Merge remote-tracking branch 'origin/master' into thiagofc/fix-orttra…

…iner-on-ortmodule-dev-branch

.flake8

@@ -3,3 +3,7 @@ max-line-length = 120
 per-file-ignores =
     __init__.py:F401
 format = [flake8 PEP8 ERROR] %(path)s:%(row)d:%(col)d: %(code)s %(text)s
+# We generally exclude using cmake/flake8.cmake. If something needs to be excluded here
+# The exclude value/s need to be on a newline otherwise it doesn't work (at least on Windows)
+# exclude = 
+#     ./onnxruntime/core/flatbuffers/ort_flatbuffers_py

.gitmodules

@@ -13,9 +13,6 @@
 [submodule "cmake/external/date"]
 	path = cmake/external/date
 	url = https://github.com/HowardHinnant/date.git
-[submodule "cmake/external/gemmlowp"]
-	path = cmake/external/gemmlowp
-	url = https://github.com/google/gemmlowp.git
 [submodule "cmake/external/nsync"]
 	path = cmake/external/nsync
 	url = https://github.com/google/nsync
@@ -25,9 +22,6 @@
 [submodule "cmake/external/eigen"]
 	path = cmake/external/eigen
 	url = https://gitlab.com/libeigen/eigen.git
-[submodule "cmake/external/horovod"]
-	path = cmake/external/horovod
-	url = https://github.com/horovod/horovod.git
 [submodule "cmake/external/cxxopts"]
 	path = cmake/external/cxxopts
 	url = https://github.com/jarro2783/cxxopts.git
@@ -62,9 +56,16 @@
 [submodule "cmake/external/SafeInt/safeint"]
 	path = cmake/external/SafeInt/safeint
 	url = https://github.com/dcleblanc/SafeInt.git
-[submodule "cmake/external/onnx-tensorrt"]
-	path = cmake/external/onnx-tensorrt
-	url = https://github.com/onnx/onnx-tensorrt.git
 [submodule "cmake/external/optional-lite"]
 	path = cmake/external/optional-lite
 	url = https://github.com/martinmoene/optional-lite.git
+[submodule "cmake/external/mp11"]
+	path = cmake/external/mp11
+	url = https://github.com/boostorg/mp11.git
+[submodule "cmake/external/coremltools"]
+	path = cmake/external/coremltools
+	url = https://github.com/apple/coremltools.git
+[submodule "cmake/external/onnx-tensorrt"]
+	path = cmake/external/onnx-tensorrt
+	url = https://github.com/onnx/onnx-tensorrt.git
+	branch = 7.1

BUILD.md

@@ -37,8 +37,12 @@
     * [iOS](#iOS)
 
 **[Training](#Training)**
+ * [Baseline CPU](#baseline-cpu)
+   * Traning Enabled Execution Providers
+     * [NVIDIA CUDA](#cuda-training)
+     * [ROCM](#ROCM)
+	 * [Intel DNNL/MKL-ML](#dnnl-training)
 
-***
 # Inferencing
 ## Start: Baseline CPU
 
@@ -133,6 +137,7 @@ GCC 4.x and below are not supported.
 |**Use OpenMP**|--use_openmp|OpenMP will parallelize some of the code for potential performance improvements. This is not recommended for running on single threads.|
 |**Build using parallel processing**|--parallel|This is strongly recommended to speed up the build.|
 |**Build Shared Library**|--build_shared_lib||
+|**Enable Training support**|--enable_training||
 
 ### APIs and Language Bindings
 |API|Command|Additional details|
@@ -339,7 +344,7 @@ Note that DNNL is built as a [shared provider library](#Execution-Provider-Share
 
 To build for Intel GPU, replace dnnl_opencl_root with the path of the Intel SDK for OpenCL Applications.
 
-##### Windows 
+##### Windows
 
 `.\build.bat --use_dnnl --dnnl_gpu_runtime ocl --dnnl_opencl_root "c:\program files (x86)\intelswtools\sw_dev_tools\opencl\sdk"`
 
@@ -353,21 +358,21 @@ To build for Intel GPU, replace dnnl_opencl_root with the path of the Intel SDK
 See more information on the OpenVINO Execution Provider [here](./docs/execution_providers/OpenVINO-ExecutionProvider.md).
 
