Description: Format all python files under onnxruntime with black and isort.
After checking in, we can use .git-blame-ignore-revs to ignore the formatting PR in git blame.
#11315, #11316
* add support for bool type
* add TVM EP support for tests
* include TVM EP in python test pool
* fix pylint
* moved technical imports to a separate file
* clean up post build actions & move _ld_preload.py extension to CMake level
* add files for include TVM EP into CI
* implement custom logger for TVM
* replace TVM logging with ONNX RT logging
* update link for TVM EP tutorial
* clean up TVM EP cmake
* add pybind auto enabling for TVM EP
* fix blank spaces
* code review fixes
* replace print with comment
* add list of EP without TVM EP
* enable onnx tests
* disable contrib ops and ml ops
* reuse Dockerfile.ubuntu
* Move install_tvm_test_dependencies.sh out of Docker context dir, update build definition.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
* improved usability of TVM EP
* moved technical import under a condition related to TVM EP only
* Revert "moved technical import under a condition related to TVM EP only"
* add conditional _ld_preload.py file extension for TVM EP
* improve readability of inserted code
* squashed commit for standalone tvm execution provider
* critical fix for correct python build with stvm ep
* get tuning log file from ep options. It has priority over AUTOTVM_TUNING_LOG
* updates and fixes
* update parsing of stvm provider options
* add support of external data for onnx model
* add conditional dump of subgraphs
* remove unused code
* get input tensor shapes through provider options. get output shapes for fixed input ones by TVM API
* support AUTO_TVM tuning log file inside ORT. Selector for Ansor and Auto_TVM is provider option (tuning_type)
* add fp16
* add functionality of conversion of model layout to NHWC if need. Necessary parameter was added to STVM provider options
* fix license text in header. fix log format
* small fixes
* fix issues from flake8
* remove model proto construction from GetCapability
* reserve memory for vector of DLTensors
* add simple tutorial for STVM EP
* STVM docs
* jroesch/tvm -> apache/tvm
* remove dead code, unneccessary logs and comments
* fix in readme
* improve tutorial notebook
* tvm update
* update STVM_EP.md
* fix default value
* update STVM_EP.md
* some TODOs for the future development
* shorten long lines
* add hyperlink to STVM_EP.md
* fix Linux CI error
* fix error in csharp test
Co-authored-by: Jared Roesch <jroesch@octoml.ai>
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* add use_tensorrt build option
* Add use_tensorrt to running tests
* add use_tensorrt for Windows
* make trt ep to skip backend test
* make trt ep to skip backend test
* Fix bug
* Add/Modify description
* modify for debug
* swtich pool to test
* modify to debug
* modify to debug
* add vobersity
* refine the code
* refine the code
* refine the code
* fix flake8 warning
* refine the code
* add pre_load check for trt as well as add cupti lib to cuda depedencies
* modify script to make trt build path the same as cuda
* show error message when user wants to run TensorRT but TensorRT is not installed in the env
* fix bug
* fix bug
* add trt lib for manylinux
* include cuda_dependencies for trt
* rewrite the condition to throw exception
* make code more compact
* schema change
* cc channges
* remove temp debug code
* Adding fbs namespace to session_state_flatbuffers_utils.h
* Add fbs namepsace to all ort format utils
* libonnxruntime_providers_rocm.so and libonnxruntime_providers_shared.so are not included in python package.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* optimize python overhead of _post_amp_backward
* overwrite apex amp's zero_grad for faster implementation
* move unscale_fp16_grads_into_fp32_grads into C++ impl
* improve the efficiency furthur, reducing 3.5ms to 1.7ms for unilm.
* unilm 1.7ms to 338us: 1). optimize python list <==> std::vector copy, 2). launch the kernels as long as num_elem reach thresh hold. This help reduce the CUDA idel time.
* refine the logic a bit after validating
Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>
* Expose symbols in onnx and protobuf namespaces in python when building with --enable_external_custom_op_schemas
* Add external onnx and protobuf files to wheel
* Added an example to demonstrate external custom ops use-case
* Added a Linux build pipeline to test external custom ops
This change adds a new pipeline for checking Python code. Currently this pipeline only runs flake8.
flake8 is also run as part of the CMake project builds, but we can switch over completely to the new pipeline later.
The .flake8 config file was also updated to make it easier to run standalone (flake8 --config ./.flake8) and some Python formatting issues were addressed in files that were not previously scanned.
ORTModule requires two PyTorch CPP extensions that are currently JIT compiled. The runtime compilation can cause issues in some environments without all build requirements or in environments with multiple instances of ORTModule running in parallel
This PR creates a custom command to compile such extensions that must be manually executed before ORTModule is executed for the first time. When users try to use ORTModule before the extensions are compiled, an error with instructions are raised
PyTorch CPP Extensions for ORTModule can be compiled by running:
python -m onnxruntime.training.ortmodule.torch_cpp_extensions.install
Full build environment is needed for this
Numpy has binary compatibility, which means "binaries compiled against a given version of NumPy will still run correctly with newer NumPy versions, but not with older versions." So, if an onnx runtime package was built with numpy version A, then at run time it requires numpy version >=A. In this change, we read numpy version from the installed packages at build time, to avoid manually keeping the build time/runtime consistency.
