Merge pull request #733 from jaybdub/torch2trt_plugins

- adds torch2trt_plugins library
- adds native group_norm converter

Author: jaybdub

CHANGELOG.md

@@ -2,6 +2,9 @@
 
 ## [Master]
 
+- Added converter for ``torch.nn.functional.group_norm`` using native TensorRT layers
+- Added converter for ``torch.nn.ReflectionPad2d`` using plugin layer
+- Added torch2trt_plugins library
 - Added support for Deep Learning Accelerator (DLA)
 - Added support for explicit batch
 - Added support for TensorRT 8

CMakeLists.txt

@@ -0,0 +1,57 @@
+cmake_minimum_required(VERSION 3.0.0)
+project(torch2trt_plugins VERSION 0.1.0)
+
+# VARIABLES
+set(CUDA_ARCHITECTURES 53 62 72 87)
+
+# BUILD PLUGINS LIBRARY
+find_package(CUDA REQUIRED)
+
+enable_language(CUDA)
+
+include_directories("${CUDA_INCLUDE_DIRS}")
+
+add_library(torch2trt_plugins SHARED
+    plugins/src/example_plugin.cu
+    plugins/src/reflection_pad_2d_plugin.cu
+)
+set_property(TARGET torch2trt_plugins PROPERTY CUDA_ARCHITECTURES ${CUDA_ARCHITECTURES})
+
+target_link_libraries(
+    torch2trt_plugins
+    nvinfer
+    ${CUDA_LIBRARIES}
+)
+
+install (TARGETS torch2trt_plugins
+         LIBRARY DESTINATION lib)
+
+# BUILD TESTS
+find_package(Catch2 QUIET)
+
+if(Catch2_FOUND)
+    include(CTest)
+    include(CPack)
+    include(Catch)
+    enable_testing()
+
+    add_executable(torch2trt_plugins_test
+        plugins/src/tests.cpp
+        plugins/src/example_plugin_test.cpp
+        plugins/src/reflection_pad_2d_plugin_test.cpp
+    )
+
+    set_property(TARGET torch2trt_plugins_test PROPERTY CUDA_ARCHITECTURES ${CUDA_ARCHITECTURES})
+
+    target_link_libraries(torch2trt_plugins_test 
+        PRIVATE 
+        Catch2::Catch2WithMain 
+        torch2trt_plugins
+        nvinfer
+        ${CUDA_LIBRARIES}
+    )
+
+    set(CPACK_PROJECT_NAME ${PROJECT_NAME})
+    set(CPACK_PROJECT_VERSION ${PROJECT_VERSION})
+    catch_discover_tests(torch2trt_plugins_test)
+endif()

README.md

@@ -103,31 +103,31 @@ We tested the converter against these models using the [test.sh](test.sh) script
 
 ## Setup
 
-> torch2trt depends on the TensorRT Python API.  On Jetson, this is included with the latest JetPack.  For desktop, please follow the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html).  You may also try installing torch2trt inside one of the NGC PyTorch docker containers for [Desktop](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) or [Jetson](https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch).
+> Note: torch2trt depends on the TensorRT Python API.  On Jetson, this is included with the latest JetPack.  For desktop, please follow the [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html).  You may also try installing torch2trt inside one of the NGC PyTorch docker containers for [Desktop](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) or [Jetson](https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch).
 
-### Option 1 - Without plugins
+### Step 1 - Install the torch2trt Python library
 
-To install without compiling plugins, call the following
+To install the torch2trt Python library, call the following
 
 ```bash
 git clone https://github.com/NVIDIA-AI-IOT/torch2trt
 cd torch2trt
 python setup.py install
 ```
 
-### Option 2 - With plugins 
+### Step 2 (optional) - Install the torch2trt plugins library
 
-To install with plugins to support some operations in PyTorch that are not natviely supported with TensorRT, call the following
-
-> Please note, this currently only includes the interpolate plugin.  This plugin requires PyTorch 1.3+ for serialization.  
+To install the torch2trt plugins library, call the following
 
 ```bash
-git clone https://github.com/NVIDIA-AI-IOT/torch2trt
-cd torch2trt
-sudo python setup.py install --plugins
+cmake -B build . && cmake --build build --target install && ldconfig
 ```
 
-### Option 3 - With support for experimental community contributed features
+This includes support for some layers which may not be supported natively by TensorRT.  Once this library is found in the system, the associated layer converters in torch2trt are implicitly enabled.
+
+> Note: torch2trt now maintains plugins as an independent library compiled with CMake.  This makes compiled TensorRT engines more portable.  If needed, the deprecated plugins (which depend on PyTorch) may still be installed by calling ``python setup.py install --plugins``.
+
+### Step 3 (optional) - Install experimental community contributed features
 
 To install torch2trt with experimental community contributed features under ``torch2trt.contrib``, like Quantization Aware Training (QAT)(`requires TensorRT>=7.0`), call the following,      
 

docker/21-06/Dockerfile

@@ -1,4 +1,9 @@
 FROM nvcr.io/nvidia/pytorch:21.06-py3
 
 
-RUN pip3 install termcolor
+RUN pip3 install termcolor
+
+RUN git clone https://github.com/catchorg/Catch2.git && \
+    cd Catch2 && \
+    cmake -Bbuild -H. -DBUILD_TESTING=OFF && \
+    cmake --build build/ --target install 

docker/21-08/Dockerfile

@@ -0,0 +1,9 @@
+FROM nvcr.io/nvidia/pytorch:21.08-py3
+
+
+RUN pip3 install termcolor
+
+RUN git clone https://github.com/catchorg/Catch2.git && \
+    cd Catch2 && \
+    cmake -Bbuild -H. -DBUILD_TESTING=OFF && \
+    cmake --build build/ --target install 

docker/21-08/build.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+docker build -t torch2trt:21-08 -f $(pwd)/docker/21-08/Dockerfile .

docker/21-08/run.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+
+docker run --gpus all -it -d --rm -v $(pwd):/torch2trt torch2trt:21-08

docker/21-09/Dockerfile

@@ -1,4 +1,9 @@
 FROM nvcr.io/nvidia/pytorch:21.09-py3
 
 
-RUN pip3 install termcolor
+RUN pip3 install termcolor
+
+RUN git clone https://github.com/catchorg/Catch2.git && \
+    cd Catch2 && \
+    cmake -Bbuild -H. -DBUILD_TESTING=OFF && \
+    cmake --build build/ --target install 

docker/21-09/run.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 
 
-docker run --gpus all -it --rm -v $(pwd):/torch2trt torch2trt:21-09
+docker run --gpus all -it -d --rm -v $(pwd):/torch2trt torch2trt:21-09