 #### Prerequisites
-1. Install the Intel<sup>®</sup> Distribution of OpenVINO<sup>TM</sup> Toolkit **Release 2021.1** for the appropriate OS and target hardware :
+1. Install the Intel<sup>®</sup> Distribution of OpenVINO<sup>TM</sup> Toolkit **Release 2021.2** for the appropriate OS and target hardware :
    * [Linux - CPU, GPU, VPU, VAD-M](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-linux)
    * [Linux - FPGA](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-linux-fpga)
    * [Windows - CPU, GPU, VPU, VAD-M](https://software.intel.com/en-us/openvino-toolkit/choose-download/free-download-windows).
 
-   Follow [documentation](https://docs.openvinotoolkit.org/2021.1/index.html) for detailed instructions.
+   Follow [documentation](https://docs.openvinotoolkit.org/2021.2/index.html) for detailed instructions.
 
-  *2021.1 is the recommended OpenVINO version. [OpenVINO 2020.2](https://docs.openvinotoolkit.org/2020.2/index.html) is minimal OpenVINO version requirement.*
-  *The minimum ubuntu version to support 2021.1 is 18.04.*
+  *2021.2 is the recommended OpenVINO version. [OpenVINO 2020.3](https://docs.openvinotoolkit.org/2020.3/index.html) is minimal OpenVINO version requirement.*
+  *The minimum ubuntu version to support 2021.2 is 18.04.*
 
 2. Configure the target hardware with specific follow on instructions:
-   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_windows.html#Install-GPU), [Linux](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps)
-   * To configure Intel<sup>®</sup> Movidius<sup>TM</sup> USB, please follow this getting started guide: [Linux](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps)
-   * To configure Intel<sup>®</sup> Vision Accelerator Design based on 8 Movidius<sup>TM</sup> MyriadX VPUs, please follow this configuration guide: [Windows](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_windows.html#hddl-myriad), [Linux](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_linux.html#install-VPU). Follow steps 3 and 4 to complete the configuration.
-   * To configure Intel<sup>®</sup> Vision Accelerator Design with an Intel<sup>®</sup> Arria<sup>®</sup> 10 FPGA, please follow this configuration guide: [Linux](https://docs.openvinotoolkit.org/2021.1/openvino_docs_install_guides_installing_openvino_linux_fpga.html)
+   * To configure Intel<sup>®</sup> Processor Graphics(GPU) please follow these instructions: [Windows](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_windows.html#Install-GPU), [Linux](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps)
+   * To configure Intel<sup>®</sup> Movidius<sup>TM</sup> USB, please follow this getting started guide: [Linux](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps)
+   * To configure Intel<sup>®</sup> Vision Accelerator Design based on 8 Movidius<sup>TM</sup> MyriadX VPUs, please follow this configuration guide: [Windows](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_windows.html#hddl-myriad), [Linux](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_linux.html#install-VPU). Follow steps 3 and 4 to complete the configuration.
+   * To configure Intel<sup>®</sup> Vision Accelerator Design with an Intel<sup>®</sup> Arria<sup>®</sup> 10 FPGA, please follow this configuration guide: [Linux](https://docs.openvinotoolkit.org/2021.2/openvino_docs_install_guides_installing_openvino_linux_fpga.html)
 
