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[RLlib](deps): Bump onnxruntime from 1.8.0 to 1.10.0 in /python/requirements/rllib #11

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[RLlib](deps): Bump onnxruntime from 1.8.0 to 1.10.0 in /python/requirements/rllib #11

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Bumps onnxruntime from 1.8.0 to 1.10.0.

Release notes

Sourced from onnxruntime's releases.

ONNX Runtime v1.10.0

Announcements

  • As noted in the deprecation notice in ORT 1.9, InferenceSession now requires the providers parameters to be set when enabling Execution Providers other than default CPUExecutionProvider. e.g. InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])
  • Python 3.6 support removed for Mac builds. Since 3.6 is end-of-life in December 2021, it will no longer be supported from next release (ORT 1.11) onwards
  • Removed dependency on optional-lite
  • Removed experimental Featurizers code

General

  • Support for plug-in custom thread creation and join functions to enable usage of external threads
  • Optional type support from opset15

Performance

  • Introduced indirect Convolution method for QLinearConv which has symmetrically quantized filter, i.e., filter type is int8 and zero point of filter is 0. The method leverages in-direct buffer instead of memcpy'ing the original data and doesn’t need to compute the sum of each pixel of output image for quantized Conv.
    • X64: new kernels - including avx2, avxvnni, avx512 and avx 512 vnni, for general and depthwise quantized Conv.
    • ARM64: new kernels for depthwise quantized Conv.
  • Tensor shape optimization to avoid allocating heap memory in most cases - #9542
  • Added transpose optimizer to push and cancel transpose ops, significantly improving perf for models requiring layout transformation

API

  • Python
    • Following through on the deprecation notice in ORT 1.9, InferenceSession now requires the providers parameters to be set when enabling Execution Providers other than default CPUExecutionProvider. e.g. InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])
  • C/C++
    • New API to query CUDA stream to launch a custom kernel for scenarios where custom ops compiled into shared libraries need implicit synchronization with ORT CUDA kernels - #9141
    • Updated Invalid -> OrtInvalidAllocator
    • Updated every item in OrtCudnnConvAlgoSearch to a safer global name
  • WinML
    • New APIs to create OrtValues from Windows platform specific ID3D12Resources by exposing DirectML Execution Provider specific APIs. These APIs allow DML to extend the C-API and provide EP specific extensions.
      • OrtSessionOptionsAppendExecutionProviderEx_DML
      • DmlCreateGPUAllocationFromD3DResource
      • DmlFreeGPUAllocation
      • DmlGetD3D12ResourceFromAllocation
    • Bug fix: LearningModel::LoadFromFilePath in UWP apps

Packages

  • Added Mac M1 Universal2 build support for a single binary that runs natively on both Apple silicon and Intel-based Macs. These are included in the official packages and can also be built using "-arch arm64 -arch x86_64"
  • Windows C API Symbols are now uploaded to Microsoft symbol server
  • Nuget package now supports ARM64 Linux C#
  • Python GPU package now includes both TensorRT and CUDA EPs. Note: EPs need to be explicitly registered to ensure the correct provider is used. e.g. InferenceSession('model.onnx', providers=['TensorrtExecutionProvider', 'CUDAExecutionProvider']). Please also ensure you have appropriate TensorRT dependencies and CUDA dependencies installed.

Execution Providers

  • TensorRT EP
    • Python GPU release packages now include support for TensorRT 8.0. Enable TensorrtExecutionProvider by explicitly setting providers parameter when creating an InferenceSession. e.g. InferenceSession('model.onnx', providers=['TensorrtExecutionProvider', 'CUDAExecutionProvider'])
    • Published quantized BERT model example
  • OpenVINO EP
    • Add support for OpenVINO 2021.4.x
    • Auto Plugin support
    • IO Buffer/Copy Avoidance Optimizations for GPU plugin

... (truncated)

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@dependabot
[RLlib](deps): Bump onnxruntime in /python/requirements/rllib
f869929 
Bumps [onnxruntime](https://github.com/microsoft/onnxruntime) from 1.8.0 to 1.10.0.
- [Release notes](https://github.com/microsoft/onnxruntime/releases)
- [Changelog](https://github.com/microsoft/onnxruntime/blob/master/docs/ReleaseManagement.md)
- [Commits](microsoft/onnxruntime@v1.8.0...v1.10.0)

---
updated-dependencies:
- dependency-name: onnxruntime
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

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@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Dec 11, 2021
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simonsays1980 pushed a commit that referenced this pull request Aug 21, 2022
@kfstorm
[Core] No RAY_LOG in the constructor of DelayManager (ray-project#26958)
34d0233 
We encountered SIGSEGV when running Python test `python/ray/tests/test_failure_2.py::test_list_named_actors_timeout`. The stack is:

```
#0  0x00007fffed30f393 in std::basic_string<char, std::char_traits<char>, std::allocator<char> >::basic_string(std::string const&) ()
   from /lib64/libstdc++.so.6
#1  0x00007fffee707649 in ray::RayLog::GetLoggerName() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#2  0x00007fffee70aa90 in ray::SpdLogMessage::Flush() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#3  0x00007fffee70af28 in ray::RayLog::~RayLog() () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#4  0x00007fffee2b570d in ray::asio::testing::(anonymous namespace)::DelayManager::Init() [clone .constprop.0] ()
   from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#5  0x00007fffedd0d95a in _GLOBAL__sub_I_asio_chaos.cc () from /home/admin/dev/Arc/merge/ray/python/ray/_raylet.so
#6  0x00007ffff7fe282a in call_init.part () from /lib64/ld-linux-x86-64.so.2
#7  0x00007ffff7fe2931 in _dl_init () from /lib64/ld-linux-x86-64.so.2
#8  0x00007ffff7fe674c in dl_open_worker () from /lib64/ld-linux-x86-64.so.2
#9  0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#10 0x00007ffff7fe5ffe in _dl_open () from /lib64/ld-linux-x86-64.so.2
#11 0x00007ffff7d5f39c in dlopen_doit () from /lib64/libdl.so.2
#12 0x00007ffff7b82e79 in _dl_catch_exception () from /lib64/libc.so.6
#13 0x00007ffff7b82f13 in _dl_catch_error () from /lib64/libc.so.6
#14 0x00007ffff7d5fb09 in _dlerror_run () from /lib64/libdl.so.2
#15 0x00007ffff7d5f42a in dlopen@@GLIBC_2.2.5 () from /lib64/libdl.so.2
#16 0x00007fffef04d330 in py_dl_open (self=<optimized out>, args=<optimized out>)
    at /tmp/python-build.20220507135524.257789/Python-3.7.11/Modules/_ctypes/callproc.c:1369
```

The root cause is that when loading `_raylet.so`, `static DelayManager _delay_manager` is initialized and `RAY_LOG(ERROR) << "RAY_testing_asio_delay_us is set to " << delay_env;` is executed. However, the static variables declared in `logging.cc` are not initialized yet (in this case, `std::string RayLog::logger_name_ = "ray_log_sink"`).

It's better not to rely on the initialization order of static variables in different compilation units because it's not guaranteed. I propose to change all `RAY_LOG`s to `std::cerr` in `DelayManager::Init()`.

The crash happens in Ant's internal codebase. Not sure why this test case passes in the community version though.

BTW, I've tried different approaches:

1. Using a static local variable in `get_delay_us` and remove the global variable. This doesn't work because `init()` needs to access the variable as well.
2. Defining the global variable as type `std::unique_ptr<DelayManager>` and initialize it in `get_delay_us`. This works but it requires a lock to be thread-safe.
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