Bump to upstream c808621

README.md

@@ -1,40 +1,40 @@
 <p align="center"><img width="50%" src="docs/images/ONNX_Runtime_logo_dark.png" /></p>
 
-**ONNX Runtime** is a cross-platform **inference and training machine-learning accelerator** compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.
+**ONNX Runtime is a cross-platform inference and training machine-learning accelerator**.
 
 # Systolic Quickstart
 
 This is a fork of upstream onnxruntime modified to work on riscv platforms and particularly focused on supporting the Gemmini accelerator. Gemmini is not necessarily *required* though, so this should also be suitable for those wanting to perform cpu-only inference on a riscv platform -- although this is less thoroughly tested so may not be as performant (the main blocker I can think of is that the `sgemm` kernel is implemented via naive matmul; ideally this should be linked with a proper BLAS implementation, but since floating point inference was not the main goal with this fork – rather, running quantized networks was – that is still a todo).
 
 Read [BUILD.md](systolic_runner/docs/BUILD.md) in `systolic_runner`(along with other documentation) for information on cross-compiling and usage instructions.
 
----
 
-ONNX Runtime uses the portable [ONNX](https://onnx.ai) computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.
+**ONNX Runtime inference** can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. [Learn more &rarr;](https://www.onnxruntime.ai/docs/#onnx-runtime-for-inferencing)
 
-Common use cases for ONNX Runtime:
-
-* Improve inference performance for a wide variety of ML models
-* Reduce time and cost of training large models
-* Train in Python but deploy into a C#/C++/Java app
-* Run with optimized performance on different hardware and operating systems
-* Support models created in several different frameworks
-
-[ONNX Runtime inference](https://www.onnxruntime.ai/docs/get-started/inference.html) APIs are stable and production-ready since the [1.0 release](https://github.com/microsoft/onnxruntime/releases/tag/v1.0.0) in October 2019 and can enable faster customer experiences and lower costs.
-
-[ONNX Runtime training](https://www.onnxruntime.ai/docs/get-started/training.html) feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.
+**ONNX Runtime training** can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. [Learn more &rarr;](https://www.onnxruntime.ai/docs/#onnx-runtime-for-training)
 
 
 ## Get Started
 
 **http://onnxruntime.ai/**
-* [Install](https://www.onnxruntime.ai/docs/get-started/install.html)
-* [Inference](https://www.onnxruntime.ai/docs/get-started/inference.html)
-* [Training](https://www.onnxruntime.ai/docs/get-started/training.html)
-* [Documentation](https://www.onnxruntime.ai/docs/)
-* [Samples and Tutorials](https://www.onnxruntime.ai/docs/tutorials/)
-* [Build Instructions](https://www.onnxruntime.ai/docs/how-to/build.html)
-* [Frequently Asked Questions](./docs/FAQ.md)
+* [Overview](https://www.onnxruntime.ai/docs/)
+* [Tutorials](https://www.onnxruntime.ai/docs/tutorials/)
+  * [Inferencing](https://www.onnxruntime.ai/docs/tutorials/inferencing/)
+  * [Training](https://www.onnxruntime.ai/docs/tutorials/training/)
+* [How To](https://www.onnxruntime.ai/docs/how-to)
+  * [Install](https://www.onnxruntime.ai/docs/how-to/install.html)
+  * [Build](https://www.onnxruntime.ai/docs/how-to/build/)
+  * [Tune performance](https://www.onnxruntime.ai/docs/how-to/tune-performance.html)
+  * [Quantize models](https://www.onnxruntime.ai/docs/how-to/quantization.html)
+  * [Deploy on mobile](https://www.onnxruntime.ai/docs/how-to/deploy-on-mobile.html)
+  * [Use custom ops](https://www.onnxruntime.ai/docs/how-to/add-custom-op.html)
+  * [Add a new EP](https://www.onnxruntime.ai/docs/how-to/add-execution-provider.html)
+* [Reference](https://www.onnxruntime.ai/docs/reference)
+  * [API documentation](https://www.onnxruntime.ai/docs/reference/api/)
+  * [Execution Providers](https://www.onnxruntime.ai/docs/reference/execution-providers/)
+  * [Releases and servicing](https://www.onnxruntime.ai/docs/reference/releases-servicing.html)
+  * [Citing](https://www.onnxruntime.ai/docs/reference/citing.html)
+* [Additional resources](https://www.onnxruntime.ai/docs/resources/)
 
 ## Build Pipeline Status
 |System|CPU|GPU|EPs|
@@ -49,7 +49,7 @@ Common use cases for ONNX Runtime:
 
 ## Data/Telemetry
 
-This project may collect usage data and send it to Microsoft to help improve our products and services. See the [privacy statement](docs/Privacy.md) for more details.
+Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the [privacy statement](docs/Privacy.md) for more details.
 