 3. Initialize the OpenVINO environment by running the setupvars script as shown below:
    * For Linux run:
@@ -591,11 +596,13 @@ See more information on the ArmNN Execution Provider [here](./docs/execution_pro
 source /opt/fsl-imx-xwayland/4.*/environment-setup-aarch64-poky-linux
 alias cmake="/usr/bin/cmake -DCMAKE_TOOLCHAIN_FILE=$OECORE_NATIVE_SYSROOT/usr/share/cmake/OEToolchainConfig.cmake"
 ```
+
 * See [Build ARM](#ARM) below for information on building for ARM devices
 
 #### Build Instructions
 ```
 ./build.sh --use_armnn
+
 ```
 The Relu operator is set by default to use the CPU execution provider for better performance. To use the ArmNN implementation build with --armnn_relu flag
 ```
@@ -606,9 +613,10 @@ The Batch Normalization operator is set by default to use the CPU execution prov
 ./build.sh --use_armnn --armnn_bn
 ```
 
-To use a library outside the normal environment you can set a custom path by using --armnn_home and --armnn_libs tags that defines the path to the ArmNN home directory and the build directory respectively.
+To use a library outside the normal environment you can set a custom path by providing the --armnn_home and --armnn_libs parameters to define the path to the ArmNN home directory and build directory respectively. 
+The ARM Compute Library home directory and build directory must also be available, and can be specified if needed using --acl_home and --acl_libs respectively.
 ```
-./build.sh --use_armnn --armnn_home /path/to/ComputeLibrary --armnn_libs /path/to/build
+./build.sh --use_armnn --armnn_home /path/to/armnn --armnn_libs /path/to/armnn/build  --acl_home /path/to/ComputeLibrary --acl_libs /path/to/acl/build
 ```
 
 ---
@@ -728,14 +736,17 @@ ORT_DEBUG_NODE_IO_DUMP_DATA_TO_FILES=1
 ---
 
 ## Architectures
-### x86
+### [64-bit x86](https://en.wikipedia.org/wiki/X86-64) (also known as x86_64 or AMD64)
+
+This is the default.
+
+### 32-bit x86
 #### Build Instructions
 ##### Windows
 * add `--x86` argument when launching `.\build.bat`
 
 ##### Linux
-* Must be built on a x86 OS
-* add --x86 argument to build.sh
+(Not officially supported)
 
 ---
 
@@ -1175,8 +1186,32 @@ Dockerfile instructions are available [here](./dockerfiles#migraphx)
 ***
 
 # Training
-## CUDA
-### Prerequisites
+
+## Baseline CPU
+
+### Build Instructions
+To build ORT with training support add `--enable_training` build instruction.
+
+All other build options are the same for inferencing as they are for training.
+
+#### Windows
+```
+.\build.bat --config RelWithDebInfo --build_shared_lib --parallel --enable_training
+```
+
+The default Windows CMake Generator is Visual Studio 2017, but you can also use the newer Visual Studio 2019 by passing
+`--cmake_generator "Visual Studio 16 2019"` to `.\build.bat`
+
+
+#### Linux/macOS
+```
+./build.sh --config RelWithDebInfo --build_shared_lib --parallel --enable_training
+```
+
+## Training Enabled Execution Providers
+
+### <a id="cuda-training">CUDA</a>
+#### Prerequisites
 
 The default NVIDIA GPU build requires CUDA runtime libraries installed on the system:
 
@@ -1188,7 +1223,7 @@ The default NVIDIA GPU build requires CUDA runtime libraries installed on the sy
 
 These dependency versions should reflect what is in [Dockerfile.training](./dockerfiles/Dockerfile.training).
 
-### Build instructions
+#### Build instructions
 
 1. Checkout this code repo with `git clone https://github.com/microsoft/onnxruntime`
 
@@ -1210,8 +1245,8 @@ These dependency versions should reflect what is in [Dockerfile.training](./dock
 
     This produces the .whl file in `./build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training.
 
-## ROCM
-### Prerequisites
+### ROCM
+#### Prerequisites
 
 The default AMD GPU build requires ROCM software toolkit installed on the system:
 
@@ -1230,4 +1265,19 @@ These dependency versions should reflect what is in [Dockerfile.training](./dock
    * Change to the ONNX Runtime repo base folder: `cd onnxruntime`
    * Run `./build.sh --config RelWithDebInfo --enable_training --build_wheel --use_rocm --rocm_home /opt/rocm --nccl_home /opt/rocm --mpi_home <location for openmpi>`
 
