(amax, src_state_dict[key].abs().max()) + + # quantize each weight tensor with the aggregate amax + for key in src_state_dict: + if 'weight' in key: + dst_state_dict[key] = tensor_quant.fake_tensor_quant(src_state_dict[key], amax, num_bits) + else: + dst_state_dict[key] = src_state_dict[key].clone() + + dst.load_state_dict(dst_state_dict) + +def compare(a, b, rtol=1e-7, atol=1e-6, ctol=1e-6): + """Compare two tensors and raise AssertionError if their difference is outside of tolerance.""" + if torch.isinf(a).any(): + raise ValueError("a contains infs") + if torch.isinf(b).any(): + raise ValueError("b contains infs") + + a = a.detach().cpu().numpy().flatten() + b = b.detach().cpu().numpy().flatten() + + # compare elements of a and b relative to the max value in b + # large fp32 values may cause quantization errors that propagate to small values + rel_diff = np.abs(a-b)/np.linalg.norm(b) + abs_diff = np.abs(a-b) + cos_diff = distance.cosine(a, b) + try: + if rel_diff.max() > rtol: + raise AssertionError("Tensor relative error > %.2e (%.2e)" % (rtol, rel_diff.max())) + if abs_diff.max() > atol: + raise AssertionError("Tensor absolute error > %.2e (%.2e)" % (atol, abs_diff.max())) + if cos_diff > ctol: + raise AssertionError("Tensor cosine distance > %.2e (%.2e)" % (ctol, cos_diff)) + # np.testing.assert_allclose(a, b, rtol=rtol, atol=atol) + # np.testing.assert_array_almost_equal_nulp(a, b) + except AssertionError as e: + print('norm(a) =', np.linalg.norm(a)) + print('norm(b) =', np.linalg.norm(b)) + print('Largest relative difference = %.2e' % rel_diff.max()) + idx = np.argmax(rel_diff) + print('a[%d] = %.10f' % (idx, a[idx])) + print('b[%d] = %.10f' % (idx, b[idx])) + print('Largest absolute difference = %.2e' % abs_diff.max()) + idx = np.argmax(abs_diff) + print('a[%d] = %.10f' % (idx, a[idx])) + print('b[%d] = %.10f' % (idx, b[idx])) + print('Cosine distance = %.2e' % cos_diff) + raise e + +def assert_min_mse(a, b, tol=1e-20): + """Assert that the mean squared error between a and b is at least tol.""" + a = a.detach().cpu().numpy() + b = b.detach().cpu().numpy() + mse = ((a-b)**2).mean() + if mse < tol: + raise AssertionError("MSE = %.2e < %.2e" % (mse, tol)) + +def quant_np(x, amax, num_bits=8, fake=False, narrow_range=True): + """Quantize x using numpy.""" + intmax = 2.0**(num_bits - 1) - 1 + intmin = -intmax if narrow_range else -intmax - 1 + scale = intmax / amax + x_q = np.round(np.clip(x * scale, intmin, intmax)) + + if fake: + x_q /= scale + + return x_q