 ## Contributions and Feedback
 

ThirdPartyNotices.txt

@@ -4685,3 +4685,31 @@ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 © 2021 GitHub, Inc.
 
 _____
+
+react-native
+
+MIT License
+
+https://github.com/facebook/react-native
+
+Copyright (c) Facebook, Inc. and its affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+_____

cmake/CMakeLists.txt

@@ -210,6 +210,8 @@ if (onnxruntime_ENABLE_BITCODE)
   endif()
   set(CMAKE_XCODE_ATTRIBUTE_ENABLE_BITCODE YES)
   set(CMAKE_XCODE_ATTRIBUTE_BITCODE_GENERATION_MODE "bitcode")
+else()
+  set(CMAKE_XCODE_ATTRIBUTE_ENABLE_BITCODE NO)
 endif()
 
 if (onnxruntime_ENABLE_MEMORY_PROFILE)

cmake/onnxruntime_java.cmake

@@ -166,7 +166,7 @@ if (CMAKE_SYSTEM_NAME STREQUAL "Android")
   add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${CMAKE_COMMAND} -E create_symlink $<TARGET_FILE:onnxruntime> ${ANDROID_PACKAGE_ABI_DIR}/$<TARGET_LINKER_FILE_NAME:onnxruntime>)
   add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${CMAKE_COMMAND} -E create_symlink $<TARGET_FILE:onnxruntime4j_jni> ${ANDROID_PACKAGE_ABI_DIR}/$<TARGET_LINKER_FILE_NAME:onnxruntime4j_jni>)
   # Generate the Android AAR package
-  add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${GRADLE_EXECUTABLE} -b build-android.gradle -c settings-android.gradle build -DjniLibsDir=${ANDROID_PACKAGE_JNILIBS_DIR} -DbuildDir=${ANDROID_PACKAGE_OUTPUT_DIR} WORKING_DIRECTORY ${JAVA_ROOT})
+  add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${GRADLE_EXECUTABLE} -b build-android.gradle -c settings-android.gradle build -DjniLibsDir=${ANDROID_PACKAGE_JNILIBS_DIR} -DbuildDir=${ANDROID_PACKAGE_OUTPUT_DIR} -DminSdkVer=${ANDROID_MIN_SDK} WORKING_DIRECTORY ${JAVA_ROOT})
 
   if (onnxruntime_BUILD_UNIT_TESTS)
     set(ANDROID_TEST_PACKAGE_ROOT ${JAVA_ROOT}/src/test/android)
@@ -180,6 +180,6 @@ if (CMAKE_SYSTEM_NAME STREQUAL "Android")
     add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${CMAKE_COMMAND} -E create_symlink ${ANDROID_PACKAGE_OUTPUT_DIR}/outputs/aar/onnxruntime-debug.aar ${ANDROID_TEST_PACKAGE_LIB_DIR}/onnxruntime-debug.aar)
     # Build Android test apk for java package
     add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${GRADLE_EXECUTABLE} clean WORKING_DIRECTORY ${ANDROID_TEST_PACKAGE_DIR})
-    add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${GRADLE_EXECUTABLE} assembleDebug assembleDebugAndroidTest WORKING_DIRECTORY ${ANDROID_TEST_PACKAGE_DIR})
+    add_custom_command(TARGET onnxruntime4j_jni POST_BUILD COMMAND ${GRADLE_EXECUTABLE} assembleDebug assembleDebugAndroidTest -DminSdkVer=${ANDROID_MIN_SDK} WORKING_DIRECTORY ${ANDROID_TEST_PACKAGE_DIR})
   endif()
 endif()

docs/ContribOperators.md

@@ -3063,6 +3063,10 @@ This version of the operator has been available since version 1 of the 'com.micr
 #### Attributes
 
 <dl>
+<dt><tt>coordinate_transformation_mode</tt> : string</dt>
+<dd></dd>
+<dt><tt>mode</tt> : string</dt>
+<dd></dd>
 <dt><tt>scales</tt> : list of ints</dt>
 <dd></dd>
 </dl>