-    This produces the .whl file in `./build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training.
+    This produces the .whl file in `./build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training.
+
+### <a id="dnnl-training"> DNNL and MKLML </a>
+
+#### Build Instructions
+##### Linux
+
+`./build.sh --enable_training --use_dnnl`
+
+##### Windows
+
+`.\build.bat --enable_training --use_dnnl`
+
+Add `--build_wheel` to build the ONNX Runtime wheel
+
+This will produce a .whl file in `build/Linux/RelWithDebInfo/dist` for ONNX Runtime Training

CONTRIBUTING.md

@@ -1,7 +1,12 @@
 # Contributing
 
-We're always looking for your help to fix bugs and improve the product. Create a pull request and we'll be happy to take a look.
-Start by reading the [Engineering Design](./docs/InferenceHighLevelDesign.md). You can find the doxygen generated documentation [here](https://microsoft.github.io/onnxruntime/).
+We're always looking for your help to improve the product (bug fixes, new features, documentation, etc).
+
+## Contributing a code change
+* Start by reading the [Engineering Design](./docs/InferenceHighLevelDesign.md). More documentation can be found in the [docs folder](./docs/) and [here](https://microsoft.github.io/onnxruntime/).
+* If your change is non-trivial or introduces new public facing APIs (discussed in more detail below) please use the [feature request issue template](https://github.com/microsoft/onnxruntime/issues/new?template=feature_request.md) to discuss it with the team and get consensus on the basic design and direction first. For all other changes, you can directly create a pull request (PR) and we'll be happy to take a look.
+* Make sure your PR adheres to the [PR Guidelines](./docs/PR_Guidelines.md) and [Coding Conventions and Standards](./docs/Coding_Conventions_and_Standards.md) established by the team.
+* If you're unsure about any of the above and want to contribute, you're welcome to [start a discussion](https://github.com/microsoft/onnxruntime/discussions) with the team.
 
 ## Proposing new public APIs
 
@@ -47,13 +52,6 @@ For making changes to the Windows Machine Learning WinRT API, please label your
 * Note: After creating a pull request, you might not see a build getting triggered right away. One of the
 onnxruntime team members will trigger the build for you.
 
-## Coding guidelines
-
-Please see [Coding Conventions and Standards](./docs/Coding_Conventions_and_Standards.md)
-
-## Guidelines for creating a good PR (pull request)
-[PR Guidelines](./docs/PR_Guidelines.md)
-
 ## Licensing guidelines
 
 This project welcomes contributions and suggestions. Most contributions require you to

NuGet.config

@@ -1,10 +1,13 @@
 <?xml version="1.0" encoding="utf-8"?>
 <configuration>
-<solution>
+  <solution>
     <add key="disableSourceControlIntegration" value="true" />
-</solution>
-<packageSources>
- <add key="NuGet Official" value="https://api.nuget.org/v3/index.json" />
- <add key="onnxruntime_public" value="https://pkgs.dev.azure.com/onnxruntime/onnxruntime/_packaging/onnxruntime_public/nuget/v3/index.json" />
-</packageSources>
+  </solution>
+  <packageSources>
+    <clear />
+    <add key="NuGet Official" value="https://api.nuget.org/v3/index.json" />
+  </packageSources>
+  <disabledPackageSources>
+    <clear />
+  </disabledPackageSources>
 </configuration>

README.md

@@ -130,7 +130,7 @@ For production scenarios, it's strongly recommended to build only from an [offic
 
 |API|Supported Versions|Samples|
 |---|---|---|
-[Python](https://aka.ms/onnxruntime-python)| 3.5, 3.6, 3.7, 3.8 (3.8 excludes Win GPU and Linux ARM)<br>[Python Dev Notes](./docs/Python_Dev_Notes.md)| [Samples](./samples#python)|
+[Python](https://aka.ms/onnxruntime-python)| 3.6, 3.7, 3.8, 3.9 (3.8/3.9 excludes Win GPU and Linux ARM)<br>[Python Dev Notes](./docs/Python_Dev_Notes.md)| [Samples](./samples#python)|
 |[C#](docs/CSharp_API.md)| | [Samples](./samples#C)|
 |[C++](./include/onnxruntime/core/session/onnxruntime_cxx_api.h)| |[Samples](./samples#CC)|
 |[C](docs/C_API.md)| | [Samples](./samples#CC)|